1. Diabatic Mesomodel Initialization Using LAPS - an effort to generate accurate short term QPF By John McGinley, NOAA Forecast Systems Lab With contributors Steve Albers, John Smart, Paul Schultz FSL BrentShaw, Weather News Intl And Gou-Ji Jian, Jen-Hsin Teng, Li-Hui Tai, CWB

2. Overview  Brief review of LAPS and Hot Start  LAPS/ Model Deployments in Sub-tropics  Brief Case Studies  Verification  Ensemble Applications  Probabilistic QPF  Future Work  Blending QPE/QPF

3. Our mission at FSL is short range high resolution forecasting. For many customers the first 12 hours is very critical. We needed an approach that allows rapid spin-up of clouds and precipitation. Useful elements to work with: 1.LAPS cloud and moisture analysis (Water in All Phases: WIAP) 2.Variational balancing scheme 3.Coupled Mesoscale Models: RAMS, MM5, WRF

4. The LAPS Diabatic Initialization Technique  LAPS includes:  Cloud analysis  Dynamic balance using variational scheme  Coupling to most mesoscale models  Links to display devices (AWIPS, others)  Sustain the “operational” tradition of LAPS  Robust data ingest, QC, and fusion  Platform and model independence  Computationally inexpensive

5. Key element: Water-In-All- Phases Analysis  Cloud Analysis – satellite, radar, surface, aircraft observations  Water Vapor Analysis – variational method  Recovery of microphysical variables using simple cloud model: Precipitation Total precipitable water Precipitation Total precipitable water Water vapor Integrated liquid water Water vapor Integrated liquid water Cloud water Cloud cover Cloud water Cloud cover Cloud Ice Ice Crystals Cloud Ice Ice Crystals

6. Cloud Analysis Scheme  Uses satellite Visible and IR  Aircraft observations  Surface observations  Radar  Interpolates cloud obs to grid with SCM  Cloud feeds back into water vapor analysis

8. Updated CWB/ FSL scheme (cloud derive subr) CB CS Downdrafts in stratiform region Less dependence on cloud type, Updraft goes to top of cloud Randomness in broad convective regions Strongest updrafts in regions of high reflectivity

9. LAPS Diabatic Initialization Cloud Analysis Univariate Data Fusion 3DVAR Dynamic Constraint LAPSPREP NWP System LAPSPOST Surface RAOB Sat ACARS GPS Radar(Vr) Profilers Radar(Z) Sat Aircraft METAR NWP FG Data Ingest/Quality Control National NWP LBC LSM IC Native Output Forecaster Isobaric Output T, , p, u, v, , RH T,  q c q i, q r q s, q g cc T, , p, u, v, RH Constraints: Mass Continuity u/v Time Tendencies Background Error Observation Error Adjust for Model: Hydrometeor Concen. Saturation Condition

10. Equations ( ) b are background quantities; (^) are solution increments from background; ( )’ are observation differences from background Dynamic Balancing and Continuity Formalism

11. Cloud, Wind and Mass Dynamic Adjustment wcwc FHFLFHFL T> 0 ^ q= q s

12. Figure 3. Cloud liquid (shaded), vertical velocity (contours) and cross-section streamlines for analyses (right) and 5-min forecasts (left). The top pair shows LAPS hot-start DI with upward vertical motions where clouds are diagnosed and properly sustained cloud and vertical motions in the forecast; the bottom pair demonstrates the artificial downdraft that usually results from simply injecting cloud liquid into a model initialization without supporting updrafts or saturation. Note that cloud liquid at the top of the updraft shown in the hot-started forecast (above right) has converted to cloud ice. Hot Start Cloud only Cold start 0-Hr 5 min forecast

13. Balanced FieldUnbalanced Field MODEL NOISE |dp/dt |

14. Difficult problem: Heavy Rain in the Subtropics - Dominican Republic and Haiti, May 2004

15. LAPS GUI – Global locatibility

16. Window MM5 Forecast for Dominican Republic/ Haiti Flash Floods 00Z May 22, 2004

17. TRMM Rainfall Estimates May 18-25, 2004 (NASA Goddard)

18. Taiwan CWB Short Term Forecast System LAPS ( Local Analysis and Prediction System ) Diabatic Initialization technique Hot-Start MM5

19. Forecast domains & Computational requirement 1km (169*151) 1368 km ( 153 points) 1260 km ( 141 points) 151 pts 9km 3km CPUs42 compaq 833 MHz Need 1.5hrs for 24hrs fcst 0.00 0.05 0.10 0.15 0.20 0.40 0.35 0.30 0.25 0.71 0.68 0.65 0.62 0.58 0.54 0.50 0.45 0.92 0.90 0.88 0.86 0.83 0.80 0.77 0.74 0.99 0.98 0.97 0.96 0.94 1.00 30 Vertical layers ( σ levels)

20. WRF Run for Typhoon Mindulle using NCEP GFS/NF-15 backgrounds Cold -GFS Hot - NFS

21. TS.60,.50,.38,.35,.26 TS.53,.51,.44,.29,.26 Verification of Hot Start Forecasts for Tropical Storm Mindulle 0-6 hr forecast - Analyzed gauge data on left; forecast on right Threat scores for precip categories 1, 10, 20,50, 100, 200mm 6-12 hr forecast - Analyzed gauge data on left; forecast on right Threat scores for precip categories 1, 10, 20,50, 100, 200mm

22. 6-H Precipitation verification for 4 tropical systems in 2003 over Taiwan (Jian and McGinley, accepted by JMSJ) ○ : HOTS ● : NCLD a Hot Start Cold Start

23. 12-H Precipitation verification for 4 tropical systems over Taiwan (Jian and McGinley) ○ : HOTS ● : NCLD b Hot Start Cold Start

24. Advantage of Ensembles Ensembles produce probability forecasts that can be more useful than single deterministic forecasts Probabilistic output can be input into economic cost/lost models Customers get a “yes-no” forecast based upon skill and spread of ensemble

25. From WRF/MM5/ RAMS Ensembles, to Probabilities, to Yes/No Forecasts 6-D model grids (variable,x,y,z,t,prob) Decision Engine Cost/Loss Thresholds Yes or No Ensemble Location, Time Customer

26. MM5-Eta MM5-AVN WRF-AVN RAMS-EtaRAMS-AVNWRF-Eta 6- Member Ensemble: 3 models, 2 boundary conditions

27. MM5-Eta N WRF-Eta H H+1 H+2 H+3 H+4 H+5 Time-Phased Ensemble: an efficient way to get many members in limited computing environments Time t0t0 Ensemble at time = t 0 Time weighting is applied to each member (Number of members) = (Number of models) x (Length of Forecast) / (Start Interval) Each pair of runs Has a unique Initial condition based on LAPS

28. Radar-observed precipitation - 24 hrs ending 13 Oct 04 12GMT New approach: time-phased ensemble - model run every hour with new initial conditions - with two models, 24 members for every time

29. WRF Time phased ensemble Model Run vt 13 Oct 04 18GMT - 18 hr forecast

30. WRF Time phased ensemble Model Run vt 13 Oct 04 12GMT - 15 hr forecast

31. WRF Time phased ensemble Model Run vt 13 Oct 04 12GMT - 12 hr forecast

32. WRF Time phased ensemble Model Run vt 13 Oct 04 12GMT - 09 hr forecast

33. WRF Time phased ensemble Model Run vt 13 Oct 04 12GMT - 06 hr forecast

34. WRF Time phased ensemble Model Run vt 13 Oct 04 12GMT - 03 hr forecast

35. Ensemble Probabilities: Threshold: 5 mm/3 hr at 12 GMT 13 Oct 04 >20 >40 > 60 >80 Precipitation Probabilities %

36. Future Work  Ensemble probabilistic post-processing and coupling ensembles to decision algorithms  Merging QPE and QPF over the 0-6 HR time frame

37. Designing a Forecast/ Observation, QPE/ QPF Blending Scheme wiwi wiwi wiwi wiwi Forecasts 0H 1H 2H 3H Observations 0H 1H 2H 3H Correlations Coefficients/ Weights from Training Set Forecast Set For New Event Post- processor Optimum Forecast Set

38. Conclusions 1.The LAPS Hot Start Method has shown improvement over operational NWP output In the 0-6 hour time frame. 2. It has demonstrated improved verification of precipitation in higher categories 3.The LAPS /NWP System is capable of running on small computers in local weather offices 4.It has run in winter and summer experiments 5.It shows promise for tropical storm QPF when combined with bogussing the initial position