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<p>MET faculty candidate seminar on Tuesday February 28th at 3:15 PM</p>
<p>Title:<b><span style="font-size:12.0pt;font-family:"Calibri",sans-serif;
mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font:
minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times
New Roman";
mso-bidi-theme-font:minor-bidi;mso-ansi-language:EN-US;mso-fareast-language:
EN-US;mso-bidi-language:AR-SA"> </span></b>Tropical
precipitation and its environmental
controls: reverse-engineering the physics from statistics</p>
<p>Speaker: Dr. Fiaz Ahmed (UCLA)</p>
<p>Abstract:<br>
Tropical rain affects us all. The local effects of tropical
rain include floods and droughts that disrupt large agrarian
societies. The remote
effects modify weather patterns even in the midlatitudes. To
understand how tropical
precipitation arises, one must study atmospheric convection—which
ultimately
generates rain—and its immediate environment. However, the
convection-environment
problem is confounded by fast timescales (a few hours), small
spatial scales (a
few km), and tight coupling between clouds and dynamics.
Consequently, our
climate model projections of future precipitation remain
uncertain. In this
talk, I present an approach in which space-borne precipitation
data are used to
build a simple physical model of tropical convection. This
approach identifies
(and helps construct) a cloud buoyancy measure from environmental
thermodynamic
variables. This buoyancy measure is the key to
convection-environment relations;
it explains land-ocean differences in precipitation statistics,
improves theoretical
understanding of tropical waves and helps diagnose process-level
errors in climate
models. However, the buoyancy measure falls short when predicting
the magnitude
of precipitation extremes. This deficiency is addressed using a
Bayesian machine
learning tool that helps fully describe the precipitation
distribution. This
talk will conclude with a forward-looking discussion about a
data-driven,
stochastic parameterization scheme to simulate rainfall
variability in intermediate-complexity
models.
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