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.

A system includes an autonomous vehicle and a control device associated with the autonomous vehicle. The control device obtains a plurality of sensor data captured by sensors of the autonomous vehicle. The control device determines a plurality of rainfall levels based on the sensor data. Each rainfall level is captured by a different sensor. the control device determines an aggregated rainfall level in a particular time period by combining the plurality of rainfall levels determined during the particular time period. The control device selects a particular object detection algorithm for detecting objects by at least one sensor. The particular object detection algorithm is configured to filter at least a portion of interference caused by the aggregated rainfall level in the sensor data. The control device causes the particular object detection algorithm to be implemented for the at least one sensor.

A system includes an autonomous vehicle and a control device associated with the autonomous vehicle. The control device obtains a plurality of sensor data captured by sensors of the autonomous vehicle. The control device determines a plurality of rainfall levels based on the sensor data. Each rainfall level is captured by a different sensor. the control device determines an aggregated rainfall level in a particular time period by combining the plurality of rainfall levels determined during the particular time period. The control device selects a particular object detection algorithm for detecting objects by at least one sensor. The particular object detection algorithm is configured to filter at least a portion of interference caused by the aggregated rainfall level in the sensor data. The control device causes the particular object detection algorithm to be implemented for the at least one sensor.

The present invention relates to a method for determining a seeding effect area and a non-seeding effect area in accordance with a wind system according to the present invention can use numerical simulation data, radar precipitation data, and ground precipitation data to systematically specify the steps of determining the seeding effect area and the non-seeding effect area in accordance with the wind system. Moreover, the method according to the present invention can easily divide the seeding effect area and the non-seeding effect area according to the physical properties of clouds and quantitatively verify the effectiveness of artificial precipitation experiments conducted by purpose in the future.

The present invention relates to a method for determining a seeding effect area and a non-seeding effect area in accordance with a wind system according to the present invention can use numerical simulation data, radar precipitation data, and ground precipitation data to systematically specify the steps of determining the seeding effect area and the non-seeding effect area in accordance with the wind system. Moreover, the method according to the present invention can easily divide the seeding effect area and the non-seeding effect area according to the physical properties of clouds and quantitatively verify the effectiveness of artificial precipitation experiments conducted by purpose in the future.

A differential emissivity imaging device for measuring evaporable particle properties can include a heated plate, a thermal camera, a memory device, and an output interface. The heated plate can have an upper surface oriented to receive falling evaporable particles. The evaporable particles have a particle emissivity and the upper surface has a plate surface emissivity. The thermal camera can be oriented to produce a thermal image of the upper surface. A memory device can include instructions that cause the imaging device to calculate a mass of the individual evaporable particle via heat conduction using a calculated surface area and an evaporation time.

A method allowing data delivered by a rain gauge to be validated in real time is provided. The method includes steps of: receiving, in a defined time window, pluviometric data from a gauge and weather data from at least one weather radar; computing a coefficient of gauge/radar similarity between the pluviometric data received from the gauge and the weather data received from the at least one weather radar; comparing the value of the coefficient of obtained gauge/radar similarity to a threshold gauge/radar value; and validating the pluviometric data of the gauge if the value of the coefficient of gauge/radar similarity is higher than or equal to the threshold gauge/radar value.

A method allowing data delivered by a rain gauge to be validated in real time is provided. The method includes steps of: receiving, in a defined time window, pluviometric data from a gauge and weather data from at least one weather radar; computing a coefficient of gauge/radar similarity between the pluviometric data received from the gauge and the weather data received from the at least one weather radar; comparing the value of the coefficient of obtained gauge/radar similarity to a threshold gauge/radar value; and validating the pluviometric data of the gauge if the value of the coefficient of gauge/radar similarity is higher than or equal to the threshold gauge/radar value.

Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.

Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.