Paul Schultz NOAA Forecast Systems Laboratory Local Analysis and Prediction Branch

1. The LAPS “hot start”Initializing mesoscale forecast models with active cloud and precipitation processes Paul Schultz NOAA Forecast Systems Laboratory Local Analysis and Prediction Branch

2. The LAPS team • John McGinley, branch chief, variational methods • Paul Schultz, project manager, modeler, your speaker today • Brent Shaw, most recent lead modeler • Steve Albers, cloud analysis, temp/wind analysis • Dan Birkenheuer, humidity analysis • John Smart, everything

3. Goals • Address NWP “spin up” problem • Explicit short-range (0-6 h) QPFs and cloud forecasts • Focus on a “local” modeling capability • Must be computationally inexpensive • Exploit all locally-available meteorological data • High-resolution grids • Robust data ingest, QC, and fusion • Develop a flexible solution for easy technology transfer • Hardware/OS independence • Choice of mesoscale model • Demonstrated in WRF, MM5, RAMS • COAMPS, ARPS, NMM?

4. Basis • Scale analysis of thermodynamic energy equation appropriate for convective-scale motions strongly suggests latent heat release forces the action • Put saturated updrafts where they belong • Relax 3-D horizontal divergence to support updrafts

5. LAPS Three-Dimensional Cloud Analysis Satellites Pilot reports METAR METAR METAR Doppler radar

6. Cloud typing “stable” “unstable”

7. Example cloud type analysis

8. Cumulus vertical motions

9. LAPS Dynamic Balance Adjustment ( ) b are background quantities; (^) are solution increments from background; ( )’ are observation differences from background

10. LAPS Dynamic Balance Adjustment FH FL

11. Results 3D Simulated Clouds 00Hr Fcst, Valid 28 Mar 01/00Z 01Hr Fcst, Valid 28 Mar 01/00Z

12. Example: first forecast hour, 5-min frames hot start loop

13. MODEL NOISE |dp/dt| Unbalanced Balanced

14. Quantitative Assessment • Comparison of parallel model runs using three kinds of initialization (hot, warm, cold); otherwise identical • Objective verification of model performance using hot start vs. other initialization methods • Approximately 40 forecast cycles during Jan 2001 • Gridded comparisons using LAPS analysis as truth • Computed various threat scores, RMSE, etc.

15. Model Initialization Comparisons Time-n Time Cold start MM5 Forecast no LAPS analysis; interpolate from larger-scale model Eta Warm start LAPS Analyses pre-forecast nudging to a series of LAPS analyses; sometimes called dynamic initialization MM5 Nudging MM5 Forecast Hot start MM5 Forecast diabatic initialization using the balanced LAPS analysis LAPS II Dynamically balanced, Cloud-consistent LAPS Eta LBC for all runs

16. Results of Initialization Comparisons

17. Results of Initialization Comparison

18. Example – 21 June 2001/0600 UTC Run MM5 00 hr Forecast, Valid 21/0600 UTC GOES IR+NOWRAD, Valid 21/0600 UTC

19. Example – 21 June 2001/0600 UTC Run MM5 01 hr Forecast, Valid 21/0700 UTC GOES IR+NOWRAD, Valid 21/0700 UTC

20. Example – 21 June 2001/0600 UTC Run MM5 02 hr Forecast, Valid 21/0800 UTC GOES IR+NOWRAD, Valid 21/0800 UTC

21. Example – 21 June 2001/0600 UTC Run MM5 03 hr Forecast, Valid 21/0900 UTC GOES IR+NOWRAD, Valid 21/0900 UTC

22. Example – 21 June 2001/0600 UTC Run MM5 04 hr Forecast, Valid 21/1000 UTC GOES IR+NOWRAD, Valid 21/1000 UTC

23. Example – 21 June 2001/0600 UTC Run MM5 05 hr Forecast, Valid 21/1100 UTC GOES IR+NOWRAD, Valid 21/1100 UTC