slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
New Remote Sensing Technologies PowerPoint Presentation
Download Presentation
New Remote Sensing Technologies

Loading in 2 Seconds...

play fullscreen
1 / 22

New Remote Sensing Technologies - PowerPoint PPT Presentation


  • 141 Views
  • Uploaded on

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). Forecasting Lake/Ocean Effect Snow.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'New Remote Sensing Technologies' - darius


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
new remote sensing technologies
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)

forecasting lake ocean effect snow1
Forecasting Lake/Ocean Effect Snow
  • 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 snow2
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
BUFKIT Guidance – Used at OSPC
  • Guidance product developed at NWSFO Buffalo that imports and displays hourly model sounding data from several models.
slide8
Brief History of NWP and LOES

* Research Cloud Model Being Run at The University of Toronto

model resolution
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
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)
eta model simulation results
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.
slide13

ETA Model Simulation Results

  • 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 results1
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.
regional scale ensemble forecasts of the 7 february 2007 lake effect snow event

REGIONAL-SCALE ENSEMBLE FORECASTSOF 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
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
northeast ensemble project
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
summary1
Summary
  • 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.
summary2
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.