Articles | Volume 11, issue 3
https://doi.org/10.5194/essd-11-1017-2019
© Author(s) 2019. 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-11-1017-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Jul 2019
Data description paper |
| 10 Jul 2019
| 10 Jul 2019
FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products
Rémy Roca
CORRESPONDING AUTHOR
Laboratoire d'Etudes Géophysiques et d'Océanographie
Spatiales, Toulouse, France
Lisa V. Alexander
Climate Change Research Centre, University of New South Wales, Sydney, Australia
ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Gerald Potter
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Margot Bador
Climate Change Research Centre, University of New South Wales, Sydney, Australia
ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Rômulo Jucá
Geoscience Environnement Toulouse, Toulouse, France
Steefan Contractor
Climate Change Research Centre, University of New South Wales, Sydney, Australia
School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
Michael G. Bosilovich
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Sophie Cloché
IPSL, Palaiseau, France
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-568, https://doi.org/10.5194/essd-2024-568, 2025
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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.
Thomas Fiolleau and Rémy Roca
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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.
M. Schröder, R. Roca, L. Picon, A. Kniffka, and H. Brogniez
Atmos. Chem. Phys., 14, 11129–11148, https://doi.org/10.5194/acp-14-11129-2014, https://doi.org/10.5194/acp-14-11129-2014, 2014
B. V. Thampi and R. Roca
Atmos. Chem. Phys., 14, 6739–6758, https://doi.org/10.5194/acp-14-6739-2014, https://doi.org/10.5194/acp-14-6739-2014, 2014
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
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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.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
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.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
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The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Duane Waliser, Peter J. Gleckler, Robert Ferraro, Karl E. Taylor, Sasha Ames, James Biard, Michael G. Bosilovich, Otis Brown, Helene Chepfer, Luca Cinquini, Paul J. Durack, Veronika Eyring, Pierre-Philippe Mathieu, Tsengdar Lee, Simon Pinnock, Gerald L. Potter, Michel Rixen, Roger Saunders, Jörg Schulz, Jean-Noël Thépaut, and Matthias Tuma
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Mia H. Gross, Markus G. Donat, Lisa V. Alexander, and Steven C. Sherwood
Earth Syst. Dynam., 11, 97–111, https://doi.org/10.5194/esd-11-97-2020, https://doi.org/10.5194/esd-11-97-2020, 2020
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This study explores the amplified warming of cold extremes relative to average temperatures for both the recent past and future in the Northern Hemisphere and the possible physical processes that are driving this. We find that decreases in snow cover and
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Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
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Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
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Ozone and water vapor in the stratosphere are important gases that affect surface climate and absorb incoming solar ultraviolet radiation. These gases are represented in reanalyses, which create a complete picture of the state of Earth's atmosphere using limited observations. We evaluate reanalysis water vapor and ozone fidelity by intercomparing them, and comparing them to independent observations. Generally reanalyses do a good job at representing ozone, but have problems with water vapor.
R. J. H. Dunn, M. G. Donat, and L. V. Alexander
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Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu
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Revised manuscript accepted for ESSD
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Thomas Fiolleau and Rémy Roca
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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.
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.
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, and Yi He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-132, https://doi.org/10.5194/essd-2024-132, 2024
Revised manuscript accepted for ESSD
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We present two sets of bias-corrected UK Climate Projections 2018 (UKCP18) regional projections of temperature, precipitation and potential evapotranspiration for 1981–2080. All 12 members of the UKCP18 regional ensemble were bias-corrected using (1) empirical quantile mapping and (2) a change-preserving variant. The two methods were evaluated and compared to guide dataset application. The datasets improve the usability of UKCP18 and serve as a reference for selecting bias correction methods.
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
Short summary
Short summary
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.
Cited articles
Adler, R. F., Gu, G., 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.
Alexander, L., Roca, R., Seneviratne, S., Becker, A., Behrangi, A., Contractor, S., Dietzsch, F., Donat, M., Dunn, R., Fowler, H., Funk, C., Guérou, A., Hollmann, R., Kirstetter, P., Lengfeld, K., Lockhoff, M., Masunanga, H., Moon, H., Muller, C., Schroeder, M., Schneider, U., Takayabu, Y., Venugopal, V., and Werscheck, M.: Joint
WCRP Grand Challenge on Weather and Climate Extremes, GEWEX GDAP Workshop on
Precipitation Extremes,
9–11 July 2018, Offenbach, Germany, GEWEX Newsl., 11–11, 2018.
Andersson, A., Fennig, K., Klepp, C., Bakan, S., Graßl, H., and Schulz, J.: The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3, Earth Syst. Sci. Data, 2, 215–234, https://doi.org/10.5194/essd-2-215-2010, 2010.
Andersson, A., Graw, K., Marc, S., Fennig, K., Liman, J., Bakan, S.,
Hollmann, R., and Klepp, C.: Hamburg Ocean Atmosphere Parameters and Fluxes
from Satellite Data – HOAPS 4.0, Satellite Application Facility on Climate
Monitoring, https://doi.org/10.5676/EUM_SAF_CM/HOAPS/V002, 2017.
Aonashi, K., Awaka, J., Hirose, M., Kozu, T., Kubota, T., Liu, G., Shige, S., Kida, S., Seto, S., Takahashi, N., and Takayabu, Y.: GSMaP Passive Microwave Precipitation Retrieval Algorithm: Algorithm Description and Validation, J. Meteorol.
Soc. Jpn. 87A, 119–136, https://doi.org/10.2151/jmsj.87A.119, 2009.
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R.,
Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR: Daily
precipitation climate data record from multisatellite observations for
hydrological and climate studies, B. Am. Meteorol. Soc., 96, 69–83,
https://doi.org/10.1175/BAMS-D-13-00068.1, 2015.
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.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013.
Bellerby, T. J.: Searchlight: Precipitation advection tracking using
multiplatform low-earth-orbiting satellite data, IEEE T. Geosci. Remote, 51, 2177–2187, https://doi.org/10.1109/TGRS.2012.2211604, 2013.
Bosilovich, M. G., Chen, J., Robertson, F. R., Adler, R. F., Bosilovich, M.
G., Chen, J., Robertson, F. R., and Adler, R. F.: Evaluation of Global
Precipitation in Reanalyses, J. Appl. Meteorol. Clim., 47,
2279–2299, https://doi.org/10.1175/2008JAMC1921.1, 2008.
Bosilovich, M. G., Mocko, D., Roads, J. O., Ruane, A., Bosilovich, M. G.,
Mocko, D., Roads, J. O., and Ruane, A.: A Multimodel Analysis for the
Coordinated Enhanced Observing Period (CEOP), J. Hydrometeorol., 10,
912–934, https://doi.org/10.1175/2009JHM1090.1, 2009.
Bosilovich, M. G., Akella, S., Coy, L., Cullather, R., Draper, C., Gelaro, R., Kovach, R., Liu, Q., Molod, A., Norris, P., Wargan, K., Chao, W., Reichle, R., Takacs, L., Vikhliaev, Y., Bloom, S., Collow, A., Firth, S., Labow, G., Partyka, G., Pawson, S., Reale, O., Schubert, S. D., and Suarez, M.: MERRA-2: Initial evaluation of the climate. NASA Tech. Rep. NASA/TM-2015-104606, Vol. 43, 136 pp., available at: https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf (last access: 8 July 2019), 2015.
Bosilovich, M. G., Robertson, F. R., Takacs, L., Molod, A. and Mocko, D.:
Atmospheric Water Balance and Variability in the MERRA-2 Reanalysis, J.
Climate, 30, 1177–1196, https://doi.org/10.1175/JCLI-D-16-0338.1, 2017.
Brocca, L., Moramarco, T., Melone, F., and Wagner, W.: A new method for
rainfall estimation through soil moisture observations, Geophys. Res. Lett.,
40, 853–858, https://doi.org/10.1002/grl.50173, 2013.
Chambon, P., Jobard, I., Roca, R., and Viltard, N.: An investigation of the
error budget of tropical rainfall accumulation derived from merged passive
microwave and infrared satellite measurements, Q. J. Roy. Meteor. Soc.,
139, 879–893, https://doi.org/10.1002/qj.1907, 2012.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Higgins, R. W.,
and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses
of global daily precipitation, J. Geophys. Res.-Atmos., 113, 1–13, https://doi.org/10.1029/2007JD009132, 2008.
Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, 2018.
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network (REGEN) – A global land-based gridded dataset of daily precipitation from 1950–2013, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-595, in review, 2019.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration
and performance of the data assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dunn, R. J. H., Donat, M. G., and Alexander, L. V.: Investigating uncertainties in global gridded datasets of climate extremes, Clim. Past, 10, 2171–2199, https://doi.org/10.5194/cp-10-2171-2014, 2014.
Fennig, K., Schröder, M., and Hollmann, R.: Fundamental Climate Data
Record of Microwave Imager Radiances, https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003, 2017.
Ferraro, R. R., Weng, F., Grody, N. C., Zhao, L., Meng, H., Kongoli, C.,
Pellegrino, P., Qiu, S., and Dean, C.: NOAA operational hydrological products
derived from the advanced microwave sounding unit, IEEE T. Geosci.
Remote, 43, 1036–1048, https://doi.org/10.1109/TGRS.2004.843249, 2005.
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, 1–21,
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., Zhao, B., 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., Silva, A. M.
da, Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.
E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D.,
Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for
Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gopalan, K., Wang, N. Y., Ferraro, R., and Liu, C.: Status of the TRMM 2A12
land precipitation algorithm, J. Atmos. Ocean. Tech., 27, 1343–1354,
https://doi.org/10.1175/2010JTECHA1454.1, 2010.
Gosset, M., Alcoba, M., Roca, R., Cloché, S., and Urbani, G.: Evaluation
of TAPEER daily estimates and other GPM-era products against dense gauge
networks in West Africa, analysing ground reference uncertainty, Q. J. Roy. Meteor. Soc, 144, 255–269, https://doi.org/10.1002/qj.3335, 2018.
Guilloteau, C., Roca, R., and Gosset, M.: A Multiscale Evaluation of the
Detection Capabilities of High-Resolution Satellite Precipitation Products
in West Africa, J. Hydrometeorol., 17, 2041–2059,
https://doi.org/10.1175/jhm-d-15-0148.1, 2016.
Haddad, Z. S. and Roca, R.: Toward a Broad Scope Assessment of Global
Precipitation Products, available at:
http://www.isac.cnr.it/~ipwg/reports/IPWG-GEWEX-Assessment2017-21_Report_1.pdf (last access: 8 July 2019), 2017.
Haddad, Z. S., Smith, E. A., Kummerow, C. D., Iguchi, T., Farrar, M. R., Durden, S. L., Alves, M., and Olson, W. S.: The TRMM `Day-1' Radar/Radiometer Combined
Rain-Profiling Algorithm, J. Meteorol. Soc. Jpn., 75, 799–809, 1997.
Herold, N., Behrangi, A., and Alexander, L. V.: Large uncertainties in observed daily precipitation extremes over land: Uncertainties in Precipitation Extremes, J. Geophys. Res.-Atmos., 122, 668–681, https://doi.org/10.1002/2016JD025842, 2017.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T.,
Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global
Precipitation at One-Degree Daily Resolution from Multisatellite
Observations, J. Hydrometeorol., 2, 36–50,
https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001.
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.
Huffman, G. J., Bolvin, D. T., and Nelkin, E. J.: Integrated MultisatellitE
Retrievals for GPM (IMERG) technical documentation, available at:
https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf (last access: 8 July 2019), 2017.
Joyce, R. J. and Xie, P.: Kalman Filter–Based CMORPH, J. Hydrometeorol.,
12, 1547–1563, https://doi.org/10.1175/JHM-D-11-022.1, 2011.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A Method
that Produces Global Precipitation Estimates from Passive Microwave and
Infrared Data at High Spatial and Temporal Resolution, J. Hydrometeorol.,
5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.
C., Ropelewski, C., Wang, J., Jenne, R., Joseph, D., Kalnay, E., Kanamitsu,
M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S.,
White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W.,
Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds,
R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-Year Reanalysis Project,
B. Am. Meteorol. Soc., 77, 437–471,
https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2,
1996.
Kidd, C., Kniveton, D. R., Todd, M. C., and Bellerby, T. J.: Satellite
Rainfall Estimation Using Combined Passive Microwave and Infrared
Algorithms, J. Hydrometeorol., 4, 1088–1104,
https://doi.org/10.1175/1525-7541(2003)004<1088:sreucp>2.0.co;2,
2003.
Klepp, C., Bumke, K., Bakan, S., and Bauer, P.: Ground validation of oceanic
snowfall detection in satellite climatologies during LOFZY, Tellus A, 62, 469–480,
https://doi.org/10.1111/j.1600-0870.2010.00459.x, 2010.
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.
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S.,
Hirose, M., Takayabu, Y. N., Ushio, T., Nakagawa, K., Iwanami, K., Kachi, M.,
and Okamoto, K.: Global Precipitation Map Using Satellite-Borne Microwave
Radiometers by the GSMaP Project: Production and Validation, IEEE T.
Geosci. Remote, 45, 2259–2275, https://doi.org/10.1109/tgrs.2007.895337, 2007.
Kummerow, C., Olson, W. S., and Giglio, L.: A simplified scheme for obtaining
precipitation and vertical hydrometeor profiles from passive microwave
sensors, IEEE T. Geosci. Remote, 34, 1213–1232,
https://doi.org/10.1109/36.536538, 1996.
Kummerow, C., Hong, Y., Olson, W. S., Yang, S., Adler, R. F., McCollum, J.,
Ferraro, R., Petty, G., Shin, D.-B., Wilheit, T. T., Kummerow, C., Hong, Y.,
Olson, W. S., Yang, S., Adler, R. F., McCollum, J., Ferraro, R., Petty, G.,
Shin, D.-B., and Wilheit, T. T.: The Evolution of the Goddard Profiling
Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors, J.
Appl. Meteorol., 40, 1801–1820, https://doi.org/10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2, 2001.
Levizzani, V., Kidd, C., Aonashi, K., Bennartz, R., Ferraro, R. R., Huffman,
G. J., Roca, R., Turk, F. J., and Wang, N.-Y.: The activities of the
International Precipitation Working Group, Q. J. Roy. Meteor. Soc., 144, 3–15, https://doi.org/10.1002/qj.3214, 2018.
Maggioni, V., Meyers, P. C., and Robinson, M. D.: A Review of Merged
High-Resolution Satellite Precipitation Product Accuracy during the Tropical
Rainfall Measuring Mission (TRMM) Era, J. Hydrometeorol., 17, 1101–1117,
https://doi.org/10.1175/jhm-d-15-0190.1, 2016.
Maidment, R. I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex,
H., Allan, R. P., Stein, T., Nkonde, E., Senkunda, S., and Alcántara, E.
M. U.: A new, long-term daily satellite-based rainfall dataset for
operational monitoring in Africa, Sci. Data, 4, 1–19,
https://doi.org/10.1038/sdata.2017.63, 2017.
Mega, T., Ushio, T., Matsuda, T., Kubota, T., Kachi, M., and Oki, R.:
Gauge-Adjusted Global Satellite Mapping of Precipitation, IEEE T.
Geosci. Remote, 57, 1928–1935, https://doi.org/10.1109/TGRS.2018.2870199, 2018.
Miao, C., Ashouri, H., Hsu, K.-L., Sorooshian, S., and Duan, Q.: Evaluation
of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of
Extreme Precipitation Events over China, J. Hydrometeorol., 16,
1387–1396, https://doi.org/10.1175/JHM-D-14-0174.1, 2015.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios,
T., Huynh, P., Braithwaite, D., Updegraff, G., Hsu, K., Kuligowski, B.,
Logan, W. S., and Sorooshian, S.: The CHRS data portal, an easily accessible
public repository for PERSIANN global satellite precipitation data, Sci.
Data, 6, 1–10, https://doi.org/10.1038/sdata.2018.296, 2019.
Novella, N. S. and Thiaw, W. M.: African rainfall climatology version 2 for
famine early warning systems, J. Appl. Meteorol. Clim., 52, 588–606,
https://doi.org/10.1175/JAMC-D-11-0238.1, 2013.
Park, K. J., Yoshimura, K., Kim, H., and Oki, T.: Chronological development
of terrestrial mean precipitation, B. Am. Meteorol. Soc., 98, 2411–2428, https://doi.org/10.1175/BAMS-D-16-0005.1, 2017.
Potter, G. L., Carriere, L., Hertz, J. D., Bosilovich, M., Duffy, D., Lee,
T., and Williams, D. N.: Enabling reanalysis research using the collaborative
reanalysis technical environment (CREATE), B. Am. Meteorol. Soc., 99,
677–687, https://doi.org/10.1175/BAMS-D-17-0174.1, 2018.
Pricope, N. G., Husak, G., Lopez-Carr, D., Funk, C., and Michaelsen, J.: The
climate-population nexus in the East African Horn: Emerging degradation
trends in rangeland and pastoral livelihood zones, Global Environ. Chang.,
23, 1525–1541, https://doi.org/10.1016/j.gloenvcha.2013.10.002, 2013.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom,
S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., Woollen, J., Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R.,
Julio Bacmeister, Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L.,
Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., Silva, A. da, Gu,
W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson,
S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A.
G., Sienkiewicz, M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective
Analysis for Research and Applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Roca, R., Chambon, P., Jobard, I., Kirstetter, P. E., Gosset, M., and
Bergés, J. C.: Comparing satellite and surface rainfall products over
West Africa at meteorologically relevant scales during the AMMA campaign
using error estimates, J. Appl. Meteorol. Clim., 49, 715–731,
https://doi.org/10.1175/2009JAMC2318.1, 2010.
Roca, R., Brogniez, H., Chambon, P., Chomette, O., Cloché,
S., Gosset, M. E., Mahfouf, J.-F., Raberanto, P., and Viltard, N.: The
Megha-Tropiques mission: a review after three years in orbit, Front. Earth
Sci., 3, 17 pp., https://doi.org/10.3389/feart.2015.00017, 2015.
Roca, R., Taburet, N., Lorant, E., Chambon, P., Gosset, M., Alcoba, M.,
Cloché, S., Dufour, C., and Guilloteau, C.: Quantifying the contribution
of the Megha-Tropiques mission to the estimation of daily accumulated
rainfall in the Tropics, 144, 49–63, https://doi.org/10.1002/qj.3327,
2018.
Roca, R., Alexander, L. V., Potter, G., Bador, M., Jucá, R., Contractor,
S., Bosilovich, M. G., and Cloché, S.: FROGs: a daily
Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U., Schröder, M., and Stender, P.: Global gridded precipitation over land: a description of the new GPCC First Guess Daily product, Earth Syst. Sci. Data, 6, 49–60, https://doi.org/10.5194/essd-6-49-2014, 2014.
Schröder, M., Lockhoff, M., Forsythe, J. M., Cronk, H. Q., Haar, T. H.
V., and Bennartz, R.: The GEWEX water vapor assessment: Results from
intercomparison, trend, and homogeneity analysis of total column water
vapor, J. Appl. Meteorol. Clim., 55, 1633–1649,
https://doi.org/10.1175/jamc-d-15-0304.1, 2016.
Schröder, M., Lockhoff, M., Fell, F., Forsythe, J., Trent, T., Bennartz, R., Borbas, E., Bosilovich, M. G., Castelli, E., Hersbach, H., Kachi, M., Kobayashi, S., Kursinski, E. R., Loyola, D., Mears, C., Preusker, R., Rossow, W. B., and Saha, S.: The GEWEX Water Vapor Assessment archive of water vapour products from satellite observations and reanalyses, Earth Syst. Sci. Data, 10, 1093–1117, https://doi.org/10.5194/essd-10-1093-2018, 2018.
Schröder, M., Lockhoff, M., Shi, L., August, T., Bennartz, R., Brogniez,
H., Calbet, X., Fell, F., Forsythe, J., Gambacorta, A., Ho, S., Kursinski,
E., Reale, A., Trent, T., and Yang, Q.: The GEWEX Water Vapor Assessment:
Overview and Introduction to Results and Recommendations, Remote Sens.,
11, 251, https://doi.org/10.3390/rs11030251, 2019.
Shiu, C.-J., Liu, S. C., Fu, C., Dai, A., and Sun, Y.: How much do
precipitation extremes change in a warming climate?, Geophys. Res. Lett.,
39, L17707, https://doi.org/10.1029/2012GL052762, 2012.
Simmons, A. J., Willett, K. M., Jones, P. D., Thorne, P. W., and Dee, D. P.:
Low-frequency variations in surface atmospheric humidity, temperature, and
precipitation: Inferences from reanalyses and monthly gridded observational
data sets, J. Geophys. Res., 115, D01110, https://doi.org/10.1029/2009JD012442,
2010.
Sorooshian, S., Hsu, K., Braithwaite, D., Ashouri, H., and NOAA CDR Program: NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1. NOAA's National C enters for Environmental Information, https://doi.org/10.7289/V51V5BWQ, 2014.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.:
Assessment of global cloud datasets from satellites: Project and database
initiated by the GEWEX radiation panel, B. Am. Meteorol. Soc., 94,
1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
review of global precipitation datasets: data sources, estimation, and
intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018.
Takacs, L. L., Suárez, M. J., and Todling, R.: Maintaining atmospheric
mass and water balance in reanalyses, Q. J. Roy. Meteor. Soc., 142,
1565–1573, https://doi.org/10.1002/qj.2763, 2016.
Tan, M. L. and Santo, H.: Comparison of GPM IMERG, TMPA 3B42 and
PERSIANN-CDR satellite precipitation products over Malaysia, Atmos. Res.,
202, 63–76, https://doi.org/10.1016/j.atmosres.2017.11.006, 2018.
Tapiador, F. J., Navarro, A., Levizzani, V., García-Ortega, E.,
Huffman, G. J., Kidd, C., Kucera, P. A., Kummerow, C. D., Masunaga, H.,
Petersen, W. A., Roca, R., Sánchez, J. L., Tao, W. K., and Turk, F. J.:
Global precipitation measurements for validating climate models, Atmos.
Res., 197, 1–20, https://doi.org/10.1016/j.atmosres.2017.06.021, 2017.
Tapiador, F. J., Roca, R., Genio, A. Del, Dewitte, B., Petersen, W., and
Zhang, F.: Is precipitation a good metric for model performance?, B. Am.
Meteorol. Soc., 100, 223–233, https://doi.org/10.1175/BAMS-D-17-0218.1, 2019.
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V.
D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A.,
Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E.,
Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. Van De, Bidlot, J.,
Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher,
M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L.,
Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette,
J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K.
E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40
re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012,
https://doi.org/10.1256/qj.04.176, 2005.
Ushio, T., Kubota, T., Shige, S., Okamoto, K., Aonashi, K., Inoue, T., Takahashi, N., Iguchi, T., Kachi, M., Oki, R., Morimoto, T., and Kawasaki, Z.: A Kalman Filter Approach to the Global Satellite
Mapping of Precipitation (GSMaP) from Combined Passive Microwave and
Infrared Radiometric Data, J. Meteorol. Soc. Jpn., 87A, 137–151,
https://doi.org/10.2151/jmsj.87a.137, 2009.
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.
Viltard, N., Burlaud, C., and Kummerow, C. D.: Rain Retrieval from TMI
Brightness Temperature Measurements Using a TRMM PR–Based Database, J.
Appl. Meteorol. Clim., 45, 455–466, https://doi.org/10.1175/jam2346.1, 2006.
Westra, S., Alexander, L. V., and Zwiers, F. W.: Global increasing trends in
annual maximum daily precipitation, J. Climate, 26, 3904–3918,
https://doi.org/10.1175/JCLI-D-12-00502.1, 2013.
Xie, P., Janowiak, J. E., Arkin, P. A., Adler, R., Gruber, A., Ferraro, R.,
Huffman, G. J., and Curtis, S.: GPCP pentad precipitation analyses: An
experimental dataset based on gauge observations and satellite estimates, J.
Climate, 16, 2197–2214, https://doi.org/10.1175/2769.1, 2003.
Xie, P., Chen, M., Yang, S., Yatagai, A., Hayasaka, T., Fukushima, Y., and Liu, C.: A Gauge-Based Analysis of Daily Precipitation over
East Asia, J. Hydrometeor., 8, 607–626, 2007.
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., and Lin, R.:
Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation
Estimates from 1998, J. Hydrometeorol., 18, 1617–1641,
https://doi.org/10.1175/JHM-D-16-0168.1, 2017.
Xie, P.-P., Chen, M., and Shi, W.: CPC unified gauge-based analysis of global daily precipitation.
24th Conf. on Hydrology, 17–21 January 2010, Atlanta, GA, USA, Amer. Meteor. Soc., 2.3A., available at: https://ams.confex.com/ams/90annual/techprogram/paper_163676.htm (last access: 8 July 2019), 2010.
Xu, L., Gao, X., Sorooshian, S., Arkin, P. A., and Imam, B.: A Microwave
Infrared Threshold Technique to Improve the GOES Precipitation Index, J. Appl. Meteorol., 38,
569–579, 1999.
Zhang, L., Kumar, A., and Wang, W.: Influence of changes in observations on
precipitation: A case study for the Climate Forecast System Reanalysis
(CFSR), J. Geophys. Res.-Atmos., 117, D08105, https://doi.org/10.1029/2011JD017347,
2012.
Ziese, M., Rauthe-Schöch, A., Becker, A., Finger, P., Meyer-Christoffer,
A., and Schneider, U.: GPCC Full Data Daily Version.2018 at 1.0∘:
Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and
Historic Data, https://doi.org/10.5676/DWD_GPCC/FD_D_V2018_100, 2018.
Short summary
This paper presents a database that is a collection of datasets of gridded 1° × 1° daily precipitation estimates from a variety of sources. It includes observations from in situ networks, satellite-based estimations and outputs from atmospheric reanalysis. All the datasets have been formatted in the same way to ease their manipulation. This database aims at facilitating intercomparisons and validation exercises performed by the scientific community.
This paper presents a database that is a collection of datasets of gridded 1° × 1° daily...
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