HRRR-AK: Status and Future of a High- Resolu8on Forecast Model for Alaska

Transcription

1 HRRR-AK: Status and Future of a High- Resolu8on Forecast Model for Alaska Trevor Alco* 1, Jiang Zhu 2, Don Morton 3, Ming Hu 4, Cur8s Alexander 1 1 ESRL Global Systems Division, Boulder, CO 2 GINA/UAF, Fairbanks, AK 3 Boreal ScienGfic CompuGng LLC, Fairbanks, AK 4 CIRA/Colorado State Univ., Boulder, CO Virtual Alaska Weather Symposium 23 Aug 2017

2 A Unique and Challenging Environment 6192 m Complex terrain PFYU Z 00000KT 1SM BR CLR M43/ A2962 RMK AO2 106 m Arc:c climate Sparse observa:ons Travel by air

3 RAP/HRRR Weather Forecast Suite NCEP-GFS 13-km Rapid Refresh (RAP) Ini8al & Lateral Boundary Condi8ons 3-km High- Resolution Rapid Refresh Alaska (HRRR-AK) 3-km High-Resolution Rapid Refresh Initial & Lateral Boundary Conditions 750-m HRRR nest Wind Forecast Improvement Project Experiment (ongoing) 3-km High-Resolution Time Lagged Ensemble (HRRR-TLE) 3-km HRRR-Smoke (VIIRS fire data) 3-km Storm-Scale Ensemble Analysis and Forecast (HRRRE) 70% CONUS HRRR Experimental (ongoing)

4 RAP/HRRR: Improving Forecast Skill Crossover in forecast skill between Nowcasting/Extrapolation vs Numerical Weather Prediction -- Extrapolation -- Persistence Forecast Skill ß Less Skill More Skill à HRRR 3-km Radar Data Assimilation RUC 13-km Radar Data Assimilation Pre-Radar Data Assimilation Improving forecast skill and halving crossover period every ~3-4 years Forecast Length (Hours)

5 HRRR-AK Model Configura8on 3-km resolu8on 1300x920x51 grid points ini8alized every 3 h 36-h forecast WRF-ARW v3.8.1 No convec8ve parametriza8on 20-s 8me step land-surface (but not full ) cycling

6 HRRR-AK Ini8aliza8on 23z RAP 242 grid 0-h forecast 3-km interpola8on 09z GEFS EnKF MRMS Alaska radar mosaic GSI 3D Hybrid GSI hydrometeor analysis conven8onal observa8ons conven8onal and satellite observa8ons 21z RAP 242 grid boundary condi8ons 00z HRRR-AK Hour -1 Pre-Forecast 00z HRRR-AK Hour 0 Full Forecast 00z HRRR-AK Hour 36 Land-surface fields from recent HRRR-AK forecast

7 HRRR-AK Applica8ons: Alaska Range Avia8on

8 HRRR-AK Applica8ons: SE Marine Interests

9 HRRR-AK Applica8ons: Interior Convec8on

10 HRRR-AK Challenges model configura8on is prone to instability in steep terrain areas >24 deg slope are selec8vely smoothed to prevent crashes Alaska and St Elias Ranges especially problema8c

11 HRRR-AK Challenges persistent WRF issue with simula8ng, maintaining sharp temperature inversions ongoing work with increasing number of ver8cal levels

12 HRRR-AK Challenges HRRR-AK shows proven skill with phenomenon of downslope windstorms but details remain highly uncertain

13 HRRR-AK Internal Verifica8on on-demand plots evaluate HRRR-AK vs METAR and RAOB observa8ons comparison with NAM- Alaska, 13-km RAP, etc. supports na8onal move to evidence based decision making

14 HRRR-AK at NCEP: Preliminary Schedule

15 Accessing HRRR-AK Forecasts h*ps://rapidrefresh.noaa.gov/alaska/ FTP access (contact LDM (NWS Alaska) Full archive since Apr 2016 on tape storage, small requests only NCEP source by spring 2018

16 HRRR-AK: Coupled Modeling Stuefer et al. (2012) HRRR-AK-Smoke real-8me smoke forecasts during fire season feedback on radia8on and microphysics now enabled Na8onal Water Model real-8me, gridded streamflow forecasts driven by HRRR-AK precipita8on HRRR-AK-Ash real-8me volcanic ash forecasts triggered by erup8ons poten8al for feedback collabora8on with Mar8n Steufer at UAF

17 Improving HRRR-AK forecasts with polar satellite data assimila8on Jiang Zhu Geographic Informa8on Network of Alaska (GINA), UAF

18 Benefit of data assimila8on Before a model runs, it needs good es8ma8on of the ini8al state. The ini8al state is es8mated by background and varied observa8ons. Data assimila8on combines observa8on and background informa8on to modify the ini8al state and thereby improves the ini8al state of the model.

19 Observa8ons Conven8onal observa8ons (METAR, RAOB, etc.) Satellite observa8ons (sounding profiles, wind profiles) Aircram observa8ons( AMDAR, etc.) Radar observa8ons (NEXRD, etc.)

20 Sumi-NPP CriS/ATMS atmospheric profile (NUCAPS) improves the WRF model short-term forecast a) Upper observa8ons in Alaska b) CrIS/ATMS humidity observa8ons at 850 mbar Figure 1. Comparison of RAOB and satellite sounding observa8ons a) shows that there are only 12 conven8onal observa8ons in Alaska. b) shows that CrIS/ATMS sounding data have much beher coverage in Alaska (Zhu, 2014). Figure 1 tells us that the conven8onal observa8on in Alaska is too coarse and the satellite sounding data make up the weakness.

21 Figure 2. Performance of assimila8on of AIRS and NUCAPS sounding data Root-mean-square error (RMSE) measures the differences between forecast and observa8on data. RMSE is composed of mean bias (RMSEa) and centered pahern RMS difference (RMSEb), and RMSE^2=RMSEa^2+RMSEb^2 (Taylor, 2001). RMSEa measures the overall bias and RMSEb measures the varia8on between the forecasts and the observa8ons. Model runs every 6 hours. The Figure 2 shows the sta8s8cs of monthly (e.g. 120 ) analyses (Zhu, 2016). Temperature, dew point, and wind speed at 300, 500, and 850 mbar pressure levels are calculated in terms of RMSE. AIRS and NUCAPS data assimila8on runs improve the analyses in all three pressure levels and all three variables.

22 Polar Satellite Wind product AVHRR polar winds (NOAA) MODIS polar winds (TERRA, AQUA) VIIRS polar winds (NPP) hhps://stratus.ssec.wisc.edu/projects/polarwinds/

23 References Jiang Zhu, E. Stevens, B. T. Zavodsky, X. Zhang, T. Heinrichs, and D. Broderson Satellite Sounder Data Assimila8on for Improving Regional NWP Forecasts in Alaska, poster, 94 th American Meteorological Society Annual Mee8ng, Atlanta, USA. Jiang Zhu, E. Stevens, T. Heinrichs, J. Cherry, and C. Dierking AIRS/CrIS sounding profile data improves the short-term weather forecast of Alaska, poster, 96 th American Meteorological Society Annual Mee8ng, New Orleans, USA