Provided are an apparatus and a method for removing noise for observation information of a weather radar, and more particularly, an apparatus and a method for removing noise for observation information of a weather radar capable of separating and removing second trip echoes corresponding to noise from precipitation echoes by simulating the reflectivity of the second trip echoes caused by a distance folding phenomenon shown in weather observation information generated using a weather radar. According to the present invention, in order to remove the second trip echo that occurs in the observation information measured in the volume observation radius during weather observation by setting the weather radar as the volume observation radius where the second trip echo occurs, the weather radar is set as a long-range observation radius in which a second trip echo exceeding the volume viewing radius does not occur.

An apparatus for composition for dual-polarization weather radar observation data includes: a coordinate system converting unit that converts a reference grid of an orthogonal coordinate system into a grid of a dual-polarization weather radar spherical coordinate system based on a latitudinal-longitudinal coordinate system for each individual dual-polarization weather radar by using an earth spherical coordinate system; a CAPPI data generating unit that generates CAPPI data based on the orthogonal coordinate system after mapping individual items of dual-polarization weather radar observation data on grid coordinates of the dual-polarization weather radar spherical coordinate system; and a CAPPI data compositing unit that performs composition of CAPPI data for each of the individual dual-polarization weather radars located at the same coordinate of the orthogonal coordinate system obtained by mapping the individual items of dual-polarization weather radar observation data thereon.

A radar system receives threat relevant data with pulses sufficiently separated to provide sufficient long-range imaging, analyzes the return data to identify features of the threat, and generate a second set of pulses to acquire more detailed, higher granularity data specific to the threat. The system may include an ESA that is configured for pulses in a higher frequency to acquire higher resolution data specific to the threat.

A ground-based radar system for weather sensing and aircraft tracking includes a ground-based radar that is configured to scan a volume of space associated with a particular aircraft for detecting a weather event in the volume of space, and an electronic control system that is configured to control the ground-based radar. The control system is adapted to track the particular aircraft via tracking data associated with the particular aircraft, and is adapted to detect the weather event via weather data associated with signals from the ground-based radar. The control system is configured to control the ground-based radar to adjust the scan of the volume of space in response to at least the tracking data associated with the particular aircraft being tracked. A geographically diverse radar network that includes multiple ground-based radar systems that communicate with each other also is provided.

The present invention belongs to the field of nowcasting early-warning technology, and discloses a thunderstorm gale early-warning method, system, equipment and terminal. The thunderstorm gale early-warning method comprises: preprocessing single radar data to identify potential thunderstorm gale areas; and in real-time service, calling the thunderstorm gale parameter model in the potential thunderstorm gale areas identified by single radar every time to perform extrapolation for 1 hour, thereby forming a thunderstorm gale early-warning product within the next hour. The thunderstorm gale early-warning method provided by the present invention makes full use of the identification technology of dual polarization radar to identify the potential of thunderstorm gale, acquires falling areas of potential thunderstorm gale within the next hour by the extrapolation technology, and has better advance and accuracy compared with the existing thunderstorm gale early-warning method.

Example embodiments present radar units capable of operating in multiple polarizations. An example radar unit may include a set of transmission antennas and a set of reception antennas. Particularly, the transmission antennas may each be configured to transmit radar signals that radiate in one or more of four potential polarizations. The four polarizations can correspond to horizontal linear, vertical linear and slanted polarizations at approximately positive forty-five degrees and negative forty-five degrees from the horizontal plane. As such, the reception antennas of the radar unit may each be configured to receive reflected radar signals that are radiating in one of the four potential polarizations. The radar unit may further include an amplifier configured to cause one or multiple transmission antennas to selectively transmit between two or more of the four polarization channels.

An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.

Systems and methods are disclosed for forecasting lightning activity. Such a method may include obtaining radar reflectivity data from a radar detection device for multiple altitudes over an environmental region for a past period of time, and dividing the reflectivity data into multiple sub-regions within the region. The method may also include obtaining lightning discharge data for the environmental region from a lightning discharge detecting device for a past period of time, and establishing a customizable mathematical operator based on the radar reflectivity data and the lightning discharge data. The method may additionally include receiving forecast radar data for at least one of the sub-region for a future time period, and forecasting a probability of lightning strikes in at least one of the sub-regions based on applying the customizable mathematical operator to the forecast radar data.

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.

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.