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Approved for Public Release – Distribution Unlimited

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

2 16 WS Presentation Overview
Land Information System (LIS) Overview Inputs/Components/Forcings Continuity Domains Schedule Post-processing/Output Way Ahead Overview 3 1

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 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 The Land Information System (LIS) is a very complex land surface characterization package that supports many different requirements. It is an active program in which we are partnered with NASA in the development and production of the LIS model source code. LIS takes the inputs of topography and soil, vegetation properties, snow cover and depth, soil moisture and skin temperature, integrates the data to produce these outputs which can then be used in specific applications (e.g., WRF). The applications vary from TDA software for the Army and AF to crop forecasts for the USDA. Important to remember, is that it is very difficult to make significant strides (similar to clouds) to improve the land surface models outputs and the applications if we lack improved inputs. Over the past 5 years, 16 WS has been closely collaborating with NASA’s Goddard Space Flight Center to improve our global AGRMET modeling system. The effort resulted in a new community land data assimilation modeling framework, called the Land Information System, which replaces the current AFWA AGRMET model. There are significant benefits associated with AFWA migrating toward using the LIS modeling framework versus the AGRMET system: LIS is not limited to a global ½ degree grid projection (e.g. AGRMET). LIS can calculated surface properties at resolutions up to 1 km, on numerous different grid projections. The higher resolution capability is important to improve our operational Army support for mobility/trafficability applications. LIS is not limited to a global domain like AGRMET. LIS can produce output on both regional and global domains. This is important for initializing the regional AFWA Weather Research and Forecasting (WRF) model domains. The LIS software contains parallel computing directives for platforms which support parallel computing. This is important when computing on very high resolution domains, in order to provide products in a timely manner. All of the LIS domains run independently, supporting concurrent computing for nested domains. The same LIS software used to support real-time operations can also be reconfigured to support re-analyses (science testing). That enables the science community to use the exact same software AFWA uses in operations, and enables a faster transition of research & development (R&D) into the operational baseline (with appropriate science testing/validation performed during integration by 16 WS). The LIS software supports ensemble weather analyses/prediction. LIS has numerous parameterizations, input forcing datasets (e.g. climatology fields, vegetation health, land surface type), and/or land surface modeling options. The additional LIS members will better improve uncertainty estimates needed by the AFWA Ensemble Prediction System and Point Analysis Intelligence System

5 LIS Inputs 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 - AFWA baseforcing component generates Shelter height temperature, wind, and humidity are provided by blending GFS 0 hour analysis with surface observations using a Barnes technique - Precipitation is calculated in the AFWA baseforcing component from a blend of SSMIS rain rate EDRs, the GEO-PRECIP model, CDFS II estimates, climatology, and observations (rain gauge and present/past weather) blended using the Barnes technique - AFWA radiation is calculated in the baseforcing module using cloud data from the World-Wide Merged Cloud Analysis (WWMCA) from AFWA’s Cloud Depiction and Forecast System (CDFS II) & snow cover/depth from the SNODEP model Longwave radiation after Idso Shortwave radiation after Wachtmann & Shapiro

6 LIS Design - Components
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 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 LIS Meteorologial Inputs ‘AGRMET’ Precipitation Forcing
AFWA Blended Global Precipitation Estimate Precipitation Forcing 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 The surface observations are retrieved from the CDMS database. The data is used to calculate rainfall based on the current or past weather reports. The processing accounts for as many 6 and 12 hourly rain gauge amounts as possible, using the information provided by each station. The processing includes special logic to handle different reporting practices of various countries. The rain rates from the SSMIS processing generates a 3 hourly SSMIS-based estimate. The climatological precip amount, climatological precip per precip day amounts, and the climatological RTNEPH cloud amounts and the actual CDFSII total cloud amounts are used to generate a CDFSII based precip estimate. The GEOPRECIP rain rates and rank values are read in to generate a precipitation estimate. The rank values provide a measure of the goodness of the estimate. (1-better than SSM/I, 2-better than present weather estimate, 3-better than CDFS-II based), 3- default value of GEOPRECIP estimate, 5-worse than climatological estimate) Finally, all the different estimates are merged based on the specified hierarchy, for each point on the grid. Depending on the cycle time, parse the 6 hour or 12 hour precip amounts into 3 hour amounts. Validate the 3 hourly real precip amounts and estimates and generate a merged precip fied using a modified barnes objective analysis technique

9 LIS Meteorologial Inputs ‘AGRMET’ Radiation Forcing
Longwave Radiation Shortwave Radiation Cloud information (coverage, top, types) from the AFWA CDFSII For the downwelling shortwave radiation, the snow free albedo for the current day is computed first. This algorithm updates the albedos based on the current snow cover. The Shapiro (1987) solar radiation scheme computes the incoming solar radiation data using WWMCA data. The method is based on a 3-layer plane-parallel atmosphere. Each layer transmits and reflects some of the solar radiation incident on it from above and below. Each layers’ transmissivity and reflectivity value is based on the cloud type and the solar zenith angle. Empirically derived reflectivity and transmissivity for each layer and the fraction of the solar radiation entering the top of the troposphere are used to calculate the surface insolation for each point. Reference: Shapiro (1987) References: Idso (1981) and Wachtmann (1975)

10 After 12 hours values have declined at most points
LIS Continuity 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 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 Two weeks of processing begins to reveal some detail but has not dried arid areas sufficiently After 12 hours values have declined at most points 10

11 Current LIS Domains 25km ‘global’ domain 15km regional domains
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 11

12 LIS Data in AFWA WRF 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 LIS Schedule LIS alternates 12-hour and 6-hour cycles
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 0Z 18Z 12Z 6Z 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

14 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)

15 LIS at 1km Resolution (prototype)
LIS can be configured to run at up to 1km resolution Currently supporting development projects 15

16 Noah in LIS vs. WRF 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 LIS Way Ahead CMORPH Precipitation
Updated (Noah 3.1 & new Land Surface models (FASST) Additional data assimilation packages (AMSR-E/SMOS/SMAP) Observed Greenness Fraction LIS-WRF Coupling Fu Liou & CRTM radiation and radiance assimilation with Cloud Optical Properties 17


19 Backup slides

20 Root Zone Layer Boundaries
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 0 cm 10 cm 40 cm 100 cm 200 cm Root Zone Layer Boundaries 20

21 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

22 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

23 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

24 Root Zone Variables Soil Temperature
0 cm 10 cm 40 cm 100 cm 200 cm Soil Temperature Volumetric Soil Moisture (liquid & solid) Volumetric Soil Moisture (liquid only) Relative Soil Moisture

25 LIS-WRF Coupling AFWA, NASA & NCAR joint study

26 LIS : Kumar et al. (2006,2008,2009), Peters-Lidard et al. (2007)
LIS Way Ahead 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. LIS : Kumar et al. (2006,2008,2009), Peters-Lidard et al. (2007)

27 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.).

28 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

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