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 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 distributed system for flight and route management (FARM) of one or more aircraft of the system may include onboard processing devices for combining sensor data local to an aircraft with cloud-based data received through the system from other aircraft or from ground-based processing devices, thereby generating situation models of each aircraft relative to its flight path and in the context of current and predictive conditions (weather, traffic, terrain, threats, etc.). The FARM system may evaluate situation models against prioritized constraint sets of business rules or policies associated with each aircraft's flight plan to determine, crosscheck, and implement possible modifications to the flight plan. Localized aircraft data and flight plan modifications may be propagated through the system via a variety of communications networks to provide synchronized, holistic airspace data portraits to aircraft and ground control alike.

A method of selectively displaying an image representative of a weather condition in relation to an aircraft includes selecting, on a display screen, a display area to display weather data based on the location of the aircraft, selecting a weather condition to display from among a plurality of weather conditions, determining if any weather conditions are available to be displayed outside the selected display area and if the weather conditions should be displayed outside the selected display area based on the location of the aircraft and the severity of the non-selected weather conditions, receiving, from a weather data source, weather data representative of the selected weather condition with respect to the selected display area, and receiving weather data representative of weather conditions that should be displayed outside the selected display area, the weather data including location data for the weather conditions, and displaying the image representative of the selected weather condition within the selected display area and the weather conditions that should be displayed outside the selected display area, the image based on the received weather data.

Apparatus and associated methods relate to detecting cloud conditions from a distance by generating a polarized microwave-frequency electromagnetic pulse and evaluating various reflected wave parameters pertaining to a corresponding cloud-reflected microwave-frequency electromagnetic reflection. Various cloud metrics can be calculated using these collected wave parameters. The microwave-frequency pulses can be scanned over multiple dimensions, using a steered beam arrangement which will lead to the ability to scanning a conical sector of the space in front of the aircraft. These collected multi-dimensional wave parameters can then be used to generate multi-dimensional maps of cloud metrics. Such cloud metrics can include relative velocities of moving cloud conditions in the flight direction, particle density distributions, ice/water ratios, estimates of particle side distributions, etc.

Embodiments of a system for simulating a radio frequency (RF) scene associated with a moving maritime surface are generally described herein. An RF scene is generated using an RF scene generation model and a moving maritime surface is generated using a maritime surface model. The RF scene is integrated with the moving maritime surface model. The RF scene generation model is configured to apply a radar model to generate and update the RF scene based on simulated radar returns at a radar pulse repetition frequency (PRF) and the maritime surface model is configured to update the moving maritime surface at a maritime surface update rate, access previous and current maritime surfaces, and interpolate surface facet properties to pulse times of the radar model, The maritime surface model is configured to update the moving maritime surface once every subdwell.

A hazard warning system can be utilized in an aircraft. The hazard warning system can include a processing system for determining a high altitude ice crystal (HAIC) condition and causing a warning of the HAIC condition to be displayed. An avionic display can be used to display the warning of the HAIC condition.

A radar system, such as a weather radar system, includes a radar antenna and a processor. The processor is configured to cause a first radar beam to be provided using a first portion of the radar antenna. The processor is configured to cause a second radar beam to be provided using a phase adjusted portion of the antenna and a remaining portion of the radar antenna. A radar method and system can allow multiple low-loss overlapping radar beams to be rapidly generated to support a sequential lobing process which may be used to generate intra-beam target angle estimates. The production of these overlapping beams does not require mechanical antenna movement but beam selection is controlled by a simple electronic switch in some embodiments.

Embodiments for a terrain following (TF) radar configured for use in an airborne system are generally described herein. In some embodiments, a radar return comprising dual polarimetry radar data is processed to determine a Correlation Coefficient (CC), a Differential Reflectivity (ZDR), and a Specific Differential Phase (KDP). Discriminator logic is applied to the CC, the ZDR and the KDP to determine whether the radar return comprises solely rain. Further signal processing may be performed on the radar return when the radar return does not comprise solely rain. When the radar signal comprises solely rain, the radar return is tagged as a rain return. Applying the discriminator logic may include applying linear and/or quadratic functions to the CC, the ZDR and the KDP to determine whether the radar return comprises solely rain.

Techniques are disclosed for attribution analysis in analytical workflows. A data processing system (DPS) obtains an overall model comprising one or more sub-models. The DPS selects an output variable of the overall model for which attribution of changes is to be performed, and a plurality of input variables against which changes are to be attributed to. The overall model is initially executed with respect to a data set of values for the plurality of input variables to generate a base result for the output variable. The overall model is iteratively executed based on a condition associated with the plurality of input variables to obtain a new result for the output variable. In each iteration, a value of an input variable is changed with respect to the data set of values and a change in the output variable with respect to the base result is attributed to the corresponding input variable.