A Prototype Precipitation Retrieval Algorithm Over Land Using Passive Microwave Observations Stratified by Surface Condition and Precipitation Vertical Structure

A prototype precipitation retrieval algorithm over land has been developed utilizing a 4-yr National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) and Special Sensor Microwave Imager/Sounder (SSMIS) coincident datasets. One of the unique features of this algorithm is using the ancillary parameters (i.e., surface type, surface temperature, land elevation and ice layer thickness) to stratify the single database into many smaller but more homogeneous databases, in which both the surface condition and precipitation vertical structure are similar. It is found that the probability of detection (POD) increases about 8.1% and 12.0% by using categorized databases for rainfall and snowfall detection, respectively. In addition, by considering the relative humidity at lower troposphere and the vertical velocity at 700 hPa in the precipitation detection process, the POD for snowfall detection is further increased by 10.4% from 56.0% to 76.4%. The better result is evident in both ends of the retrieved rainrate when the categorized databases are used, which is particularly obvious when the rainrate is greater than 30 mm/hr. Similarly, the retrieved snowfall rate using categorized databases also outperform that using single database. The correlation between retrieved and observed rainrates from categorized databases is 0.63 while it is 0.42 using the single database. The root mean square error is reduced by 50.3% from 2.07 to 0.98 by using categorized databases. The retrieved snowrate from categorize database are also better correlated with observations and possess smaller root mean square error. Additionally, the precipitation overestimation from the single database over the western United States is largely mitigated when the categorized databases are utilized. It is further demonstrated that over the majority of the categorized databases, the relationship between precipitation rate and brightness temperature is much closer to that from the corresponding category in the validation databases, rather than that from the single database. Therefore, overall superior performance using the categorized databases for both the precipitation detection and retrieval is achieved.