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

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

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….

Model Domain Configurations Study Focused On Inner Domains

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

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.

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

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

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

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

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

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 18 2004. 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

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

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

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

Model Predictions & Observed Results Precip Start Diff Precip End Diff Precip Accum Diff Wind Diff 1.776875 2.98125 0.579688 9.3125 Mean 1.03719955 1.078671 0.41262 8.451578 Std Dev 1.07578292 1.163532 0.170255 71.42917 Variance