Retrieval of vertical air motion in precipitating clouds using mie scattering and comparison with in situ measurements

Ming Fang, Bruce Albrecht, Eunsil Jung, Pavlos Kollias, Haflidi Jonsson, Ivan PopStefanija

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

For the first time, the Mie notch retrieval technique is applied to airborne cloud Doppler radar observations in warm precipitating clouds to retrieve the vertical air velocity profile above the aircraft. The retrieval algorithm prescribed here accounts for two major sources of bias: aircraft motion and horizontal wind. The retrieval methodology is evaluated using the aircraft in situ vertical air velocity measurements. The standard deviations of the residuals for the retrieved and in situ measured data for an 18-s time segment are 0.21 and 0.24 m s-1, respectively; the mean difference between the two is 0.01 m s-1. For the studied cases, the total theoretical uncertainty is less than 0.19 m s-1 and the actual retrieval uncertainty is about 0.1 m s-1. These results demonstrate that the Mie notch technique combined with the bias removal procedure described in this paper can successfully retrieve vertical air velocity from airborne radar observations with low spectral broadening due to Doppler fading, which enables new opportunities in cloud and precipitation research. A separate spectral peak due to returns from the cloud droplets is also observed in the same radar Doppler spectra and is also used to retrieve vertical air motion. The vertical air velocities retrieved using the two different methods agree well with each other, and the correlation coefficient is as high as 0.996, which indicates that the spectral peak due to cloud droplets might provide another way to retrieve vertical air velocity in clouds when the Mie notch is not detected but the cloud droplets' spectral peak is discernable.

Original languageEnglish
Pages (from-to)537-553
Number of pages17
JournalJournal of Applied Meteorology and Climatology
Volume56
Issue number3
DOIs
StatePublished - 2017

Keywords

  • Aircraft observations
  • Algorithms
  • Cloud retrieval
  • Cloud tracking/cloud motion winds

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