The Effects of Surface Heterogeneity on Boundary-Layer Structure and Fluxes Margaret LeMone and Fei Chen, with acknowledgments to collaborators: T. Horst, S. Semmer, R. Roberts, Jim Wilson (NCAR) B. Geerts (Univ. of Wyoming) R. Cuenca (Oregon State) + R. Grossman (CoRA) + P. Blanken (CU) + D. Niyogi (NCSU) + E. Small (CU) + D. Stauffer and K. Davis (PSU), T. Holt (NRL) Goals: 1. Collect and analyze IHOP surface/vegetation/soil/aircraft data 2. Validate and improve models (Noah LSM, CLM, HRLDAS, Coupled WRF/Noah system) 3. Investigate the relationships between land variability and convection initiation
Transcript
The Effects of Surface Heterogeneity on Boundary-Layer Structure and Fluxes
Margaret LeMone and Fei Chen, with acknowledgments to collaborators:
T. Horst, S. Semmer, R. Roberts, Jim Wilson (NCAR) B. Geerts (Univ. of Wyoming) R. Cuenca (Oregon State) + R. Grossman
(CoRA) + P. Blanken (CU) + D. Niyogi (NCSU) + E. Small (CU) + D. Stauffer and K. Davis (PSU), T. Holt (NRL)
Goals: 1. Collect and analyze IHOP surface/vegetation/soil/aircraft
data 2. Validate and improve models (Noah LSM, CLM, HRLDAS,
Coupled WRF/Noah system)3. Investigate the relationships between land variability and
Evaluation of flux measurements for relating to surface heterogeneity
Lx = record lengthF = flux (average w’s’)F = turbulence integral scalerw’s’’
2 = correlation between w’ and s’
2''
2''
2
1
12
sw
sw
x
FF
r
r
LF
1. Statistics
2. Adherence to surface energy balance constraint:
H + LE = Rn – G ~ constant spatially H = sensible heat flux LE = latent heat flux Rn = net radiatioin G = heat flux into ground
3. Existence of horizontal variability
June 17, 20, 22 composite (21 legs)
17 J 201 deg at 7.7 m/s20 J 163 deg at 5.3 m/s22 J 179 deg at 9.4 m/s
Last rain on15 June
For comparison…CASES-97 Sensible Heat Flux (H)
-96.9o is site of lowH; maximumshifted to east
CASES-97 LE (-96.9o High LE)
Net result of flux difference: boundary-layer depth 150-250 m deeper at west end of track
17 June – W 250 m deeper than E20 June – W 150 m deeper than E22 June – W 240 m deeper than E
Eastern Track Synthesis reinforces findings of Grossman et al. 2004.
Land use affects radiometric surface temperature (Ts),potential temperature () , fluxes (H and LE),
and turbulence level (w’2)
Ts, , H, w’2 higher and LE lower over dormant vegetation; reverse for green vegetationVegetation related to terrain April-early May (CASES): Winter wheat to W and
in riparian zones green, grass dormant late May-June (IHOP): Grass green, winter wheat
senescent and then harvested.Soil moisture effect less obvious (long drydown for 17,20, 22 June)
Western Track: Radiometric Surface Temperature
Rainfall:16-17 May < 5 mm23 May ~ 3 mm26-27 May: 20-30 mm
in north to >80 mm to south
4-5 June ~15-20 mm
Silty Clay Loam Sandy Loam Soils Land cover
Air Temperature
Rainfall:
16-17 May < 5 mm 23 May ~ 3 mm26-27 May: 30-30 mm north 90 mm south 4-5 June 15-20 mm
Distribution of Fluxes (Sensible Heat)
177/13.2
191/4.9
133/2.5199/10.6
Distribution of Fluxes (Latent Heat)
29 May 2002: Contrast Between Station 3 (north) and Station 1 (south) ~50 km apart
Site 1 (western track) Site 3 (western track)
rain
Soil moisture
Net RadLatent heat
Sensible heat
Site3: drier, larger sensible heat flux
Turbulence – 29 May 2002
Wyoming Cloud Radar: Independent Confirmation of More vigorous BL to north on 29 May
NORTH – More Vigorous Convection
SOUTH – Less Vigorous Convection Bart Geerts
Synthesis for Western Track
Potential temperature H, and Ts larger tonorth most days (but not all)-- Sandy soil to north (better drained)-- More rain to south
29 May strong heterogeneity due heavy rain onsouth end of track 2 days previous.
Mesoscale variability on some days of currentlyunknown origin
IHOP Boundary Layer/Surface Data
• Data sets completed – Near-surface weather conditions, PAR, surface incoming and net radiation
(full component at sites 1,8, and 9), precipitation, surface heat fluxes, ground heat flux
– Latent heat fluxes recalculated from energy budgets– Soil bulk density, soil texture, saturated hydraulic conductivity, unsaturated
hydraulic conductivity function, thermal conductivity, and the soil-water retention function
– Weekly vegetation data: NDVI, leaf area index (LAI), stomatal resistance, transpiration
– CO2 concentrations at sites 1 and 8– MODIS data – Diurnal cycle of stomatal resistance and transpiration for a few selected
sties– Aircraft fluxes and NDVI along the flight tracks– Available @ www.rap.ucar.edu/projects/land/IHOP/index.htm
• Data sets being processed – Soil moisture content, soil water tension (potential), and soil temperature
profiles from the surface to a depth of 90 cm (about seven weeks). Three profiles at sites 1 and 9
– Landsat data
Modeling and Analysis Effort
• Validate and improve LSMs– Weather Community Noah LSM (CU/NCAR, NCSU/NCAR): – CLM (CU/NCAR)
• Verify WRF/Noah LSM coupled model (NCAR)• Verify high-resolution land data assimilation
system (HRLDAS) • Understand relationships between soil moisture
and convection initiation (extension of our USWRP work)
High-Resolution Land Data Assimilation System (HRLDAS) : Capturing Small-Scale Variability
• Input: – 4-km hourly NCEP Stage-II
rainfall– 1-km landuse type and soil
texture maps – 0.5 degree hourly GOES
downward solar radiation – 0.15 degree AVHRR vegetation
fraction – T,q, u, v, from model based
analysis• Output: long term evolution of
multi-layer soil moisture and temperature, surface fluxes, and runoff
4-km HRLDAS surface soil moisturein IHOP domain 12 Z May 29 2002
High-Resolution Land Data Assimilation System (HRLDAS) Concept
Run uncoupled LSM on the same grid as MM5/WRF to avoid:• Mismatch of terrain, land use
type, soil texture, physical parameters between sources of soil data and NWP models
• Need for interpolation
4-month (2002) HRLDAS Soil Moisture vs Oklahoma Mesonet Observation
