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 article
| 
10 Jul 2019
Data description article |
| 10 Jul 2019
| 10 Jul 2019
FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products
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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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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