A new methodology of rainfall retrievals from indirect measurements

A new methodology for rainfall retrievals from indirect measurements is proposed and illustrated using IR brightness temperature and radar rainfall observations collected during TOGA-COARE. Since (1) rain rate has a mixed distribution with a delta-function for a zero rain and lognormal distribution for non-zero and (2) the least squares method which is used to calculate regression coefficients gives a priori consistent estimates only for normally distributed data, it is proposed to convert the rain rate to a normally distributed set and only after that to develop a retrieval method and estimate the skill of this method. Consideration of the physics of clouds and cloud ensembles, the goal to minimize errors in the radar data, and the desire to remove the influence of cirrus clouds lead us to use: a) minimum of IR brightness temperatures over a 1°1° area and a 3 hour interval as a predictor and b) radar rainfall, averaged over 3 hours over a 1°1° area, with the radar in its center, as the truth. Results using the TOGA-COARE data show that the correlation of the rain rate transformed to normal distribution is significantly higher with minimum temperature than with the fraction of area covered by high clouds. The sizes of heavy rainfall areas obtained using the new methodology are reasonable. The regression coefficients should change with latitude, season and location.

Taken together, the results indicate it is possible, in principle, to retrieve rainfall from IR satellite observations and obtain reliable rainfall data. To realize this goal it is necessary to process radar and IR data using the new methodology for different latitudes, seasons, over land and ocean.

To illustrate this methodology a case with a hurricane Geoges is discussed.