Use of Cloud Model Microphysics for Passive Microwave-Based Precipitation Retrieval: Significance of Consistency between Model and Measurement Manifolds

Abstract

Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud-radiation databases. In this study cloud-radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space-time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space-time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud-radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

“…See Panegrossi et al (1998), Casella (2010 and Casella et al (2013) for explanations of simulationobservation database TB manifolds. This agreement is tantamount to the consistency required in data assimilation between modeled and observed influence variables.…”

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

“…Special functions of TBs already proposed for rainfall retrieval (Kidd, 1998;Ferraro and Marks, 1995;Grody, 1991), such as the polarization corrected temperature (PCT 85 ) and the scattering index, have also been considered as the NN inputs (Sarma et al, 2008;Mahesh et al, 2011). Some geographical and meteorological parameters (e.g., surface type, surface height, season, latitude) are often considered as auxiliary input data in order to reduce the ambiguity intrinsic to the PMW precipitation retrievals based only on observed TBs (e.g., Panegrossi et al, 1998;Kummerow et al, 2011;You and Liou, 2012;You et al, 2015).…”

Comparing rain retrievals from GPROF with ECMWF 1D‐Var products

“…In the first approach, retrievals are performed to convert satellite observations to model output variables (e.g., Zhou et al, 2007;Geer et al, 2008). In the second approach, observation operators such as radiative transfer models (RTMs) are used to simulate observed radiances or T b s from the model variables (e.g., Panegrossi et al, 1998;Chevallier and Bauer, 2003;Matsui et al, 2009). However, both approaches ultimately lead to comparing either rainfall or radiance maps where the comparison in radiance space has the advantage that at least the observations are very accurately known.…”