Subtropical Marine Low Clouds and Surface Wind Distributions

This book is designed to represent subtropical marine
stratus and
stratocumulus (MSC) and surface winds in a simple but
physical
consistent way, which has applications in climate
modeling and
offshore wind energy development. A simple low cloud
cover
scheme is developed in Chapter 2, in which the
variation of MSC is
formulated by connecting low cloud amount with
lower tropospheric stability (LTS), large-scale
subsidence, and
dynamical transport of available dry inhibition
energy below trade
wind inversion. The new scheme produces realistic
seasonal and
inter-annual variations of MSC amounts and
systematically reduces
the NCAR CAM3.1 model biases as shown in Chapter 3.
The lower
troposphere available thermal inhibition energy is
also shown in
Chapter 4 to be a skillful predictor in diagnosing
MSC in monthly
and seasonal time-scales. The influence of MSC, ocean
SST, and
large-scale divergence on surface wind probability
distribution are
addressed in Chapter 5 using satellite observations
and a simple
stochastic model, which can successfully reproduce
the observed
mean, the standard deviation, and skewness of surface
wind speeds
in the southeast Pacific.

Autorentext
Dr. Yanping He was born in Beijing, China. She got her Bachelor and Master degree from Peking University in China and her PhD degree from Georgia Institute of Technology in USA in May, 2007. Now She is continuing her research in the beautiful city of Victoria, BC, Canada.

Klappentext
This book is designed to represent subtropical marine stratus and stratocumulus (MSC) and surface winds in a simple but physical consistent way, which has applications in climate modeling and offshore wind energy development. A simple low cloud cover scheme is developed in Chapter 2, in which the variation of MSC is formulated by connecting low cloud amount with lower tropospheric stability (LTS), large-scale subsidence, and dynamical transport of available dry inhibition energy below trade wind inversion. The new scheme produces realistic seasonal and inter-annual variations of MSC amounts and systematically reduces the NCAR CAM3.1 model biases as shown in Chapter 3. The lower troposphere available thermal inhibition energy is also shown in Chapter 4 to be a skillful predictor in diagnosing MSC in monthly and seasonal time-scales. The influence of MSC, ocean SST, and large-scale divergence on surface wind probability distribution are addressed in Chapter 5 using satellite observations and a simple stochastic model, which can successfully reproduce the observed mean, the standard deviation, and skewness of surface wind speeds in the southeast Pacific.