Margaret 'Peggy' LeMone

Senior Scientist
TIIMES - MMM
BEACHON

Contact Information:
PO Box 3000, Boulder, CO 80307-3000
Office: FL3 - 3050
Telephone: 303-497-8962
Email: lemone@ucar.edu
Home Page

Project Summary:

My current research interests involve the atmospheric boundary layer and its interaction with the surface and clouds; and the structure of deep precipitating convection and its effect on momentum.

Strassberg, LeMone, and Warner. 

Click on picture to view the entire figure.

Figure 1. Comparison of winds estimated from aircraft data using Monin-Obukhov similarity theory. Date gathered around local noon on fair-weather days.

Abstract. 
Comparisons of 10-m AGL wind speeds from numerical weather prediction (NWP) models to point observations consistently show that model daytime wind speeds are slow compared to observations, even after improvement of model physics and going to smaller grid spacing.  Previous authors have attributed the discrepancy to the difference in the areas represented and the difference in wind-vane exposure from what is typical for the area the model represents.  Using daytime fair-weather data from the May-June 2002 International H2O Experiment (IHOP_2002), the effect of wind-vane exposure is explored by comparing observed 10-m winds from 9 surface flux towers in well-exposed locations to modeled 10-m winds found by applying Monin-Obukhov (M-O) similarity for unstable conditions to flight-track-averaged data collected by the University of Wyoming King Air over flat to rolling terrain with occasional trees and buildings.  In the calculations, King-Air winds and fluxes are supplemented with thermodynamic means and fluxes from the surface flux towers.  After considerable care in characterizing and reducing biases in aircraft winds and fluxes, M-O based surface winds averaged 0.5 – 0.7 ± 0.2 m s-1 less than those measured – about the same as the smaller reported discrepancies between NWP model and observed winds.

Narrative.
The figure summarizes the results from the four days for which Monin-Obukhov similarity applied.  Note that in all cases, the aircraft-based estimates of 10-m wind speed are lower than those measured at the surface flux towers, with differences typically about 0.5 m s -1.  In all cases except for 31 May, the differences between the measured and estimated 10-m wind exceed the estimated error by about a factor of two.  The overall difference between estimated and observed 10-m wind is 0.5 – 0.7 ± 0.2 m s-1.

The aircraft winds are sampled along a 50-60 km track that traverses houses and trees as well as grasses, while the flux tower winds are sampled in open areas with large fetch. Thus the aircraft-based 10-m wind estimates are probably more comparable to the winds predicted by numerical weather prediction models.

This work was Diane Strassberg’s undergraduate thesis at CU.  Publication was funded by MMM and the TIIMES Water cycle Initiative.

LeMone., Chen, Alfieri, Cuenca, Hagimoto, Blanken, Niyogi,  Kang,  Davis, and Grossman, 2007

Click on picture to view the entire figure.

Figure 2. For 29-30 May, volumetric soil moisture (%) for the 4 Western Track surface flux sites. Squares: 24-h average Oregon State University soil moisture profiles (3 at Site 1), 5-cm point from hand-collected Trime data; triangle: NCAR soil moisture, upside-down triangle: average Trime soil moisture for transects S and W of sites. For Site 1, data from 30 May; Data from Sites 2 and 3 are plotted for 29 May (solid squares) and 30 May (hollow squares).

Abstract. 
The May-June 2002 International H2O Project was held in the U.S. Southern Great Plains to determine ways that moisture data could be collected and utilized in numerical forecast models most effectively.  We describe the surface and boundary-layer components, and indicate how the data can be acquired.  These data document the eddy transport of heat and water vapor from the surface to the atmosphere (in terms of sensible heat flux H and latent heat flux LE), and the radiative, atmospheric, soil, and vegetative factors that affect it; so that moisture and heat supply to the atmosphere can be related to surface properties both for observational studies and testing land surface models.  The surface dataset was collected at 10 surface-flux towers at locations representing the major types of land cover and extending from SE Kansas to the Oklahoma Panhandle.  At each location, the components of the surface energy budget  (H and LE, net radiation, and soil heat flux)

are documented each half hour, along with the weather (wind, temperature, mixing ratio, air pressure, precipitation) and soil temperature, moisture, and matric potential down to 70-90 cm beneath the surface at 9 of the 10 sites. Observations of soil and vegetation properties and their horizontal changes were taken near all 10 towers during periodic visits.  Aircraft measurements of H and LE from repeated low-level flight tracks along three tracks collocated with the surface sites extend the flux-tower measurements horizontally.  We illustrate the effects of vegetation and soil moisture on the H and LE and their horizontal variability.

Narrative
The particularly exciting aspect of this dataset is all the supplemental data (metadata) taken during periodic visits, and the soil-moisture profiles, produced using support from the Water Cycle Initiative, in collaboration with Richard Cuenca of Oregon State University.  An example of the profiles observed appears in figure 2.  The leftmost profile is an average of three; the data are a day later than for the other sites due to a malfunction.

This additional information enables us to perform an approximate water budget, which appears in figure 3.  Of particular interest is the large difference around 27 May (Day 147) at Site 1 – the remaining water almost certainly was lost to runoff.  This rain event produced flooding that influenced the horizontal variation of sensible and latent heat fluxes for several days afterward.

LeMone, Chen, Alfieri, Tewari, Geerts, Miao, Grossman, and Coulter

Abstract
Analysis of daytime fair-weather aircraft and surface-flux tower data from the May-June 2002 International H2O Project (IHOP_2002) and the April-May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in SE Kansas.  Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation.  If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope DxyLE/DxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area.  Based on the surface energy balance, H+LE = Rnet – Gsfc, where Rnet is the net radiation and Gsfc the flux into the soil, Rnet – Gsfc ~constant over the area implies an approximately –1 slope.   Right after rainfall, H and LE vary too little horizontally to define a slope.  After sufficient drying to produce enough horizontal variation to define a slope, a steep (~-2) slope emerges. The slope becomes shallower and better defined with time, as H and LE horizontal variability increases.  Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE.  These trends are based on land-surface model (LSM) runs and observations collected under nearly-clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

This paper explores the reasons for systematic horizontal variation of sensible and latent heat flux under nearly-clear skies and an amply-watered surface, namely that areas with higher (lower) H are associated with lower (higher) LE in such a way that a plot of H vs LE for a given time interval will yield a negative slope.  We argue using data from aircraft (for IHOP_2002) and the surface (from CASES-97) and runs from land-surface models, that this slope is steeper for higher soil moisture.

Narrative
Central to this work is the surface energy budget,

H + LE = Rnet - Gsfc

where H is the sensible heat flux, LE is the latent heat flux, Rnet is the net radiation, and Gsfc is the flux into the

soil.  Note that for Rnet - Gsfc = constant, H + LE is constant, and the slope of the line on a scatter plot of LE as a function of H averaged over a given time interval should be exactly -1.  Departures from this value can be related to horizontal variations in Rnet and Gsfc. On the days of interest, the skies are clear or nearly clear, thus, averaged over time, downwelling radiation is nearly constant, so Rnet variation is produced primarily by upwelling radiation which is proportional to Ts4, where Ts is the surface temperasture.

In figure 4, there are four Noah model scenarios, two for early spring, and two for summer.  The dominant types of ground cover are grasslands and winter wheat.  In the early spring, the winter wheat is green and actively photosynthesizing, while the grass is still dormant or partially dormant.  For the spring scenarios, the latent heat flux stays high over the winter wheat (the simulation starts out with saturated soil everywhere), with small sensible heat flux.  Available energy (H+LE) is large because flux into the soil is reduced by the presence of the lush vegetation.  On the other hand, the dormant grass has large sensible heat flux and small latent heat flux, and smaller available energy due to larger flux into the soil, putting the “grass” point closer to the graph’s origin.  This leads to large negative slopes for the Day 1 lines.  For the first several days, H and LE (and flux into the soil) change little at the winter wheat point, but a reduction of flux into the soil as it dries out beneath the grass site

leads to increased available energy with time, and the slope of the line connecting the two points becomes closer to -1 with time.   The arrows indicate the additional effect of horizontal water transport through the soil on the rate of dry-down:  the grass sites are associated with ridges, producing what we hypothesize to be a faster drydown than accounted for in these model runs.  Conversely, the green winter wheat is located in lower-lying areas potentially supplied by water from higher up, which should slow the dry-down relative to the simulation.

In the summer the grass acts as the high-LE low-H surface; with the senescent/mature/harvested winter wheat acting as the low-LE high-H surface.  Again, the modeled slope becomes shallower with time.  The arrows are drawn to suggest that drainage of the grassy areas will speed up drydown there, while the drydown in the winter wheat area is slowed down.

This pattern is consistent with an observed tendency for the slope ΔLE/ΔH to become shallower with time after rainfall.  This would suggest slightly shallower slopes in the spring compared to summer. The data we have is consistent with this tendency, but the sample is too small to confirm this trend. 

Consistent with the scenarios, there seems to be a general relationship of slope to soil moisture for this particular region, as shown by the figure 5 to the right.

LeMone, Tewari, Chen, Alfieri, and Niyogi:

Abstract

Sources of significant differences between observations and simulations using the Noah-model based High-Resolution Land Data Assimilation System (HRLDAS) are examined for sensible and latent heat fluxes H and LE, surface temperatureT_s, and vertical temperature gradients T₀-T_s, where T₀ is at 2 m.  The observational data were collected on 29 May 2002, using the University of Wyoming King Air and four surface towers placed along the sparsely-vegetated 60-km International H2O Project (IHOP_2002) Western Flight Track.  This day had nearly clear skies and a strong north-south soil-moisture gradient, with wet soils and widespread puddles at the south end of the track and drier soils to the north.  Relative amplitudes of H and LE horizontal variation were estimated by taking the slope of the least-squares best-fit straight line ΔLE / ΔH on plots of time-averaged LE as a function of time-averaged H for values along the track.  It is argued that observed H and LE values departing significantly from their slope line are not associated with surface processes and hence need not be replicated by HRLDAS.

Reasonable agreement between HRLDAS results and observed data was found only after adjusting the coefficient C in the Zilitinkevich equation relating the roughness lengths for momentum and heat in HRLDAS from its default value of 0.1 to 0.5.  Using C = 0.1, adjusting soil moisture to match the observed near-surface values increased horizontal variability in the right sense, raising LE and lowering H in the moist south end, but both the magnitude of H and the amplitude of its horizontal variability relative to LE remained too large; adjustment of the green vegetation fraction had only a minor effect. With C = 0.5, model-input green vegetation fraction, and our best-estimate soil moisture, H, LE, ΔLE / ΔH, and T₀-T_s. were all close to observed values.  The remaining inconsistency between model and observations – too high H and too low LE over the wet southern end of the track – could be due to HRLDAS ignoring the effect of open water.  Neglecting the effect of moist soils on the albedo could also have contributed.

Narrative.

The figure above compares the surface fluxes from the original (“Control”) HRLDAS run to the sensitivity run with our best-guess soil-moisture, and with the coefficient C in the Zilitenkevich equation relating the roughness lengths for heat and momentum in the model adjusted from the default value of 0.1 to 0.5.  The latter value produces the observed exchange coefficient for H based on the surface data, as well as the observed ratio of amplitudes of H to LE horizontal variability (about 2 to 3), as determined from the slope DLE/DLE.   In addition, the average simulated difference between surface and air temperature is much closer to the observed values than the control value.  We suspect that some of the remaining discrepancy at the southern end of the figure above is due to the presence of widespread puddles, which are not represented in the model.  The puddles are illustrated in figure 6.

Publications:

Gochis, D. J., G. Bonan, E. Brandes, F. Chen, D. Lenschow, M. LeMone, R. Rasmussen, T. T. Warner, M. Ek, K. Mitchell, 2007: A ten-year vision for advancing coupled land-atmosphere prediction. Water Resources Research. (Submitted)

LeMone, M., M. Tewari, F. Chen, J. G. Alfieri, D. Niyogi, 2007: Adding horizontal heterogeneity as a criterion for evaluating a land-surface model. Mon. Wea. Rev.. (Submitted)

Alfieri, J. G., D. Niyogi, M. A. LeMone, F. Chen, S. Fall, 2007: A simple reclassification method for correcting uncertainty in land use/land cover datasets used with land surface models. Pure Appl. Geophys., 164, 1789-1809, doi: 10.1007/s00024-007-0241-4.

Armstrong, J. A., S. K. Avery, H. B. Bluestein, E. W. Friday, M. A. Geller, E. A. Holland, C. F. Kolb, M. A. LeMone, R. E. Lopez, S. Solomon, J. M. Wallace, R. A. Weller, S. E. Zebiak, 2007: Strategic guidance for the National Science Foundation's support of the atmospheric sciences. National Research Council of the National Academy of Sciences, Board of Atmos. Sci. and Climate, A.M Staudt and C. Mengelt, Ed., National Research Council.

Trier, S. B., F. Chen, K. W. Manning, M. A. Lemone, C. A. Davis, 2007: Sensitivity of the PBL and precipitation in 12-day simulations of warm-season convection using different land surface models and soil wetness conditions. Mon. Wea. Rev.. (Submitted)

Sun, J., S. P. Burns, A. C. Delaney, S. P. Oncley, A. A. Turnipseed, B. B. Stephens, D. H. Lenschow, M. A. LeMone, R. K. Monson, D. E. Anderson, 2007: CO2 Transport over Complex Terrain. Agric. For. Meteorol., 145, 1-21, doi: 10.1016/j.agrformet.2007.02.007.

Chen, F., K. W. Manning, M. A. LeMone, S. B. Trier, J. G. Alfieri, R. Roberts, M. Tewari, D. Niyogi, T. W. Horst, S. P. Oncley, J. B. Basara, P. D. Blanken, 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climat., 46, 694-713, doi: 10.1175/JAM2463.1.

Kiemle, C., G. Ehret, A. Fix, M. Wirth, G. Poberaj, R. M. Hardesty, W. Brewer, C. Senff, M. LeMone, 2007: Latent Heat Flux Profiles from Collocated Airborne Water Vapor and Wind Lidars during IHOP. J. Atmos. Ocean. Technol., 24, 627-639.

Kang, S.-L., K. Davis, M. LeMone, 2007: Observation of variable ABL structures over a heterogeneous land surface. J. Hydrometeorol., 8, 221-244.

Strassberg, D., M. A. LeMone, T. T. Warner, J. G. Alfieri, 2007: Comparison of observed 10-m wind speeds to those based on Monin-Obukhov similarity theory using IHOP_2002 aircraft and surface data. Mon. Wea. Rev.. (In Press)

Alfieri, J., X. Xiao, D. Niyogi, R. A. Pielke, Sr., F. Chen, M. A. Lemone, 2007: Satellite-based modeling of transpiration and evaporation of grasslands and croplands in the Southern Great Plains, USA. Global Planetary Changes. (Submitted)

LeMone, M. A., F. Chen, J. G. Alfieri, M. Tewari, B. Geerts, Q. Miao, R. L. Grossman, R. L. Coulter, 2007: Influence of land cover and soil moisture on the horizontal distribution of sensible and latent heat fluxes in southeast Kansas during IHOP_2002 and CASES-97. J. Hydrometeorol., 8, 68-87, doi: 10.1175/JHM554.1.

LeMone, M. A., F. Chen, J. Alfieri, R. Cuenca, Y. Hagimoto, P. Blanken, D. Niyogi, S. Kang, K. Davis, R. Grossman, 2007: NCAR/CU surface, soil, and vegetation observation network during the IHOP_2002 field campaign. Bull. Amer. Meteor. Soc., 88, 65-81.

Kang, S.-L., K. J. Davis, M. A. LeMone, 2006: Observations of the ABL over a heterogeneous land surface during IHOP_2002. J. Hydrometeorol.. (In Press)