Error characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42) products over India for different seasons

Journal of Hydrology

Volumn 529, Issue , 2015, Pages 1302-1312

  Prakash, Satya ^a   Mitra, Ashis K ^a   AghaKouchak, Amir ^b   Pai, D S ^c  

^a NATIONAL CENTRE FOR MEDIUM RANGE WEATHER FORECASTING   (India)

^b Irvine   (United States)

^c India Meteorological Department   (India)

Author keywords

Error decomposition; Hydrometeorology; Indian monsoon rainfall; Multisatellite precipitation; Surface rain gauge

Indexed keywords

ATMOSPHERIC THERMODYNAMICS; ERRORS; QUALITY CONTROL; RAIN GAGES; SATELLITES;

ERROR CHARACTERIZATIONS; ERROR DECOMPOSITION; GLOBAL PRECIPITATION MEASUREMENT MISSIONS; HYDROMETEOROLOGY; INDIAN MONSOON RAINFALL; PRECIPITATION PRODUCTS; RAIN GAUGES; TROPICAL RAINFALL MEASURING MISSIONS;

RAIN;

ALGORITHM; ANNUAL VARIATION; CALIBRATION; DATA SET; ERROR ANALYSIS; HYDROMETEOROLOGY; MOUNTAIN REGION; PRECIPITATION ASSESSMENT; RAINGAUGE; SATELLITE DATA; SEASONALITY; TRMM;

INDIA;

EID: 84945496481     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2015.08.062     Document Type: Article

References (47)

1. AghaKouchak A., Nasrollahi N., Habib E. Accounting for uncertainties of the TRMM satellite estimates. Remote Sens. 2009, 1:606-619. 10.3390/rs1030606.
2. AghaKouchak A., Mehran A., Norouzi H., Behrangi A. Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett. 2012, 39:L09406. 10.1029/2012GL051592.
3. AghaKouchak A., Mehran A. Extended contingency table: performance metrics for satellite observations and climate model simulations. Water Resour. Res. 2013, 49:7144-7149. 10.1002/wrcr.20498.
4. Behrangi A., Khakbaz B., Jaw T.C., AghaKouchak A., Hsu K., Sorooshian S. Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol. 2011, 397:225-237. 10.1016/j.jhydrol.2010.11.043.
5. Bytheway J.L., Kummerow C.D. Inferring the uncertainty of satellite precipitation estimates in data-sparse regions over land. J. Geophys. Res.: Atmospheres 2013, 118:9524-9533. 10.1002/jgrd.50607.
6. Chen S., Hong Y., Cao Q., Gourley J.J., Kirstetter P.E., Yong B., Tian Y., Zhang Z., Shen Y., Hu J., Hardy J. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China. J. Geophys. Res.: Atmospheres 2013, 118:13060-13074. 10.1002/2013JD019964.
7. Ciach G.J., Krajewski W.F., Villarini G. Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J. Hydrometeorol. 2007, 8:1325-1347. 10.1175/2007JHM814.1.
8. Collins M., AchutaRao K., Ashok K., Bhandari S., Mitra A.K., Prakash S., Srivasatva R., Turner A. Observational challenges in evaluating climate models. Nat. Climate Change 2013, 3(11):940-941. 10.1038/nclimate2012.
9. Dash S.K., Kulkarni M.A., Mohanty U.C., Prasad K. Changes in the characteristics of rain events in India. J. Geophys. Res. 2009, 114:D10109. 10.1029/2008JD010572.
10. Gadgil S. The Indian monsoon and its variability. Annu. Rev. Earth Planet. Sci. 2003, 31:429-467.
11. Gebregiorgis A.S., Hossain F. How well can we estimate error variance of satellite precipitation data around the world?. Atmos. Res. 2015, 154:39-59. 10.1016/j.atmosres.2014.11.005.
12. Gopalan K., Jones W.L., Biswas S., Bilanow S., Wilheit T., Kasparis T. A time-varying radiometric bias correction for the TRMM Microwave Imager. IEEE Trans. Geosci. Remote Sens. 2009, 47(11):3722-3730. 10.1109/TGRS.2009.2028882.
13. Goswami B.N., Venugopal V., Sengupta D., Madhusoodanan M.S., Xavier P.K. Increasing trend of extreme rain events over India in a warming environment. Science 2006, 314:1442-1445. 10.1126/science.1132027.
14. Houze R.A. Orographic effects on precipitating clouds. Rev. Geophys. 2012, 50:RG1001. 10.1029/2011RG000365.
15. Huffman G.J., Adler R.F., Bolvin D.T., Gu G., Nelkin E.J., Bowman K.P., Hong Y., Stocker E.F., Wolff D.B. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 2007, 8:38-55. 10.1175/JHM560.1.
16. Huffman G.J., Adler R.F., Bolvin D.T., Nelkin E.J. The TRMM multi-satellite precipitation analysis (TMPA). Satellite Applications for Surface Hydrology 2010, 3-22. Springer. F. Hossain, M. Gebremichael (Eds.).
17. Huffman, G.J., Bolvin, D.T., 2013. TRMM and other data precipitation data set documentation. Global Change Master Directory, NASA, p. 40. http://ftp://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.pdf.
18. Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., 2014. NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD), version 4.4, NASA, p. 30. http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.4.pdf.
19. Joyce R., Janowiak J., Huffman G. Latitudinally and seasonally dependent zenith-angle corrections for geostationary satellite IR brightness temperatures. J. Appl. Meteorol. 2001, 40:689-703.
20. Kumar P., Kishtawal C.M., Pal P.K. Impact of satellite rainfall assimilation on weather research and forecasting model predictions over the Indian region. J. Geophys. Res.: Atmospheres 2014, 119:2017-2031. 10.1002/2013JD020005.
21. Li J., Hsu K., AghaKouchak A., Sorooshian S. An object-based approach for verification of precipitation estimation. Int. J. Remote Sens. 2015, 36:513-529. 10.1080/01431161.2014.999170.
22. Liu Z. Comparison of versions 6 and 7 3-hourly TRMM multi-satellite precipitation analysis (TMPA) research products. Atmos. Res. 2015, 163:91-101. 10.1016/j.atmosres.2014.12.015.
23. Mehran A., AghaKouchak A. Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations. Hydrol. Process. 2014, 28:2262-2270. 10.1002.hyp.9779.
24. Mitra A.K., Bohra A.K., Rajeevan M.N., Krishnamurti T.N. Daily Indian precipitation analyses formed from a merge of rain-gauge with TRMM TMPA satellite derived rainfall estimates. J. Meteorol. Soc. Jpn. 2009, 87A:265-279.
25. Mitra A.K., Momin I.M., Rajagopal E.N., Basu S., Rajeevan M.N., Krishnamurti T.N. Gridded daily Indian monsoon rainfall for 14 seasons: merged TRMM and IMD gauge analyzed values. J. Earth Syst. Sci. 2013, 122(5):1173-1182.
26. Moradi I., Meng H., Ferraro R.R., Bilanow S. Correcting geolocation errors for microwave instruments aboard NOAA satellites. IEEE Trans. Geosci. Remote Sens. 2013, 51(6):3625-3637. 10.1109/TGRS.2012.2225840.
27. Nguyen P., Thorstensen A., Sorooshian S., Hsu K., AghaKouchak A. Flood forecasting and inundation mapping using HiResFlood-UCI and near real-time satellite precipitation data: the 2008 Iowa flood. J. Hydrometeorol. 2015, 16:1171-1183. 10.1175/JHM-D-14-0212.1.
28. Pai D.S., Sridhar L., Rajeevan M., Sreejith O.P., Satbhai N.S., Mukhopadhyay B. Development of a new high spatial resolution (0.25°×0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 2014, 65(1):1-18.
29. Pai D.S., Sridhar L., Badwaik M.R., Rajeevan M. Analysis of the daily rainfall events over India using a new long period (1901-2010) high spatial resolution (0.25°×0.25°) gridded rainfall data set. Clim. Dyn. 2015, 45(3):755-776. 10.1007/s00382-014-2307-1.
30. Palazzi E., Hardenberg J.von., Provenzale A. Precipitation in the Hindu-Kush Karakoram Himalaya: observations and future scenarios. J. Geophys. Res.: Atmospheres 2013, 118:85-100. 10.1029/2012JD018697.
31. Poe G.A., Conway R.W. A study of the geolocation errors of the special sensor microwave/imager (SSM/I). IEEE Trans. Geosci. Remote Sens. 1990, 28(5):791-799.
32. Prakash S., Mahesh C., Gairola R.M. Comparison of TRMM multisatellite precipitation analysis (TMPA)-3B43 version 6 and 7 products with rain gauge data from ocean buoys. Remote Sens. Lett. 2013, 4(7):677-685. 10.1080/2150704X.2013.783248.
33. Prakash S., Gairola R.M. Validation of TRMM-3B42 precipitation product over the tropical Indian Ocean using rain gauge data from the RAMA buoy array. Theoret. Appl. Climatol. 2014, 115(3-4):451-460. 10.1007/s00704-013-0903-3.
34. Prakash S., Sathiyamoorthy V., Mahesh C., Gairola R.M. An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region. Int. J. Remote Sens. 2014, 35(9):3018-3035. 10.1080/01431161.2014.894661.
35. Prakash S., Mitra A.K., Momin I.M., Rajagopal E.N., Basu S. Agreement between monthly land rainfall estimates from TRMM-PR and gauge-based observations over South Asia. Remote Sens. Lett. 2014, 5(6):558-567. 10.1080/2150704X.2014.934401.
36. Prakash S., Gairola R.M., Papa F., Mitra A.K. An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data. Curr. Sci. 2014, 107(9):1582-1586.
37. Prakash S., Mitra A.K., Momin I.M., Pai D.S., Rajagopal E.N., Basu S. Comparison of TMPA-3B42 versions 6 and 7 precipitation products with gauge based data over India for the south-west monsoon period. J. Hydrometeorol. 2015, 16:346-362. 10.1175/JHM-D-14-0024.1.
38. Prakash S., Mitra A.K., Pai D.S. Comparing two high-resolution gauge-adjusted multisatellite rainfall products over India for the southwest monsoon period. Meteorol. Appl. 2015, 22(3):679-688. 10.1002/met.1502.
39. Prakash S., Mitra A.K., Momin I.M., Rajagopal E.N., Basu S., Collins M., Turner A.G., AchutaRao K., Ashok K. Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season. Int. J. Climatol. 2015, 35(9):2326-2338. 10.1002/joc.4129.
40. Shen Y., Xiong A., Hong Y., Yu J., Pan Y., Chen Z., Saharia M. Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibet Plateau. Int. J. Remote Sens. 2014, 35:6843-6858. 10.1080/01431161.2014.960612.
41. Sorooshian S., AghaKouchak A., Arkin P., Eylander J., Foufoula-Georgiou E., Harmon R., Hendrickx J.M.H., Imam B., Kuligowski R., Skahill B., Skofronick-Jackson G. Advanced concepts on remote sensing of precipitation at multiple scales. Bull. Am. Meteorol. Soc. 2011, 92:1353-1357. 10.1175/2011BAMS3158.1.
42. Tian Y., Peters-Lidard C., Eylander J., Joyce R., Huffman G., Adler R., Hsu K.-L., Turk F.J., Garcia M., Zeng J. Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res. 2009, 114:D24101. 10.1029/2009JD011949.
43. Tian Y., Peters-Lidard C.D. A global map of uncertainties in satellite-based precipitation measurements. Geophys. Res. Lett. 2010, 37:L24407. 10.1029/2010GL046008.
44. Turner A., Annamalai H. Climate change and the South Asian summer monsoon. Nat. Climate Change 2012, 2:587-595.
45. Wilks D.S. Statistical Methods in the Atmospheric Sciences 2006, Academic, p. 648. second ed.
46. Xu S.-G., Niu Z., Shen Y. Understanding the dependence of the uncertainty in a satellite precipitation data set on the underlying surface and a correction method based on geographically weighted regression. Int. J. Remote Sens. 2014, 35:6508-6521. 10.1080/01431161.2014.955147.
47. Yong B., Chen B., Gourley J.J., Ren L., Hong Y., Chen X., Wang W., Chen S., Gong L. Intercomparison of the Version-6 and Version-7 TMPA precipitation products over high and low latitudes basins with independent gauge networks: is the newer version better in both real-time and post-real-time analysis for water resources and hydrologic extremes?. J. Hydrol. 2014, 508:77-87. 10.1016/j.jhydrol.2013.10.050.