VERIFICATION OF NDFD GRIDDED FORECASTS IN THE WESTERN UNITED

STATES

John Horel1, David Myrick1, Bradley Colman2, Mark Jackson3

1NOAA Cooperative Institute for Regional Prediction2National Weather Service, Seattle

3National 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

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

Validation of NDFD Forecast GridsDeveloping 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

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

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.

Arctic Outbreak: 21-25 November 2003

NDFD 48 h forecast ADAS Analysis

RMS differenceRUC2-OBS: 2.7C (0z) 4.0C (12z)

RMS differenceADAS-OBS:1.7C (0z) 2.4C (12z)

ADAS Analysis

Average 00Z Temperature: 18 Nov.- 23 Dec. 2003 48 H NDFD Forecast

48 h Forecast Bias (NDFD –ADAS)

00z 18 Nov.-23 Dec. 2003

Average RMS Differences between NDFD Forecasts and ADAS grids over the Western United States

0

1

2

3

4

5

6

7

RM

S D

iffer

ence

24 48 72 96 120 144

Forecast (h)

Temperature ( C)Dew Point Temperature ( C)Wind Speed (m/s)

NDFD Forecasts Issued 00z. Period: 12 Nov.-24 Dec. 2003

Valid at 0z

Arctic Outbreak: 21-25 November 2003

NDFD 48 h forecast ADAS Analysis

NDFD and ADAS sample means removed

-0.6-0.4-0.2

00.20.40.60.8

1

18 22 26 30 4 8 12 16 20

Verification Date

Ano

mal

y C

orre

latio

n 24487296120144168

Temperature spatial anomaly pattern correlation as a function of NDFD forecast length during 12 Nov.-24 Dec. 2003

Anomaly relative to sample average for NDFD and ADAS

Nov. Dec.

Comparison of daily temperature anomaly maps

Temperature spatial anomaly pattern correlation as a function of NDFD forecast length. Average 12 Nov.-24 Dec. 2003

Anomaly relative to sample average for NDFD and ADAS

0

0.2

0.4

0.6

0.8

1

12 24 36 48 60 72 84 96 108 120 132 144 156 168

Forecast Duration (h)

Ano

mal

y pa

tter

n co

rrel

atio

n

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

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

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 gridsinstantaneous/time average temperature obs vs. max/min

Objectively identify regions where forecaster skill limited by sparse data

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 8:30 Tuesday

Average RMS Differences between NDFD Forecasts and ADAS grids over the Western United States

0

1

2

3

4

5

6

RM

S D

iffer

ence

12 36 60 84 108 132

Forecast (h)

Temperature ( C)

NDFD Forecasts Issued 00z. Period: 12 Nov.-20Dec. 2003

Valid at0z and 12z

48 h Forecast RMS

Difference(NDFD –ADAS)

00z 18 Nov.-23 Dec. 2003