What does CloudSat reveal about global land precipitation detection by other...

Behrangi, A., Y. Tian, B. Lambrigtsen, and G. Stephens (2014), What does CloudSat reveal about global land precipitation detection by other spaceborne sensors?, Water Resour. Res., 50, 4893-4905, doi:10.1002/2013WR014566.

Current orbital land precipitation products have serious shortcomings in detecting light rain and snowfall, the most frequent types of global precipitation. The missed precipitation is then propagated into the merged precipitation products that are widely used. Precipitation characteristics such as frequency and intensity and their regional distribution are expected to change in a warming climate. It is important to accurately capture those characteristics to understand and model the current state of the Earth’s climate and predict future changes. In this work, the precipitation detection performance of a suite of precipitation sensors, commonly used in generating the merged precipitation products, are investigated. The high sensitivity of CloudSat Cloud Profiling Radar (CPR) to liquid and frozen hydrometeors enables superior estimates of light rainfall and snowfall within 80 S–80 N. Three years (2007–2009) of CloudSat precipitation data were collected to construct a climatology reference for guiding our analysis. In addition, auxiliary data such as infrared brightness temperature, surface air temperature, and cloud types were used for a more detailed assessment. The analysis shows that no more than 50% of the tropical (40 S–40 N) precipitation occurrence is captured by the current suite of precipitation measuring sensors. Poleward of 50 latitude, a combination of various factors such as an abundance of light rainfall, snowfall, shallow precipitation-bearing clouds, and frozen surfaces reduces the space-based precipitation detection rate to less than 20%. This shows that for a better understanding of precipitation from space, especially at higher latitudes, there is a critical need to improve current precipitation retrieval techniques and sensors.

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