In an embodiment, radar observation data for time points are received at an input layer of a rain rate prediction neural network. The radar observations are forward propagated via hidden layers of the network to determine rain rates for the time points. The rain rates are integrated over a time period, determined based on the time points, to determine a predicted rainfall amount. The predicted rainfall amount is compared with an actual rainfall amount, determined based on received rainfall measurements, to determine an error. If the error does not satisfy certain criteria, then the error is apportioned to each of the time points, the apportioned errors are back propagated via the hidden layers, and weights associated with nodes in the hidden layers are updated. The radar observation data is again forward propagated via the layers, multiplied by the updated weights, and used to determine new rain rates.

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.

Computerized method and system for estimating a rain attribute on microwave communications, the estimation being carried out by: obtaining quantized minimum and maximum levels of received signals and transmitted signals over a microwave link during a period; subtracting the quantized maximum level of received signals from the quantized minimum level of transmitted signals to provide a minimal attenuation value; subtracting the quantized minimal level of received signals from the quantized maximal level of transmitted signals to provide a maximal attenuation value; calculating an attenuation difference related to the period by subtracting the minimal attenuation value from the maximal attenuation value; calculating a bias compensated attenuation difference based on the attenuation difference, and bias value related to the microwave link; and calculating the rain attribute, including the average rain during the period, based on the bias compensated attenuation difference.

Methods and systems of displaying weather data for a cockpit display system of an aircraft. The methods and systems include generating a display to include a first graphical map of real-time weather data from a weather radar. The display further includes a notification graphic associated with a portion of part of a displayed flight plan in which a significant weather condition exists. When the notification graphic is selected, the display includes the first graphical map of the real time weather data based on weather data from the weather radar and a second graphical map of significant weather conditions data derived from transmitted weather data.

A system and method for computing radar based precipitation estimates using inverse distance weighting is provided. In an embodiment, an agricultural intelligence computer system receives first electronic digital data comprising a first plurality of values representing precipitation gauge measurements at a plurality of gauge locations. The agricultural intelligence computer system obtains second electronic digital data comprising a second plurality of values representing radar based precipitation estimates at the plurality of gauge locations. For each radar based precipitation estimate value at the plurality of gauge locations, the agricultural intelligence computer identifies one or more corresponding precipitation gauge measurement values, computes a gauge radar differential value for the radar based precipitation estimate based, at least in part, on one or more corresponding precipitation gauge measurement values and the radar based precipitation estimate value, and stores the gauge radar differential value with location data identifying a corresponding location of the plurality of gauge locations. The agricultural intelligence computer system then obtains a particular radar based precipitation estimate at a non-gauge location. The agricultural intelligence computer system determines that one or more particular gauge radar differential values at one or more particular gauge locations correspond to the particular radar based precipitation estimate at the non-gauge location and computes a particular radar based precipitation estimate error at the non-gauge location based, at least in part, on the one or more particular gauge radar differential values at the one or more particular gauge locations and one or more distances between the non-gauge location and the one or more particular gauge locations.

A system and method for computing radar based precipitation estimates using inverse distance weighting is provided. In an embodiment, an agricultural intelligence computer system receives first electronic digital data comprising a first plurality of values representing precipitation gauge measurements at a plurality of gauge locations. The agricultural intelligence computer system obtains second electronic digital data comprising a second plurality of values representing radar based precipitation estimates at the plurality of gauge locations. For each radar based precipitation estimate value at the plurality of gauge locations, the agricultural intelligence computer identifies one or more corresponding precipitation gauge measurement values, computes a gauge radar differential value for the radar based precipitation estimate based, at least in part, on one or more corresponding precipitation gauge measurement values and the radar based precipitation estimate value, and stores the gauge radar differential value with location data identifying a corresponding location of the plurality of gauge locations. The agricultural intelligence computer system then obtains a particular radar based precipitation estimate at a non-gauge location. The agricultural intelligence computer system determines that one or more particular gauge radar differential values at one or more particular gauge locations correspond to the particular radar based precipitation estimate at the non-gauge location and computes a particular radar based precipitation estimate error at the non-gauge location based, at least in part, on the one or more particular gauge radar differential values at the one or more particular gauge locations and one or more distances between the non-gauge location and the one or more particular gauge locations.

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 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 clutter suppressing device for suppressing echo data of static clutter components indicating reflection waves caused by radar transmission signals reflecting on a static object is provided. The device includes a static clutter component suppressor configured to receive reception signals containing the static clutter components, and suppress the static clutter components, a reference data memory configured to store, as reference data, echo data of the reception signals obtained in fine weather and in which the static clutter components are suppressed by the static clutter component suppressor, and a rain component extracting module configured to extract echo data indicating rain components contained in the reception signals, by removing the reference data stored in the reference data memory from echo data of the reception signals obtained in rainy weather and in which the static clutter components are suppressed by the static clutter component suppressor.

A clutter suppressing device for suppressing echo data of static clutter components indicating reflection waves caused by radar transmission signals reflecting on a static object is provided. The device includes a static clutter component suppressor configured to receive reception signals containing the static clutter components, and suppress the static clutter components, a reference data memory configured to store, as reference data, echo data of the reception signals obtained in fine weather and in which the static clutter components are suppressed by the static clutter component suppressor, and a rain component extracting module configured to extract echo data indicating rain components contained in the reception signals, by removing the reference data stored in the reference data memory from echo data of the reception signals obtained in rainy weather and in which the static clutter components are suppressed by the static clutter component suppressor.