Hybrid Assimilation of Satellite Rainfall Product with High Density Gauge Network to Improve Daily Estimation: A Case of Karnataka, India

ournal of the Meteorological Society of Japan

article

Prashant KUMAR¹  Rakesh GAIROLA¹  Takuji KUBOTA²  Chandra KISHTAWAL¹ 

[1] Atmospheric and Oceanic Sciences Group, Space Applications Centre;Earth Observation Research Center

关键词: GSMaP rainfall;    Karnataka State Natural Disaster Monitoring Centre rain gauge network;    twodimensional variational method;    orography;    Kalman filter;   

DOI  :  10.2151/jmsj.2021-037

来源: Meteorological Society of Japan

【 摘 要 】

Accurate rainfall estimation during the Indian summer monsoon (ISM) is one of the most crucial activities in and around the Indian Sub-continent. Japan Aerospace Exploration Agency (JAXA) provides a couple of Global Satellite Mapping of Precipitation (GSMaP) rainfall products, namely, the GSMaP_MVK, which is a satellite-based product calculated with ancillary data including global objective analysis data, and the GSMaP_Gauge, which is adjusted by global rain gauges. In this study, the daily rainfall amount from the GSMaP rainfall product (version 7) is validated against a dense rain gauge network over Karnataka, one of the southwestern states of India, during ISM 2016–2018. Furthermore, as the primary objective of this study, these dense rain gauge observations are assimilated in the GSMaP rainfall product using a hybrid assimilation method to improve the final rainfall estimate. The hybrid assimilation method is a combination of the two-dimensional variational (2D-Var) method and the Kalman filter, in which the 2D-Var method is utilized to merge rain gauge observations and the Kalman filter is applied to update background error in the 2D-Var method. Preliminary verification results suggest that GSMaP_Gauge rainfall has sufficient skill over north interior Karnataka and south interior Karnataka regions, with large errors over the orographic heavy rainfall region of the Western Ghats. These errors are larger in the GSMaP_MVK rainfall product over orographic heavy rainfall regions. Hybrid assimilation results of randomly selected rain gauge observations improve the skill of GSMaP_Gauge and GSMaP_MVK rainfall products when compared with independent rain gauge observations. These improvements in daily rainfall are more prominent over orographic heavy rainfall regions. GSMaP_MVK rainfall product shows larger improvement due to the absence of the gauge adjustment in the JAXA operational processing. The superiority of the hybrid assimilation method against Cressman and optimal interpolation methods for impacts of utilized rain gauge numbers are also presented in the present study.

【 授权许可】

Unknown