Shallow Precipitation Detection and Classification using Multifrequency Radar Observations and Model Simulations

Shallow Precipitation Detection and Classification using Multifrequency Radar Observations and... AbstractDetection 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 IPHEx in the Appalachian Mountains (MV), US, and BAECC in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% respectively for MV and TMP, 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% respectively for deep and shallow rainfall in TMP, the latter linked to reflectivity profiles with dark-band, whereas insufficient radar sensitivity to light rainfall (<2 mm/h) 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-CPR and GPM-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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

Shallow Precipitation Detection and Classification using Multifrequency Radar Observations and Model Simulations

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-17-0060.1
Publisher site
See Article on Publisher Site

Abstract

AbstractDetection 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 IPHEx in the Appalachian Mountains (MV), US, and BAECC in Hyytiala (TMP), Finland. Detection is successful with false alarm errors of 2.64% and 4.45% respectively for MV and TMP, 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% respectively for deep and shallow rainfall in TMP, the latter linked to reflectivity profiles with dark-band, whereas insufficient radar sensitivity to light rainfall (<2 mm/h) 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-CPR and GPM-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.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Jul 19, 2017

References

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