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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. COMAP Symposium on NWP 20 May 1999 Boulder, Colorado. Kelvin K. Droegemeier School of Meteorology and Center for Analysis and Prediction of Storms University of Oklahoma. What Are Models Predicting?.

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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 COMAP Symposium on NWP20 May 1999Boulder, Colorado Kelvin K. DroegemeierSchool of Meteorology and Center for Analysis and Prediction of StormsUniversity of Oklahoma

  2. What Are 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 Are We Using? • 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 Would We Like 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!

  6. What Do We Need? • 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 • Probability forecasts and othermeasures of uncertainty • A single tool that integratesmodel output and observations

  7. Trends in Large-Scale Forecast Skill

  8. 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

  9. Center for Analysis and Prediction of Storms (CAPS) • One of first 11 NSF Science and Technology Centers established in 1989 • 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 • 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?

  11. 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

  12. 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

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

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

  15. Present NWS Operations

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

  22. 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!!

  23. 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

  24. 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???

  25. 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 12-hr forecast @ 10 km resolution 2-hr forecast @ 1 km resolution 6-hr forecast @ 4 km resolution

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

  27. The CAPS Advanced Regional Prediction System (ARPS)

  28. NEXRAD Doppler Radar Data

  29. Single-Doppler Velocity Retrieval (SDVR) • observe ... • One (radial) wind component • reflectivity • 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

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

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

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

  33. 5 April 1999 - Impact of Level II Data Initial 700 mb Vertical Velocity Using NIDS Initial 700 mb Vertical Velocity Using Level II Data and SDVR 12 Z Reflectivity

  34. 5 April 1999 - Impact of Level II 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

  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

  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. CRAFT Phase II

  39. Regional Collection Concept Must await open-RPG

  40. Abilene Network 1999 Network - All Participants Seattle Eugene Minneapolis Westfield New York New Haven Cleveland Newark Detroit Trenton Salt Lake City Philadelphia Wilmington Pittsburgh Columbus Sacramento Lincoln Indianapolis Washington Oakland Denver Kansas City Raleigh Albuquerque Oklahoma City Nashville Los Angeles Atlanta Anaheim Dallas Phoenix Abilene Router Node Access Node Directly Connected Participant New Orleans Houston GigaPop Connected Participant Any color GigaPoPs UW Pacific North West OARnet Miami OneNet CENIC Pittsburgh (CMU) Texas Westnet MREN MCNC 33 Total Access Points Serving 64 Members Great Plains MERIT MAX MAGPI

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

  42. GOES Satellite Data

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

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

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

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

  47. 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

  48. High-Density Surface Networks

  49. Commercial Aircraft Wind and Temperature Observations

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