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APRFC NCEP WX MODEL PROJECT

APRFC NCEP WX MODEL PROJECT . David Streubel and Larry Rundquist Alaska Pacific River Forecast Center. APRFC Objectives. Assess the value of using NCEP operational models (ETA & GFS) for observed and forecast data within NWSRFS basins in Alaska.

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APRFC NCEP WX MODEL PROJECT

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  1. APRFC NCEP WX MODEL PROJECT David Streubel and Larry Rundquist Alaska Pacific River Forecast Center

  2. APRFC Objectives • Assess the value of using NCEP operational models (ETA & GFS) for observed and forecast data within NWSRFS basins in Alaska. • Evaluate the use of NCEP reanalysis data in the NWSRFS calibration process; specifically determine whether its use provides an unbiased procedure for introducing NCEP data operationally into NWSRFS.

  3. Previous Work • Clark et Al. (2004) developed statistical downscale procedure for use with reanalysis 2m temperature and precipitation data and ran a USGS runoff model. • Significant bias was found with the reanalysis precipitation data and to a lesser extent with the 2m temperature data when compared with observed. • Temperature driven hydrologic events such as snow melt performed better than precipitation dominated processes. • CBRFC developed methods in conjunction with Clark et al (2004) to use reanalysis data to create ensembles of 14 day forecasts. Ongoing project • Hagemann & Gates (2001) used data from the ECMWF reanalysis project to force a rainfall runoff model.

  4. What is Reanalysis Data? • Time series data generated from the NCEP reanalysis project using 1995 frozen MRF model. • Time series of most major operational model parameters in 6hr or 24hr format since 1948. • Grid is 1.9 – 2.5 degrees.

  5. NCEP Operational WX Model Data • The GEMPAK analysis package supported by UCAR is used for real time extraction of GFS and ETA model output. • Model run times: GFS 6hrs - ETA 12 hrs • Data is interpolated by GEMPAK decoders and scripts are used to put data into SHEF for use in NWSRFS.

  6. Temperature is Initial Focus Area • Concentrate on extreme snow and ice melt events in data sparse basins. • Evaluate the effects of including reanalysis temperature data at 700MB, 850MB, Surface, and 2M in the calibration process and its benefit. • Introduce operational ETA and GFS as both observed and forecast data if confident that significant bias is minimized by using reanalysis data in calibration process.

  7. Tanana River Basin • Upper basin 19400 mi^2 • Elevation Range is from 400 to 16390 feet • 11 real time temperature gauges in basin all below 2700 feet. • Significant component of water balance is glacier melt. • Poor simulation in unusual weather events

  8. 2004 Lapse Rate Example

  9. Temperature Bias • ETA-GFS and reanalysis data bias small • “Free Air” vs ground temperature bias larger

  10. Temperature Bias Cont. • GFS-ETA-Reanalysis have significantly less diurnal fluctuations than HRDA2 ground observations. • Model derived temperatures follow observed relatively well but amount of departure is seasonally dependent.

  11. Potential Use of Precipitation Data • Precipitation bias is large when comparing NCEP model, reanalysis, and observed data sets. • Clark et al (2004) found precipitation bias as “severe” when comparing reanalysis data with observed data and implemented a downscaling procedure to help.

  12. Precipitation Bias • Usually large accumulation bias are present between models and reanalysis data. • Timing errors are very dependent on location and season. • Downscaling methods need to be implemented.

  13. Conclusions or Initial Thoughts... • We are just getting started… • In basins with limited low elevation station data the inclusion of NCEP model temperature data should help during extreme events when standard lapse rates do not apply. • NCEP reanalysis data likely needs to be included in calibration process of NWSRFS in order to reduce bias when NCEP model data is used operationally . • Methods for downscaling reanalysis data need to be investigated further

  14. References • Clark, M.P., and L. E. Hay 2004. Use of Medium Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow. The Journal of Hydrometeorology 5:15-32. • Hagemann, S., and L. D Gates, 2001. Validation of hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model. The Journal of Geophysical .Research 106 D2:1503-1510.

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