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Precipitation Dataset for Statistical Post Processing and Downscaling. 29 November 2007. Goals. Create a historical precipitation dataset Needs to be quality controlled Needs to have a high spatial resolution

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Presentation Transcript
goals
Goals
  • Create a historical precipitation dataset
    • Needs to be quality controlled
    • Needs to have a high spatial resolution
  • Use this climatic precipitation dataset to bias-correct GEFS and NAEFS precipitation forecasts (1° resolution)
  • Use this dataset to downscale precipitation forecasts to the high resolution NDFD grid (5 km resolution)
  • Compute climate statistics for precipitation
dataset
Dataset

U.S. Domain (downscaling and bias correction)

  • Combine information from two datasets:
    • CPC precipitation analysis
      • Specifications
        • Daily
        • Since 2002
        • 1/8° resolution
        • 6-7 thousand daily reports
      • Methods used to generate
        • Uses inverse distance weighting for spatial interpolation of gages
        • PRISM climatology for incorporating orographic
        • Quality controlled (radar, satellite, station checks)
    • RFC QPE
      • Specifications
        • Every 6 hours
        • Since 2000
        • 4-5 km resolution
      • Methods used to generate
        • Combines rain gage data w/ radar data (Mountain Mapper in West)
        • Quality controlled by individual RFCs, then mosaicked.
dataset1
Dataset

North American Domain (NAEFS)

  • Combine information from multiple sources:
    • CMORPH
    • CPC ¼° Precipitation Analysis
      • Same as 1/8° dataset, except uses modified Cressman scheme to interpolate to grid
    • Canadian Precipitation Analysis (CaPA)
      • Specifications
        • 6-hourly
        • 15 km grid
        • Covers Canada and the U.S.
      • Methods
        • 6 hour forecast precip (GEM model) used as background
        • Adjusted with rain gauges and radar data
      • Being developed
dataset2
Dataset

Global Domain (bias correction)

  • Combine information from multiple sources:
    • CMORPH
      • Specifications
        • Every 30 minutes
        • Since late 2002
        • 8 km resolution
      • Methods used to generate
        • Precipitation estimates derived from low orbiter satellite microwave observations
        • Precipitation features transported using satellite infrared data, when microwave observations are not available
      • Issues
        • Poor estimates of precipitation over ice/snow cover
        • Only extends to 60° N/S
      • Will be replaced by RMORPH
        • Early next year (March?)
        • Incorporate rain gage data
        • Back to 2000 (maybe 1998?)
dataset3
Dataset

Global Domain (bias correction) (continued)

    • GFS
      • Will fill in data missing from CMORPH dataset (> 60° N/S)
      • Use 12 hour forecast of 6-hour precipitation (between 6 and 12 hours after initialization).
    • Issues
      • Lower resolution
      • Not observed precipitation
  • Blending global and regional domains
    • Precipitation values in U.S. domain may differ between CMORPH and RFC QPE-CPC merged dataset
    • Should be better with RMORPH
    • For now, scale CMORPH toward higher resolution RFC QPE-CPC data, and incorporate Mexico/Canada CPC rain gage data to help scale CMORPH
combining datasets
Combining Datasets
  • Methods similar to work by Ken Mitchell and Youlong Xia:
    • Temporally disaggregated the 1/8° CPC precipitation analyses using Stage IV precipitation (http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/).
    • Maintained 24 hour totals from CPC precipitation, but added time variability from Stage IV precipitation.
  • Original Resolutions
    • CPC dataset: ~15 km resolution
    • RFC dataset: ~5 km resolution
    • NDFD: 5 km resolution
  • Interpolate grids to make combining simple
    • RFC data  5 km NDFD grid
    • CPC data  15 km grid that fits exactly over the 5 km NDFD grid
new method first interpolate grids
New Method – First Interpolate Grids

CPC grid

RFC QPE grid

CPC grid fits exactly over RFC QPE grid (which matches NDFD grid)

scaling rfc qpe against cpc
Scaling RFC QPE Against CPC
  • Step 1
    • CPC daily precip defined as precip from 12 UTC – 12 UTC
    • Sum 6-hourly RFC QPE over same period to obtain daily total

12-18 UTC

18-00 UTC

00-06 UTC

06-12 UTC

12-12 UTC

12-12 UTC

12-12 UTC

scaling rfc qpe against cpc1
Scaling RFC QPE Against CPC
  • Step 2
    • Obtain average of all 9 points of RFC QPE grid within each CPC grid box
    • Obtain scaling factor (1/2 in this example)

12-12 UTC

12-12 UTC

Average: 20

Scaling factor = CPCval / RFCtot (1/2 in this example)

scaling rfc qpe against cpc2
Scaling RFC QPE Against CPC
  • Step 3
    • Scale each 6-hourly RFC QPE grid by the scaling factor obtained in step 2

12-18 UTC

18-00 UTC

00-06 UTC

06-12 UTC

* ½

12-18 UTC

18-00 UTC

00-06 UTC

06-12 UTC

end result
End Result
  • 5-year precipitation dataset
  • 5 km resolution
  • Confident in values (CPC = ‘truth’)
  • Already on NDFD grid
  • Already incorporates quality controls
missing data
Missing Data
  • Obtain average of all N points of RFC QPE grid within each CPC grid box, where N is the number of gridpoints with data
  • Obtain scaling factor (1/2 in this example)

12-12 UTC

12-12 UTC

  • Average = SumAllGoodPoints / N = 140/7 = 20
  • Scaling factor = CPCval / RFCtot (1/2 in this example)
  • Perform step 3 as usual
  • For data missing across time dimension (eg. no data at 18 UTC), fill gridpoint with time-average of remaining points