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Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications
Author(s) -
Bennartz Ralf,
Thoss Anke,
Dybbroe Adam,
Michelson Daniel B
Publication year - 2002
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1017/s1350482702002037
Subject(s) - advanced microwave sounding unit , nowcasting , precipitation , radar , environmental science , microwave , depth sounding , meteorology , remote sensing , quantitative precipitation estimation , weather radar , computer science , telecommunications , geology , geography , oceanography
We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA‐15. Each observation is assigned a probability of belonging to four classes: precipitation‐free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation‐sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight‐month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU‐B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation‐free areas, as well as heavily precipitating areas (>5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non‐perfect matching of the different data sources and sub‐optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces. Copyright © 2002 Royal Meteorological Society

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