1. The Emergence of Land-Surface Modeling in Modern-Era NWP: The NCEP Experience and Collaborations NWP 50-Year Anniversary Symposium 15-17 June 2004 Ken Mitchell NCEP Environmental Modeling Center NCEP: Where America's Weather and Climate Services Begin

2. Improving Weather and Climate Prediction: Becoming a Complete Earth System Endeavor 1 - ATMOSPHERE: troposphere, stratosphere (GARP) - initial conditions require atmosphere data assimilation 2 - OCEAN:deep ocean, seas, coastal ocean, sea ice (TOGA/CLIVAR) - initial conditions require ocean data assimilation 3 - LAND:soil moisture, snowpack, vegetation, runoff (GEWEX/GAPP) - initial conditions require land data assimilation

3. Historical Timeline of NCEP LSMs: With respect to NCEP atmospheric models 1955-1965: Barotropic Model –no land surface, no radiation, no diurnal cycle 1965-1985: Multi-layer PE and LFM models –simple surface friction effect on wind velocity –surface sensible/latent heat fluxes over ocean only –assume zero sensible/latent heat flux over land –no diurnal cycle, no radiation 1986-1995: global MRF, regional NGM & Eta models –first viable land surface models included –bucket model hydrology and slab model thermodynamics –first diurnal cycle of land surface energy balance & radiative forcing

4. Historical Timeline of NCEP LSMs: With respect to NCEP atmospheric models 1995-2004: global GFS, regional Eta & WRF models –The Oregon State University (OSU) LSM –The NCEP Noah LSM descendant of the OSU LSM Four soil layers –Includes liquid and frozen soil moisture (OHD) –Vertical profiles of soil moisture and soil temperature Explicit vegetation canopy with root zone –Satellite NDVI-based seasonal cycle of green vegetation fraction (NESDIS) Snowpack physics, including water content and density –Daily snow cover and snowpack analyses from NESDIS and AFWA –Dynamic snowmelt and snow sublimation Stream network and streamflow simulation

6. Multi-institution Land-Surface Partners: 1990 - present Air Force (AFWA and AFRL) NESDIS Land Team (ORA) NWS Office of Hydrological Development (OHD) NOAA Office of Global Programs (OGP): –GEWEX Programs: GAPP, GCIP, PILPS, ISLSCP –NLDAS: N.American Land Data Assimilation System Six university partners plus above partners Many are GAPP/OGP sponsored NASA Hydrological Sciences Branch and GMAO NCAR WRF Land Surface Working Group –USWRP sponsored

7. NESDIS Interactive Multi-sensor Snow (IMS) Product: Daily 4-km Snow/Ice Analysis Used along with AFWA Snowdepth Analysis for the daily Initialization of snowpack in NCEP global and regional models 28 Feb 2004 13 May 2004

8. Partitioning of Incoming Solar Radiation 34% reflected to space -- 25% reflected by clouds -- 7% back scatter by air -- 2% reflected by earth sfc 19% absorbed by atmos -- 17% absorbed by air -- 2% absorbed by clouds 47% absorbed by earth sfc

9. Land Surface Energy Balance (Exp: Monthly mean, mid-day summer, central U.S.) Sd - αSd + Ld - Lu = H + LE + G 800 - 150 + 400 - 550 = 125 + 300 + 75 Complexity of LSM driven by representation of LE and G Sd = Downward solar insolation: 800 W/m**2 -αSd = Reflected solar insolation:-150 Ld = Downward longwave radiation: 400 -Lu = Upward longwave radiation:-550 (based on land skin temp) G = Ground heat flux 75 H = Sensible heat flux:125 LE = L*E = Latent heat flux (evaporation)300

10. Land Surface Water Balance (Exp: monthly, summer, central U.S.) dS = P – R – E dS = change in soil moisture content: - 75 mm P = precipitation: 75 R = runoff 25 E = evaporation 125 (P-R) = infiltration Evaporation is a function of soil moisture and vegetation type, rooting depth/density, fractional cover, greenness. All terms in units of mm.

11. Simple “one-layer” slab LSMs of 1985-1995 era at NCEP Bucket Model for hydrology Surface Evaporation: LE = B * EP B = Surface Wetness coefficient (fraction) EP = potential evaporation: function of atmospheric conditions (humidity, wind speed, temperature) Slab Model (“force-restore”) for ground heat flux

12. The Surface Wetness Field in the NGM Model (Range: ~ 0.04 – 0.20 ) (values plotted are actual * 100)

13. Land Surface Evaporation Treatment in modern-era land models WHEREIN: E = total evapotranspiration from combined soil/vegetation E dir = direct evaporation from top soil layer E c = evaporation from canopy-intercepted precipitation or dew E t = transpiration through plant canopy via root uptake, and constrained by the canopy resistance to evaporation

14. Noah Land Model Prognostic Equations _Soil Moisture : – “ Richard’s Equation” for soil water movement – D, K functions (soil texture) – F  represents sources (infiltration) and sinks (evaporation) _ Soil Temperature – C, Kt functions (soil texture, soil moisture) – Soil temperature information used to compute ground heat flux

15. Vegetation Greenness April Climatology Vegetation Greenness July Climatology Developed and provided by NESDIS/ORA -- New NESDIS realtime weekly update now being tested by NCEP

17. Ground Heat Flux Evaluation in Eta Model using FIFE Field Exp: Slab/Bucket LSM versus Noah LSM (Betts et al., 1997, MWR)

18. Validation of surface fluxes of four LSMs vs 15 ARM flux stations. Monthly mean Rnet, LE, H and G for Jan 98 to Sep 99

19. Improving the Mesoscale NWP Forecasts via Land-Surface Influences NWP prediction improvement goals - 2 meter air temperature and humidity - 10 meter wind vector - PBL T and Td profiles - convective stability indices - integrated moisture flux convergence - precipitation and cloud cover

20. July 2003 Monthly Mean Diurnal Cycle of 2-m Air Temperature: Obs vs NCEP Models (3) for Midwest U.S.: Eta, GFS/AVN, NGM NGM Obs ETA AVN

21. NCEP Eta model forecast during July 1998: Texas/Oklahoma drought, 24-hour forecast valid 00Z 27 July 1998 In late July 1998, after nearly two months of self-cycling the land states in the EDAS, the Eta model successfully captured the extremely dry soil moisture (upper left) and warm soil temps (upper right) over the Texas/Oklahoma region, yielding forecasts of high 2-m air temps (lower left) and deep, dry, hot boundary layers that verified well against raobs (e.g., at Norman, OK – lower right). air temperature (2-meter) Norman, OK sonde (obs=solid, model=dashed) soil moisture availability (1-m)soil temperature (5-cm)

22. In the forecast period between the analysis steps of the 12h pre-forecast data assimilation period, at each time step and at each point where observed precipitation is available, we compare P mod to P obs, then modify the model’s temperature, moisture, cloud and rain field to be more consistent with observed precipitation. The Eta Data Assimilation System: EDAS A Coupled Land Data Assimilation System with hourly assimilation of observed precipitation Pre-forecast data assimilation period Free forecast period

23. Figure 8. (a) 1-15 July 1998 gage-observed total precip (mm), (b) 'snapshot' of hourly Stage IV radar/ Gage precip (06Z, 15 July 1998); EDAS total precip of 1-15 July 1998 for (c) control run without precip assim, and (d) test run with hourly Stage IV precip assim; EDAS soil moisture availability (% saturation) of top 1-m soil column valid at 12Z 15 July 1998 (e) without precip assim, and (f) with precip assim. IMPACT OF HOURLY PRECIPITATION ASSIMILATION IN ETA MODEL (a) (c) (e) (f) (d) (b) OPS EDAS: TEST EDAS: 15-DAY OBS PRECIP (1-15 JUL 98) SOIL MOISTURE 15 JUL 98 15-DAY PRECIP 1-15 JUL 1-HR STAGE IV PRECIP

24. 25-Year EDAS-based Regional Reanalysis: Example of July 1988 vs. 1993 Difference of observed monthly total precipitation from gauge- only analysis (Higgins and Shi, Schaake personal comm.) Difference of monthly total precipitation produced by Regional Reanalysis with its precipitation assimilation Drought of 1988 vs Flood of 1993

25. SAMPLE LAND-SURFACE OUTPUT FROM RR DROUGHT 1988 July 15 July, 21Z FLOOD 1993 July 15 July, 21Z soil moisture (percent of saturation) in top 1-meter

26. DROUGHT YEAR (1988): 15 July, 21Z FLOOD YEAR (1993): 15 July, 21Z Boundary layer depth [m] SAMPLE LAND-SURFACE OUTPUT FROM RR

27. Conclusions Land surface modeling has advanced intensely at NCEP from mid 1980’s to present Above advancements have benefited greatly from multi-institution and multi-disciplinary partnerships These land surface advancements have improved the skill/accuracy of NWP predictions