WRF-RTFDDA Optimization and Wind Farm Data Assimilation
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1 2009, University Corporation for Atmospheric Research. All rights reserved. WRF-RTFDDA Optimization and Wind Farm Data Assimilation William Y.Y. Cheng, Yubao Liu, Yuewei Liu, and Gregory Roux NCAR/Research Application Laboratory Acknowledgements: Luca Delle Monache, Tom Warner, Becky Ruttenberg, John Exby and Brice Lambi NATIONAL CENTER FOR ATMOSPHERIC RESEARCH
2 Outline 1) Motivation 2) Impact of WRF Model Physics 3) Assimilation of Wind Farm Meteorological Data 4) On-going R&D 2010, University Corporation for Atmospheric Research. All rights reserved.
3 Motivation Errors in limited-area NWP model forecasts Imperfect model physics and numerics (finite difference) initial conditions lateral boundary conditions surface boundary conditions Goal: to optimize model performance by focusing on model physics and initial conditions Sensitivity of various WRF physics and the best combination of physics options, Improve DA by using wind farm meteorological data.
4 Impact of WRF Physics: Cases Dec Ramp-Up Case Jun Ramp-Up Case Focused on ramp-up in XXXX Wind Farm (~300 MW).
5 WRF Model Physics in Two Weather Cases Focused on wind speed forecast in XXXX wind farm Winter ramp-up: fronts/cyclone Dec 13-14, 2008 case Physics sensitivity: PBL/surface layer schemes Land surface model Summer ramp-up: convective storm Jun 26-27, 2009 case Physics sensitivity: Cumulus parameterization schemes (CPSs) Cloud microphysics
6 Impact of WRF Model Physics: Setup d01 d02 d03 x = 30 km First guess + 3-hourly BC: GFS003 x = 10 km x = 3.3 km Data ingest : MADIS, ACARS, WMO Mesonet, wind profilers d01: 127 X 113 X 36 d02: 252 X 231 X 36 d03: 540 X 570 X 36
7 WRF Physics Sensitivity Experiments for Winter Case surface land PBL µphysics cumulus layer surface scheme param scheme model (d01 & d02) CTRL M-O Noah YSU Lin Kain-Fritsch RUC M-O RUC YSU Lin Kain-Fritsch MYJ M-O (Janjic) Noah MYJ TKE Lin Kain-Fritsch QNSE QNSE Noah QNSE Lin Kain-Fritsch MYNN2.5 MYNN Noah MYNN2.5 Lin Kain-Fritsch MYNN3 MYNN Noah MYNN3 Lin Kain-Fritsch Winter cases focused on land surface and PBL physics.
8 WRF Physics Sensitivity Experiments for Summer Case surface land PBL µphysics cumulus layer surface scheme param scheme model (d01/d02) CTRL M-O Noah YSU Lin Kain-Fritsch BMJ M-O Noah YSU Lin BMJ GD M-O Noah YSU Lin Grell-Devenyi MORR M-O Noah YSU Morrison Kain-Fritsch THOM M-O Noah YSU Thompson Kain-Fritsch WDM6 M-O Noah YSU WDM6 Kain-Fritsch WSM6 M-O Noah YSU WSM6 Kain-Fritsch Summer cases focused on moist physics.
9 Impact of WRF Model Physics: Winter Case turbine-avg wind speed (m/s) OBS CTRL RUC MYJ QNSE MYNN 2.5 MYNN 3 Cedar Creek Dec Dec
10 Comparison of Error Statistics Dec , 2008 Case XX farm averaged wind speed BE (m/s) XX farm averaged wind speed MAE (m/s)
11 Impact of WRF Model Physics: Summer Case Stage IV CTRL BMJ GD MORR THOM WDM6 WSM6
12 Impact of WRF Model Physics: Summer Case turbine-avg wind speed (m/s) OBS CTRL BMJ GD MORR THOM WDM6 WSM6 Cedar Creek Jun Jun
13 Comparison of Error Statistics Jun 26-27, 2009 Case XX farm averaged wind speed BE (m/s) XX farm averaged wind speed MAE (m/s)
14 Summary: Impact of WRF Model Physics For the winter case (frontal passage): Large-scale forcing dominant. Improvements in DA important. Lowest error scores: RUC and MYJ. For the summer case (convective storm): sensitivity to microphysics and outer grid CPS. Double moment microphysics (Morrison and WDM6) the best. Feasibility of sophisticated physics options in realtime forecast?.
15 Assimilation of Wind Farm Met. Data Met Tower wind spd/dir Turbine Hub wind spd Average Met Tower wind directions and assign them for turbines Wind farm Data Processing Averages turbine wind speed to closest WRF grid points Wind farm data reformat WRF RTFDDA.
16 Assimilation of Wind Farm Met. Data Access to ~2000 turbine and over 30 Met Tower observations in real time in CO, NM, TX, and MN. 8 wind farm turbine data (with access to Met Towers) are already being assimilated: More wind farms will eventually be added.
17 Assimilation of Wind Farm Met. Data Met Towers N/A? Solution: wind direction from previous forecast (easy to implement) or directly from model during forecast (harder to implement) Test case: Dec XXX Wind Farm 3 grid setup: x = 30, 10, 3.3. km (smaller d03). XXXX turbine/met Tower data only Exp 1: DA test of turbine data with various sources of wind direction. Exp 2: Nudging coefficient test..
18 EXP 2: NUDGING COEFFICIENT TEST changed nudging coefficient for wind (10 times the default) Worked with run using current model wind direction. default: obs_coef_wind = 6.E-4,6.E-4,8.E-4 obs_rinxy = 200, 90, 36 obs_rinfmx = 2., 6., 18., extra nudging obs_coef_wind = 6.E-3,6.E-3,8.E-3, obs_rinxy = 300, 200, 100 obs_rinfmx = 4., 12., 36.,
19 Assimilation of Wind Farm Data Experiments turbine-avg wind speed (m/s) turbine-avg wind speed (m/s) obs Previous forecast WD false ramp forecast starts here Met Tower WD forecast starts here Current model WD forecast starts here small false ramp DA: u, v u always a bit smaller C 2010, University Corporation for Atmospheric Research. All rights reserved.
20 NUDGING COEFFICIENT TEST turbine-avg wind speed (m/s) obs turbine-avg wind speed (m/s) default nudging small false ramp forecast starts here 10X nudging forecast starts here
21 Data Assimilation Tests: Findings The best wind speed forecasts (best to worst): Met Tower wind direction, Current model wind direction, Previous forecast wind direction. Better solution: using current model wind direction when Met Tower unavailable. Extra nudging can improve wind speed forecast, but parameters have limits.
22 Ongoing Work For physics impact studies, 2 more summer cases and 1 more winter case. Currently modifying WRF to selectively increase nudging weights for turbine data. Make WRF accept wind speed only turbine data. Improve algorithms to cope with inconsistencies between model terrain and observation station elevation. UCAR Confidential and Proprietary.
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