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Validating a rapid-update satellite precipitation analysis across telescoping space and time scales

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Abstract

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and time-resolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25° spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1° spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8° produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1° time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day−1.

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References

  1. Adler RF, Kidd C, Petty G, Morrissey M, Goodman HM (2001) Intercomparison of global precipitation products: the third Precipitation Intercomparison Project (PIP-3). Bull Am Meteorol Soc 82:1377–1396

  2. Arkin PA, Xie P (1994) The global precipitation climatology project: first algorithm intercomparison project. Bull Am Meteorol Soc 75:401–419

  3. Barrett EC, Dodge J, Goodman HM, Janowiak J, Kidd C, Smith EA (1994) The First WetNet Precipitation Intercomparison Project. Remote Sens Rev 11:49–60

  4. Bauer P, Burose D, Schulz J (2002) Rain detection over land surfaces using passive microwave satellite data. Meteorol Z 11:37–48

  5. Ebert EE, Manton MJ, Arkin PA, Allam RJ, Holpin CE, Gruber A (1996) Results from the GPCP Algorithm Intercomparison Programme. Bull Am Meteorol Soc 77:2875–2887

  6. Ebert EE, Kidd C, Janowiak J (2007) Comparison of near real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc 88:47–64

  7. Gebremichael M, Krajewski WF (2004) Characterization of the temporal sampling error in space-time averaged rainfall estimates from satellites. J Geophys Res 109:D11110. doi:10.1029/2004JD004509

  8. Gebremichael M, Krajewski WF, Morissey ML, Huffman GJ, Adler RF (2005) A detailed evaluation of GPCP 1-degree daily rainfall estimates over the Mississippi river basin. J Appl Meteorol 44:665–681

  9. GPM (2008) GPM science objectives. Global Precipitation Measurement, NASA Goddard Spaceflight Center, http://gpm.gsfc.nasa.gov/science.html

  10. Grose A, Smith EA, Chung HS, Ou M-L, Sohn BJ, Turk FJ (2002) Possibilities and limitations for QPF using nowcasting methods with infrared geosynchronous satellite imagery. J Appl Meteorol 41:763–785

  11. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55

  12. Joyce R, Ferraro RR (2006) Improvements of CMORPH resulting from limb adjustments and normalization of AMSU-B rainfall. 14th AMS Conference on Satellite Meteorology and Oceanography, 29 Jan to 3 Feb, Atlanta

  13. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503

  14. Joyce R, Janowiak JE, Arkin PA, Xie P (2006) The combination of a passive microwave based satellite rainfall estimation algorithm with an IR-based algorithm. 14th AMS Conference on Satellite Meteorology and Oceanography, 29 Jan to 3 Feb, Atlanta

  15. Kummerow CD, Simpson J, Thiele O, Barnes W, Chang ATC, Stocker E, Adler RF, Hou A, Kakar R, Wentz F, Ashcroft P, Kozu T, Hong Y, Okamoto K, Iguchi T, Kuroiwa H, Im E, Haddad Z, Huffman G, Ferrier B, Olson WS, Zipser E, Smith EA, Wilheit TT, North G, Krishnamurti T, Nakamura K (2000) The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J Appl Meteorol 39:1965–1982

  16. Levizzani V, Bauer P, Turk J (2007) Measuring precipitation from space: EURAINSAT and the future. Springer, Berlin, p 722

  17. Negri AJ, Bell TL, Xu L (2002) Sampling of the diurnal cycle of precipitation using TRMM. J Atmos Ocean Technol 19:1333–1344

  18. Oh HJ, Sohn BJ, Smith EA, Turk FJ, Seo AS, Chung HS (2002) Validating infrared-based rainfall retrieval algorithms with 1-minute spatially dense raingauge measurements over the Korean peninsula. Meteorol Atmos Phys 81:273–287

  19. Sapiano MRP, Arkin PA (2009) An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J Hydrometeorol 10:149–166

  20. Schmetz J, Pili P, Tjemkes S, Just D, Kerkmann J, Rota S, Raiter A (2002) An introduction to Meteosat Second Generation (MSG). Bull Am Meteorol Soc 83:977–992

  21. Smith EA, Lamm JE, Adler R, Alishouse J, Aonashi K, Barrett E, Bauer P, Berg W, Chang A, Ferraro R, Ferriday J, Goodman S, Grody N, Kidd C, Kniveton D, Kummerow C, Liu G, Marzano F, Mugnai A, Olson W, Petty G, Shibata A, Spencer R, Wentz F, Wilheit T, Zipser E (1998) Results of WetNet PIP-2 Project. J Atmos Sci 55:1483–1536

  22. Soman VV, Valdes JB, North GR (1995) Satellite sampling and the diurnal cycle statistics of Darwin rainfall data. J Appl Meteorol 34:2481–2490

  23. Sorooshian S, Hsu K, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81:2035–2046

  24. Steiner M, Bell TL, Zhang Y, Wood EF (2003) Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset. J Clim 16:3759–3778

  25. Tian Y, Peters-Lidard CD, Choudhury B, Garcia M (2007) Multitemporal analysis of TRMM based satellite precipitation products for land data assimilation applications. J Hydrometeorol 8:1165–1183. doi:10.1175/2007JHM859.1

  26. Turk J, Bauer P (2006) The International Precipitation Working Group and its role in the improvement of quantitative precipitation measurements. Bull Am Meteorol Soc 87:643–647

  27. Turk FJ, Miller SD (2005) Toward improving estimates of remotely-sensed precipitation with MODIS/AMSR-E blended data techniques. IEEE Trans Geosci Remote Sens 43:1059–1069

  28. Turk FJ, Arkin P, Ebert E, Sapiano M (2008) Evaluating high resolution precipitation products. Bull Am Meteorol Soc 89:1911–1916

  29. Turk FJ, Mostovoy G, Anantharaj V (2009) Satellite rainfall applications for surface hydrology. In: Gebremichael M, Hossain F (eds) The NRL-Blend high resolution precipitation product and its application to land surface hydrology, Chapter 6. Springer, Berlin, p 445. ISBN 978-90-481-2914-0

  30. Ushio T, Okamoto K, Isoda F, Iida Y, Aonashi K, Inoue T, Takahashi N, Iguchi T, Hanado H, Iwanami K (2004) The Global Satellite Mapping of Precipitation (GSMaP) Project: integration of microwave and infrared radiometers for a global precipitation map. Proceedings of 2nd international precipitation working group, 25–28 October, Monterey. EUMETSAT, Darmstadt, p 44, ISBN 92-9110-070-6. Online at http://www.isac.cnr.it/~ipwg

  31. Vicente GA, Davenport JC, Scofield RA (2002) The role of orographic and parallax correction on real time, high resolution satellite rain rate observation. Int J Remote Sens 23:221–230

  32. Xian P, Reid J, Turk FJ, Hyer EJ, Westphal D (2009) Impact of modeled versus satellite measured tropical precipitation on regional smoke optical thickness in an aerosol transport model. Geophys Res Lett 36. doi:10.1029/2009GL038823

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Acknowledgments

The first author acknowledges the support of the research sponsors, the Office of Naval Research, Program Element (PE-0602435N) and the National Aeronautics and Space Administration (NASA) under grant NNG04HK11I. We acknowledge the efforts of the Microwave Surface and Precipitation Products System (MSPPS) at NOAA/NESDIS for the AMSU-B and MHS rainfall datasets, and the TRMM Precipitation Processing System (PPS) for the TMI and PR rainfall datasets.

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Correspondence to Francis Joseph Turk.

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Turk, F.J., Sohn, B., Oh, H. et al. Validating a rapid-update satellite precipitation analysis across telescoping space and time scales. Meteorol Atmos Phys 105, 99–108 (2009). https://doi.org/10.1007/s00703-009-0037-4

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Keywords

  • Tropical Rainfall Measure Mission
  • Automate Weather Station
  • Geostationary Earth Orbit
  • False Alarm Ratio
  • Defense Meteorological Satellite Program