Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China
- 296 Downloads
Daily precipitation data from 42 stations in Inner Mongolia, China for the 10 years period from 1 January 2001 to 31 December 2010 was utilized along with downscaled data from the Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25° × 0.25° for the same period based on the statistical relationships between the normalized difference vegetation index (NDVI), meteorological variables, and digital elevation models (DEM) using the leave-one-out (LOO) cross validation method and multivariate step regression. The results indicate that (1) TRMM data can indeed be used to estimate annual precipitation in Inner Mongolia and there is a linear relationship between annual TRMM and observed precipitation; (2) there is a significant relationship between TRMM-based precipitation and predicted precipitation, with a spatial resolution of 0.50° × 0.50°; (3) NDVI and temperature are important factors influencing the downscaling of TRMM precipitation data for DEM and the slope is not the most significant factor affecting the downscaled TRMM data; and (4) the downscaled TRMM data reflects spatial patterns in annual precipitation reasonably well, showing less precipitation falling in west Inner Mongolia and more in the south and southeast. The new approach proposed here provides a useful alternative for evaluating spatial patterns in precipitation and can thus be applied to generate a more accurate precipitation dataset to support both irrigation management and the conservation of this fragile grassland ecosystem.
Our cordial thanks are extended to the editor, Prof. Dr. Dominique Ruffieux, and the journal’s anonymous reviewers for their professional and pertinent comments, which have greatly improved the quality of this manuscript.
This study was financially supported by the following contracts: the Excellent Young Scientist Foundation of Inner Mongolia Agricultural University of China (Grant No.: 2014XYQ-11), the National Natural Science Foundation of China (Grant No.: 51509131, 51369016, 51620105003), the International S&T Cooperation Program of China (2015DFA00530), the Ministry of Education Innovative Research Team (Grant No.: IRT_17R60), the Natural Science Foundation of Inner Mongolia (Grant No.: 2015BS0514, 2015MS0566), the Innovation Team in Priority Areas Accredited by the Ministry of Science and Technology (Grant No.: 2015RA4013), and the National Science Foundation for Distinguished Young Scholars of China (Grant No.: 51425903).
- Ba MB, Gruber A (2001) GOES multispectral rainfall algorithm (GMSRA). J Appl Meteorol 40(8):1500–1514. https://doi.org/10.1175/1520-0450(2001)040<1500:GMRAG>2.0.CO;2 CrossRefGoogle Scholar
- Dingman SL (2002) Physical hydrology. Prentice Hall, Boca RatonGoogle Scholar
- Hou AY, Kakar R, Neeck S, Azabarzin A, Kummerow C, Kojima M, Oki R, Nakamura K, Iguchi T (2013) The Global Precipitation Measurement (GPM) mission. Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-13-00164 1 (e-view)
- Huffman GJ, Bolvin TD, Nelkin JE, Wolff BD, Adler FR, Gu G, Hong Y, Bowman PK, Stocker FE (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55. https://doi.org/10.1175/JHM560.1 CrossRefGoogle Scholar
- 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(3):487–503. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 CrossRefGoogle Scholar
- Kummerow C, 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(12):1965–1982. https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2 CrossRefGoogle Scholar
- Sorooshian S, Hsu K-L, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81(9):2035–2046. https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2 CrossRefGoogle Scholar
- Sun Y, Guo P, Yan X, Zhao T (2010) Dynamics of vegetation cover and its relationship with climate change and human activities in Inner Mongolia. J Nat Resour 25(3):407–414 (In Chinese with English abstract)Google Scholar
- Vicente-Serrano SM, Gouveia C, Camarero JJ, Beguería S, Trigo R, López-Moreno JI, Azorín-Molina C, Pasho E, Lorenzo-Lacruz J, Revuelto J, Morán-Tejeda E, Sanchez-Lorenzo A (2013) Response of vegetation to drought time-scales across global land biomes. Proc Natl Acad Sci 110(1):52–57. https://doi.org/10.1073/pnas.1207068110 CrossRefGoogle Scholar
- Wilheit TT (1986) Some comments on passive microwave measurement of rain. Bull Am Meteorol Soc 67(10):1226–1232. https://doi.org/10.1175/1520-0477(1986)067<1226:SCOPMM>2.0.CO;2 CrossRefGoogle Scholar