Sensitivity of Atmospheric Boundary-Layer Winds and Stability to
Soil Moisture and Cloud Properties
David R. Stauffer
Department of Meteorology
Penn State University
503 Walker Building
University Park PA 16802-5013
Email: stauffer@meteo.psu.edu
The objective of the project is to understand the fundamental relationships between the
scales of heterogeneous forcing (land surface, clouds) and the atmospheric response of boundary
layer (BL) characteristics in stable and unstable conditions.
The relative amount of solar radiation partitioned to the surface sensible and latent heat
fluxes to the atmosphere has a dominant effect on daytime BL properties, where these fluxes are
significantly influenced by soil moisture and cloud cover. Effects of soil-moisture and cloud
signatures on the BL should include effects on mixing depth, static stability, and thermally
forced circulations. The BL response to heterogeneous soil-moisture and cloud forcing is
presently poorly understood and rarely validated in model predictions. The proposed research
will seek to evaluate the fundamental mechanisms that control the nature and scale of the
response of the unstable BL wind and turbulence characteristics in models and observations.
Existing turbulence formulations have focused primarily on the convectively unstable
BL, but tend to break down under statically stable conditions with light winds. Moreover, as the
stable atmospheric boundary layer (SABL) develops under conditions with strong cooling,
meandering motions and gravity-driven currents develop over even gently complex terrain.
These tend to disrupt the locally driven SABL and may even become dominant. Meandering
motions are physically not well understood, but are known to feed back on the shear-driven
generation of turbulence in a way that is not included in existing parameterizations. It is now
practical to review the observational evidence and idealized modeling results in stable
conditions, and to attempt to apply these lessons in an advanced 3D numerical modeling system
designed for such purposes.
The methodology to study the boundary layer responses involves detailed field-program
observational data sets and both large eddy simulations (LES) and mesoscale models with real
and idealized forcings. Figure 1 illustrates the effects of observed heterogeneous soil moisture
on the BL in a mesoscale model. Mesoscale model-simulated BL depths are compared between
an experiment utilizing high-resolution observed soil moisture data (line labeled “Detailed SM”)
and one using a smoother soil moisture field (“Smooth SM”). When validated against observed
BL depth (via airborne lidar, “Lidar Obs”), the model simulation using the high-resolution soil
moisture better predicts the secondary observed maximum in BL depth at about 37N. This
illustration shows how surface heterogeneity can affect BL structure and indicates the potential
importance of better understanding surface-BL interaction processes. LES results in Figure 2
show the potential temperature distributions at 100 m above ground. The simulation in Figure 2
(a) uses homogeneous surface fluxes, and the simulation in Figure 2 (b) uses heterogeneous
surface fluxes prescribed as a sinusoidal function. In the homogeneous surface flux case only
turbulent variations of potential temperature can be seen but in the heterogeneous case mesoscale
and turbulence variations coexist. LES-based research allows for a better understanding of the
mesoscale and turbulent processes in the BL over heterogeneous surfaces.
During the first 12-month period, idealized and realistic specifications of surface/cloud
parameters will be used in 1D mesoscale modeling and 4-km (horizontal resolution) 3D
mesoscale modeling, while idealized LES using 100-m resolution will be used to investigate
scales down to 1 kilometer or less. The second 12-month period will involve case-specific, realdata 3D mesoscale modeling and more sophisticated LES using heterogeneous terrain and noncyclic lateral boundary conditions. Work on the stable atmospheric boundary layer will begin
during this period, starting with a review of existing datasets and modeling, as well as scoping a
field study in Central PA. The final 12-month period will involve final model simulations and
increased focus on understanding the theoretical implications of the modeling work as well as
possible impacts on transport and diffusion simulations. For example, we will test relevant
similarity theories (e.g., Monin-Obukhov and mixed-layer similarities for homogeneous surfaces,
and “patch” similarity theories for heterogeneous surfaces) for the full ranges of scales and
identify necessary parameterization changes where this work indicates that the theories do not
apply. Alternative parameterizations of the SABL for 3D mesoscale models will be tested and
evaluated against datasets that were analyzed in the previous 12-month period. A range of
configurations will “stress” the models and parameterizations to analyze their ability to simulate
complex SABL flow characteristics and identify limits of the physical representations. Analysis
of the new datasets from the Central PA scoping field study will begin, and a more
comprehensive field program with turbulence measurements in stable conditions will be planned.
Figure 3 shows a preliminary 9-h prediction of surface winds in the SABL at 1 m above
ground level using the WRF-ARW model at 444-m horizontal resolution over the complex ridgeand-valley topography (color code to right of figure) of central PA. Figure 4 shows the
corresponding circulation simulated in a vertical cross section through the central part of the
WRF domain (yellow line in Fig. 3) under stable conditions. Evaluations of twice daily
simulations will determine the skill of WRF and its most advanced BL turbulence schemes for
SABL conditions using vertical resolutions as fine as 2 m.
In addition to the knowledge gained about the relationship between soil moisture and
cloud variability, and BL properties, the results will advance our knowledge for generating
mesoscale-model ensemble members that contain sufficient spread near the surface. Developing
this variability is essential for estimating transport and diffusion (T&D) uncertainty with
ensemble forecasts using coupled mesoscale and T&D models.
Figure 1
a.
b.
Figure 2
Figure 3
Figure 4