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Toward Operational Convection-Allowing Ensembles over the United States

Toward Operational Convection-Allowing Ensembles over the United States. Craig Schwartz, Glen Romine, Ryan Sobash, and Kate Fossell The National Center for Atmospheric Research ensemble@ucar.edu. NCAR is sponsored by the National Science Foundation

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Toward Operational Convection-Allowing Ensembles over the United States

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  1. Toward Operational Convection-Allowing Ensembles over the United States Craig Schwartz, Glen Romine, Ryan Sobash, and Kate Fossell The National Center for Atmospheric Research ensemble@ucar.edu NCAR is sponsored by the National Science Foundation This work partially supported by NOAA Grant No. NA15OAR4590238

  2. Real-time 3-km ensemble forecasts • Since April 7, 2015, we have been producing real-time, 10-member, 48-hrensemble forecasts • 3-km horizontal grid spacing • Initialized at 0000 UTC daily www.ensemble.ucar.edu

  3. Why are we doing this work? • One of the forefronts of NWP model research is how to design high-resolution ensembles • Also verification and data visualization challenges • Demonstrate feasibility of real-time convection-allowing ensemble forecasting over large areas • The USA currently does not have an operational convection-allowing ensemble

  4. Challenge with high-resolution ensembles • How to design high-resolution ensembles? • Vary just initial conditions? • Configure different members with different physics or dynamics? • We only vary initial and boundary conditions • Single set of physics and dynamics for all members • Equal likelihood among ensemble members • Facilitates investigation of model deficiencies

  5. Components of the NCAR ensemble 1) Ensemble analysis system • Assimilate real observations every 6 hours with an ensemble Kalman filter (EnKF) • 80 ensemble members • 15-km horizontal grid spacing 2) Ensemble prediction system • 10-member, 3-km ensemble forecasts • 48-hr forecasts initialized at 0000 UTC • WRF-ARW model

  6. Continuously cycling EnKF 6-hr WRF model forecast • Continuous cycling EnKF ens mem 1 background ens mem 1 analysis … … (Members 2-79) Observations ens mem 80 background ens mem 80 analysis 6-hr WRF model forecast

  7. NCAR ensemble analysis domain 15-km 415 x 325

  8. Observations used in the analysis system Radiosonde Aircraft Satellite wind METAR Oklahoma MESONET Marine GPSRO

  9. EnKF-initialized 3-km ensemble forecasts 6-hr WRF model forecast • Dynamically consistent initial conditions Observations Downscale to 3-km and initialize forecast EnKF ens mem 1 background ens mem 1 analysis … … (Members 2-79) Downscale to 3-km and initialize forecast ens mem 80 background ens mem 80 analysis 6-hr WRF model forecast

  10. NCAR ensemble forecast domain • 48-hr, 10-member, 3-km forecasts 15-km 3-km initial conditions from downscaled 15-km analyses 3-km 1581 x 986 415 x 325

  11. How to initialize high-resolution ensembles? • Useensemble data assimilation (NCAR way) • Ensemble Kalman filter (EnKF) • Use existing operational ensembles • Cheap and easy but potential for mismatches • Add random noise to a single field • A bit ad hoc • Derive perturbations from external models and add to a single field • Potential for mismatches

  12. “Snowzilla” • East coast blizzard of January 22-24, 2016 • Forecast uncertainty about the northern extent of heavy snow

  13. Forecast initialized 0000 UTC 22 January • Ensemble mean 24-hr accumulated snow between 0000 UTC 23 – 0000 UTC 24 January 24-48-hr forecast inches

  14. Forecast initialized 1200 UTC 22 January • Ensemble mean 24-hr accumulated snow between 0000 UTC 23 – 0000 UTC 24 January 12-36-hr forecast inches

  15. Forecast initialized 0000 UTC 23 January • Ensemble mean 24-hr accumulated snow between 0000 UTC 23 – 0000 UTC 24 January 0-24-hr forecast inches

  16. “Plume diagram” for New York City • Forecast initialized 0000 UTC 22 January

  17. “Plume diagram” for New York City • Forecast initialized 0000 UTC 23 January

  18. Forecast initialized 0000 UTC 22 January • 48-hr accumulated snow between 0000 UTC 22 – 0000 UTC 24 January

  19. Probabilities of 48-hr snowfall > 1 foot within 25 miles of a point Initialized 0000 UTC 22 January Initialized 0000 UTC 23 January Probability

  20. Probabilities of 48-hr snowfall > 2 feetwithin 25 miles of a point Initialized 0000 UTC 22 January Initialized 0000 UTC 23 January Probability

  21. Simulated reflectivity > 40 dBz • Error initializing convection • 6-hr forecast Initialized 0000 UTC 22 January

  22. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period May 25, 2015 Probability

  23. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period June 20, 2015 Probability

  24. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period June 23, 2015 Probability

  25. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period Feb. 23, 2016 Probability

  26. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period Feb. 24, 2016 Probability

  27. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period July 8, 2016 Probability

  28. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period July 18, 2016 Probability

  29. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period July 25, 2016 Probability

  30. Severe weather guidance • Smoothed probabilities of the union of hail > 1 inch, wind exceeding 25 m/s, and 2-5-km updraft helicity > 75 m2/s2 within 25 miles of a point over a 24-hr period Aug. 13, 2016 Probability

  31. NCAR ensemble bias • Aggregate biases over 451 forecasts 0.25 mm/hr 0.5 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr 20.0 mm/hr

  32. NCAR ensemble ROC areas • ROC areas for 24-hr forecasts of 1-hr accumulated precipitation aggregated over 451 forecasts

  33. NCAR ensemble calibration • Attributes diagrams for 24-hr forecasts of 1-hr accumulated precipitation aggregated over 451 forecasts

  34. Seasonal forecast skill • 24-hr fractions skill scores (50-km radius of influence) between June 15, 2015 – June 15, 2016 0.25 mm/hr 1.0 mm/hr Date Date

  35. Other research activates • Compare EnKF-initialized ensemble forecasts with other, more traditional, methods of initializing high-resolution ensemble forecasts • Objective verification against National Weather Service watches and warnings • Ensemble sensitivity analysis • e.g., relate Snowzilla displacement errors to the initial conditions

  36. Toward 1-km ensembles • Fractions skill scores over 32 1-12-hr forecasts

  37. Future work • 3-km analysis on current 3-km forecast grid • More frequent analyses (at least hourly) • Assimilate radar, satellite, lightning observations • Requires substantially more computational resources • Post-processing and interpretation • New tools to interpret predictions of hazards • Calibration

  38. Documentation • Paper in Weather and Forecasting describes the system (Schwartz et al. 2015) AMS tweeted about our paper

  39. Summary • NCAR ensemble represents a glimpse of future operational systems • We have demonstrated convection-allowing ensembles are operationally feasible over the U.S. • Contact us if you’d like to collaborate or want real-time data: ensemble@ucar.edu

  40. Surrogates for tornado forecasting Observed tornadoes 2-5-km AGL updraft helicity 0-3-km AGL updraft helicity 1-km AGL relative vorticity Sobash et al. (2016), Wea. Forecasting

  41. Why ensemble forecasts are desirable • Quantification of uncertainty • Naturally produces probabilities! • Allows forecasters to forecast their “true beliefs” • Allows users to make decisions based on expected value and cost-loss scenarios • Forecasts combining information across all members are usually more skillful than single deterministic forecasts

  42. NCAR ensemble forecast domain 15-km 3-km initial conditions from downscaled 15-km analyses 3-km 1581 x 986 415 x 325

  43. NCAR ensemble forecast domain 15-km 3-km initial conditions from true 3-kmanalyses 3-km 1581 x 986 415 x 325

  44. Mean analysis increments August 2015 mean analysis increments at 0000 UTC Lowest model level temperature (K) Lowest model level water vapor (g/kg) Pattern not geographically uniform

  45. Mean analysis increments December 2015 mean analysis increments at 0000 UTC Lowest model level temperature (K) Lowest model level water vapor (g/kg) Different characteristics compared to summer

  46. Why ensembles vs. deterministic forecasts? Ensemble probabilistic forecast skill exceeds performance of deterministic forecasts from the same prediction system

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