Impact of proxy variables of the rain column height on monthly oceanic rainfall estimations from passive microwave sensors

A major uncertainty in physically based algorithms that are used to estimate rainfall from passive microwave sensors arises from a lack of information on physical parameters such as the rain column height and the freezing level in rainy conditions. This uncertainty occurs because the rainfall integrated along a path on the rain column determines the relationship between the brightness temperature and the rainfall. The rain column height, however, is not well determined directly from simultaneous measurements. Most estimation models use the freezing level derived from an indirect method to obtain the unknown parameter. In this study, the characteristics of three variables that may be used as a proxy variable of the rain column height are investigated.The two variables are derived from the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and precipitation radar (TPR). They include the TMI-estimated freezing level (TFL) and the TPR-estimated bright-band height (BBH). The third variable is the freezing-level altitude derived from National Centers for Environmental Prediction (NCEP) reanalysis data (NCEP reanalysis freezing level (NFL)). Monthly oceanic rainfall estimations were then performed using the three aforementioned variables in place of the rain column height. As expected, the results show that differences in the rainfall estimates are greater in the regions where larger differences exist among the three variables. The analysis confirmed that an underestimate of the rain column height causes an overestimate of the rainfall. In addition, rainfalls that were underestimated with the BBH or NFL can be corrected with an empirical adjustment. This suggests that the TFL, BBH and NFL contain information related to the rain column height. However, the BBH and NFL require a correction in the mid-latitudes when their magnitude is low.