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Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology
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Author and Affiliation:
Meng, Huan(National Environmental Satellite, Data, and Information Service, Camp Springs, MD, United States);
Ferraro, Ralph(National Environmental Satellite, Data, and Information Service, Camp Springs, MD, United States);
Kongoli, Cezar(Maryland Univ., Cooperative Inst. for Climate and Satellites, College Park, MD, United States);
Yan, Banghua(National Environmental Satellite, Data, and Information Service, Camp Springs, MD, United States);
Zavodsky, Bradley(NASA Marshall Space Flight Center, Huntsville, AL, United States);
Zhao, Limin(National Environmental Satellite, Data, and Information Service, Camp Springs, MD, United States);
Dong, Jun(Maryland Univ., Cooperative Inst. for Climate and Satellites, College Park, MD, United States);
Wang, Nai-Yu(National Environmental Satellite, Data, and Information Service, Camp Springs, MD, United States)
Abstract: (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.
Publication Date: Jan 04, 2015
Document ID:
20150002901
(Acquired Mar 19, 2015)
Subject Category: METEOROLOGY AND CLIMATOLOGY
Report/Patent Number: M14-3975
Document Type: Conference Paper
Meeting Information: Annual American Meteorological Society Conference (AMS); 95th; 4-8 Jan. 2015; Phoenix, AZ; United States
Conference on Satellite Meteorology and Oceanography; 20th; 4-8 Jan. 2015; Phoenix, AZ; United States
Meeting Sponsor: American Meteorological Society; Boston, MA, United States
Financial Sponsor: NASA Marshall Space Flight Center; Huntsville, AL, United States
Organization Source: NASA Marshall Space Flight Center; Huntsville, AL, United States
Description: 1p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: Copyright; Distribution as joint owner in the copyright
NASA Terms: WEATHER FORECASTING; CLOUD PHYSICS; HYDROLOGY; SNOW; MEASURING INSTRUMENTS; TERMINAL VELOCITY; RADAR TRACKING; MICROWAVE SOUNDING; ALGORITHMS; WATER VAPOR; REGRESSION ANALYSIS; ATMOSPHERIC TEMPERATURE; PROBABILITY THEORY
Availability Notes: Abstract Only
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