Abstract
The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.
Highlights
Precipitation is a vital component of the water cycle, and understanding the characteristics of precipitation is essential for hydro-meteorological applications [1,2]
In the comparison of observed spatial distribution with the satellite-based precipitation (SBP) datasets, all four SBP datasets generally showed the main characteristic, in which the high precipitation occurs in central Nepal
The Integrated Multi-satellite Retrievals for GPM (IMERG)-UC shows the maximum precipitation at 27.9oN, 84.8oE (Figure 3b), while IMERG-C shows high precipitation at 28.2o N, 84oE (Figure 3d). Another area (26.5oN, 88oE) of the highest rainfall in IMERG-C might be associated with the monsoon trough as seen over the lower ranches of the eastern region (Figure 3d)
Summary
Precipitation is a vital component of the water cycle, and understanding the characteristics of precipitation is essential for hydro-meteorological applications [1,2]. Rain gauge-based measurements provide relatively accurate measurements of precipitation on the ground surface [8,9] These observations developed by the Department of Hydrology and Meteorology (hereafter, DHM) in Nepal are relatively dense in the lowlands but sparse in high mountain areas [10,11]. The scarcity of rain gauge observations is a major challenge in hydro-meteorological studies and for effective water and disaster management This scarcity of measurements limits knowledge of precipitation patterns across the country [12]. High-resolution satellite-based precipitation (hereafter, SBP) products provide potential alternatives for monitoring precipitation on regular high-resolution grids, yielding unprecedented levels of detail especially over remote areas and mountainous regions where stations are very sparse These estimates are indirect measurements and must be verified and calibrated using gauge observations before further application [13,14]
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