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Precipitation Dataset for Statistical Post Processing and Downscaling

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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Precipitation Dataset for Statistical Post Processing and Downscaling

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  1. Precipitation Dataset for Statistical Post Processing and Downscaling 29 November 2007

  2. 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

  3. 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.

  4. 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

  5. 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?)

  6. 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

  7. Comparison

  8. 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

  9. New Method – First Interpolate Grids CPC grid RFC QPE grid CPC grid fits exactly over RFC QPE grid (which matches NDFD grid)

  10. 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

  11. 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)

  12. 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

  13. End Result • 5-year precipitation dataset • 5 km resolution • Confident in values (CPC = ‘truth’) • Already on NDFD grid • Already incorporates quality controls

  14. 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

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