Abstract
The effect of rainfall inhomogeneity within the sensor fleld-of-view (FOV), the so-called beam-filling error, affects significantly the accuracy of rainfall retrievals. Observational analyses of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data show that the beam-filling error can be examined in terms of the coefficient of variation (CV, standard deviation divided by mean rain rate) that provides a measure of the spatial variability. Furthermore, the CV of surface rainfall from PR is related to its vertical structure and has some correlation with the TMI 85 GHz brightness temperature (Tb), especially at the high rain rates. Based on these findings, we exploit the 85 GHz spatial variability in the context of a Bayesian-type inversion method for rainfall retrieval. The spatial variability at various domain sizes (thus CV in a vector form) is blended with the sets of multi-channel brightness temperatures (Tb vector) for the Bayesian inversion. The a-priori databases for the inversion are constructed from the collocated TMI and PR observations at the PR resolution. Through synthetic retrievals we demonstrated that the inclusion of CV information of the Tb remarkably improved the rainfall retrieval accuracy by reducing the effect of the rainfall inhomogeneity.
| Original language | English |
|---|---|
| Pages | 3169-3171 |
| Number of pages | 3 |
| Publication status | Published - 2003 |
| Event | 2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France Duration: 2003 Jul 21 → 2003 Jul 25 |
Other
| Other | 2003 IGARSS: Learning From Earth's Shapes and Colours |
|---|---|
| Country/Territory | France |
| City | Toulouse |
| Period | 03/7/21 → 03/7/25 |
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Earth and Planetary Sciences(all)