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

Abstract

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 1 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 91 % when compared to the manually determined PP for 149 635 min of precipitation from RV Polarstern. Including mixed phase (165 632 min), an accuracy of 81.2 % 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 1 min resolution.

Towards an along‐track validation of HOAPS precipitation using OceanRAIN optical disdrometer data over the Atlantic Ocean

“…Since Klepp (2015) describes the OceanRAIN data post-processing and quality-checking in detail, while Burdanowitz et al (2016) introduce the algorithm to distinguish between rain, snow and mixed-phase precipitation. The OceanRAIN dataset is publicly available free of charge ; more information is listed at http://oceanrain.org/.…”

“…Second, collocated OceanRAIN tracks that consist of very few measurements also lead to more point-like structures. Third, in borderline cases the precipitation phase distinction algorithm explained in Burdanowitz et al (2016) might assign an erroneous precipitation phase that could lead to an order-of-magnitude different precipitation rate; e.g. snowflakes misclassified as rain strongly overestimate the precipitation rate.…”

The Latitudinal Variability of Oceanic Rainfall Properties and Its Implication for Satellite Retrievals: 1. Drop Size Distribution Properties

“…The total number of 1-min DSD measurements with precipitation in the OceanRAIN database is 702,641 (about 10% of all measurements including non-rainy minutes). Out of these, 40.7% are classified as snow or mixed phase using an automatic precipitation phase detection algorithm (Burdanowitz et al 2016) and are therefore discarded from our analysis of rainfall properties. Due to the requirement in our analysis that at least 10 bins are filled with data to produce a fit, 47% of all DSDs are discarded.…”

The sensitivity of oceanic precipitation to sea surface temperature

“…Nevertheless, heterogeneous spatial sampling by the ships can lead to a biased picture (see Fig. 3 in Burdanowitz et al, 2018); e.g., the eastern Atlantic has been more densely sampled compared to the western Atlantic, which might have an effect on the occurrence of very long-lasting precipitation events. Second, the ship movement relative to cloud move- ment can affect the retrieved event duration.…”