Sheldon Drobot

Vehicles as Mobile Weather Observation Systems

Anyone who has recently purchased a new car or truck knows that vehicles are now very sophisticated and full of computer-controlled systems and advanced consumer electronics. What many people do not realize is that modern passenger vehicles contain data-management systems that monitor nearly every operation, from controlling engine performance to recording seat belt use, headlamp status, door positions, and hundreds of other parameters. Most vehicles also measure air temperature and pressure, and an increasing number have solar and rain sensors. Can these data be accessed, processed, and utilized by the transportation and weather enterprise to improve weather diagnostics and predictions? The authors not only believe they can, but they are actively engaged in research, development, and outreach programs to make this a reality. High-resolution spatial and temporal observations are crucial to advance our understanding of meso- and microscale meteorology, improve weather forecasts and products, and ultimately protect life and property. Over the last few decades, significant strides have been made to increase the quality and quantity of weather observations. Recently, the National Research Council (NRC) report, “Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks,” focused attention on the U.S. national needs and progress toward development of a nationwide “network of networks” observational system. Arguably, one of the most promising possibilities envisioned in the NRC report is the potential

Improving Road Weather Hazard Products with Vehicle Probe Data: Vehicle Data Translator Quality-Checking Procedures

One of the goals of RITAs IntelliDrive initiative is utilization by the public and private organizations that collect, process, and generate weather products of vehicle sensor data to improve weather and road condition hazard products. Some users may not be able to, or not want to, contend with the complexities associated with vehicle data, such as data quality, representativeness, and format. With funding and support from the U.S. Department of Transportations RITA IntelliDrive initiative and direction from FHWAs Road Weather Management Program, the National Center for Atmospheric Research is conducting research to develop a vehicle data translator (VDT) to address these vehicle-based data challenges. This paper first describes the VDT quality check (QCh) concept and then examines QCh pass rates for temperature and pressure data collected from 11 specially equipped vehicles operating in the Detroit test bed in April 2009. Results show that temperature pass rates are higher than pressure pass rates. Additionally, pass rates are somewhat affected by vehicle type, vehicle speed, ambient temperature, and precipitation occurrence for both temperature and pressure.

Diagnosing Road Weather Conditions with Vehicle Probe Data: Results from Detroit IntelliDrive Field Study

Over the past 2 years, the U.S. Department of Transportation RITA funded an IntelliDrive vehicle probe data collection test bed in the northwest Detroit, Michigan, area. The purpose of the test bed was to provide the infrastructure for both public and private organizations to collect, process, and generate a robust observation data set for multiple purposes (e.g., crash avoidance, automated toll services, weather diagnostics). During April 2009, a weather-specific field study was performed over an 11-day period. The resulting data set was processed by a vehicle data translator (VDT), which parsed, quality controlled, and combined these data (with ancillary weather data) in the generation of road weather-specific algorithms. This paper briefly describes the VDT concept and then examines the accuracy of the quality-controlled temperature and pressure data (for several different stratifications) collected from 11 specially equipped vehicles operated during the study time period. Results show that the vehicles accurately measure the temperature (compared with a nearby fixed weather station, KDTW), but are not as accurate at measuring the barometric pressure. In addition, stratification by speed, vehicle type, time of day, and occurrence of precipitation do not affect the accuracy of the temperature and barometric pressure measurements.

U.S. public preferences for weather and road condition information

In 2008, the American Meteorological Society (AMS) Board on Enterprise Planning (BEP) established the Committee on Mobile Observations to discuss the application and utilization of mobile weather and road condition data in the context of supporting the weather and transportation communities and how these data could be used to improve safety and mobility across the nations surface transportation system. The goal of the committee is to articulate a clear vision for mobile data that captures the immense opportunities for these data to improve road weather services and transportation safety and mobility. The Committee on Mobile Observations is engaged in numerous activities to accomplish its goal, which includes a nationwide survey of the traveling public to obtain better information on their preferences for and interests in obtaining weather and road condition information, their willingness to share vehicle data, and their willingness to pay for enhanced services. This paper outlines the results of the surv...

Using Vehicles as Mobile Weather Platforms

One of the goals of the Research and Innovative Technology Administration’s IntelliDriveSM initiative is for the public and private organizations that collect, process, and generate weather products to utilize vehicle sensor data to improve weather and road condition products. It is likely that some users will not be able to contend with the complexities associated with vehicle data, such as data quality, representativeness, and format. A solution for addressing this issue is to utilize a Vehicle Data Translator (VDT) to pre-process weather-related vehicle data before it is distributed to data subscribers. This paper will describe the VDT and how vehicle data sets are processed by the prototype VDT to generate derived weather and road condition information.

A comparison between SSM/I passive microwave melt onset dates and satellite-derived albedo melt onset dates in the Arctic

Arctic sea ice undergoes a very strong annual cycle. This study sets out to look at the transition when the Arctic sea ice starts to melt using satellite-obtained passive microwave brightness temperatures and satellite-derived albedo data for 13 points within the Arctic, including both first-year and multiyear ice locations, for 1995–2000. Special sensor microwave imager (SSM/I) brightness temperature differences are used to determine melt onset dates once surface temperatures approach freezing. Independently, satellite-derived albedo data are obtained and a melt onset date is derived. Generally, the two methods produce the same date for melt onset with optimum conditions. However, in most cases there are clouds present, which for the albedo data restrict observations and generate melt dates that are several days later than the passive microwave melt onset which is not affected by cloud cover. Melt onset dates, determined from the passive microwave brightness temperatures, are compared to those from the albedo observation to determine differences between the two methods. For first-year ice (FYI) locations, the average differences in melt onset dates for the study locations between the passive microwave and albedo-derived methods are +/−3 days. The average difference for multiyear ice (MYI) locations melt onset dates is around 8 days, slightly longer than the (FYI) locations, however, this is due to more cloudy conditions. The results indicate that the passive microwave-derived melt onset dates and albedo-derived dates are very close and either method could be used to determine melt. The advantage of using microwave data would be the independence of having to have cloud free conditions.