1. Description and Preliminary Evaluation of the Expanded UW S hort R ange E nsemble F orecast System Maj. Tony Eckel, USAF University of Washington Atmospheric Sciences Department Advisor: Prof. Cliff Mass March 2002

2. Overview UW SREF Methodology Generating New Initial Conditions Case Study Error Statistics Individual SREF Members Ensemble-Based Probability Summary

3. - Construct the initial state of the atmosphere with multiple, equally likely analyses, or initial conditions (ICs) Ensemble Forecasting Theory Frequency Initial State - From our point of view, truth is random sample from the pdf 24hr Forecast State48hr Forecast State - Let all ICs evolve to build PDF at future time (i.e., a forecast pdf) - Error growth spreads out PDF as forecast lead time increases

4. Difficult to consistently construct the “correct” analysis/forecast pdf. Errors in mean and spread result from: 1) Model error 2) Choice of ICs 3) Under sampling due to limits of computer processing Result: EF products don’t always perform the way they should. (especially a problem for SREF) Limitations of EF Frequency Initial State24hr Forecast State48hr Forecast State analysis pdf ensemble pdf

5. phase space T 48hr forecast state (core) 48hr true state Analysis pdf : Forecast pdf : 7 “independent” atmospheric analyses, the centroid, plus 7 “mirrored” ICs 15 divergent, “equally likely” solutions using the same primitive equation model, MM5 Forecast pdf 48hr forecast state (perturbation) ngp uk eta cmc gsp avn Analysis pdf cwb C ngp eta cmc avn gsp cwb uk UW SREF Methodology Overview A point in phase space completely describes an instantaneous state of the atmosphere. For a model, a point is the vector of values for all parameters (pres, temp, etc.) at all grid points at one time.

6. cmcg* Filling in the Holes of the IC Cloud STEP 1: Calculate best guess for truth (the centroid) by averaging all analyses. STEP 2: Find error vector in model phase space between one analysis and the centroid by differencing all state variables over all grid points. STEP 3: Make a new IC by mirroring that error about the centroid. cmcg C cmcg* Sea Level Pressure (mb) ~1000 km 1006 1004 1002 1000 998 996 994 cent 170°W 165°W 160°W 155°W 150°W 145°W 140°W 135°W eta ngps tcwb gasp avn ukmo cmcg

7. ICs: Analyses, Centroid, and Mirrors Strengths Good representation of analysis error Perturbations to synoptic scale disturbances Magnitude of perturbation(s) set by spread among analyses Bigger spread  Bigger perturbations Dynamically conditioned ICs Computationally affordable Weaknesses Limited by number and quality of available analyses May miss key features of analysis error Analyses must be independent (i.e., dissimilar biases) Calibration difficult; no stability since analyses may change techniques

8. CASE STUDY: Thanksgiving Day Non-Wind Event eta-MM5 Initialized: 00z, 21 Nov 2001 (Tuesday evening) 39h, valid 22 Nov 15z 42h, valid 22 Nov 18z (Thursday 7AM) (Thursday 10AM) Note: This study used only 13 ensemble members since missing gasp grids.

9. ZCZC SEANPWSEA WWUS45 KSEA 212348 URGENT - WEATHER MESSAGE NATIONAL WEATHER SERVICE SEATTLE WA 344 PM PST WED NOV 21 2001 AN INTENSE LOW PRESSURE SYSTEM WILL MOVE ALONG THE NORTH WASHINGTON COAST EARLY MORNING THANKSGIVING DAY...AND MOVE INLAND OVER THE NORTH INTERIOR OF WESTERN WASHINGTON BY MIDDAY. STRONG SOUTH WINDS WILL DEVELOP ALONG THE COAST AFTER MIDNIGHT. AS THIS SYSTEM MOVES INLAND THANKSGIVING MORNING IT HAS THE POTENTIAL TO CAUSE HIGH WINDS ACROSS THE INTERIOR OF WESTERN WASHINGTON AFTER ABOUT 8 AM....HIGH WIND WATCH FOR THURSDAY MORNING THROUGH THURSDAY EVENING REMAINS IN EFFECT...

10. Root Mean Square Error (RMSE) by Model Verification RMSE of MSLP (mb) 36km Outer Domain cmcg cmcg* avn avn* eta eta* ngps ngps* ukmo ukmo* tcwb tcwb* cent 12h 24h 36h 48h 12km Inner Domain cmcg cmcg* avn avn* eta eta* ngps ngps* ukmo ukmo* tcwb tcwb* cent

11. 00h cmcg 21 Nov 00z 00h cmcg* 21 Nov 00z 00h cent 21 Nov 00z Initialization cmcg C cmcg*

12. 36h cmcg 22 Nov 12z 36h cmcg* 22 Nov 12z 36h cent 22 Nov 12z 36 Hour Forecast 00h cent 22 Nov 12z “Verification”

13. 42h (18z, 22 Nov) mslp and sfc wind. avn* ngps* cmcg* tcwb* ukmo* eta* “Verification” cent avn ngps cmcg tcwb ukmo eta

14. Ensemble-Based Probability of Wind Speed Prob. of (sustained) Winds > 21 kt 39h, 22 Nov 15z 42h, 22 Nov 18z 39h, 22 Nov 21z (Thursday 7AM) (Thursday 1PM) (Thursday 10AM) 10% 30% 50% 10% 90%

15. Summary Set of 15 ICs for UW SREF are not optimal, but may be good enough to represent important features of analysis error The centroid may be the best bet deterministic model run, in the big picture Need further evaluation How often does the ensemble fail to capture the truth? How reliable are the probabilities? Does the ensemble dispersion represent forecast uncertainty?