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Variability of Passive Microwave Radiometric Signatures at Different Spatial Resolutions and Its Implication for Rainfall Estimation

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4 Author(s)
Dong-Bin Shin ; Dept. of Atmos. Sci., Yonsei Univ., Seoul ; Bowman, K.P. ; Jung-Moon Yoo ; Chiu, L.S.

Analysis of precipitation radar (PR) and Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data collected from the TRMM satellite shows that rainfall inhomogeneity, as represented by the coefficient of variation (CV), depends on a spatial scale, i.e., the CV appears to be nearly constant at all rain rates within the field of view (FOV) of the TMI 37-GHz channel, while it decreases with rain rate at lower spatial resolutions, such as the FOV sizes of the low-frequency TMI channels (10.7 and 19.4 GHz). It is known that the brightness temperature (Tb) for a low-frequency channel decreases with increasing rainfall inhomogeneity for a given rain rate. As such, more inhomogeneous rainfall at low rain rates leads to a lower Tb compared with that of a FOV with homogeneous rainfall; however, less inhomogeneous rainfall at high rain rates tends to produce a Tb similar to that of homogeneous rainfalls. These results indicate that the observed radiometric signatures of low-frequency channels at low spatial resolutions are characterized by a larger response range and smaller variability than those at a higher spatial resolution. Based on the observational characteristics of the TMI and PR data sets, we performed synthetic retrievals of rainfalls, employing a Bayesian retrieval methodology at different retrieval resolutions corresponding to the FOV sizes of the TMI channels at 10.7, 19.4, and 37 GHz. Comparisons of the rainfalls retrieved at the different resolutions and their temporal and regional averages show that the systematic bias resulting from the rainfall inhomogeneity is smaller in the lower resolution data than in their higher resolution counterparts. We note that such low-resolution rainfall retrievals are not expected to describe the instantaneous features of rain fields; however, they could be useful for climatological estimates at large temporal and spatial scales.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:47 ,  Issue: 6 )