Land-Surface evolution forced by predicted precipitation corrected by high-frequency radar/satellite assimilation – the RUC Coupled Data Assimilation System.

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Presentation transcript:

Land-Surface evolution forced by predicted precipitation corrected by high-frequency radar/satellite assimilation – the RUC Coupled Data Assimilation System (CDAS) IUGG - 1 July 2003 Tatiana Smirnova NOAA / FSL Stan Benjamin John M. Brown Dongsoo Kim

2 Schematic presentation of processes included into RUC-LSM Cycling of soil moisture, soil temperature, snow cover/depth/temperature in RUC 1h cycle since 1997: - more accurate lower boundary for weather prediction in RUC - 5-year long record of surface grids provided to GCIP/GAPP community for climate studies

3 RUC Coupled Data Assimilation System – RUC CDAS RUC CDAS is a four-dimensional system which: Uses a forward full-physics model Cycles surface/soil fields depending on the RUC atmospheric forcing Cycles 5 hydrometor species : cloud, ice, rain, snow and graupel. Cloud clearing/building based on GOES data new compared to RUC Control: Radar reflectivity ( since April 2002, lightning added as convection proxy in Sept 2002) GPS precipitable water Boundary-layer profilers Mesonet observations collected at FSL Main Goal: - improve 1-h precipitation forcing and the land surface model climate. Important for both seasonal and short-range prediction.

4 RUC radar reflectivity assimilation Hypothesis: Mesoscale model forecast of precipitation and precipitation type may be better than analyses from observations in some situations: - orographic precip, especially in cold season - data void area Assimilation of radar reflectivity allows use of beam-blockage information Don’t change background where there are no obs 1.Modify hydrometeor/vapor 2.Project onto cu parm for sub-grid precip Radar lowest beam height (km)

5 Snow water Depth 30 January 2003 RUC ControlRUC CDAS w/radar NOHRSC snow analysis

6 Motivation – correct excessively cold temperatures at night (with clear skies, low winds) over thin snow layer; improve estimation of the snow melting rate. 5 cm 4 cm 7.5 cm S n o w S o i l combined snow-soil layer 1-layer snow model 2-layer snow model 2002 Modifications to the RUC snow model – changed vertical structure of the snow model snow albedo reduction for thin snow layer

7 Improvement of frozen soil physics algorithm - needed when both soil moisture and soil temperature increase – typical situation for the snow melting season. Tested in 1-D for Valdai, Russia Tested in Experimental RUC at FSL Daily averaged skin temperature (C), April-May 1980

8 RUC CDAS - with radar reflectivity/lightning data ETA June

9 Resolution 20 km, 50  levels Analysis 3DVAR, hydrometeor analysis w/GOES Assimilation Intermittent 1-h cycle Stable clouds New version of MM5/RUC / precipitation microphysics (FSL and NCAR), much shorter microphysics time step, less graupel, more supercooled liq water Sub-grid-scale New Grell scheme w/ ensemble cloud, precipitation shallow convection, detrainment of cloud water to microphysics Turbulence Modified Burk-Thompson Radiation MM5 scheme with fix to SW lag error Land-sfc processes Improved soil model, 2-layer snow model, improved cold season processes improved diurnal cycle Sfc conditions High-res USGS land-use, CONUS soil type, albedo Lat boundary cond. Eta model initialized every 6h RUC20 operational at NCEP since 17 April short-range numerical forecasts for aviation, severe weather and general public forecasting