Weather Applications and Products Enabled Through Vehicle Infrastructure Integration (VII)

List of Figures

Figure 4.1 - Representation of VII Network Architecture (courtesy of Booz Allen Hamilton)

Figure 4.2 - Potential rural RSE deployment based on national highway system intersections and interstate interchanges. (12)

Figure 5.1 - Atmospheric absorption (average) of millimeter-waves at sea level (line A, Temperature=20oC, Pressure=1013.25 mb, Water Vapor Density= 7.5 g/m3) and altitude of 4 kilometers (line B, Temperature=0oC, Water Vapor Density= 1 g/m3) (15).

Figure 5.2 - Impact of rain and fog (15).

Figure 6.1 - Vehicle data valid at 17:12Z (A), 17:58Z (B), 23:25Z (C), and 23:49Z (D) on 16 February 2006 overlaid with WSR-88D radar data. Vehicle data include vehicle position (white circle), wiper state (to the upper left of vehicle location), atmospheric temperature (oF) (upper right), and barometric pressure (inches Hg) (lower right). Vehicle data courtesy of DaimlerChrysler.

Figure 6.2 - As in Figure 6.1, except valid times are 21:56Z (A), 22:21Z (B), 22:32Z (C), and 22:41Z (D) on 25 May 2006. Arrow denotes the location of outflow boundary.

Figure 7.1 - Doppler weather radar mosaic showing intensity (reflectivity) at Aberdeen, South Dakota on 15 March 2002 at 02:19 UTC. The image illustrates the impact of anomalous propagation (AP) on data quality. Although echoes are displayed in the image, no precipitation was occurring at the ground. None of the echoes displayed are precipitation.

Figure 7.2 - Denver NEXRAD radar image from 9 February 2006 showing virga in northeast Colorado (left image). Conceptual illustration (right image) of an improved surface precipitation product showing no precipitation at the ground based on an algorithm that correct the product by taking advantage of vehicle data from vehicles traveling along I-76.

Figure 7.3 - Illustration of a precipitation type product near Denver, Colorado from October 2005 overlaid with conceptual observations taken by winter maintenance vehicle drivers. The vehicle observations, which would supplement the standard surface observations, could be used by precipitation type diagnosis algorithms to improve the temporal and spatial resolution of the product. The locations of NWS surface observations stations are shown as circles.

Figure 7.4 - Conceptual illustration of vehicle data along a roadway in Kansas whereby the vehicles are moving through a foggy region. In this example, vehicle data contributing to a fog product include speed, relative humidity, headlamp setting, outside air temperature (OAT), and brake usage.

Figure 7.5 - Graphical illustration of the NWS surface observation network in the Northern Plains States of North and South Dakota, Minnesota, Nebraska, and western Iowa. The density of surface observations decreases dramatically west of the Mississippi River.

Figure 7.6 - Contours of temperature in the Detroit area during the passage of a simulated cold front. Warmer temperatures are in the southeast, and colder temperatures are in the northwest. Contours are based on simulated data from area ASOS stations.

Figure 7.7 - As in Figure 7.6, except simulated data from 165 vehicles located along the interstate highways are included in the analysis.

Figure 7.8 - Photo of winding road through complex terrain (top) and bottom half of a SKEWT/LOG-P atmospheric sounding diagram (bottom) from Denver, Colorado on October 27, 1999. The (partial) sounding diagram shows air temperature and dewpoint temperature measurements plotted at different altitudes (pressure levels) from a NWS balloon sounding. The data indicate that a very strong inversion was present in the lowest few hundred feet of the atmosphere near Denver. Wind barbs (right side) indicate wind speed and direction and pressure (left axis) is on hPa. For reference, 700 hPa is approximately 10,000 ft above sea level. Data collected by vehicles in mountainous areas could provide additional information (temperature, pressure, etc.) about the vertical structure of the atmosphere beyond what is currently supplied by traditional atmospheric soundings.

Figure 7.9 - Generalized data flow for air quality models. Vehicles would provide real-time data that would contribute to the calculation of emission rates.

Figure 7.10 - Refractivity field from the Front Range WSR-88D near Denver for the 0.5 degree elevation scan from 10 September 2006. Refractivity is provided in non-dimensional "N" units. A 1 g/kg change in atmospheric moisture is equivalent to 4 N units. The figure illustrates a moisture gradient from south to north.

Figure 7.11 - Conceptual illustration of a VII-enabled road condition product that provides users with information on Anti-lock Brake System (ABS) status. Positive reports of ABS events from multiple vehicles could provide winter maintenance personnel with information that suggests that pavement friction is low in specific regions. Travelers in the vicinity of vehicles reporting ABS events could also receive notifications of the activity as a "heads-up" advisory.

Figure 7.12 - Conceptual illustration of a graphical pavement temperature product over central Iowa that combines data from both fixed sites and vehicles. Iowa DOT RWIS sites are shown as yellow diamonds with RWIS pavement temperature values shown in selected white boxes. Vehicle measured pavement temperatures are shown along I-35 in blue boxes. The vehicle data help define the location of freezing pavement temperatures between the fixed RWIS sites.

Figure 7.13 - Conceptual illustration of an in-vehicle navigation display containing VII-enabled weather alerts. In this example, an alert for slippery road conditions is displayed notifying the driver of hazardous conditions prior to the encounter. (Courtesy of Andrew Stern - Mitretek Systems)

Figure 7.14 - Conceptual illustration of an in-vehicle navigation display containing VII-enabled weather alerts overlaid with weather radar data. In this example, the vehicle is about to enter a region of heavy precipitation (Courtesy of Andrew Stern - Mitretek Systems)

Figure 7.15 - Sample image from the FHWA prototype MDSS for a plow route along Interstate 70 near Denver, CO. Winter maintenance personnel use this dialog (inset image) to enter the actual snow and ice control treatments and to explore alternative treatments. Treatment type, amount, and timing can be entered.

Figure 7.16 - Output from an urban-scale plume dispersion model in an urban setting. Concentration levels are shown for a generic chemical released from a single source.

Figure 8.1 - Conceptual illustration of the VII Weather Data Translator (WDT).

Figure 9.1 - Crisp (red line) and fuzzy (green line) sets of "hot outside".

Figure 9.2 - Schematic of a "heavy precipitation" fuzzy logic algorithm that utilizes VII data, radar data, model data, satellite data, and supplemental surface observations.

Figure 9.3 - Representation of a data fusion algorithm for slick roadways.

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