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Statistical Evaluation of TAMDAR Data in Short-Range Mesoscale Models

This study evaluates the impact of increasing model vertical resolution on forecast accuracy using TAMDAR data for two weather events. Results show improved forecast skill when TAMDAR data is properly utilized.

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Statistical Evaluation of TAMDAR Data in Short-Range Mesoscale Models

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  1. A STATISTICAL EVALUATION OF TAMDAR DATA IN SHORT-RANGE MESOSCALE NUMERICAL MODELS Neil A. Jacobs1 and Yubao Liu2 1AirDat, LLC, Morrisville, NC 27560 2National Center for Atmospheric Research, Boulder, CO 80307

  2. Vertical Resolution Case Studies Hypothesis: Increasing the number of model -levels in the lower to mid-troposphere will better utilize the greater observation density provided by TAMDARs, and result in a more accurate forecast.

  3. Great Lakes late-season snow event (April 22-25, 2005) Model Description: RT-FDDA  First-guess field Little_R (MM5 Objective analysis)  INTERPF  MM5 Domains 1/2 (36-km/12-km) Grell CP, MRF PBL, Mixed-phase (Reisner-1) microphysics 4 Simulations (96 h…was 14 h): “TAMDAR” (36 -levels) “Cntl” No TAMDAR (36 -levels…same as “TAMDAR”) “TAMDAR+” (48 -levels: 6  1.5 km / all 12  5.5 km) “Cntl+” No TAMDAR (48 -levels…same as “TAMDAR+”) Initialized 1100 UTC 22 April 2005

  4. TAMDAR Cntl TAMDAR+ Cntl+ Stage IV (4 km) 1-h precipitation forecast (mm) and sea-level pressure (mb), as well as the 1-h Stage-IV analysis (mm), valid 1200 UTC 22 April 2005.

  5. Cntl / Cntl+ Scatter plot of 1-h QPF totals versus the 1-h Stage-IV analysis comparing matching grid point magnitudes (above 5 mm threshold) summed over each of the 14 forecast hours. The Cntl is blue, and the Cntl+ is red. TAMDAR / TAMDAR+ Scatter plot of 1-h QPF totals versus the 1-h Stage-IV analysis comparing matching grid point magnitudes (above 5 mm threshold) summed over each of the 14 forecast hours. The TAMDAR is blue, and the TAMDAR+ is red.

  6. Precipitation cell isolation: who cares? A crude method to quantify QPF performance We can isolate cells based on magnitude, and retain only the precipitation associated with that cell Laplacian edge detection: Can also detect noise, so a smoothing Gaussian filter was tested…Laplacian of Gaussian: • Both methods yield near-identical results • All forecasts are regridded to 12-km • An assumption is made that the closest cell (radial search) was the predicted cell. • A weighted score was applied to the magnitude of the cell based on the linear distance • (to the maximum) from “truth” (Stage-IV). • For example, Stage-IV compared against itself would receive full weight. A score of 0 • would mean either no cell was detected, or the distance was > 2(dm_cell+ds4_cell).

  7. Total 5-mm 10-mm 15-mm 20-mm EXAMPLE: Raw Stage-IV 3-h accumulated precipitation data (Total), and the postprocessed (no minimum) isolated cells. The domain-2 data are mapped on the x-y grid.

  8. Comparison of 12-h (4x3-h) QPF between TAMDAR+, TAMDAR, Cntl+, Cntl, and other various models regridded to the smallest grid (12-km), as well as the Stage-IV analysis (“truth”) for 20-mm cells with 2-mm minimum bound. The results presented here are consistent with preliminary findings from similar studies conducted at NCAR.

  9. Results suggest that the addition of TAMDAR data in conjunction with increased vertical resolution improves the forecast skill for certain output parameters. • GFS, NAM, and RUC were included as “reference” models, and the improvement of the Cntl over these models is attributed to the 4DVAR ingestion technique of the RT-FDDA system. • However, the TAMDAR+ run shows significant improvements of 18-22% over the TAMDAR, Cntl, and the Cntl+ for this case. • This suggests that proper utilization of TAMDAR data plays a crucial role in forecast skill.

  10. Hurricane Katrina (August 29, 2005) Model Description: RT-FDDA  First-guess field Little_R (MM5 Objective analysis)  INTERPF  MM5 Domains 1/2 (36-km/12-km) Grell CP, MRF PBL, Mixed-phase (Reisner-1) microphysics 4 Simulations (96 h…was 14 h): “TAMDAR” (36 -levels) “Cntl” No TAMDAR (36 -levels…same as “TAMDAR”) “TAMDAR+” (48 -levels: 6  1.5 km / all 12  5.5 km) “Cntl+” No TAMDAR (48 -levels…same as “TAMDAR+”) Initialized 2300 UTC 29 August 2005

  11. TAMDAR+ Cntl+ Stage-IV Sea-level pressure (mb) and 1-h precip. (in) Valid 0600 UTC 30 AUG 2005 (7-h Fcst)

  12. TAMDAR+ Cntl+ Stage-IV Sea-level pressure (mb) and 1-h precip. (in) Valid 0900 UTC 30 AUG 2005 (10-h Fcst)

  13. TAMDAR+ Cntl+ Stage-IV Sea-level pressure (mb) and 1-h precip. (in) Valid 1200 UTC 30 AUG 2005 (13-h Fcst)

  14. TAMDAR+ Cntl+ Stage-IV Sea-level pressure (mb) and 1-h precip. (in) Valid 1500 UTC 30 AUG 2005 (16-h Fcst)

  15. TAMDAR+ Cntl+ Stage-IV Sea-level pressure (mb) and 1-h precip. (in) Valid 1800 UTC 30 AUG 2005 (19-h Fcst) Precipitation bands TAMDAR+ is 4 mb deeper

  16. 850-hPa Relative Humidity (%) Valid 1800 UTC 30 AUG 2005 (19-h Fcst) TAMDAR+ Cntl+

  17. 850-hPa Relative Humidity (%) Valid 0600 UTC 30 AUG 2005 (7-h Fcst) TAMDAR+ Cntl+

  18. 850-hPa RH Analysis Difference TAMDAR+ minus Cntl+ 2300 UTC 29 AUG 2005 Regions of RH responsible for future band formation From outer 36-km grid 850-hPa T Analysis Difference TAMDAR+ minus Cntl+ 2300 UTC 29 AUG 2005

  19. RAOB verification of RH band (case 2) is tough because of the space-time void. TAMDAR was meant to fill this void, but verification against itself is a last choice. Grell CP scheme trigger function is dependent on saturation (or near saturation) of moisture fields. Minor differences in magnitude that exist near the CP scheme’s trigger threshold can tip the scales in a huge way hours later, which can be good or bad. Thus, proper assimilation of accurate data is key!

  20. Why was the cyclone in TAMDAR+ 4 mb deeper when increased RH was the only difference seen in the analysis? An increase in lower-tropospheric PV seen in the TAMDAR+ run appears to be linked to latent heat release from the precipitation bands around the cyclone (e.g., Bretherton 1966). Preliminary findings suggest that the majority of geopotential height difference can be attributed to this additional PV.

  21. Precipitation Forecast Comparison 46 cases (22 March 2005- 4 June 2005) AIRDAT (RT-FDDA-MM5 - TAMDAR) AIRNOT (RT-FDDA-MM5 - no TAMDAR) RUC, NAM, GFS Stage-IV "truth" Originally 49 cases, but 3 cases in May omitted based on initialization errors.

  22. NCAR verification 0.2 to 15-mm threshold Object-oriented verification technique Developed by Barbara Brown et al. (NCAR), and presented at previous GLFE. All 49 cases…3 May outliers not removed GFS not shown

  23. Improvement of QPF accuracy for short-range severe precipitation • 8-11% - Control RT-FDDA-MM5 w/out TAMDAR (apples-to-apples) • 34-55% - RUC w/out TAMDAR (apples-to-oranges) • 71-84% - NAM (apples-to-squash) • >90% - GFS (apples-to-spaghetti) • A conservative estimate of potential improvement because… • 4-month study utilized only 36 -levels • Stage-II/IV bias adjustment typically on low side (Smith and Krajewski 1991) • Weighting/optimization of ingestion/parameterizations are still being refined • AirDat and NCAR findings are consistent despite different techniques

  24. Cold-Start-MM5 Sensitivity Tests 168 simulations on a CONUS 36-km grid 7 winter events (12 combinations) Table: 144-h average of 3-h error Error = | Forecast - ASOS* | Blackadar good in winter despite 5-layer LSM Snow cover / lack of veg.  LSM’s influence KF -> Grell may =  feedback in warm-start 12/12 combinations:  error with TAMDAR *Automated Surface Observing System (NWS/FAA/DOD)

  25. 2-m temp. error averaged for all 7 cases using KF/Blackadar Cntl (No TAMDAR)Exp (TAMDAR) • This is a trend seen in all 7 cases! • …not just an artifact of one “outlier”. • ? • Better QPF > more accurate snow cover, albedo, and/or surface radiation > long- range surface temp. impact ? • Better forecasted feedback from downstream “blocking” ? • Weird Hovmoller teleconnection ? • Lucky-7 ? Objective was to obtain CPU speed benchmark for new 3GHz dual-core

  26. Upcoming studies… • Sampling Rate Impact Study • 3 Parallel Simulations on Cold-Start MM5 • 36-km CONUS  12-km GLFE • 48 -levels • CNTL = No TAMDAR data • EXP1 = TAMDAR data at “old” original sample rate • EXP2 = TAMDAR data at “new” increased sample rate • Variable Sampling Rate ? • Based on forecasted dynamics • Weighting Studies… • Independent testing of ascent/descent • …and independent testing of RH, T, winds • Testing of radius and magnitude w.r.t. seasonal and diurnal variations • QPF Verification: Round 2 • Additional QPF verification studies, as well as other surface and • upper-level verification will be needed after OSSE / weighting.

  27. Acknowledgments Barbara Brown, Randy Bullock, and Wei Yu (NCAR’s object-based QPF verification) Stan Benjamin and William Moninger (NOAA/ERL/FSL/GSD) NCAR (OSSE computer support, etc.) NASA Aeronautics Research Office’s Aviation Safety Program FAA Aviation Weather Research Program AirDat, LLC

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