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Evaluating the impact of a time-evolving constellation on multi-platform satellite based daily precipitation estimates

Oliveira, Rômulo Augusto Jucá, Roca, Rémy, Finkensieper, Stephan, Cloché, Sophie, Schröder, Marc·2022·Atmospheric Research

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Research works printed but not linked: 55

  1. Adler, 2018, The Global Precipitation Climatology Project (GPCP) monthly analysis (New Version 2.3) and a review of 2017 global precipitation, Atmosphere (Basel)., 9
  2. Alexander, 2020, Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based and reanalysis products, Environ. Res. Lett., 15, 10.1088/1748-9326/ab79e2 · DOI · Paper page
  3. Ayat, 2021, How do different sensors impact IMERG precipitation estimates during hurricane days?, Remote Sens. Environ., 259, 10.1016/j.rse.2021.112417 · doi:10.1016/j.rse.2021.112417
  4. Bador, 2020, Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets, Environ. Res. Lett., 10.1088/1748-9326/ab6a22 · doi:10.1088/1748-9326/ab6a22
  5. Beck, 2017, Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201, 10.5194/hess-21-6201-2017 · DOI · Paper page
  6. Biscaro, 2021, What drives daily precipitation over the Central Amazon? Differences observed between wet and dry seasons, Atmos. Chem. Phys., 21, 6735, 10.5194/acp-21-6735-2021 · DOI
  7. Chambon, 2012, An investigation of the error budget of tropical rainfall accumulation derived from merged passive microwave and infrared satellite measurements, Q. J. R. Meteorol. Soc., 139, 879, 10.1002/qj.1907 · doi:10.1002/qj.1907
  8. Chambon, 2013, The sensitivity of tropical rainfall estimation from satellite to the configuration of the microwave imager constellation, IEEE Geosci. Remote Sens. Lett., 10, 996, 10.1109/LGRS.2012.2227668 · DOI
  9. Chambon, 2013, The Sensitivity of Tropical Rainfall Estimation from Satellite to the Configuration of the Microwave Imager Constellation, IEEE Geosci. Remote Sens. Lett., 10, 996, 10.1109/LGRS.2012.2227668 · DOI
  10. Chen, 2019, Thermodynamic and dynamic mechanisms for hydrological cycle intensification over the full probability distribution of precipitation events, J. Atmos. Sci., 76, 497, 10.1175/JAS-D-18-0067.1 · DOI
  11. Coelho, 2016, Precipitation diagnostics of an exceptionally dry event in São Paulo, Brazil, Theor. Appl. Climatol., 125, 769, 10.1007/s00704-015-1540-9 · doi:10.1007/s00704-015-1540-9
  12. Contractor, 2021, Changes in observed daily precipitation over global land areas since 1950, J. Clim., 34, 3, 10.1175/JCLI-D-19-0965.1 · DOI
  13. Costa, 2018, An examination of microwave rainfall retrieval biases and their characteristics over the Amazon, Atmos. Res., 213, 323, 10.1016/j.atmosres.2018.06.011 · doi:10.1016/j.atmosres.2018.06.011
  14. Fiolleau, 2020, Homogenization of geostationary infrared imager channels for cold cloud studies using megha-tropiques/ScaRaB, IEEE Trans. Geosci. Remote Sens., 1–14
  15. Gosset, 2018, Evaluation of TAPEER daily estimates and other GPM-era products against dense gauge networks in West Africa, analysing ground reference uncertainty, Q. J. R. Meteorol. Soc., 144, 255, 10.1002/qj.3335 · doi:10.1002/qj.3335
  16. Gupta, 2009, Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80, 10.1016/j.jhydrol.2009.08.003 · DOI · Paper page
  17. Hersbach, 2020, The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 1999–2049
  18. Huffman, 2001, Global precipitation at one-degree daily resolution from multisatellite observations, J. Hydrometeorol., 2, 36, 10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2 · DOI · Paper page
  19. Joyce, 2004, CMORPH: a Method that Produces Global Precipitation estimates from Passive Microwave and infrared Data at High Spatial and Temporal Resolution, J. Hydrometeorol., 5, 487, 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 · DOI · Paper page
  20. Karlsson, 2017, CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809, 10.5194/acp-17-5809-2017 · doi:10.5194/acp-17-5809-2017
  21. Kidd, 2003, Satellite rainfall estimation using combined passive microwave and infrared algorithms, J. Hydrometeorol., 4, 1088, 10.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2 · doi:10.1175/1525-7541(2003)004<1088:sreucp>2.0.co;2
  22. Kidd, 2021, The global satellite precipitation constellation: current status and future requirements, Bull. Am. Meteorol. Soc., 1–47
  23. Kidd, 2021, Precipitation retrievals from passive microwave cross-track sensors: the precipitation retrieval and profiling scheme, Remote Sens., 13, 1, 10.3390/rs13050947 · doi:10.3390/rs13050947
  24. Kirstetter, 2013, An error model for instantaneous satellite rainfall estimates: evaluation of BRAIN-TMI over West Africa, Q. J. R. Meteorol. Soc., 139, 894, 10.1002/qj.1964 · DOI
  25. Kling, 2012, Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264, 10.1016/j.jhydrol.2012.01.011 · DOI · Paper page
  26. Kummerow, 1996, A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE Trans. Geosci. Remote Sens., 34, 1213, 10.1109/36.536538 · DOI · Paper page
  27. Kummerow, 2015, The evolution of the Goddard profiling algorithm to a fully parametric scheme, J. Atmos. Ocean. Technol., 32, 2265, 10.1175/JTECH-D-15-0039.1 · doi:10.1175/jtech-d-15-0039.1
  28. Lerch, 2020, Distribution Optimization: an evolutionary algorithm to separate Gaussian mixtures, Sci. Rep., 10, 648, 10.1038/s41598-020-57432-w · doi:10.1038/s41598-020-57432-w
  29. Levizzani, 2019, Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a changing climate, Remote Sens., 11, 2301, 10.3390/rs11192301 · DOI · Paper page
  30. Levizzani, 2018, The activities of the International Precipitation Working Group, Q. J. R. Meteorol. Soc., 10.1002/qj.3214 · DOI
  31. Liu, 2012, Multisatellite observed responses of precipitation and its extremes to interannual climate variability, J. Geophys. Res. Atmos., 117, 1
  32. Maggioni, 2016, A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall measuring Mission (TRMM) era, J. Hydrometeorol., 17, 1101, 10.1175/JHM-D-15-0190.1 · DOI · Paper page
  33. Marengo, 2012, Recent developments on the south American monsoon system, Int. J. Climatol., 32, 1, 10.1002/joc.2254 · DOI · Paper page
  34. Marengo, 2017, Meteorological context of the onset and end of the rainy season in Central Amazonia during the GoAmazon2014/5, Atmos. Chem. Phys., 17, 7671, 10.5194/acp-17-7671-2017 · DOI
  35. Masunaga, 2019, Inter-product biases in global precipitation extremes, Environ. Res. Lett., 14, 10.1088/1748-9326/ab5da9 · doi:10.1088/1748-9326/ab5da9
  36. Morin, 2011, 47, 1
  37. Oliveira, 2022, A simple statistical model of the uncertainty distribution for daily gridded precipitation multi-platform satellite products. 2022, Remote Sens., 14, 3726, 10.3390/rs14153726 · doi:10.3390/rs14153726
  38. Oliveira, 2016, Characteristics and diurnal cycle of GPM rainfall estimates over the Central Amazon region, Remote Sens., 8, 10.3390/rs8070544 · DOI
  39. Petković, 2017, Understanding the sources of satellite passive microwave rainfall retrieval systematic errors over land, J. Appl. Meteorol. Climatol., 56, 597, 10.1175/JAMC-D-16-0174.1 · doi:10.1175/jamc-d-16-0174.1
  40. Potter, 2020, Histogram anomaly time series : a compact graphical representation of spatial time series data sets, Bull. Am. Meteorol. Soc., 101, E2133, 10.1175/BAMS-D-20-0130.1 · doi:10.1175/bams-d-20-0130.1
  41. Raia, 2008, The Life Cycle of the South American Monsoon System, J. Clim., 21, 6227, 10.1175/2008JCLI2249.1 · doi:10.1175/2008jcli2249.1
  42. Rajagopal, 2021, Comparisons of IMERG version 06 precipitation at and between passive microwave overpasses in the tropics, J. Hydrometeorol., 22, 2117
  43. Reboita, 2010, Regimes de precipitação na América do Sul: uma revisão bibliográfica, Rev. Bras. Meteorol., 25, 185, 10.1590/S0102-77862010000200004 · DOI · Paper page
  44. Roca, 2020, Extreme precipitation in the tropics is closely associated with long-lived convective systems, Commun. Earth Environ., 1, 1, 10.1038/s43247-020-00015-4 · DOI
  45. Roca, 2010, Comparing satellite and surface rainfall products over West Africa at meteorologically relevant scales during the AMMA campaign using error estimates, J. Appl. Meteorol. Climatol., 49, 715, 10.1175/2009JAMC2318.1 · DOI
  46. Roca, 2015, The Megha-Tropiques mission: a review after three years in orbit, Front. Earth Sci., 3, 17, 10.3389/feart.2015.00017 · DOI
  47. Roca, 2018, 144, 49
  48. Roca, 2019, FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products, Earth Syst. Sci. Data, 11, 1017, 10.5194/essd-11-1017-2019 · DOI
  49. Roca, 2020, Merging the infrared fleet and the microwave constellation for tropical hydrometeorology (TAPEER) and global climate monitoring (GIRAFE) applications, Adv. Global Change Res., 67 of, 429, 10.1007/978-3-030-24568-9_24 · doi:10.1007/978-3-030-24568-9_24
  50. Stephens, 2020, Earth’s water reservoirs in a changing climate, Proc. R. Soc. A Math. Phys. Eng. Sci., 476
  51. Sun, 2018, A review of global precipitation data sets: data sources, estimation, and intercomparisons, Rev. Geophys., 56, 79, 10.1002/2017RG000574 · DOI · Paper page
  52. Tan, 2018, Evaluation of global precipitation measurement rainfall estimates against three dense gauge networks, J. Hydrometeorol., 19, 517, 10.1175/JHM-D-17-0174.1 · DOI
  53. Utsumi, 2020, Evaluation of precipitation vertical profiles estimated by GPM-Era satellite-based passive microwave retrievals, J. Hydrometeorol., 22, 95, 10.1175/JHM-D-20-0160.1 · doi:10.1175/jhm-d-20-0160.1
  54. Xu, 1999, A microwave infrared threshold technique to improve the GOES precipitation index, J. Appl. Meteorol., 38, 569, 10.1175/1520-0450(1999)038<0569:AMITTT>2.0.CO;2 · doi:10.1175/1520-0450(1999)038<0569:amittt>2.0.co;2
  55. You, 2020, Evaluation of v05 precipitation estimates from gpm constellation radiometers using kupr as the reference, J. Hydrometeorol., 21, 705, 10.1175/JHM-D-19-0144.1 · doi:10.1175/jhm-d-19-0144.1

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Metadata

Authors
Oliveira, R. A. J., Roca, R., Finkensieper, S., Cloché, S., & Schröder, M.
Venue
Atmospheric Research
Publisher
Elsevier BV
Published
2022-12
Volume
279
Pages
106414
ISSN
0169-8095
Cited by
4
References
4
Language
en
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Oliveira, R. A. J., Roca, R., Finkensieper, S., Cloché, S., & Schröder, M. (2022). Evaluating the impact of a time-evolving constellation on multi-platform satellite based daily precipitation estimates. Atmospheric Research. https://doi.org/10.1016/j.atmosres.2022.106414

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