Probabilistic Precipitation Estimation with a Satellite Product

Abstract

Satellite-based precipitation products have been shown to represent precipitation well over Nepal at monthly resolution, compared to ground-based stations. Here, we extend our analysis to the daily and subdaily timescales, which are relevant for mapping the hazards caused by storms as well as drought. We compared the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT product with individual stations and with the gridded APHRODITE product to evaluate its ability to retrieve different precipitation intensities. We find that 3B42RT, which is freely available in near real time, has reasonable correspondence with ground-based precipitation products on a daily timescale; rank correlation coefficients approach 0.6, almost as high as the retrospectively calibrated TMPA 3B42 product. We also find that higher-quality ground and satellite precipitation observations improve the correspondence between the two on the daily timescale, suggesting opportunities for improvement in satellite-based monitoring technology. Correlation of 3B42RT and 3B42 with station observations is lower on subdaily timescales, although the mean diurnal cycle of precipitation is roughly correct. We develop a probabilistic precipitation monitoring methodology that uses previous observations (climatology) as well as 3B42RT as input to generate daily precipitation accumulation probability distributions at each 0.25• × 0.25• grid cell in Nepal and surrounding areas. We quantify the information gain associated with using 3B42RT in the probabilistic model instead of relying only on

Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal

“…Land-cover change could be evaluated from Landsat imagery before 1990 and after 2010. A precipitation product at higher resolution, which could be based on remote sensing calibrated to available weather stations, would better resolve the sharp elevation and orographic gradients within Nepal and thus help to clarify the impact of moisture stress [63][64][65].…”

Error-Component Analysis of TRMM-Based Multi-Satellite Precipitation Estimates over Mainland China

“…Due to the lack of ground observations over these regions, we cannot directly quantify the error components of satellite precipitation estimates and analyze their error sources by the validation data CGDPA. The difference between different satellite estimates in total bias was especially remarkable over these areas, which might be primarily attributed to the gauge validation product rather than to the satellite retrieval algorithm [40]. In our knowledge, there certainly exist large uncertainties for the satellite rainfall evaluation over regions where gauge network is very sparse.…”

The Impact of Climate Change on Biodiversity in Nepal: Current Knowledge, Lacunae, and Opportunities

“…The analysis of trends in observed temperature and precipitation data in Nepal is limited by the dearth of weather stations, high altitude rain gauges not adapted to measure snowfall [42], and the relatively short period of data collection: about 30-40 years [43,44]. With the limited extent of available ground observations, remote sensing has been useful in resolving spatial and temporal variability in climate variables, especially precipitation [45][46][47][48].…”