Cloud model–based Bayesian technique for precipitation profile retrieval from the Tropical Rainfall Measuring Mission Microwave Imager

Abstract

[1] A cloud model-based statistical retrieval technique for estimating surface precipitation and cloud profiles over ocean, called Bayesian Algorithm for Microwave Precipitation Retrieval (BAMPR), is described. The inversion scheme, based on the Bayesian estimation theory, is trained by a CRD obtained by inputting the numerical outputs of a mesoscale microphysical model into a three-dimensional radiative transfer model. Since the performances of the retrieval are strictly dependent on the a priori information given by the CRD, the generation of the database itself, and the coupling between the forward and the inverse problem are carefully discussed. Particular emphasis is given to the database representativeness of the meteorological event under investigation and to the quantification of modeling errors. The retrieval uncertainties are provided with the estimates themselves by choosing the Minimum Mean Square technique as a Bayesian inversion method. As an example, the algorithm is applied to some case studies in the Tropics using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager data. The analysis is focused on the evaluation of the CRD performances with respect to the various events (i.e., a tropical cyclone, a tropical storm, a summer front, and some isolated convective cells in the Atolls region) and different CRDs (i.e., two hurricanes from the University of Wisconsin Nonhydrostatic Modeling System and a tropical squall line from the Goddard Cumulus Ensemble model). A detailed examination is carried out on the case of the hurricane Bonnie on 25 August 1998, which is discussed by using TRMM official products as a comparison.

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

“…Table 2 provides a brief summary of the attributes of the CDRD and PNPR algorithms, including descriptors pertaining to the official H-SAF algorithm version codes, H-SAF algorithm names, ISAC-Rome algorithm names, products and data sources, methodologies and finally the current status of each algorithm in terms of H-SAF nomenclature. The PR-OBS-1 algorithm represents a modified and improved methodology applied to the now conventional Cloud Radiation Database (CRD) methodology originally developed by Smith et al (1992Smith et al ( , 1994a and Mugnai et al (1993), and applied repeatedly since that time, e.g., see Pierdicca et al (1996), Bauer et al (2000Bauer et al ( , 2001Bauer et al ( , 2005, , Chen and Staelin (2003, Tassa et al (2003Tassa et al ( , 2006, Grecu et al (2004), Mugnai et al (2008) and Surussavadee and Staelin (2008a, b) as cases in point. In physical terms, the new methodology is referred to as a Cloud Dynamics and Radiation Database (CDRD) algorithm; see the series of papers by Sanò et al (2013), Casella et al (2013) and which explain Neural Network Operational the attributes of the new methodology and its performance when used in an applied retrieval setting.…”

Three‐dimensional variational assimilation of Special Sensor Microwave/Imager data into a mesoscale weather‐prediction model: A case study

“…The possible causes of the bias in the 3D-Var RT model have been analysed elsewhere (Memmo et al, 2006). The aim is to compare the 3D-Var RT model with a fairly detailed RT scheme implementing both extinction and multiple scattering processes due to sphere-equivalent hydrometeors in various thermodynamic phases (Smith et al, 2002;Tassa et al, 2003). The main result is that cloud-absorption parametrization within the 3D-Var RT model is not always accurate because of empirical approximations of droplet-size distribution effects.…”

“…In raining areas, scattering becomes significant, especially at higher frequencies. In these conditions, the coding of the algorithm becomes complicated (Marzano and Bauer, 2001;Smith et al, 2002;Amerault and Zou, 2003;Tassa et al, 2003). Amerault and Zou (2003) provide an example of assimilation of raw SSM/I data in raining areas.…”

Preface to special section: Remote Sensing of the Earth's Environment by Microwave Radiometers and Radar

“…[2003] (a proposed statistical methodology to combine measurements from spaceborne microwave radar and radiometers); and Tassa et al [2003] (a cloud-model based statistical retrieval technique for estimating surface precipitation and cloud profiles over ocean, called Bayesian Algorithm for Microwave Precipitation Retrieval (BAMPR)). The effect of retrieval algorithms is studied by Mitnik and Mitnik [2003].…”