FinalPRICIP.doc 1 Integrating Data from NASA ...

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Integrating Data from NASA Missions into NOAA’s Pacific Region Integrated Climatology Information Products (PRICIP) Project Casey Teske1, Nicole Simons1, Frank Garcia1, Joshua Ingham1, Seema Gupta1, J.W. Skiles1, Cindy Schmidt2 1NASA-Ames Research Center, Moffett Field CA 2 San Jose State University, San Jose CA ABSTRACT Hurricanes, typhoons, and cyclones are de v astating to coastal areas throughout the world, especially in the Pacific Region. The strong winds, hea vy rains, and high seas elements that accompany tropical storm events are of interest to researchers and forecasters alike. In a recent collaboration, NASA DEVELOP interns ha ve teamed up with NOAA researchers to enhance their ongoing Pacific Region Integrated Climatology Products (PRICIP) project by integrating NASA mission data products. The PRICIP project will eventually become an interactiv e decision support tool that will assist decision makers in mitigating and recovering from natural hazards inevitably reducing coastal vulnerability. DEVELOP’S contribution to this ongoing project included creating hindcasts for three past extreme storm events. The hindcasts were in the form of interacti ve geovisualizations, and highlighted the strong winds, hea vy rains, and high sea storm elements that were of interest to NOAA researchers. These interactive geovisualizations will contribute directly to NOAA’s PRICIP decision support tool, and will be accessible to researchers and the public through a web browser. INTRODUCTION Earth is a very dynamic and dangerous place. Its oceans ha ve contributed dramatically to extreme weather conditions as they are directly linked to the global climate system through atmospheric circulation and winds (Woolf et al. 2003). At the extremes, these dynamic weather phenomena ha ve the potential to threaten various ocean species, coastal communities, and sea-going vessels (Easterling et al. 2000; Woolf et al. 2003). For example, intense tropical storms ha ve caused erosion and property losses along the shoreline due to changing ocean variables such as sea surface height and mean sea le vel (Allan and Komar 2006). To reduce the effects of these ever increasing extreme storm events, a thorough understanding of the earth’s physical processes and how they interact with each other is vital and necessary (JPL 2006). While water covers 71% of Earth’s surface, 97% of that water is stored in the oceans. The influence and feedback loops of ocean processes and climate are thus highly relevant, and observations, measurements, and long-term records can provide the scientific community insight into these linked processes. The National Oceanic and Atmospheric Administration (NOAA) and various other government agencies are embarking on a pioneering project called Pacific Region Integrated Climatology Information Products (PRICIP). Project partners include the

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National Aeronautics and Space Administration (NASA), the Federal Emergency Management Agency (FEMA), the National Weather Service (NWS), and the Coastal Service Center (CSC). The objecti ve of the PRICIP project is to gain a thorough understanding of patterns and trends of storm frequency and intensity such that the integrated information products de veloped will assist emergency managers, planners, and decision makers in various sectors to ultimately reduce vulnerability to risks associated with extreme storm events (NOAA PRICIP 2007). Our contribution to this project was to animate tracks of three Pacific Rim extreme storm events using remotely sensed imagery from various NASA sensors. We processed the imagery to create storm track animations highlighting four main variables: surface wind speed and direction, precipitation accumulation, sea surface temperature, and sea surface height. The purpose for animating storm tracks was to provide a visualization of each storms’ life cycle with the possibility of observing connections between storm elements. Students working on an internship at NASA-Ames Research Center were tasked with a portion of the PRICIP project. NASA’s DEVELOP internship is a Human Capital Development Program funded by NASA’s Applied Sciences Program of the Earth Science Division in the Science Mission Directorate at NASA Headquarters. Student-run and student-led teams collaborate on projects during the summer, especially those projects that utilize remote sensing and GIS technologies (Skiles and Schmidt 2007). In an intensive 10-week program, students work with collaborators to design projects, gather and analyze necessary data, and present the findings in both oral and written formats. This paper describes the collaborative project between DEVELOP and the PRICIP project, beginning with an overview of our methodology and ending with a review of the project and recommendations and implications for future work. BACKGROUND Extreme Storm Events Tropical storms typically occur in the eastern Pacific and northern Atlantic regions between June 1 and November 30 of each year. This portion of the year is commonly referred to as hurricane season by local residents, researchers, and decision makers. Factors that influence the development of extreme storms in the Pacific Rim include sea surface temperatures greater than 26.5°C, vertical shear, and mid-tropospheric moisture (Goldenburg et al. 2001, Webster et al. 2005). Once an oceanic depression has reached sustained winds in excess of 34 knots it is upgraded to a tropical storm and assigned a name. Ratings for extreme storm events are based on the Saffir-Simpson Scale from lowest intensity (Category 1) to highest intensity (Category 5) as described in Table 1. The number of named storms, and the number of storms that ha ve reached hurricane intensity, ha ve increased in recent years (Webster et al. 2005). For instance, in 2004 nine of fourteen named storms in the North Atlantic were classified as a Category 1

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storm or higher (Webster et al. 2005); in 2005 alone, there were 27 named storms, four of which reached Category 5 strength (NCDC 2006). In a 2005 study, Webster et al. performed a comprehensive analysis of global tropical cyclone statistics for each tropical ocean basin within a 34-year period between 1970 and 2004, which they referred to as the satellite era. Their study revealed a trend that intense hurricanes were becoming more frequent. This led them to declare that more knowledge about the role of hurricanes in the general circulation of the atmosphere and ocean is vital for understanding climate and coping with catastrophic natural events. TABLE 1: Saffir-Simpson hurricane intensity scale table (adapted from Unisys 2007).

Winds (knots)

Winds (km/h)

Winds (mph)

TD

< 34

155

>5.4

1

Minimal

64-82

118-152

74-95

2

Moderate

83-95

153-176

96-110

3

Extensive

96-112

177-208

4

Extreme

113-135

209-248

5

Catastrophic

>135

>248

Surge (ft)

4-5 6-8 9-12 13-18 >18

One of the costliest and deadliest Category 5 storms to affect the United States was Hurricane Katrina. Katrina made landfall in August of 2005 on the Louisiana Gulf Coast as a Category 3 hurricane with a Category 5 storm surge. More than 1800 deaths were reported and nearly 250,000 Gulf Coast residents displaced. The damages from Hurricane Katrina were estimated to be more than US$125 billion as of August 2006 (Graumann et al. 2005). Extreme storms, such as Hurricane Katrina, are serious problems that affect coastal populations around the world each year. While much attention is focused in the Atlantic Region, the Pacific Rim region has more storms than the Atlantic Region (NMFC/JTWC 2007). For example, between 1980 and 2005 there were 304 named storms in the Atlantic Region as compared to 808 in the Pacific Region. Efforts to use existing satellite technologies to aid in making accurate and timely forecasts can minimize the damaging effects of storms such as Katrina. Missions and Sensors In 1960, the first successful weather satellite, TIROS-1, was launched into orbit capturing infrared images of clouds around the globe (NOAA 2000, Ohring et al. 2002). These images were combined with ground-based local observations yielding a broad-scale

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weather picture. As a result, a greater understanding of weather patterns and storm formations drastically improved the weather forecasting capabilities of the time (Franklin Institute 2007, NOAA 2000, Ohring et al. 2002). Remotely-sensed data ha ve increasingly improved since TIROS-1 and ha ve been assimilated into operational and research forecast models (Ohring et al. 2002), decisions support systems and decision support tools (Skiles and Schmidt 2006), teaching tools (JPL 2006), animations, and various other applications. By assimilating different data products into a single model one can greatly enhance the data’s v alue. For example, Pennsylvania State University researchers created a data assimilation tool named MM5 4DVAR that can dynamically adjust a storm’s thermal structure and moisture structure. This tool, which is essentially a mesoscale model, assimilated data from multiple sensors into a single data product which resulted in enhanced storm thermal and moisture structure images (Zhang et al. 2007). Although satellite data products ha ve improved o ver time, there are still many v ariables to consider while using satellite data. For example, data characteristics and acquisition methods differ between satellites, therefore it is important to choose sensors capable of capturing data for the specific variables of interest at appropriate resolutions (i.e. spatial, radiometric, and temporal). For instance, the 15km spatial resolution of SSM/I data would be inappropriate for use in projects analyzing isolated wind patterns over small areas (Turk et al. 2000). Another consideration is the acceptable range of variation between satellite data and in situ data sources. QuikSCAT, for example, has been shown to overestimate wind speeds in the morning and underestimate wind speeds in the e venings when compared to buoy data (Mostovoy et al. 2005). In spite of these limitations, data from satellite sources ha ve significantly improved extreme storm event forecasting. Forecasters now ha ve the ability to view near realtime formation of vortices thus increasing their ability to accurately place the central point of severe oceanic storms, such as an eye of a hurricane (Atlas et al. 2005). The storm forecast products can now be passed to the public via internet, television, and radio more effectively than ever before. With current technology, these forecast products can be represented visually in order to raise public awareness of severe storm events. Geovisualizations Widespread access to television and the internet has provided the public near realtime information about storm events throughout the globe. The visualizations provided by meteorologists enable the public to visually understand complex layers of weather data easily and make informed decisions based on current and expected weather. These visualizations of weather patterns and other naturally occurring phenomena are not always georeferenced, or correlated with a ground reference. Howe ver

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animations that contain internal georeferencing ha ve been scientifically recognized as geovisualizations. The National Science Foundation defined geovisualization in 1987 (see McCormick et al. 1987) thereby establishing a new academic discipline which further ad vanced the worlds of geography, cartography, image analysis, and geographic information science. Geo visualizations integrate approaches from multiple disciplines including cartography, image analysis, information visualization, exploratory data analysis (EDA), geographic information systems (GISystems), and visualizations in scientific computing (ViSC) (MacEachren and Kraak 2001) and are established as “an important technique for understanding time- varying data” (Rosenblum 1994). These provide theories, methods, and tools for visual exploration, analysis, synthesis, and presentation of geospatial data (MacEachren and Kraak 2001). Advancements in GIS and image analysis ha ve allowed geovisualization methods to become more integrated thereby enabling decision-makers to rapidly assess crisis situations, such as earthquakes, fires, tornadoes, floods, and hurricanes. Properly understanding, assessing, and estimating possible damages is a time consuming and potentially costly process for decision-makers. By ha ving quick access to complex geospatial information in an understandable interface, decision-makers can be assured that their decisions will result in the best outcomes. For example, in a 2005 study, Gra ves et al. focused on the benefits of providing decision makers in Mesoamerica with geovisualizations as part of an en vironmental monitoring DSS known as SERVIR. A Spanish verb meaning “to serve” or “to be useful”, SERVIR is an acronym for Sistema Regional de Visualizacion y Monitoreo (Regional System of Visualization and Monitoring). SERVIR enhanced decision-makers’ ability to monitor, forecast, respond, and understand both natural disasters and human induced ecological change. The PRICIP Project The PRICIP project will combine and analyze historical and near-real time data of severe weather phenomena (i.e. hurricanes, typhoons, and cyclones) from multiple sources in order to put the current weather into a longer term perspecti ve. The project will focus on the interaction between three thematic variables of extreme storm events: hea vy rains, strong winds, and high seas. The derived data product suite will be useful for guiding coastal climatology programs and disaster management policies. The storm related information products will be a vailable in an online portal eventually becoming an important component to the decision support system associated with coastal management throughout the Pacific Rim. To achieve the stated goals of the PRICIP project, NOAA created project de velopment teams to undertake indi vidual tasks, which included analyzing historical records and integrating climatological analyses with near-real time observations (PRICIP 2007). Students from NASA’s DEVELOP internship program collaborated with

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NOAA researchers to form one of these teams. While NOAA researchers concentrated on the societal impacts of extreme storm events, the NASA-Ames DEVELOP PRICIP team combined multiple sources of remotely sensed imagery to create animated storm tracks of three specific tropical storms that occurred in the Pacific Rim region; these animations will be used as part of NOAA’s PRICIP portal for historical e vent anatomies. METHODOLOGY Storms In the initial stages of the PRICIP project, NOAA created a list of over forty significant storm events that had occurred in the Pacific region since 1980. Events were selected to be animated based on the extensive damages and effects to islands in the Pacific Rim region. They were also required to ha ve three of the following components in order to be selected: strong winds, hea vy rains, and high seas. Four significant storm events (Hurricane Iniki, Typhoon Chata’an, Super Typhoon Pongsona, and Cyclone Heta) were chosen. The specific characteristics of each storm are highlighted in Table 2. Ultimately, Hurricane Iniki was not animated, as the sensors in existence in 1992 were not capable of acquiring the data required to highlight the storm’s elements.

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TABLE 2: Storm Information for the four selected storms.

STORM NAME Hurricane Iniki 9/8/92 to 9/13/92

B

Typhoon Chata'an

6/28/02 to 7/11/02

A,D,E

Super Typhoon Pongsona 12/2/02 to 12/11/02

A,C

Cyclone Heta 1/2/04 to 1/8/04

A,F

IMPORTAN T DATES

CLASSIFICATION

WIND SPEED (Knots)

9/8/1992 9/9/1992 9/10/1992 9/11/1992 9/12/1992 9/13/1992 6/29/2002 7/3/2002 7/4/2002 7/5/2002 7/7/2002 7/9/2002 7/10/2002 7/11/2002 12/3/2002 12/5/2002 12/6/2002 12/7/2002 12/8/2002 12/9/2002 12/10/2002 12/11/2002 1/2/2004 1/3/2004 1/4/2004 1/5/2004 1/6/2004 1/7/2003

Tropical Storm Cat. 1 Cat. 3 Cat. 4 Cat. 1 Tropical Storm Tropical Storm Cat. 1 Cat. 2 Cat. 3 Cat. 4 Cat. 2 Tropical Storm Declassified Tropical Storm Cat. 1 Cat. 2 Cat. 3 Cat. 4 Cat. 3 Cat. 2 Cat. 1 Tropical Storm Cat. 1 Cat. 4 Cat. 5 Cat. 4 Cat. 1

50 80 100 125 80 40 45 65 95 110 130 85 55 40 50 70 90 110 130 110 90 75 55 75 125 140 115 65

1/8/2004

Tropical Storm

35

DAMAGES $ (millions)

Loss of Life

3,000+

6

245+

43

700+

1

73+

1

Sources: A: ht tp://weather.unisys.com B: ht tp://www.prh.noaa.gov

D: ht tp://pubs.usgs.gov E: ht tp://www.fema.gov

C: ht tp://www.weather.gov

F: ht tp://www.ncdc.noaa.gov

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Sensors and Processing Methods Based on NOAA’s requirements for the PRICIP portal, datasets were chosen from sensors with the ability to capture surface wind speed and direction, surface precipitation accumulation, sea surface temperature (SST), and sea surface height (SSH). These parameters directly relate to the strong winds, hea vy rains, and high seas theme specified by the PRICIP project protocols. Datasets for each of the parameters and storms of interest were acquired. While all data were acquired by NASA missions, the sources of the datasets varied. Once downloaded, the datasets were processed using one or more of the following tools: HEGTool (Praderas et al. 2004), Microsoft Access (Microsoft Corp. 2003), IDL 6.3 (ITT VIS 2006), ESRI ArcMap 9.2 GIS software (ESRI 2006), and Adobe Photoshop CS3 (Adobe Systems Incorporated 2007a) and Adobe Flash CS3 (Adobe Systems Incorporated 2007b). The following contains a summary about the sensors and datasets used in this project. QuikSCAT A major component of any tropical storm is its wind speed and direction. The ability to measure these parameters from space is possible with scatterometers, and is useful for forecasters and researchers alike. NASA’s Quick Scatterometer satellite (QuikSCAT), which contains a Sea Winds scatterometer, was launched into a sun-synchronous orbit 803km above the Earth in 1999 in response to the catastrophic failure of the Japanese satellite, ADEOS-1 (Advanced Earth Orbiting Satellite 1), and the loss of NASA’s NSCAT scatterometer which was housed onboard ( v on Ahn et al. 2006, Liu 1999). While only designed to be in operation through the summer of 2004 (Pickett et al. 2003), QuikSCAT is still operational today. QuikSCAT’s twice daily overpasses (once ascending and once descending) of the world’s oceans, coupled with its moderate resolution make it ideal for tracking extreme storm events. As an active radar that uses the microwa ve portion of the spectrum (13.4GHz), QuikSCAT has the ability to capture the formation of vortices much earlier than other remote sensing systems (Atlas et al. 2005). Sea surface wind speeds between 3 and 20m/s can be measured with an accuracy of ±2m/s, and wind direction is accurate to ±20° except in areas of high rainfall (in which case data are flagged) (Liu 2003). QuikSCAT Level 2B datasets for the area of interest were acquired from NASA’s PO.DAAC website (http://podaac.jpl.nasa.gov/) as hierarchical data format (HDF) files. Daily ocean vector winds acquired by the Sea Winds scatterometer during one full orbital revolution of QuikSCAT are represented in each dataset at a 12.5km spatial resolution. The variables of interest were extracted from the HDF files using an IDL script. An “XY E vent Layer” was then created in ArcGIS using the latitude and longitude v ariables as Y and X values, respectively. A custom model built in ArcGIS was used to clip the geographic area of interest for each storm from the global dataset. Sea

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surface wind speed and direction were mapped and classified, and the images were exported as Portable Network Graphic (.PNG) files for animation. TRMM In a joint mission between the National Aeronautics and Space Administration (NASA) of the United States and the National Space Development Agency (NASDA) of Japan, the Tropical Rainfall Measuring Mission (TRMM ) was launched in 1997 with the primary purpose of providing detailed and accurate precipitation measurements over the tropics (Lee et al., 2002, Lonfat et al., 2004). Although other satellite instruments such as the Special Sensor Microwa ve Imager (SSM/I) and the Advanced Microwa ve Scanning Radiometer (AMSR) capture precipitation measurements over the oceans, their resolutions (15km and 5km respectively) are too coarse to provide enough detail of isolated storm events such as hurricanes, typhoons, and cyclones (Spencer 2000, Lee et al., 2002). TRMM’s sensors, howe ver, ha ve the ability to obtain these precipitation measurements at a finer resolution (Alcala and Dessler, 2002, Kummerow et al., 1998, Ricciarduli and Wentz, 2004). TRMM carries five instruments onboard providing se veral different products including, sea surface temperature, sea surface wind v elocities, precipitation rates, cloud liquid water vapor, and integrated water vapor. Two of these five instruments were used to complete this project. TRMM Microwa ve Instrument or TMI, a primary instrument, is a passive microwa ve radiometer that measures surface precipitation intensity and has the ability to obtain information about sea surface temperature and sea surface winds. It can obtain information at nine different frequencies five of which are used extensively (10.7, 19.4, 21.3, 37, and 85.5 GHz) (Kummerow et al. 1998, Ricciarduli and Wentz 2004). Another primary instrument, the Precipitation Radar (PR), is the first onboard satellite active radar system to be used for measuring rainfall (Alcala and Dessler 2002, Kummerow et al. 1998). PR is designed to measure precipitation intensity and its associated vertical structure and this data can be used to produce 3-Dimensional images of extreme storm events. The PR has a vertical resolution of 250m and a horizontal resolution of 3.1km providing improved detail about storm vertical precipitation structures. Although PR has good spatial resolution its ground track is relatively small (Alcala and Dessler 2002, Kummerow et al. 1998, NASA GSFC 2007b). In August 2001, scientists boosted TRMM’s altitude from 350km to 402km in order to reduce drag and increase the life expectancy of the satellite (Lonfat et al., 2004). This resulted in an increased swath width of the instruments onboard TRMM but the swaths did not increase equally for each instrument. For example, TMI’s swath width increased from 760km to 880km and PR’s swath width increased from 215km to 247km (NASA GSFC 2007b, NASA GSFC 2007c). The boost did not, howe ver, affect the 35° inclination or the original scan surface between 38°N and 38°S (Lonfat et al. 2004).

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This project used Level 3B42 TRMM Merged High Quality/Infrared Precipitation datasets downloaded from the NASA Rainfall Archives (http://disc2.nascom.nasa.gov/Giovanni/tov as/TRMM_V6.3B42.shtml) in ASCII format. This site provides gridded 3-hour temporal resolution data for an area of interest delineated by the user and extends the scan surface from 38°N and 38°S to 50°N and 50°S. The acquired dataset is a combination of precipitation measurements from TMI onboard TRMM, AMSR-E on AQUA, NOAA’s AMSU measurements, GOES IR data, and several SSM/I sensors onboard the Department of Defense satellites. The spatial resolution of the data is 0.25° by 0.25° and data is a veraged within 90 minutes of each 3-hour time period (NASA GSFC 2007a). Once downloaded, the ASCII files were imported to database files (.dbf) using a Microsoft Access database. In ArcGIS, an “XY Event Layer” was created from each individual .dbf using the longitude and latitude v alues and then con verted to a grid. The precipitation values were then mapped and classified and the images were exported as .PNG files for animation. A custom model built in ArcGIS was used to derive a “precipitation accumulation” grid; each individual grid (representing 3-hr precipitation totals) was added in series such that a running total for precipitation was created. This created a final grid of total precipitation accumulated for each storm ev ent, and each time-step was exported for animation in .PNG format. Jason-1 Jason-1, a joint mission between NASA and the French Space Agency Centre National d’Etudes Spatiales (CNES), was launched on December 7, 2001, as a follow-on mission to the TOPEX/POSEIDON (T/P) mission launched in 1992 by the same agencies (JPL 2006). To ensure the continuous acquisition of T/P data, Jason-1 (J-1) was created with identical error budgets and orbital characteristics as T/P (Picot et al. 2003). The goals of both the J-1 and T/P missions were to observe global ocean circulation from space to better understand the ocean’s role in Earth’s climate (Picot et al. 2003, Rosmorduc et al. 2006). The two satellites flew in a tandem orbit collecting, calibrating, and v alidating J-1 measurements for five years before T/P was terminated. Since that time, J-1 sensors ha ve continuously provided near-real-time SSH and sea-state measurements to research and ocean forecasting communities. These J-1 datasets are currently being incorporated into research programs such as the Climate Variability and Predictability program (CLIVAR) and the Global Ocean Data Assimilation Experiment (GODAE) (Rosmorduc et al. 2006, Picot et al. 2003). J-1 uses a combination of radar altimetry and microwa ve systems to determine sea surface height, wa ve heights, sea surface wind speeds, and water vapor concentrations (JPL 2006). Altimetry data are collected in the Ku (13.575 GHz) and C (5.3GHz) bands from an orbital altitude of 1336km and a 10-day repeat cycle (Rosmorduc et al. 2006). In conjunction with ground stations and GPS satellites, J-1’s altimeter measurements and exact location can be verified and corrected to yield

FinalPRICIP.doc accurate sea-le vel measurements to

11 3cm (Rosmorduc et al. 2006, Naranjo 2003, JPL

2006). Altimeter products produced by SSALTO/DUACS and distributed by AVISO with support from CNES were used to derive sea surface maps. The global delayed time maps of sea level anomalies (DT_MSLA) were downloaded from the AVISO Live Access Server (http://las.a viso.oceanobs.com/las/) using the “GRID format” option and a userdefined region of interest; the resulting dataset was an ASCII file of values representing sea level anomalies at a spatial resolution of 1/3° x 1/3°. The DT_MSLA datasets are created using a se ven-day a verage of data composited from the Jason-1, TOPEX/POSEIDON, En visat, ERS-1 and ERS-2, GFO, and Geosat missions (Rosmorduc 2006). Once downloaded, the datasets for each storm were con verted to rasters using the “ASCII to Raster” tool in ArcGIS; the mapped and colored images were exported for animation in .PNG format. Aqua Another joint project between NASA and NASDA is the Advanced Microwa ve Scanning Radiometer for EOS (AMSR-E) which is aboard the Aqua mission that was launched on May 4, 2002 (NSIDC 2007a). AMSR-E utilizes passive microwa ve technology to observe sea surface temperature, sea surface wind speed, precipitation rate, and other land- and ocean-based physical characteristics from a 705km orbital altitude (NSIDC 2007b, Spencer 2000). O verflights of the Aqua spacecraft occur twice daily, allowing two opportunities for AMSR-E to capture measurements along its 1445km swath width. AMSR-E is a passive-microwa v e radiometer system, collecting data at six microwa ve frequencies and in two polarizations (Lobl 2001, NSIDC 2007b). The spatial resolution of this sensor ranges from approximately 5km for the 89.0GHz frequency to approximately 56km for the 6.9GHz frequency (Spencer 2000, NSIDC 2007b). The AMSR-E Level 3 Daily Ocean Gridded product that is a vailable from the National Snow and Ice Data Center and the EOS Data Gateway (http://nsidc.org/~imswww/pub/imswelcome/index.html) was used. This global dataset of sea surface temperatures was downloaded as a hierarchical data format (HDF) file with a spatial resolution of 0.25° x 0.25°. Using HEGTools, the low resolution sea surface temperature data was extracted from the HDF and sa ved out in GeoTIFF format. Using a custom model that was built in ArcGIS, the sea surface temperature GeoTIFF image was clipped to the region of interest (for each storm), and the values con verted to the exact temperature value in degrees Celsius. The final color-coded temperature images were exported as .PNG files for animation. Geovisualizations Geovisualizations were created from each dataset for each storm and for each storm element. To create the geovisualizations, Adobe Photoshop CS3 and FlashCS3 were used. First, the .PNG files were manipulated in Photoshop. The background from each

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of the files was removed for a “transparent” effect. The files were then animated in Flash. Each file represents a step in time; to create a time-series geovisualization, each image was added in sequence from the earliest image to the most recent image. Every geovisualization is interacti ve and can be incorporated into a web portal. RESULTS Our team created interacti ve geovisualizations for the three selected storms and animated annual storm tracks for 1992, 2002, and 2004. These animations and geovisualizations will be incorporated into NOAA’s PRICIP web portal. When in operation, this decision support tool will be useful to managers, forecasters, and decision makers for tropical storm planning, education, and research purposes. The interacti ve aspect will allow users to understand the connections between storm events and storm elements ine vitably reducing coastal vulnerability. Our first resulting product was an interactive geovisualization. To display this, one of three storm events must first be chosen by the user (e.g., Super Typhoon Pongsona, Typhoon Chata’an, or Cyclone Heta). The geovisualization screen appears for the selected storm allowing the user to select the storm element that most interests them (e.g., wind speed and direction, rainfall accumulation, SST, or MSLA (mean sea level anomaly), see Figure 1). In addition, the wind and rain elements can be displayed on top of each other, creating a “composite” effect showing how they relate to one another. When the “Play” button is selected, the storm’s track is animated across a static map image of the region. The symbol moves along the storm track and changes size according to Saffir-Simpson’s classifications of storm intensity. When one of the buttons for a storm element is selected that image appears under the storm track. For instance, when the user selects “Wind”, the wind speed and direction vectors appear and show how the winds change for the duration of the storm event. The user can pause, play, and control the playback of the storm event by selecting the appropriate buttons in the toolbar. The “Info” button, also located in the toolbar, gives the user information about the storm once selected. Additionally, the user can explore mission and sensor information that corresponds to the displayed data by selecting the “i” button that appears on the screen when that particular storm element is chosen. This information appears in a “pop-up” window format, and can be closed at any time (Figure 2).

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Figure 1: Interactive Geovisualization of Wind and Rain Composite for Super Typhoon Pongsona.

Figure 2: Examples of Storm and Sensor Quick Facts that can be displayed in the Interactive Geovisualizations.

The second product that our team created was annual storm track animations. When these annual storm tracks are played, the location of a storm’s eye appears as a colorcoded dot that represents storm intensity (Figure 3). The storm track animations progress throughout the year, showing all storms for a given year from the time they were first classified as tropical depressions until the time they were declassified. The storms appear chronologically and are tracked at 6-hour intervals. The user can control the annual storm track animations by using the slider bar at the bottom of the screen.

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Figure 3: 2002 Animated Annual Storm Tracks.

CONCLUSIONS Researchers, forecasters, and the general public pay attention to hurricanes, typhoons, and cyclones, as these storms are devastating to coastal areas throughout the world, especially in the Pacific Region. The interactions of the strong winds, hea vy rains, and high seas elements that accompany these tropical storm events warrant a thorough examination. People can be educated about past events so that they can better prepare for future e vents with the use of interacti ve visual tools. For this project, our DEVELOP team created interactive geo visualizations for three particular storms, Cyclone Heta, Super Typhoon Pongsona, and Typhoon Chata’an, and also animated annual storm tracks for 2004, 2002, and 1992. These products will be included in NOAA’s PRICIP web portal, thereby demonstrating the utility of combining data from different NASA sensors into geovisualizations of extreme storm events for use within a decision support tool. The decision makers, managers, researchers, and general public will benefit from these products. To continue enhancing the PRICIP project, NOAA and their partners will create nowcasts and futurecasts of storm events to be included in the web portal. This will ultimately reduce coastal vulnerability by improving the a vailable decision support tools.

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