1. CONVECTIVE FORECAST PERFORMANCE OF AN OPERATIONAL MESOSCALE MODELLING SYSTEM
Anthony P. Praino, Lloyd A. Treinish
IBM Thomas J. Watson Research Center, Yorktown Heights, NY

2. Forecast Study Details
Four Geographic Regions Examined
Baltimore-Washington, Chicago
Kansas City, New York
Seven Convective Events Studied
Baltimore-Washington: 1 case
Chicago: 2 cases
Kansas City: 1 case
New York: 3 Cases

3. Forecast Model Description
Deep Thunder: Highly Customized Version of RAMS
24 Hour Forecast Period
Full Three Dimensional Non-Hydrostatic
31 Vertical Levels
Triple Nested Configuration
16km, 4km, 1km – NY
32km, 8km, 2km – BW, Chi, KC
Two Way Interactive Domains
Explicit Cloud Microphysics
Talk about model details: What products are available, model run times in different domains, etc. Refernce earlier work in Winter Storm verification, initial model verification study, etc. Motivation of end to end automated systems approach.
Market specific applications: Transportation, energy, Emergency planning, recreation, construction….

4. Model Domain Configurations
Study Focused On Inner Domains

5. Quantitative Study Specific Airport or Metar Locations Used
NYC, EWR, JFK, LGA in New York Domain
ORD, MDW in Chicago Domain
FDK in Baltimore-Washington Domain
MKC, MCI in Kansas City Domain
Datasets
Metar Reports, Daily Climate Summaries
Radar Precipitation Estimates

6. Quantitative Summary Precipitation Onset Precipitation Cessation
Model Was Late in 13 of 16 Cases
Mean Error 1.8 Hours
Mean Observation Uncertainty 39 min.
Precipitation Cessation
Mean Error 3 Hours
Mean Observation Uncertainty 38 min.

7. Quantitative Summary Precipitation Accumulation Wind Speed Maxima
Model Underpredicted in 9 of 16 Cases
Model Overpredicted in 7 of 16 Cases
Mean Error 0.6 inches
Wind Speed Maxima
Mean Error 9 mph

8. Qualitative Study
Radar Composite Reflectivity Compared To Model Predictions For Overall Storm Structure, Intensity And Timing
Radar Total Precipitation Compared To Model For Spatial Distribution And Accumulation Of Rainfall
NWS Upton Radar – New York
NWS Chicago Radar – Chicago
NWS Kansas City Radar – Kansas City
NWS Sterling, VA Radar – Baltimore/DC

9. Deep Thunder Model Prediction 28 Oct 0120 UTC
Radar Image
28 Oct 0120 UTC
New York Region
A line of heavy showers and thunderstorms to the east of a frontal boundary moved east through the New York-New Jersey metropolitan area. The storms spawned an F0 tornado which touched down in Staten Island, NY.
Wind speeds were estimated at 70 mph with numerous uprooted trees and light structural damage to several houses. Model predictions for this event were available about 7 hours before the showers and thunderstorms occurred. Figure shows an image of one of the visualizations produced from the model prediction which was available 1630 UTC on 10/27/03, five hours before the thunderstorms impacted the region. The model visualization snapshot is paired with the local NWS Upton radar image for the same time. The two panel radar image shows reflectivity on the left side and radar estimated precipitation on the right side. The timing, location, distribution and intensity of the model predicted precipitation is in good agreement with the radar image.
Deep Thunder Model Prediction 28 Oct 0120 UTC

10. Deep Thunder Model Prediction 28 Oct 0120 UTC
Radar Image
02 March 130 UTC
Chicago Region
Another case examined was an area of early spring thunderstorms which developed over northern Illinois and northwestern Indiana on 3/01/04.These were low topped storms that developed in an environment of significant cold air aloft. There were several reports of hail between 0.75 and 1.00 inches in diameter. The storms also produced wind gusts as high as 59 knots.
In this case spatial distribution and intensity of the predicted convective activity was in good agreement with radar observations. Timing however was off with the model prediction lagging the actual passage of the storms through the region by about three hours. Considering the model products availability, there was still significant lead time (7 hours) in predicting convective activity for the region.
Deep Thunder Model Prediction 28 Oct 0120 UTC

11. Radar Image 18 May 1230 UTC Kansas City Region Deep Thunder
In the Kansas City region a line of early morning thunderstorms moved developed and moved southeast through the metropolitan area on 5/18/04. Wind gusts as high as 65 mph were recorded with some trees down and some structural damage.
Model forecast results for the 24 hour period beginning 0600Z on May 18, 2004 were available at 1030UTC, about 1.5 hours prior to the storms impacting the area. Figure shows the model forecast visualization for 1230 UTC along with the local NWS radar image for approximately the same time.
Comparison of the images show that the model forecast did produce convective activity over the region but it was less organized and of lesser intensity than the radar observations show during this time interval. Later model times show more structure and intensity to the southeast of Kansas City.
Deep Thunder
Model Prediction
18 May 1230 UTC

12. Baltimore/Washington Region
Radar Image
16 Oct 2038 UTC
Baltimore/Washington Region
convective event which occurred in the Washington D.C. – Baltimore area on May A line of thunderstorms developed in the late afternoon and moved quickly eastward through the metropolitan area. The storm impacted traffic on interstate 95 north of Baltimore with heavy rain and pea sized hail causing 11 separate traffic accidents including one involving 90 vehicles.
Model forecast products for the Washington- Baltimore region were completed at 1030 UTC, more than 9 hours before the storms moved through. Figure shows the model forecast visualization for 2030 UTC along with the local weather service radar image at 2038 UTC.
Comparison of the images shows the model results to be in good agreement with radar observation in timing, intensity and spatial distribution with the exception of the southern portion of the squall line.
Deep Thunder Prediction
16 Oct 2030 UTC

13. Qualitative Summary
Deep Thunder Exhibited Considerable Skill in Modelling Structure and Spatial Distribution of Convective Events
Model Predictions Available With Significant Lead Time (mean 6.5 hours) Before Storms Impacted Area

14. Summary
Deep Thunder Demonstrates Good Skill In Modelling Convective Events
Negative Bias in Precipitation Timing
Positive Bias in Precipitation Amount
Model Predictions In Several Cases Had Considerable Lead Time When Compared to Other Forecast Data

15. Future Work
Use Of High Resolution Eta 218 Data For Model Initial And Boundary Conditions
Application Of Additional Mesonet Data For Point Specific Model Verification
Application Of Model Ensemble Methodology

16. Model Predictions & Observed Results
Precip Start Diff
Precip End Diff
Precip Accum Diff
Wind Diff
9.3125
Mean
Std Dev
Variance