S19 Water Level Prediction Model for the Port of Baltimore

Sunday, 6 January 2013
Exhibit Hall 3 (Austin Convention Center)
Whitney Rutledge, Texas A&M University, Corpus Christi, TX; and P. Tissot

Handout (3.8 MB)

A model was developed to predict the occurrence of thunderstorms within the next 3 to 12 hours based on Sea Surface Temperatures and Atmospheric Predictions. The model was developed for a grid of equidistant 20-km x 20-km box regions offshore of the South Texas Gulf of Mexico coast (29.3N- 26.9S, 100.6W – 96.1W). Inputs to the model combine predictions from a Numerical Weather Prediction (NWP) mesoscale models (Eta, WRF-NMM) and high-resolution/subgrid scale sea surface temperature data. The NWP model predictions provide information as to the likely mesoscale environment while subgrid scale data provides information on the meso- scale microscale forcing. The meso-scale input is composed of statistics (mean, maximum, maximum gradient and cluster based measures of the sea surface temperature in each box). Sea surface temperatures are obtained from NASA's Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center. A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis is produced daily on an operational basis at the JPL Physical Oceanography DAAC using weighted averages on a regional 0.011 degree grid. This Research to Operations (RTO) analysis is based upon a composite of either nighttime or daytime GHRSST L2P skin SST from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Aqua and Terra platforms, and subskin SST observations from the Advanced Microwave Scanning Radiometer-EOS (AMSRE). Four unique products (composites) are created: MODIS Terra/AMSRE day and night, and MODIS Aqua/AMSRE day and night. The particular datasets represents a MODIS Aqua/MODIS Terra and AMSRE composite using either a daytime or nighttime data. The algorithm is based on a weighting scheme and compositing whereby MODIS data are used if they exist to preserve the highest resolution possible. The product is categorized as blended because no attempt is made to correct for foundation or skin temperature. Project Data covers the period December 2010 to September 2012. The model is calibrated over a randomly selected portion of the data set and evaluated on the remaining portion of the data. The performance of the calibrated ANN is evaluated by comparing predictions with observations from the US National Lightning Detection Network (NLDN). The performance parameters used included the Heidke, Pierce, and Yule's Q skill scores. A threshold for the occurrence of thunderstorm is determined subjectively based on the respective Probability of Detection (POD) and False Alarm Rate (F) output at each point on the receiver/relative operating characteristic (ROC) curve generated from the testing data set during calibration.
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