Statistical Downscaling Approach - PowerPoint PPT Presentation

1 / 9
About This Presentation
Title:

Statistical Downscaling Approach

Description:

St phane Beauregard3, David Unger4 , Richard Wobus1. 1SAIC at Environmental Modeling Center, NCEP/NWS. 2Environmental Modeling Center, NCEP/NWS ... – PowerPoint PPT presentation

Number of Views:28
Avg rating:3.0/5.0
Slides: 10
Provided by: boc4
Category:

less

Transcript and Presenter's Notes

Title: Statistical Downscaling Approach


1
Statistical Downscaling Approach its
Application
  • Bo Cui1, Zoltan Toth2, Yuejian Zhu2
  • Stéphane Beauregard3, David Unger4 , Richard
    Wobus1
  • 1SAIC at Environmental Modeling Center, NCEP/NWS
  • 2Environmental Modeling Center, NCEP/NWS
  • 3Canadian Meteorological Centre, Meteorological
    Service of Canada
  • 4Climate Prediction Center, NCEP/NWS
  • Acknowledgements
  • Richard Verret, Poulin Lewis CMC/MSC
  • Dingchen Hou EMC/NCEP/NWS/NOAA
  • David Michaud, Brent Gorden, Luke Lin
    NCO/NCEP/NWS/NOAA
  • Valery J. Dagostaro MDL/NWS/NOAA

2
Statistical Post-Processing Issues
  • Goal
  • Improve reliability while maintaining resolution
    in NWP forecasts
  • Reduce systematic errors (improve reliability)
    while
  • Not increasing random errors (maintaining
    resolution)
  • Retain all useful information in NWP forecast
  • No variation added on finer scales (spatial,
    temporal and across variables covariance)
  • Approach
  • Bias Correction estimate/eliminate lead-time
    dependent bias on model grid
  • Working on coarser model grid
  • Feedback on systematic errors to model
    development
  • Downscaling project ( interpolate ) coarse
    resolution info onto finer grid
  • Needed due to computational resource limitations
  • Interpolation in space, time, across variables
  • Not directly related to numerical models
  • Relationship 2-step processing
  • Common
  • Both intend to improve statistical reliability
  • Difference
  • Bias correction

3
Downscaling
  • Goal
  • Establish relationship between coarse and fine
    resolution data
  • Assume difference between low hires analysis is
    systematicreproducible
  • Not dependent on lead time
  • Downscaling approaches
  • Climatological downscaling
  • Eg, difference in climate mean low hires
    analysis fields
  • Regime dependent
  • Eg, time mean difference in recent low hires
    analysis fields
  • Bo Cui, decaying averaging difference
  • Gridded MOS, Analog approaches, etc
  • Case dependent
  • Hybrid ensemble (Jun Du others), dual
    resolution 3/4DVAR or ens-DA
  • Compare high-low res control forecasts
  • Adjust all ensemble members by difference
  • Physics considered (INFORM) simplified
    numerical models
  • One-way coupled LAM integrations most costly

4
Statistical downscaling for NAEFS forecast
  • Proxy for truth
  • RTMA at 5km resolution
  • Variables (surface pressure, 2-m temperature, and
    10-meter wind)
  • Downscaling vector
  • Interpolate GDAS analysis to 5km resolution
  • Compare difference between interpolated GDAS and
    RTMA
  • Apply decaying weight to accumulate this
    difference downscaling vector
  • Downscaled forecast
  • Interpolate bias corrected 11 degree NAEFS to
    5km resolution
  • Add the downscaling vector to interpolated NAEFS
    forecast
  • Application
  • Ensemble mean, mode, 10, 50(median) and 90
    forecasts

5
Downscaling Method with Decaying Averaging
Algorithm
  • True high resolution analysis
  • Operational North American Real-Time Mesoscale
    Analysis (RTMA)
  • 5x5 km National Digital Forecast Database (NDFD)
    grid (e.g. G. DiMego et al.)
  • 4 variables available surface pressure, T2m, 10m
    U and V
  • Other data can also be used
  • Downscaling method apply decaying averaging
    algorithm

Downscaling Vector5km (t0) (1-w) prior
DV5km (t-1) w (GDAS5km(t 0) RTMA5km(t0))
  • GDAS5km GDAS 1x1 analysis interpolated to
    RTMA5km grids by bilinear interpolation
  • 4 cycles, individual grid point, DV5km
    Downscaling Vector on 5km grids
  • choose different weight 0.5, 1, 2, 5, 10
  • Downscaling Process

Downscaled Forecast5km(t) Bias-corrected
Forecast5km(t) DV5km (t0)
  • Bias-corrected Forecast5km interpolated to
    RTMA5km grids by bilinear interpolation
  • subtract DV5km from bias-corrected forecast5km
    valid at analysis time

6
GEFS bias-corr. down scaling fcst.
GEFS raw forecast
12hr 2m T forecast Mean Absolute Error w.r.t
RTMA for CONUS Average for September
NAEFS forecast
7
NCEP/GEFS raw forecast
4 days gain from NAEFS
NAEFS final products
From Bias correction (NCEP, CMC) Dual-resolution
(NCEP only) Down-scaling (NCEP,
CMC) Combination of NCEP and CMC
8
NCEP/GEFS raw forecast
8 days gain
NAEFS final products
From Bias correction (NCEP, CMC) Dual-resolution
(NCEP only) Down-scaling (NCEP,
CMC) Combination of NCEP and CMC
9
Summary and Future Plan
  • Downscaling NAEFS products has been implemented
    in NCEP by
  • December 4th 2007 1200UTC
  • CONUS only
  • 4 variables only
  • Surface pressure, 2m temperature and 10m u and v
  • Downscaling NAEFS products
  • Reduced mean absolute errors (by 4 days)
  • Improved probabilistic skills (by 8 days)
  • This method could apply to SREF, too.
  • Apply statistical downscaling method to other
    regions, Alaska, Hawaii, Puerto Rico and Guam,
    when RTMA is available
  • Needed in support of Alaska Desk, etc
  • Following after RTMA implementation
  • Add new variables to NDFD grids, such as wind
    speed/direction, maximum/minimum temperature,
    2-meter dew point temperature etc
  • Enhance products by
  • Improvements to RTMA
  • Bias correction of forecast first guess using
    recursive algorithm
  • Under testing
Write a Comment
User Comments (0)
About PowerShow.com