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Meso- and Storm-Scale NWP: Scientific and Operational Challenges for the Next Decade

Meso- and Storm-Scale NWP: Scientific and Operational Challenges for the Next Decade. COMET Faculty Course on NWP 9 June 1999 Boulder, Colorado. Kelvin K. Droegemeier School of Meteorology and Center for Analysis and Prediction of Storms University of Oklahoma.

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Meso- and Storm-Scale NWP: Scientific and Operational Challenges for the Next Decade

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  1. Meso- and Storm-Scale NWP:Scientific and Operational Challenges for the Next Decade COMET Faculty Course on NWP9 June 1999Boulder, Colorado Kelvin K. DroegemeierSchool of Meteorology and Center for Analysis and Prediction of StormsUniversity of Oklahoma

  2. What Are Operational Models Predicting? • Global and synoptic flow patterns • Precipitation via crude parameterizations that are unable to resolve individual clouds • Topographic forcing • Coastal and lakeinfluences • Crude linkagesbetween the landsurface andatmosphere

  3. What Do Forecasters Use? • Single forecasts • Output frequency of 3 to 12 hours • Accumulated precipitation and other traditional fields • Graphical overlays of model, radar, satellite GETTING THIS FROM THIS

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

  5. Why? • Local high-impact weather causes economic losses in the US that average $300 M per week • Over 10% of the $7 trillion US economy is impacted each year • Commercial aviation losses are $1-2 B per year (one diverted flight costs $150K) • Agriculture losses exceed $10 B/year • Other industries (power utilities, surface transport) • About 50% of the loss is preventable! Pielke Jr. (1997)

  6. What is Needed? • Models that • run at high spatial resolution (1-3 km) • utilize high-resolution observations (e.g., from theWSR-88D network) • handle terrain well • represent important physicalprocesses, especially microphysicsand land-surface interactions • Physical/theoretical understanding • Tools for integrating modeloutput, observations

  7. Role of the University Community • Educating students about NWP -- a whole new ballgame! • Physical processes • Data sets & observing platforms • Numerical models & methods • Data assimilation & predictability • Research in all facets of NWP • Running models in in real time • More than 25 universities do this today! • Major change from 20 years ago! • Academia is driving operational NWP • Collecting data • GPS, WSR-88D, other

  8. Trends in Large-Scale Forecast Skill

  9. Predictability: Hitting the Wall • 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 • 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 km 30 km 30 km 10 km 10 km 2 km

  10. Center for Analysis and Prediction of Storms (CAPS) • One of first 11 NSF Science and Technology Centers established in 1989 • STCs were designed to attack problems of fundamental research that eventually would yield important benefits to society • Mission of CAPS: 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

  11. The Key Scientific Questions • Can value be added to present-day NWP and radar-based nowcasting by storm-resolving models? • Which storm-scale events are most predictable, and will fine-scale details enhance or reduce predictability? • What physics is required, and do we understand it well enough for practical application? • 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? • What networking and computational infrastructures are needed to support high-resolution NWP? • How can useful decision making information be generated from forecast model output?

  12. Prediction Targets • Somewhat problematic • 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 • Initiation • Movement • Intensity • Duration

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

  14. Storm-scale NWP • The prediction of explicit updraft/downdrafts and related features (e.g., gust fronts, meso-cyclones) NEXRAD Radar Observations ARPS 90 min Forecast (3 km)

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

  16. Present NWS Operations

  17. NWS Forecast Offices

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

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

  20. The Future of Operational NWP??

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

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

  23. Large-scale: Dynamics and predictability limits are fairly well understood; model physics and numerics are reasonably mature • 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) • Large-scale: Conventional data assimilation techniques work well; large-scale features evolve slowly • 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???

  24. Large-scale: Computing power reasonably sufficient • Small-scale: Need 100 to 1000 times more computing power than is now available commercially • Large-scale: No lateral boundary conditions to worry about for global and hemispheric models • Small-scale: Lateral boundaries in limited-area models exert a tremendous influence on the solution; compromise between high spatial resolution and domain size

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

  26. The CAPS Advanced Regional Prediction System (ARPS)

  27. NEXRAD Doppler Radar Data

  28. Single-Doppler Velocity Retrieval (SDVR) real wind • We observe ... • one (radial) wind component • reflectivity • We need ... • 3 wind components • temperature • humidity • pressure • water substance (6-10 fields) • SDVR solves the inverse problem • control theory (adjoint), simpler methods • computationally very intensive observed component

  29. Sample SDVR Result Dual-Doppler SDVR-Retrieved Weygandt (1998)

  30. Sample SDVR Result Dual-Doppler SDVR-Retrieved Weygandt (1998)

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

  32. 5 April 1999 - Impact of Radar Data Initial 700 mb Vertical Velocity Using NIDS Initial 700 mb Vertical Velocity Using Level II Data and SDVR 12 Z Reflectivity

  33. 5 April 1999 - Impact of Radar Data 3 hr ARPS CREF Forecast (9 km) Using NIDS Data Valid 15Z 3 hr ARPS CREF Forecast (9 km) Using Level II Data and SDVR Valid 15Z 15 Z Reflectivity

  34. The Lahoma, OK Hailstorm Conway et al. (1996)

  35. Availability of Base Data • 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 • 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 • No national strategy yet exists for the real time collection and distribution of Level II data • An example of universities leading the way!!

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

  37. CRAFT Phase I

  38. Regional Collection Concept Must await open-RPG Great opportunity for universities!

  39. The CAPS Vision

  40. Real Time Testing • 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 • Over 25 groups around the US are running models in real time in collaboration with NWS Offices or NCEP Centers; few are assimilating observations

  41. CAPS’ Real Time Testing • Daily operational forecasts with full-physics at spatial resolutions down to 3 km • 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 • More than 2000 products produced each hour and posted on the web (http://hubcaps.ou.edu) • Execution on the 256-node Origin 2000 at NCSA

  42. ARPSView Decision Support System

  43. 1999 Special Operational Period 5-Member, 30 km Ensemble 9 km 3 km WSR-88D Base Data Being Ingested WSR-88D Base Data Pending

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

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

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

  47. 6 January 1999 ARPS 12 h Forecast Visibility (27 km) Valid 18Z, 6 Jan 99 GOES Visible Image 1745Z, 6 Jan 99

  48. 9-10 May 1999 NCEP Eta 12-hour Forecast Valid 00 Z Monday, 10 May 1999 Composite Radar Valid 2347 Z on Sunday, 9 May 1999

  49. 9-10 May 1999 ARPS 4-hour, 3 km CREF Forecast Valid 04 Z Monday, 10 May 1999 Composite Radar Valid 0344 Z on Monday, 10 May 1999

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