EPSAT-SG: A satellite method for precipitation estimation; Its concepts and implementation for the AMMA experiment

Annales Geophysicae

Volumn 28, Issue 1, 2010, Pages 289-308

  Berges J C ^a   Jobard, I ^b,c   Chopin, F ^b   Roca, R ^b  

^a UNIVERSITÉ PARIS 1 PANTHÉON SORBONNE   (France)

^b ECOLE POLYTECHNIQUE   (France)

^c UNIVERSITÉ PARIS SACLAY   (France)

Author keywords

Meteorology and atmospheric dynamics (Precipitation; Tropical meteorology; Instruments and techniques)

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DOWNSCALING; METEOSAT; PRECIPITATION ASSESSMENT; PRECIPITATION INTENSITY; RAINFALL; SATELLITE DATA; TROPICAL METEOROLOGY;

EID: 76649102098     PISSN: 09927689     EISSN: 14320576     Source Type: Journal    
DOI: 10.5194/angeo-28-289-2010     Document Type: Article

References (50)

1. Adler, R. F. and Negri, A. J.: A Satellite Infrared Technique to Estimate Tropical Convective and Stratiform Rainfall, J. Appl. Meteorol., 27, 30-51, 1988.
2. Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present), J. Hydrometeorol., 4, 1147- 1167, 2003.
3. Ali, A., Amani, A., Diedhiou, A., and Lebel, T.: Rainfall Estimation in the Sahel. Part II: Evaluation of Rain Gauge Networks in the CILSS Countries and Objective Intercomparison of Rainfall Products, J. Appl. Meteor. ,44, 1707-1722, 2005.
4. Arkin, P. A.: The relationship between the fractional coverage of high cloud and rainfall accumulations during GATE over the Barray, Mon. Weather Rev., 107, 1382-1387, 1979.
5. Arkin, P. A. and Meisner, B. N.: The Relationship between Large-Scale Convective Rainfall and Cold Cloud over the Western Hemisphere during 1982-1984, Mon. Weather Rev., 115, 51-74, 1987.
6. Ba, M. B. and Gruber, A.: GOES Multispectral Rainfall Algorithm (GMRA), J. Appl. Meteorol., 40, 1500-1514, 2000.
7. Balme, M., Vischel, T., Lebel, T., Peugeot, C., and Galle, S.: Assessing the water balance in the Sahel: Impact of small scale rainfall variability on runoff. Part 1: Rainfall variability analysis, J. Hydrol., 331, 336-348, 2006.
8. Berg̀es, J. C., Jobard, I., and Roca, R.: A new index to estimate precipitation using cloud growing rate, Geophys. Res. Lett., 36, L08808, doi:10.1029/2008GL036665, 2009.
9. Coppola, E., Grimes, D. I. F., Verdecchia, M., and Visconti, G.: Validation of Improved TAMANN Neural Network for Operational Satellite-Derived Rainfall Estimation in Africa, J. Appl. Meteor. Climatol., 45, 1557-1572, 2006.
10. Derrien, M., Farki, B., Harang, L., LeGleau, H., Noyalet, A., Pochic, D., and Sairouni, A.: Automatic cloud detection applied to NOAA-11/AVHRR imagery, Rem. Sens. Environ., 46, 246-267, 1993.
11. Eyre, J. R. and Menzel, P. W.: Retrieval of Cloud Parameters from Satellite Sounder Data: A Simulation Study, J. Appl. Meteorol., 28, 267-275, 1989.
12. Grimes, D. I. F. and Diop, M.: Satellite-based rainfall estimation for river flow forecast in Africa. I Rainfall estimates and hydrological forecasts, Hydrol. Sci. J., 48(4), 567-584, 2003. (Pubitemid 37036140)
13. Grody, N. C.: Classification of snow cover and precipitation using the Special Sensor Microwave Imager, J. Geophys. Res., 96, 7423-7435, 1991.
14. Gruber, A. and Levizzani, V.: Assessment of Global Precipitation Products, WCRP-128, WMO Technical Document, 1430, 57 pp, 2008.
15. Herman, A., Kumar, V. B., Arkin, P. A., and Kousky, J. V.: Objectively Determined 10-Day African Rainfall Estimates Created for Famine EarlyWarning Systems, Int. J. Rem. Sens., 18, 2147-2159, 1997.
16. Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-366, 1989.
17. Hornik, K.: Approximation Capabilities of Multilayer Feedforward Networks, Neural Networks, vol.4, 1991.
18. Hsu, K., Gao, X., Sorooshian, S., and Gupta, H. V.: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks, J. Appl. Meteorol., 36, 1176-1190, 1997.
19. Hsu, K., Gupta, H. V., Gao, X., and Sorooshian, S.: Estimation of physical variables from multichannel remotely sensed imagery using neural networks: Application to rainfall estimation, Water Resour. Res., 35, 1605-1618, 1999.
20. Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, B.: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2, 36-50, 2001.
21. Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasiglobal, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol., 8, 38-55, 2007.
22. Iguchi, T., Kozu, T., Meneghini, R., Awaka, J., and Okamoto, K.: Rain-profiling algorithm for the TRMM precipitation radar, J. Appl. Meteor., 39, 2038-2052, 2000. (Pubitemid 32214705)
23. Inoue, T.: An instantaneous delineation of convective rainfall area using split window data of NOAA-7 AVHRR, J. Meteorol. Soc. Japan, 65, 469-481, 1987.
24. Jobard, I. and Desbois, M.: Remote sensing of rainfall over the tropical Africa using Meteosat IR imagery; sensitivity to time and space averaging, Int. J. Rem. Sens., 13(14), 2683-2700, 1992.
25. Jobard, I. and Desbois, M.: Satellite estimation of the tropical precipitation using the METEOSAT ans SSM/I data, Atmos. Res., 34, 285-298, 1994.
26. Jobard, I., Chopin, F., Berg̀es, J. C., Ali, A., and Roca, R.: An intercomparison of 10 days satellite precipitation products during West African monsoon, Int. J. Rem. Sens., in press, 2010.
27. 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, 2004.
28. Kerkmann, J., Rosenfeld, D., and Bridge, G.: Applications of Meteosat Second Generation (MSG) RGB images: Part 4 RGB composites with channels 01-11 and their interpretation, MSG CHANNELS Interpretation Guide, http://oiswww.eumetsat.org/ WEBOPS/msg interpretation/index.html, 2005.
29. Kiruno, T.: A rainfall estimation associated with tropical cyclones with the GMS-5 infra-red split windows and water vapour measurements. Proceedings of the Meteorological Satellite Dara Users' Conference, Brussels, 523-528, EUM P 21, ISSN 1011-3932, 29 September-3 October 1998.
30. 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 Trans. Geosci. Rem. Sens., 45, 2259-2275, 2007.
31. Kummerow, C., Olson, W. S., and Giglio, L.: A simplified scheme for obtaining precipitation and vertical hydrometer profiles from passive microwave sensors, IEEE Trans. Geosci. Rem. Sens., 34, 1213-1232, 1996.
32. Kummerow, C., Hong, Y., Olson, W. S., Yang, S., Adler, R. F., Mc-Collum, 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, 2001.
33. Lattanzio, A., Watts, P. D., and Govaerts, Y.: Activity report on Physical Interpretation of Warm Water Vapour Pixels, EUMETSAT, Technical Memorandum, 14, 2006.
34. Levizzani, V., Bauer, P., and Turk, F. J. (Eds.): Measuring precipitation from space, Springer, Dordrecht, 722 pp., 2007.
35. Milford, J. R. and Dugdale, G.: Estimation of rainfall using geostationary satellite data. Applications of Remote Sensing in Agriculture, Proceedings of 48th Easter School in Agricultural Science, Butterworth, 1990.
36. Nieman, S. J., Schmetz, J., and Menzel, W. P.: A Comparison of Several Techniques to Assign Heights to Cloud Tracers, J. Appl. Meteorol., 32, 1559-1568, 1993.
37. Redelsperger, J. L.: The mesoscale organization of deep convection, in: The Physics and Organization of Deep Atmospheric Convection, edited by: Smith, R. K., pp 159-160, Springer, London, 1997.
38. Richards, F. and Arkin, P. A.: On the relationship between satelliteobserved cloud cover and precipitation, Mon. Weather Rev., 5, 1081-1093, 1981.
39. Roca, R., Chambon, P., Jobard, I., Kirstetter, P. E., Gosset, M., and Berg̀es, J. C.: Comparing satellite and surface rainfall products over West Africa at meteorological relevant scales during the AMMA campaign using error estimates, J. Appl. Meteorol. Climatol., in press, 2010.
40. Rumelhart, D. E., Hinton, G. E., andWilliams, R. J.: Learning internal representations by error propagation, in: Parallel Distributed Processing, edited by: Rumelhart, D. E. and McClelland, J. L., MIT Press, 1, 318-362, 1986.
41. Santurette, P. and Georgiev, C. G.: Weather Analysis and Forecasting: Applying Satellite Water Vapor Imagery and Potential Vorticity Analysis, Academic Press, Burlington, MA, San Diego, London. 179 pp, 2005.
42. Schmetz, J., Pili P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An Introduction to Meteosat Second Generation (MSG), B. Am. Meteorol. Soc., 83, 977-992, 2002.
43. Takahashi, N., Iguchi, T., Aonashi, K., Awaka, J., Eito, H., Fujita, M., Hashizume, H., Hirose, M., Inoue, T., Kashiwagi, H., Kozu, T., Satoh, S., Seto, S., Shige, S., Shimizu, S., Takayabu, Y., and Okamoto, K.: The Global Satellite Mapping of Precipitation (GSMaP) Project Part II Algorithm and precipitation model development, 2nd TRMM International Science Conference, Nara, 2004.
44. Tefft, J., McGuire, M., and Maunder, N.: Planning for the future: an assessment of food security early warning systems in sub-Saharan Africa, FAO synthesis report, Rome, TC/D/A0838E/1/11.06/50, 2006.
45. Todd, M. C., Kidd, C., Kniveton, D., and Bellerby, T. J.: A Combined Satellite Infrared and Passive Microwave Technique for Estimation of Small-Scale Rainfall, J. Atmos. Oceanic Technol., 18, 742-755, 2001.
46. Vicente, G., Scofield, R. A., and Menzel, W. P.: The operational GOES infrared rainfall estimation technique, B. Am. Meteorol. Soc., 79, 1883-1898, 1998. (Pubitemid 29085634)
47. Viltard, N., Burlaud, C., and Kummerow, C. D.: Rain Retrieval from TMI Brightness Temperature Measurements Using a TRMM PR-Based Database, J. Appl. Meteorol. Climatol., 45, 455-466, 2006.
48. Von Storch, H., Langenberg, H., and Feser, F.: A spectral nudging technique for dynamical downscaling purposes, Mon. Weather Rev., 128, 3664-3673, 2000.
49. Walczak, S. and Cerpa, N.: Heuristic principles for the design of artificial neural networks, Information and software technology, 41, 107-117, 1999.
50. Wolters, E. L. A., Roebeling, R. A., and Feijt, A. J.: Evaluation of cloud phase retrieval methods for SEVIRI onboard METEOSAT-8 using ground-based lidar and cloud radar data, J. Appl. Meteor. Climatol., 47, 1723-1738, 2008.