Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.248 IF 3.248
  • IF 5-year value: 3.650 IF 5-year 3.650
  • CiteScore value: 3.37 CiteScore 3.37
  • SNIP value: 1.253 SNIP 1.253
  • SJR value: 1.869 SJR 1.869
  • IPP value: 3.29 IPP 3.29
  • h5-index value: 47 h5-index 47
  • Scimago H index value: 60 Scimago H index 60
Volume 9, issue 4 | Copyright
Atmos. Meas. Tech., 9, 1637-1652, 2016
https://doi.org/10.5194/amt-9-1637-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Apr 2016

Research article | 13 Apr 2016

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

Jörg Burdanowitz1, Christian Klepp2, and Stephan Bakan1 Jörg Burdanowitz et al.
  • 1Max Planck Institute for Meteorology, Bundesstraße 53, Hamburg, Germany
  • 2University of Hamburg, CliSAP/CEN, Bundesstraße 55, Hamburg, Germany

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 1min 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 149635min of precipitation from RV Polarstern. Including mixed phase (165632min), 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 1min resolution.

Publications Copernicus
Download
Short summary
We develop a new automatic algorithm to distinguish oceanic precipitation into rain, snow and mixed phase using optical disdrometers deployed on board research vessels. In combination, air temperature, relative humidity and the maximum precipitation particle diameter outperform human observer data and yield highest skill to predict the precipitation phase. This knowledge allows deriving accurate rain and snowfall rates with dense global ocean sampling, which enables satellite sensor validation.
We develop a new automatic algorithm to distinguish oceanic precipitation into rain, snow and...
Citation
Share