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A Report to the WWRP Working Group on Nowcasting Research, 9 February Science of Nowcasting Winter Weather for Vancouver 2010 (SNOW-V10) by George Isaac Cloud Physics and Severe Weather Research Section Environment Canada Nowcasting Winter Weather in Complex Terrain – Experiences from SNOW-V10 Presentation Shortened to Remove Material Still Under Review Conclusions Remain the Same
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Acknowledgements Paul Joe1, Jocelyn Mailhot2, Monika Bailey1, Stephane Bélair2, Faisal Boudala1, Mindy Brugman4, Edwin Campos1, Richard Carpenter3, Stewart Cober1, Bertrand Denis4, Chris Doyle4. Doug Forsyth5, Ismail Gultepe1, Thomas Haiden6, Ivan Heckman1, Laura Huang1, Jason Milbrandt2, Ruping Mo4, Roy Rasmussen7, Janti Reid1, Trevor Smith4, Ron Stewart 8, and Donghai Wang9 1 Cloud Physics and Severe Weather Research Section, Environment Canada 2 Recherche en prévision numérique, Environment Canada 3Weather Decision Technologies, Oklahoma, USA 4 Meteorological Service of Canada, Environment Canada 5National Severe Storms Laboratory, Norman, Oklahoma, USA 6Central Institute for Meteorology and Geodynamics (ZAMG), Austria 7 National Center for Atmospheric Research, Colorado, USA 8 Department of Environment and Geography, University of Manitoba 9Chinese Academy of Meteorological Science, China
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Main Goals of SNOW-V10 Related to Nowcasting in Complex Terrain (Developed at March 08 Workshop)
To improve our understanding and ability to forecast/nowcast low cloud, and visibility; To improve our understanding and ability to forecast precipitation amount and type; To improve forecasts of wind speed, gusts and direction; To develop better forecast system production system(s). Assess and evaluate value to end users; To increase the capacity of WMO member states (Training component).
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Participating Groups Observing Systems and Associated Nowcast Systems
EC Region OAN Sites EC Research Sites UBC (McKendry) and U. Manitoba (Stewart) NCAR WSDDM System + (Rasmussen) BOM STEPS System (Seed) NSSL Radar (Forsyth) FMI (Koistinen) Modeling Systems EC Research Models WDT (Carpenter) China (Donghai Wang) Austria INCA (Haiden) Germany (Bott) Switzerland (Mueller) Verification EC Team NCAR (Brown) Others: CSU (Chandra & Vaisala) U. Washington (House)
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The Winter Olympic Challenge
Steep topography, highly variable weather elements in space and time Village Creekside 5 km
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Cypress Mountain
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Summary of Instruments
Callaghan scanning C-Band Radar located near Whistler. NOAA/OU X-Band Dual- Polarization Radar located at Canada/U.S. Border south of Vancouver. Vertically Pointing X-Band Radars were located at TFT, RND and WWA. A 915 MHz Boundary Layer Wind Profiler located at Squamish Airport (WSK). A profiling microwave radiometer located at Timing Flats (TFT) A rawindsonde system located at Nesters (VOC). Ceilometers located at YVR, WWA, TFT, WSK, WGP, and VOG, Recording visibility meters located at VOA, VOL, RND, VOT, VOG and YVR. Hot Plates located at YVR, VOG, RND, VOA, VOL and TFT Present weather sensors, either the Vaisala FD12P, Parsivel or POSS located at VOA, VOL, VOD, WWA, WSK, YVR, TFT, VOT, RND, and VOG, A 3D anemometer located at the ski jump (VOW). Temperature, relative humidity and GPS sensors were installed on the Whistler Village to Roundhouse Gondola, and the Whistler to Blackcomb gondola. The Roundhouse site, near the helipad, was instrumented with a significant list of equipment. as an extension of the Fog Remote Sensing and Modeling Project (FRAM) (Gultepe et al., 2009). Special surface sites measuring temperature and humidity were installed by UBC up Whistler Mountain Snow Density measurements were made at some selected sites, mainly VOA, VOL RND and VOC after significant snowstorms. Snow Photographs were made continuously at RND as a special project during some intensive periods and then irregularly outside those periods.
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Whistler Peak Looking East Towards the Telecomm Tower
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Roundhouse Helipad Site (RND)
FD12P MRR VRG GEONOR K-Z Solar Radiation Wind LPM Parsivel D Anemometer Sentry Vis FMD YorkU PC TPU SFC Radiometer X SW IR Radiation/Surface Radiatioin WXT Aerosol TPU Icing Detector GCIP DMIST
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VOA Three Platforms SNOW-V10 OAN Whistler Maintenance
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VOA (Pig Alley) Precipitation Occurrence Sensing System (POSS)
X Band, Continuous Wave Radar Large sample volume Present weather, precipitation type and intensity Doppler Spectrum, Drop Size Distribution Parsivel - Laser-imaging system,particle shadows - Present weather, precipitation type and intensity - Doppler Spectrum, Drop Size Distribution Visibility due to precipitation reported Hot Plate – heated top/bottom surface Designed for snowfall precipitation measurements Wind Vaisala FD12P – laser scattering system Visibility All 1 minute (1 and 1)!
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VOL Mid-station/Raven’s Nest
Harvey Fellowes
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VOB (No Power, 15/1 min data)
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Timing Flats
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Timing Flats (VOT) Ceilometer 25K, (also CL31 – aerosol mode)
Radiometer Parsivel OAN (1 min) POSS MRR Move in summer
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Whistler Village Gondola (WVG)
Local Beer Company
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1 degree
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P2P (Peak to Peak)
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Snow Photography McGill University U Manitoba
Need to help develop higher accuracy radar retrievals.
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Network Radar Coverage
100 km NSSL XBand DualPol Whistler Radar
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Whistler Doppler Weather Radar
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Blocked flow (downslope winds)
means Intense precipitation is on the slope and not on mountain peak Precipitation: the intense precipitation is on the slope. Doppler velocity: Blue means air is moving to the left or downslope
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EC OAN Squamish Airport LAP-3000 Wind Profiler co-located with
Full compound autostation. Site also had Remote Video System and Automatic Weather Station EC OAN
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Wind Profile for 12 hours duration.
Normally, just 1 profile with radiosondes. Considerable structure within 12 hours.
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Canadian Models Used in SNOW-V10
Model Name Organization Country Spatial Resolution Temporal Resolution Available Times of Day Run (UTC) Length of Forecst (hours) General Description ABOMLAM1km Environment Canada Canada 1 Km 15 min Every 15 min Max 6 h Adaptive Blending of Observation and Models using GEM LAM1k ABOMREG 15 km Adaptive Blending of Observation and Models using GEM Regional INTW 1 and 15 km INTegrated Weighted Model using LAM1k, GEM Regional and Observations LAM1k 1 km 30 s (Model), 15 min (Tables) 11 and 20 UTC 19 h Limited-Area version of GEM model LAM2.5k 2.5 km 1 min (Model), 15 min (Tables) 06 and 15 UTC 33 h REG 7.5 min (Model), 15 min (Tables) 00, 06, 12, 18 UTC 48 h Regional version of GEM (Global Environmental Multiscale) model
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Other Countries Models Used in SNOW-V10
Model Name Organization Country Spatial Resolution Temporal Resolution Available Times of Day Run (UTC) Length of Forecst (hours) General Description CMA Chinese Meteorological Administration China 15 km & 3 km 1 hour 00 and 12 UTC 48 h & 24 h CMA GRAPES-Meso NWP model WDTUSL Weather Decision Technologies and NanoWeather USA pointwise or 100 m grid 02, 08, 14, 20 UTC 48 h Surface layer model nested in NAM. Works particularly well in quiescent condistions.. WSDDM National Center for Atmospheric Research (NCAR) Radar Resolution 10 min (based on radar update) Every 10 min 2 hours Nowcast based on storm tracking of radar echo using cross correlation and real-time calibration with surface precipitation gauges. ZAMGINCA Central Institute for Meteorology and Geodynamics (ZAMG) Austria 1 km Every hour 18 hours The Integrated Nowcasting Through Comprehensive Analysis (INCA) system uses downscaled ECMWF forecasts as a first guess and applies corrections according to the latest observation. ZAMGINCARR 15 min Every 15 min The precipitation module of INCA combines raingauge and radar data, taking into account intensity-dependant elevation effects. The forecasting mode is based on displacement by INCA motion vectors, merging into the ECMWF model through prescribed weighting.
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Products During Olympics
Each group produced a Table showing 24 hour forecast of significant variables for main venue sites (hourly intervals and 10 to 15 min intervals in first two hours). Similar to what forecasters produce A Research Support Desk was run during Olympics and Paralympics (virtual and on-site) providing real time support to forecasters. A SNOW-V10 Web site was created with many of the products (time series for sites, remote sensing products, area displays, soundings (gondola and others), and a very successful Blog.
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Research Support Desk (RSD) VANOC
SNOW-V10 Research Support Desk (RSD) On-Site (3 scientists) WebEx (at least once per day) VANOC Chris Doyle Central Forecast Office Trevor Smith et al. SNOW-V10 Web Site SNOW-V10 Blog 1 min Data from Research Sites Whistler Radar Microwave Radar MRR Wind Profiler Gondola Profiles Model and Data Charts Forecast Tables Nowcasts etc Venue Forecasters Cypress Freestyle Callaghan Nordic Callaghan Ski Jump Whistler Alpine Whistler Sliding Pacfic Weather Centre Data 15 Min Data Soundings Meteograms Satellite Data Other Model Data
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Harvey’s Cloud Equipment on Whistler mountain provided good data for forecasters and help in understanding weather processes Remote Sensors Most of SNOW-V10 data from instruments are now being displayed on Web site ( Model data is also being shown.
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Weather Decision Technologies (WDT) Products for Vancouver 2010
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INCA (Integrated Nowcasting through Comprehensive
Analysis) developed at the Austrian national weather service Products were available in real time for forecaster use and evaluation 34 34
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Adaptive Blending of Observations and Model (ABOM): What is it how does it work
Statistical method for generating point forecasts using model and observation data The current observation + a weighted combination of forecasts from three different methods: Extrapolation of the current observation trend The change predicted by a model forecast Observation persistence The weights are determined from recent history and are updated every 15 minutes with new observation data (an hour was used in the studies here)
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Background of INTW INTW refers to integrated weighted model
LAM 1k, LAM 2.5k and REG 15k were selected to generate INTW Major steps of INTW generation Data pre-checking - defining the available NWP models and observations Extracting the available data for specific variable and location Calculating statistics from NWP model data, e.g. MAE, RMSE Deriving weights from model variables based on model performance Defining and performing dynamic and variational bias correction Generating Integrated Model forecasts (INTW)
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Summary and Future …1 Many physical processes were well documented during SNOW-V10 such as: diabatic effects due to melting snow, wind flow around and over terrain, diurnal flow reversal in valleys associated with daytime heating, precipitation reductions and increases due to local terrain influences, etc Weather changes rapidly in complex terrain and it is necessary to get good measurements at time resolutions of at least min. SNOW-V10 attempted to get measurements at 1 min resolution where possible. Because of the rapidly changing nature of the weather, weather forecasts also must be given at high time resolution. There are many difficulties in measuring parameters, especially precipitation amount and type in this environment. Special scanning strategies were necessary for the scanning radar placed at Whistler and significant differences were observed with many different types of surface precipitation sensors placed at several locations. 38 38
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Summary and Future …2 SNOW-V10 has made progress in forecasting “non traditional” variables like ceiling, visibility, wind gusts, etc. Nowcast schemes are showing skill. There is value in presenting real-time obs and several models to forecasters doing Nowcasting. The techniques tested are not yet good enough to issue standalone nowcasts. Significant errors exist of high impact events that require forecaster intervention. Weather Forecasts, both long and short term, were critical in the conduct of these games, both long term and short term. Statistical verification of models and nowcasts are continuing. Papers are being written on SNOW-V10 data. At the 4th SNOW-V10 Workshop held in Huntsville, north of Toronto, January, with 35 participants, it was decided to prepare a special issue of Pure and Applied Geophysics (Pageoph) with a deadline of 1 October 2011 for submission of papers. 31 tentative titles have been received. The idea of a forecaster training workshop is being explored. 39 39
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