Physically based statistical integration of TRMM microwave measurements for precipitation profiling

Abstract

[1] A statistical methodology to combine measurements from space-borne microwave radar and radiometers is proposed. The approach is fairly general, even though the combination technique is here tailored for the two instruments onboard the Tropical Rainfall Measuring Mission (TRMM) satellite specifically devoted to rainfall measurements, that is, the Precipitation Radar (PR) and the TRMM Microwave Imager (TMI). Two combined retrieval algorithms are proposed, both derived from the previously developed Bayesian algorithm for microwave-based precipitation retrieval from passive sensors (BAMPR-P), which is based on a Bayesian inversion method and is trained by a modeled cloud radiation database. The first combined technique, called BAMPR-C (BAMPR combined), operates in the narrower common swath aiming at exploiting the simultaneous measurements of PR and TMI instruments. Within BAMPR-C the hydrometeor profiles, retrieved from TMI, are used as a constraint for the PR-based inversion: this two step cascade allows us to overcome the difficulty to take into account the different scan geometries of TMI and PR. The second combined technique is called BAMPR-B (BAMPR broadening) and aims at improving the TMI-only retrieval outside the common swath. In this approach, first optimal retrieved profiles are generated by reversing the order of the previous two steps of BAMPR-C cascade within the common swath. Then the resulting profile data set and the corresponding TMI brightness temperatures are used to define the cloud radiation database to be employed outside the common swath for the TMI-only retrieval, achieving the so-called radar swath synthetic broadening. Numerical internal tests, using simulated data, are illustrated to quantify the features of the proposed synergetic algorithms. Finally, an application to measured TRMM data for a selected case study (hurricane Bonnie on August 1998) is shown and discussed.

Neural-network approach to ground-based passive microwave estimation of precipitation intensity and extinction

“…The latter problem is related to the fact that microwave radiometers are installed aboard low-Earth-orbit (LEO) platforms which exhibits an overpass period of about 12 h. Synergies of more platforms, carrying microwave radiometers, and integration with infrared radiometers aboard geostationary-Earth-orbit platforms have been recently envisaged to overcome these limitations . More recently, spaceborne microwave radars, installed aboard LEO platforms such the Tropical Rainfall measuring Mission (TRMM), have reinforced the essential role of satellite rain meteorology (e.g., Marzano et al, 1999a;Di Michele et al, 2003).…”

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.…”

“…These schemes precede the application of the retrieval solution solvers for either algorithm in order to ensure that the solvers are acting only on precipitating pixels. Since the PNPR algorithm is used with the AMSU-A/MHS and /AMSU-B radiometers, it uses the high-end frequencies screening procedure following Chen and Staelin (2003) as described in detail in Appendix A.…”

“…The high-end frequency screening procedure follows the algorithm of Chen and Staelin (2003), which they developed for the AMSU-A/MHS and/AMSU-B radiometers. For the CDRD algorithm, this procedure is adapted for applications with the SSMIS radiometer whereas for the PNPR algorithm, it is adapted for applications with the AMSU-A/MHS radiometer.…”

On the potential of sub-mm passive MW observations from geostationary satellites to retrieve heavy precipitation over the Mediterranean Area

“…The precipitation retrieval method -BAMPR -is characterized by a detailed description of the estimation uncertainties, a careful coupling of the forward and inverse problem and a quantitative evaluation of the representativeness of the cloud-radiation database (Mugnai et al, 2001;Di Michele et al, 2003, 2005. The solution profiles were derived by using an iterative Bayesian method based on weighting different database profiles according to the proximity of measured and modeled TB's and on a priori probabilities of occurrence of given profile structures.…”