An automatic precipitation-phase distinction algorithm for optical disdrometer data over the global ocean

Abstract

The lack of high-quality in situ surface precipitation data over the global ocean so far limits the capability to validate satellite precipitation retrievals. The first systematic ship-based surface precipitation data set OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) aims at providing a comprehensive statistical basis of in situ precipitation reference data from optical disdrometers at 1ĝ€min resolution deployed on various research vessels (RVs). Deriving the precipitation rate for rain and snow requires a priori knowledge of the precipitation phase (PP). Therefore, we present an automatic PP distinction algorithm using available data based on more than 4 years of atmospheric measurements onboard RV Polarstern that covers all climatic regions of the Atlantic Ocean. A time-consuming manual PP distinction within the OceanRAIN post-processing serves as reference, mainly based on 3-hourly present weather information from a human observer. For automation, we find that the combination of air temperature, relative humidity, and 99th percentile of the particle diameter predicts best the PP with respect to the manually determined PP. Excluding mixed phase, this variable combination reaches an accuracy of 91ĝ€% when compared to the manually determined PP for 149ĝ€635ĝ€min of precipitation from RV Polarstern. Including mixed phase (165ĝ€632ĝ€min), an accuracy of 81.2ĝ€% is reached for two independent PP distributions with a slight snow overprediction bias of 0.93. Using two independent PP distributions represents a new method that outperforms the conventional method of using only one PP distribution to statistically derive the PP. The new statistical automatic PP distinction method considerably speeds up the data post-processing within OceanRAIN while introducing an objective PP probability for each PP at 1ĝ€min resolution.