A storage unit stores ranking information including an index value relating to past rainfall and a corresponding date-time and damage information including details of damage that occurred due to past rainfall and a corresponding date-time. A real-time data reception unit acquires a spot value and a forecast value of current precipitation. Also, a comparison operation unit computes an index value relating to current rainfall using the acquired spot value and forecast value. The comparison operation unit specifies, with reference to the ranking information, the date-time of an index value relating to past rainfall whose similarity to the computed index value relating to current rainfall is not less than a threshold value. An information output unit presents, with reference to the damage information, details of damage that occurred in a period whose difference from the specified date-time of the index value relating to past rainfall is in a predetermined range.

An artificially intelligent irrigation system on a property may include an irrigation management server with the information for the irrigation system. An artificial intelligence feature may retrieve and access inputs from a plurality of resources or data sources. These sources may include current weather data, historical weather data, current moisture levels, historical moisture levels, sensor information from sensors on or near the property, water utility usage data, and other data. Other inputs may be events on the property as well the frequently or consistently occur and may also be considered historical data. The artificial intelligence feature may manage the schedule and predict the upcoming water schedule based on this information and appropriately water, or not water, or change duration of watering or output of watering based on the information gathered without human intervention.

An artificially intelligent irrigation system on a property may include an irrigation management server with the information for the irrigation system. An artificial intelligence feature may retrieve and access inputs from a plurality of resources or data sources. These sources may include current weather data, historical weather data, current moisture levels, historical moisture levels, sensor information from sensors on or near the property, water utility usage data, and other data. Other inputs may be events on the property as well the frequently or consistently occur and may also be considered historical data. The artificial intelligence feature may manage the schedule and predict the upcoming water schedule based on this information and appropriately water, or not water, or change duration of watering or output of watering based on the information gathered without human intervention.

Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Example methods and systems for detecting weather conditions using vehicle onboard sensors are provided. An example method includes receiving laser data collected for an environment of a vehicle, and the laser data includes a plurality of laser data points. The method also includes associating, by a computing device, laser data points of the plurality of laser data points with one or more objects in the environment, and determining given laser data points of the plurality of laser data points that are unassociated with the one or more objects in the environment as being representative of an untracked object. The method also includes based on one or more untracked objects being determined, identifying by the computing device an indication of a weather condition of the environment.

Systems and methods are provided for estimating atmospheric properties using a LIDAR sensor. A control system includes a LIDAR sensor configured to detect a distance to an object, and an intensity of light reflected by the object. A controller's a target selection module determines whether the object is a target for use in estimating the atmospheric properties. A data collection module collects values of the distance and the intensity as detected by the LIDAR sensor. The atmospheric properties are determined based on the values of the distance and the intensity. In response to the determined atmospheric properties, the actuator is operated to effect an action.

A computerized method of processing data for use in weather modeling is provided. The method includes receiving, from a first data source, by a first server, microwave link data including signal attenuation information. The method also includes pre-processing, in real time, by the first server, the microwave link data, thereby producing pre-processed microwave link data. The method also includes storing the pre-processed microwave link data in a first data store. The method also includes receiving, from the first data store, by a second server, the pre-processed microwave link data. The method also includes processing, on a scheduled routine, by the second server, the pre-processed microwave link data using a data transform, thereby producing first weather data.

Discloses is a precipitation measuring apparatus using a fiber Bragg grating sensor, which includes: a base horizontally installed at a position for measuring precipitation; a cylindrical cover fixedly installed on the base; a bucket for collecting a predetermined amount of water introduced into the cover to discharge the collected water; and a detecting unit for detecting the number of times of discharging the water from the bucket by using the fiber Bragg grating sensor, wherein the change in the load applied onto the water collecting tank at a predetermined amount of water discharged from the bucket is detected using the fiber Bragg grating sensor, and the amount of precipitation is precisely measured using the number of changes in the wavelength of the light outputted from the fiber Bragg grating sensor.

Discloses is a precipitation measuring apparatus using a fiber Bragg grating sensor, which includes: a base horizontally installed at a position for measuring precipitation; a cylindrical cover fixedly installed on the base; a bucket for collecting a predetermined amount of water introduced into the cover to discharge the collected water; and a detecting unit for detecting the number of times of discharging the water from the bucket by using the fiber Bragg grating sensor, wherein the change in the load applied onto the water collecting tank at a predetermined amount of water discharged from the bucket is detected using the fiber Bragg grating sensor, and the amount of precipitation is precisely measured using the number of changes in the wavelength of the light outputted from the fiber Bragg grating sensor.

Disclosed is a method for distinguishing sunny-rainy weather based on time division long-term evolution network, including the following steps: acquiring sunny-rainy feature by extracting communication measurement statistics of time division long-term evolution network base stations in a certain area; establishing a training set according to that observation result of multiple statistical periods, multiple base stations and multiple rain gauges in the region; establishing a sunny-rainy discrimination model combined with machine learning binary classification algorithm, so as to realize the identification of rainfall events covered by a single base station; calculating the reliability of rainfall events at specific locations based on the comprehensive judgment results of multiple base stations.