Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part I: Improved Method and Uncertainties

Abstract

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

Verification of Hydrometeor Properties Simulated by a Cloud-Resolving Model Using a Passive Microwave Satellite and Ground-Based Radar Observations for a Rainfall System Associated with the Baiu Front

“…Cloud resolving models (CRMs) with complicated cloud microphysical parameterizations explicitly predict various hydrometeors at high time and space resolution; therefore, CRMs serve as valuable tools for satellite remote sensing of precipitation for inferring information about precipitating clouds that cannot be directly observed (Adler et al 1991;Smith et al 1994;Kummerow et al 1996Kummerow et al , 2001Panegrossi et al 1998;Olson et al 2006). However, to use CRMs in precipitation remote sensing, their output must be verified with observational data to confirm that the information derived from them is reliable.…”

Convective and stratiform rainfall and heating associated with the summer monsoon over the South China Sea based on TRMM data

“…Although the space borne radar observations of TRMM have provided new perspectives in tropical precipitation, to some extent there are uncertainties in estimating latent heating and convective=stratiform rain from TRMM data. There are numbers of TRMM heating algorithms so far (Tao et al 2001;Shige et al 2004;Olson et al 2006;Yang et al 2006). Among them the CSH algorithm has its own strengths ): it's a robust algorithm with long history based on the cloud-resolving model; it adheres to convective= stratiform heating variational characteristics based on diagnostic budget studies; it experiences extensive simulation and Q 1 validation studies involving field campaign datasets from GATE, EMEX, PRE-STORM, TOGA COARE, SCSMEX, TRMM LBA, KWAJEX, and DOE ARM programs, etc.…”

“…Magagi and Barros (2004) estimated the latent heating of rainfall during the onset of the Indian monsoon. Olson et al (2006) and Yang et al (2006) described an improved algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles. And yet, there are no conclusive results on the annual variations of rainfall and heating over the South China Sea.…”

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

“…The G-SDSU includes satellite orbit-scanning geolocation calculations, generalized single-scattering databases/calculations, and various radiative transfer models (RTMs) that can be applied to most of the existing satellite sensors. Most of the RTMs have been previously applied to construct various remote sensing algorithms [Nakajima et al, 1991;Higurashi and Nakajima, 1999;Dubovik and King, 2000;Kummerow et al, 2001;Masunaga and Kummerow, 2006;Olson et al, 2006]. The particle size distributions (PSD) of hydrometeors and aerosols are treated with the model (NU-WRF) microphysics assumptions and are nearly identical in various simulator components.…”

“…G-SDSU passive microwave simulator computes TOA emergent microwave Tb by treating two-stream radiative transfer calculations with the Eddington's second approximation along the slant radiance path [Kummerow, 1993;Olson et al, 2006;. For bottom boundary conditions, wind-induced changes to the water body emissivity at vertical and horizontal polarization are considered over ocean and lake.…”