Meteorology Commons^™

Adaptive Monte Carlo Sampling For Cloud And Microphysics Calculations, Thomas Franz-Peter Roessler

Theses and Dissertations

An important problem in large-scale modeling of the atmosphere is the parametrization of clouds and microphysics on subgrid scales. The framework Cloud Layers Unified By Binormals (CLUBB) was developed to improve the parametrization of subgrid variability. Monte Carlo sampling is used to couple the different physical processes, which improves the grid average of subgrid tendencies.

In this Thesis we develop an adaptive Monte Carlo sampling algorithm that re-uses sample points of the previous time step by re-weighting them according to the change of the underlying distribution. This process is called 'what-if sampling' and is an application of importance sampling. An …

Using A Semiprognostic Test To Elucidate Key Model Errors Of Warm Rain Processes Within A Unified Parameterization Of Clouds And Turbulence, Justin Kyle Weber

Theses and Dissertations

The representation of clouds and turbulence remains one of the foremost challenges in modeling earth's climate system and continues to remain one of the greatest sources of uncertainty in future climate projections. Increased attention has been given to unifying cloud and turbulence parameterizations in order to avoid the artificial categorization of cloud and turbulence regimes. One such unified parameterization is known as the Cloud Layers Unified by Binormals (CLUBB). CLUBB is a single column model of clouds and turbulence that assumes subgrid scale variability can be represented by a joint probability density function (PDF) of temperature, moisture, momentum, and hydrometeors. …

A Crowdsourced Hail Dataset: Potential, Biases, And Inaccuracies, Joseph Robert Pehoski

Theses and Dissertations

Hail is a substantial severe weather hazard in the USA, with significant damage to property and

crops occurring annually. Traditional methods of forecasting hail size have limited accuracy, and despite

improvements in remote sensing of precipitation, the fall characteristics of hail make quantification of

hail imprecise. Research into hail is ongoing, but traditional hail datasets have known biases and low

spatiotemporal resolution. The increased usage of smartphones creates the opportunity to use a

crowdsourced dataset provided by the Precipitation Identification Near the Ground (PING) program, a

program developed by the National Severe Storms Laboratory. PING data is compared to approximate …