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Evaluation of Operational WRF Model Simulations (in the SF Bay Area)

Evaluation of Operational WRF Model Simulations (in the SF Bay Area). By Ellen METR 702. Background Research Question and Hypothesis Method & Programs Used Data Example & Expected Plots More to Expect. Research Advisor: Prof. Dave Dempsey Funded by the ASERG Grant.

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Evaluation of Operational WRF Model Simulations (in the SF Bay Area)

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  1. Evaluation of Operational WRF Model Simulations (in the SF Bay Area) By Ellen METR 702 • Background • Research Question and Hypothesis • Method & Programs Used • Data Example & Expected Plots • More to Expect Research Advisor: Prof. Dave Dempsey Funded by the ASERG Grant

  2. Two Forecast Model Gotten: mNAM & GFS • 40km CONUS NAM • 4 times/ day (model cycle run time: 00z, 06z,12z,18z) • Forecast Length: 84hrs But, we only use 48hrs in the reserach for now. • Forecast Interval in one model run: 6hrs until the 48thhr; 24hrs after that until the 84thhr (e.x. inital 00z, f06, f12 … f48, f60 … f84) • Includes: SLP & station P; T; RH; u-comp and v-comp of Wind Speed; Precip; GeoHt; etc. • Gridded Data • Global longitude-latitude grid • -- 0.5 degree resolution (north-south direction: about 55 km -- lower than mNAM’s resolution) • More distances between 2 gridded point, more details being skipped in-between, lower resolution • 4 times/ day • (model cycle run time: • 00z, 06z,12z,18z) • Forecast Length: 120 hrs (also only use 48hrs) • Forecast Interval in one model run: • Same as mNAM, but going further • until the 120thhr • Includes: Surface P; T; RH; u and v; precip; geoht; etc. • Gridded Data (different from mNAM because: • different resolution • be represented spatially in different ways on the map)

  3. Observation (Station) Data Gotten:MADIS UrbaNet data in NorthCA area • NOAA data sources & non-NOAA data providers • Small data size (small but enough amount of stations in the file)– short time to process with the programming software • Decodes the data and then encodes all of the observational data into a common format with uniform observation units and time stamps. • Quality control checks • Includes: SLP; T; RH; Wind Direction & Wind speed; Precip; etc.

  4. Visualize the Differences GFS  Meso NAM  MADIS Mesonet station data 

  5. Questions and Hypotheses • At a specific time in a day, which forecast model (WRF-GFS or WRF-mNAM) does the forecast better? (maybe WRF-mNAM, considering it based on a little bit higher resolution) • In which field does WRF-mNAM model forecast better? How about WRF-GFS? (Temperature) • How long does WRF-mNAM forecast relatively precise, compared to itself? How about WRF-GFS? (24hr) • Overall, which model does forecast better in a shorter period (24hrs)? (WRF-mNAM) • Overall, Which model does forecast better in a longer period (48hrs)? (WRF-GFS) • Overall, in which domain of the 3 domains does WRF-mNAM forecast better? How about WRF-GFS? (SFMarin) Potential Q: • In a specific phenomenon (need to be chose), which model does forecast better?(depends on the phoneme) • At what time in a day, WRF-mNAM has higher potential to forecast better than other time? How about WRF-GFS? What’s in common at that specific time( i.e. What phoneme is relative to that time)? (don’t have a hypotheses yet; need read more)

  6. Interesting Examples of Similar Study • Evaluating Weather Research and Forecasting (WRF) Model Predictions of Turbulent Flow Parameters in a Dry Convective Boundary Layer Jeremy A. Gibbs, EvgeniFedorovich, and Alexander M. J. van Eijk, 2011: Evaluating weather research and forecasting (wrf) model predictions of turbulent flow parameters in a dry convective boundary layer. J. Appl. Meteor. Climatol., 50, 2429–2444. doi: http://dx.doi.org/10.1175/2011JAMC2661.1 • Suitability of the Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska Nicole Mölders. Weather and Forecasting. Volume 23, Issue 5 (October 2008) pp. 953-973 doi: http://dx.doi.org/10.1175/2008WAF2007062.1 • Evaluation and Comparison of Microphysical Algorithms in ARW-WRF Model Simulations of Atmospheric River Events Affecting the California Coast IsidoraJankov, Jian-Wen Bao, Paul J. Neiman, Paul J. Schultz, Huiling Yuan, Allen B. White. Journal of Hydrometeorology. Volume 10, Issue 4 (August 2009) pp. 847-870 doi: http://dx.doi.org/10.1175/2009JHM1059.1

  7. Methods – WRF-ARW System & UPP, MET Programs Flow Chart for Real Case (Having Input Forecast Data – mNAM & GFS): Model Forecast Data (one run)  (Including:) UPP madisnc2nc (involving MET program) Observation Data point_stat (MET program)  stat_analysis(MET program)

  8. WPS(WRF Preprocessing System) • geogrid.execreates terrestrial data (static) basing on “namelist.wps” file. (the biggest domain’s resolution, number of nest, latitude/longitude, ratio between the parent domain--the biggest one and the nest domains) • ungrib.exeunpacks GRIB meteorological data (mNAM & GFS) and packs it into an intermediate file format – reformatting the data. • metgrid.exeinterpolates the meteorological data horizontally onto your model domain. • Output from metgrid.exeis used as input to WRF.

  9. with ARW (Advanced Research WRF) core • real.exeinterpolatesmeteorological fields vertically to 40 WRF grid levels. • basing on “namelist.input” (forecast length which can be edited by provide arguments of the script “wrf_run_nest.sh”, time steps, forecast interval and resolution of the domains of the output of wrf) • Output fromreal.exeis used as input (initial and boundary conditions-interval:6hrs) to WRF program wrf.exe • wrf.exe generates atmospheric simulations using the ARW (Advanced Research WRF) core dynamical solver (physic calculations for higher resolution points)

  10. UPP (NCEP’s Unified Post Processor) • Script "run_unipost_frames.sh" (a). interpolates 3D fields of wrf output from model grid surfaces to isobaric surfaces and (b). saves the results in separated GRIB1 format files (according to forecast intervals). • Outputs of run_unipost are used as input to point_stat • Example of output files: • WRFPRS_2014-09-16_12_NAM_CenCal_BayArea_SFMarin.06.grb WRFPRS_2014-09-16_12_NAM_CenCal_BayArea_SFMarin.07.grb • WRFPRS_2014-09-16_12_NAM_CenCal_BayArea.06.grb WRFPRS_2014-09-16_12_NAM_CenCal_BayArea.07.grb • WRFPRS_2014-09-16_12_NAM_CenCal.06.grb WRFPRS_2014-09-16_12_NAM_CenCal.09.grb

  11. madisnc2nc(a script we wrote): • To getMADIS NetCDF (nc) format observations for stations within the CenCA domain(not readable directly by point_stats) • To reformate MADIS nc data to a particular ASCII ncformat readable by MET* program: ascii2nc *MET(Model Evaluation Tools) • To runasscii2ncprogram – to reformateASCII ncformat to a particular nc format (met.nc) readable by MET program: point_stats Example of output files: • 20140916_1200.asc • 20140916_1200.met.nc (as input to point_stat)

  12. point_stat (MET Pr0gram) To compare Obs data and Fcst data for each forecast hour according to the Configuration file: PointStatConfig Using: Observation Window ("obs_win") = period from 1 minute before the forecast hour to 4 minutes after.  need 2 Adjacent observation files to compute verification statistics.

  13. In the PointStatConfig Specifying: Fields: SLP(Pa); T(K); Wind Speed(m/s); & RH(%) Level information for the fields. (e.x. T: Z2– 2 m above the ground SLP: L0 – default: mean sea level) Categories used in the contingency table statistics. (e.x. SLP: >=99500, >=100000) Specific Interpolation Procedures: Median & (distance-weighted) Mean Verification/Statistics Methods (e.x. fho - Forecast, Hit, Observation Rates; mcts - Multi-category Contingency Table Statistics; cnt- Continuous Statistics) Output data = key to answer the questions!

  14. More scripts were wrote to make the whole process(with dynamic start)run automatically ! Model Forecast Data  (Including:) (WRF System; run by the script “wrf_run_nest.sh”) UPP madisnc2nc (involving MET program) Observation Data point_stat (MET program)  Every step of one run involves a lot of DATA! 48 fcst hrs/run * 4 run/day = 192 sets of point_stat output/day Output data is helpful to answer the questions!

  15. One Example of the point_stat Data: point_stat_20140914_06_f54_NAM_CenCal_fho (observation window) 9 fcst gridded points around the obs station will be used to calculate MEDIAN & DW_MEAN (range/category)

  16. (Line_Type) # of Station (n) (Base Rate) (# to fit[Yes] or not fit[No] into each category) OBS FCST F_RATE = (a+b)/n= total “fcst_Y”/total H_RATE = a/n =FY_OY/total O_RATE = a+c/n = total “obs_Y”/total ratio

  17. Visualizing data & Expected plots F_RATE e.x. F_RATE WRF-GFS WRF-mNAM Borrowed from: http://www.esrl.noaa.gov/fiqas/presentations/tollerud-mccaslin-met_summit.pptx.

  18. Another Example of Expected Plot Borrowed from:http://www.esrl.noaa.gov/fiqas/presentations/tollerud-mccaslin-met_summit.pptx. WRF-GFS WRF-mNAM My plot won’t have so many lines/fcst models RMSE (Root Mean Square Error) in CNT(Continuous Statistics) output line type = ([Σ (Xf-Xo)2] / n)-1/2

  19. Future Goals • To look for more field: 1hr-accumulated precipitation, dew point temperature… (need to write scripts to do extra calculations and reformation) • To configure the STAT_ANALYSIS to compute statistics for the wind direction errors; and to filter, summary as well as aggregate the STAT outputs from point stat • To visualizing the outputs (in graphs maybe with METViewand in plots with IDV-the Integrated Data Viewer); to make the IDV output automatically for public purpose • To move on to more research questions and to involve more forecast model data (e.x. How accurate does the RUC model forecast, compared to GFS &/or mNAM?) Output data can answer the questions

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