1. Meso- and Storm-Scale NWP: Scientific and Operational Challenges for the Next Decade Kelvin K. Droegemeier School of Meteorology and Center for Analysis and Prediction of Storms University of Oklahoma COMAP Symposium on NWP 20 May 1999 Boulder, Colorado

2. What Are Models Predicting? n Global and synoptic flow patterns n Precipitation via crude parameterizations that are unable to resolve individual clouds n Topographic forcing n Coastal and lake influences n Crude linkages between the land surface and atmosphere

3. What Are We Using? n Single forecasts n Output frequency of 3 to 12 hours n Accumulated precipitation and other traditional fields n Graphical overlays of model, radar, satellite GETTING THIS FROM THIS

4. What Would We Like to Predict? n Individual thunderstorms and squall lines n Lake effect snow storms n Down-slope wind storms n Convective initiation n Seabreeze convection n Stratocumulus decks off the coast n Cold air damming n Post-frontal rainbands

5. Why? n Local high-impact weather causes economic losses in the US that average $300 M per week n Over 10% of the $7 trillion US economy is impacted each year n Commercial aviation losses are $1-2 B per year (one diverted flight costs $150K) n Agriculture losses exceed $10 B/year n Other industries (power utilities, surface transport) n About 50% of the loss is preventable!

6. What Do We Need? n Models that –run at high spatial resolution (1-3 km) –utilize high-resolution observations (e.g., from the WSR-88D network) –handle terrain well –represent important physical processes, especially microphysics and land-surface interactions n Probability forecasts and other measures of uncertainty n A single tool that integrates model output and observations

7. Trends in Large-Scale Forecast Skill

8. Predictability: Hitting the Wall n For global models, the predictability increases for all resolvable scales as the spatial resolution increases (quasi 2-D dynamics) –The improvement is bounded –Going beyond a few 10s of km gives little payoff n The next quantum leap in NWP will come when we start resolving explicitly the most energetic weather features, e.g., individual convective storms (3-D) 60 km30 km 10 km 2 km

9. Center for Analysis and Prediction of Storms (CAPS) n One of first 11 NSF Science and Technology Centers established in 1989 n Mission: To demonstrate the practicability of numerically predicting local, high-impact storm-scale spring and winter weather, and to develop, test, and help implement a complete analysis and forecast system appropriate operational, commercial, and research applications

10. The Key Scientific Questions n Can value be added to present-day NWP and radar- based nowcasting by storm-resolving models? n Which storm-scale events are most predictable, and will fine-scale details enhance or reduce predictability? n What physics is required, and do we understand it well enough for practical application? n What observations are most critical, and can data from the national NEXRAD Doppler radar network be used to initialize NWP models? Can this be done in real time? n What networking and computational infrastructures are needed to support high-resolution NWP? n How can useful decision making information be generated from forecast model output?

11. Prediction Targets n Somewhat problematic n For 1-3 km resolution grids, location to within –200 km 6 hours in advance –100 km 4 hours in advance –50 km 2 hours in advance –10 km 1 hour in advance n Initiation n Movement n Intensity n Duration

12. Meso-scale NWP n The prediction of the general characteristics associated with mesoscale weather phenomena 6-hour ARPS Forecast at 9 km resolution WSR-88D CREF (02 UTC 30 Nov 1999)

13. Storm-scale NWP n The prediction of explicit updraft/downdrafts and related features (e.g., gust fronts, meso-cyclones) 1-hour ARPS Forecast at 2 km resolutionWSR-88D CREF (Lahoma Storm)

14. Model Spatial Resolution Breadth of Application Economic Impact Negative Consequences of a Bad Forecast 1980’s 1970’s 1990’s 2000-2010

15. Present NWS Operations

17. NWS Forecast Offices

18. Small-Scale Weather is LOCAL! Severe Thunderstorms FogRain and Snow Rain and Snow Intense Turbulence Snow and Freezing Rain

19. The Future of Operational NWP 10 km 3 km 1 km 20 km CONUS Ensembles

20. The Future of Operational NWP??

21. The Emerging Concept of a National Scale “Information Power Grid”

22. Principal Differences Between Large- and Small-Scale NWP n Large-scale: Rawinsondes observe “everything” that is needed to initialize a model (T, RH, u, v) n Small-scale: Doppler radar observes only the radial wind and reflectivity in precipitation regions; clear- air PBL data available in some situations n Large-scale: Well-known balances can be applied to reconcile wind and mass fields (e.g., geostrophy, balance equation) n Small-scale: Only simple balances available (mass continuity); otherwise, it’s the full equations!!

23. n Large-scale: Forecasts are of sufficient duration to be produced and disseminated in reasonable time frames n Small-scale: Forecasts are of very short duration and thus are highly perishable n Large-scale: Observing network is mature and errors and natural variability are understood n Small-scale: Key observing system (WSR-88D) is new; only a few links exist for providing base data in real time

24. n Large-scale: Dynamics and predictability limits are fairly well understood; model physics and numerics are reasonably mature n Small-scale: Dynamics fairly well understood, but predictability limits have not been established; model physics still evolving; physical processes complicated (addition of detail a double-edged sword) n Large-scale: Conventional data assimilation techniques work well; large-scale features evolve slowly n Small-scale: Conventional data assimilation techniques not applicable; events are spatially intermittent and evolve rapidly; how to remove an incorrect thunderstorm and insert the correct one???

25. n Large-scale: Computing power reasonably sufficient n Small-scale: Need 100 to 1000 times more computing power than is now available commercially n Large-scale: No lateral boundary conditions to worry about for global and hemispheric models n Small-scale: Lateral boundaries in limited-area models exert a tremendous influence on the solution; compromise between high spatial resolution and domain size 12-hr forecast @ 10 km resolution 6-hr forecast @ 4 km resolution 2-hr forecast @ 1 km resolution

26. Recipe for a Storm-Scale NWP System n Advanced numerical model with appropriate physics parameterizations n High-resolution observations (WSR-88D, profilers, satellites, MDCRS) n Powerful computers and networks n A way to retrieve quantities that cannot be observed directly n Strategies for converting output to useful decision making information

27. The CAPS Advanced Regional Prediction System (ARPS)

29. NEXRAD Doppler Radar Data

30. n observe... –One (radial) wind component –reflectivity n need... –3 wind components –temperature –humidity –pressure –water substance (6-10 fields) n SDVR solves the inverse problem –control theory (adjoint), simpler methods –computationally very intensive Single-Doppler Velocity Retrieval (SDVR)

31. Sample SDVR Result Dual-DopplerSDVR-Retrieved Weygandt (1998)

32. Sample SDVR Result Dual-DopplerSDVR-Retrieved Weygandt (1998)

33. Dual-DopplerSDVR-Retrieved Sample SDVR Result Weygandt (1998)

34. 5 April 1999 - Impact of Level II Data Initial 700 mb Vertical Velocity Using NIDS 12 Z Reflectivity Initial 700 mb Vertical Velocity Using Level II Data and SDVR

35. 5 April 1999 - Impact of Level II Data 15 Z Reflectivity 3 hr ARPS CREF Forecast (9 km) Using Level II Data and SDVR Valid 15Z 3 hr ARPS CREF Forecast (9 km) Using NIDS Data Valid 15Z

36. n CAPS has been using Level II (base) NEXRAD data in case study predictions down to 1 km resolution and Level III data (NIDS) in its daily operational forecasts n Although NIDS data are available in real time from all radars, they are insufficient in many cases for storm-scale NWP –Precision is degraded via value quantization –Only the lowest 4 tilts are transmitted n No national strategy yet exists for the real time collection and distribution of Level II data Availability of Base Data

37. Real Time Test Bed for Acquiring WSR-88D Base Data (Project CRAFT) INX DDC AMA LBB FWS TLX KFSM ICT Radars Online Approval Pending

38. CRAFT Phase I

39. CRAFT Phase II

40. Regional Collection Concept Must await open-RPG

42. The CAPS Vision n Distributed data acquisition (NEXRAD radars) n Distributed dynamic computing - model grids respond to the evolving weather n Requires intelligent networking, not just high bandwidth n Distributed decision making - local weather/local decisions

43. GOES Satellite Data

44. ADAS Cloud Analysis Scheme GOES Visible Image at 1745 UTC on 07 May 1995 A B

45. ADAS Cloud Analysis Scheme Vertical E/W Cross-Section: METAR Only

46. ADAS Cloud Analysis Scheme Vertical E/W Cross-Section: METAR + GOES IR

47. ADAS Cloud Analysis Scheme Vertical E/W Cross-Section: METAR + GOES IR + WSR-88D

48. ADAS Cloud Analysis Scheme PW and Vertically Integrated Condensate Valid 13 UTC on 12 April 1999 GOES Visible Image Valid 13 UTC on 12 April 1999

49. High-Density Surface Networks

50. Commercial Aircraft Wind and Temperature Observations

51. n Daily operation of experimental forecast models is critical for –involving operational forecasters in R&D –evaluating model performance under all conditions –testing new forecast strategies (e.g., rapid model updates, forecasts on demand, re-locatable domains) –developing measures of skill and reliability based on a long- term data base of model output –learning how to integrate new forecast information into operational decision making n Over 25 groups around the US are running models in real time in collaboration with NWS Offices or NCEP Centers Real Time Testing

52. Uniqueness n Daily operational forecasts with full-physics at spatial resolutions down to 3 km n Assimilation of high-resolution observations consistent with the model high spatial resolution –WSR-88D Level II (base) data –WSR-88D Level III (NIDS) data –GOES satellite data for quantitative vapor/cloud/precip –MDCRS commercial aircraft T and V –Surface mesonets n More than 2000 products produced each hour and posted on the web (http://hubcaps.ou.edu) n Execution on the 256-node Origin 2000 at NCSA

53. 1998 Operational Configuration 06Z00Z 12Z00Z 18Z 06Z 00Z 18Z 20Z02Z 06Z00Z 1 DAY

54. 1998 Hourly Analysis Domains D/FW Region NE Corridor ORD Region Central/Eastern US

55. ARPSView Decision Support System Proprietary

56. ARPSView Decision Support System Proprietary

57. ARPSView Decision Support System Proprietary

58. ARPSView Decision Support System Proprietary

59. ARPSView Decision Support System Proprietary

60. Forecast Status Page

61. Sample ARPSView Products Cloud Type and LWC at FL 050 Cloud Type and LWC at FL 320 Cloud Type and LWC N/S X-Section

62. Sample ARPSView Products Downburst PotentialSurface Isotachs & Streamlines CAPE & Helicity

63. Sample ARPSView Products Lifted Index & CAP Strength Sfc Moisture Convergence and Theta-e BRN & BRN Shear

64. Sample ARPSView Products 700 mb Winds, T, and RH 500 mb Height, Vort700 mb Vert Velocity

65. Sample ARPSView Products N/S X-Section of Vert Vel and Winds N/S X-Section of RH and Winds Montgomery Stream Function and Winds on 320K Isentropic Sfc

66. Sample ARPSView Products Sounding and HodographMeteogram

67. 3-4 December 1998 24 h Eta Valid 00Z 4 Dec 98 9 h RUC Valid 00Z 4 Dec 98

68. 3-4 December 1998 KTLX 00Z 4 Dec 98 KFWS 00Z 4 Dec 98

69. 3-4 December 1998 ARPS 4 h Forecast CREF (9 km) Valid 00Z 4 Dec 98 KFWS 00Z 4 Dec 98

70. 3-4 December 1998 ARPS 12 h Accumulated Precipitation (27 km) Valid 12Z 4 Dec 98 Observed 24-hour Accumulated Precip (Valid 12Z 4 Dec 98)

71. 3-4 December 1998 ARPS 6 h Accumulated Precipitation (9 km) Valid 02Z 4 Dec 98 Observed 24-hour Accumulated Precip (Valid 12Z 4 Dec 98)

72. 23 December 1998 NORTH TEXAS FORECAST DISCUSSION NATIONAL WEATHER SERVICE FORT WORTH TX 935 PM CST TUE DEC 22 1998... HOW ABOUT THE WINTER STORM WARNING?: MY CONFIDENCE IN IT IS LOW...BUT NOT LOW ENOUGH TO CANCEL IT...GIVEN THAT MOST OF THE PRECIP HAS YET TO DEVELOP (CEILINGS ARE BEGINNING TO DECREASE AT DFW...WHICH IS A FAVORABLE TREND FOR PRECIP). THE ADVISORY LOOKS OK...AS MOST OF THE PRECIP WILL BE FREEZING RAIN/SLEET...AND LIGHT PRECIP COULD CAUSE WIDESPREAD ROAD PROBLEMS. THUS...WE WILL KEEP THE ADVYS AS IS...AND KEEP THE WARNING...POSSIBLY EVEN EXPANDING IT TO INCLUDE THE EXTREME SOUTHEAST COUNTIES THAT ADJOIN THE WINTER STORM WARNING AREA THAT HOUSTON HAS GOING. ANOTHER COMMENT: MESOSCALE MODELS ARE NOT HELPING MUCH IN THIS TOUGH SITUATION...AS THEY SHOW RATHER DISPARATE SOLUTIONS. THE RUC II SAYS "NON-EVENT", THE ARPS SHOWS PRECIP STREAKING DIRECTLY ACROSS DFW...AND THE SYNOPTIC ETA SAYS NORTHEAST TEXAS!

73. 23 December 1998 3 h RUC Valid 12Z 23 Dec 98

74. 23 December 1998 12Z Surface Obs REUTERS. At least 10 people died in road accidents in Texas Wednesday as storms brought widespread ice and wreaked havoc on routes to Dallas from San Antonio, 250 miles southwest, officials said. Two people died when 59 cars crashed in two pile-ups on an Austin highway as snow and rain combined with freezing temperatures, said Austin Police Department spokeswoman Tracy Karol.Hundreds of flights were canceled at Dallas-Fort Worth International Airport (DFW). "We're having definite problems at DFW today. Our best guess is that...we'll be operating approximately 50 percent of our flights,'' said American Airlines spokesman Tim Smith. The U.S. carrier usually has 525 flights a day leaving the airport, its main hub, and a similar number of arrivals, he added. Extensive de-icing of planes had slowed schedules, while road conditions prevented employees reaching work.

75. 23 December 1998 ARPS 6 h Forecast Explicit (left) and Conditional (right) Precipitation Type (27 km) Valid 12Z 23 Dec 98

76. 1999 Special Operational Period 5-Member, 30 km Ensemble 9 km 3 km WSR-88D Base Data Being Ingested WSR-88D Base Data Pending

77. 6 January 1999 GOES Visible Image 1745Z, 6 Jan 99 ARPS 12 h Forecast Visibility (27 km) Valid 18Z, 6 Jan 99

78. 6 February 1999 - Bust! Fort Worth Radar at 00Z Sunday, 7 Feb 1999 ARPS 4-hour Forecast Reflectivity (9 km grid) Valid 00Z Sunday, 7 Feb 1999

79. 1 May 1999 Radar Valid 1930 Z Saturday, 1 May 1999 NWS RUC Model Forecast Valid 21 Z Saturday, 1 May 1999

80. 1 May 1999 Radar (1930 Z Saturday, 1 May 1999) ARPS 9 km CREF Forecast Valid 20 Z Saturday, 1 May 1999

81. ARPS 32 km Forecast - AR Tornadoes Radar (Tornadoes in Arkansas) ARPS 12-hour, 32 km Resolution Forecast CREF Valid at 00Z on 1/22/99 Proprietary Radar

82. ARPS 9km Forecast - AR Tornadoes Radar (Tornadoes in Arkansas) ARPS 6-hour, 9 km Forecast CREF Valid at 00Z on 1/22/99 Proprietary Radar

83. ARPS 3km Forecast - AR Tornadoes Weather Channel Radar at 2343 Z ARPS 6-hour, 3 km Forecast CREF Valid at 00Z

84. ARPS 3km Forecast - AR Tornadoes ARPS 6-hour, 3 km (E/W x-section) Forecast Reflectivity and Cld/Ice Valid at 00Z

85. 3 May 1999 Oklahoma Tornadoes KTLX CREF 00 Z on Tuesday, 4 May 1999 (7 pm CDT on 3 May) ARPS 9 km CREF Forecast Valid 00 Z Tuesday, 4 May 1999

86. 3 May 1999 Oklahoma Tornadoes KTLX CREF 06 Z on Tuesday, 4 May 1999 (1 am CDT on 4 May) ARPS 4-hour 9 km CREF Forecast Valid 06 Z Tuesday, 4 May 1999

87. 9-10 May 1999 Composite Radar Valid 0344 Z on Monday, 10 May 1999 ARPS 4-hour, 3 km CREF Forecast Valid 04 Z Monday, 10 May 1999

88. How Good are the Forecasts? ForecastVerification 40 km for 6 Hour Forecast D/FW Airport

89. How Good Are the Forecasts?

90. n Traditional skill measures (e.g., threat score or “overlap” agreement) not appropriate for intermittent storm-scale phenomena n SPC concern is the specific character of storms (intensity, motion, initiation, decay); precipitation is less of a concern n We forecast more things than we can observe/verify (how to verify 500 mb height fields that contain thunderstorms?) n Point verification is rather meaningless The Issues

91. n Phase-shifting verification –maximize spatial correlation –generates a shift vector n Qualitative (by hand) verification –location, speed, timing, duration, intensity, orientation, mode –“With 4 hours of lead time, the location of storms was within 30 km of observed 80% of the time” –“The model predicted storms 10% of the time when none were observed” n Seeking to create a unified approach n Will eventually have to consider cost-benefit and reliability Approaches

92. n Storm-scale models are not reflectivity generators, yet reflectivity is what we’re used to seeing! –Must be careful not to focus on the final outcome –Forecasters not used to seeing storms on a 500 mb map! –Even when reflectivity is incorrect, many other features may be accurate n Fine resolution –means thinking across many more scales of motion –gives more detail but also greater uncertainty and sensitivity (e.g., caps, outflow boundaries) n Forecasters easily overwhelmed by zillions of new products –must determine what’s really needed and useful n More experience needed with ensemble output Lessons Learned

93. Ensemble Forecasting n The Need –Small errors in numerical weather forecasts can grow quickly and render the solution indistinguishable from a randomly chosen forecast at some later time –Errors are unavoidable: observations, models, understanding –We desire to predict forecast uncertainty as well as the weather

94. Ensemble Forecasting n Strategy –In addition to a control forecast, create a number of other forecasts whose initial conditions are equally plausible but differ slightly from those of the control –Ensemble averaging acts as a non-linear filter to smooth out the unpredictable components of the flow

97. Ensemble Forecasting n Advantages –Ensemble mean is generally superior to control forecast –Ensembles provide n a measure of expected skill or confidence n a quantitative basis for probabilistic forecasting n a rational framework for forecast verification n information for targeted observations

98. Ensemble Forecasting n Limitations/Challenges –Not clear how to optimally specify the initial conditions (singular vectors, breeding, perturbed observations) –Requires more computer resources

99. n Collaborative effort among CAPS, NCAR, AFWA, NCEP and NSSL n Performed during May, 1998 n Goal: Examine the value of coarse-resolution, multi- model ensemble forecasts versus single high-resolution deterministic forecasts n Expose operational forecasters to both types of output Storm and Mesoscale Ensemble Experiment (SAMEX)

100. SAMEX Domains

103. 3-hour Accumulated Precipitation 25-Member Ensemble POP > 0.1 inches/hour

104. Explicit 9 km Prediction 3-hour Accumulated Precipitation 9 km, 15-hour ARPS Forecast Reflectivity

105. 500 mb Errors

107. n Storm-scale NWP is a significant scientific and technological challenge n Predictability appears plausible at storm scales n More work needed in –data assimilation, especially from satellite, GPS, WSR-88D –physics parameterizations (especially cloud microphysics, radiation, and land-atmosphere exchanges) –fundamental predictability and sensitivity n Transition to operations will be a major challenge –centralized versus distributed? –verification techniques –creation of useful products –forecaster interpretation and utilization n NWS FO involvement in R&D will be critical Summary