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On the impact of Land Data on Mesoscale Numerical Weather Prediction

On the impact of Land Data on Mesoscale Numerical Weather Prediction. Vince Wong & Michael Ek EMC/NCEP/NOAA. Implementation of MODIS-IGBP Vegetation Data Implementation of MODIS Albedo Data Implementation of AVHRR Green Vegetation Fraction data. IGBP. USGS.

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On the impact of Land Data on Mesoscale Numerical Weather Prediction

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  1. On the impact of Land Data onMesoscale Numerical Weather Prediction Vince Wong & Michael Ek EMC/NCEP/NOAA • Implementation of MODIS-IGBP Vegetation Data • Implementation of MODIS Albedo Data • Implementation of AVHRR Green Vegetation Fraction data

  2. IGBP USGS Change of Vegetation Type ==> Change of Vegetation Parameters: 1. Roughness Length (Z0) 2. Minimum Stomatal Resistance (RSmin) 3. Radiation Stress Function (RGL) 4. Root Depth (NRoot) 5. Threshold Snow Depth that implies 100% Snow Cover(SNUP) 6. etc.

  3. Comparison between N1 & CN Runs for 10 cases N1: IGBP (Parallel Testing with Operational Model since last August) CN: Same as N1 except using USGS vegetation map NAM: present mesoscale operational model

  4. Relative Humidity Bias & Root-mean-square Error NAM IGBP USGS

  5. Temperature Bias & Root-mean-square Error

  6. Z Bias & Root-mean-square Error

  7. Surface Dew Point Temperature Bias & Root-mean-square Error NMMB(USGS) NAM IGBP

  8. Surface Temperature Bias & Root-mean-square Error USGS NAM IGBP

  9. Precipitation Score Precipitation Bias

  10. Surface Relative Humidity Bias & Root-mean-square Error NAM IGBP USGS

  11. MODIS Max Albedo & Snow-free Albedo • Visible Direct Albedo (Solar zenith angle dependent) • Visible Diffuse Albedo • Near Infrared Direct Albedo (Solar Zenith angle dependent) • Near Infrared Diffuse Albedo • ~1km Resolution over the Globe • Monthly Averaged

  12. Snow-free Albedo

  13. Green Vegetation Fraction requires “Real Time” Data

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