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16 th Weather Squadron Overview of Land Surface Modeling at AFWA Jeff Cetola and Chris Franks 5 th Interagency Surface Dynamics Working Group Meeting Tuscon.

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Presentation on theme: "16 th Weather Squadron Overview of Land Surface Modeling at AFWA Jeff Cetola and Chris Franks 5 th Interagency Surface Dynamics Working Group Meeting Tuscon."— Presentation transcript:

1 16 th Weather Squadron Overview of Land Surface Modeling at AFWA Jeff Cetola and Chris Franks 5 th Interagency Surface Dynamics Working Group Meeting Tuscon 1 March 2011 Approved for Public Release – Distribution Unlimited

2 Aim High … Fly, Fight, Win 16 WS Presentation Overview Land Information System (LIS) Overview Inputs/Components/Forcings Continuity Domains Schedule Post-processing/Output Way Ahead 2

3 Aim High … Fly, Fight, Win 3 Land Information System (LIS) LIS is a framework for hydrological modeling and data assimilation An object-oriented framework with abstractions defined for customization and extension which allows User defined domains Multiple land surface models Multiple forcing options Multiple data assimilation algorithms Designed to facilitate reuse and community sharing of tools, data resources, and assimilation algorithms Support for high-performance computing 3

4 Aim High … Fly, Fight, Win Land Information System (LIS) AFWA, NASA, NCAR, NCEP effort w/following benefits… Capability to run at different spatial resolutions to match NWP model (1/4 deg, 15 km, 1 km, etc.) Produces output for both global and regional domains using same software Parallel computations for increased efficiency Ability to run on Weather Research and Forecasting (WRF) supported projections (Lambert Conformal, Mercator and polar stereographic) Ability to run nested domains concurrently Ability to run with different modes – analysis, forecast, or a prototyping mode that can incorporate various cycling that typically goes on in an operational environment. Ability to run with different land surface models (Noah, VIC, CLM) Highly configurable infrastructure, use multiple forcing datasets 4

5 Aim High … Fly, Fight, Win LIS Inputs 5 AFWA GEO-PRECIP model Precipitation estimate based on geostationary IR Roughly 6km resolution 50 South to 50 North CMORPH CPC Morphing Technique Estimates precipitation with PMR & ‘morphs’ the data over time with geostationary IR data AFWA World-Wide Merged Cloud Analysis Analysis of cloud cover world-wide using all available satellite data AFWA SNODEP Snow depth anaylsis from observations with FITL Soon to add satellite based estimates

6 Aim High … Fly, Fight, Win LIS Design - Components 6 LIS provides many options for domains, LSM, run mode, inputs, & data assimilation AFWA configuration Noah LSM Unique ‘AGRMET’ run mode Unique input/baseforcing Unique AFWA radiation based on Shapiro, Wachtman, & Idso Direct insertion of snow depth

7 Aim High … Fly, Fight, Win 7 LIS Meteorologial Inputs Meteorological Surface Forcing  Lower troposphere temperature and humidity profile  NCEP GFS blended with synoptic observations  Near surface winds derived from 10-meter GFS winds

8 Aim High … Fly, Fight, Win 8 Barnes analysis method blends observations from: Gauge reports AFWA Geostationary IR satellite precipitation estimate AFWA CDFSII precipitation estimate DMSP SSM/I & SSMI/S rainrate estimates (Tropics only) Climatology AFWA Blended Global Precipitation Estimate LIS Meteorologial Inputs ‘AGRMET’ Precipitation Forcing

9 Aim High … Fly, Fight, Win 9 Cloud information (coverage, top, types) from the AFWA CDFSII Reference: Shapiro (1987) References: Idso (1981) and Wachtmann (1975) LIS Meteorologial Inputs ‘AGRMET’ Radiation Forcing

10 Aim High … Fly, Fight, Win 10 LIS Continuity There is insufficient observational data for soil moisture on which to base an analysis LIS starts at a set soil moisture and progresses using the previous cycle or time step as a starting point After one hour, the values at some points have been reduced 10 After 12 hours values have declined at most points Two weeks of processing begins to reveal some detail but has not dried arid areas sufficiently Two years of processing matched AGRMET for the 0 to 10 cm layer but in deserts the deeper layers were still too moist Initial conditions for LIS come from a restart file produced from a previous cycle Production data was based on a file generated from AGRMET and then processed for two years

11 Aim High … Fly, Fight, Win 11 Current LIS Domains 25km ‘global’ domain Supports external users Provide land surface inputs to WRF domains which assimilate data at 45km Post-processed to produce 12-hourly and 24-hourly averages/accumulations 15km regional domains Provide land surface inputs to WRF domains which assimilate data at 15km Unclassified SW Asia WRF began using 15km LIS on 23 November Sized to support multiple planned WRF domains Support contingencies and classified WRF domains Currently Lat/lon projection Other projections upcoming

12 Aim High … Fly, Fight, Win LIS Data in AFWA WRF 12 WPS/Metgrid ingests 25 or 15km LIS data for cycle minus 6 hours, interpolates to the WRF grid, and outputs to WRF input file The WRF input is used as initial conditions by 6 hour ‘init’ run of WRF Noah LSM within WRF modifies land surface data for the 6 hour period and writes land surface and atmospheric variables to the WRF output file WPS reads the outputs of the WRF output file, applies 3DVAR, and creates the initial conditions for the WRF forecast, but land surface data are not modified

13 Aim High … Fly, Fight, Win LIS Schedule LIS alternates 12-hour and 6-hour cycles The global 25km theater for 0/12Z cycles run for around 10 minutes, the 6/18Z cycles run around 5 minutes The afr_asia 15km theater takes 8/4 minutes and the others less Post-processing for the global theater takes 1 minute 13 0Z 18Z12Z6Z0Z 12Z prior day to 0Z global 05:30 afr_asia 06:05 wpac 06:20 s_amer 06:40 0Z to 6Z global 11:30 afr_asia 12:05 wpac 12:20 s_amer 12:40 0Z to 12Z global 17:30 afr_asia 18:05 wpac 18:20 s_amer 18:40 12Z to 18Z global 23:30 afr_asia 23:05 wpac 23:20 s_amer 23:40

14 Aim High … Fly, Fight, Win Post-processed LIS/ LIS Users 3-hourly LIS is used by AFWA/WRF, NCEP, AFTAC, CG/AR, FNMOC, NASA, NRL, NMSU, NGA, NWS- Corpus Christi The global LIS domain is post-processed to generate a number of output variables which are summed and/or averaged for 12 and 24 hours 12-hourly post-processed LIS is an input to the SFCTMP model 24-hourly post-processed LIS is sent to external customers (e.g., 14 WS, USACE-ERDC, USDA-FAS) 14

15 Aim High … Fly, Fight, Win 15 LIS at 1km Resolution (prototype) 15 LIS can be configured to run at up to 1km resolution Currently supporting development projects

16 Aim High … Fly, Fight, Win Noah in LIS vs. WRF 16 AFWA LIS is using Noah while WRF is using Noah 3.1 Parameter data for LIS provided by NASA while WRF uses files provided by NCAR Both use USGS 24 class land use WRF adds a category for inland lakes Would like to move to 20 class MODIS land use

17 Aim High … Fly, Fight, Win 17 LIS Way Ahead CMORPH Precipitation Updated (Noah 3.1 & new Land Surface models (FASST) Additional data assimilation packages (AMSR-E/SMOS/SMAP) 17 Observed Greenness Fraction LIS-WRF Coupling Fu Liou & CRTM radiation and radiance assimilation with Cloud Optical Properties

18 Aim High … Fly, Fight, Win

19 BACKUP SLIDES 19

20 Aim High … Fly, Fight, Win 20 LIS Output LIS output is in GRIB edition 1 format Center 57 sub-center 2 All records for each output time are bundled in a single file Land surface variables Shelter level variables Static variables Root Zone variables 20 0 cm 10 cm 40 cm 100 cm 200 cm Root Zone Layer Boundaries

21 Aim High … Fly, Fight, Win Land Surface Variables Latent heat flux, sensible heat flux, & ground heat flux Accumulated precipitation, surface runoff, subsurface runoff, snow depth, and water equivalent snow depth Skin Temperature, Albedo, short wave radiation, and long wave radiation Surface pressure Actual and potential evapotranspiration 21

22 Aim High … Fly, Fight, Win Shelter Level Variables 2 meter AGL temperature 2 meter AGL maximum temperature for previous 3 hours 2 meter AGL minimum temperature for previous 3 hours 2 meter AGL specific humidity 10 meter AGL wind run (km/24 hours) 2 meter AGL relative humidity at the time of minimum temperature 22

23 Aim High … Fly, Fight, Win Static Variables Land mask Vegetation type/Land Use – 24 categories Soil type – composite STATSGO (CONUS) and FAO (OCONUS) Terrain Height Greenness fraction – monthly climatological values interpolated to the date 23

24 Aim High … Fly, Fight, Win Root Zone Variables Soil Temperature Volumetric Soil Moisture (liquid & solid) Volumetric Soil Moisture (liquid only) Relative Soil Moisture 24 0 cm 10 cm 40 cm 100 cm 200 cm

25 Aim High … Fly, Fight, Win LIS-WRF Coupling AFWA, NASA & NCAR joint study

26 Aim High … Fly, Fight, Win Ensemble Kalman Filter data assimilation algorithms have been developed and tested for LIS at NASA Goddard, resulting in several conference presentations and peer reviewed, published articles. Retrieved moisture and snow fields have been used in test cases, for example. LIS parameter optimization and Bayesian parameter/ output uncertainty estimators have been developed and tested at NASA Goddard. 26 LIS Way Ahead LIS : Kumar et al. (2006,2008,2009), Peters-Lidard et al. (2007)

27 Aim High … Fly, Fight, Win LIS Meteorologial Inputs Surface Forcing Issue Background: - AGRMET used a 20m height for T, RH, and wind values that are from near surface and 2m (T and RH) and 10m (wind) measurements and GFS output; effects of this are seen in fields computed by Noah as a result. - Noah uses single ZLVL to handle T, RH, and wind. - T and RH are observations (per WMO guidance for smooth, flat fetches, e.g.) at 2m or very simply interpolated to "surface pressure" from GFS pressure levels, while wind is measured at 10m or the GFS 10m wind. - WMO measurements are over smooth flat ground where z0 should be very small. - Noah seems to consider Z from "ground", and to define "ground" as top of canopy when there is one. - Meanwhile, Noah might sometimes use, e.g., 10m (or lowest model level) values without any scaling thereof - thus no accounting for z0 - *upon ingest*. - Noah accounts for z0 in later physics ("later" meaning well after ingesting the winds, e.g.). 27

28 Aim High … Fly, Fight, Win LIS Meteorologial Inputs Surface Forcing Issue Potential solutions (while generally scaling wind from 10m to 2m, since T and RH profile might be less predictable (nocturnal situations etc)): - natural log incorporating z0 - power law excluding consideration of z0, possibly with different exponents given some measure of stability - more elaborate similarity scaling that also accounts for stability Potential issues under consideration: - uncertainty in scaling seems it might result in scaling factors (going from 10m to 2m, e.g.) on order of from.3 or.4 to as much as.9; a factor of 2 or more re surface d(wind)/dz - questions concerning accounting for z0 in this scaling and then accounting for z0 in physics(e.g., it’s already accounted for in physics; accounting for it in scaling upon ingest might simply add uncertainty) - power law exponents may also ill-represent wind profile under some conditions and may be uncertain given uncertain local conditions - time and effort towards a solution in the midst of other uncertainties of a grid cell; might need to perform a more elaborate sensitivity/validation study to discern 28


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