Lab Affiliation(s):
Ralph M. Parsons Laboratory for Environmental Science and Engineering
Dara Entekhabi
Areas of Expertise:
  • Remote sensing
  • Boundary-layer meteorology
  • Hydrology
Expected date of graduation:
September 9, 2016

Kaighin McColl

  • PhD


  • Civil and Environmental Engineering

Lab Affiliation(s): 

Ralph M. Parsons Laboratory for Environmental Science and Engineering


Dara Entekhabi

Top 3 Areas of Expertise: 

Remote sensing
Boundary-layer meteorology

Personal Statement: 

My research interests include microwave remote sensing of the hydrosphere and biosphere, land-atmosphere interactions, environmental turbulence and convection.

From October 2016, I will be moving to Harvard University as a Ziff Environmental Fellow.

Expected date of graduation: 

September 9, 2016


Thesis Title: 

Spectral modeling of an idealized atmospheric surface layer

Thesis Abstract: 

Almost all of humanity resides in the atmospheric surface layer (ASL), so its state
(e.g., temperature, humidity, wind velocity) is relevant to a range of applications in
human health, agriculture, and ecosystem health. However, the ASL is turbulent,
and therefore characterized by complex dynamics across a wide range of spatial and
temporal scales. Explicitly modelling turbulent motions in the ASL at all scales is
computationally expensive and beyond current capabilities. In this thesis, a framework
is proposed for parsimoniously modelling a broad range of turbulent motions in
wall-bounded turbulent flows such as the ASL, using spectra of turbulent fluctuations
as inputs. Turbulent spectra contain information on turbulent motions across scales,
and are constrained by theory and observations. By propagating spectra through a
cospectral budget, a model of the mean velocity profile (MVP) is obtained. Comparison
with a direct numerical simulation (DNS) of a neutral channel flow reveals a
good correspondence between the MVPs of the cospectral budget model and DNS,
provided the pressure-decorrelation model in the cospectral budget includes established
effects of wall-blocking. The cospectral budget model is then extended to the
case where the wall-bounded flow is heated from below, as in an unstable ASL. The
MVP and mean buoyancy profile (MBP) of the cospectral budget model and the
DNS agree qualitatively, with remaining differences attributable to neglected terms
in the cospectral budget, and the low Reynolds number of the DNS. The normalized
turbulent statistics of the heated duct flow DNS agree surprisingly well with ASL
measurements, despite the low Reynolds number of the DNS and other differences.
Treating the DNS as an idealized ASL, a spectral model is derived to describe the
partitioning of turbulent kinetic and potential energy between turbulent transport of
heat and momentum in the ASL. The model reproduces observed dissimilarity between
turbulent heat and momentum transport in unstable conditions. It attributes
the dissimilarity to contributions from large eddies in turbulent heat transport, which
are largely ignored in existing ASL parameterizations in weather and climate models.

Top 5 Awards and honors (name of award, date received): 

NSF Graduate Research Fellowship, 2013-2016
Dean's Honors Prize at The University of Melbourne, 2009

5 Recent Papers: 

Contact Information:
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MIT Building 48-216