Abstract
Abstract. This study presents a comparison of new generation weather observatory satellites Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall products with field data collected for Gangotri glacier in India. The meteorological analysis of rainfall estimates has been performed on GPM IMERG Final, Late and Early precipitation products available at daily scale with a spatial resolution of 0.1° × 0.1° for melting season from May to September for the year 2014 and 2015 respectively. The comparison of satellite products with field data was done using correlation coefficient and standard anomaly. The Late run curve showed a high degree of similarity with final run curve while early run showed variation from them. The satellite meteorological data correctly identified non-rainy days with an average of ∼86.7%, ∼67.5% and ∼95% for pre-monsoon, monsoon and post-monsoon season respectively. The rmse for final run data product for 2014 and 2015 are 4.5, 1.23, 1.55, 1.24, 0.8 and 1.14, 7.1, 1.82, 1.15, 1.52 from May to September respectively. Overall, it has been observed that for medium to heavy rainfall final run estimates are close to field data and for light to medium rainfall late run estimates are close. Similar results have been obtained from both datasets for non-rainy days in the study area.
Highlights
Precipitation is considered as one of the major driver of land surface hydrology and the most uncertain part of hydrologic cycle as well (Futrell et al, 2005)
This study aims at the assessment of accuracy of different precipitation products available through Global Precipitation Measurement (GPM)-Integrated Multi-satellite Retrievals for GPM (IMERG) data for a mountainous region present in Indian Himalayan region
The GPM-IMERG satellite precipitation products were evaluated at daily scale using field data from meteorological station for Gangotri glacier in Himalayas
Summary
Precipitation is considered as one of the major driver of land surface hydrology and the most uncertain part of hydrologic cycle as well (Futrell et al, 2005). There are challenges in monitoring, modelling and prediction of precipitation as precipitation varies at both temporal and spatial scales (Futrell et al, 2005). The introduction of satellite remote sensing for study of precipitation and its patterns, has helped in better understanding of weather and climate related research. Tropical Rainfall Measuring Mission (TRMM) mission launched in November 1997 was one such mission where global tropical rainfall dataset was delivered that helped in improved knowledge of cyclone structure and evolution, climate and weather modeling, lighting-storm relationships
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