ABSTRACT. by the Division of Nearshore Research (DNR) at Texas A & M University Corpus

ABSTRACT The Atmospheric Wind Predictions for the Gulf of Mexico project is a joint effort by the Division of Nearshore Research (DNR) at Texas A & M University Corpus Christi (TAMU-CC) and the National Weather Service (NWS) forecasting offices at Corpus Christi and Brownsville. The main objective of the project to design and implement a user-friendly VISTA (Visualization, Statistical and Analysis) tool which enables researcher`s, and modeler`s to compare and evaluate different wind model forecasts in an efficient manner has been developed. The VISTA tool facilitates the data archival, analysis and visualization of the stored and recent available wind forecast. The tool also evaluates the different wind model forecasts by comparing the wind forecast parameter values with the actual measured data of DNR-Texas Coastal Observation Network (TCOON) stations at same latitude and longitude. The project has been developed and the resulting tool has been integrated with the framework of the TAMUCC-DNR systems and Website. ii

TABLE OF CONTENTS Abstract...... ii Table of Contents....iii List of Figures... v List of Tables......vi 1. Background and Rationale.. 1 1.1 Introduction... 1 1.2 Importance of the Project......2 1.3 Intended Audience of the Application......3 1.4 Similar Works 4 2. Narrative..8 2.1. Current Wind Models Visualization and Comparison..8 2.2. VISTA Tool for Visualization and Comparison... 9 2.2.1. Model Forecast Acquisition...9 2.2.2. Model Forecast Data Archival...9 2.2.3. Model Forecast Statistics and Visualization 10 2.2.4. Model Forecast Analysis and Evaluation 11 2.3. MesoEta File Formats.11 3. System Design......13 3.1 System Requirements..13 3.2 Main Components...13 3.3.1 Forecast Data Acquisition Module..16 iii

3.3.2 Real-time Statistics and Visualization Module...21 3.3.3 Forecast Model Analysis and Evaluation Module...25 3.4 Table Structure...27 4. Testing and Evaluation..29 4.1 Software Testing..29 4.2 Usability Testing...29 5. Results and Conclusion...31 Acknowledgements 34 Bibliography and References. 35 APPENDIX A.... 37 APPENDIX B.... 38 APPENDIX C.... 43 APPENDIX D.... 46 APPENDIX E.... 49 APPENDIX F 51 APPENDIX G.... 53 APPENDIX H.... 56 APPENDIX I.59 APPENDIX J.....62 APPENDIX K.... 69 APPENDIX L.... 73 APPENDIX M... 76 APPENDIX N.... 80 APPENDIX O.... 84 APPENDIX P.... 90 APPENDIX Q.... 94 APPENDIX R.... 99 APPENDIX S......103 iv

APPENDIX T....107 APPENDIX U....111 APPENDIX V....115 APPENDIX W....119 v

LIST OF FIGURES Figure 1.1 Division of Nearshore Research TCOON.....1 Figure 1.2 Forecast Data Presentation TCOON....... 4 Figure 1.3 Numerical Model Output....... 6 Figure 2.1 Visualization of Eta-12 Wind Model Forecast over California......8 Figure 2.2 Model Forecasts Archival...10 Figure 2.3 Real-time visualization of Wind Forecast Statistics Data... 10 Figure 3.1 Map of locations for model forecasts around the Gulf of Mexico...14 Figure 3.2 Overview of the System Design..15 Figure 3.3 User Interface for Searching Archived Forecast....17 Figure 3.4 Real-Time Comparative Recently Available Forecast Data for Models 19 Figure 3.5 TCOON vs. Model Comparative Recent Available Forecast Data 20 Figure 3.6 Generated Comparison Graphs for Model Forecast...20 Figure 3.7 Spatio-temporal Comparative Visualization Map..22 Figure 3.8 User Interface for Wind Statistics Visualization 23 Figure 3.9 Visualization of Wind Statistics for Model Forecasts...24 Figure 3.10 User Interface for Error Statistics Data 26 vi

LIST OF TABLES Table 2.1 File Format Description of Wind Forecast Data.... 10 Table 3.1 Structure of the Locations Table....28 Table 3.2 Structure of the Models Table.....28 Table 3.3 Structure of the Location Forecasts Table....28 Table 1 Table of Locations for Model Forecasts Around the Gulf Of Mexico.....37 vii

1. BACKGROUND AND RATIONALE 1.1 Introduction The Division of Nearshore Research (DNR) at Texas A&M University-Corpus Christi maintains a large network of stations along the coast of Texas measuring near real-time water level, wind, salinity, water quality, and other environmental parameters along the Texas coast. These data are reported on the World Wide Web and used by national, state, and local organizations for littoral boundary determination, construction, marine safety, and research. [DNR 2005] [Tissot 2003] Figure 1.1 Division of Nearshore Research TCOON [DNR 2005] DNR has developed systems for the automated acquisition, archival, quality control, and distribution of environmental data. Automation and reliance on open systems 1

and Web-based protocols are keys to effective operation and maintenance of the networks. [Tissot 2005] As part of the project, wind, barometric pressure, and air temperature predictions are archived for locations in and on the coastlines of the Gulf of Mexico. The predictions are collected 4 times a day (0000 0600 1200 1800 UTC) at the Corpus Christi Weather Forecasting Office (CCWFO) and archived in the DNR database. Predicted values are stored for 3 hour intervals up to 48 hour forecasts. Atmospheric predictions have been archived since 2002 for the Eta-12 or MesoEta model. Predictions from the MesoEta interpolated, GFS models have been stored since the middle of July 2004. The database has been created to compare predictions from different atmospheric models and for use within Artificial Neural Networks (ANN) models developed both at DNR and at the WFO. [Stearns 2002] [Stearns 2002] 1.2 Importance of the Project The main objective of the project was to develop a tool named VISTA (VIsualization, STatistical and Analysis) that facilitates the data archival, provide statistical analysis and visualization of the stored and recently available wind forecasts. The VISTA tool facilitates the user`s in evaluating and comparing the different wind model forecasts with the real-time measured TCOON winds and generate the relative error statistics. The development of the tool has eliminated many of the difficulties associated with archival, visualization, evaluation and analysis of different atmospheric model forecasts. The project has developed a tool which can archive, visualize, compare, analyze and evaluate the wind model forecasts. The VISTA tool will be the first of its kind to be 2

developed with model evaluation and comparison features. The system will serve as a vital tool with features which will enable its users to study and evaluate the performance of the atmospheric models with the TCOON measurements. The VISTA tool has been integrated with the framework of the TAMUCC-DNR systems and Website. The project serves as a user-friendly tool which enables researchers, modelers and wind forecast data users to compare and evaluate different models in an efficient manner. 1.3 Intended Audience for the Application The project primarily targets three different categories of audience namely environmental researchers, neural network and statistical modelers, navigators and the common public. The system aids the environmental researchers to study and evaluate the performance and difference between the atmospheric model forecasts. The following features of the system were designed to help the researchers in their study and evaluation: 1. Real-time comparison of recently available data, 2.Comparative graphs generated by the system 3.Real-time visualization of wind speed and wind direction, 4.Seasonal comparative study of atmospheric forecasts and 5. Sliding window self-model evaluation The system enables neural network and statistical modelers to study the difference between the model forecasts. The study enables them to use the knowledge appropriately to design, implement and tune their models. The following features of the system aids the modelers: 1.Searching and extracting the archived wind model forecasts, 2. Real-time comparison of recently available data, 3.Comparative graphs generated, and 4. Real-time visualization of wind speed and wind direction. 3

The system serves as an informative tool for the navigators and common public by providing the following features: 1. Real-time comparison of recently available data, 2.Comparative graphs generated for atmospheric models, and 3.Real-time visualization of wind speed and wind direction. The above specified features provide information about recent wind forecast for the specific locations or locations that are nearby to the public. 1.4 Similar Works For more than ten years the Texas Coastal Ocean Observation Network (TCOON) has measured and archived water levels as well as other coastal variables that have been recorded in CC Bay and along the coast of Texas. TCOON was constructed and operated by Texas A&M University-Corpus Christi (TAMUCC) Conrad Blucher Institute (CBI) Division of Nearshore Research (DNR).Figure 1.2 illustrates a visual display of near-real time data presentation of TCOON. [Michaud 2001] [Patrick 2002] Figure1.2 Forecast Data Presentation TCOON [DNR 2005] 4

The overall network provides real-time or near real-time coastal measurements such as water levels, wind speeds and wind directions, barometric pressures as well as other variables such as dissolved oxygen, salinity and wave climates depending on the station. This abundance of archived measurements provides a unique opportunity to test data intensive modeling and forecasting techniques such as the application of Artificial Neural Networks (ANN) to forecast future water levels and improve on the presently inadequate harmonic forecasts [DNR 2005] The VISTA and TCOON have a similar methods of archival and visualization of real-time data from the TCOON stations. The reason is to facilitate integration of the system with the DNR systems and Website. Another relevant system is operated by the Desert Research Institute - Program for Climate, Ecosystem and Fire Applications provides visualization of model forecast output. (See Figure 2.1 Page 8) from the most recent 00 UTC and 12 UTC Eta Model run for the state of California. Derived variables include the mixing height and mean transport wind for every 6 hour forecast out to 48 hours. The product provided is considered experimental. [DRI 2005] The National Weather Service provides real-time animated videos for wind forecast parameters for MesoEta and GFS and MM5 models. The forecast provided by the NWS is available from http://www.nco.ncep.noaa.gov/.[collins 2004] The Ohio State University WWW weather server provides objective numerical model guidance, both regional and global as shown in Figure1.3. The Ohio State 5

Figure 1.3 Numerical Model Output. [DRI 2005] University WWW weather server provides forecast data and visual images for individual forecast models. The forecast provided by the Ohio State University WWW weather server is available from http://twister.sbs.ohio-state.edu/models.html. The above stated weather forecast systems provide individual forecast data and visualization for the atmospheric models such as MM5, MesoEta, and GFS. The current researchers and modelers do not have any means to access historic data and tools for analysis, evaluation, visualization and study of wind model forecast in comparison with the actual measured parameters. 6

The system is design and implemented as a part of this project and provides the following features which are important for modelers and researchers and generally not integrated within a easily accessible web based tool.1.comparison and visualization of atmospheric forecast difference between the models, and 2. Evaluation and Statistical data for models in comparison with DNR-TCOON measured wind parameters. 7

2. NARRATIVE 2.1 Current Systems for Visualization and Comparison The current systems focus on particular wind forecast models. The focus is either MesoEta or GFS or MM5 wind forecast model. The systems visualize the wind forecast for specific models as images. The systems do not compare the difference between model forecasts. Current Systems do not evaluate the atmospheric models in comparison with measured wind parameters for the same location. Figure 2.1 illustrates the visualization of the MesoEta wind model forecast over California. Figure 2.1 Visualization of Eta-12 Wind Model Forecast over California [DRI 2005] 8

Images are generated from the most recent 00 UTC and 12 UTC Eta Model run for the state of California. Derived variables include the mixing height and mean transport wind for every 6 hour forecast out to 48 hours. [DRI 2005] 2.2. VISTA Tool for Visualization and Comparison The VISTA Tool has primarily focused on features that are not available in the current systems. They are comparison, visualization of atmospheric forecast variation between the models, and Evaluation, Statistical data for models in comparison with DNR-TCOON measured wind parameters. The application contains four major modules which are briefly described in the sections 2.2.1-2.2.4. 2.2.1. Model Forecasts Acquisition The model forecasts acquisition module has been designed to import the files into the VISTA tool and to store them for further processing by the other modules. The module will also format the incoming raw files into usable text files. The functional system has been integrated with TCOON database to compare the model forecasts with TCOON measured winds. The programs are scheduled in the DNR system servers and are executed every six hours to extract data and update the forecast database. 2.2.2. Model Forecasts Archive The model forecasts archive module archives the incoming data into text files and will organize, store them in text files according to models, stations and years. This feature will help the neural network modelers to search, extract and use the forecast data as training inputs to their neural network models. Figure 2.2 demonstrates the archival process of the VISTA Tool. 9

Incoming data Processing Storage Model name Processing Module Year Location name Figure 2.2 Model Forecasts Archive 2.2.3. Model Forecasts Statistics and Visualization This feature helps in visualizing the wind forecast and enables users to compare and study the difference between the wind model forecasts. Figure 2.3 demonstrates a method by which the wind statistics has been visualized by the VISTA tool. Figure 2.3 Real-time Visualization of Wind Forecast Statistical Data The system also generates real-time maps with wind vectors to visualize the wind speed and wind direction. The comparative graphs and tables generated by the tool helps the 10

users to compare the difference among the models in contrast with the TCOON measured parameters. 2.2.4. Model Forecasts and Evaluation One of the primary objectives of the system is to evaluate the performance of the models. The objective has been made possible through the following features of the module: 1.Real-time generated statistical data, 2.Forecast comparison with TCOON measured winds and 3.Sliding window self-test model evaluation. In the sliding window self-test model evaluation, the model stability is analyzed by comparing the model forecast with common time interval for the same model between two forecast intervals. 2.3. Model File Formats Files of MesoEta data come from NWS with the following file name format: TAMUCCneuralnet_01052004_1800_test The MesoEta data comes in a file consisting of a simple header followed by the data. The header is a blank line, a title line and another blank line. The following table describes the file formats of incoming data. Table 2.1 File Format Description of Wind Forecast Data File Format Description TAMUCC Destination of the data from NWS Neuralnet Project with which the data has been used _01052004 Date the prediction was made (mmddyyyy) _1800 Time of day the prediction was made using military time _test Optional Component Each line following the header is a full record with elements delimited by commas as follows: Station, Latitude, Longitude, Date, Time, Time Zone, Length of Prediction, U - NS wind vector component, V - EW wind vector component, Barometric Pressure, 11

Temperature The other models also have the same file formats. The wind speed and wind direction are calculated in real-time from the U - NS wind vector component and V - EW wind vector component. 12

3. SYSTEM DESIGN 3.1. System Requirements The tool is implemented on the Linux operating system on an apache Web server. The system uses Perl and Common Gateway Interface Scripts (CGI) for programming interfaces and with MySQL as its database back-end. [Addison-Wesley 2001] The project modules have been implemented on the Division of Nearshore Research systems. The tool have been implemented and tested for evaluation purposes in testing systems of DNR. After development and testing of the system, the tool has been merged into the production system of DNR. [O Reilly Inc. 1998] [O Reilly Inc. 2000] From the user s point of view, the Web-based user interfaces has been made independent of operating system, browser and screen resolutions. The only need for the user is access to Web browser in order to study the wind model forecast data. 3.2. Main Components There are three major components of the project. They are: a. Atmospheric Wind forecast data-mining and archival module: This module extracts the forecast data from the storage base to be fed in as inputs to neural network models. b. Real-time visualization and comparative statistics module: This module primarily supports features for real-time comparison and visualization of wind speed and wind direction on maps. The module generates comparative graphs to study the difference among the models. The module also generates real-time statistics on wind speed and wind direction. 13

c. Model evaluation and analysis module: This module generates statistical evaluation information related to each model for a particular location. The module enables features that provide self-model evaluation using sliding window model test. The module also enables the users to study the seasonal variability among forecast models. The user is needed just to click on interface forms or needed to select the data they require and programs in the system automates or generates the required data making the system more user-friendly. The following figure3.2 shows the map of locations for the wind model forecast around the Gulf of Mexico. Figure 3.1 Map of Locations for Model Forecasts around the Gulf of Mexico Figure 3.2 provides an overview of the system design. 14

Figure 3.2 Overview of the System Design 15

The following sub-sections provide a detailed description of various modules of system. 3.2.1. Wind forecast data-mining and archival module This module has been programmed using Perl scripts scheduled on the Linux server to archive the incoming raw data into usable text files which can be used as input to neural network applications. The sub-modules in the program will be programmed in such a way that the system can import any external data by which it could be ported into usable data in database. The programs are scheduled in the production servers of Division of Nearshore Research. [O Reilly Inc. 2001] The programs are scheduled to be executed on the incoming forecast data for ten minutes after every six hours a day. The lag of ten minutes helps to overcome the difficulty associated in the missing forecast data due to the delay in incoming forecast data. The source code for the archival module can be found in APPENDIX C. In order to keep track of the missing forecast files, three programs have been written and scheduled on the DNR servers. They are scheduled to check for the forecast files on daily basis, monthly basis and yearly basis. On successful checking the archive directory of the forecast data, if a missing forecast file is detected, it automatically sends out mails to the National Weather Service officials, Dr.Philippe Tissot, Assistant Professor of Physics and Physical Sciences and system administrators at DNR. After converting the raw data, it archives the incoming data into categories like model, location names and year in which it was received. The Perl programs in this module enable features to access and compare real-time recent available wind model forecast data. The module also enables users to search and extract the historic data 16

available in the database. Figure 3.3 provides a visual display of the user interface for searching archived forecasts. Figure 3.3 User Interface for Searching Archived Forecast 17

The VISTA Tool has user-friendly clickable maps, through which the users have been able to access the recent available data for the specific locations just by clicking on the locations on the map. The maps are designed such that users can differentiate the arbitrary locations for which model forecasts are received and also arbitrary locations where TCOON stations are present, through which they can compare the model forecasts with real-time measured data. The users need to select the following parameters on the forms to perform the data query: a) Select a location from a list of locations for which data needs to be retrieved b) Select single or multiple forecast parameters from list of forecast parameters. The following are the forecast parameters available for the users: U Wind Vector (m/s) V Wind Vector (m/s) Barometric Pressure (mbar) Wind Speed (miles/hour) Wind Direction c) Select the model(s) from the list of forecast models. The following are the list of forecast models available. MesoEta-12 Forecast Model NCEP Global Forecast System MesoEta-12 Interpolated Forecast Model Compare TCOON Stations d) Select the date range intervals in the following format. 18

all 3 models. yyyy-mm-dd (yyyy-year,mm-month,dd-date). Figure 3.4 provides a screen shot of the recently available wind forecast data for Figure 3.4 Real-Time Comparative Recently Available Forecast Data for Models The VISTA tool also allows users to compare the model forecast data with realtime TCOON measurements for which TCOON stations are present. The following figure 3.5 provides a screen shot of the recently available wind forecast data for all 3 models in comparison with TCOON measured winds. 19

Figure 3.5 TCOON vs. Model Comparative Recent Available Forecast Data The following figure 3.6 demonstrates a model comparison graph generated by the tool. Figure 3.6 Generated Comparison Graphs for Model Forecast 20

The module would provide the following functionalities to the users. 1. To compare and study the wind parameters of recent available wind forecast data for different wind model forecasts in comparison with TCOON measured winds. 2. The comparison graphs of wind speed and wind direction is computed and generated real-time based on the values of U and V wind parameter values. Comparison with TCOON measured winds helps in evaluating the performance of the models. 3. Search and obtain the archived wind forecasts to be fed in as inputs to neural network and statistical modeling. 3.2.2. Real-Time Visualization and Comparative Statistics Module The real-time visualization and comparative statistics module allows the users to access real-time visualization of wind speed, wind direction, comparison graphs with interactive maps and real-time wind statistics. The module is implemented using CGI scripts using additional Perl modules installed and configured in server to serve the additional requirements of the users. [PERL 2002] Spatio-temporal comparative visualization maps with the dynamic plot of locations are generated on the homepage of the project as shown in the figure 3.5. The Spatio-temporal comparative visualization map visualizes the wind speed and direction real-time recent available data of various locations around Gulf of Mexico. 21

Figure 3.7 Spatio-temporal Comparative Visualization Map The generated map is user-friendly and upon clicking on specific locations the scripts generate a page of comparison tables and graphs which compares the different models for the wind parameters. The page also allows easy navigation enabling the users to study all the information and details needed for the station through menubar which appears on all the pages. Figure 3.8 gives a model of the user interface form for the wind statistics visualization. The users need to select the following parameters on the forms to perform the data query: a) Select a location from a list of locations for which data needs to be retrieved b) Select single or multiple forecast parameters from list of forecast parameters. The following are the forecast parameters available for the users: 22

U Wind Vector (m/s) V Wind Vector (m/s) Barometric Pressure (mbar) Wind Speed (miles/hour) Wind Direction Figure 3.8 User Interface for Wind Statistics Visualization c) Select the model(s) from the list of forecast models. The following are the list of forecast models available. MesoEta-12 Forecast Model NCEP Global Forecast System MesoEta-12 Interpolated Forecast Model 23

Compare TCOON Stations d) Select the date range intervals in the from and to options with the following format. yyyy-mm-dd (yyyy-year,mm-month,dd-date). Figure 3.9 provides the output of wind statistics visualization for a forecast location between two intervals of time. Figure 3.9 Visualization of Wind Statistics for Forecast Models [Tufte 1983] The module would provide the following functionalities to the users. 1. Visualize the wind speed and wind direction of the recent available data for the forecast locations 24

2. Visualize the wind statistics between any two intervals of time. 3. Determine the predominant wind speed and wind direction between two intervals of time 4. Visualize the wind speed and wind direction for the following 48 hours through animated maps. 3.2.3 Forecast Model Analysis and Evaluation Module This module helps the users in analyzing and evaluating the models. The module enables the users to perform the following features using the interactive tab options: 1. To compare the seasonal differences in model winds where the user is given option to select and compare the difference in models between any intervals of time. 2. To evaluate the model s previous forecast and the current forecast with sliding window self-model test. The common hours of forecast between the two forecasts are studied to study and evaluate the stability of same model in forecasting wind. The graphs are generated in similar fashion for all the forecast models. 3. To study, analyze and compare the performance of models with evaluation statistics generates the following statistical information : a) RMSE between pairs of models or models and DNR/TCOON observations b) Average error (bias) between pairs of models or models and DNR/TCOON observations 25

c) Absolute average error between pairs of models or models and DNR/TCOON observations Figure 3.10 User Interface for Error Statistics Data The users need to select the following parameters on the forms to perform the data query: a) Select a location from a list of locations for which data needs to be retrieved b) Select single or multiple forecast parameters from list of forecast parameters. The following are the forecast parameters available for the users: U Wind Vector (m/s) V Wind Vector (m/s) Barometric Pressure (mbar) 26

Wind Speed (miles/hour) Wind Direction c) Select the model(s) from the list of forecast models. The following are the list of forecast models available. MesoEta-12 Forecast Model NCEP Global Forecast System MesoEta-12 Interpolated Forecast Model Compare TCOON Stations d) Select the error statistics of your choice Root Mean Square Error Absolute Average Error Central Frequency e) Select the date range intervals in the following format. yyyy-mm-dd (yyyy-year,mm-month,dd-date). 3.4. Table Structure The database is designed to achieve efficient minimal storage and quick access. The database consists of a table for maintaining the list of locations for which the forecasts are received, a table for models to keep track the number of models being received and a table for storing the forecast parameters for each location. [MySQL 2002] The tool uses a simple database structure for efficient storage and faster access. The database initially consists of 51 tables. The database contains a table to monitoring and tracking locations for which the forecasts are received, a table for monitoring and tracking the number of models for 27

which the model forecasts are received, and a table for each station consisting of various forecast parameters needed for generation of other wind parameters. The programs are scripted and automated such that it updates the database with upcoming new models and locations for which the forecasts are received. Tables 3.2-3.4 describe the table structure of the various tables. Table 3.1 Description of the Locations Table Field Type Null Key Default Extra LID tinyint(2) PRI NULL auto_increment LNAME varchar(20) PRI LAT double(3,2) PRI 0 LON double(3,2) PRI 0 Table 3.2 Description of the Models Table Field Type Null Key Default Extra MID tinyint(1) PRI NULL auto_increment MNAME varchar(20) PRI Table 3.3 Description of the Location Forecasts Table Field Type Null Key Default Extra MID tinyint(1) PRI 0 DATE_TIME datetime PRI 0000-00-00 00:00:00 FORECAST int(2) PRI 0 U double(3,2) 0 V double(3,2) 0 BP double(3,2) 0 TEMP double(3,2) 0 4. TESTING AND EVALUATION 28

The testing and evaluation of the tool was tested by the use of software testing and usability testing. [McGraw Hill 2000] 4.1 Software Testing The project consists of three main modules. The main modules are sub-divided into sub modules. The sub-modules were tested individually and merged with the main module after testing. The modules were tested individually to check if the projects met the requirements of the intended audience. After module testing, the overall functionality, efficiency of the tool in terms of resource utilization and quick access were tested. The software tool was programmed and built in such a way that it does not require any maintenance and could be maintained by the computer novice. The software built was initially tested by the developer. After development and testing by the developer, the tool was tested for efficiency by Mr. Scott Duff and Mr. James Davis, Senior Systems Analysts, at the Division of Nearshore Research. The following were some of the fixes as a result of test cases that were found through software testing 1. The forecast archival programs were optimized for faster performance. 2. Dynamic database driven use interface forms 3. Merging the project with DNR Real time systems 4. Automated management of the programs 4.2. Usability Testing The user interfaces have been built to serve as a maximum user-friendly tool. Most of the user interfaces have been made interactive maps or a drop down menu to provide maximum flexibility to the users. The tool was tested for usability by the 29

developer during the development of the tool. After development of the tool, Dr.Philippe Tissot and Meteorologists from National Weather Service, Faculties of Department of Computer and Mathematical Sciences and Members of DNR-Neural Network Research group members tested the tool for usability for the following features. a. Ease of use and maintenance b. Faster Access c. Browser independent d. Completeness of automation Easy maintenance by a computer novice. e. Ease of comparison of models f. Statistical comparison of models g. Ease of use through Interactive maps h. Visualization of wind speed, wind direction and wind statistics i. Self model evaluation and evaluation statistics The users of the prototype version of the tool have evaluated usability of the application for the following characteristics: a.access and Usability, b. Completeness of the application, c. Appearance of the forms and controls on it and d. Functionality of the application. The following were the suggestions made by the prototype users which aided in improving the tools efficiency and thereby better meeting the requirements of the users. i. Faster Access to Wind Speed Visualization. ii. Uniform Date and time format throughout the system. iii. Adding features to evaluate itself. iv. Faster Access to archived forecast data 30

5. RESULTS AND CONCLUSION The VISTA tool that facilitates the data archival, analysis and visualization of the stored and recent available wind forecast have been developed. The tool enables users to evaluate and compare the different wind model forecasts through the generated statistical data for all forecast models. The VISTA tool is composed of forecast data acquisition module, real-time statistics and visualization module and forecast model analysis and evaluation module. The model forecasts archival module enables the archiving of the incoming data into text files and organizes, stores them in text files according to models, stations and years. The module provides the following functionalities to the users. 1. To compare and study the wind parameters of recent available wind forecast data for different wind model forecasts in comparison with TCOON measured winds. 2. The comparison graphs of wind speed and wind direction is computed and generated real-time based on the values of U and V wind parameter values. Comparison with TCOON measured winds helps in evaluating the performance of the models. 3. Search and obtain the archived wind forecasts to be fed in as inputs to neural network and statistical modeling. Model forecasts statistics and visualization module helps in visualizing the wind forecast.the model forecasts statistics and visualization module also compares and studies the difference in wind model forecasts in contrast with TCOON measured winds. The module provides the following functionalities to the users. 31

1. Visualize the wind speed and wind direction of the recent available data for the forecast locations 2. Visualize the wind statistics between any two intervals of time. 3. Determine the predominant wind speed and wind direction between two intervals of time 4. Visualize the wind speed and wind direction for the following 48 hours through animated maps. Model forecasts analysis and evaluation module help the users in evaluating the performance of the models. The evaluation feature will be made possible through realtime generated statistical data in comparison with TCOON measured winds. The sliding window self-test model evaluation feature helps in evaluating the individual model stability when it will be compared to the common time interval of forecast for same model. The module enables the users to perform the following features: 1. To compare the seasonal differences in model winds where the user is given option to select and compare the difference in models between any intervals of time. 2. To evaluate the model s previous forecast and the current forecast with sliding window self-model test. The common hours of forecast between the two forecasts are studied to evaluate the stability of the same model in forecasting wind. The graphs are generated in similar fashion for all the forecast models. 3. To study, analyze and compare the performance of models with evaluation statistics generates the following statistical information : 32

d) RMSE between pairs of models or models and DNR/TCOON observations e) Average error (bias) between pairs of models or models and DNR/TCOON observations The system has been built in a simple and automated fashion such that it could be maintained by a computer novice. The system has been developed such that the resulting tool has been integrated with the framework of the TAMUCC-DNR systems and Website. The system serves as a user-friendly tool which enables researchers, modelers and wind forecast data users to compare and evaluate different models in an effective manner. 33

ACKNOWLEDGEMENTS I would like to express my deepest gratitude to Dr. Philippe E Tissot, Assistant Professor of Physics and Physical Sciences, Texas A&M University Corpus Christi, for his excellent guidance, encouragement and his valuable suggestions throughout the period of this project work. This project would not have been possible without the support of many people. I would like to thank Mr. James Davis and Mr. Scott Duff, Senior Systems Analysts at the Division of Nearshore Research for providing me with constant support and technical suggestions. I would like to thank the members of DNR-Neural Network Research Group for their valuable suggestions and feedback. My sincere thanks to Dr. Mario Al. Garcia, Associate Professor of Computer Science and Dr. David Thomas, Associate Professor of Computer Science for their keen interest, support and motivation in completing my project. I take this opportunity to thank Mr. Waylon Collins and Mr. Andy Patrick, Meteorologists at National Weather Service Offices at Corpus Christi and Brownsville for their extended support in this project. I would also like to thank the members of DNR- Neural Network Research Group, Officials from National Weather Service and Faculties at the Department of Computer and Mathematical Sciences at Texas A & M University- Corpus Christi for taking part in the Usability testing and providing their valuable feedbacks which helped in the betterment of the project. 34

BIBLIOGRAPHY AND REFERENCES [Addison-Wesley 2001] Meltzer, Kevin, Writing CGI Applications with Perl Kevin Meltzer/Brent Michalski, Addison-Wesley, Pearson Education MA 2001 [Collins 2004] Division of Nearshore Research Atmospheric Wind Predictions for the Gulf of Mexico Homepage. Available from http://lighthouse.tamucc.edu/apgm/ (visited Feb 1, 2005). [DNR 2005] Division of Nearshore Research -Texas Coastal Ocean Observation Network Homepage. Available from http://lighthouse.tamucc.edu/tcoon/homepage (visited Feb 1, 2005). [DRI 2005] Desert Research Institute - Program for Climate, Ecosystem and Fire Applications. Available from http://www.cefa.dri.edu/ (visited Feb 1, 2005). [McGraw Hill 2000] Ian Sommerville, Software Engineering. McGraw Hill Publications August 2000 [Michaud 2001] Michaud, P., G.A. Jeffress, R.S. Dannelly, and C. Steidley, Real-Time Data Collection and the Texas Coastal Ocean Observation Network, International Measurement and Control (InterMAC) 2001 Joint Technical Conference (Tokyo, Japan), November 2001 [MySQL 2002] MySQL Reference Manual Documentation Indexing and Optimization Available from www.mysql.com/doc (visited Feb.10, 2005). [O Reilly Inc. 1998] Christiansen Tom, Nathan Torkington, Perl Cookbook, O Reilly Associates Inc., California, August 1998 [O Reilly Inc. 2000] Alligator Descartes and Tim Bunce, Programming the Perl DBI, O Reilly Associates Inc., California, Feb. 2000 [O Reilly Inc. 2001] Randal L. Schwartz, Tom Phoenix, Learning Perl, O Reilly Associates Inc., California, July 2001 [Patrick 2002] Patrick, A.R., W.G. Collins, P.E. Tissot, A. Drikitis, J. Stearns, and P. Michaud, Use of the NCEP MesoEta Data in a Water Level Predicting Neural Network, Proc. of 19th AMS Conf. on Weather Analysis and Forecasting/15th AMS Conf. on Numerical Weather Prediction (San Antonio, Texas), August 2002. [PERL 2002] Perl Manual Documentation - Available from http://www.perldoc.com/ (visited Feb.10, 2005). 35

[Stearns 2002] Stearns, J., P.E. Tissot, P.R. Michaud, A.R. Patrick, and W.G. Collins, Comparison of MesoEta Wind Forecasts with TCOON Measurements along the Coast of Texas, Proc. of 19th AMS Conf. on Weather Analysis and Forecasting/15th AMS Conf. on Numerical Weather Prediction (San Antonio, Texas), August 2002. [Tissot 2005] P.E. Tissot, S. Duff, G. Jeffress and P. Michaud, DNR-TCOON: An Integrated Observation and Operational Forecasts System for the Gulf of Mexico, accepted for publication in the proceedings of the Symposium on Living in the Coastal Zone, San Diego, California, January 9-13, 2005 [Tissot 2003] P.E. Tissot, D.T. Cox, and P.R. Michaud, Optimization and Performance of a Neural Network Model Forecasting Water Levels for the Corpus Christi, Texas, Estuary, 3rd Conference on the Applications of Artificial Intelligence to Environmental Science, Long Beach, California, February 2003 [Tufte 1983] Edward R Tufte, The Visual Display of Quantitative Information.Graphics Press, Connecticut, 1993 36

APPENDIX A Table 1 contains a list of locations for model forecasts around Gulf of Mexico with their respective latitudes and longitudes. Table 1 Table of Locations for Model Forecasts around the Gulf of Mexico No Location Name Latitude Longitude 1 PortAransas 27.84-97.07 2 Rockport 28.02-97.48 3 NASCORPUS 27.7-97.28 4 GLSPleasurePier 29.29-94.79 5 BobHallPier 27.58-97.22 6 Freeport 28.95-97.31 7 MorgansPoint 29.68-97.98 8 PortIsabelle 26.05-97.22 9 PortOconnor 28.45-96.4 10 MesquitePoint 29.77-93.89 11 RincondelSanJose 26.8-97.47 12 ArroyoColorado 26.32-97.33 13 BaffinBay 27.3-97.41 14 CorpusChristiBay 27.78-97.3 15 BirdIsland 27.48-97.32 16 CopanoBay 28.12-97.1 17 AransasBay 28-97.02 18 SanAntonioBay 28.28-96.72 19 MatagordaBay 28.53-96.3 20 LavacaBay 28.63-96.58 21 EastMatagordaBay 28.71-95.83 22 WestBay 29.25-94.97 23 SabineLake 29.92-93.82 24 Beeville 28.4-97.73 25 Dallas 32.77-96.78 26 Austin 30.28-97.7 27 Amarillo 35.18-101.83 28 FlowerGardens 27.9-93.57 29 TABSF 26.18-94.23 30 NWSWX1 27.75-96.78 31 TABSJ 26.19-97.05 32 ShorelineSouthwest 27-97 33 OffshoreSouthwest 26.5-95.5 34 OffshoreNorthwest 27.67-95.67 35 ShorelineNorthwest 28.5-95.83 36 GalvestonOffshore 29.12-94.51 37 OffshoreSoutheast 26.5-93.5 38 BayofCampeche 21-94 39 MiddleofGulf 25-91 40 GalvestonBay 28.58-94.83 41 NWSCorpusChristi 27.77-97.52 42 ASOSVCT 28.86-96.93 43 ASOSRKP 28.08-97.05 44 ASOSPSX 28.73-97.51 45 ASOSNGP 27.69-97.22 46 ASOSNQI 27.5-97.81 47 ASOSALI 27.74-98.02 37

APPENDIX B The appendix has been extracted from the Website for the Atmospheric Wind Predictions for the Gulf of Mexico homepage written by Mr. Waylon Collins, meteorologist at National Weather Service at Corpus Christi available from the following link: http://lighthouse.tamucc.edu/vista/ 1. Model Winds The predicted winds from the respective models come in the (u, v) format where u is the North-South Wind component and v is the East-West component both in meter/second. The signs of the u and v components are such that a negative u and negative v component correspond to a north-easterly wind (i.e. a wind coming from the northeast direction). Note that when plotted by the MATLAB feather function, the northeasterly wind vectors point toward the southwesterly portion of the graph. 1.1. NCEP Eta-12 Model The NCEP Environmental Modeling Center (EMC), part of the National Weather Service (NOAA/NWS), developed the Eta-12 model (herein Eta), which is a limited-area, numerical atmospheric model. The model integrates the primitive hydrostatic equations in three dimensions. The vertical coordinate is known as the Eta, which is achieved by modifying the terrain following sigma coordinate. The advantage of the Eta coordinate is that the surfaces are quasi-horizontal, thus avoiding errors associated with steep slopes of the coordinate surfaces (Mesinger, 1984) and hence improving the solution over highly variable topography such as over the Western United States. Initial values to each Eta forecast model run are provided by the 38

fully-cycled Eta Data Assimilation System (EDAS), which incorporates the 3- dimensional variation analysis (3D-VAR) technique. Presently, NCEP Eta surface data (from net CDF files containing Eta forecast output mapped to AWIPS Grid 215, which has a horizontal grid spacing of 20 km) is sent every six hours to TAMUCC-DNR.A significant number of locations and forecasts other than surface forecasts complements the present database. NCEP will likely discontinue providing Eta model outputs during the project duration. The model was selected for this project to complete the ongoing database and to test how a change in numerical weather product will affect the ANN models. This transition will be tested as the WRF model outputs become available. All other three model outputs are expected to be available throughout the duration of this project and beyond. 1.2. The Weather Research and Forecasting (WRF) Model This model is being developed collaboratively between a number of organizations including the Mesoscale, Microscale, and Meteorology Division of the National Center for Atmospheric Research (NCAR/MMM), NOAA/NWS, the Forecast Systems Laboratory (NOAA/FSL), the Oklahoma University Center for Analysis and Prediction of Storms, and the Air Force Weather Agency. The philosophy behind the development of this model is to create an advanced Mesoscale community (non-proprietary) model and data assimilation system. Also, a close relationship between the research and operational communities that will utilize the model is encouraged. The WRF is a non-hydrostatic, limited-area, numerical model in which the domain and physics parameterizations can be chosen amongst several options. 39

The current WRF contains several options for explicit microphysics, cumulus parameterization, planetary boundary layer, radiation, and parameterization of the surface. With version 1.3 of the WRF, initial values to each model run can be provided by static initialization, with or without 3DVAR. Eventually, dynamic initialization techniques will be available. Of importance to this project, NCEP plans to discontinue operational Eta runs by October 2004, and replace them with North American Mesoscale runs of the WRF (at a resolution similar to the Eta) and make them available to NWS forecasters to support operations. WRF output to the forecasters will be available on grids currently used for the Eta, which include AWIPS Grids 215, 218, 212, and 221, with horizontal grid spacing of 20km, 12km, 40km, and 32km respectively. The project will include testing of the ANN models with both the Eta and NCEP WRF forecasts to determine potential differences. Project objectives also include running models initially trained with Eta forecasts and testing them with WRF forecasts to measure the impact of the model transition on operational forecasts. 1.3. NCEP Global Forecast System (GFS) This Model is a hydrostatic, global, and spectral model maintained by NCEP/EMC. The GFS runs four times a day (0, 6, 12, 18 UTC) with forecasts computed for up to 384 hours. The spectral nature of the model affects the manner in which quantities are calculated. The computation of quantities in the horizontal plane is handled by the following methodology: space derivatives are computed in spectral space and selected quantities are transformed to a Gaussian grid for the computation of nonlinear products in grid space. The advantage of this method is that derivatives are computed 40

accurately in spectral space, yet non linear products are computed efficiently in physical space. Initial values to each GFS model forecast are provided by the Global Data Assimilation System (GDAS). The GDAS incorporates a technique referred to as Spectral Statistical Interpolation (SSI), a type of 3DVAR, to create each analysis. GFS output from each run, like the Eta, is sent to each NWS Weather Forecasting Office via the NWS AWIPS Satellite Broadcast Network, in the GRIB file format. The data is then written to netcdf files, containing the data mapped to several numerical grids of varying spatial resolution. For the GFS, the AWIPS grids are 202, 211, and 213 with horizontal resolutions of approximately 180 km, 80 km, and 90 km respectively. 1.4. PSU-NCAR MM5 Model This Model was selected as the local model. The MM5 model is the Fifth Generation of a Mesoscale Model, originally developed at Penn State University, with current development and support provided by NCAR. The MM5, like the new WRF, is a non-hydrostatic, limited-area, community numerical model in which the domain and physics parameterizations can be chosen amongst several options. The current MM5 contains several options for explicit microphysics, cumulus parameterization, planetary boundary layer, radiation, and parameterization of the surface. The model will provide increased resolution as compared to the other girded model and the ability of the local office to adapt the physics of the model. The model will be frozen early in the project and will serve as a control for comparison with the other models for the rest of the project. 41

Initial values to each MM5 model forecast can be provided via static or dynamic initialization. Static initialization can be performed with or without 3DVAR.Dynamic initialization is performed via Four Dimensional Data Assimilation (FDDA.) The MM5 Modeling System will be configured by the CC-WFO participants to the project. 42

APPENDIX C VISTA MODULE ####################################################################### # # # This file is part of the Pharos environmental data # collection/dissemination system. # # Pharos is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # Pharos is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Pharos; if not, write to the Free Software Foundation, # Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # # For more information on Pharos, see # # http://lighthouse.tamucc.edu/pharos # # or contact one of # # dnr-info@lighthouse.tamucc.edu # duff@lighthouse.tamucc.edu # ####################################################################### # package PSeries; use PDB; use PDL; use DBI; #use Math::Round; use Math::Trig(rad2deg); sub vista my($self) = shift; my(%args) = (%$self,@_); my($ser,$gmt0,$gmt1) = @args'ser','gmt0','gmt1'; $gmt0 = $gmt0 - $gmt0%(3600*3); ############################ Database Connection ###################### my $dsn = "DBI:mysql:database=test;host=wip.cbi.tamucc.edu"; my $dbh = DBI->connect($dsn,'test','', RaiseError => 1, AutoCommit=> 43