An Update on the Stony Brook University Ensemble Forecast System        Brian A. Colle, Matthew Jones, Yanluan Lin, and Joseph B. Olson Institute for Terrestrial and Planetary Sciences Stony Brook University / SUNY Jefferey S. Tongue NOAA/NWS Upton, NY

SUNY-SB Realtime MM5 Domains

NCEP grids MM5 V3.6 Web page Stony Brook Applications Collaborative Applications 60-h Deterministic Runs (00/12Z) 48-h 18-member MM5 Ensemble Wavewatch III Ocean Model (storm surge) MM5 Verification NWS AWIPS NWS-NERFC hydro model BNL- NYC Observatory Energy Load Forecasts (Eta, GFS, NCEP breds) 32-level, Grell CP (36/12 km), Simple ice, MRF PBL) Stony Brook Real-time System Computers: 4-processor Compaq ES40, 2-processor DS20, 10-node dual Athlon, 15-node dual Xeon) 6-member 12kmWRF ensemble

l Explore the benefits and weakenesses of running a multi-physics and initial condition ensemble over the Northeast at moderately high resolution (12-km grid spacing). l Learn how to use ensemble data in operations. l Determine how the various physical parameterizations in the ensemble perform during the warm and cool seasons. Ensemble Objectives

INITIAL CONDITION ENSEMBLE MATRIX (each uses Grell-MRF physical combination) PHYSICS ENSEMBLE MATRIX 18-Member MM5 Ensemble (00 UTC cycle, 36/12 km domains)

ENSEMBLE BASIC CONCEPTS 1.) Ensemble Mean In a good ensemble, “truth” looks like a member of the ensemble. Over many simulation periods, the ensemble mean outperforms individual ensemble members in terms of mean-square error (Leith 1974). Mean solution True solution Member trajectories

BASIC CONCEPTS 2.) Probabilistic forecasts If an ensemble forecast system properly captures the uncertainty, the ensemble solution will identify the possible weather outcomes, including those with low probability of occurrence, reducing the “element of surprise” (Brooks and Doswell 1993). Mean solution True solution Member trajectories

BASIC CONCEPTS 3.) Predict forecast skill (reliability) Ensemble spread should be related to ensemble-mean skill. That is, high (low) spread events correspond to low (high) forecast skill (Houtekamer 1993). High skill And confidence Low spread Low skill High spread

Model Surface Temperature Errors ● Errors in excess of 1.5ºC dominate portions of the model domain ● Model errors have a seasonal dependence ● Temperature error reverses sign at the coast. MM5 Eta Winter Summer Errors are averages for forecast hours Solid contours are positive biases, dashed contours are negative biases. Contour intervals of 0.5 ºC.

48-h mean errors (at 44004) during the cool season SLP TEMP MRF fix and TOGA COARE FLUX

5-6 December 2003 Nor-easter (the first test) 1200 UTC 12/6 surface 1200 UTC 12/6 radar

Operational Model Failure (Hour 48, valid 12/06/12Z) Eta (24-48 h pcp) MM5 (new MRF) MM5 (45-48 h pcp) GFS (24-48 h pcp) mm

MM5 Prob cat: 1> 0.01…4 =12-25 mm Eta (12-36 h pcp) OBS (12Z 27Jan04 – 12Z 28Jan04) GFS (12-36 h pcp) Darker shading = more confidence in category mm

Operational model qpf 9/18 case OBS (12Z 17Sep04 – 12Z 18Sep04) GFS (12-36 h pcp) Eta (12-36 h pcp) MM5 Prob cat: 6=53-126mm, 7 > 126 mm

1200 UTC 15 October 2003

Real-time Verification

18-member MM5 ensemble MOS using: Hourly data ● Surface temperature ● Suface relative humidity ● Sea-level pressure ● Surface wind speed ● Surface wind direction ● Precipitation Climatic data ● cosine(day of year) 6-hourly data ● LW out ● SW out ● LW down ● SW down ● PBL height ● LH flux ● SH flux ● 500 mb geopotential height ● Moisture (upper/mid/low levels) ● 850 mb temperature Equations are created for each forecast hour (1-48), for each variable, for each ensemble member, and for each spatial point of interest. Two month training period (June-July 2003).

Aug-Sept 2003 Temp MAEs Islip, NY °C forecast hour Black: raw model output control member Dashed: 6 variable mos; blue: control member MOS, green: mean MOS Solid: 18 variable mos; blue: control member MOS, green: mean MOS m/s Black: raw model output Dashed: 6 variable mos; blue: control member MOS green: mean MOS Solid: 18 variable mos; blue: control member MOS green: mean MOS Aug-Sept 2003 WSP MAEs 44008

l Running the Weather and Research Forecast (WRF) Model in real-time since summer 2004 at 36- and 12-km grid spacing. l Using Mass coordinate with 32 vertical levels. Control run uses Kain-Fritsch, simple ice (3-class) micro, Dudhia radiation, and YSU PBL. l Objectives: Compare WRF with MM5 and Eta at moderate resolution and evaluate WRF for different mesoscale weather. l A 6 member WRF ensemble has been developed by varying some ICs (Eta vs GFS), CPs (K-F vs Grell) and PBLs (YSU vs Eta). The GFS-WRF (YSU-KF) will be run at 4-km with no Conv Param.and compared with the 5-km WRF CONUS using Eta for ICs for the NCEP/FSL winter experiment. Real-time WRF Modeling

MM5 vs WRF: 2100 UTC 23 July km MM Z precip12-km WRF 18-21Z precip Both runs used Grell, MRF PBL, and simple ice

WRF (Grell and YSU PBL)WRF (KF and YSU PBL) 0000 UTC 20 October 2004 (12-km hour 48) Spurious bulls-eyes over water

WRF Grell-YSU (Conv pcp)WRF Grell (explicit pcp) 0000 UTC 20 October 2004 (12-km, hours 45-48)

Summary and Future Work An ensemble MM5 forecast system has been developed. For the evening forecast cycle, 18 separate forecasts are generated using different initializations and model physics. See The 12-km ensemble is has been expanded to include a 6-member Weather and Research Forecasting (WRF) model. Will help complement the deterministic NCEP/FSL 5-km WRF Winter Forecast Experiment over the CONUS. The ensemble system has proved to be a useful addition to the NCEP model suite. Future work will explore benefits of ensemble post-processing as well additional ensemble diversification using different initial conditions (MM5/WRF breds, NOGAPS, and CMC ICs).