1. Alan Rezek National Weather Association Annual Meeting October 17, 2006

2. WHY NWS has forecaster resources (100+ offices), local expertise and tools to forecast high resolution space and time Private sector has analysis, interpretation and distribution expertise to use high resolution forecast data to provide services

3. WHY NWS has been making investments. Higher resolution modeling, WFO local high resolution model capability, increased frequency of model runs and local WFO model diagnostic capability, etc. have all been developed and are available to forecasters over the last several years. With a new level of services in place with ESTO, we will be positioned to use improved technology & organizational structure to take us to a second level of services by 2015.

4. 12 to 18 Hour forecast focus High Detail in space Stress being deterministic as possible Hourly Grids – High detail in time Aviation forecast grids (ceiling and visibility) Repeated three hour forecast preparation cycle Analysis, model/guidance interpretation, build forecast grids, transmit forecast Modified shift activities to have at least one forecaster dedicated 24/7 to short term forecast activities

5. Focus off text onto meteorology Prioritized forecaster efforts to maximize service through detail increase emphasis on our role as detailed forecast data service providers decrease emphasis on private role of creating “desirable, pleasant, focused use” text information Full Automation of many text products from grids: AFM, PFM, SFP, FWF

6. 90% Automation of 4 “primary” text products from grids Alerted when produced – checked and sent Adjusted wording to better facilitate computer generation and better match the role of the 4 “primary” text products in an operation oriented to high resolution forecast data. ZFP: 4/day, Low detail, 1 Cnty/1 Zn, for general use NOW: 8/day, 1 Cnty/1 Zn, four 3 hr periods, high detail text for short term decision making (next 12 hours) NWR ZFP: Every 3HRs, 3 HR detail 1st 12 hrs, 6 hr detail period 2 and 3, then 12 hour detail, both general use and short term decision making text TAF: data extracted from grids; TAFs are formatted, finalized and sent every 6 hours, updated every 3 hours

7. SCIENCE For Short Term Consolidate and organize data and information availability to forecasters for their use to meet ESTO expectations Improve high resolution model data availability Develop model diagnostic techniques to assist in preparing high detail forecasts Improve forecaster performance feedback focus on short term forecast operations real time/next day Three Primary Components

8. SCIENCE For Short Term * Improved MESONET CAPTURED ALL AVAILABLE DATA ADDED RIDGETOP SITE AT NWR COORDINATED WV IFLOWS MESONET 2 MET SENSOR SYSTEMS/COUNTY EXPANDED LOCAL LAPS DOMAIN FOR IMPROVED LOCAL MODELING INCREASED LAPS RESOLUTION - 5KM

9. SCIENCE For Short Term LOCAL SHORT RANGE MODELS/FCSTS * WSETA FORECAST EVERY 6 HOURS HRLY OUT 18 HR, 15KM RESOLUTION * SHIFTED FINANCIAL RESOURCES TO CREATE A CLUSTER TO SUPPORT LOCAL WRF * WRF FORECAST EVERY 3 HOURS HOURLY OUT 18 HR, 5KM RESOLUTION HIGH LOW LEVEL LAYER RESOLUTION INCLUDE PBZ (BACKUP PARTNER) IN DOMAIN “HOT START” INITIALIZING ON LAPS EXPERIMENTED WITH “LOCAL” PRODUCED FIELDS

10. SCIENCE For Short Term Worked to assure availability and reliability of other operational short range and aviation models HOURLY SHORT TERM MOS (LAMP) HOURLY FORECASTS EVERY 3 HR FWC BASED MAV BASED RUC40 AND RUC13 NCV

11. SCIENCE For Short Term High Resolution model data made available in AWIPS and GFE (2.5 KM) Enhanced the role of GFE in forecasting Developed diagnostic capabilities and output EX - Individual model diagnosis and short term model consensus forecasts of hourly precipitation, severe potential, winter weather Developed SMART tools for the preparation of high detail forecasts in space and time– EX – fog in individual valleys/individual hours

12. SCIENCE For Short Term Real time model & Forecaster first period temperature and POP verification data Available in AWIPS at end of first period Verification data for each model and the forecaster 2 and 10 day trends/rankings for each model and WFO Higher resolution POP verification developed 3 hour sub period of 12 hour forecast period Local MOS and consensus MOS developed for 1 st period Review of verification of NDFD elements by hour undertaken and feedback provided to forecasters Verification developed for station, forecast team and individual

13. 0115 PM Exploring a New Approach to Improving Severe Weather Warning Lead Times Using GFE. Andy Roche, NOAA/NWS Charleston, WV. 0830 AM Generation and Application of Gridded Aviation Forecast Parameters in GFE and AvnFPS. Chris Leonardi, NOAA/NWS Charleston, WV. Thursday For more details regarding the aviation activities For more details regarding the use of GFE for model diagnostics

15. Percent Improvement Max/Min over MAV January through September Traditional Period 1 First Year Short Term

16. Percent Improvement Max/Min over MAV Traditional Period 1 First convective season exceptional Second convection season no improvement First winter season good improvement

17. Brier Scores June ‘05 through July ‘06 Traditional Period 1 Zero being perfect

18. Aviation POD IFR FY ‘06 Essentially about the same as the previous year

19. Aviation FAR IFR FY ‘06 Large improvement over previous year

21. 15 to 20 representatives from varied WFO user groups Ex. Aviation, Education, Construction, Media They provide periodic feedback regarding services forming a “core” for a service verification program They provide feedback regarding specific issues (ie. NWR, river forecast services)

22. What is the place of detail in the forecast? Web products most used: Radar, AHPS, Hourly Wx Graph, Point and click forecast – Products with high detail in space and time POPs of highest value to users: zero and 60 to 100 percent POPs of lowest value to users: 10-50 percent Ease of getting a detailed forecast on our web site = 4.1 out of 5 (being best)

23. Difficult to overcome organizational “can’t” biased culture and belief system which says: “we are not that good”, “we can’t predict summertime convection”, etc. Historic examples where believing few overcame “can’t”: QPF, river forecasts based on QPF, hourly grid forecasts Challenging for forecasters to change forecasting approach Getting forecasters to shift from traditional “big period” approach of model interpretation Getting forecasters to use new forecast models/tools Getting forecasters to forecast more deterministically

24. Forecaster continuously engaged in short term during shift: lose of touch with long term, no training/focal pt “extra time” During complex short term weather, Lead Forecaster must delegate “overseer” role or remove themselves from the short term desk Collaboration difficult in rapidly changing situation

25. Days 4 through 7 forecasting taking from short term forecasting time Poor cost/benefit for time spent forecasting for days 4 thru 7 relative to forecasting in short term Lack of good low level modeling for aviation ESTO technology dependent, susceptible to failure Meso Networks, Local modeling, Network speed

26. SOO – Jeffrey.Hovis@NOAA.govJeffrey.Hovis@NOAA.gov WCM - Daniel.Bartholf@NOAA.govDaniel.Bartholf@NOAA.gov MIC – Alan.Rezek@NOAA.govAlan.Rezek@NOAA.gov IT – Kevin.Mcgrath@NOAA.govKevin.Mcgrath@NOAA.gov

29. Enhanced Short Term Forecasts Deterministic Where it is and is not When it is and is not New forecast every 3 hours 7 AM/PM 10 AM/PM 1 AM/PM 4 AM/PM Focus Hourly Weather in first 12 to 24 hours

30. Additional Thoughts on WHY Customers have good detail for decision making A better warning service/advance notice in both space and time of specific threat areas With automation of text, more time to focus on, and provide, detail in the forecast Skill/verification improves with more focus on detail in short term Focus on the area in the forecast process where the technology and availability of the human/forecast resources of the NWS can enhance the information to the private sector for their use. Additional aviation services available with aviation grids Cost/Benefit ratio high with high detail forecasts

31. July 04 – WFO Enhanced Short Term Team established Aug 04-Mar 05 – Put in place hardware and software requirements, conducted training Mar 05 – Began Enhanced Short Term Operations (ESTO) Aug-Sep 05 - Put in place software requirements, conducted training for aviation grids Oct 05 – Added aviation grids to ESTO

32. Available to forecasters when they return to work the next day

33. THIS WRKVER PRODUCT COMPARES MOS FCSTS WITH CCF AND OBS DATA IN THE RTP RANKINGS LISTED AT THE BOTTOM VERIFICATION PRODUCT CREATED: 09/21/2006 at 13:00 UTC QUICK VIEW - ONE DAY MINIMUM BIASES FOR SELECT MODELS: STN OBS CCF BIAS MAV BIAS MET BIAS FLP(L) BIAS LMOS BIAS --- --- --- ---- --- ---- --- ---- ----- ---- --- ---- BKW 37 | 37 0 | 35 -2 | 39 2 | 42 5 | 36 -1 CKB 41 | 41 0 | 40 -1 | 43 2 | 999 MM | 42 1 CRW 43 | 42 -1 | 38 -5 | 44 1 | 45 2 | 44 1 EKN 41 | 39 -2 | 36 -5 | 38 -3 | 999 MM | 39 -2 HTS 40 | 41 1 | 37 -3 | 43 3 | 44 4 | 44 4 PKB 41 | 40 -1 | 38 -3 | 41 0 | 44 3 | 42 1 --- --- --- --- --- ABS SUMS: 5 | 19 | 11 | 14 | 10...CCF-MOS Rankings...(ABS) 2 DAY RANK: 10 DAY RANK : ----------- ------------ 1.CCF 1.83 1. CCF 1.70 2.MOS 2.08 2. CON 1.80 3.FLE 2.25 3. MOS 1.80 4.MEX 2.25 4. MET 1.90 5.FLL 2.50 5. MEX 1.98 6.FWC 2.50 6. FWC 2.18 7.CON 2.60 7. MAV 2.35 8.MET 2.92 8. FLL 2.58 9.MAV 3.33 9. MEN 2.72 10.MEN 3.67 10. FLE 2.80

34. THIS WRKPOP PRODUCT PROVIDES POPS SCORES FOR MOS AND CCF FCSTS RANKINGS LISTED AT THE BOTTOM VERIFICATION PRODUCT CREATED: 09/21/2006 at 13:00 UTC QUICK VIEW - ONE DAY POP VALUES AND VERIFICATIONS FOR SELECT MODELS: STN PCP? CCF BIAS MAV BIAS MET BIAS FLP BIAS FWC BIAS --- --- --- ---- --- ---- --- ---- --- ---- --- ---- BKW N | 5 5 | 6 6 | 10 10 | 0 0 | 0 0 CKB N | 5 5 | 5 5 | 11 11 | 2 2 | 0 0 CRW N | 5 5 | 6 6 | 10 10 | 1 1 | 0 0 EKN N | 5 5 | 6 6 | 14 14 | 1 1 | 0 0 HTS N | 0 0 | 6 6 | 9 9 | 2 2 | 0 0 PKB N | 0 0 | 5 5 | 10 10 | 0 0 | 0 0 --- --- --- --- --- ABS SUMS: 20 | 34 | 64 | 6 | 0 BRIER SCORES FOR ALL SITES: 2 DAY RANK: 10 DAY RANK: ------------ ----------- 1.MOS 0.0000 1. MET 0.0946 2.FLL 0.0020 2. CCF 0.0959 3.FLE 0.0021 3. CON 0.0973 4.MEN 0.0063 4. MOS 0.1000 5.CCF 0.0083 5. FWC 0.1032 6.FWC 0.0095 6. MAV 0.1042 7.MAV 0.0112 7. MEN 0.1048 8.MEX 0.0150 8. FLE 0.1072 9.CON 0.0150 9. MEX 0.1147 10.MET 0.0296 10. FLL 0.1149

35. THE WRKTHR PRODUCT COMPARES POP FCSTS FROM THE PAST DAY WITH OBSERVED DATA AT VARIOUS GAGE SITES FOR THREE HOUR PERIODS..15Z PACKAGE. For ALL SITES/ALL TIMES: OverFcst UnderFcst MidPOP HitsPcp HitsNoPcp #/% #/% #/% #/% #/% ------- --------- ------- -------- --------- 3842/39.80 | 435/70.96 | 3494/34.03 | 178/29.04 | 5811/60.20 FOR SITE: 8137: DaysBack OverFcst UnderFcst MidPOP HitsPcp HitsNoPcp #/% #/% #/% #/% #/% ------- --------- ------- -------- --------- -------- 10 5/12.82 | 2/50.00 | 6/13.95 | 2/50.00 | 34/87.18 30 36/31.30 | 9/50.00 | 42/31.58 | 9/50.00 | 79/68.70 90 140/35.81 | 30/69.77 | 152/35.02 | 13/30.23 | 251/64.19 FOR SITE: 1234:………. Breakdown by 3 hour period also planned

37. Percent Improvement over MAV June ‘05 through July ‘06 Period 2

38. Brier Scores June ‘05 through July ‘06 Period 2 Zero being perfect

39. Y - Short Term (12-24 hr) Z - Long Term Winter H - Short Term J – Extended (Day 4-7)/Extra K - “Mid Term” (Period 2 or 3 thru Day 3) Summer H - Short Term J - Extended then “Mid Term” L – Assist with forecast/Extra/Convection Q - Short Term 10 forecasters

41. By going to grids we have increased the services to the aviation community without adversely impacting TAF forecasts

46. Ability Of Atmosphere to Support Severe Weather Each Hour As Created in GFE