Abstract: Apparatuses, methods, systems, and program products are disclosed for compression and distribution of meteorological data using machine learning. An apparatus includes a processor and a memory that stores code executable by the processor to receive a raw meteorological data set for a time frame, the raw meteorological data set comprising a plurality of dimensions. The code is executable by the processor to compress the raw meteorological data set using a machine learning encoding model to create an encoded meteorological data set that has a storage size that is smaller than a storage size of the raw meteorological data set, wherein the encoded meteorological data set can be decoded to create a decoded meteorological data set that is substantially similar to the raw meteorological data set. The code is executable by the processor to make the encoded meteorological data set accessible to one or more end users.

Abstract: Examples are disclosed that related to providing flight-delay estimations of airborne flights in a real-time environment. In one example, an aircraft information message for a current aircraft flight is received. The aircraft information message has a designated format consumable by a machine learning model previously trained to assess delay predictions for aircraft flights. The aircraft information message includes one or more aircraft flight-plan parameters, one or more aircraft surveillance parameters, and one or more weather parameters for the current aircraft flight. The aircraft information message is provided as input to the machine learning model to assess a real-time delay prediction for the current aircraft flight based at least on the one or more flight-plan parameters, the one or more aircraft surveillance parameters, and the one or more weather parameters included in the aircraft information message.