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Atmospheric Science [clear filter]
Tuesday, April 24

10:15am PDT

Why Cloud Iridescence Mimics Birefringent Colors
Cloud iridescence is one commonly overlooked, yet spectacular atmospheric optical phenomenon, in which clouds become vividly multi-colored, like oil slicks in the sky. However, unlike an oil slick, which is the result of thin-film interference, the selection of colors in iridescent clouds are due to a different optical physical process called diffraction. Sunlight diffracts around thin cloud layers, interfering constructively and destructively at different locations for different wavelengths. This interference is seen as a range of colors that appear to follow the Michel-Levy Birefringence chart. Supported by observations of the polarization state (degree and angle of linear polarization) of iridescent clouds, this research suggests why cloud iridescence, a diffraction phenomenon, mimics the familiar color patterns of thin-film interference. The chromatic effects due to interference from both diffraction from cloud droplets and thin films are compared based on the points in time and space in which the phase of incident unpolarized light is altered as a function of wavelength. This comparison illustrates why chromatic patterns match those of the birefringence interference chart, regardless of the process by which different wavelengths shift phases.


Tuesday April 24, 2018 10:15am - 10:35am PDT
213 Rhoades Robinson Hall

10:35am PDT

Climate Change In India: An Analysis Of Future Precipitation Rates
In recent decades, water stress in India has become increasingly severe. Lower crop yields, civil unrest, and increased political tensions have become more commonplace as severe weather events are becoming more extreme and sporadic. Because of this emerging reality and its potential consequences, it is vital to be able to anticipate potential changes in India’s precipitation rates as a byproduct of global climate change. Previous research points to an increase in precipitation as the climate warms, but with large regional disparity and more severe extremes in both rain events as well as droughts. To provide further support for this consensus across a variety of potential outcomes, multiple runs using the Global Circulation Model (GCM) EdGCM, have been used in an attempt to support these claims. These runs consist of different potential concentrations of major Greenhouse Gases based on a variety of possible trends found in the most recent Intergovernmental Panel on Climate Change (IPCC) report. This presentation will highlight expected changes in annual precipitation and interannual variability through the end of this century, as well as their underlying causes, in an attempt to determine potential issues facing future policymakers.

Tuesday April 24, 2018 10:35am - 10:55am PDT
213 Rhoades Robinson Hall

10:55am PDT

Improving The Prediction Of Daily Maximum Temperatures Using A Neural Network
A weather forecaster uses model data, persistence, and historical data to predict values for weather variables on the following days. The application of a neural network to use the same data and train to identify a relationship between the predictors and the predicted value would result in a more refined source of data for weather forecasters to use. In this study a neural network is used to account for consistent bias in the models, overall trends in temperature, and current conditions to predict a maximum temperature for the next day. The model is trained on a dataset containing two models, historical daily maximum temperatures, and the previous day’s maximum temperature over two years from 2014 to 2016. The model is evaluated using data from 2016 to 2017, but will be able to take current data and predict the next day’s temperature with greater accuracy than model output. The neural network used is a multi-layer perceptron classifier that trains using backpropagation. The model will be assessed for accuracy and used to predict maximum daily temperatures for the Asheville area. This could result in improved reliability when predicting maximum temperatures. The model is easily scalable and could be used with various other weather variables to create more accurate data for forecasters to use and make short term weather forecasts that are more accurate than model output statistics.

Tuesday April 24, 2018 10:55am - 11:15am PDT
213 Rhoades Robinson Hall

11:15am PDT

Case Study Analysis Of Ozone Concentrations In Houston TX During Hurricane Harvey
Ozone and sulfur dioxide are two of the six criteria pollutants monitored by the Clean Air Act, as they pose many human health and environmental risks, which is of particular concern in Houston TX. Due to the several hundred petrochemical plants and the several dozen crude oil refineries in Houston, the city experiences some of the highest concentrations of ozone, sulfur dioxide, and peroxy radicals of any city in the United States. The complex geography, influence of the inertial gravity wave (i.e. sea breeze), and the normal synoptic-scale flow makes Houston unique regarding ozone attainment. The landfall of Hurricane Harvey in Houston in August 2017 created an opportunity to assess the city’s ozone response. Ozone, wind, and volatile organic compound (VOC) data collected before, during, and after the hurricane are analyzed. Using a network of automated observation sites between the Houston ship channel and the city center, VOC (e.g. Benzene, Ethane, Ethylene, and Sulfur Dioxide) increases were correlated with non-typical ozone changes (NTOCs). As a result of the flooding, winds, and shutdown of petrochemical plants, several tons of VOCs were released, contributing to the highest 8 h ozone average in 2017 for the entire state of Texas. Additionally, from September 1 to September 5 average wind speeds were ≤ 5 mph, and wind directions were primarily from the East, allowing ozone and VOCs to advect to and build up in the Houston downtown area.

Tuesday April 24, 2018 11:15am - 11:35am PDT
213 Rhoades Robinson Hall