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Land Surface Physics_ Observational examples and relevance to NWP_ Attributes of NCEP land-surface physics (NOAH model)_ Milestones of land-surface physics upgrades
PBL Physics_ Attributes of PBL physics
Recent Verification of Land-Surface / PBL schemes
Future Work
Land-Surface Physics
Is the Land Surface Important to NWP? “The atmosphere and the upper layers of soil or sea form together a united system.
This is evident since the first few meters of ground has a thermal capacity comparable with 1/10 that of the entire atmospheric column standing upon it, and since buried thermometers show that its changes for temperature are considerable. Similar considerations apply to the sea, and to the capacity of the soil for water. “
L.F. Richardson, 1922
Weather Prediction by Numerical Processes
“Much improved understanding of land-atmosphere interaction and far better measurements of land-surface properties, especially soil moisture, would constitute a major intellectual advancement and may hold the key to dramatic improvements in a number of forecasting problems, including the location and timing of deep convection over land, quantitative precipitation forecasting in general, and seasonal climate prediction.”
National Research Council, 1996
Goals of Improved Land-Surface Physics
Better diurnal cycle of surface heating and evaporation (2 meter TAIR and TDEW)
Reproduce diurnal growth and decay of PBL Improved convective index forecasts Better QPF Expand use of model outputs for hydrologic
and agricultural applications (runoff, snowmelt, soil moisture and temperature)
Notable Examples
Examples of the influence of land-surface processes on the
atmosphere in both models and observations
Atmospheric signature over Oklahoma wheat fields (dark green area from north-central through southwest Oklahoma) during peak
greenness.
Relatively moist, cool PBL over wheat fields Densely cultivated vegetation increases
evapotranspiration Sun’s energy used less for sensible heating Result: surface layer more moist than surrounding areas
by as much as 10 F Result: surface layer cooler than surrounding areas by a
few degrees
1998 Texas /Oklahoma Drought10% Moisture Availability over Region by late July 1998
Parched, dry ground heats quickly under afternoon insolation. Note very warm Eta model soil temperatures over the Red River
Valley
The hot, dry ground results in large sensible heat flux into the PBL, with very hot 2 meter temperatures (>40 C) over the area
So what does a land-surface scheme do?
_ Provides albedo for calculating reflected shortwave radiation_ Calculates evapotranspiration (latent heat flux) from soil and
vegetation canopy_ Provides ground surface (“skin”) temperature for determining
surface sensible heat flux and upward longwave radiation_ Determine impact of snowpack on surface radiation and
heat budgets THE UPSHOT: PROVIDE MORE REALISTIC SURFACE
FLUXES TO PBL SCHEME THAN OLDER, SIMPLE TREATMENTS (e.g, NGM)
Attributes of Eta Land-Surface Physics
4 soil layers (10, 30, 60, 100 cm thick)– predict soil moisture/temperature– Continuous 3-hour update in fully cycled EDAS
Explicit vegetation physics– 12 vegetation classes over Eta domain– annual cycle of vegetation greenness
Explicit snowpack physics– prognostic treatment of snowmelt
COMING SOON:– frozen ground (soil ice) treatment and patchy snow– explicit streamflow routing
Key Assumption: Surface Energy Balance:
Rn=H +LE +GRn = Net Radiation
H = Surface Sensible Heat Flux
LE = Surface Latent Heat Flux
G = Soil (Ground) Heat Flux
Rn − G = H + LE
“Availabl eEner ”gy for Turbulent Fluxes
Prognostic Equations
Soil Moisture:
∂ θ
∂ t
=
∂
∂ z
D
∂ θ
∂ z
⎛
⎝
⎜
⎞
⎠
⎟ +
∂ K
∂ z
+ F θ
– “Richard’s Equation” for soil water movement
– D, K functions (soil texture)
– Fθ represents sources (rainfall) and sinks (evaporation)
Soil Temperature
C θ( )
∂ T
∂ t
=
∂
∂ z
K t θ( )
∂ T
∂ z
⎛
⎝
⎜
⎞
⎠
⎟
– C, Kt functions (soil texture, soil moisture)
– Soil temperature information used to compute ground heat flux
Operational Soil Texture Database
Evapotranspiration Treatment
E = E dir + E t + E c
WHERE:
E = total evapotranspiration from combined soil/vegetation
Edir = direct evaporation from soil
Et = transpiration through plant canopy
Ec = evaporation from canopy-intercepted rainfall
Evapotranspiration (continued)
These terms represent a flux of moisture, that can be parameterized in terms of “resistances” to the “potential” flux. Borrowing from electrical physics (Ohm’s Law):
FLUX = POTENTIAL/RESISTANCE
Potential ET can roughly be thought of as the rate of ET from an open pan of water. In the soil/vegetation medium, what are some resistances to this?
– Available amount of soil moisture
– Canopy (stomatal) resistance: function of vegetation type and amount of green vegetation)
– atmospheric stability, wind speed
Canopy Resistance Issues
Canopy transpiration determined by:
– Amount of photosynthetically active (green) vegetation. Green vegetation fraction (f) partitions direct (bare soil) evaporation from canopy transpiration:
Et/Edir ≈ f(f)
– Green vegetation in Eta based on 5 year NDVI climatology of monthly values
– Not only the amount, but the TYPE of vegetation determines canopy resistance (Rc):
R c =
R c min
LAI F 1 F 2 F 3 F 4
Canopy Resistance (continued)
Where:
Rcmin ≈ f(vegetation type)
F1 ≈ drying power of the sun
F2, F3 ≈ drying power of the air mass
F4 ≈ soil moisture stress
Thus: hot air, dry soil, and strong insolation lead to stressed vegetation!
Eta model uses database of 12 separate vegetation classes
Operational Vegetation Type Database at NCEP
December Green Vegetation Fraction
June Green Vegetation Fraction
Annual Time Series of Green Fraction Over Oklahoma Wheat Country
Early Spring intense green up
Rapid senescence Harvesting and
return of land to near bare soil by early summer
Annual Time Series of Green Fraction over Iowa Corn Fields
Maturity of corn occurs less rapidly than for wheat
Corn harvested much later in the warm season than wheat
Annual Time Series of Green Fraction over Arizona Desert
Not much vegetation to speak of year around!
Any evaporation in model is from bare soil
Eta Model Albedo (snow free)
Snow Cover Treatment
Why so important? Marked effect on albedo and hence the surface fluxes
Snow cover / sea ice product from NESDIS analysis ingested daily at 0000 UTC into NCEP models
Prognostic snow depth during Eta integration, but not in NGM (snowfall computed using 5:1 density ratio from model QPF)
Available energy for snowmelt computed from surface energy balance assumptions
More Snow Information
cover: 23-km N. Hemisphere grid
produced daily by human analyst
multiple data sources:– GOES visible– SSMI snow cover– station reports– NIC ice cover – AVHRR visible
Example NESDIS snow/ice cover
cover: http://hpssd1en.wwb.noaa.gov/SSD/DATA/snow/archive
depth:
http://lnx29.wwb.noaa.gov
Milestones of Eta Land-Surface Physics 31Jan 1996
– Multi-layer soil/veg/snow model introduced – Initial soil moisture/temp from GDAS
18 Feb 1997– new vegetation greenness database from NESDIS– refined adjustment of initial GDAS soil moisture– refined evaporation over snow and bare soil
09 Feb 1998– increase from 2 to 4 soil layers
03 Jun 1998– full self-cycling of EDAS/Eta soil moisture/temp– new NESDIS daily 23-km snow cover and sea ice
PBL Physics
Purpose of PBL Scheme
Two separate schemes for:– Surface layer (constant flux layer)– PBL turbulence above surface layer
Surface layer– Exchange of heat (water vapor) and momentum
with the land surface
PBL turbulence– Vertical dispersion of heat (water vapor) and
momentum throughout the PBL
Attributes of PBL Treatment
Surface layer– Monin-Obukhov similarity theory applied to
determine exchange coefficient. Use of Paulson (1970) stability functions. Does not allow turbulence to diminish to zero near ground in nighttime hours.
– Roughness length for heat differentiated from that for momentum(very important!)
PBL turbulence– Mellor-Yamada level 2.5 turbulence closure– local diffusion
Atmospheric Surface Layer
Sensible Heat Flux Calculation:
H = ρ c p C h u a θ s − θ a( )
- Traditional “bulk aerodynamic” approach
- Ua = wind speed at first eta surface
- θs = “skin temperature”, from land-surface scheme!
- θa = Air temperature at first eta surface
- Ch, Cd = Exchange coefficients for heat and momentum
- diagnosed using “similarity theory”
τ=ρCdua 2
Momentum Flux:
What the heck is “Similarity Theory”?
An empirical technique for drawing vertical profiles of wind and temperature in the surface layer
Rests on the assumption that all profiles have a “similar”shape that can be adjusted with “scaling parameters”
In practice, scaling parameters used to determine magnitide of the surface exchange coefficient
PBL Above the Surface Layer
- Vertical Mixing of heat, moisture, and momentum based on prognostic “turbulent kinetic energy” (TKE):
q = TKE ~ (u’)2 + (v’)2 + (w’)2
- Turbulent eddys:
− w ' u ' = K M
∂ U
∂ z
− w ' θ v ' = K H
∂ θ v
∂ z
K M = lqS M G M ( ) K H = lqS H ( G H )
- KM, KH (mixing coefficients) use info about TKE (q)
- Vertical gradients computed using “local” as opposed to “non-local” information (a local mixing scheme is employed)
Local Versus Non-Local Mixing
– Zi represents height of PBL, diagnosed with minimum TKE threshold– Non-local scheme employs Richardson Number criteria for diagnosing height of PBL top
Z = Z iZ = 0“Non-Local” Diffusion“Local” Diffusion
Recent Verification and New Initiatives
Improved soil moisture via continuous self-cycling Prior to June 1998, soil moisture was initialized from the Global Data Assimilation System, resulting in a severe positive bias !!!
0.1
0.15
0.2
0.25
0.3
0.35
0.4
5 10 15 20 25 30
JULY 1997 (J97) AND JULY 1998 (J98) ETA MODEL AND OBSERVED 5 CM SOIL MOISTURE AT NORMAN, OK
J97 SOIM (obs)J97 SOIM (model)J98 SOIM (obs)J98 SOIM (model)
DAY OF MONTHCurtis MarshallNCEP/EMC
Soil Moisture Improvement (continued)
QuickTime™ and aVideo decompressor
are needed to see this picture.
QuickTime™ and aVideo decompressor
are needed to see this picture.
Comparison of July 1997 and July 1998 bias fields (forecast minus observed) of Eta model top-layer soil moisture (from daily averaged observations and model values). Note the dramatic reduction from 1997 to 1998 as a result of continuous self-cycling.
Validation of Surface Fluxes
-100
0
100
200
300
400
500
600
700
0 8 16 24 32 40 48
18 July 1998 RNET
RNET (obs)RNET (model)
FCST HOUR
0
200
400
600
800
1000
1200
0 8 16 24 32 40 48
18 July 1998 SWRD
SWRD (obs)SWRD (model)
FCST HOUR
Verification of model net radiation (RNET)at Norman, OK shows a positive bias.
This positive bias in RNET bias appears to be the result of a high bias in downward shortwave radiation (SWRD).
Validation of Surface Fluxes (continued)
Too much RNET at the surface results in too much available energy for the other fluxes (ground, sensible and latent). Key question: how is this excess being partitioned among the three?
-50
0
50
100
150
0 8 16 24 32 40 48
July 1998 FXGH
FXGH (model)FXGH (obs)
FCST HOUR
Model ground heat flux (FXGH) appears to be underestimated in this case. Thus, excess RNET not being realized in FXGH.
Validation of Surface Fluxes (continued)
-100
0
100
200
300
400
500
0 8 16 24 32 40 48
July 1998 FXSH
FXSH (model)FXSH (obs)
FXSH (W m
-2)
FCST HOUR
Low ground heat flux results in overly warm skin temperature, which, coupled with high RNET, serves to exaggerate surface sensible heat flux.
Validation of Surface Fluxes (continued)
0
50
100
150
200
0 8 16 24 32 40 48
July 1998 FXLH
FXLH (model)FXLH (obs)
FCST HOUR
Surface evapotranspiration (latent heat flux, FXLH) also appears to be slightly high owing to excess net radiation at the surface, among other factors. Remember: this is a single point validation example.
So what does this all mean?
- In this particular case over Oklahoma, the surface flux biases seem to result in a warm, dry bias in the surface layer
- Be aware! This verification case is during the height of the warm season, over relatively dry soils. The situation can be quite different for other soil moisture regimes at different times of the year!
Areas Needing Improvement
Reduce remaining Eta surface insolation bias Revise ground heat flux physics
– too small (large) over dry (moist) soils Add frozen soil and patchy snow physics
– current 2 m cool bias over shallow snow (assumes complete coverage)
Higher resolution vegetation and soil classes Refine infiltration and runoff formulations
– prevent long-term drift of soil water in EDAS Expand validation effort
Major Initiative: LDAS
A new Land Data Assimilation System (LDAS) for the Eta model
Goal: provide soil moisture/temperature initial conditions superior to current EDAS
Method: drive land-surface “off-line” with gage/radar precipitation and satellite-derived solar radiation
Additions: assimilate satellite-derived soil moisture and skin temperature
Conclusions
New initiatives in improvement of physical parameterizations
An ongoing process External comments and verification
studies VERY helpful Model biases: change with each
upgrade to physics!!!
