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Questions from Hydromet Group:

Questions from Hydromet Group:. See handout on ongoing QPE grid generation – real-time and historical reanalysis Is there a need for PRISM-type precip climatology over northern Mexico? Is MPE input being used in river modeling?

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Questions from Hydromet Group:

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  1. Questions from Hydromet Group: • See handout on ongoing QPE grid generation – real-time and historical reanalysis • Is there a need for PRISM-type precip climatology over northern Mexico? • Is MPE input being used in river modeling? • Any ongoing use of Hydroestimator or other satellite precipitation input?

  2. GOES-based estimates ofPotential Evapo-transpiration (PET) Yu Zhang Hydrometeorology Group Hydrology Laboratory Office of Hydrologic Development NOAA/National Weather Service

  3. Background • PET is needed as forcing factor for the Hydrologic model • Currently most RFCs use climatologic PET as input • monthly grids with no inter-annual variation • Data with intra-month and inter-annual variation can be helpful for hydrologic forecasting • ABRFC has developed a module for ingesting GOES-based sky cover product for daily PET estimation • OHD is working on developing a similar module and evaluating the impacts of daily PET on hydrologic simulations

  4. PET flowchart GOES sky cover Radiation module Net radiation T RTMA grids PET module T, Td, wind PET grids

  5. GOES sky cover • Produced by NESDIS from GOES sounder data • 15-minute resolution • archived by OST/MDL • Quality issues reported • the severity has not been confirmed by preliminary investigation • Large chunk of missing data

  6. Testing Plans • Test period: 2006-9 • Three-step testing • Radiation • SURFRAD and NRCS-SCAN sites • PET evaluation • DMIP basins • Hydrologic experiments • DMIP basins

  7. Radar QPE Quality Indicators(OHD analysis – Dave K, Bob Setzenfand, Wanru Wu) • Earlier in 2009 we (Bob Setzanfand and I) analyzed the correlation between daily Stage2 (DPA-based mosaic) accumulations and 24-h NAM precipitation forecast totals, at individual boxes of the HRAP grid • Visual inspection and some analysis of corresponding Stage2-gauge correlations indicate that, at any place, the Stage2/NAM correlation is a good proxy for Stage2-gauge correlation – and the NAM forecast is available at all geographic locations • Correlations were calculated over all available days October-March 206-2009. • Results for relative frequency of daily 1mm precip from DPA and NAM, and the correlation grid, shown below. • Pretty consistent with the misbin grids that Jay Breidenbach developed

  8. Relative Frequency of Dates with at least 1 mm 24-h precipitationWINTERS 2006-07, 2007-08, 2008-09 NAM Radar % 8

  9. Radar/NAM Linear Correlation Coefficient (r)WINTERS 2006-07, 2007-08, 2008-09 9

  10. Radar/NAM Linear Correlation Coefficient (r)WINTERS 2006-07, 2007-08, 2008-09

  11. Comparison of NMQ and DPA-based precipitation grids – CONUS and RFC areas Wanru Wu Hydrometeorology Group Hydrology Laboratory Office of Hydrologic Development NOAA/National Weather Service

  12. ≥0 1072 ≥0.25 495 ≥10 49 ≥25 9 Case # Evaluation of 24-h NMQ Radar-Only and NEXRAD PPS Precipitation Estimates Against ASOS Rain Gauge Observations – Correlation Coefficients Warm Season (Apr. 2009 – Sep. 2009) (c) CBRFC

  13. ≥0 2962 ≥0.25 821 ≥10 53 ≥25 7 Case # Evaluation of 24-h NMQ Radar-Only and NEXRAD PPS Precipitation Estimates Against ASOS Rain Gauge Observations – Correlation Coefficients Cool Season (Oct. 2009 – Mar. 2010) (c) CBRFC

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