Systems and methods for accurate measurement and transmission of water level parameters during weather events, such as floods, are provided. The solid-state system can effectively measure water level without utilizing moving parts, pumps, or floats and may implement an improved water level determination method that compensates for inherent sources of error. Additionally, the system may be comprised of a network of sensor units that can communicate weather measurements wirelessly via a hybrid mesh network consisting variously of wireless terrestrial radio, cellular, and satellite communication links. By doing so, the status of water level and other environmental parameters may be reported in real time to first responders and emergency planners.

Systems and methods for accurate measurement and transmission of water level parameters during weather events, such as floods, are provided. The solid-state system can effectively measure water level without utilizing moving parts, pumps, or floats and may implement an improved water level determination method that compensates for inherent sources of error. Additionally, the system may be comprised of a network of sensor units that can communicate weather measurements wirelessly via a hybrid mesh network consisting variously of wireless terrestrial radio, cellular, and satellite communication links. By doing so, the status of water level and other environmental parameters may be reported in real time to first responders and emergency planners.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Disclosed is a method for calculating instantaneous sprinkler strength comprising: ensuring that a translational sprinkler (1) maintains a stable operating state, placing b rain barrels (3) at a distance of a metres from the translational sprinkler (1), and moving the translational sprinkler (1) to obtain measurement data; calculating movement time, and the average sprayed water depth received by the rain barrels (3); assuming the distribution form of the amount of water of the translational sprinkler (1), establishing a function relationship between an instantaneous sprinkler strength ht and the movement time t, and calculating a variable in the function relationship; and substituting into the established function relationship a specific numerical value of an instantaneous point in time t of the movement of the translational sprinkler (1), so that the value of ht obtained is a numerical value of the instantaneous sprinkler strength of the translational sprinkler (1). The calculation method has a simple operation, is fast and can obtain a precise calculation result with relatively low experiment costs.

Disclosed is a method for calculating instantaneous sprinkler strength comprising: ensuring that a translational sprinkler (1) maintains a stable operating state, placing b rain barrels (3) at a distance of a metres from the translational sprinkler (1), and moving the translational sprinkler (1) to obtain measurement data; calculating movement time, and the average sprayed water depth received by the rain barrels (3); assuming the distribution form of the amount of water of the translational sprinkler (1), establishing a function relationship between an instantaneous sprinkler strength ht and the movement time t, and calculating a variable in the function relationship; and substituting into the established function relationship a specific numerical value of an instantaneous point in time t of the movement of the translational sprinkler (1), so that the value of ht obtained is a numerical value of the instantaneous sprinkler strength of the translational sprinkler (1). The calculation method has a simple operation, is fast and can obtain a precise calculation result with relatively low experiment costs.

Systems, methods, and apparatus related to determining ice hazards on roads based on crowdsourced data from vehicles. In one approach, a server receives weather data and location data from each of several vehicles. The weather data is timestamped when received. The server determines, using the location data, a geographic region in which each vehicle is located. The weather data is stored in a database associated with the respective geographic region for the vehicle that transmitted the weather data. The server periodically scans the database to select weather data received over a selected time period. The selected data is analyzed to determine whether an ice hazard exists for one or more regions. A communication is sent to vehicles in those regions having the determined ice hazard.