Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2381-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
02 Oct 2020
Data description paper |
| 02 Oct 2020
| 02 Oct 2020
SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Martyn P. Clark
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Andrew J. Newman
National Center for Atmospheric Research, Boulder, CO, USA
Andrew W. Wood
National Center for Atmospheric Research, Boulder, CO, USA
Simon Michael Papalexiou
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Vincent Vionnet
Environmental Numerical Research Prediction, Environment and Climate Change Canada, Dorval, Quebec, Canada
Paul H. Whitfield
University of Saskatchewan Coldwater Lab, Canmore, Alberta, Canada
Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Related authors
Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, and Naoki Mizukami
EGUsphere, https://doi.org/10.5194/egusphere-2025-38, https://doi.org/10.5194/egusphere-2025-38, 2025
Short summary
Short summary
We present a new strategy to calibrate large-domain land/hydrology models over diverse and extensive regions. Using SUMMA and mizuRoute models, our approach integrates catchment attributes, model parameters, and performance metrics to optimize streamflow simulations. By leveraging recent innovations in machine learning methods and concepts for hydrology, we improve calibration outcomes and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, and Paul H. Whitfield
Earth Syst. Sci. Data, 13, 3337–3362, https://doi.org/10.5194/essd-13-3337-2021, https://doi.org/10.5194/essd-13-3337-2021, 2021
Short summary
Short summary
Probabilistic estimates are useful to quantify the uncertainties in meteorological datasets. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018. It is expected to be useful for hydrological and meteorological applications in North America.
Ziqiang Ma, Jintao Xu, Siyu Zhu, Jun Yang, Guoqiang Tang, Yuanjian Yang, Zhou Shi, and Yang Hong
Earth Syst. Sci. Data, 12, 1525–1544, https://doi.org/10.5194/essd-12-1525-2020, https://doi.org/10.5194/essd-12-1525-2020, 2020
Short summary
Short summary
Focusing on the potential drawbacks in generating the state-of-the-art IMERG data in both the TRMM and GPM era, a new daily calibration algorithm on IMERG was proposed, as well as a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality than IMERG for Asian scientific research and applications. The proposed daily calibration algorithm for GPM is promising and applicable in generating the future IMERG in either an operational scheme or a retrospective manner.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, https://doi.org/10.5194/essd-17-3619-2025, 2025
Short summary
Short summary
In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Norway, Finland, Switzerland, Russia, and Nepal over the period 1979–2021. It includes more than 11.5 million observations from more than 10 000 different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
Alireza Amani, Marie-Amélie Boucher, Alexandre R. Cabral, Vincent Vionnet, and Étienne Gaborit
Hydrol. Earth Syst. Sci., 29, 2445–2465, https://doi.org/10.5194/hess-29-2445-2025, https://doi.org/10.5194/hess-29-2445-2025, 2025
Short summary
Short summary
Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil-freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid in water resource management decisions in cold climates.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
Short summary
Short summary
Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 29, 2361–2375, https://doi.org/10.5194/hess-29-2361-2025, https://doi.org/10.5194/hess-29-2361-2025, 2025
Short summary
Short summary
Hydrologic models are needed to provide simulations of water availability, floods, and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models and thus cannot easily be used to select an appropriate model for a specific place.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Wooley, Nicolas R. Leroux, Paul Siqueira, J. Max Adams, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2317, https://doi.org/10.5194/egusphere-2025-2317, 2025
Short summary
Short summary
This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, TSMM. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–19. We detail important considerations to have in the context of an Earth Observation mission over a vast region such as Canada. The results show that it is possible to achieve the desired accuracy for TSMM, over an Arctic environment.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Mozhgan A. Farahani, Andrew W. Wood, Guoqiang Tang, and Naoki Mizukami
EGUsphere, https://doi.org/10.5194/egusphere-2025-38, https://doi.org/10.5194/egusphere-2025-38, 2025
Short summary
Short summary
We present a new strategy to calibrate large-domain land/hydrology models over diverse and extensive regions. Using SUMMA and mizuRoute models, our approach integrates catchment attributes, model parameters, and performance metrics to optimize streamflow simulations. By leveraging recent innovations in machine learning methods and concepts for hydrology, we improve calibration outcomes and enable regionalization to ungauged basins, which is valuable for national-scale water security studies.
Mari R. Tye, Ming Ge, Jadwiga H. Richter, Ethan D. Gutmann, Allyson Rugg, Cindy L. Bruyère, Sue Ellen Haupt, Flavio Lehner, Rachel McCrary, Andrew J. Newman, and Andy Wood
Hydrol. Earth Syst. Sci., 29, 1117–1133, https://doi.org/10.5194/hess-29-1117-2025, https://doi.org/10.5194/hess-29-1117-2025, 2025
Short summary
Short summary
There is a perceived mismatch between the spatial scales on which global climate models can produce data and those needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We assessed the credibility of a set of water management decision metrics in the Community Earth System Model v2 (CESM2). CESM2 shows potentially greater use of its output in long-range water management decisions.
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025, https://doi.org/10.5194/tc-19-769-2025, 2025
Short summary
Short summary
This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
Short summary
Short summary
Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3169, https://doi.org/10.5194/egusphere-2024-3169, 2024
Short summary
Short summary
Measuring the snow mass from radar measurements is possible with information on the snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layer yielding richer information but at increased computational cost. Here, we show the capabilities of a new method to simplify a complex snowpack, while preserving the scattering behavior of the snowpack and conserving the mass.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Shervan Gharari, Paul H. Whitfield, Alain Pietroniro, Jim Freer, Hongli Liu, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 28, 4383–4405, https://doi.org/10.5194/hess-28-4383-2024, https://doi.org/10.5194/hess-28-4383-2024, 2024
Short summary
Short summary
This study provides insight into the practices that are incorporated into discharge estimation across the national Canadian hydrometric network operated by the Water Survey of Canada (WSC). The procedures used to estimate and correct discharge values are not always understood by end-users. Factors such as ice cover and sedimentation limit accurate discharge estimation. Highlighting these challenges sheds light on difficulties in discharge estimation and the associated uncertainty.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
EGUsphere, https://doi.org/10.5194/egusphere-2024-1626, https://doi.org/10.5194/egusphere-2024-1626, 2024
Short summary
Short summary
This study compared global satellite and one reanalysis precipitation dataset to assess diurnal variability. We found that all datasets capture key diurnal precipitation patterns, with maximum precipitation in the afternoon over land and early morning over the ocean. However, there are differences in the exact timing and amount of precipitation. This suggests that it is better to use a combination of datasets for potential applications rather than relying on a single dataset.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Tek Kshetri, Amir Khatibi, Yiwen Mok, Shahabul Alam, Hongli Liu, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2023-3049, https://doi.org/10.5194/egusphere-2023-3049, 2024
Preprint withdrawn
Short summary
Short summary
This study reveals a crucial discovery: when tweaking model parameters, similar performance metrics might mislead—different parameter settings can yield comparable results in snow depth predictions. This "equifinality" challenges past studies, suggesting that evaluating model tweaks based on performance alone might not reflect actual variations in snow depth forecasts.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
Short summary
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Mijael Rodrigo Vargas Godoy, Yannis Markonis, Oldrich Rakovec, Michal Jenicek, Riya Dutta, Rajani Kumar Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, Simon Michael Papalexiou, and Martin Hanel
Hydrol. Earth Syst. Sci., 28, 1–19, https://doi.org/10.5194/hess-28-1-2024, https://doi.org/10.5194/hess-28-1-2024, 2024
Short summary
Short summary
The study introduces a novel benchmarking method based on the water cycle budget for hydroclimate data fusion. Using this method and multiple state-of-the-art datasets to assess the spatiotemporal patterns of water cycle changes in Czechia, we found that differences in water availability distribution are dominated by evapotranspiration. Furthermore, while the most significant temporal changes in Czechia occur during spring, the median spatial patterns stem from summer changes in the water cycle.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
Short summary
Short summary
The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
Short summary
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Ulises M. Sepúlveda, Pablo A. Mendoza, Naoki Mizukami, and Andrew J. Newman
Hydrol. Earth Syst. Sci., 26, 3419–3445, https://doi.org/10.5194/hess-26-3419-2022, https://doi.org/10.5194/hess-26-3419-2022, 2022
Short summary
Short summary
This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly used macroscale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
Short summary
Short summary
This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
Short summary
Short summary
Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
Short summary
Short summary
Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, and Paul H. Whitfield
Earth Syst. Sci. Data, 13, 3337–3362, https://doi.org/10.5194/essd-13-3337-2021, https://doi.org/10.5194/essd-13-3337-2021, 2021
Short summary
Short summary
Probabilistic estimates are useful to quantify the uncertainties in meteorological datasets. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018. It is expected to be useful for hydrological and meteorological applications in North America.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
Short summary
Short summary
Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 25, 2513–2541, https://doi.org/10.5194/hess-25-2513-2021, https://doi.org/10.5194/hess-25-2513-2021, 2021
Short summary
Short summary
Using only warm season streamflow records, regime and change classifications were produced for ~ 400 watersheds in the Nelson and Mackenzie River basins, and trends in water storage and vegetation were detected from satellite imagery. Three areas show consistent changes: north of 60° (increased streamflow and basin greenness), in the western Boreal Plains (decreased streamflow and basin greenness), and across the Prairies (three different patterns of increased streamflow and basin wetness).
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, https://doi.org/10.5194/tc-15-743-2021, 2021
Short summary
Short summary
Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Shervan Gharari, Martyn P. Clark, Naoki Mizukami, Wouter J. M. Knoben, Jefferson S. Wong, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 24, 5953–5971, https://doi.org/10.5194/hess-24-5953-2020, https://doi.org/10.5194/hess-24-5953-2020, 2020
Short summary
Short summary
This work explores the trade-off between the accuracy of the representation of geospatial data, such as land cover, soil type, and elevation zones, in a land (surface) model and its performance in the context of modeling. We used a vector-based setup instead of the commonly used grid-based setup to identify this trade-off. We also assessed the often neglected parameter uncertainty and its impact on the land model simulations.
Manuela I. Brunner, Lieke A. Melsen, Andrew J. Newman, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 24, 3951–3966, https://doi.org/10.5194/hess-24-3951-2020, https://doi.org/10.5194/hess-24-3951-2020, 2020
Short summary
Short summary
Streamflow seasonality is changing and expected to further change under the influence of climate change. We here assess how annual streamflow hydrographs will change in future by using a newly developed classification scheme. Our comparison of future with current annual hydrograph classes shows that robust changes are expected only for currently melt-influenced regions in the Rocky Mountains. These upstream changes may require the adaptation of management strategies in downstream regions.
Ziqiang Ma, Jintao Xu, Siyu Zhu, Jun Yang, Guoqiang Tang, Yuanjian Yang, Zhou Shi, and Yang Hong
Earth Syst. Sci. Data, 12, 1525–1544, https://doi.org/10.5194/essd-12-1525-2020, https://doi.org/10.5194/essd-12-1525-2020, 2020
Short summary
Short summary
Focusing on the potential drawbacks in generating the state-of-the-art IMERG data in both the TRMM and GPM era, a new daily calibration algorithm on IMERG was proposed, as well as a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality than IMERG for Asian scientific research and applications. The proposed daily calibration algorithm for GPM is promising and applicable in generating the future IMERG in either an operational scheme or a retrospective manner.
Vincent Vionnet, Vincent Fortin, Etienne Gaborit, Guy Roy, Maria Abrahamowicz, Nicolas Gasset, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 24, 2141–2165, https://doi.org/10.5194/hess-24-2141-2020, https://doi.org/10.5194/hess-24-2141-2020, 2020
Short summary
Short summary
The 2013 Alberta flood in Canada was typical of late-spring floods in mountain basins combining intense precipitation with rapid melting of late-lying snowpack. Hydrological simulations of this event are mainly influenced by (i) the spatial resolution of the atmospheric forcing due to the best estimate of precipitation at the kilometer scale and changes in turbulent fluxes contributing to snowmelt and (ii) uncertainties in initial snow conditions at high elevations. Soil texture has less impact.
Louis Quéno, Fatima Karbou, Vincent Vionnet, and Ingrid Dombrowski-Etchevers
Hydrol. Earth Syst. Sci., 24, 2083–2104, https://doi.org/10.5194/hess-24-2083-2020, https://doi.org/10.5194/hess-24-2083-2020, 2020
Short summary
Short summary
In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. Satellite-derived products of incoming radiation were assessed in the French Alps and the Pyrenees and compared to meteorological forecasts, reanalyses and in situ measurements. We showed their good quality in mountains. The different radiation datasets were used as radiative forcing for snowpack simulations with the detailed model Crocus. Their impact on the snowpack evolution was explored.
Andrew J. Newman and Martyn P. Clark
Geosci. Model Dev., 13, 1827–1843, https://doi.org/10.5194/gmd-13-1827-2020, https://doi.org/10.5194/gmd-13-1827-2020, 2020
Short summary
Short summary
This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes to turn observations of precipitation and temperature into spatial maps. TIER allows our understanding of complex atmospheric processes such as terrain-enhanced precipitation to be modeled in a very simple way. TIER lets users change the model so they can experiment with different ways of making maps. A key conclusion is that small changes in TIER will change the final map.
Jennifer R. Dierauer, Diana M. Allen, and Paul H. Whitfield
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-676, https://doi.org/10.5194/hess-2019-676, 2020
Preprint withdrawn
Short summary
Short summary
Increasing temperatures due to climate change will change patterns of snow accumulation and melt. How these changes in snow impact summer streamflow, however, is not well understood. This study combined computer-based watershed models with climate change projections to show how warm winters with less snow accumulation lead to less streamflow in the following summer season. In seasonally snow-covered regions, warm winters could become a common stressor on summer surface water supplies.
Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-671, https://doi.org/10.5194/hess-2019-671, 2020
Revised manuscript not accepted
Short summary
Short summary
Using partial year streamflow records a regime and change classification were produced for ~ 400 watersheds in the Saskatchewan and Mackenzie River basins, and trends in water storage and vegetation were detected from satellite imagery. Three areas show consistent changes; north of 60° [increased streamflow and basin greenness], in the western Boreal Plains [decreased streamflow and basin greenness], and across the Prairies [three different patterns of increased streamflow and basin wetness].
Kabir Rasouli, John W. Pomeroy, and Paul H. Whitfield
Hydrol. Earth Syst. Sci., 23, 4933–4954, https://doi.org/10.5194/hess-23-4933-2019, https://doi.org/10.5194/hess-23-4933-2019, 2019
Short summary
Short summary
The combined effects of changes in climate, vegetation, and soils on mountain hydrology were modeled in three mountain basins. In the Yukon, an insignificant increasing effect of vegetation change on snow was found to be important enough to offset the climate change effect. In the Canadian Rockies, a combined effect of soil and climate change on runoff became significant, whereas their individual effects were not significant. Only vegetation change decreased runoff in the basin in Idaho.
Satoru Yamaguchi, Masaaki Ishizaka, Hiroki Motoyoshi, Sent Nakai, Vincent Vionnet, Teruo Aoki, Katsuya Yamashita, Akihiro Hashimoto, and Akihiro Hachikubo
The Cryosphere, 13, 2713–2732, https://doi.org/10.5194/tc-13-2713-2019, https://doi.org/10.5194/tc-13-2713-2019, 2019
Short summary
Short summary
The specific surface area (SSA) of solid precipitation particles (PPs) includes detailed information of PP. This work is based on field measurement of SSA of PPs in Nagaoka, the city with the heaviest snowfall in Japan. The values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. An equation to empirically estimate the SSA of fresh PPs with WS and Tw was established and the equation successfully reproduced the fluctuation of SSA in Nagaoka.
Amirhossein Mazrooei, A. Sankarasubramanian, and Andrew W. Wood
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-288, https://doi.org/10.5194/hess-2019-288, 2019
Revised manuscript not accepted
Short summary
Short summary
Reliable long-range hydrologic forecasts (e.g. month-ahead streamflow conditions) greatly help to facilitate water resources management. Toward this, climate forecasts are utilized to implement hydrologic models, with past hydrologic simulations used to initialize the land-surface conditions of models. Since there are uncertainties associated with the simulated conditions, Data Assimilation (DA) techniques can be employed to reduce such errors and consequently improve hydrologic forecasting.
Naoki Mizukami, Oldrich Rakovec, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, Hoshin V. Gupta, and Rohini Kumar
Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, https://doi.org/10.5194/hess-23-2601-2019, 2019
Short summary
Short summary
We find that Nash–Sutcliffe (NSE)-based model calibrations result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. The use of Kling–Gupta efficiency (KGE) results in annual peak flow estimates that are better than from NSE, with only a slight degradation in performance with respect to other related metrics.
Gwo-Jong Huang, Viswanathan N. Bringi, Andrew J. Newman, Gyuwon Lee, Dmitri Moisseev, and Branislav M. Notaroš
Atmos. Meas. Tech., 12, 1409–1427, https://doi.org/10.5194/amt-12-1409-2019, https://doi.org/10.5194/amt-12-1409-2019, 2019
Short summary
Short summary
This paper proposes a method for snow rate (SR) estimation using observations collected by NASA dual-frequency dual-polarized (D3R) radar during the GPM Cold-season Precipitation Experiment (GCPEx). The new method utilizes dual-wavelength radar reflectivity ratio (DWR) and 2-D-video disdrometer (2DVD) measurements to improve SR estimation accuracy. It is validated by comparing the D3R radar-retrieved SR with accumulated SR directly measured by a Pluvio gauge for an entire GCPEx synoptic event.
Gilbert Guyomarc'h, Hervé Bellot, Vincent Vionnet, Florence Naaim-Bouvet, Yannick Déliot, Firmin Fontaine, Philippe Puglièse, Kouichi Nishimura, Yves Durand, and Mohamed Naaim
Earth Syst. Sci. Data, 11, 57–69, https://doi.org/10.5194/essd-11-57-2019, https://doi.org/10.5194/essd-11-57-2019, 2019
Short summary
Short summary
The paper introduces a meteorological and blowing snow data set from Col du Lac Blanc (2720 m a.s.l., French Alps) allowing physical parameterizations and numerical models of blowing snow to be developed and evaluated. In situ winter season data consist of wind, snow depth, air temperature measurements and a database of blowing snow occurrence (2000–2016) complemented by measurements of blowing snow fluxes (2010–2016). Atmospheric data from a meteorological reanalysis and a DEM are also provided.
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Christine Lac, Jean-Pierre Chaboureau, Valéry Masson, Jean-Pierre Pinty, Pierre Tulet, Juan Escobar, Maud Leriche, Christelle Barthe, Benjamin Aouizerats, Clotilde Augros, Pierre Aumond, Franck Auguste, Peter Bechtold, Sarah Berthet, Soline Bielli, Frédéric Bosseur, Olivier Caumont, Jean-Martial Cohard, Jeanne Colin, Fleur Couvreux, Joan Cuxart, Gaëlle Delautier, Thibaut Dauhut, Véronique Ducrocq, Jean-Baptiste Filippi, Didier Gazen, Olivier Geoffroy, François Gheusi, Rachel Honnert, Jean-Philippe Lafore, Cindy Lebeaupin Brossier, Quentin Libois, Thibaut Lunet, Céline Mari, Tomislav Maric, Patrick Mascart, Maxime Mogé, Gilles Molinié, Olivier Nuissier, Florian Pantillon, Philippe Peyrillé, Julien Pergaud, Emilie Perraud, Joris Pianezze, Jean-Luc Redelsperger, Didier Ricard, Evelyne Richard, Sébastien Riette, Quentin Rodier, Robert Schoetter, Léo Seyfried, Joël Stein, Karsten Suhre, Marie Taufour, Odile Thouron, Sandra Turner, Antoine Verrelle, Benoît Vié, Florian Visentin, Vincent Vionnet, and Philippe Wautelet
Geosci. Model Dev., 11, 1929–1969, https://doi.org/10.5194/gmd-11-1929-2018, https://doi.org/10.5194/gmd-11-1929-2018, 2018
Short summary
Short summary
This paper presents the Meso-NH model version 5.4, which is an atmospheric non-hydrostatic research model that is applied on synoptic to turbulent scales. The model includes advanced numerical techniques and state-of-the-art physics parameterization schemes. It has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling.
Marion Réveillet, Delphine Six, Christian Vincent, Antoine Rabatel, Marie Dumont, Matthieu Lafaysse, Samuel Morin, Vincent Vionnet, and Maxime Litt
The Cryosphere, 12, 1367–1386, https://doi.org/10.5194/tc-12-1367-2018, https://doi.org/10.5194/tc-12-1367-2018, 2018
Lawrence R. Mudryk, Chris Derksen, Stephen Howell, Fred Laliberté, Chad Thackeray, Reinel Sospedra-Alfonso, Vincent Vionnet, Paul J. Kushner, and Ross Brown
The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, https://doi.org/10.5194/tc-12-1157-2018, 2018
Short summary
Short summary
This paper presents changes in both snow and sea ice that have occurred over Canada during the recent past and shows climate model estimates for future changes expected to occur by the year 2050. The historical changes of snow and sea ice are generally coherent and consistent with the regional history of temperature and precipitation changes. It is expected that snow and sea ice will continue to decrease in the future, declining by an additional 15–30 % from present day values by the year 2050.
Lieke A. Melsen, Nans Addor, Naoki Mizukami, Andrew J. Newman, Paul J. J. F. Torfs, Martyn P. Clark, Remko Uijlenhoet, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 22, 1775–1791, https://doi.org/10.5194/hess-22-1775-2018, https://doi.org/10.5194/hess-22-1775-2018, 2018
Short summary
Short summary
Long-term hydrological predictions are important for water management planning, but are also prone to uncertainty. This study investigates three sources of uncertainty for long-term hydrological predictions in the US: climate models, hydrological models, and hydrological model parameters. Mapping the results revealed spatial patterns in the three sources of uncertainty: different sources of uncertainty dominate in different regions.
Cameron Wobus, Ethan Gutmann, Russell Jones, Matthew Rissing, Naoki Mizukami, Mark Lorie, Hardee Mahoney, Andrew W. Wood, David Mills, and Jeremy Martinich
Nat. Hazards Earth Syst. Sci., 17, 2199–2211, https://doi.org/10.5194/nhess-17-2199-2017, https://doi.org/10.5194/nhess-17-2199-2017, 2017
Short summary
Short summary
We linked modeled changes in the frequency of historical 100-year flood events to a national inventory of built assets within mapped floodplains of the United States. This allowed us to project changes in inland flooding damages nationwide under two alternative greenhouse gas (GHG) emissions scenarios. Our results suggest that more aggressive GHG reductions could reduce the projected monetary damages from inland flooding, potentially saving billions of dollars annually by the end of the century.
Nans Addor, Andrew J. Newman, Naoki Mizukami, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, https://doi.org/10.5194/hess-21-5293-2017, 2017
Short summary
Short summary
We introduce a data set describing the landscape of 671 catchments in the contiguous USA: we synthesized various data sources to characterize the topography, climate, streamflow, land cover, soil, and geology of each catchment. This extends the daily time series of meteorological forcing and discharge provided by an earlier study. The diversity of these catchments will help to improve our understanding and modeling of how the interplay between catchment attributes shapes hydrological processes.
Jesús Revuelto, Grégoire Lecourt, Matthieu Lafaysse, Isabella Zin, Luc Charrois, Vincent Vionnet, Marie Dumont, Antoine Rabatel, Delphine Six, Thomas Condom, Samuel Morin, Alessandra Viani, and Pascal Sirguey
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-184, https://doi.org/10.5194/tc-2017-184, 2017
Revised manuscript not accepted
Short summary
Short summary
We evaluated distributed and semi-distributed modeling approaches to simulating the spatial and temporal evolution of snow and ice over an extended mountain catchment, using the Crocus snowpack model. The distributed approach simulated the snowpack dynamics on a 250-m grid, enabling inclusion of terrain shadowing effects. The semi-distributed approach simulated the snowpack dynamics for discrete topographic classes characterized by elevation range, aspect, and slope.
Mathieu Barrere, Florent Domine, Bertrand Decharme, Samuel Morin, Vincent Vionnet, and Matthieu Lafaysse
Geosci. Model Dev., 10, 3461–3479, https://doi.org/10.5194/gmd-10-3461-2017, https://doi.org/10.5194/gmd-10-3461-2017, 2017
Short summary
Short summary
Global warming projections still suffer from a limited representation of the permafrost–carbon feedback. This study assesses the capacity of snow-soil coupled models to simulate the permafrost thermal regime at Bylot Island, a high Arctic site. Significant flaws are found in the description of Arctic snow properties, resulting in erroneous heat transfers between the soil and the snow in simulations. Improved snow schemes are needed to accurately predict the future of permafrost.
Pablo A. Mendoza, Andrew W. Wood, Elizabeth Clark, Eric Rothwell, Martyn P. Clark, Bart Nijssen, Levi D. Brekke, and Jeffrey R. Arnold
Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, https://doi.org/10.5194/hess-21-3915-2017, 2017
Short summary
Short summary
Water supply forecasts are critical to support water resources operations and planning. The skill of such forecasts depends on our knowledge of (i) future meteorological conditions and (ii) the amount of water stored in a basin. We address this problem by testing several approaches that make use of these sources of predictability, either separately or in a combined fashion. The main goal is to understand the marginal benefits of both information and methodological complexity in forecast skill.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
Short summary
Short summary
The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Matthieu Lafaysse, Bertrand Cluzet, Marie Dumont, Yves Lejeune, Vincent Vionnet, and Samuel Morin
The Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, https://doi.org/10.5194/tc-11-1173-2017, 2017
Short summary
Short summary
Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC by implementing new representations of different physical processes in a coupled multilayer ground/snowpack model. This system is a promising tool to integrate snow modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack modelling applications.
Chengcheng Huang, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, and Xiaogu Zheng
Hydrol. Earth Syst. Sci., 21, 635–650, https://doi.org/10.5194/hess-21-635-2017, https://doi.org/10.5194/hess-21-635-2017, 2017
Short summary
Short summary
This study examined the potential of snow water equivalent data assimilation to improve seasonal streamflow predictions. We examined aspects of the data assimilation system over basins with varying climates across the western US. We found that varying how the data assimilation system is implemented impacts forecast performance, and basins with good initial calibrations see less benefit. This implies that basin-specific configurations and benefits should be expected given this modeling system.
Louis Quéno, Vincent Vionnet, Ingrid Dombrowski-Etchevers, Matthieu Lafaysse, Marie Dumont, and Fatima Karbou
The Cryosphere, 10, 1571–1589, https://doi.org/10.5194/tc-10-1571-2016, https://doi.org/10.5194/tc-10-1571-2016, 2016
Short summary
Short summary
Simulations are carried out in the Pyrenees with the snowpack model Crocus, driven by meteorological forecasts from the model AROME at kilometer resolution. The evaluation is done with ground-based measurements, satellite data and reference simulations. Studying daily snow depth variations allows to separate different physical processes affecting the snowpack. We show the benefits of AROME kilometric resolution and dynamical behavior in terms of snowpack spatial variability in a mountain range.
A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan
Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, https://doi.org/10.5194/hess-19-209-2015, 2015
Short summary
Short summary
The focus of this paper is to (1) present a community data set of daily forcing and hydrologic response data for 671 unimpaired basins across the contiguous United States that spans a very wide range of hydroclimatic conditions, and (2) provide a calibrated model performance benchmark using a common conceptual snow and hydrologic modeling system. This benchmark provides a reference level of model performance across a very large basin sample and highlights regional variations in performance.
V. Vionnet, E. Martin, V. Masson, G. Guyomarc'h, F. Naaim-Bouvet, A. Prokop, Y. Durand, and C. Lac
The Cryosphere, 8, 395–415, https://doi.org/10.5194/tc-8-395-2014, https://doi.org/10.5194/tc-8-395-2014, 2014
F. Lombardo, E. Volpi, D. Koutsoyiannis, and S. M. Papalexiou
Hydrol. Earth Syst. Sci., 18, 243–255, https://doi.org/10.5194/hess-18-243-2014, https://doi.org/10.5194/hess-18-243-2014, 2014
V. Masson, P. Le Moigne, E. Martin, S. Faroux, A. Alias, R. Alkama, S. Belamari, A. Barbu, A. Boone, F. Bouyssel, P. Brousseau, E. Brun, J.-C. Calvet, D. Carrer, B. Decharme, C. Delire, S. Donier, K. Essaouini, A.-L. Gibelin, H. Giordani, F. Habets, M. Jidane, G. Kerdraon, E. Kourzeneva, M. Lafaysse, S. Lafont, C. Lebeaupin Brossier, A. Lemonsu, J.-F. Mahfouf, P. Marguinaud, M. Mokhtari, S. Morin, G. Pigeon, R. Salgado, Y. Seity, F. Taillefer, G. Tanguy, P. Tulet, B. Vincendon, V. Vionnet, and A. Voldoire
Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, https://doi.org/10.5194/gmd-6-929-2013, 2013
Related subject area
Meteorology
An updated reconstruction of Antarctic near-surface air temperatures at monthly intervals since 1958
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in southern Spain (Sierra Nevada)
Estimation of long-term gridded cloud radiative kernel and radiative effects based on cloud fraction
Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain
Over three decades, and counting, of near-surface turbulent flux measurements from the Atmospheric Radiation Measurement (ARM) user facility
Homogenized daily sunshine duration over China from 1961 to 2022
Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River watershed during SPLASH, 2021–2023
MDG625: a daily high-resolution meteorological dataset derived by a geopotential-guided attention network in Asia (1940–2023)
The SAIL dataset of marine atmospheric electric field observations over the Atlantic Ocean
CLIMADAT-GRid: A high-resolution daily gridded precipitation and temperature dataset for Greece
The Dutch real-time gauge-adjusted radar precipitation product
LARA: a Lagrangian Reanalysis based on ERA5 spanning from 1940 to 2023
Global projections of heat stress at high temporal resolution using machine learning
An ensemble-based coupled reanalysis of the climate from 1860 to the present (CoRea1860+)
Low-level atmospheric turbulence dataset in China generated by combining radar wind profiler and radiosonde observations
A new high-resolution multi-drought-index dataset for mainland China
Updates of C-LSAT 2.1 and the development of high-resolution LSAT and DTR datasets
Data collected by a drone backpack for air quality and atmospheric state measurements during Pallas Cloud Experiment 2022 (PaCE2022)
GIRAFE v1: a global climate data record for precipitation accompanied by a daily sampling uncertainty
A new upgraded high-precision gridded precipitation dataset considering spatiotemporal and physical correlations for mainland China
What is climate change doing in Himalaya? Thirty years of the Pyramid Meteorological Network (Nepal)
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
The PAZ polarimetric radio occultation research dataset for scientific applications
Water vapor Raman lidar observations from multiple sites in the framework of WaLiNeAs
An observational record of global gridded near surface air temperature change over land and ocean from 1781
SARAH-3 – satellite-based climate data records of surface solar radiation
PL1GD-T – gridded dataset of the mean, minimum and maximum daily air temperature at the level of 2 m for the area of Poland at a resolution of 1 km × 1 km
A New Database of Extreme European Winter Windstorms
A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)
Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data
Special Observing Period (SOP) data for the Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP)
Dataset of spatially extensive long-term quality-assured land–atmosphere interactions over the Tibetan Plateau
A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes
Multifrequency radar observations of marine clouds during the EPCAPE campaign
Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)
GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022
LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics
Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg
Earth Virtualization Engines (EVE)
The 2023 National Offshore Wind data set (NOW-23)
Dataset of stable isotopes of precipitation in the Eurasian continent
A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations
Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations
A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica
Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau
High-resolution (1 km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Year-long buoy-based observations of the air–sea transition zone off the US west coast
The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations
David Bromwich, Sheng-Hung Wang, Xun Zou, and Alexandra Ensign
Earth Syst. Sci. Data, 17, 2953–2962, https://doi.org/10.5194/essd-17-2953-2025, https://doi.org/10.5194/essd-17-2953-2025, 2025
Short summary
Short summary
Antarctica is a major player in Earth’s climate, with the most direct influence arising from its potential to raise the global sea level by 1 m or more in the coming decades. Near-surface air temperature is the primary variable used to monitor the climate of this remote but important region. Continent-wide direct but sparse measurements that started around 1958 are used to construct a monthly air temperature dataset for all of Antarctica, spanning the period from 1958 to 2022.
Matilde García-Valdecasas Ojeda, Feliciano Solano-Farias, David Donaire-Montaño, Emilio Romero-Jiménez, Juan José Rosa-Cánovas, Yolanda Castro-Díez, Sonia R. Gámiz-Fortis, and María Jesús Esteban-Parra
Earth Syst. Sci. Data, 17, 2809–2829, https://doi.org/10.5194/essd-17-2809-2025, https://doi.org/10.5194/essd-17-2809-2025, 2025
Short summary
Short summary
This work aims to present a series of climate datasets for Sierra Nevada, a region especially vulnerable to climate change in southern Spain. The database consists of primary climate variables such as precipitation, temperature, radiation, wind speed, pressure, and atmospheric humidity and also bioclimatic variables and extreme indices, which are both useful pieces of information for assessing the impact of climate change in this region. These datasets are only available on Zenodo at https://doi.org/10.5281/zenodo.14883471.
Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu
Earth Syst. Sci. Data, 17, 2405–2435, https://doi.org/10.5194/essd-17-2405-2025, https://doi.org/10.5194/essd-17-2405-2025, 2025
Short summary
Short summary
This study addresses the challenge of how clouds affect the Earth's energy balance, which is vital for understanding climate change. We developed a new method to create long-term cloud radiative kernels to improve the accuracy of measurements of sunlight reaching the surface, which significantly reduces errors. Findings suggest that prior estimates of cloud cooling effects may have been overstated, emphasizing the need for better strategies to manage climate change impacts in the Arctic.
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, and Yi He
Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, https://doi.org/10.5194/essd-17-2113-2025, 2025
Short summary
Short summary
We present bias-corrected UK Climate Projections 2018 (UKCP18) regional datasets for temperature, precipitation, and potential evapotranspiration (1981–2080). All 12 members of the 12 km ensemble were corrected using quantile mapping and a change-preserving variant. Both methods effectively reduce biases in multiple statistics while maintaining projected climatic changes. We provide guidance on using the bias-corrected datasets for climate change impact assessment.
Ryan C. Sullivan, David P. Billesbach, Sebastien Biraud, Stephen Chan, Richard Hart, Evan Keeler, Jenni Kyrouac, Sujan Pal, Mikhail Pekour, Sara L. Sullivan, Adam Theisen, Matt Tuftedal, and David R. Cook
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-168, https://doi.org/10.5194/essd-2025-168, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Turbulent fluxes quantify energy, water, or trace gases exchange into and out of the atmosphere. The US Department of Energy Atmospheric Radiation Measurement user facility has been making atmospheric measurements since the early 1990's, including of turbulent fluxes using two well-established methods: energy balance Bowen ratio and eddy covariance. This manuscript documents key aspects of these datasets, including their history, changes through time, and best use practices.
Yanyi He, Kaicun Wang, Kun Yang, Chunlüe Zhou, Changkun Shao, and Changjian Yin
Earth Syst. Sci. Data, 17, 1595–1611, https://doi.org/10.5194/essd-17-1595-2025, https://doi.org/10.5194/essd-17-1595-2025, 2025
Short summary
Short summary
To address key gaps in data availability and homogeneity with regard to sunshine duration, we compiled raw data and made a homogenization protocol to produce a homogenized daily observational dataset of sunshine duration from 1961 to 2022 in China. The dataset avoids a sharp drop in zero-value frequency after 2019 as caused by the instrument upgrade but is also more continuous for various periods. This dataset is crucial for accurately assessing dimming and brightening and for supporting other applications.
Christopher J. Cox, Janet M. Intrieri, Brian J. Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data, 17, 1481–1499, https://doi.org/10.5194/essd-17-1481-2025, https://doi.org/10.5194/essd-17-1481-2025, 2025
Short summary
Short summary
Snow is an essential water resource in the intermountain western United States, and predictions are made using models. We made observations to validate, constrain, and develop the models. The data are from the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign in Colorado's East River valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
Zijiang Song, Zhixiang Cheng, Yuying Li, Shanshan Yu, Xiaowen Zhang, Lina Yuan, and Min Liu
Earth Syst. Sci. Data, 17, 1501–1514, https://doi.org/10.5194/essd-17-1501-2025, https://doi.org/10.5194/essd-17-1501-2025, 2025
Short summary
Short summary
It is hard to access long-time series and high-resolution meteorological data for past years. In this paper, we propose the Geopotential-guided Attention Network (GeoAN) for downscaling which can produce high-resolution data using given low-resolution data. Quantitative and visual comparisons reveal our GeoAN produces better results with regard to most metrics. Using GeoAN, a historical meteorological dataset called MDG625 has been produced daily for the period since 1940.
Susana Barbosa, Nuno Dias, Carlos Almeida, Guilherme Amaral, António Ferreira, António Camilo, and Eduardo Silva
Earth Syst. Sci. Data, 17, 1393–1405, https://doi.org/10.5194/essd-17-1393-2025, https://doi.org/10.5194/essd-17-1393-2025, 2025
Short summary
Short summary
The electric field in the Earth's atmosphere reflects global planetary conditions. It is influenced by both atmospheric processes (such as thunderstorms, pollution, and aerosols) and space weather. Marine measurements of the electric field are rare. Here, we present a unique dataset of atmospheric electric field measurements taken over the Atlantic Ocean. This dataset is valuable not only for atmospheric electricity studies but also for research on climate and space–Earth interactions.
Konstantinos V. Varotsos, Gianna Kitsara, Anna Karali, Ioannis Lemesios, Platon Patlakas, Maria Hatzaki, Vassilis Tenentes, George Katavoutas, Athanasios Sarantopoulos, Basil Psiloglou, Aristeidis G. Koutroulis, Manolis G. Grillakis, and Christos Giannakopoulos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-29, https://doi.org/10.5194/essd-2025-29, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
CLIMADAT-GRid is the first, publicly available, daily air temperature and precipitation gridded climate dataset for Greece at a high resolution of 1 km x 1 km and for the period 1981–2019.The dataset is based on quality-controlled station data while various interpolation techniques were evaluated for generating the daily grids. CLIMADAT-GRid serves as a valuable resource for research and information in climate studies as well as in other areas such as hydrology, agriculture, energy and health.
Aart Overeem, Hidde Leijnse, Mats Veldhuizen, and Bastiaan Anker
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-160, https://doi.org/10.5194/essd-2025-160, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The Dutch real-time gauge-adjusted radar product provides 5 min precipitation accumulations every 5 min covering the Netherlands and the area around it. It plays a key role in hydrological decision-support systems and as input for short-term weather forecasts. Major changes were implemented on 31 January 2023 and the associated quality improvement is presented. Moreover, the employed radar and rain gauge datasets and the algorithms needed to produce this real-time radar product are described.
Lucie Bakels, Michael Blaschek, Marina Dütsch, Andreas Plach, Vincent Lechner, Georg Brack, Leopold Haimberger, and Andreas Stohl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-26, https://doi.org/10.5194/essd-2025-26, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Meteorological reanalyses are crucial datasets. Most reanalyses are Eulerian, providing data at specific, fixed points in space and time. When studying how air moves, it is more convenient to follow air masses through space and time, requiring a Lagrangian reanalysis (LARA). We explain how the LARA dataset is organized, and provide four examples of applications. These include studying the evolution of wind patterns, understanding weather systems, and measuring air mass travel time over land.
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira
Earth Syst. Sci. Data, 17, 1153–1171, https://doi.org/10.5194/essd-17-1153-2025, https://doi.org/10.5194/essd-17-1153-2025, 2025
Short summary
Short summary
Climate change is posing increasing challenges in the dairy cattle farming sector, as heat stress adversely affects the animals' health and milk production. To accurately assess these impacts, we developed a machine learning model to downscale daily climate data to hourly Temperature Humidity Index (THI) values. We utilized historical weather data to train our model and applied them to future climate projections, under two climate scenarios.
Yiguo Wang, François Counillon, Lea Svendsen, Ping-Gin Chiu, Noel Keenlyside, Patrick Laloyaux, Mariko Koseki, and Eric de Boisseson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-127, https://doi.org/10.5194/essd-2025-127, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to today. By using advanced modeling techniques and incorporating sea surface temperature observations, it provides a consistent picture of long-term climate variability. The dataset captures key ocean, sea ice and atmosphere changes, helping scientists understand past climate changes and variability.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-138, https://doi.org/10.5194/essd-2025-138, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China’s growing low-altitude economy and accessible at https://doi.org/10.5281/zenodo.14959025.
Qi Zhang, Chiyuan Miao, Jiajia Su, Jiaojiao Gou, Jinlong Hu, Xi Zhao, and Ye Xu
Earth Syst. Sci. Data, 17, 837–853, https://doi.org/10.5194/essd-17-837-2025, https://doi.org/10.5194/essd-17-837-2025, 2025
Short summary
Short summary
Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland China, offering detailed insights from 1961 to 2022 at a spatial resolution of 0.1°. This dataset incorporates six key drought indices, including multi-scale versions, facilitating early detection and monitoring of droughts. Through the provision of consistent and reliable data, CHM_Drought enhances our understanding of drought patterns, aiding in effective water management and agricultural planning.
Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-70, https://doi.org/10.5194/essd-2025-70, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study introduces the update to the C-LSAT 2.1 station data and its gridded dataset (5° × 5°). Based on this, we develop high-resolution (0.5° × 0.5°) LSAT (C-LSAT HRv1) and DTR (C-LDTR HRv1) datasets. The C-LSAT 2.1 station data integrates over 3000 additional global stations, significantly improving spatial coverage. The global and regional variations in C-LSAT HRv1 and C-LDTR HRv1 align well with their 5° × 5° datasets (C-LSAT 2.1 and C-LDTR).
David Brus, Viet Le, Joel Kuula, and Konstantinos Doulgeris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-61, https://doi.org/10.5194/essd-2025-61, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This manuscript provides datasets collected during Pallas Cloud Experiment campaign in norther Finland during the autumn of 2022. We provided an overview of the custom-built drone backpack for air quality and atmospheric state variables carried on top of the consumer-grade drone (DJI Mavic 2 pro). We described the flight strategies, and provided an overview of the datasets obtained, including a description of the measurement against the reference for data validation.
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-568, https://doi.org/10.5194/essd-2024-568, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research, allowing also studies for water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
Jinlong Hu, Chiyuan Miao, Jiajia Su, Qi Zhang, Jiaojiao Gou, and Qiaohong Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-20, https://doi.org/10.5194/essd-2025-20, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We developed a high-precision daily precipitation dataset for mainland China called CHM_PRE V2. Using data from 3,476 rain gauges, 11 related precipitation variables and advanced machine learning methods, we created a daily precipitation dataset spanning 1960–2023 with unprecedented accuracy. Compared to existing datasets, it better captures rainfall events while reducing false alarms. This work provides a reliable tool for studying water resources, climate change, and disaster management.
Franco Salerno, Nicolas Guyennon, Nicola Colombo, Maria Teresa Melis, Francesco Gabriele Dessì, Gianpietro Verza, Kaji Bista, Ahmad Sheharyar, and Gianni Tartari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-591, https://doi.org/10.5194/essd-2024-591, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Climate change is deeply impacting mountain areas around the globe, especially in Himalaya. Here, we present the Pyramid Meteorological Network, located in Himalaya (Nepal), on the southern slopes of Mt. Everest. The network is composed of 7 meteorological stations located between 2660 and 7986 m a.s.l., which have collected continuous climatic data during the last 30 years (1994–2023). The dataset is available freely accessible from https://zenodo.org/records/14450214 (Salerno et al., 2024).
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan-Peter Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, and Manuel de la Torre Juárez
Earth Syst. Sci. Data, 16, 5643–5663, https://doi.org/10.5194/essd-16-5643-2024, https://doi.org/10.5194/essd-16-5643-2024, 2024
Short summary
Short summary
This dataset provides, for the first time, combined observations of clouds and precipitation with coincident retrievals of atmospheric thermodynamics obtained from the same space-based instrument. Furthermore, it provides the locations of the ray trajectories of the observations along various precipitation-related products interpolated into them with the aim of fostering the use of such dataset in scientific and operational applications.
Frédéric Laly, Patrick Chazette, Julien Totems, Jérémy Lagarrigue, Laurent Forges, and Cyrille Flamant
Earth Syst. Sci. Data, 16, 5579–5602, https://doi.org/10.5194/essd-16-5579-2024, https://doi.org/10.5194/essd-16-5579-2024, 2024
Short summary
Short summary
We present a dataset of water vapor mixing ratio profiles acquired during the Water Vapor Lidar Network Assimilation campaign in fall and winter 2022 and summer 2023, using three lidar systems deployed on the western Mediterranean coastline. This innovative campaign provides access to lower-tropospheric water vapor variability to constrain meteorological forecasting models. The scientific objective is to improve forecasting of heavy-precipation events that lead to flash floods and landslides.
Colin Peter Morice, David I. Berry, Richard C. Cornes, Kathryn Cowtan, Thomas Cropper, Ed Hawkins, John J. Kennedy, Timothy J. Osborn, Nick A. Rayner, Beatriz R. Rivas, Andrew P. Schurer, Michael Taylor, Praveen R. Teleti, Emily J. Wallis, Jonathan Winn, and Elizabeth C. Kent
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-500, https://doi.org/10.5194/essd-2024-500, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a new data set of global gridded surface air temperature change extending back to the 1780s. This is achieved using marine air temperature observations with newly available estimates of diurnal heating biases together with an updated land station database that includes bias adjustments for early thermometer enclosures. These developments allow the data set to extend further into the past than current data sets that use sea surface temperature rather than marine air temperature data.
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024, https://doi.org/10.5194/essd-16-5243-2024, 2024
Short summary
Short summary
The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.
Adam Jaczewski, Michał Marosz, and Mirosław Miętus
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-433, https://doi.org/10.5194/essd-2024-433, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper introduces a high-resolution dataset of daily air temperatures in Poland from 1951 to 2020, with a 1 km2 spatial resolution. PL1GD-T dataset was developed using radial basis functions applied to quality-controlled observations from ground weather stations and evaluated using cross-validation methods. This open-access dataset is crucial for climate change impact studies on a smaller scale and can serve a wide range of users, including researchers, administrative bodies, and society.
Clare Marie Flynn, Julia Moemken, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-298, https://doi.org/10.5194/essd-2024-298, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We created a new, publicly available database of the Top 50 most extreme European winter windstorms from each of four different meteorological input data sets covering the years 1995–2015. We found variability in all aspects of our database, from which storms were included in the Top 50 storms for each input to their spatial variability. We urge users of our database to consider the storms as identified from two or more input sources within our database, where possible.
Thomas Fiolleau and Rémy Roca
Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, https://doi.org/10.5194/essd-16-4021-2024, 2024
Short summary
Short summary
This paper presents a database of tropical deep convective systems over the 2012–2020 period, built from a cloud-tracking algorithm called TOOCAN, which has been applied to homogenized infrared observations from a fleet of geostationary satellites. This database aims to analyze the tropical deep convective systems, the evolution of their associated characteristics over their life cycle, their organization, and their importance in the hydrological and energy cycle.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
Short summary
Short summary
This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
Short summary
Short summary
During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, https://doi.org/10.5194/essd-16-3017-2024, 2024
Short summary
Short summary
Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-112, https://doi.org/10.5194/essd-2024-112, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We develop a high-resolution (4 km and hourly) observational derecho dataset over the United States east of the Rocky Mountains from 2004 to 2021 by using a mesoscale convective system dataset, bow echo detection based on a machine learning method, hourly gust speed measurements, and physically based identification criteria.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
Earth Syst. Sci. Data, 16, 2701–2715, https://doi.org/10.5194/essd-16-2701-2024, https://doi.org/10.5194/essd-16-2701-2024, 2024
Short summary
Short summary
This paper describes multifrequency radar observations of clouds and precipitation during the EPCAPE campaign. The data sets were obtained from CloudCube, a Ka-, W-, and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals. This data collection provides a unique opportunity to study hydrometeors with diameters in the millimeter and submillimeter size range that can be used to better understand the drop size distribution within clouds and precipitation.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
Earth Syst. Sci. Data, 16, 2525–2541, https://doi.org/10.5194/essd-16-2525-2024, https://doi.org/10.5194/essd-16-2525-2024, 2024
Short summary
Short summary
This article provides an overview of the lower-atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as part of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. The data were collected through boundary layer transitions, through sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, and Xiaoyu Yu
Earth Syst. Sci. Data, 16, 2407–2424, https://doi.org/10.5194/essd-16-2407-2024, https://doi.org/10.5194/essd-16-2407-2024, 2024
Short summary
Short summary
We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
Short summary
Short summary
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Finn Burgemeister, Marco Clemens, and Felix Ament
Earth Syst. Sci. Data, 16, 2317–2332, https://doi.org/10.5194/essd-16-2317-2024, https://doi.org/10.5194/essd-16-2317-2024, 2024
Short summary
Short summary
Knowledge of small-scale rainfall variability is needed for hydro-meteorological applications in urban areas. Therefore, we present an open-access data set covering reanalyzed radar reflectivities and rainfall estimates measured by a weather radar at high spatio-temporal resolution in the urban environment of Hamburg between 2013 and 2021. We describe the data reanalysis, outline the measurement’s performance for long time periods, and discuss open issues and limitations of the data set.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
Short summary
Short summary
This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Longhu Chen, Qinqin Wang, Guofeng Zhu, Xinrui Lin, Dongdong Qiu, Yinying Jiao, Siyu Lu, Rui Li, Gaojia Meng, and Yuhao Wang
Earth Syst. Sci. Data, 16, 1543–1557, https://doi.org/10.5194/essd-16-1543-2024, https://doi.org/10.5194/essd-16-1543-2024, 2024
Short summary
Short summary
We have compiled data regarding stable precipitation isotopes from 842 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51 753 data records. The collected data have undergone pre-processing and statistical analysis. We also analysed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Leah Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, https://doi.org/10.5194/essd-16-1301-2024, 2024
Short summary
Short summary
The vertical structure of clouds has a major impact on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here, we present the 3S-GEOPROF-COMB product, a globally gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
Short summary
Short summary
We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
Short summary
Short summary
This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
Short summary
Short summary
Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
Short summary
Short summary
Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
Short summary
Short summary
The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
Short summary
Short summary
Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
Short summary
Short summary
The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
Short summary
Short summary
We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Cited articles
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Tank,
A. M. G. K., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Kumar, K. R.,
Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J., Aguilar, E., Brunet, M.,
Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes
in daily climate extremes of temperature and precipitation, J. Geophys. Res.-Atmos., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006.
Anderson, B. T., Wang, J., Salvucci, G., Gopal, S., and Islam, S.: Observed Trends in
Summertime Precipitation over the Southwestern United States, J. Climate, 23, 1937–1944,
https://doi.org/10.1175/2009JCLI3317.1, 2009.
Beaulieu, C., Seidou, O., Ouarda, T. B. M. J., Zhang, X., Boulet, G., and Yagouti, A.:
Intercomparison of homogenization techniques for precipitation data, Water Resour. Res., 44, W02425, https://doi.org/10.1029/2006WR005615, 2008.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G.,
Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global gridded precipitation
(1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21,
589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk,
A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1∘
Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500,
https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324,
2001.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM Precipitation by
Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?, J. Climate, 28,
6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
Che Ghani, N. Z., Abu Hasan, Z., and Tze Liang, L.: Estimation of Missing Rainfall Data
Using GEP: Case Study of Raja River, Alor Setar, Kedah, Adv. Artif. Intell., 2014, 716398,
https://doi.org/10.1155/2014/716398, 2014.
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF atmospheric
reanalyses of the global climate, Copernic. Clim. Change Serv. Clim. Data Store CDS Access Date
July 2019, available at: https://cds.climate.copernicus.eu/cdsapp{#}!/home (last access: 19 October 2019), 2017.
Coulibaly, P. and Evora, N. D.: Comparison of neural network methods for infilling
missing daily weather records, J. Hydrol., 341, 27–41, https://doi.org/10.1016/j.jhydrol.2007.04.020, 2007.
Dastorani, M. T., Moghadamnia, A., Piri, J., and Rico-Ramirez, M.: Application of ANN
and ANFIS models for reconstructing missing flow data, Environ. Monit. Assess., 166, 421–434,
https://doi.org/10.1007/s10661-009-1012-8, 2010.
Devi, U., Shekhar, M. S., Singh, G. P., Rao, N. N., and Bhatt, U. S.: Methodological
application of quantile mapping to generate precipitation data over Northwest Himalaya,
Int. J. Climatol., 39, 3160–3170, https://doi.org/10.1002/joc.6008, 2019.
Devine, K. A. and Mekis, E.: Field accuracy of Canadian rain measurements,
Atmos.-Ocean, 46, 213–227, 2008.
Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., and Arnold, J. G.:
Constructing Retrospective Gridded Daily Precipitation and Temperature Datasets for the
Conterminous United States, J. Appl. Meteorol. Climatol., 47, 475–497,
https://doi.org/10.1175/2007JAMC1356.1, 2008.
Di Piazza, A., Conti, F. L., Noto, L. V., Viola, F., and La Loggia, G.: Comparative
analysis of different techniques for spatial interpolation of rainfall data to create a serially
complete monthly time series of precipitation for Sicily, Italy, Int. J. Appl. Earth Obs. Geoinf.,
13, 396–408, https://doi.org/10.1016/j.jag.2011.01.005, 2011.
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.: Comprehensive
Automated Quality Assurance of Daily Surface Observations, J. Appl. Meteorol. Climatol., 49,
1615–1633, https://doi.org/10.1175/2010JAMC2375.1, 2010.
Eischeid, J. K., Bruce Baker, C., Karl, T. R., and Diaz, H. F.: The Quality Control of
Long-Term Climatological Data Using Objective Data Analysis, J. Appl. Meteorol., 34, 2787–2795,
https://doi.org/10.1175/1520-0450(1995)034<2787:TQCOLT>2.0.CO;2, 1995.
Eischeid, J. K., Pasteris, P. A., Diaz, H. F., Plantico, M. S., and Lott, N. J.:
Creating a Serially Complete, National Daily Time Series of Temperature and Precipitation for the
Western United States, J. Appl. Meteorol., 39, 1580–1591,
https://doi.org/10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2, 2000.
Gao, L., Bernhardt, M., and Schulz, K.: Elevation correction of ERA-Interim temperature
data in complex terrain, Hydrol. Earth Syst. Sci., 16, 4661–4673,
https://doi.org/10.5194/hess-16-4661-2012, 2012.
Gao, L., Wei, J., Wang, L., Bernhardt, M., Schulz, K., and Chen, X.: A high-resolution
air temperature data set for the Chinese Tian Shan in 1979–2016, Earth Syst. Sci. Data, 10,
2097–2114, https://doi.org/10.5194/essd-10-2097-2018, 2018.
Gardner, A. S., Sharp, M. J., Koerner, R. M., Labine, C., Boon, S., Marshall, S. J.,
Burgess, D. O., and Lewis, D.: Near-Surface Temperature Lapse Rates over Arctic Glaciers and Their
Implications for Temperature Downscaling, J. Climate, 22, 4281–4298, https://doi.org/10.1175/2009jcli2845.1,
2009.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R.,
Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R.,
Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research
and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1,
2017.
Gruber, S.: Derivation and analysis of a high-resolution estimate of global permafrost
zonation, The Cryosphere, 6, 221–233, https://doi.org/10.5194/tc-6-221-2012, 2012.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean
squared error and NSE performance criteria: Implications for improving hydrological modelling,
J. Hydrol., 377, 80–91, 2009.
Hamada, A., Arakawa, O., and Yatagai, A.: An Automated Quality Control Method for Daily
Rain-gauge Data, Glob. Environ. Res., 15, 183–192, 2011.
Hasanpour Kashani, M., and Dinpashoh, Y.: Evaluation of efficiency of different
estimation methods for missing climatological data, Stoch. Environ. Res. Risk Assess., 26,
59–71, https://doi.org/10.1007/s00477-011-0536-y, 2012.
Hubbard, K. G. and You, J.: Sensitivity Analysis of Quality Assurance Using the Spatial
Regression Approach—A Case Study of the Maximum/Minimum Air Temperature, J. Atmospheric
Ocean. Technol., 22, 1520–1530, https://doi.org/10.1175/JTECH1790.1, 2005.
Kanda, N., Negi, H. S., Rishi, M. S., and Shekhar, M. S.: Performance of various
techniques in estimating missing climatological data over snowbound mountainous areas of Karakoram
Himalaya, Meteorol. Appl., 25, 337–349, https://doi.org/10.1002/met.1699, 2018.
Kemp, W. P., Burnell, D. G., Everson, D. O., and Thomson, A. J.: Estimating Missing
Daily Maximum and Minimum Temperatures, J. Clim. Appl. Meteorol., 22, 1587–1593,
https://doi.org/10.1175/1520-0450(1983)022<1587:EMDMAM>2.0.CO;2, 1983.
Kenawy, A. E., López-Moreno, J. I., Stepanek, P., and Vicente-Serrano, S. M.: An
assessment of the role of homogenization protocol in the performance of daily temperature series
and trends: application to northeastern Spain, Int. J. Climatol., 33, 87–108,
https://doi.org/10.1002/joc.3410, 2013.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under
an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, 2012.
Knowles, N., Dettinger, M. D., and Cayan, D. R.: Trends in Snowfall versus Rainfall in
the Western United States, J. Climate, 19, 4545–4559, https://doi.org/10.1175/JCLI3850.1, 2006.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K.,
Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 Reanalysis:
General Specifications and Basic Characteristics, J. Meteorol. Soc. Jpn. Ser II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015.
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R.,
Cayan, D. R., and Brekke, L.: A spatially comprehensive, hydrometeorological data set for Mexico,
the U.S., and Southern Canada 1950–2013, Sci. Data, 2, 150042, https://doi.org/10.1038/sdata.2015.42, 2015.
Longman, R. J., Frazier, A. G., Newman, A. J., Giambelluca, T. W., Schanzenbach, D.,
Kagawa-Viviani, A., Needham, H., Arnold, J. R., and Clark, M. P.: High-Resolution Gridded Daily
Rainfall and Temperature for the Hawaiian Islands (1990–2014), J. Hydrometeorol., 20, 489–508,
https://doi.org/10.1175/JHM-D-18-0112.1, 2019.
Longman, R. J., Newman, A. J., Giambelluca, T. W., and Lucas, M.: Characterizing the
Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii,
J. Appl. Meteorol. Climatol., 59, 1261–1276, https://doi.org/10.1175/JAMC-D-20-0007.1, 2020.
Ma, C., Fassnacht, S. R., and Kampf, S. K.: How Temperature Sensor Change Affects
Warming Trends and Modeling: An Evaluation Across the State of Colorado, Water Resour. Res., 55,
9748–9764, 2019.
Ma, L., Zhang, T., Li, Q., Frauenfeld, O. W., and Qin, D.: Evaluation of ERA-40,
NCEP-1, and NCEP-2 reanalysis air temperatures with ground-based measurements in China,
J. Geophys. Res., 113, D15115, https://doi.org/10.1029/2007jd009549, 2008.
Ma, Y., Hong, Y., Chen, Y., Yang, Y., Tang, G., Yao, Y., Long, D., Li, C., Han, Z., and
Liu, R.: Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic
Bayesian Model Averaging Scheme Over the Tibetan Plateau, J. Geophys. Res.-Atmos., 123, 814–834,
https://doi.org/10.1002/2017jd026648, 2018.
Maraun, D.: Bias Correction, Quantile Mapping, and Downscaling: Revisiting the
Inflation Issue, J. Climate, 26, 2137–2143, https://doi.org/10.1175/JCLI-D-12-00821.1, 2013.
Marshall, S. J., Sharp, M. J., Burgess, D. O., and Anslow, F. S.:
Near-surface-temperature lapse rates on the Prince of Wales Icefield, Ellesmere Island, Canada:
implications for regional downscaling of temperature, Int. J. Climatol., 27, 385–398,
https://doi.org/10.1002/joc.1396, 2007.
Mekis, É. and Brown, R.: Derivation of an adjustment factor map for the estimation
of the water equivalent of snowfall from ruler measurements in Canada, Atmos.-Ocean, 48, 284–293,
https://doi.org/10.3137/AO1104.2010, 2010.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An overview
of the global historical climatology network-daily database, J. Atmospheric Ocean. Technol.,
https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Metcalfe, J. R., Routledge, B., and Devine, K.: Rainfall measurement in Canada:
changing observational methodsand archive adjustment procedures, J. Climate, 10, 92–101, 1997.
Minder, J. R., Mote, P. W., and Lundquist, J. D.: Surface temperature lapse rates over
complex terrain: Lessons from the Cascade Mountains, J. Geophys. Res.-Atmos., 115, D14122.
https://doi.org/10.1029/2009JD013493, 2010.
Newman, A. J., Clark, M. P., Craig, J., Nijssen, B., Wood, A., Gutmann, E., Mizukami,
N., Brekke, L., and Arnold, J. R.: Gridded Ensemble Precipitation and Temperature Estimates for
the Contiguous United States, J. Hydrometeorol., 16, 2481–2500, https://doi.org/10.1175/JHM-D-15-0026.1,
2015.
Newman, A. J., Clark, M. P., Longman, R. J., Gilleland, E., Giambelluca, T. W., and
Arnold, J. R.: Use of Daily Station Observations to Produce High-Resolution Gridded Probabilistic
Precipitation and Temperature Time Series for the Hawaiian Islands, J. Hydrometeorol., 20,
509–529, https://doi.org/10.1175/JHM-D-18-0113.1, 2019.
Papalexiou, S. M. and Montanari, A.: Global and regional increase of precipitation
extremes under global warming, Water Resour. Res., 55, 4901–4914, 2019.
Papalexiou, S. M., AghaKouchak, A., Trenberth, K. E., and Foufoula-Georgiou, E.:
Global, regional, and megacity trends in the highest temperature of the year: Diagnostics and
evidence for accelerating trends, Earths Future, 6, 71–79, 2018.
Pappas, C., Papalexiou, S. M., and Koutsoyiannis, D.: A quick gap filling of missing
hydrometeorological data, J. Geophys. Res.-Atmos., 119, 9290–9300, 2014.
Paulhus, J. L. H. and Kohler, M. A.: Interpolation of missing precipitation records,
Mon. Weather Rev., 80, 129–133,
https://doi.org/10.1175/1520-0493(1952)080<0129:IOMPR>2.0.CO;2, 1952.
Pielke Sr, R., Nielsen-Gammon, J., Davey, C., Angel, J., Bliss, O., Doesken, N., Cai,
M., Fall, S., Niyogi, D., and Gallo, K.: Documentation of uncertainties and biases associated with
surface temperature measurement sites for climate change assessment, B. Am. Meteorol. Soc., 88,
913–928, 2007.
Ramos-Calzado, P., Gómez-Camacho, J., Pérez-Bernal, F., and Pita-López,
M. F.: A novel approach to precipitation series completion in climatological datasets: application
to Andalusia, Int. J. Climatol., 28, 1525–1534, https://doi.org/10.1002/joc.1657, 2008.
Reeves, J., Chen, J., Wang, X. L., Lund, R., and Lu, Q. Q.: A Review and Comparison of
Changepoint Detection Techniques for Climate Data, J. Appl. Meteorol. Climatol., 46, 900–915,
https://doi.org/10.1175/JAM2493.1, 2007.
Rubin, D. B.: Inference and missing data, Biometrika, 63, 581–592,
https://doi.org/10.1093/biomet/63.3.581, 1976.
Santos, L., Thirel, G., and Perrin, C.: Technical note: Pitfalls in using
log-transformed flows within the KGE criterion, Hydrol. Earth Syst. Sci., 22, 4583–4591,
https://doi.org/10.5194/hess-22-4583-2018, 2018.
Sattari, M.-T., Rezazadeh-Joudi, A., and Kusiak, A.: Assessment of different methods
for estimation of missing data in precipitation studies, Hydrol. Res., 48, 1032–1044,
https://doi.org/10.2166/nh.2016.364, 2017.
Scaff, L., Yang, D., Li, Y., and Mekis, E.: Inconsistency in precipitation measurements
across the Alaska–Yukon border, The Cryosphere, 9, 2417–2428,
https://doi.org/10.5194/tc-9-2417-2015, 2015.
Serrano-Notivoli, R., Beguería, S., and de Luis, M.: STEAD: a high-resolution
daily gridded temperature dataset for Spain, Earth Syst. Sci. Data, 11, 1171–1188,
https://doi.org/10.5194/essd-11-1171-2019, 2019.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data, in:
Proceedings of the 1968 23rd ACM national conference, pp. 517–524, ACM, Association for Computing Machinery, New York, NY, United States, 1968.
Simolo, C., Brunetti, M., Maugeri, M., and Nanni, T.: Improving estimation of missing
values in daily precipitation series by a probability density function-preserving approach,
Int. J. Climatol., 30, 1564–1576, https://doi.org/10.1002/joc.1992, 2010.
Stooksbury, D. E., Idso, C. D., and Hubbard, K. G.: The Effects of Data Gaps on the
Calculated Monthly Mean Maximum and Minimum Temperatures in the Continental United States: A
Spatial and Temporal Study, J. Climate, 12, 1524–1533,
https://doi.org/10.1175/1520-0442(1999)012<1524:TEODGO>2.0.CO;2, 1999.
Tang, G., Behrangi, A., Long, D., Li, C., and Hong, Y.: Accounting for spatiotemporal
errors of gauges: A critical step to evaluate gridded precipitation products, J. Hydrol., 559,
294–306, https://doi.org/10.1016/j.jhydrol.2018.02.057, 2018a.
Tang, G., Behrangi, A., Ma, Z., Long, D., and Hong, Y.: Downscaling of ERA-Interim
Temperature in the Contiguous United States and Its Implications for Rain–Snow Partitioning,
J. Hydrometeorol., 19, 1215–1233, https://doi.org/10.1175/jhm-d-18-0041.1, 2018b.
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V.,
Whitfield, P. H.: SCDNA: a serially complete precipitation and temperature dataset for North
America from 1979 to 2018, Zenodo, https://doi.org/10.5281/zenodo.3735533, 2020.
Tang, Q., Wood, A. W., and Lettenmaier, D. P.: Real-Time Precipitation Estimation Based
on Index Station Percentiles, J. Hydrometeorol., 10, 266–277, https://doi.org/10.1175/2008JHM1017.1, 2009.
Teegavarapu, R. S. V. and Chandramouli, V.: Improved weighting methods, deterministic
and stochastic data-driven models for estimation of missing precipitation records, J. Hydrol.,
312, 191–206, https://doi.org/10.1016/j.jhydrol.2005.02.015, 2005.
Ustaoglu, B., Cigizoglu, H. K., and Karaca, M.: Forecast of daily mean, maximum and
minimum temperature time series by three artificial neural network methods, Meteorol. Appl., 15,
431–445, https://doi.org/10.1002/met.83, 2008.
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P.,
Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier,
G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas,
K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti,
M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking
homogenization algorithms for monthly data, Clim. Past, 8, 89–115,
https://doi.org/10.5194/cp-8-89-2012, 2012.
Vicente-Serrano, S. M., Saz-Sanchez, M. A., and Cuadrat, J. M.: Comparative analysis of
interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and
temperature, Clim. Res., 24, 161–180, https://doi.org/10.3354/cr024161, 2003.
Vincent, L. A., Zhang, X., Bonsal, B. R., and Hogg, W. D.: Homogenization of Daily
Temperatures over Canada, J. Climate, 15, 1322–1334,
https://doi.org/10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2, 2002.
Vincent, L. A., Milewska, E. J., Hopkinson, R., and Malone, L.: Bias in Minimum
Temperature Introduced by a Redefinition of the Climatological Day at the Canadian Synoptic
Stations, J. Appl. Meteorol. Climatol., 48, 2160–2168, https://doi.org/10.1175/2009JAMC2191.1, 2009.
Vincent, L. A., Wang, X. L., Milewska, E. J., Wan, H., Yang, F., and Swail, V.: A
second generation of homogenized Canadian monthly surface air temperature for climate trend
analysis, J. Geophys. Res.-Atmos., 117, D18110, https://doi.org/10.1029/2012JD017859, 2012.
Wambua, R. M., Mutua, B. M., and Raude, J. M.: Prediction of Missing
Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River
Basin, Kenya, Am. J. Water Resour., 4, 35–43, , 2016.
Wang, X. L., Xu, H., Qian, B., Feng, Y., and Mekis, E.: Adjusted daily rainfall and
snowfall data for Canada, Atmos.-Ocean, 55, 155–168, 2017.
Werner, A. T., Schnorbus, M. A., Shrestha, R. R., Cannon, A. J., Zwiers, F. W., Dayon,
G., and Anslow, F.: A long-term, temporally consistent, gridded daily meteorological dataset for
northwestern North America, Sci. Data, 6, 1–16, https://doi.org/10.1038/sdata.2018.299, 2019.
Whitfield, P. H.: Climate Station Analysis and Fitness for Purpose Assessment of
3053600 Kananaskis, Alberta, Atmos.-Ocean, 52, 363–383, https://doi.org/10.1080/07055900.2014.946388, 2014.
Woldesenbet, T. A., Elagib, N. A., Ribbe, L., and Heinrich, J.: Gap filling and
homogenization of climatological datasets in the headwater region of the Upper Blue Nile Basin,
Ethiopia, Int. J. Climatol., 37, 2122–2140, https://doi.org/10.1002/joc.4839, 2017.
Wood, A. W.: The University of Washington Surface Water Monitor: An experimental platform for national hydrologic assessment and prediction, in: Proceedings of the AMS 22nd Conference on Hydrology, New Orleans, Louisiana, USA, 2008.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C.,
Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations,
Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Yang, D., Kane, D., Zhang, Z., Legates, D., and Goodison, B.: Bias corrections of
long-term (1973-2004) daily precipitation data over the northern regions, Geophys. Res. Lett., 32,
L19501, https://doi.org/10.1029/2005gl024057, 2005.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.:
APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense
Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/bams-d-11-00122.1,
2012.
Young, K. C.: A Three-Way Model for Interpolating for Monthly Precipitation Values,
Mon. Weather Rev., 120, 2561–2569,
https://doi.org/10.1175/1520-0493(1992)120<2561:ATWMFI>2.0.CO;2, 1992.
Short summary
Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Station observations are critical for hydrological and meteorological studies, but they often...
Altmetrics
Final-revised paper
Preprint