New Remote Sensing Technologies http://cimss.ssec.wisc.edu/goes/blog/about NOAA’s Cooperative Institute for Meteorological Satellite Studies (CIMSS), located at the University of Wisconsin – Madison’s Space Science and Engineering Center (SSEC)Cooperative Institute for Meteorological Satellite Studies (CIMSS)Space Science and Engineering Center (SSEC)
LOES are difficult phenomena to forecast: Mesoscale temporal/spatial resolution I can predict thunderstorms will occur but can’t tell you exactly where. Standard rawinsonde network lacks spatial/temporal resolution to adequately sample/observe this phenomenon. Onset, intensity, orientation, exact location very sensitive to wind direction and thermal stratification in the lower troposphere. Operational NWP is still not sophisticated enough to simulate the air-sea interface and lower atmospheric processes or resolve the physical scale of the snowbands.
Forecasting Lake/Ocean Effect Snow Empirical Forecast Rules Assess: Localized Instability. Depth of the mixed layer. Ambient moisture of the airmass. Wind direction and speed through mixed layer. Then determine how long such conditions remain steady-state to sustain the snow band.
BUFKIT Guidance – Used at OSPC Guidance product developed at NWSFO Buffalo that imports and displays hourly model sounding data from several models.
Numerical Weather Prediction Mesoscale Modelling of LOES
Brief History of NWP and LOES DateMode l ResolutionLand/Sea Mask (Great Lakes) pre 1980LFM ~160km No Great Lakes 1980-90 NGM ~ 80km1-2 grid points per lake 1995- 2000 ETA 80km then 40k m A few more lake points 2000-2003ETA 32kmEven more lake points 2003-2005 ETA 12kmResolves lakes well. 2006WRF 4-8kmWell-defined Great Lakes?? 2006* YYZ500mIndividual Rolls 2007-09WRF EnsemblesConsensus Solutions * Research Cloud Model Being Run at The University of Toronto
Model Resolution It takes roughly 4 to 8 points to resolve a wave. To resolve a 20km wide snowband: –20km / 4 points ~ 5km horizontal model resolution. Therefore, only the larger, single banded snows have any chance of being explicitly simulated by most operational NWP today.
Bua (2002) Outlined Success/Deficiency of a Mesoscale Model for a Single Band Event Using ETA- 12km. Precipitation deficiencies seen may result from: –Latent and sensible heat fluxes from Lake Erie that were lower than occurred because surface winds used to calculate the fluxes were too light. Sensible Heat (temperature) Flux Fs = rCDCp|V|(Tw-Ta) Latent Heat (moisture) Flux Fh= rCqLv|V|(qw-qa)
Model Wind - 250° at 10 kts Observed Wind - 200° at 35 kts ETA Model Simulation Cloud Water and Surface Wind – Observed Wind Green – observed wind Blue – Model Wind From Bua, 2002
ETA Model Simulation Results General Precipitation Deficiencies Result From: Eta-12 surface winds underforecast by up to 20 kts Latent and sensible heat fluxes that are too low, perhaps by as much as a factor of 3. The steady state single lake effect snow band will thus get too little moisture and heat from below, which will result in: too little instability too little convection too little precipitation.
Additionally, convective scheme does not draw moisture out of the boundary layer. Leaves it too moist and warm. This reduces the vertical gradient of moisture and temperature, and thus even further reduces already low latent and sensible heat flux from the lake. ETA Model Simulation Results
In spite of the physics limitations, the model captured the essence of the mesoscale circulation generated by the passage of cold air over Lake Erie. We can conclude that the Eta-12 got synoptic/gross scale features of the mesoscale environment correct, since it predicted a single snow band. ETA Model Simulation Results
REGIONAL-SCALE ENSEMBLE FORECASTS OF THE 7 FEBRUARY 2007 LAKE EFFECT SNOW EVENT Justin Arnott and Michael Evans NOAA/NWS Binghamton, NY Richard Grumm NOAA/NWS State College, PA
What is the Northeast Regional Ensemble? 12 km Workstation WRF –24 hr run length 2007-2008: 8 Members –2 CTP members –1 Operational Goal: Improve operational forecasts of lake effect snowfall
The Ensemble – 2008/09 OfficeCoreBCsMicroCPS#Z Lev OperationalNM M NAMFerrierBMJ60 BGMARWNAMLinKF31 CLEARWGFSLinKF40 CTP-1NM M NAMLinBMJ31 CTP-2ARWNAMLinBMJ31 BUFNM M GFSFerrierKF31 BTVNM M GFSFerrierBMJ31
Northeast Ensemble Project Case Study Conclusions –Suggests ensemble approach to LES may be valuable –Hone in on high-probability impact areas –Highlight outlier (low-probability) outcomes
NWP much improved but limited in abilities: –Initialization and data assimilation –Microphysics –Convective parameterization –Other factos As a result, QPF and subsequently, snowfall forecasts are a tremendous challenge. Summary
Better data initialization/assimilation, improved physics and other improvements will enhance our understanding and further the development of new and improved conceptual models. Development of more local expertise (e.g., focal point meteorologists to build local guidance packages, do case studies, etc.) will also lead to improved forecasts. Summary