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.

An imaging system for a moving vehicle aggregates pre-existing data with sensor data to provide an image of the surrounding environment in real-time. The pre-existing data are combined with data from one or more 3-D sensors, and 2-D information from a camera, to create a scene model that is rendered for display. The system accepts data from a 3-D sensor, transforms the data into a 3-D data structure, fuses the pre-existing scene data with the 3-D data structure and 2-D image data from a 2-D sensor to create a combined scene model, and renders the combined scene model for display. The system may also weight aspects of data from first and second sensors to select at least one aspect from the first sensor and another aspect from the second sensor; wherein fusing the pre-existing scene data with the sensor data uses the selected aspect from the first sensor and the selected aspect from the second sensor.

A radar system to detect and track objects in three dimensions. The radar system including antennae, transmit, receive and processing electronics is all in a small, lightweight, low-cost, highly integrated package. The radar system uses a wide azimuth, narrow elevation radar pattern to detect objects and a Wi-Fi radio to communicate to one or more receiving and display units. One application may include mounting the radar system in an existing radome on an aircraft to detect and avoid objects during ground operations. Objects may include other moving aircraft, ground vehicles, buildings or other structures that may be in the area. The system may transmit information to both pilot and ground crew.

A system and method for enhanced vehicle efficiency through smart automation for an onboard weather update is provided. The system comprises a processor, and a non-transitory processor readable medium including instructions, executable by the processor, to perform a method comprising: receiving vehicle data from an onboard vehicle data source; receiving real-time weather data from one or more weather data sources; detecting when onboard forecast weather data is out-of-date or irrelevant based on the vehicle data and the real-time weather data; estimating one or more potential benefits from an update of the onboard forecast weather data; and activating the update of the onboard forecast weather data.

Predicting weather radar images by building a first machine learning model to generate first predictive radar images based upon input weather forecast data, and a second machine learning model to generate second predictive radar images based upon historical radar images and the first predictive radar images. Further by generating enhanced predictive radar images by providing the first machine learning model weather forecast data for a location and time and providing the second machine learning model with historical radar images for the location and an output of the first machine learning model.

Predicting weather radar images by building a first machine learning model to generate first predictive radar images based upon input weather forecast data, and a second machine learning model to generate second predictive radar images based upon historical radar images and the first predictive radar images. Further by generating enhanced predictive radar images by providing the first machine learning model weather forecast data for a location and time and providing the second machine learning model with historical radar images for the location and an output of the first machine learning model.

A clutter suppressing device for suppressing echo data of reflection waves caused by radar transmission signals reflecting on a static object is provided. Each of the radar transmission signals is transmitted at a predetermined azimuth from a radar antenna at a predetermined time interval. The clutter suppressing device includes an echo data memory configured to sequentially store a plurality of echo data of reflection waves caused by the radar transmission signals reflecting on objects, a filter configured to select, from the plurality of echo data, a data row in the azimuth direction for a predetermined distance, and suppress, in the data row, echo data of a target object moving at a speed within a predetermined range, and a suppression echo data output unit configured to output suppression echo data containing the echo data suppressed by the filter.

A system and method for improving radar based precipitation estimates using spatiotemporal interpolation is provided. In an embodiment, an agricultural intelligence computer system receives a plurality of radar based precipitation rate values representing precipitation rate measurements at a plurality of locations and a plurality of times. The agricultural intelligence computer system identifies a first non-zero radar based precipitation rate value associated with a first location of the plurality of locations and a first time of the plurality of times. The agricultural intelligence computer also identifies a second non-zero radar based precipitation rate value associated with a second location of the plurality of locations and a second time of the plurality of times. The agricultural intelligence computer system determines that the first non-zero radar based precipitation rate value corresponds to the second non-zero radar based precipitation rate value. Based on the first non-zero radar based precipitation rate value and the second non-zero radar based precipitation rate value, the agricultural intelligence computer system computes a non-zero precipitation accumulation value at a third location and a third time.

A system and method for improving radar based precipitation estimates using spatiotemporal interpolation is provided. In an embodiment, an agricultural intelligence computer system receives a plurality of radar based precipitation rate values representing precipitation rate measurements at a plurality of locations and a plurality of times. The agricultural intelligence computer system identifies a first non-zero radar based precipitation rate value associated with a first location of the plurality of locations and a first time of the plurality of times. The agricultural intelligence computer also identifies a second non-zero radar based precipitation rate value associated with a second location of the plurality of locations and a second time of the plurality of times. The agricultural intelligence computer system determines that the first non-zero radar based precipitation rate value corresponds to the second non-zero radar based precipitation rate value. Based on the first non-zero radar based precipitation rate value and the second non-zero radar based precipitation rate value, the agricultural intelligence computer system computes a non-zero precipitation accumulation value at a third location and a third time.