VERIFICATION OF NDFD GRIDDED FORECASTS IN THE WESTERN UNITED STATES John Horel1, David Myrick1, Brad

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VERIFICATION OF NDFD GRIDDED FORECASTS IN THE
WESTERN UNITED STATESJohn Horel1, David
Myrick1, Bradley Colman2, Mark Jackson31NOAA
Cooperative Institute for Regional
Prediction2National Weather Service,
Seattle3National Weather Service, Salt Lake
City
Objective Verify month sample of NDFD gridded
forecasts of temperature, dew point temperature,
and wind speed over the western United States
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IFPS and NDFD

• NWS has undergone major change in procedures to
  generate and distribute forecasts
  
• Interactive Forecast Preparation System (IFPS
  Ruth 2002) used to create experimental
  high-resolution gridded forecasts of many weather
  elements
• Forecast grids at resolutions of 1.25, 2.5, or 5
  km produced at each NWS Warning and Forecast
  Office (WFO) and cover their respective County
  Warning Area (CWA)
• CWA grids combined into National Digital Forecast
  Database (NDFD Glahn and Ruth 2003) at 5-km
  resolution
  
• NDFD elements include temperature, dewpoint,
  wind speed, sky cover, maximum and minimum
  temperature, probability of precipitation, and
  weather
• Available up to hourly temporal intervals with
  lead times up to 7 days
  
• Products can be
• viewed graphically
• downloaded by customers and partners
• linked to formatting software to produce
  traditional NWS text products

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Validation of NDFD Forecast Grids

• Developing effective gridded verification scheme
  is critical to identifying the capabilities and
  deficiencies of the IFPS forecast process (SOO
  White Paper 2003)
• National efforts led by MDL to verify NDFD
  forecasts underway
  
• Forecasts available from NDFD for a particular
  grid box are intended to be representative of the
  conditions throughout that area (a 5 x 5 km2
  region)
• Many complementary validation strategies
• Interpolate gridded forecasts to observing sites
• Compare gridded forecasts to gridded analysis
  based upon observations
  
• Objective of this preliminary study
• Compare NDFD forecasts to analyses created at the
  Cooperative Institute for Regional Prediction
  (CIRP) at the University of Utah, using the
  Advanced Regional Prediction System Data
  Assimilation System (ADAS)
• Period examined 12 November 24 December 2003

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ADAS ARPS Data Assimilation System

• ADAS is run in near-real time to create analyses
  of temperature, relative humidity, and wind over
  the western U. S. (Lazarus et al. 2002 WAF)
  
• Analyses on NWS GFE grid at 2.5, 5, and 10 km
  spacing
  
• Typically 2000 surface temperature and wind
  observations available via MesoWest for analysis
  
• The 20km Rapid Update Cycle (RUC Benjamin et al.
  2002) is used for the background field
  
• Background and terrain fields help to build
  spatial temporal consistency in the surface
  fields
  
• Current ADAS analyses are a compromise solution
  suffer from many fundamental problems due to
  nature of optimum interpolation approach
  

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MesoWest

• MesoWest Cooperative sharing of current weather
  information around the nation
  
• Real-time and retrospective access to weather
  information through state-of-the-art database
  
• http//www.met.utah. edu/mesowest
• Horel et al. (2002) Bull. Amer. Meteor. Soc.

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Arctic Outbreak 21-25 November 2003
NDFD 48 h forecast
ADAS Analysis
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Average 00Z Temperature 18 Nov.- 23 Dec. 2003
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48 h Forecast Bias (NDFD ADAS)00z 18 Nov.-23
Dec. 2003
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Average RMS Differences between NDFD Forecasts
and ADAS grids over the Western United States
NDFD Forecasts Issued 00z. Period 12 Nov.-24
Dec. 2003
Valid at 0z
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Arctic Outbreak 21-25 November 2003
NDFD and ADAS sample means removed
NDFD 48 h forecast
ADAS Analysis
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Temperature spatial anomaly pattern correlation
as a function of NDFD forecast length during 12
Nov.-24 Dec. 2003Anomaly relative to sample
average for NDFD and ADAS
Comparison of daily temperature anomaly maps
Nov.
Dec.
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Temperature spatial anomaly pattern correlation
as a function of NDFD forecast length. Average 12
Nov.-24 Dec. 2003Anomaly relative to sample
average for NDFD and ADAS
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Summary

• Assimilation of surface data is critical for
  generating and verifying gridded forecasts of
  surface parameters
  
• MDL is using RUC for national NDFD validation and
  is exploring use of ADAS in the West
  
• Differences between ADAS analysis and NDFD
  forecast grids result from combination of
  analysis and forecast errors
  
• Difference between ADAS temperature analysis on 5
  km grid and station observations is order
  1.5-2.5C
  
• Difference between NDFD temperature forecast and
  ADAS temperature analysis is order 3-5C. May
  reflect upper bound of forecast error since ADAS
  analysis contains biases
• Anomaly pattern correlations between NDFD and
  ADAS temperature grids over the western United
  States suggest forecasts are most skillful out to
  48 h
• Major issue for NDFD validation true state of
  atmosphere is unknown
  
• Specific issues for NDFD Validation in Complex
  Terrain
  
• Scales of physical processes
• Analysis methodology
• Validation techniques

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Issues for NDFD Validation in Complex Terrain

• Analysis Methodology
• Analysis of record will require continuous
  assimilation of surface observations, as well as
  other data resources (radar, satellite, etc.)
  
• Requires considerable effort to quality control
  observations (surface stations siting issues,
  radar terrain clutter problems, etc.)
  
• Quality control of precipitation data is
  particularly difficult
  
• NWP model used to drive assimilation must resolve
  terrain without smoothing at highest possible
  resolution (2.5 km)
  
• NCEP proposing to provide analysis of record for
  such applications

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Issues for NDFD Validation in Complex Terrain

• Validation technique
• Upscaling of WFO grids to NDFD grid introduces
  sampling errors in complex terrain
  
• Which fields are verified?
• Max/min T vs. hourly temperature?
• Max/min spikes
• fitting of sinusoidal curve to Max/Min T to
  generate hourly T grids
  
• instantaneous/time average temperature obs vs.
  max/min
  
• Objectively identify regions where forecaster
  skill limited by sparse data
  

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Related Presentations

• Monday Poster Session. David Myrick. A
  Modification to the Bratseth Method of Successive
  Corrections for Complex Terrain
  
• Mike Splitt. Geospatial Uncertainty Analysis and
  Gridded Forecast Verification. Room 3A 830
  Tuesday

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Average RMS Differences between NDFD Forecasts
and ADAS grids over the Western United States
NDFD Forecasts Issued 00z. Period 12
Nov.-20Dec. 2003
Valid at 0z and 12z
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48 h Forecast RMS Difference(NDFD ADAS)00z 18
Nov.-23 Dec. 2003