Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2635-2023
© Author(s) 2023. 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-15-2635-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Jun 2023
Data description paper |
| 28 Jun 2023
| 28 Jun 2023
The EUPPBench postprocessing benchmark dataset v1.0
Jonathan Demaeyer
CORRESPONDING AUTHOR
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
European Meteorological Network (EUMETNET), Brussels, Belgium
Jonas Bhend
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Sebastian Lerch
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Cristina Primo
Deutscher Wetterdienst, Offenbach, Germany
Bert Van Schaeybroeck
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
Aitor Atencia
GeoSphere Austria, Vienna, Austria
Zied Ben Bouallègue
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Jieyu Chen
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Markus Dabernig
GeoSphere Austria, Vienna, Austria
Gavin Evans
Met Office, Exeter, United Kingdom
Jana Faganeli Pucer
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Ben Hooper
Met Office, Exeter, United Kingdom
Nina Horat
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
David Jobst
Institute of Mathematics and Applied Informatics, University of Hildesheim, Hildesheim, Germany
Janko Merše
Slovenian Environment Agency, Ljubljana, Slovenia
Peter Mlakar
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Slovenian Environment Agency, Ljubljana, Slovenia
Annette Möller
Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
Olivier Mestre
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Toulouse, France
Maxime Taillardat
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Toulouse, France
Stéphane Vannitsem
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
European Meteorological Network (EUMETNET), Brussels, Belgium
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Preprint withdrawn
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Clemens Wastl, Yong Wang, Aitor Atencia, and Christoph Wittmann
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The El Niño–Southern Oscillation phenomenon is a slow dynamics present in the coupled ocean–atmosphere tropical Pacific system which has important teleconnections with the northern extratropics. These teleconnections are usually believed to be the source of an enhanced predictability in the northern extratropics at seasonal to decadal timescales. This question is challenged by investigating the causality between these regions using an advanced technique known as convergent cross mapping.
Maite Bauwens, Trissevgeni Stavrakou, Jean-François Müller, Bert Van Schaeybroeck, Lesley De Cruz, Rozemien De Troch, Olivier Giot, Rafiq Hamdi, Piet Termonia, Quentin Laffineur, Crist Amelynck, Niels Schoon, Bernard Heinesch, Thomas Holst, Almut Arneth, Reinhart Ceulemans, Arturo Sanchez-Lorenzo, and Alex Guenther
Biogeosciences, 15, 3673–3690, https://doi.org/10.5194/bg-15-3673-2018, https://doi.org/10.5194/bg-15-3673-2018, 2018
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Biogenic isoprene fluxes are simulated over Europe with the MEGAN–MOHYCAN model for the recent past and end-of-century climate at high spatiotemporal resolution (0.1°, 3 min). Due to climate change, fluxes increased by 40 % over 1979–2014. Climate scenarios for 2070–2099 suggest an increase by 83 % due to climate, and an even stronger increase when the potential impact of CO2 fertilization is considered (up to 141 %). Accounting for CO2 inhibition cancels out a large part of these increases.
Lesley De Cruz, Sebastian Schubert, Jonathan Demaeyer, Valerio Lucarini, and Stéphane Vannitsem
Nonlin. Processes Geophys., 25, 387–412, https://doi.org/10.5194/npg-25-387-2018, https://doi.org/10.5194/npg-25-387-2018, 2018
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The predictability of weather models is limited largely by the initial state error growth or decay rates. We have computed these rates for PUMA, a global model for the atmosphere, and MAOOAM, a more simplified, coupled model which includes the ocean. MAOOAM has processes at distinct timescales, whereas PUMA surprisingly does not. We propose a new programme to compute the natural directions along the flow that correspond to the growth or decay rates, to learn which components play a role.
Alexander Pasternack, Jonas Bhend, Mark A. Liniger, Henning W. Rust, Wolfgang A. Müller, and Uwe Ulbrich
Geosci. Model Dev., 11, 351–368, https://doi.org/10.5194/gmd-11-351-2018, https://doi.org/10.5194/gmd-11-351-2018, 2018
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We propose a decadal forecast recalibration strategy (DeFoReSt) which simultaneously adjusts unconditional and conditional bias, as well as the ensemble spread while considering the typical setting of decadal predictions, i.e., model drift and a climate trend. We apply DeFoReSt to decadal toy model data and surface temperature forecasts from the MiKlip system and find consistent improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
Lesley De Cruz, Jonathan Demaeyer, and Stéphane Vannitsem
Geosci. Model Dev., 9, 2793–2808, https://doi.org/10.5194/gmd-9-2793-2016, https://doi.org/10.5194/gmd-9-2793-2016, 2016
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Large-scale weather patterns such as the North Atlantic Oscillation, which dictates the harshness of European winters, vary over the course of years. By recreating it in a simple ocean-atmosphere model, we hope to understand what drives this slow, hard-to-predict variability. MAOOAM is such a model, in which the resolution and included physical processes can easily be modified. The modular system allowed us to show the robustness of the slow variability against changes in model resolution.
Cristina Primo
Adv. Sci. Res., 13, 113–120, https://doi.org/10.5194/asr-13-113-2016, https://doi.org/10.5194/asr-13-113-2016, 2016
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Warnings have become a standard product in meteorological centres since they help the public prevent major disasters and minimize costs or losses. They happen rarely and add a new temporal dimension, namely the time window of the forecasted event. Thus, specific verification methods are required to verify warnings. This paper analyses the warning verification issues and includes a real life example, the verification of wind gust warnings at the German Meteorological Centre (Deutscher Wetterdienst).
Olivier Giot, Piet Termonia, Daan Degrauwe, Rozemien De Troch, Steven Caluwaerts, Geert Smet, Julie Berckmans, Alex Deckmyn, Lesley De Cruz, Pieter De Meutter, Annelies Duerinckx, Luc Gerard, Rafiq Hamdi, Joris Van den Bergh, Michiel Van Ginderachter, and Bert Van Schaeybroeck
Geosci. Model Dev., 9, 1143–1152, https://doi.org/10.5194/gmd-9-1143-2016, https://doi.org/10.5194/gmd-9-1143-2016, 2016
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The Royal Meteorological Institute of Belgium and Ghent University have performed two simulations with different horizontal resolutions of the past observed climate of Europe for the period 1979–2010. Of special interest is the new way of handling convective precipitation in the model that was used. Results show that the model is capable of representing the European climate and comparison with other models reveals that precipitation patterns are well represented.
Y. Brugnara, R. Auchmann, S. Brönnimann, R. J. Allan, I. Auer, M. Barriendos, H. Bergström, J. Bhend, R. Brázdil, G. P. Compo, R. C. Cornes, F. Dominguez-Castro, A. F. V. van Engelen, J. Filipiak, J. Holopainen, S. Jourdain, M. Kunz, J. Luterbacher, M. Maugeri, L. Mercalli, A. Moberg, C. J. Mock, G. Pichard, L. Řezníčková, G. van der Schrier, V. Slonosky, Z. Ustrnul, M. A. Valente, A. Wypych, and X. Yin
Clim. Past, 11, 1027–1047, https://doi.org/10.5194/cp-11-1027-2015, https://doi.org/10.5194/cp-11-1027-2015, 2015
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A data set of instrumental pressure and temperature observations for the early instrumental period (before ca. 1850) is described. This is the result of a digitisation effort involving the period immediately after the eruption of Mount Tambora in 1815, combined with the collection of already available sub-daily time series. The highest data availability is therefore for the years 1815 to 1817. An analysis of pressure variability and of case studies in Europe is performed for that period.
S. Vannitsem and L. De Cruz
Geosci. Model Dev., 7, 649–662, https://doi.org/10.5194/gmd-7-649-2014, https://doi.org/10.5194/gmd-7-649-2014, 2014
S. Brönnimann, J. Bhend, J. Franke, S. Flückiger, A. M. Fischer, R. Bleisch, G. Bodeker, B. Hassler, E. Rozanov, and M. Schraner
Atmos. Chem. Phys., 13, 9623–9639, https://doi.org/10.5194/acp-13-9623-2013, https://doi.org/10.5194/acp-13-9623-2013, 2013
Related subject area
Domain: ESSD – Atmosphere | Subject: 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
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
LARA: a Lagrangian Reanalysis based on ERA5 spanning from 1940 to 2023
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
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)
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)
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
The 2023 National Offshore Wind data set (NOW-23)
Dataset of stable isotopes of precipitation in the Eurasian continent
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
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
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
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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.
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
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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.
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
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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
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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
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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.
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
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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
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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
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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).
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
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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 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
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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
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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
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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.
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
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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
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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
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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.
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.
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
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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
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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.
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.
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.
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.
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.
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.
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.
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.
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.
Cited articles
Ashkboos, S., Huang, L., Dryden, N., Ben-Nun, T., Dueben, P., Gianinazzi, L.,
Kummer, L., and Hoefler, T.: Ens-10: A dataset for post-processing ensemble
weather forecast, arXiv [preprint], https://doi.org/10.48550/arXiv.2206.14786, 29 June 2022. a, b
Ben Bouallègue, Z.: Accounting for representativeness in the verification
of ensemble forecasts, ECMWF Technical Memoranda, 865, https://doi.org/10.21957/5z6esc7wr, 2020. a
Ben Bouallègue, Z.: EUPP-benchmark/ESSD-ASRE: version 1.0 release, Zenodo [code],
https://doi.org/10.5281/zenodo.7477735, 2023. a
Benjamini, Y. and Hochberg, Y.: Controlling the False Discovery Rate: A
Practical and Powerful Approach to Multiple Testing, J. Roy.
Stat. Soc. B Met., 57, 289–300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x, 1995. a
Bhend, J., Dabernig, M., Demaeyer, J., Mestre, O., and Taillardat, M.:
EUPPBench postprocessing benchmark dataset – station data, Zenodo [data set],
https://doi.org/10.5281/zenodo.7708362, 2023. a, b
Bremnes, J. B.: Ensemble postprocessing using quantile function regression
based on neural networks and Bernstein polynomials, Mon. Weather Rev.,
148, 403–414, 2020. a
Chapman, W. E., Monache, L. D., Alessandrini, S., Subramanian, A. C., Ralph,
F. M., Xie, S.-P., Lerch, S., and Hayatbini, N.: Probabilistic Predictions
from Deterministic Atmospheric River Forecasts with Deep Learning, Mon.
Weather Rev., 150, 215–234, https://doi.org/10.1175/MWR-D-21-0106.1, 2022. a
Chen, J., Janke, T., Steinke, F., and Lerch, S.: Generative machine learning
methods for multivariate ensemble post-processing, arXiv [preprint],
https://doi.org/10.48550/arXiv.2211.01345, 26 September 2022. a
Chen, J., Dabernig, M., Demaeyer, J., Evans, G., Faganeli Pucer, J., Hooper,
B., Horat, N., Jobst, D., Lerch, S., Mlakar, P., Möller, A., Merše, J., and
Bouallègue, Z. B.: ESSD benchmark output data, Zenodo [data set], https://doi.org/10.5281/zenodo.7798350,
2023a. a
Chen, J., Horat, N., and Lerch, S.: EUPP-benchmark/ESSD-DRN: version 1.0
release, Zenodo [code], https://doi.org/10.5281/zenodo.7477698, 2023b. a
Copernicus Land Monitoring Service, E. U.: EU-DEM, European
Environment Agency, CLC
https://land.copernicus.eu/imagery-in-situ/eu-dem, last access: 1 August 2022. a
Dabernig, M.: EUPP-benchmark/ESSD-EMOS: version 1.0 release, Zenodo [code],
https://doi.org/10.5281/zenodo.7477749, 2023. a
Dabernig, M., Mayr, G. J., Messner, J. W., and Zeileis, A.: Spatial Ensemble
Post-Processing with Standardized Anomalies, Q. J. Roy.
Meteor. Soc., 143, 909–916, https://doi.org/10.1002/qj.2975,
2017. a
Demaeyer, J.: Climdyn/pythie: Version 0.1.0 alpha release, Zenodo [code],
https://doi.org/10.5281/zenodo.7233538, 2022a. a
Demaeyer, J.: EUPPBench postprocessing benchmark dataset – gridded data – Part
I, Zenodo [data set], https://doi.org/10.5281/zenodo.7429236, 2022b. a, b
Demaeyer, J.: EUPP-benchmark/ESSD-mbm: version 1.0 release, Zenodo [code],
https://doi.org/10.5281/zenodo.7476673, 2023. a
Demaeyer, J. and Vannitsem, S.: Correcting for model changes in statistical postprocessing – an approach based on response theory, Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020, 2020. a
Dueben, P. D., Schultz, M. G., Chantry, M., Gagne, D. J., Hall, D. M., and
McGovern, A.: Challenges and Benchmark Datasets for Machine Learning in the
Atmospheric Sciences: Definition, Status, and Outlook, Artificial
Intelligence for the Earth Systems, 1, e210002,
https://doi.org/10.1175/AIES-D-21-0002.1, 2022. a
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Caron, J., Signell,
R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M., Raspaud, M., Horne, R.,
Whiteaker, T., Blodgett, D., Zender, C., and Lee, D.: NetCDF Climate and Forecast
(CF) metadata conventions,
http://cfconventions.org/Data/cf-conventions/cf-conventions-1.8/cf-conventions.pdf (last access: 2 June 2023),
2003. a
EUMETNET: EUPPBench datasets documentation, EUMETNET [data set], https://eupp-benchmark.github.io/EUPPBench-doc/files/EUPPBench_datasets.html, last access: 2023. a
Evans, G. and Hooper, B.: EUPP-benchmark/ESSD-reliability-calibration: version
1.0 release, Zenodo [code], https://doi.org/10.5281/zenodo.7476590, 2023. a
Flowerdew, J.: Calibrating ensemble reliability whilst preserving spatial
structure, Tellus A, 66, 22662,
https://doi.org/10.3402/tellusa.v66.22662, 2014. a, b
Garg, S., Rasp, S., and Thuerey, N.: WeatherBench Probability: A benchmark
dataset for probabilistic medium-range weather forecasting along with deep
learning baseline models, arXiv [preprint], https://doi.org/10.48550/arXiv.2205.00865, 2 May 2022. a
Glahn, H. R. and Lowry, D. A.: The use of model output statistics (MOS) in
objective weather forecasting, J. Appl. Meteorol.
Clim., 11, 1203–1211, 1972. a
Gregory, J.: The CF metadata standard, CLIVAR Exchanges, 8, 4, http://cfconventions.org/Data/cf-documents/overview/article.pdf (last access: June 2022), 2003. a
Groß, J. and Möller, A.: ensAR: Autoregressive postprocessing methods
for ensemble forecasts, R
package version 0.2.0, https://github.com/JuGross/ensAR (last access: 2 June 2023), 2019. a
Hafizi, H. and Sorman, A. A.: Assessment of 13 Gridded Precipitation Datasets
for Hydrological Modeling in a Mountainous Basin, Atmosphere, 13, 143,
https://doi.org/10.3390/atmos13010143, 2022. a
Hagedorn, R., Hamill, T. M., and Whitaker, J. S.: Probabilistic Forecast
Calibration Using ECMWF and GFS Ensemble Reforecasts. Part I: Two-Meter
Temperatures, Mon. Weather Rev., 136, 2608–2619,
https://doi.org/10.1175/2007MWR2410.1, 2008. a
Hamill, T. M., Hagedorn, R., and Whitaker, J. S.: Probabilistic Forecast
Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation,
Mon. Weather Rev., 136, 2620–2632, https://doi.org/10.1175/2007MWR2411.1, 2008. a
Han, J., Miao, C., Gou, J., Zheng, H., Zhang, Q., and Guo, X.: A new daily gridded precipitation dataset based on gauge observations across mainland China, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-373, in review, 2022. a
Haupt, S. E., Chapman, W., Adams, S. V., Kirkwood, C., Hosking, J. S.,
Robinson, N. H., Lerch, S., and Subramanian, A. C.: Towards implementing
artificial intelligence post-processing in weather and climate: proposed
actions from the Oxford 2019 workshop, Philos. T.
Roy. Soc. A, 379, 20200091, https://doi.org/10.1098/rsta.2020.0091, 2021. a, b
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570, 2000. a
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., De Chiara, G., 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. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Horton, P.: Analogue methods and ERA5: Benefits and pitfalls, Int.
J. Climatol., 42, 4078–4096,
https://doi.org/10.1002/joc.7484, 2022. a
Hoyer, S. and Joseph, H.: xarray: N-D labeled Arrays and Datasets in Python,
Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Jobst, D.: EUPP-benchmark/ESSD-DVQR: version 1.0 release, Zenodo [code],
https://doi.org/10.5281/zenodo.7477640, 2023. a
Kim, T., Ho, N., Kim, D., and Yun, S.-Y.: Benchmark Dataset for Precipitation
Forecasting by Post-Processing the Numerical Weather Prediction, arXiv
[preprint], https://doi.org/10.48550/arXiv.2206.15241, 30 June 2022. a
Klein, W. H. and Lewis, F.: Computer Forecasts of Maximum and Minimum Temperatures, J. Appl. Meteorol. Clim., 9, 350–359, https://doi.org/10.1175/1520-0450(1970)009<0350:CFOMAM>2.0.CO;2, 1970. a, b
Kraus, D. and Czado, C.: D-vine copula based quantile regression, Comput.
Stat. Data An., 110, 1–18, https://doi.org/10.1016/j.csda.2016.12.009,
2017. a
Lakatos, M., Lerch, S., Hemri, S., and Baran, S.: Comparison of multivariate
post-processing methods using global ECMWF ensemble forecasts, Q.
J. Roy. Meteor. Soc., 149, 856–877, https://doi.org/10.1002/qj.4436, 2023. a
Lalaurette, F.: Early detection of abnormal weather conditions using a
probabilistic extreme forecast index, Q. J. Roy.
Meteor. Soc., 129, 3037–3057, 2003. a
Lang, M. N., Lerch, S., Mayr, G. J., Simon, T., Stauffer, R., and Zeileis, A.: Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression, Nonlin. Processes Geophys., 27, 23–34, https://doi.org/10.5194/npg-27-23-2020, 2020. a
Lenkoski, A., Kolstad, E. W., and Thorarinsdottir, T. L.: A Benchmarking
Dataset for Seasonal Weather Forecasts, NR-notat,
https://nr.brage.unit.no/nr-xmlui/bitstream/handle/11250/2976154/manual.pdf (last access: 22 December 2022),
2022. a
Lerch, S., Baran, S., Möller, A., Groß, J., Schefzik, R., Hemri, S., and Graeter, M.: Simulation-based comparison of multivariate ensemble post-processing methods, Nonlin. Processes Geophys., 27, 349–371, https://doi.org/10.5194/npg-27-349-2020, 2020. a
Maciel, P., Quintino, T., Modigliani, U., Dando, P., Raoult, B., Deconinck, W.,
Rathgeber, F., and Simarro, C.: The new ECMWF interpolation package MIR,
36–39, https://doi.org/10.21957/h20rz8, 2017. a
Messner, J. W., Mayr, G. J., and Zeileis, A.: Heteroscedastic Censored and
Truncated Regression with crch, The R Journal, 8, 173–181,
https://journal.r-project.org/archive/2016-1/messner-mayr-zeileis.pdf (last access: 2 June 2023),
2016. a
Miles, A., Kirkham, J., Durant, M., Bourbeau, J., Onalan, T., Hamman, J.,
Patel, Z., shikharsg, Rocklin, M., raphael dussin, Schut, V., de Andrade,
E. S., Abernathey, R., Noyes, C., sbalmer, pyup.io bot, Tran, T., Saalfeld,
S., Swaney, J., Moore, J., Jevnik, J., Kelleher, J., Funke, J., Sakkis, G.,
Barnes, C., and Banihirwe, A.: zarr-developers/zarr-python: v2.4.0, Zenodo [code],
https://doi.org/10.5281/zenodo.3773450, 2020. a
Mlakar, P., Merše, J., and Pucer, J. F.: Ensemble weather forecast
post-processing with a flexible probabilistic neural network approach, arXiv [preprint],
https://doi.org/10.48550/arXiv.2303.17610, 29 March 2023a. a
Mlakar, P., Pucer, J. F., and Merše, J.: EUPP-benchmark/ESSD-ANET: version 1.0
release, Zenodo [code], https://doi.org/10.5281/zenodo.7479333,
2023b. a
Möller, A.: EUPP-benchmark/ESSD-AR-EMOS: version 1.0 release, Zenodo [code],
https://doi.org/10.5281/zenodo.7477633, 2023. a
Möller, A. and Groß, J.: Probabilistic temperature forecasting based on
an ensemble autoregressive modification, Q. J. Roy.
Meteor. Soc., 142, 1385–1394,
https://doi.org/10.1002/qj.2741, 2016. a
Möller, A. and Groß, J.: Probabilistic Temperature Forecasting with a
Heteroscedastic Autoregressive Ensemble Postprocessing model, Q.
J. Roy. Meteor. Soc., 146, 211–224,
https://doi.org/10.1002/qj.3667, 2020. a
Möller, A., Spazzini, L., Kraus, D., Nagler, T., and Czado, C.: Vine copula
based post-processing of ensemble forecasts for temperature, arXiv [preprint], https://doi.org/10.48550/arXiv.1811.02255, 6 November 2018. a
Murphy, A. H.: What Is a Good Forecast? An Essay on the Nature of Goodness in
Weather Forecasting, Weather Forecast., 8, 281–293,
https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2, 1993. a
Nagler, T.: vinereg: D-Vine Quantile Regression, r package version
0.7.2, CRAN [code],
https://CRAN.R-project.org/package=vinereg (last access: 17 November 2020), 2020. a
Perrone, E., Schicker, I., and Lang, M. N.: A case study of empirical copula
methods for the statistical correction of forecasts of the ALADIN-LAEF
system, Meteorol. Z., 29, 277–288,
https://doi.org/10.1127/metz/2020/1034, 2020. a
Primo-Ramos, C., Bhend, J., Atencia, A., Van schaeybroeck, B., and Demaeyer,
J.: EUPP-benchmark/ESSD-Verification: version 1.0 release, Zenodo [code],
https://doi.org/10.5281/zenodo.7484371, 2023. a
Rabault, J., Müller, M., Voermans, J., Brazhnikov, D., Turnbull, I.,
Marchenko, A., Biuw, M., Nose, T., Waseda, T., Johansson, M., Breivik, Ø., Sutherland, G., Hole, L. R., Johnson, M., Jensen, A., Gundersen, O., Kristoffersen, Y., Babanin, A., Tedesco, P., Christensen, K. H., Kristiansen, M., Hope, G., Kodaira, T., de Aguiar, V., Taelman, C., Quigley, C. P., Filchuk, K., and Mahoney, A. R.: A
dataset of direct observations of sea ice drift and waves in ice, arXiv
[preprint], https://doi.org/10.48550/arXiv.2211.03565, 25 October 2022. a
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using
Bayesian model averaging to calibrate forecast ensembles, Mon. Weather
Rev., 133, 1155–1174, 2005. a
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., and Thuerey,
N.: WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting,
J. Adv. Model. Earth Sy., 12, e2020MS002203,
https://doi.org/10.1029/2020MS002203, 2020. a
Roberts, N., Ayliffe, B., Evans, G., Moseley, S., Rust, F., Sandford, C.,
Trzeciak, T., Abernethy, P., Beard, L., Crosswaite, N., Fitzpatrick, B.,
Flowerdew, J., Gale, T., Holly, L., Hopkinson, A., Hurst, K., Jackson, S.,
Jones, C., Mylne, K., Sampson, C., Sharpe, M., Wright, B., Backhouse, S.,
Baker, M., Brierley, D., Booton, A., Bysouth, C., Coulson, R., Coultas, S.,
Crocker, R., Harbord, R., Howard, K., Hughes, T., Mittermaier, M., Petch, J.,
Pillinger, T., Smart, V., Smith, E., and Worsfold, M.: IMPROVER: the new
probabilistic post processing system at the UK Met Office, B.
Am. Meteorol. Soc., 104, E680–E697, https://doi.org/10.1175/BAMS-D-21-0273.1, 2023. a
Taillardat, M., Mestre, O., Zamo, M., and Naveau, P.: Calibrated ensemble
forecasts using quantile regression forests and ensemble model output
statistics, Mon. Weather Rev., 144, 2375–2393, 2016. a
Vannitsem, S., Wilks, D. S., and Messner, J. W.: Statistical Postprocessing of
Ensemble Forecasts, 1st edn., edited by: Vannitsem, S., Wilks, D. S., and Messner, J. W., Elsevier, ISBN 978-0-12-812372-0, 2018. a
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J.,
Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Bouallègue,
Z. B., Bhend, J., Dabernig, M., Cruz, L. D., Hieta, L., Mestre, O., Moret,
L., Plenković, I. O., Schmeits, M., Taillardat, M., den Bergh, J. V.,
Schaeybroeck, B. V., Whan, K., and Ylhaisi, J.: Statistical Postprocessing
for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World,
B. Am. Meteorol. Soc., 102, E681–E699,
https://doi.org/10.1175/BAMS-D-19-0308.1, 2021.
a, b
Van Schaeybroeck, B. and Vannitsem, S.: Ensemble post-processing using
member-by-member approaches: theoretical aspects, Q. J.
Roy. Meteor. Soc., 141, 807–818,
https://doi.org/10.1002/qj.2397, 2015. a
Wang, W., Yang, D., Hong, T., and Kleissl, J.: An archived dataset from the
ECMWF Ensemble Prediction System for probabilistic solar power forecasting,
Sol. Energy, 248, 64–75,
https://doi.org/10.1016/j.solener.2022.10.062, 2022. a
Watson-Parris, D., Rao, Y., Olivié, D., Seland, Ø., Nowack, P.,
Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J.,
Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard,
L., and Roesch, C.: ClimateBench v1.0: A Benchmark for Data-Driven Climate
Projections, J. Adv. Model. Earth Sy., 14,
e2021MS002954, https://doi.org/10.1029/2021MS002954, 2022. a
Wilks, D. S.: Multivariate ensemble Model Output Statistics using
empirical copulas, Q. J. Roy. Meteor. Soc.,
141, 945–952, https://doi.org/10.1002/qj.2414, 2015. a
Zandler, H., Haag, I., and Samimi, C.: Evaluation needs and temporal
performance differences of gridded precipitation products in peripheral
mountain regions, Scientific Reports, 9, 15118, https://doi.org/10.1038/s41598-019-51666-z, 2019. a
Zantedeschi, V., Falasca, F., Douglas, A., Strange, R., Kusner, M. J., and
Watson-Parris, D.: Cumulo: A dataset for learning cloud classes, arXiv
[preprint], https://doi.org/10.48550/arXiv.1911.04227, 5 November 2019. a
Zsótér, E.: Recent developments in extreme weather forecasting, ECMWF
Newsletter, 107, 8–17, 2006. a
Short summary
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.
A benchmark dataset is proposed to compare different statistical postprocessing methods used in...
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