Shallow precipitation detection and classification using multifrequency radar observations and model simulations

Published

Journal Article

© 2017 American Meteorological Society. Detection of shallow warm rainfall remains a critical source of uncertainty in remote sensing of precipitation, especially in regions of complex topographic and radiometric transitions, such as mountains and coastlines. To address this problem, a new algorithm to detect and classify shallow rainfall based on space-time dual-frequency correlation (DFC) of concurrent W- and Ka-band radar reflectivity profiles is demonstrated using ground-based observations from the Integrated Precipitation and Hydrology Experiment (IPHEx) in the Appalachian Mountains (MV), United States, and the Biogenic Aerosols-Effects on Clouds and Climate (BAECC) in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% for MV and TMP, respectively, corresponding to one order of magnitude improvement over the skill of operational satellite-based radar algorithms in similar conditions. Shallow rainfall is misclassified 12.5% of the time at MV, but all instances of low-level reverse orographic enhancement are detected and classified correctly. The classification errors are 8% and 17% for deep and shallow rainfall, respectively, in TMP; the latter is linked to reflectivity profiles with dark band but insufficient radar sensitivity to light rainfall (< 2 mm h-1) remains the major source of error. The potential utility of the algorithm for satellite-based observations in mountainous regions is explored using an observing system simulation (OSS) of concurrent CloudSat Cloud Profiling Radar (CPR) and GPM Dual-Frequency Precipitation Radar (DPR) during IPHEx, and concurrent satellite observations over Borneo. The results suggest that integration of the methodology in existing regime-based classification algorithms is straightforward, and can lead to significant improvements in the detection and identification of shallow precipitation.

Full Text

Duke Authors

Cited Authors

  • Arulraj, M; Barros, AP

Published Date

  • September 1, 2017

Published In

Volume / Issue

  • 34 / 9

Start / End Page

  • 1963 - 1983

Electronic International Standard Serial Number (EISSN)

  • 1520-0426

International Standard Serial Number (ISSN)

  • 0739-0572

Digital Object Identifier (DOI)

  • 10.1175/JTECH-D-17-0060.1

Citation Source

  • Scopus