Retrieving Snowfall Rate with Satellite Passive Microwave Measurements

Transcription

1 Retrieving Snowfall Rate with Satellite Passive Microwave Measurements Huan Meng 1, Ralph Ferraro 1, Banghua Yan 1, Cezar Kongoli 2, Nai-Yu Wang 2, Jun Dong 2, Limin Zhao 1 1 NOAA/NESDIS, USA 2 Earth System Science Interdisciplinary Center, UMCP, USA

2 Overview of Snowfall Rate Products Satellite retrieved water equivalent snowfall rate (SFR) over global land Uses measurements from passive microwave sensors, AMSU/MHS/ATMS AMSU/MHS SFR is operational at NESDIS ATMS SFR algorithm is under development Up to eight AMSU/MHS and two ATMS observations at any location over land and maybe more at higher latitudes Resolution: 16 km at nadir Maximum snowfall rate: 5 mm/hr AMSU/MHS SFR was validated against NMQ, StageIV, and gauge snowfall data from the contiguous United States

3 AMSU, MHS, & ATMS Sensors AMSU: Advanced Microwave Sounding Unit MHS: Microwave Humidity Sounder ATMS: Advanced Technology Microwave Sounder Cross track scanning, mixed polarizations AMSU/MHS onboard four NOAA POES and EUMETSAT Metop satellites, ATMS onboard SNPP and future JPSS satellites AMSU/MHS ATMS Ch GHz Pol Ch GHz Pol QV QV QV QV QV QH QH QV QH ± QH ± QH QH QH QV QH QH QH 9 fo = QH 10 fo = QH 10 fo ± QH 11 fo±0.3222±0.217 QH 11 fo±0.3222±0.048 QH 12 fo± ±0.048 QH 12 fo ±0.3222±0.022 QH 13 fo±0.3222±0.022 QH 13 fo± ± fo±0.3222± QH 14 fo± ±0.010 QH 15 fo± ± QH QH QV QV QV QV QH ± 1 QH ± 7 QH ± 3 QH ± 4.5 QH QV ± 3 QH ± 1.8 QH ± 1 QH

4 SFR Methodology 1. Detect snowfall areas 2. Retrieve cloud properties with an inversion method 3. Compute snow particle terminal velocity and derive snowfall rate

5 Snowfall Detection Start with two products: AMSU snowfall detection (SD) (Kongoli et al., 2003) and AMSU rain rate (RR) (Ferraro et al., 2005; Zhao and Weng, 2002) Apply filters (based on GFS T and RH profiles) to RR and SD to determine snowfall (Foster, et al., 2011) A new AMSU/MHS snowfall detection algorithm has been developed (Kongoli s and Dong s talk) and will replace the operational AMSU/MHS snowfall detection algorithm. ATMS SD follows the same approach. NMQ (radar) Phase SFR w/ Operational SD SFR w/ the new SD

6 Retrieval of Cloud Properties Inversion method I c D ε ε ε ε ε e Simulation of Tb s with a two-stream, one-layer RTM (Yan et al., 2008) 89/ / /176 = ( A T A + E) 1 A T T T T T T B23 B31 B89/88 B157 /165 B190/176 Ic: ice water path D e : ice particle effective diameter ε i : emissivity at 23.8, 31.4, 89(MHS)/88.2(ATMS), 157/165.5, and /183±7 GHz T Bi : brightness temperature at 23.8, 31.4, 89/88.2, 157/165.5, and /183±7 GHz A: derivatives of T Bi over IWP, D e, and ε i E: error matrix Iteration scheme with ΔT Bi thresholds IWP and De are retrieved when iteration stops

7 Snowfall Rate Terminal velocity: Heymsfield and Westbrook (2010): Snowfall rate Assume spherical habit An adjusting factor to compensate for non-uniform ice water content distribution in cloud column SFR model: Integration is solved numerically

8 Snowfall Rate Products AMSU/MHS Snowfall Rate Operational product Comprehensive validation ATMS Snowfall Rate ATMS has advantages (more water vapor channels, improved sampling etc.) and challenges (frequency and polarization changes etc.) Both snowfall detection and snowfall rate algorithms are still under development. Show agreement in basic patterns, maybe more false alarms than the AMSU/MHS SD in the shown case. AMSU/MHS, 8:37Z ATMS 8:05Z

9 Validation of AMSU/MHS SFR Validate over contiguous United States Validation Sources Station hourly accumulated precipitation data StageIV radar and gauge combined hourly precipitation data National Mosaic & Multi-Sensor QPE (NMQ) instantaneous radar precipitation data Validation challenges Spatial scale difference with station data: 16+ km footprint vs. point measurement Temporal scale difference with station/stageiv data: instantaneous vs. hourly Other issues with ground observations and radar snowfall data Station: Undercatch (underestimation) due to dynamic effect StageIV/NMQ: Range effect, overshooting, beam blockage, overestimation at the presence of melting snowflakes, etc.

10 Validation with StageIV and Station Data Five large-scale heavy snowfall events from Jan 27-28, 2009 Mar 23-24, 2009 Dec 08-09, 2009 Jan 28-30, 2010 Feb 05-06, 2010 The events cover diverse geographic areas and climate zones

11 Summary Statistics Combined statistics of all five events Bias RMSE Correlation Coeff. StageIV Station Sanity check - Comparison between StageIV and Station

12 Validation with NMQ Validation with NMQ instantaneous radar precipitation rate NMQ (Q2) radar only data: 0.01 degree, every 5 minutes Better comparability in spatial (radar weighted average) and temporal collocations between satellite and radar NMQ has considerable positive bias against StageIV Satellite NMQ (Radar)

13 Validation with NMQ Time series of mean snowfall rate from satellite and radar Time series of bias and correlation coefficient between satellite and radar snowfall rate

14 Applications Hydrology: Contribute to global blended precipitation products (most currently lacking satellite winter precipitation estimates) Weather forecast: Pair with GOES images to track snowstorm Identify snowstorm extent and areas with most intense snowfall Fill in data gaps in regions with limited radar and gauge observations Collaborating with NASA SPoRT and several NWS WFOs to evaluate AMSU/MHS SFR in this snow season (Courtesy of NASA/SPoRT)

15 Conclusions The operational AMSU/MHS snowfall rate product can provide up to eight snowfall rate retrievals per day over global land at near realtime. ATMS will add two more retrievals per day. The ATMS/MHS product follows well the evolution of StageIV and station hourly snowfall data; and especially well the evolution of instantaneous NMQ snowfall data. In spite of the large scale discrepancies, validation shows that AMSU/MHS SFR have reasonable correlation with the validation data ( on average), and the bias ranges from to 0.07 mm/hr. ATMS snowfall rate algorithm is under development but already shows reasonable agreement with the AMSU/MHS retrievals. The snowfall rate product may contribute to hydrology and weather forecast.