Articles | Volume 13, issue 7
https://doi.org/10.5194/essd-13-3337-2021
© Author(s) 2021. 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-13-3337-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
13 Jul 2021
Data description paper |
| 13 Jul 2021
| 13 Jul 2021
EMDNA: an Ensemble Meteorological Dataset for North America
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta,
Canada
Martyn P. Clark
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta,
Canada
Department of Geography and Planning, University of Saskatchewan,
Saskatchewan, Canada
Simon Michael Papalexiou
Centre for Hydrology, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
Department of Civil, Geological and Environmental Engineering,
University of Saskatchewan, Saskatchewan, Canada
Andrew J. Newman
National Center for Atmospheric Research, Boulder, Colorado, USA
Andrew W. Wood
National Center for Atmospheric Research, Boulder, Colorado, USA
Dominique Brunet
Meteorological Research Division, Environment and Climate Change
Canada, Toronto, Ontario, Canada
Paul H. Whitfield
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta,
Canada
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Guoqiang Tang, Martyn P. Clark, Andrew J. Newman, Andrew W. Wood, Simon Michael Papalexiou, Vincent Vionnet, and Paul H. Whitfield
Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, https://doi.org/10.5194/essd-12-2381-2020, 2020
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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
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Ziqiang Ma, Jintao Xu, Siyu Zhu, Jun Yang, Guoqiang Tang, Yuanjian Yang, Zhou Shi, and Yang Hong
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Andrew J. Newman and Martyn P. Clark
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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Preprint withdrawn
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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
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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
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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
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Gwo-Jong Huang, Viswanathan N. Bringi, Andrew J. Newman, Gyuwon Lee, Dmitri Moisseev, and Branislav M. Notaroš
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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
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
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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
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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
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Pablo A. Mendoza, Andrew W. Wood, Elizabeth Clark, Eric Rothwell, Martyn P. Clark, Bart Nijssen, Levi D. Brekke, and Jeffrey R. Arnold
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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
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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.
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
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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.
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
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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.
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
Related subject area
Meteorology
SARAH-3 – satellite-based climate data records of surface solar radiation
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
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 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
Multifrequency radar observations of marine clouds during the EPCAPE campaign
The PAZ Polarimetric Radio Occultation Research Dataset for Scientific Applications
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
Water vapor Raman-lidar observations from multiple sites in the framework of WaLiNeAs
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
Global high-resolution drought indices for 1981–2022
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
ET-WB: water-balance-based estimations of terrestrial evaporation over global land and major global basins
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
IWIN: the Isfjorden Weather Information Network
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
The EUPPBench postprocessing benchmark dataset v1.0
MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
LegacyClimate 1.0: a dataset of pollen-based climate reconstructions from 2594 Northern Hemisphere sites covering the last 30 kyr and beyond
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
Combined wind lidar and cloud radar for high-resolution wind profiling
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
The AntAWS dataset: a compilation of Antarctic automatic weather station observations
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
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
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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.
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
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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
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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.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-329, https://doi.org/10.5194/essd-2024-329, 2024
Revised manuscript accepted for ESSD
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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-hour temporal resolution, using machine learning model. These can be valuable for filling observational data gaps, advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
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
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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
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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.
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
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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.
Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan P. Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, and Manuel de la Torre Juárez
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-150, https://doi.org/10.5194/essd-2024-150, 2024
Revised manuscript accepted for ESSD
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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 to foster the use of such dataset in scientific and operational applications.
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
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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
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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
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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.
Frédéric Laly, Patrick Chazette, Julien Totems, Jérémy Lagarrigue, Laurent Forges, and Cyrille Flamant
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-73, https://doi.org/10.5194/essd-2024-73, 2024
Revised manuscript accepted for ESSD
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We present a dataset of water vapor mixing ratio profiles acquired during the WaLiNeAs campaign in fall and winter 2022 and summer 2023, using 3 lidar systems deployed on the Western Mediterranean coastline. This innovative campaign gives access to low tropospheric water vapor variability to constrain meteorological forecasting models. The scientific objective is to improve forecasting of heavy precipation events that lead to severe flash floods.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
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We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
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We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar, James S. Famiglietti, Chong Zhang, Gionata Ghiggi, Shenglian Guo, Yun Pan, and Bramha Dutt Vishwakarma
Earth Syst. Sci. Data, 15, 4571–4597, https://doi.org/10.5194/essd-15-4571-2023, https://doi.org/10.5194/essd-15-4571-2023, 2023
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To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
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The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
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Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
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This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Santiago Beguería, Dhais Peña-Angulo, Víctor Trullenque-Blanco, and Carlos González-Hidalgo
Earth Syst. Sci. Data, 15, 2547–2575, https://doi.org/10.5194/essd-15-2547-2023, https://doi.org/10.5194/essd-15-2547-2023, 2023
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A gridded dataset on monthly precipitation over mainland Spain between spans 1916–2020. The dataset combines ground observations from the Spanish National Climate Data Bank and new data rescued from meteorological yearbooks published prior to 1951, which almost doubled the number of weather stations available during the first decades of the 20th century. Geostatistical techniques were used to interpolate a regular 10 x 10 km grid.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
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The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
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A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
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Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
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This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
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This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
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We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
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Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
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Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
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We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Cited articles
Aalto, J., Pirinen, P., and Jylhä, K.: New gridded daily climatology of
Finland: Permutation-based uncertainty estimates and temporal trends in
climate, J. Geophys. Res.-Atmos., 121, 3807–3823,
https://doi.org/10.1002/2015JD024651, 2016.
Adler, R. F., Gu, G. J., Sapiano, M., Wang, J. J., and Huffman, G. J.:
Global Precipitation: Means, Variations and Trends During the Satellite Era
(1979–2014), Surv. Geophys., 38, 679–699,
https://doi.org/10.1007/s10712-017-9416-4, 2017.
Arias-Hidalgo, M., Bhattacharya, B., Mynett, A. E., and van Griensven, A.: Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills, Hydrol. Earth Syst. Sci., 17, 2905–2915, https://doi.org/10.5194/hess-17-2905-2013, 2013.
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 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.
Beck, H. E., Wood, E. F., McVicar, T. R., Zambrano-Bigiarini, M.,
Alvarez-Garreton, C., Baez-Villanueva, O. M., Sheffield, J., and Karger, D.
N.: Bias Correction of Global High-Resolution Precipitation Climatologies
Using Streamflow Observations from 9372 Catchments, J. Clim., 33,
1299–1315, https://doi.org/10.1175/JCLI-D-19-0332.1, 2020.
Brier, G. W.: Verification of forecasts expressed in terms of probability,
Mon. Weather Rev., 78, 1–3,
https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2, 1950.
Bromwich, D. H., Wilson, A. B., Bai, L., Liu, Z., Barlage, M., Shih, C.-F.,
Maldonado, S., Hines, K. M., Wang, S.-H., and Woollen, J.: The Arctic system
reanalysis, version 2, B. Am. Meteorol. Soc., 99, 805–828, 2018.
Caillouet, L., Vidal, J.-P., Sauquet, E., Graff, B., and Soubeyroux, J.-M.: SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France, Earth Syst. Sci. Data, 11, 241–260, https://doi.org/10.5194/essd-11-241-2019, 2019.
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. Clim., 28, 6938–6959,
https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
Chen, Y., Yuan, W., Xia, J., Fisher, J. B., Dong, W., Zhang, X., Liang, S.,
Ye, A., Cai, W., and Feng, J.: Using Bayesian model averaging to estimate
terrestrial evapotranspiration in China, J. Hydrol., 528, 537–549,
https://doi.org/10.1016/j.jhydrol.2015.06.059, 2015.
Clark, M. P. and Hay, L. E.: Use of medium-range numerical weather
prediction model output to produce forecasts of streamflow, J.
Hydrometeorol., 5, 15–32, 2004.
Clark, M. P. and Slater, A. G.: Probabilistic Quantitative Precipitation
Estimation in Complex Terrain, J. Hydrometeorol., 7, 3–22,
https://doi.org/10.1175/JHM474.1, 2006.
Clark, M. P., Slater, A. G., Barrett, A. P., Hay, L. E., McCabe, G. J.,
Rajagopalan, B., and Leavesley, G. H.: Assimilation of snow covered area
information into hydrologic and land-surface models, Adv. Water Resour., 29,
1209–1221, https://doi.org/10.1016/j.advwatres.2005.10.001, 2006.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones,
P. D.: An ensemble version of the E-OBS temperature and precipitation data
sets, J. Geophys. Res.-Atmos., 123, 9391–9409,
https://doi.org/10.1029/2017JD028200, 2018.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064,
https://doi.org/10.1002/joc.1688, 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.
Dinku, T., Anagnostou, E. N., and Borga, M.: Improving radar-based
estimation of rainfall over complex terrain, J. Appl. Meteorol., 41,
1163–1178, 2002.
Donat, M. G., Sillmann, J., Wild, S., Alexander, L. V., Lippmann, T., and
Zwiers, F. W.: Consistency of Temperature and Precipitation Extremes across
Various Global Gridded In Situ and Reanalysis Datasets, J. Clim., 27,
5019–5035, https://doi.org/10.1175/JCLI-D-13-00405.1, 2014.
Duan, Q. and Phillips, T. J.: Bayesian estimation of local signal and noise
in multimodel simulations of climate change, J. Geophys. Res.-Atmos.,
115, D18123, https://doi.org/10.1029/2009JD013654, 2010.
Duan, S.-B. and Li, Z.-L.: Spatial Downscaling of MODIS Land Surface
Temperatures Using Geographically Weighted Regression: Case Study in
Northern China, IEEE Trans. Geosci. Remote Sens., 54, 6458–6469,
https://doi.org/10.1109/TGRS.2016.2585198, 2016.
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.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1 km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315,
2017.
Folland, C. K.: Numerical models of the raingauge exposure problem, field
experiments and an improved collector design, Q. J. R. Meteor. Soc., 114,
1485–1516, https://doi.org/10.1002/qj.49711448407, 1988.
Fortin, V., Roy, G., Donaldson, N., and Mahidjiba, A.: Assimilation of radar
quantitative precipitation estimations in the Canadian Precipitation
Analysis (CaPA), J. Hydrol., 531, 296–307,
https://doi.org/10.1016/j.jhydrol.2015.08.003, 2015.
Fortin, V., Roy, G., Stadnyk, T., Koenig, K., Gasset, N., and Mahidjiba, A.:
Ten Years of Science Based on the Canadian Precipitation Analysis: A CaPA
System Overview and Literature Review, Atmos.-Ocean, 56, 178–196,
https://doi.org/10.1080/07055900.2018.1474728, 2018.
Frei, C. and Isotta, F. A.: Ensemble Spatial Precipitation Analysis From
Rain Gauge Data: Methodology and Application in the European Alps, J.
Geophys. Res.-Atmos., 124, 5757–5778,
https://doi.org/10.1029/2018JD030004, 2019.
Fuchs, T., Rapp, J., Rubel, F., and Rudolf, B.: Correction of synoptic
precipitation observations due to systematic measuring errors with special
regard to precipitation phases, Phys. Chem. Earth Pt. B, 26, 689–693, 2001.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations–a new environmental
record for monitoring extremes, Sci. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015.
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. Clim., 30,
5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1, 2017.
Goodison, B. E., Louie, P. Y., and Yang, D.: WMO solid precipitation
measurement intercomparison, Report no. 67, World Meteorological Organization, 1998.
Habib, E., Haile, A. T., Sazib, N., Zhang, Y., and Rientjes, T.: Effect of
Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the
Source of the Upper Blue Nile, Remote Sens., 6, 6688–6708,
https://doi.org/10.3390/rs6076688, 2014.
Hamilton, A. S. and Moore, R. D.: Quantifying Uncertainty in Streamflow
Records, Can. Water Resour. J., 37, 3–21,
https://doi.org/10.4296/cwrj3701865, 2012.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci. Data,
7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P.
D., and New, M.: A European daily high-resolution gridded data set of
surface temperature and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008.
Hellinger, E.: Neue begründung der theorie quadratischer formen von
unendlichvielen veränderlichen, J.Reine Angew. Math. Crelles
J., 1909, 210–271, 1909.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Henn, B., Newman, A. J., Livneh, B., Daly, C., and Lundquist, J. D.: An
assessment of differences in gridded precipitation datasets in complex
terrain, J. Hydrol., 556, 1205–1219,
https://doi.org/10.1016/j.jhydrol.2017.03.008, 2018.
Herrnegger, M., Senoner, T., and Nachtnebel, H.-P.: Adjustment of
spatio-temporal precipitation patterns in a high Alpine environment, J.
Hydrol., 556, 913–921, https://doi.org/10.1016/j.jhydrol.2016.04.068, 2018.
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570, 2000.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G.,
de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.: Bayesian
Model Averaging: A Tutorial, Stat. Sci., 14, 382–401, 1999.
Hong, Y., Hsu, K., Moradkhani, H., and Sorooshian, S.: Uncertainty
quantification of satellite precipitation estimation and Monte Carlo
assessment of the error propagation into hydrologic response, Water Resour.
Res., 42, W08421, https://doi.org/10.1029/2005wr004398, 2006.
Hu, Q., Li, Z., Wang, L., Huang, Y., Wang, Y., and Li, L.: Rainfall Spatial
Estimations: A Review from Spatial Interpolation to Multi-Source Data
Merging, Water, 11, 579, https://doi.org/10.3390/w11030579, 2019.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor
Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/jhm560.1, 2007.
Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.
W., Zimmermann, N. E., Linder, H. P., and Kessler, M.: Climatologies at high
resolution for the earth's land surface areas, Sci. Data, 4, 170122,
https://doi.org/10.1038/sdata.2017.122, 2017.
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.
Khedhaouiria, D., Bélair, S., Fortin, V., Roy, G., and Lespinas, F.:
High Resolution (2.5 km) Ensemble Precipitation Analysis across Canada, J.
Hydrometeorol., 21, 2023–2039, https://doi.org/10.1175/JHM-D-19-0282.1, 2020.
Kiang, J. E., Gazoorian, C., McMillan, H., Coxon, G., Coz, J. L.,
Westerberg, I. K., Belleville, A., Sevrez, D., Sikorska, A. E.,
Petersen-Øverleir, A., Reitan, T., Freer, J., Renard, B., Mansanarez, V.,
and Mason, R.: A Comparison of Methods for Streamflow Uncertainty
Estimation, Water Resour. Res., 54, 7149–7176,
https://doi.org/10.1029/2018WR022708, 2018.
Kirstetter, P.-E., Gourley, J. J., Hong, Y., Zhang, J., Moazamigoodarzi, S.,
Langston, C., and Arthur, A.: Probabilistic precipitation rate estimates
with ground-based radar networks, Water Resour. Res., 51, 1422–1442,
https://doi.org/10.1002/2014WR015672, 2015.
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.
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Meyers, T., Buisan, S., Isaksen, K., Brækkan, R., Landolt, S., and Jachcik, A.: Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE, Hydrol. Earth Syst. Sci., 22, 1437–1452, https://doi.org/10.5194/hess-22-1437-2018, 2018.
Lader, R., Bhatt, U. S., Walsh, J. E., Rupp, T. S., and Bieniek, P. A.:
Two-Meter Temperature and Precipitation from Atmospheric Reanalysis
Evaluated for Alaska, J. Appl. Meteorol. Climatol., 55, 901–922,
https://doi.org/10.1175/JAMC-D-15-0162.1, 2016.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis,
K. M., Maurer, E. P., and Lettenmaier, D. P.: A Long-Term Hydrologically
Based Dataset of Land Surface Fluxes and States for the Conterminous United
States: Update and Extensions, J. Clim., 26, 9384–9392,
https://doi.org/10.1175/JCLI-D-12-00508.1, 2013.
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.
Lu, X., Tang, G., Wang, X., Liu, Y., Wei, M., and Zhang, Y.: The Development
of a Two-Step Merging and Downscaling Method for Satellite Precipitation
Products, Remote Sens., 12, 398, https://doi.org/10.3390/rs12030398, 2020.
Ma, Y., Yang, Y., Han, Z., Tang, G., Maguire, L., Chu, Z., and Hong, Y.:
Comprehensive evaluation of Ensemble Multi-Satellite Precipitation Dataset
using the Dynamic Bayesian Model Averaging scheme over the Tibetan plateau,
J. Hydrol., 556, 634–644, https://doi.org/10.1016/j.jhydrol.2017.11.050,
2018a.
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, 2018b.
Ma, Z., Xu, J., Zhu, S., Yang, J., Tang, G., Yang, Y., Shi, Z., and Hong, Y.: AIMERG: a new Asian precipitation dataset (0.1∘/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE, Earth Syst. Sci. Data, 12, 1525–1544, https://doi.org/10.5194/essd-12-1525-2020, 2020.
Mahfouf, J.-F., Brasnett, B., and Gagnon, S.: A Canadian precipitation
analysis (CaPA) project: Description and preliminary results,
Atmos.-Ocean, 45, 1–17, https://doi.org/10.3137/ao.v450101, 2007.
Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., and Nijssen,
B.: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and
States for the Conterminous United States, J. Clim., 15, 3237-3251, 2002.
Mears, C. A., Wentz, F. J., Thorne, P., and Bernie, D.: Assessing
uncertainty in estimates of atmospheric temperature changes from MSU and
AMSU using a Monte-Carlo estimation technique, J. Geophys. Res.-Atmos.,
116, D08112, https://doi.org/10.1029/2010JD014954, 2011.
Mendoza, P. A., Wood, A. W., Clark, E., Rothwell, E., Clark, M. P., Nijssen, B., Brekke, L. D., and Arnold, J. R.: An intercomparison of approaches for improving operational seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 21, 3915–3935, https://doi.org/10.5194/hess-21-3915-2017, 2017.
Mooney, P. A., Mulligan, F. J., and Fealy, R.: Comparison of ERA-40,
ERA-Interim and NCEP/NCAR reanalysis data with observed surface air
temperatures over Ireland, Int. J. Climatol., 31, 545–557,
https://doi.org/10.1002/joc.2098, 2011.
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys. Res.-Atmos., 117, D08101, https://doi.org/10.1029/2011JD017187, 2012.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: A state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-82, in review, 2021.
Nešpor, V. and Sevruk, B.: Estimation of Wind-Induced Error of Rainfall
Gauge Measurements Using a Numerical Simulation, J. Atmos. Ocean.
Tech., 16, 450–464, https://doi.org/10.1175/1520-0426(1999)016<0450:EOWIEO>2.0.CO;2, 1999.
Newman, A. J. and Clark, M. P.: TIER version 1.0: an open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fields, Geosci. Model Dev., 13, 1827–1843, https://doi.org/10.5194/gmd-13-1827-2020, 2020.
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., and Giambelluca, T. W.:
Methodological Intercomparisons of Station-Based Gridded Meteorological
Products: Utility, Limitations, and Paths Forward, J. Hydrometeorol., 20,
531–547, https://doi.org/10.1175/JHM-D-18-0114.1, 2019a.
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, 2019b.
Newman, A. J., Clark, M. P., Wood, A. W., and Arnold, J. R.: Probabilistic
Spatial Meteorological Estimates for Alaska and the Yukon, J. Geophys. Res.-Atmos., 125, e2020JD032696, https://doi.org/10.1029/2020JD032696, 2020.
Papalexiou, S. M.: Unified theory for stochastic modelling of hydroclimatic
processes: Preserving marginal distributions, correlation structures, and
intermittency, Adv. Water Resour., 115, 234–252, 2018.
Papalexiou, S. M. and Koutsoyiannis, D.: Battle of extreme value
distributions: A global survey on extreme daily rainfall, Water Resour.
Res., 49, 187–201, https://doi.org/10.1029/2012WR012557, 2013.
Papalexiou, S. M. and Serinaldi, F.: Random Fields Simplified: Preserving
Marginal Distributions, Correlations, and Intermittency, With Applications
From Rainfall to Humidity, Water Resour. Res., 56, e2019WR026331,
https://doi.org/10.1029/2019WR026331, 2020.
Parker, W. S.: Reanalyses and Observations: What's the Difference?, B.
Am. Meteorol. Soc., 97, 1565–1572,
https://doi.org/10.1175/BAMS-D-14-00226.1, 2016.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using
Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev.,
133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005.
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti, J.
S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers,
D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J.,
Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A.,
Sheffield, J., and Wood, E. F.: The Observed State of the Water Cycle in the
Early Twenty-First Century, J. Clim., 28, 8289–8318,
https://doi.org/10.1175/JCLI-D-14-00555.1, 2015.
Sampson, A. A., Wright, D. B., Stewart, R. D., and LoBue, A. C.: The role of
rainfall temporal and spatial averaging in seasonal simulations of the
terrestrial water balance, Hydrol. Process., 34, 2531–2542,
https://doi.org/10.1002/hyp.13745, 2020.
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.
Schepen, A. and Wang, Q. J.: Model averaging methods to merge operational
statistical and dynamic seasonal streamflow forecasts in Australia, Water
Resour. Res., 51, 1797–1812, https://doi.org/10.1002/2014WR016163, 2015.
Sevruk, B.: International comparison of national precipitation gauges with a
reference pit gauge, WMO Instrum. Obs. Methods Rep. No. 17, 111 pp., 1984.
Shen, Y., Zhao, P., Pan, Y., and Yu, J. J.: A high spatiotemporal
gauge-satellite merged precipitation analysis over China, J. Geophys.
Res.-Atmos., 119, 3063–3075, https://doi.org/10.1002/2013jd020686,
2014a.
Shen, Y., Zhao, P., Pan, Y., and Yu, J.: A high spatiotemporal
gauge-satellite merged precipitation analysis over China, J. Geophys. Res.-Atmos., 119, 3063–3075, https://doi.org/10.1002/2013JD020686, 2014b.
Shen, Y., Hong, Z., Pan, Y., Yu, J., and Maguire, L.: China's 1 km Merged
Gauge, Radar and Satellite Experimental Precipitation Dataset, Remote Sens.,
10, 264, https://doi.org/10.3390/rs10020264, 2018.
Sinclair, S. and Pegram, G.: Combining radar and rain gauge rainfall
estimates using conditional merging, Atmos. Sci. Lett., 6, 19–22,
https://doi.org/10.1002/asl.85, 2005.
Slater, A. G. and Clark, M. P.: Snow Data Assimilation via an Ensemble
Kalman Filter, J. Hydrometeorol., 7, 478–493,
https://doi.org/10.1175/JHM505.1, 2006.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
Review of Global Precipitation Data Sets: Data Sources, Estimation, and
Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017rg000574, 2018.
Tang, G., Zeng, Z., Long, D., Guo, X., Yong, B., Zhang, W., and Hong, Y.:
Statistical and Hydrological Comparisons between TRMM and GPM Level-3
Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA
3B42V7?, J. Hydrometeorol., 17, 121–137,
https://doi.org/10.1175/jhm-d-15-0059.1, 2016.
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., Papalexiou, S. M., Newman, A. J., Wood, A. W.,
Brunet, D., and Whitfield, P. H.: EMDNA: Ensemble Meteorological Dataset for
North America [Dataset], FRDR, https://doi.org/10.20383/101.0275, 2020a.
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., and Hong, Y.: Have
satellite precipitation products improved over last two decades? A
comprehensive comparison of GPM IMERG with nine satellite and reanalysis
datasets, Remote Sens. Environ., 240, 111697,
https://doi.org/10.1016/j.rse.2020.111697, 2020b.
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., and Whitfield, P. H.: SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018, Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, 2020c.
Tang, G., Clark, M. P., and Papalexiou, S. M.: The use of serially complete
station data to improve the temporal continuity of gridded precipitation and
temperature estimates, J. Hydrometeorol., 22, 1553–1568, 2021.
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456–457, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The
Changing Character of Precipitation, B. Am. Meteorol. Soc., 84,
1205–1218, https://doi.org/10.1175/BAMS-84-9-1205, 2003.
Vila, D. A., de Goncalves, L. G. G., Toll, D. L., and Rozante, J. R.:
Statistical Evaluation of Combined Daily Gauge Observations and Rainfall
Satellite Estimates over Continental South America, J. Hydrometeorol., 10,
533–543, https://doi.org/10.1175/2008JHM1048.1, 2009.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and
Viterbo, P.: The WFDEI meteorological forcing data set: WATCH Forcing Data
methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50,
7505–7514, https://doi.org/10.1002/2014wr015638, 2014.
Willkofer, F., Schmid, F.-J., Komischke, H., Korck, J., Braun, M., and
Ludwig, R.: The impact of bias correcting regional climate model results on
hydrological indicators for Bavarian catchments, J. Hydrol. Reg. Stud., 19,
25–41, https://doi.org/10.1016/j.ejrh.2018.06.010, 2018.
Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D. P.: Hydrologic
Implications of Dynamical and Statistical Approaches to Downscaling Climate
Model Outputs, Clim. Change, 62, 189–216,
https://doi.org/10.1023/B:CLIM.0000013685.99609.9e, 2004.
Wu, H., Adler, R. F., Tian, Y., Huffman, G. J., Li, H., and Wang, J.:
Real-time global flood estimation using satellite-based precipitation and a
coupled land surface and routing model, Water Resour. Res., 50, 2693–2717,
https://doi.org/10.1002/2013wr014710, 2014.
Xie, P. and Xiong, A.-Y.: A conceptual model for constructing
high-resolution gauge-satellite merged precipitation analyses, J. Geophys.
Res.-Atmos., 116, D21106, https://doi.org/10.1029/2011JD016118, 2011.
Xu, S., Wu, C., Wang, L., Gonsamo, A., Shen, Y., and Niu, Z.: A new
satellite-based monthly precipitation downscaling algorithm with
non-stationary relationship between precipitation and land surface
characteristics, Remote Sens. Environ., 162, 119–140,
https://doi.org/10.1016/j.rse.2015.02.024, 2015.
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., Goodison, B. E., Ishida, S., and Benson, C. S.: Adjustment of
daily precipitation data at 10 climate stations in Alaska: Application of
World Meteorological Organization intercomparison results, Water Resour.
Res., 34, 241–256, https://doi.org/10.1029/97WR02681, 1998.
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.
Yin, J., Gentine, P., Zhou, S., Sullivan, S. C., Wang, R., Zhang, Y., and
Guo, S.: Large increase in global storm runoff extremes driven by climate
and anthropogenic changes, Nat. Commun., 9, 4389,
https://doi.org/10.1038/s41467-018-06765-2, 2018.
Zhang, Y., Ren, Y., Ren, G., and Wang, G.: Bias Correction of Gauge Data and
its Effect on Precipitation Climatology over Mainland China, J. Appl.
Meteorol. Climatol., 58, 2177–2196,
https://doi.org/10.1175/JAMC-D-19-0049.1, 2019.
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.
Probabilistic estimates are useful to quantify the uncertainties in meteorological datasets....
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