Published on August 21, 2007
A Radar Wind Profiler Snow-Level Algorithm for NWS Forecast Applications and Climate Research: A Radar Wind Profiler Snow-Level Algorithm for NWS Forecast Applications and Climate Research Allen B. White1,2, Dan J. Gottas1,2, Paul J. Neiman2, F. Marty Ralph2, Eric T. Strem3, and Elizabeth J. Carter4 1Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 2NOAA Environmental Technology Laboratory, Boulder, CO 3NWS California/Nevada River Forecast Center, Sacramento, CA 4WeatherExtreme LLC, Beverly Hills, CA Slide2: Outline Introduction Development of ETL snow-level algorithm Technology transfer: research to operations Operational and research applications Ways in which an operational snow-level product could help address NOAA’s strategic plan Summary Slide3: Groups who participated in the PACJET Planning Workshop of September 1999: Forecast community NOAA/NWS/National Centers for Environmental Prediction (HPC, EMC, MPC) NOAA/NWS/Western Region (7 Weather Forecast Offices and a River Forecast Center) Navy (San Diego) Television Broadcasting Forecast user community Flood Control-Water Supply (CA Department of Water Resources, Army Corps of Engineers) Emergency Management (CA Governor’s Office of Emergency Services, San Mateo County Sheriff) Commercial Fishing (Pacific Coast Federation of Fishermen’s Associations) Research community NOAA Research (Environmental Technology Lab., National Sever Storms Lab., Forecast Systems Lab., AOC) NOAA/NESDIS (CIMMS-University of Wisconsin, CIRA-Colorado State University) Navy (Naval Postgraduate School, Naval Research Lab.) Universities (University of California, University of Colorado, University of Nevada) PACJET: LINKING RESEARCH, OPERATIONS andamp; FORECAST USERS TO ADDRESS WEST-COAST WINTER STORMS Groups involved in winter weather forecasting and hydrometeorological prediction identified the need for more information on the snow level in storms Slide4: NOAA/ETL S-band Radar NOAA/ETL 915-MHz Integrated Wind Profiler Observing System ETL Snow-level Algorithm Slide5: Slide6: The bright band height provides a better estimate of the snow level than the melting level (i.e., 0o C level), because of the time required for frozen precipitation particles to melt as they descend through the melting layer. This is especially true when the vertical profile of temperature in the melting layer is nearly isothermal. PACJET-2001 Snow-level Evaluation Slide7: ETL Snow-level Product Web-based Display www.etl.noaa.gov/et7/data snow Table added at the request of Portland, OR WFO rain Slide8: Snow-level Applications NOAA Strategic Mission Goal #3: Serve society’s needs for weather and water information Slide9: Operational Forecasting Snow Advisory raised to Winter Storm Warning Prototype profiler snow-level product from PACJET showed 2700-ft snow level at the coast, 1300 ft lower than the snow level that had been predicted before landfall. NWS’ Portland OR SOO (Bill Schneider) upgraded earlier Snow Advisory to a Winter Storm Warning based on this lower snow level. Forecasters’ use of these data provided valuable lead time. Predicted snow level - Slide10: Low-level jets Snow level Neiman et al., MWR, 2002 White et al., JTech, 2002 Slide11: Snow-level Applications NOAA Strategic Mission Goal #4: Support the Nation’s commerce with information for safe, efficient, and environmentally sound transportation Transportation Applications: Transportation Applications Highway maintenance 6,600 fatalities/yr in adverse weather 544 million hours ($2 billion) lost productivity Transportation Applications: BAND I II BBY 19 Feb 01 - 10 UTC GVY Donner Pass BBY GVY Donner Pass SL: 4100 ft 1030 UTC BAND I SL: 4600 ft 1430 UTC 19 Feb 01 - 14 UTC II BBY GVY Donner Pass I II SL: 4000 ft 2030 UTC 19 Feb 01 - 20 UTC BBY GVY Donner Pass II 20 Feb 01 - 00 UTC SL: 4300 ft 0030 UTC (a) (b) (c) (d) Transportation Applications Snow-level lead time Slide14: Bodega Bay, CA Grass Valley, CA Slide15: Snow-level Applications NOAA Strategic Mission Goal #2: Understand climate variability and change to enhance society’s ability to plan and respond Knowles and Cayan, GRL, 2001 +0.6 oC +1.6 oC +2.1 oC Slide16: How well do climate models represent impact of climate change on snow level? Use climatology of snow-level datasets collected at Grass Valley, CA and surrounding sites and use ENSO as a surrogate for climate change 1997-98 – El Nino (strong) 2000-01 – La Nina (mod. strong) 2001-02 – Neutral 2002-03 – El Nino (weak-mod.) 2003-04 – Neutral? Climate Research Application Slide17: Summary Snow-level product provides value-added information for winter weather forecasts. Snow-level is an important parameter for hydrometeorological forecasting, especially in the complex terrain of the intermountain West. Research comparing observed and modeled snow-level climatology in the Sierra could provide useful information for climate modelers and water managers. Snow level data will be available during winter 2003-04 from seven sites in west and three sites in the east. Slide18: 2003 NOAA Honor Awards Ceremony PACJET Research and Development Team: For the development of a snow-level algorithm for boundary-layer wind profilers which will improve national winter weather forecasts.