Evaluation of ShARP Passive Rainfall Retrievals over Snow-Covered Land Surfaces and Coastal Zones

Abstract

For precipitation retrievals over land, using satellite measurements in microwave bands, it is important to properly discriminate the weak rainfall signals from strong and highly variable background surface emission. Traditionally, land rainfall retrieval methods often rely on a weak signal of rainfall scattering on high-frequency channels (85 GHz) and make use of empirical thresholding and regression-based techniques. Due to the increased ground surface signal interference, precipitation retrieval over radiometrically complex land surfaces-especially over snow-covered lands, deserts and coastal areas-is of particular challenge for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken locally linear embedding Algorithm for Retrieval of Precipitation (ShARP), over a radiometrically complex terrain and coastal areas using the data provided by the Tropical Rainfall Measuring Mission (TRMM) satellite. To this end, the ShARP retrieval experiments are performed over a region in South and Southeast Asia, partly covering the Tibetan Highlands, Himalayas, Ganges-Brahmaputra-Meghna river basins and its delta. This region is unique in terms of its diverse land surface radiation regime and precipitation type within the TRMM field of view. We elucidate promising results by ShARP over snow covered land surfaces and at the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM-2A12 product. Specifically, using the TRMM-2A25 radar product as a reference, we provide evidence that the ShARP algorithm can significantly reduce the rainfall over estimation due to the background snow contamination and markedly improve detection and retrieval of rainfall at the vicinity of coastlines. During the calendar year 2013, we demonstrate that over the study domain the root mean squared difference can be reduced up to 38% annually, while the reduction can reach up to 70% during the cold months.

Linking Global Changes of Snowfall and Wet-Bulb Temperature

“…Satellite-based precipitation datasets are expected to provide improved estimates of the global precipitation and enhance the accuracy of the reanalysis products. However, passive microwave satellite data are not free of error, especially over complex topography where the microwave signal of shallow precipitation is weak (Ebtehaj et al 2016). For example, intercomparison studies over Europe have shown that satellite products have a tendency to overestimate precipitation over flat regions and underestimate it over mountainous regions (Prein and Gobiet 2017).…”

An observationally based method for stratifying a priori passive microwave observations in a Bayesian‐based precipitation retrieval framework

“…In principle, the separation and search of the a priori database by the EPC‐based method applies similar reasoning as other empirically based retrievals (Petty and Li, ; ; Ebtehaj et al ., ), the common factor being that the TB observations (or transformations involving the TB) play a much more direct role in the selection and weighting of a priori candidates. The EPC is essentially based upon the obscuration of the surface emissivity signal as clouds and precipitation enter the scene.…”

“…A surface emissivity-based a priori database indexing scheme that incorporates information directly from the TB observations may be advantageous for precipitation retrievals to better transition across oceanic, land, and the complex nature of mixed land-water and land-ice scenes, with minimal surface-related artefacts in the resultant precipitation products. The Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) embeds TB spectral surface information within its a priori database search and has demonstrated improvements to the detection and estimation of precipitation across complex coastal and snow cover scenes (Ebtehaj et al, 2016).…”

A Prognostic Nested k-Nearest Approach for Microwave Precipitation Phase Detection over Snow Cover

“…The nearest neighbor matching has been successfully utilized for passive microwave retrieval of rainfall using the TRMM data (Ebtehaj et al, 2015(Ebtehaj et al, , 2016 and for microwave mapping of flood inundation using the Special Sensor Microwave Imager/Sounder observations (Takbiri et al, 2017). In this section, we introduce a prognostic algorithm that relies on a nested k-nearest neighbor matching that finds the best representation of a query brightness temperature in the database to detect precipitation occurrence and phase.…”