Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-4097-2025
© Author(s) 2025. 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-17-4097-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GIRAFE v1: a global climate data record for precipitation accompanied by a daily sampling uncertainty
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Rémy Roca
Laboratoire d'Etudes Géophysiques et d'Océanographie Spatiales, 14, av. Edouard Belin, 31401 Toulouse CEDEX 9, France
Anja Niedorf
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Stephan Finkensieper
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Marc Schröder
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Sophie Cloché
Institut Pierre-Simon Laplace, Sciences du Climat, route de Saclay, 91128 Palaiseau, France
Giulia Panegrossi
National Research Council of Italy, Institute of Atmospheric Sciences and Climate, Via del Fosso del Cavaliere 100, 00133 Roma, Italy
Paolo Sanò
National Research Council of Italy, Institute of Atmospheric Sciences and Climate, Via del Fosso del Cavaliere 100, 00133 Roma, Italy
Christopher Kidd
University of Maryland, Earth System Science Interdisciplinary Center, 5825 University Research Ct., College Park, MD 20740, USA
NASA/Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
Rômulo Augusto Jucá Oliveira
Laboratoire d'Etudes Géophysiques et d'Océanographie Spatiales, 14, av. Edouard Belin, 31401 Toulouse CEDEX 9, France
now at: Hydro Matters, 1 Chemin de la Pousaraque, 31460 Le Faget, France
Karsten Fennig
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Thomas Sikorski
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Madeleine Lemoine
Laboratoire d'Etudes Géophysiques et d'Océanographie Spatiales, 14, av. Edouard Belin, 31401 Toulouse CEDEX 9, France
Rainer Hollmann
Deutscher Wetterdienst, Satellite-Based Climate Monitoring, Frankfurter Str. 135, 63067 Offenbach am Main, Germany
Related authors
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Louis Netz, Thomas Fiolleau, and Rémy Roca
EGUsphere, https://doi.org/10.5194/egusphere-2025-2247, https://doi.org/10.5194/egusphere-2025-2247, 2025
Short summary
Short summary
Convective systems are the primary drivers of rainfall and climate on Earth, yet the spatial organisation of associated convection remains poorly understood. This study presents a simple approach to describing this organisation. First, the convective field is decomposed into elementary structures. Then, four scores are computed to describe the size, density, spacing scale and departure from randomness of the cores. This method robustly characterises the organisation of convection.
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024, https://doi.org/10.5194/essd-16-5243-2024, 2024
Short summary
Short summary
The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.
Thomas Fiolleau and Rémy Roca
Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, https://doi.org/10.5194/essd-16-4021-2024, 2024
Short summary
Short summary
This paper presents a database of tropical deep convective systems over the 2012–2020 period, built from a cloud-tracking algorithm called TOOCAN, which has been applied to homogenized infrared observations from a fleet of geostationary satellites. This database aims to analyze the tropical deep convective systems, the evolution of their associated characteristics over their life cycle, their organization, and their importance in the hydrological and energy cycle.
Tim Trent, Marc Schröder, Shu-Peng Ho, Steffen Beirle, Ralf Bennartz, Eva Borbas, Christian Borger, Helene Brogniez, Xavier Calbet, Elisa Castelli, Gilbert P. Compo, Wesley Ebisuzaki, Ulrike Falk, Frank Fell, John Forsythe, Hans Hersbach, Misako Kachi, Shinya Kobayashi, Robert E. Kursinski, Diego Loyola, Zhengzao Luo, Johannes K. Nielsen, Enzo Papandrea, Laurence Picon, Rene Preusker, Anthony Reale, Lei Shi, Laura Slivinski, Joao Teixeira, Tom Vonder Haar, and Thomas Wagner
Atmos. Chem. Phys., 24, 9667–9695, https://doi.org/10.5194/acp-24-9667-2024, https://doi.org/10.5194/acp-24-9667-2024, 2024
Short summary
Short summary
In a warmer future, water vapour will spend more time in the atmosphere, changing global rainfall patterns. In this study, we analysed the performance of 28 water vapour records between 1988 and 2014. We find sensitivity to surface warming generally outside expected ranges, attributed to breakpoints in individual record trends and differing representations of climate variability. The implication is that longer records are required for high confidence in assessing climate trends.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
Short summary
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024, https://doi.org/10.5194/amt-17-2195-2024, 2024
Short summary
Short summary
The paper describes a new machine-learning-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder observations developed to retrieve high-latitude snowfall events. The main novelty of the approach is the radiometric characterization of the background surface at the time of the overpass, which is ancillary to the retrieval process. The algorithm shows a unique capability to retrieve snowfall in the environmental conditions typical of high latitudes.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
Short summary
Short summary
This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
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
Short summary
Short summary
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.
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
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
Short summary
Short summary
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.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Tim Trent, Richard Siddans, Brian Kerridge, Marc Schröder, Noëlle A. Scott, and John Remedios
Atmos. Meas. Tech., 16, 1503–1526, https://doi.org/10.5194/amt-16-1503-2023, https://doi.org/10.5194/amt-16-1503-2023, 2023
Short summary
Short summary
Modern weather satellites provide essential information on our lower atmosphere's moisture content and temperature structure. This measurement record will span over 40 years, making it a valuable resource for climate studies. This study characterizes atmospheric temperature and humidity profiles from a European Space Agency climate project. Using weather balloon measurements, we demonstrated the performance of this dataset was within the tolerances required for future climate studies.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
Short summary
Short summary
Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
Marloes Gutenstein, Karsten Fennig, Marc Schröder, Tim Trent, Stephan Bakan, J. Brent Roberts, and Franklin R. Robertson
Hydrol. Earth Syst. Sci., 25, 121–146, https://doi.org/10.5194/hess-25-121-2021, https://doi.org/10.5194/hess-25-121-2021, 2021
Short summary
Short summary
The net exchange of water between the surface and atmosphere is mainly determined by the freshwater flux: the difference between evaporation (E) and precipitation (P), or E−P. Although there is consensus among modelers that with a warming climate E−P will increase, evidence from satellite data is still not conclusive, mainly due to sensor calibration issues. We here investigate the degree of correspondence among six recent
satellite-based climate data records and ERA5 reanalysis E−P data.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
Short summary
Short summary
We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Cited articles
A lexander, L. V., Bador, M. Roca, R., Contractor, S. Donat, M. G., and Nguyen, P. L.: Intercomparison of Annual Precipitation Indices and Extremes over Global Land Areas from in Situ, Space-Based and Reanalysis Products, Environ. Res. Lett., 15, 055002, https://doi.org/10.1088/1748-9326/ab79e2, 2020.
Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986.
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.
Arkin, P. A.: The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-Scale Array, Mon. Weather Rev., 107, 1382–1387, https://doi.org/10.1175/1520-0493(1979)107<1382:TRBFCO>2.0.CO;2, 1979.
Bador, M., Alexander, L. V., Contractor, S., and Roca, R.: Diverse Estimates of Annual Maxima Daily Precipitation in 22 State-of-the-Art Quasi-Global Land Observation Datasets, Environ. Res. Lett., 15, 035005, https://doi.org/10.1088/1748-9326/ab6a22, 2020.
Bagaglini, L., Sanò, P., Casella, D., Cattani, E., and Panegrossi, G.: The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification, Remote Sens., 13, 1701, https://doi.org/10.3390/rs13091701, 2021.
Berg, W.: GPM AMSR-E on AQUA Common Calibrated Brightness Temperatures L1C 1.5 hours 10.5 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/AMSRE/AQUA/1/07, 2021.
Berg, W.: GPM MHS on METOP-A Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/GPM/MHS/METOPA/1C/07, 2022a.
Berg, W.: GPM MHS on METOP-B Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/METOPB/1C/07, 2022b.
Berg, W.: GPM MHS on METOP-C Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/METOPC/1C/07, 2022c.
Berg, W.: GPM MHS on NOAA-18 Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/NOAA18/1C/07, 2022d.
Berg, W.: GPM MHS on NOAA-19 Common Calibrated Brightness Temperatures L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/NOAA19/1C/07, 2022e.
Berg, W.: GPM ATMS on NOAA-20 Common Calibrated Brightness Temperatures L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/ATMS/NOAA20/1C/07, 2022f.
Berg, W.: GPM ATMS on SUOMI-NPP Common Calibrated Brightness Temperature L1C 1.5 hours 16 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/ATMS/NPP/1C/07, 2022g.
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, 2013.
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.
De Meyer, V. and Roca, R.: Thermodynamic Scaling of Extreme Daily Precipitation over the Tropical Ocean from Satellite Observations, J. Meteorol. Soc. Jpn., 99 , 423–36, https://doi.org/10.2151/jmsj.2021-020, 2021.
Fennig, K.: Technical Report – Microwave Imager Intercalibration, https://www.cmsaf.eu/SharedDocs/Literatur/document/2022/saf_cm_dwd_rep_mii_v1_pdf (last access: 18 August 2025), 2022.
Fennig, K., Schröder, M., Andersson, A., and Hollmann, R.: A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures, Earth Syst. Sci. Data, 12, 647–681, https://doi.org/10.5194/essd-12-647-2020, 2020.
Fennig, K., Schröder, M., Konrad, H., and Hollmann, R.: Fundamental Climate Data Record of Microwave Imager Radiances, Edition 4, Satellite Application Facility on Climate Monitoring, EUMETSAT [data set], https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V004, 2022.
Ferraro, R. R., Smith, E. A., Berg, W., and Huffman, G. J.: A Screening Methodology for Passive Microwave Precipitation Retrieval Algorithms, J. Atmos. Sci., 55, 1583–1600, https://doi.org/10.1175/1520-0469(1998)055%3C1583:ASMFPM%3E2.0.CO;2, 1998.
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses, Remote Sens., 12, 720, https://doi.org/10.3390/rs12040720, 2020.
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.
GPM Science Team: GPM GMI Brightness Temperatures L1B 1.5 hours 13 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/GMI/GPM/1B/07, 2022.
Grecu, M., Olson, W. S., Munchak, S. J., Ringerud, S., Liao, L., Haddad, Z. S., Kelley, B. L., and McLaughlin, S. F.: The GPM combined algorithm, J. Atmos. Ocean. Tech., 33, 2225–2245, https://doi.org/10.1175/JTECH-D-16-0019.1, 2016.
Hans, I., Burgdorf, M., Buehler, S. A., Prange, M., Lang, T., and John, V. O.: An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders, Remote Sens., 11, 548, https://doi.org/10.3390/rs11050548, 2019.
Hans, I., Burgdorf, M., and Wooliams, E.: FIDUCEO: Fundamental Climate Data Record of Microwave Brightness Temperatures with uncertainties, 1994–2017, v4.1, Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/a8e9f44965434f3b861eba77688701ef, 2020.
Hawkins, D. M.: Testing a Sequence of Observations for a Shift in Location, J. Am. Stat. Assoc., 72, 180–186, https://doi.org/10.1080/01621459.1977.10479935, 1977.
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.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2023.
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., Nelkin, E. J., and Adler, R. F.: TRMM (TMPA) Precipitation L3 1 d 0.25°×0.25° V7, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/TRMM/TMPA/DAY/7, 2016.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), in: Satellite Precipitation Measurement, vol. 1, edited by Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., Springer, Cham, Switzerland, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020.
Huffman, G. J., Adler, R. F., Behrangi, A., Bolvin, D. T., Nelkin, E. J., Gu, G., and Ehsani, M. R.: The New Version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily Precipitation Products, J. Climate, 36, 7635–7655, https://doi.org/10.1175/JCLI-D-23-0123.1, 2023a.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 d 0.1°×0.1° V07, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERGDF/DAY/07, 2023b.
Kidd, C.: GPM SAPHIR on MT1 (PRPS) Radiometer Precipitation Profiling L2 1.5 hours 10 km V06, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SAPHIR/MT1/PRPS/2A/06, 2019.
Kidd, C., Matsui, T., and Ringerud, S.: Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme, Remote Sens., 13, 947, https://doi.org/10.3390/rs13050947, 2021.
Konrad, H., Schröder, M., Roca, R., and Niedorf, A.: Validation Report Global Interpolated RAinFall Estimation, version 1 (GIRAFE v1), https://www.cmsaf.eu/SharedDocs/Literatur/document/2024/saf_cm_dwd_val_girafe_v1_1_pdf (last access: 18 August 2025), 2024.
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., and Seto, S.: 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. D., Ringerud, S., Crook, J., Randel, D., and Berg, W.: An Observationally Generated A Priori Database for Microwave Rainfall Retrievals, J. Atmos. Ocean. Tech. 28, 113–130, https://doi.org/10.1175/2010jtecha1468.1, 2011.
Lebel, T., Cappelaere, B., Galle, S., Hanan, N., Kergoat, L., Levis, S., Vieux, B., Descroix, L., Gosset, M., Mougin, E., Peugeot, C., and Seguis, L.: AMMA-CATCH studies in the Sahelian region of West-Africa: An overview, J. Hydrol., 375, 3–13, https://doi.org/10.1016/j.jhydrol.2009.03.020, 2009.
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.
Levizzani, V. and Cattani, E.: Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate, Remote Sens., 11, 2301, https://doi.org/10.3390/rs11192301, 2019.
Marine Data Store: Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed, E.U. Copernicus Marine Service Information (CMEMS) [data set], https://doi.org/10.48670/moi-00168, 2025.
Masunaga, H., Schröder, M., Furuzawa, F. A., Kummerow, C., Rustemeier, E., and Schneider, U.: Inter-product biases in global precipitation extremes, Environ. Res. Lett., 14, 125016, https://doi.org/10.1088/1748-9326/ab5da9, 2019.
Mieruch, S., Schröder, M., Noël, S., and Schulz, J.: Comparison of decadal global water vapor changes derived from independent satellite time series, J. Geophys. Res.-Atmos., 119, 489–499, https://doi.org/10.1002/2014JD021588, 2014.
Muller, C. and Takayabu, Y.: Response of Precipitation Extremes to Warming: What Have We Learned from Theory and Idealized Cloud-Resolving Simulations, and What Remains to Be Learned?, Environ. Res. Lett., 15, 035001, https://doi.org/10.1088/1748-9326/ab7130, 2020.
Nguyen, K. N., Bock, O., and Lebarbier, E.: A statistical method for the attribution of change-points in segmented Integrated Water Vapor difference time series, Int. J. Climatol., 44, 2069–2086, https://doi.org/10.1002/joc.8441, 2024.
Niedorf, A., Finkensieper, S., Konrad, H., Roca, R., Schröder, M., Cloché, S., Panegrossi, G., Sanò, P., Kidd, C., Jucá Oliveira, R. A., Fennig, K., Sikorski, T., Penning de Vries, M., Radovan, A., Dietzsch, F., Pondrom, M., Selbach, N., and Hollmann, R.: GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/GIRAFE/V001, 2024a.
Niedorf, A., Konrad, H., Finkensieper, S., Juca Oliveira, R., Pondrom, M., Radovan, A., Cloché, S., Fennig, K., Sikorski, T., Roca, R., Schröder, M., and Kidd, C.: Algorithm Theoretical Basis Document Global Interpolated RAinFall Estimation, version 1 (GIRAFE v1), https://www.cmsaf.eu/SharedDocs/Literatur/document/2024/saf_cm_dwd_atbd_girafe_v1_3_pdf (last access: 18 August 2025), 2024b.
NOAA: Multivariate ENSO Index Version 2 (MEI.v2), NOAA [data set], https://psl.noaa.gov/enso/mei/, last access: 20 August 2025.
Oliveira, R. A. J. and Roca, R: A Simple Statistical Model of the Uncertainty Distribution for Daily Gridded Precipitation Multi-Platform Satellite Products, Remote Sens., 14, 3726, https://doi.org/10.3390/rs14153726, 2022.
Oliveira, R. A. J., Roca, R., Finkensieper, S., Cloché, S., and Schröder, M.: Evaluating the Impact of a Time-Evolving Constellation on Multi-Platform Satellite Based Daily Precipitation Estimates, Atmos. Res., 279, 106414, https://doi.org/10.1016/j.atmosres.2022.106414, 2022.
Olson, W. S., Hong, Y., Kummerow, C. D., and Turk, J.: A Texture-Polarization Method for Estimating Convective–Stratiform Precipitation Area Coverage from Passive Microwave Radiometer Data, J. Appl. Meteorol. Clim., 40, 1577–1591, https://doi.org/10.1175/1520-0450(2001)040<1577:ATPMFE>2.0.CO;2, 2001.
Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J., van der Plas, E., and Leijnse, H.: EURADCLIM: The European climatological gauge-adjusted radar precipitation dataset (24-h accumulations), KNMI Data Platform [data set], https://doi.org/10.21944/1a54-gg96, 2022.
Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J. F., van der Plas, E., and Leijnse, H.: EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset, Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, 2023.
Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M.-P., Frappart, F., Calmant, S., and Mendes, C. A. B.: Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin, Water Resour. Res., 49, 1226–1243, https://doi.org/10.1002/wrcr.20067, 2013.
Reeves, J., Chen, J., Wang, X. L., Lund, R., and Lu, Q. Q.: A Review and Comparison of Changepoint Detection Techniques for Climate Data, J. Appl. Meteorol. Clim., 46, 900–915, https://doi.org/10.1175/JAM2493.1, 2007.
Roca, R.: Estimation of extreme daily precipitation thermodynamic scaling using gridded satellite precipitation products over tropical land, Environ. Res. Lett., 14, 095009, https://doi.org/10.1088/1748-9326/ab35c6, 2019.
Roca, R. and Fiolleau, T.: Extreme Precipitation in the Tropics Is Closely Associated with Long-Lived Convective Systems, Commun. Earth Environ., 1, 1–6, https://doi.org/10.1038/s43247-020-00015-4, 2020.
Roca, R., Chambon, P., Jobard, I., Kirstetter, P., Gosset, M., and Bergès, 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., Taburet, N., Lorant, E., Chambon, P., Alcoba, M., Brogniez, H., Cloché, S., Dufour, C., Gosset, M., and Guilloteau, C.: Quantifying the contribution of the Megha-Tropiques mission to the estimation of daily accumulated rainfall in the Tropics, Q. J. Roy. Meteor. Soc., 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 1°×1° gridded precipitation database of rain gauge, satellite and reanalysis products, Earth Syst. Sci. Data, 11, 1017–1035, https://doi.org/10.5194/essd-11-1017-2019, 2019a.
Roca, R., Alexander, L. V., Potter, G., Bador, M., Jucá, R., Contractor, S., Bosilovich, M. G., and Cloché, S.: FROGs: a daily gridded precipitation database of rain gauge, satellite and reanalysis products, IPSL Data Catalog, https://doi.org/10.14768/06337394-73A9-407C-9997-0E380DAC5598, 2019b.
Roca, R., Haddad, Z. S., Akimoto, F. F., Alexander, L., Behrangi, A., Huffman, G., Kato, S., Kidd, C., Kirstetter, P. E., Kubota, T., Kummerow, C., L'Ecuyer, T. S., Levizzani, V., Maggioni, V., Massari, C., Masunaga, H., Schröder, M., Tapiador, F. J., Turk, F. J., and Utsumi, N.: The Joint IPWG/GEWEX Precipitation Assessment, World Climate Research Programme, GMU, https://doi.org/10.13021/gewex.precip, 2021.
Rüthrich, F., John, V. O., Roebeling, R. A., Quast, R., Govaerts, Y., Woolliams, E. A., and Schulz, J.: MVIRI Level 1.5 Climate Data Record Release 1 – MFG – 0°, European Organisation for the Exploitation of Meteorological Satellites [data set], https://doi.org/10.15770/EUM_SEC_CLM_0009, 2020a.
Rüthrich, F., John, V. O., Roebeling, R. A., Quast, R., Govaerts, Y., Woolliams, E. A., and Schulz, J.: MVIRI Level 1.5 Climate Data Record Release 1 – MFG – 57°, European Organisation for the Exploitation of Meteorological Satellites [data set], https://doi.org/10.15770/EUM_SEC_CLM_0012, 2020b.
Rüthrich, F., John, V. O., Roebeling, R. A., Quast, R., Govaerts, Y., Woolliams, E. A., and Schulz, J.: MVIRI Level 1.5 Climate Data Record Release 1 – MFG – 63°, European Organisation for the Exploitation of Meteorological Satellites [data set], https://doi.org/10.15770/EUM_SEC_CLM_0013, 2020c.
Sanò, P. Panegrossi, G., Bagaglini, L., Cattani, E., Konrad, H., Sikorski, T., Schröder, M.: COBRA: Algorithm Theoretical Basis Document, https://confluence.ecmwf.int/pages/viewpage.action?pageId=278552349 (last access: 18 August 2025), 2021.
Schär, C., Ban, N., Fischer, E. M., Rajczak, J., Schmidli, J., Frei, C., Giorgi, F., Karl, T. R., Kendon, E. J., Klein Tank, A. M. G., O'Gorman, P. A., Sillmann, J., Zhang, Y., and Zwiers, F. W.: Percentile Indices for Assessing Changes in Heavy Precipitation Events, Climatic Change, 137, 201–16, https://doi.org/10.1007/s10584-016-1669-2, 2016.
Schneider, U., Finger, P., Rustemeier, E., Ziese, M., Hänsel, S.: Global Precipitation Analysis Products of the GPCC, revision 010 for v2022, https://opendata.dwd.de/climate_environment/GPCC/PDF/GPCC_intro_products_v2022.pdf (last access: 18 August 2025), 2022.
Schröder, M., Lockhoff, M., Forsythe, J. M., Cronk, H. Q., Vonder Haar, T. H., 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., Shi, L., August, T., Bennartz, R., Brogniez, H., Calbet, X., Fell, F., Forsythe, J., Gambacorta, A., Ho, S.-P., Kursinski, E. R., 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.
Stephens, G., Polcher, J., Zeng, X., van Oevelen, P., Poveda, G., Bosilovich, M., Ahn, M., Balsamo, G., Duan, Q., Hegerl, G., Jakob, C., Lamptey, B., Leung, R., Piles, M., Su, Z., Dirmeyer, P., Findell, K. L., Verhoef, A., Ek, M., L'Ecuyer, T., Roca, R., Nazemi, A., Dominguez, F., Klocke, D., and Bony, S.: The First 30 Years of GEWEX, B. Am. Meteorol. Soc., 104, 126–157, https://doi.org/10.1175/BAMS-D-22-0061.1, 2023.
Szantai, A., Six, B., Cloché, S., and Sèze, G.: Quality of geostationary satellite images, Megha-Tropiques Tech. Memo. 3, https://meghatropiques.ipsl.fr/download/megha-tropiques-technical-memorandum-n3/ (last access: 18 August 2025), 2011.
Tan, J., Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Rajagopal, M.: SHARPEN: A Scheme to Restore the Distribution of Averaged Precipitation Fields, J. Hydrometeorol., 22, 2105–2116, https://doi.org/10.1175/JHM-D-20-0225.1, 2021.
Tropical Rainfall Measuring Mission: TRMM Microwave Imager Calibrated Radiances L1B 1.5 hours V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://disc.gsfc.nasa.gov/datacollection/TRMM_1B11_7.html (last access: 20 August 2025), 2011.
Trent, T., Schröder, M., Ho, S.-P., Beirle, S., Bennartz, R., Borbas, E., Borger, C., Brogniez, H., Calbet, X., Castelli, E., Compo, G. P., Ebisuzaki, W., Falk, U., Fell, F., Forsythe, J., Hersbach, H., Kachi, M., Kobayashi, S., Kursinski, R. E., Loyola, D., Luo, Z., Nielsen, J. K., Papandrea, E., Picon, L., Preusker, R., Reale, A., Shi, L., Slivinski, L., Teixeira, J., Vonder Haar, T., and Wagner, T.: Evaluation of total column water vapour products from satellite observations and reanalyses within the GEWEX Water Vapor Assessment, Atmos. Chem. Phys., 24, 9667–9695, https://doi.org/10.5194/acp-24-9667-2024, 2024.
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking homogenization algorithms for monthly data, Clim. Past, 8, 89–115, https://doi.org/10.5194/cp-8-89-2012, 2012.
Wang, X. L.: Penalized maximal F test for detecting undocumented mean shift without trend change, J. Atmos. Ocean. Tech., 25, 368–384, https://doi.org/10.1175/2007JTECHA982.1, 2008a.
Wang, X. L.: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or F test, J. App. Meteorol. Clim., 47, 2423–2444, https://doi.org/10.1175/2008JAMC1741.1, 2008b.
Wang, X. L., Feng, Y., Cheng, V. Y. S., and Xu, H.: Observed Precipitation Trends Inferred from Canada's Homogenized Monthly Precipitation Dataset, J. Climate, 36, 7957–7971, https://doi.org/10.1175/JCLI-D-23-0193.1, 2023.
Weatherhead, E. C., Reinsel, G. C., Tiao, G. C., Meng, X., Choi, D., Cheang, W., Keller, T., Luisi, J., Wuebbles, D. J., Kerr, J. B., Miller, A. J., Oltmans, S. J., and Frederick, J. E.: Factors affecting the detection of trends: Statistical considerations and applications to environmental data, J. Geophys. Res., 103, 17149–17161, https://doi.org/10.1029/98JD00995, 1998.
WMO – World Meteorological Organization: WMO Guidelines on the Calculation of Climate Normals, https://library.wmo.int/idurl/4/55797 (last access: 18 August 2025), 2017.
Wolter, K. and Timlin, M. S.: El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext), Int. J. Climatol., 31, 1074–1087, https://doi.org/10.1002/joc.2336, 2011.
Wongchuig, S., Paiva, R., Siqueira, V., Papa, F., Fleischmann, A., Biancamaria, S., Paris, A., Parrens, M., and Al Bitar, A.: Multi-satellite data assimilation for large-scale hydrological-hydrodynamic prediction: Proof of concept in the Amazon basin, Water Resour. Res., 60, e2024WR037155, https://doi.org/10.1029/2024WR037155, 2024.
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.
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. Clim., 38, 569–579, https://doi.org/10.1175/1520-0450(1999)038<0569:AMITTT>2.0.CO;2, 1999.
Yamamoto, M. K. and Kubota, T.: Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders, Remote Sens., 14, 4445, https://doi.org/10.3390/rs14184445, 2022.
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., and Kitzmiller, D.: Multi-Radar Multi- Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities, B. Am. Meteorol. Soc., 97, 621–637, https://doi.org/10.1175/BAMS-D-14-00174.1, 2016.
Ziese, M., Rauthe-Schöch, A., Becker, A., Finger, P., Rustemeier, E., Hänsel, S., and Schneider, U.: GPCC Full Data Daily Version 2022 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_V2022_100, 2022.
Short summary
GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research and allows studies on water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines...
Altmetrics
Final-revised paper
Preprint