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

Abstract

Estimation of precipitation from space‐based passive microwave (PMW) radiometric brightness temperature (TB) observations that adapts to the wide variety of Earth surface background and environmental conditions is a long‐standing issue. Since these conditions are generally unknown from the TB observations, PMW‐based precipitation estimation techniques commonly utilize independent ancillary data sources, such as interpolated prognostic variables from numerical weather prediction forecast models, and discrete surface emissivity classifications. In some situations, the selection of these variables may restrain the algorithm performance under particular surface and atmospheric conditions. The objective of this article is to examine the emissivity principal component (EPC) analysis as a common stratification method for indexing, searching and weighting candidate precipitation profiles from a priori databases, adaptable for Bayesian‐based precipitation estimation algorithms applied to the Global Precipitation Measurement (GPM) Microwave Imager (GMI) or other PMW sensors, to minimize dependence upon ancillary data sources. The EPC has been previously shown to track the joint variability between the 10–89 GHz surface emissivity, total column precipitable water vapour (TPW) and surface temperature (Ts) conditions directly from the TB observations, and identify global locations of similar conditions. A parallel GMI precipitation retrieval was carried out where the identical a priori database was indexed by TPW, Ts and a surface emissivity class index. An independent validation of each precipitation retrieval scheme was carried out using GMI pixel‐matched Multi‐Radar Multi‐Source (MRMS) ground radar data over the continental USA and surrounding ocean waters. While the EPC‐based estimates demonstrated similar performance to the TPW‐based estimates over ocean backgrounds, a markedly improved detection, and reduction in bias, was found for moderate and higher (>5 mm/hr) rainfall rates over other backgrounds, especially vegetated surfaces and coastlines.

Linkage among ice crystal microphysics, mesoscale dynamics, and cloud and precipitation structures revealed by collocated microwave radiometer and multifrequency radar observations

“…CloudSat-GPM Coincidence dataset version 3B is a collection of collocated and coincident GMI, DPR and CPR measurements, which can be conveniently used for our current study. Details of collocation criteria and procedures can be found in Turk (2017). This dataset has been used by some other researchers (e.g., Gong et al, 2017;Zeng et al, 2019).…”

“…On the other hand, since match-up is defined to happen whenever the CPR beam intercepts with the DPR beam at any altitude and at any DPR view angle, the line-of-sight volume is quite different when DPR is at an off-nadir view angle, and this problem is even more severe for GMI which always views at a slant angle. Even though a cosine function is multiplied to slightly mitigate this issue (Turk, 2017), 3D cloud inhomogeneity and beam-filling effects are again the culprit of uncertainty that is hard to justify. These two problems, however, are expected to be not too serious for our current study, because cloud inhomogeneity inside anvil and stratiform clouds is not as large as in deep convective scenes (Kirstetter et al, 2014).…”

Linkage among Ice Crystal Microphysics, Mesoscale Dynamics and Cloud and Precipitation Structures Revealed by Collocated Microwave Radiometer and Multi-frequency Radar Observations

“…To overcome these shortcomings, efforts have been putting forward to use spaceborne active sensor information to help improve or constrain the errorbar of the ice hydrometer retrieval products from passive microwave sensors [e.g., Evans et al, 2012;Gong and Wu, 2014]. In particular, the GPM team uses DPR retrieved hydrometer vertical profiles as either the apriori database or "training" datasets to generate their official passive microwave and joint retrieval products [Kummerrow et al, 2018;Turk et al, 2018].…”

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

“…The high-frequency radiometer channel observations are very sensitive to environmental conditions (e.g., humidity, temperature, frozen or snow-covered soils), which affect the measured signal. This problem is particularly acute at high latitudes, where the low and variable emissivity of snow or ice-covered surfaces [22,[30][31][32] can mask snowflakes' scattering signatures [33]. Moreover, low humidity in high latitude regions makes the atmosphere more transparent for channels that probe around the water vapor absorption line and thus increase surface contamination.…”