A bad weather judgment apparatus and a bad weather judgment method thereof are disclosed. The apparatus includes a target recognizer configured to recognize targets in detection areas of a plurality of heterogeneous sensors based on sensor recognition information received from the heterogeneous sensors, a counter configured to count the number of cases based on detection states of the heterogeneous sensors about a same target among the targets, and a bad weather judger configured to determine whether the same target is present in bad weather judgment zones of the detection areas of the heterogeneous sensors, control the counter to increment or decrement the number of the cases based on detection states of the heterogeneous sensors about whether the same target is present in the bad weather judgment zones, and judge current weather to be bad weather when the number of the cases is greater than a threshold value.

A bad weather judgment apparatus and a bad weather judgment method thereof are disclosed. The apparatus includes a target recognizer configured to recognize targets in detection areas of a plurality of heterogeneous sensors based on sensor recognition information received from the heterogeneous sensors, a counter configured to count the number of cases based on detection states of the heterogeneous sensors about a same target among the targets, and a bad weather judger configured to determine whether the same target is present in bad weather judgment zones of the detection areas of the heterogeneous sensors, control the counter to increment or decrement the number of the cases based on detection states of the heterogeneous sensors about whether the same target is present in the bad weather judgment zones, and judge current weather to be bad weather when the number of the cases is greater than a threshold value.

A method and a device for measuring altitude of an aircraft relative to a point on the ground, said aircraft carrying a radar system comprising a directional antenna to transmit a radio frequency signal along a aiming axis, including. controlling the transmission of a radiofrequency signal along the axis, calculating received powers as a function of radial distance on a sum channel and an elevation deviation channel, calculating tilt angular deviation values, determining an estimator of the radial distance of the aircraft relative to the point on the ground intercepted by the aiming axis as a function of at least one zero crossing of the angular deviation measurement in a selected area of the angular deviation measurement curve, calculating an aircraft altitude relative to said point on the ground as a function of the estimator of the radial distance and the elevation angle of the aiming axis.

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.

Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

In some examples, a system includes a memory configured to store a buffer storing volumetric weather data. The system also includes processing circuitry configured to retrieve weather radar data from the buffer by retrieving a first reflectivity value associated with a first cell of the buffer, where the first cell represents a first volume of space. The processing circuitry is further configured to determine an altitude of a first location based on a parameter and an altitude extent of the first volume of space. The altitude of the first location is lower than an altitude of a centroid of the first volume of space. The processing circuitry is also configured to associate the first reflectivity value with the first location.

A weather radar system initiates a horizontal scan at a first tilt angle, then adjusts the tilt angle down during an intermediate phase of the horizontal scan, and finally adjust the tilt angle back to the first tilt angle for an end phase of the horizontal scan. The intermediate phase may comprise a fifteen-to-twenty-degree arc centered about a centerline of the weather radar antenna. The radar system may perform test horizontal scans at intervals within a predefined range, each having a different fixed tilt angle to identify tilt angles having desirable signal-to-noise ratio and signal-to-clutter ratio at different phases of the scan. Alternatively, the system may execute a vertical scan and at one or more horizontal positions to identify desirable tilt angles at various phases.

An aircraft-based synthetic vision system (SVS) is disclosed. In embodiments, the SVS includes avionics processors in communication with onboard lightning detection sensors, which provide the SVS with real-time lightning data (e.g., bearing to, and distance from, the aircraft) about proximate lightning strikes. Based on the real-time lightning data, the avionics processors generate flight deck effects (FDE) corresponding to identified areas of lightning activity (e.g., a sufficient quantity of strikes, exceeding a strike threshold, within a particular airspace during a time window), each FDE having a particular bearing to and distance from the aircraft. The FDE data is processed by a display system aboard the aircraft (e.g., a cockpit-based primary flight display (PFD) or head-worn/heads-up display (HWD/HUD)) which incorporates the generated FDEs into the SVS status display provided to the flight crew or pilot at the appropriate bearing and distance relative to the aircraft.

This water vapor observation device comprises an antenna, an RF amplifier, and processing circuitry. The antenna receives radio waves radiated from an atmosphere including water vapor. The RF amplifier amplifies the received radio waves and generates an observation signal. The processing circuitry is programmed to at least: use the observation signal to select a plurality of observation frequencies excluding an accuracy degraded frequency; and calculate a water vapor index using the spectral intensities of the plurality of observation frequencies. This configuration makes it possible to accurately observe a water vapor amount.

Embodiments of the present disclosure provide a method and apparatus for predicting a severe convection weather. The method may include: acquiring a current radar echo map sequence, the current radar echo map sequence being a radar echo map sequence within a current time period; generating, based on the current radar echo map sequence, a future radar echo map sequence, the future radar echo map sequence being a radar echo map sequence within a future time period; and inputting the future radar echo map sequence into a pre-trained severe convection weather predicting model to obtain a severe convection weather intensity predicting map, where the severe convection weather predicting model is used to predict the intensity of a severe convection weather.