Snowfall Rate Retrieval Using Passive Microwave Measurements

1. Snowfall Rate Retrieval Using Passive Microwave Measurements Huan Meng1 (Huan.Meng@noaa.gov), Banghua Yan2, Ralph Ferraro1, Cezar Kongoli3, Nai-Yu Wang3, Limin Zhao21NOAA/NESDIS/STAR, College Park, MD; 2NOAA/NESDIS/OSPO, College Park, MD; 3ESSIC/CICS/University of Maryland, College Park, MD Introduction Validation Validation with NMQ Radar Precip Satellite snowfall rate (SR) retrieval is an emerging topic in satellite remote sensing. Passive microwave (MW) measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. A land snowfall rate algorithm has been developed using the measurements from Advanced Microwave Sounding Unit (AMSU) and Microwave Humidity Sounder (MHS), and is running operationally at NOAA/NESDIS. Currently, there are four satellites (NOAA-18, NOAA-19, Metop-A, and Metop-B) that carry these sensors which allow up to eight SR retrievals per day at any location over global land. • Validation sources include NEXRAD combined reflectivity images, NMQ radar instantaneous precipitation data, StageIV radar and gauge combined hourly precipitation data, and station hourly accumulated precipitation data. Snowfall product validation is especially challenging due to 1) the spatial and temporal differences between satellite retrieval and validation data, 2) errors in validation data, and 3) time lag between the satellite product and station observations due to the slow snow particle terminal velocity. AMSU/MHS Snowfall Rate NMQ Phase Composite NEXRAD Reflectivity Case Study - Snowmaggedon: Feb 5-6, 2010 Methodology 1. Detect precipitation based on two operational products: AMSU rain rate (Ferraro et al., 2005; Zhao and Weng, 2002) and AMSU snowfall detection (Kongoli et al., 2003). A set of temperature and water vapor filters are used to determine snowfall. 2. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream Radiative Transfer Model (Yan et. al, 2008). IWP: ice water path De: ice particle effective diameter e: emissivity A: Jacobian matrix E: error matrix TB: brightness temperature 3. Compute snow particle terminal velocity (Heymsfield and Westbrook, 2010) and determine snowfall rate by numerically solving a complex integral. SFR (mm/hr) Combined Statistics from Five Cases New Development • Two algorithms are being developed with the support of JPSS PGRR Program: ATMS snowfall detection and snowfall rate. In addition to the improved spatial resolution, ATMS also includes more channels that are sensitive to the scattering effect of snow particles than AMSU/MHS. Therefore, ATMS has the potential to support an SR algorithm that is more advanced than the AMSU/MHS algorithm. The development is expected to complete by the end of 2013. Sanity Check – Station vs. StageIV • Ferraro, R.R., F. Weng, N. Grody, L. Zhao, H. Meng, C. Kongoli, P. Pellegrino, S. Qiu and C. Dean, 2005: NOAA operational hydrological products derived from the AMSU. IEEE Trans. Geo. Rem. Sens., 43, 1036 – 1049. • Heymsfield, A.J. and C.D. Westbrook, 2010, Advances in the Estimation of Ice Particle Fall Speeds Using Laboratory and Field Measurements. J. Atmos. Sci., 67, 2469-2482 doi: 10.1175/2010JAS3379.1 • - Kongoli, C., P. Pellegrino, R.R. Ferraro, N.C. Grody, and H. Meng, 2003. A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU), Geophys. Res. Lett., 30(14), 1756, doi:10.1029/2003GL017177. • Yan, B., F. Weng, and H. Meng, 2008. Retrieval of snow surface microwave emissivity from the advanced microwave sounding unit, J. Geophys. Res., 113, D19206, doi:10.1029/2007JD009559. • Zhao, L. and F. Weng, 2002: Retrieval of ice cloud parameters using the AMSU. J. Appl. Meteor., 41, 384-295. This study was partially supported by NOAA grant NA09NES4400006 (Cooperative Institute for Climate and Satellites -CICS) at the University of Maryland/ESSIC.