1. An FSL-RUC/RR Proposal for AoR Stan Benjamin Dezso Devenyi Steve Weygandt John M. Brown NOAA / FSL Help from NOHRSC/NWS – Chanhassen, MN - Tom Carroll, Don Cline, Greg Fall USWRP AoR Workshop 29 June 2004

2. 2 Outline of proposal Combined approach - Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC)  13km RUC  10km RR) - Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis) - Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite RUC analysis 1-2km downscaled grids 1-2km analysis background

3. 3 Advantages for FSL combined-approach AoR proposal Extension of existing and planned NCEP operational products Much less expensive for computer power than full-model- downscaling Can produce hourly AoRs within 30 min of valid time Builds on ongoing work to assimilate full METAR/sfc obs incl. ceiling, cloud levels, visibility, current wx (dev RUC) to be added to GSI for future Rapid Refresh and other NCEP models Builds on current hourly 1km CONUS downscaling from National Operational Hydrologic Remote Sensing Center (NOHRSC). Other downscaling methods (e.g., PRISM) also applicable. Builds on collaborative GSI development with NCEP Applicable to Eta/WRF-North American input as well as RUC/WRF-Rapid Refresh (use ensemble approach).

4. 4 Outline of proposal Combined approach (sequential 3 steps) - Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC)  13km RUC  10km RR) - Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis) - Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite RUC analysis 1-2km downscaled grids 1-2km analysis

5. NCEP model hierarchy – RUC (1h frequency)  Eta (6h)  Global (6h ) The 1-h Version of the Rapid Update Cycle at NCEP

6. 6 10km RUC 9-h forecast surface wind-speed and barbs overlaid on sfc reports - valid 15z 28 Mar 02 Forecast max wind-speed 48 kts

7. 7 Verify RUC sfc fcsts against all U.S. sfc obs 10-m wind speed 2-m temperature SUM (Apr–Sep) WIN (Oct–Dec) Persist 0-h 1-h 3-h 6-h 9-h 12-h Fcst Length 0-h 1-h 3-h 6-h 9-h 12-h Fcst Length RUC improves surface wind, temp skill down to 1-h fcst Much better than 1-h, 3-h persistence forecasts

8. 8 PBL-based METAR assimilation Use METAR data through PBL depth from 1h fcst RUC oper analysis 18z 3 Apr 02 IAD x x x x Effect of PBL-based METAR assimilation

9. 9 Assimilation of surface cloud, visibility, current weather observations into RUC Goal: Modify hydrometeor, RH fields to 1) force near match to current ceiling/vis obs when passed through ceiling/vis translation algorithms 2) improve short-range predictions Running in real-time test since Oct 2003 Clearing/building of RUC 3-d hydrometeor fields Use QC with GOES and radar Part of RUC cloud/precip analysis w/ GOES, radar, surface obs, background 1-h forecast IFRLIFR VFR CLR MVFR

10. 10 Cloud ceiling (m) RUC – with and without METAR cloud assimilation 18z Obs 17 Nov 2003 Diagnosed ceiling from RUC hydrometeors Corresponding Ceiling height - meters IFRLIFR VFR CLR MVFR METAR Flight Rules Oper RUC - w/o METAR cloud assim With METAR cloud assim

11. 11 17z 27 Jan 04 analysis – After assimilation of METAR cloud obs Cloud water mixing ratio (qc),  Background – 1h fcst

12. 12 Added assimilation of visibility obs - Feb 2004 Use FG or BR reports from METARS Only when Precip is not also reported T-Td < 1K Build at lowest 2 levels in RUC (5 m, 20 m)

13. 13 Characteristics of RUC analysis appropriate for AoR Hourly mesoscale analysis (digital filter essential) Designed to fit observations (within expected error) (incl. Sfc 2m temp (as  ), dewpoint, altimeter, wind ) Consistent with full-physics 1-h forecast (most important in physics – PBL, land-surface) (real-time testing at FSL in RUC20 and RUC13) Accounting for local PBL depth in assimilation of surface data Accounting of land-water contrast Assimilation of METAR cloud, vis, current wx Assimilation of full mesonet obs Assimilation of GPS PW, PBL profiler QC criteria for mesonet different than METARs Assimilation of hourly radar reflectivity/lightning and GOES cloud-top data into initial fields of 3-d hydrometeors (5 types)

14. 14 Outline of proposal Combined approach - Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC)  13km RUC  10km RR) - Step 2. Non-model downscaling using ~1-2km topography, land- use, roughness length, land/water (e.g., NOHRSC 1km snow analysis) - Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite RUC analysis 1-2km downscaled grids 1-2km analysis

15. 15 Interactive Snow informationNational and Regional Snow Analyses Airborne Gamma Snow Survey The interactive website includes time series plots of modeled and observation data for stations, the ability to choose physical elements and shapefile overlays to display images, and create basin averaged text and map products. The national and regional snow analyses provide daily comprehensive snow information for the coterminous United States. The products include daily regional maps, text summaries, and model analyses. The airborne snow survey page includes current survey information, schedule of surveys, historical and current airborne gamma data, and background information for the Airbrone Gamma Snow Survey program. National Snow Summary Weak upper-level ridging over the West with weak surface lows continues to bring warm but unsettled weather. Heat-of-the-day scattered showers and thunderstorms continue across the South. more... The National Operational Hydrologic Remote Sensing Center (NOHRSC) - Chanhassen, MN provides remotely-sensed and modeled hydrology products for the coterminous U.S. and Alaska for the protection of life and property and the enhancement of the national economy. produces snow data/products - airborne, satellite, and modeled snow data and products - used by NWS, other govt agencies, private sector, and public to support operational/ research hydro programs across nation. produces snow products and information that include estimates of: snow water equivalent, snow depth, snow pack temperatures, snow sublimation, snow evaporation, estimates of blowing snow, modeled and observed snow information, airborne snow data, satellite snow cover, historic snow data, and time-series for selected modeled snow products. NOHRSC Bulletin Board | Mission Statement | Contact Bulletin BoardMission StatementContact National Weather Service National Operational Hydrologic Remote Sensing Center 1735 Lake Drive W. Chanhassen, MN 55317 Page last modified: Dec 30, 2003 Disclaimer Privacy Policy

16. 16 NOHRSC Daily Snow Analysis National Operational Hydrologic Remote Sensing Center – Chanhassen, Minnesota http://www.nohrsc.nws.gov

17. 17 NOHRSC Hourly analyses at 1 km 1000z 29 June 2004 2m temp, RH- RUC Snow precip, non-snow precip – RUC (later corrected w/ obs) Surface wind- RUC Solar radiation- GOES Contour interval = 5K

18. 18 NOHRSC Hourly analyses at 1 km 0600z 29 June 2004 2m temp, RH- RUC Snow precip, non-snow precip - RUC Surface wind- RUC Solar radiation- GOES

19. 19 Geospatial Relational Database Geospatial Relational Database Product Generation Product Generation Field Offices Field Offices NOHRSC Snow Mapping NOHRSC Snow Mapping Temperature Relative Humidity Wind Speed Solar Radiation Atmos. Radiation Precipitation Precipitation Type RUC 20km Hourly Input Gridded Data Downscaled to 1 km RUC 20km Hourly Input Gridded Data Downscaled to 1 km Soils Land Use/Cover Silvics Static Gridded Data (1 km) Static Gridded Data (1 km) Snow Energy and Mass Balance Model Blowing Snow Model Radiative Transfer Model State Variables for Multiple Vertical Snow and Soil Layers - Thickness - Density - Temperature - Liquid Water Content - Grain Size - Melt - Sublimation -Mass Transport State Variables for Multiple Vertical Snow and Soil Layers - Thickness - Density - Temperature - Liquid Water Content - Grain Size - Melt - Sublimation -Mass Transport State Variables for Multiple Vertical Snow & Soil Layers NOHRSC SNODAS Snow Model

20. 20 Full-Res (Internet) CONUS Hourly Mesoscale Input Data RUC20 Analyses (20 km, 50 levels) RUC20 12-h Forecasts (20 km, 50 levels) FSL RUC Analyses (20 km, 50 levels) FSL RUC 12-h Forecasts (20 km, 50 levels) GOES Two-Stream Solar (0.5 o ) (Direct Beam and Diffuse) FSL RUC20 NCEP RUC20 NESDIS SOLAR Hourly Snow Model Forcing (1 km) Surface, Spatially & Temporally Continuous Air Temperature Relative Humidity Wind Speed Precipitation (Snow) Precipitation (Non-Snow) Solar Radiation Physically Based Downscaling (1 km) Physically Based Downscaling (1 km) Spatial/Temporal Gap Filling Spatial/Temporal Gap Filling Preprocessing: Forcing Data (RUC20)

21. 21 Downscaling: Solar Radiation GOES Two-Stream Solar Radiation 0.5 degree Direct Beam and Diffuse Irradiance GOES Two-Stream Solar Radiation 0.5 degree Direct Beam and Diffuse Irradiance Terrain Cross-Section Direct Beam Irradiance Terrain Cross-Section Diffuse Irradiance Sky-View Factor Terrain Reflection Terrain Reflection Topographic Shading Topographic Shading Sky-View Factor Sky-View Factor Incidence Angles Incidence Angles

22. 22 NRCS SNOTEL Snow Water Equivalent NRCS SNOTEL Snow Water Equivalent Point CADWR Snow Water Equivalent CADWR Snow Water Equivalent Point NOHRSC GOES/AVHRR Snow Cover NOHRSC GOES/AVHRR Snow Cover Grid NOHRSC Airborne Gamma Snow Water Equivalent NOHRSC Airborne Gamma Snow Water Equivalent Area NWS/Cooperative Snow Water Equivalent Snow Depth NWS/Cooperative Snow Water Equivalent Snow Depth Point Snow Model Snow Model Gridded Data Sets Auto QC Point Data Sets Preprocessing: Update Data

23. 23 NOHRSC data- Boulder Snowstorm in Colorado (18-19 March 2003) RUC forcing Observed 6 days

24. 24 Wind speed downscaling -- Use u* from model grid scale to calculate wind speed at 1km grid scale using 1km roughness length Other improved downscaling? - PRISM - simple PBL/near-sfc wind models - … Zo at 20km based on USGS 1km data Enhancements needed for NOHRSC-like downscaling

25. 25 Outline of proposal Combined approach - Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC)  13km RUC  10km RR) - Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis) - Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite RUC analysis 1-2km downscaled grids 1-2km analysis

26. 26 STEP 3: 1-2 km analysis w/ high-resolution observations Background = 1-2km downscaled grids (from step 2). (Step 2 grids are downscaled from Step 1 grids) Possible tools – all fast analysis steps on 1-2km scale Barnes- or Bratseth-type analysis using innovations (high- res obs minus results of downscaling in step 2) Simple, fast 2dVAR or 3dVAR of innovations, using wavelet or digital- filter modeled covariances. (Problem is mathematically better conditioned than standard 3dVAR, also parallelizable.) Optimum interpolation (OI): Fast, reliable, easy to parallelize Ensemble Kalman filters also applicable in 3-step method proposed here RUC-like use of PBL height, cloud/radar/vis/current wx Note: RUC/GSI 3dvar also assimilate radial winds

27. 27 Our position: 3-d model component necessary for AoR. But what is the trade-off? Only way to allow physical consistency in analysis fields for topography land use (including land-water), land-sfc parameterization boundary-layer, cloud physics, radiation, … Essential to produce best possible skill at grid points between observations Problem with model component for AoR Bias in favor of NDFD forecasts that are taken from same model as used in AoR. Brad Colman (and others) goal: AoR should be independent as possible from any given model Our guarded hopes: 1) Steps 2 and 3 will provide independence from Step 1. 2) Step 1 can have multiple models.

28. 28 Advantages for FSL combined-approach AoR proposal Extension of existing and planned NCEP operational products Much less expensive for computer power than full-model- downscaling Can produce hourly AoRs within 30 min of valid time Build on ongoing work to assimilate full METAR/sfc obs incl. ceiling, cloud levels, visibility, current wx (dev RUC) to be added to GSI for future Rapid Refresh and other NCEP models Build on current hourly 1km CONUS downscaling from National Operational Hydrologic Remote Sensing Center (NOHRSC). Other downscaling methods (e.g., PRISM) also applicable. Builds on collaborative GSI development with NCEP Applicable to Eta/WRF-North American input as well as RUC/WRF-Rapid Refresh (use ensemble approach).