A spatial autocorrelation machine learning-based downscaling method of satellite precipitation data includes obtaining the TRMM precipitation data and the land surface parameters; preprocessing the land surface parameters to obtain DEM, day land surface temperature, night surface land temperature, day-and-night land surface temperature difference and NDVI with spatial resolutions of 1 km (0.621 miles) and 25 km (15.534 miles); performing a spatial autocorrelation analysis of the TRMM precipitation data to obtain an estimated spatial autocorrelation value of the precipitation data with a spatial resolution of 25 km (15.534 miles); downscaling the spatial resolution of the spatial autocorrelation value of the precipitation data from 25 km (15.534 miles) to 1 km (0.621 miles); establishing a nonlinear regression model; obtaining a precipitation downscaling data with a spatial resolution of 1 km (0.621 miles) based on the nonlinear regression model. A system and a terminal are also provided.

A spatial autocorrelation machine learning-based downscaling method of satellite precipitation data includes obtaining the TRMM precipitation data and the land surface parameters; preprocessing the land surface parameters to obtain DEM, day land surface temperature, night surface land temperature, day-and-night land surface temperature difference and NDVI with spatial resolutions of 1 km (0.621 miles) and 25 km (15.534 miles); performing a spatial autocorrelation analysis of the TRMM precipitation data to obtain an estimated spatial autocorrelation value of the precipitation data with a spatial resolution of 25 km (15.534 miles); downscaling the spatial resolution of the spatial autocorrelation value of the precipitation data from 25 km (15.534 miles) to 1 km (0.621 miles); establishing a nonlinear regression model; obtaining a precipitation downscaling data with a spatial resolution of 1 km (0.621 miles) based on the nonlinear regression model. A system and a terminal are also provided.

The system and method for generating a forecast or simulation in a hydrologic environment includes the comparison of real-world observations with archived model states to generate or obtain initial conditions for the generation of the forecast or simulation. By using archived model states to generate forecast initial conditions, a more realistic simulation may be generated. The output of the simulation may then be stored as new model states with the other archived model states to maintain an updated archive of model states.

The system and method for generating a forecast or simulation in a hydrologic environment includes the comparison of real-world observations with archived model states to generate or obtain initial conditions for the generation of the forecast or simulation. By using archived model states to generate forecast initial conditions, a more realistic simulation may be generated. The output of the simulation may then be stored as new model states with the other archived model states to maintain an updated archive of model states.

According to an exemplary embodiment of the present disclosure, a method of classifying a precipitation type based on deep learning performed by a computing device is disclosed. The method may include: receiving first sensor data and second sensor data measured in a satellite; and generating training data based on at least a part of the first sensor data overlapping the second sensor data.

According to an exemplary embodiment of the present disclosure, a method of classifying a precipitation type based on deep learning performed by a computing device is disclosed. The method may include: receiving first sensor data and second sensor data measured in a satellite; and generating training data based on at least a part of the first sensor data overlapping the second sensor data.

Provided herein is technology relating to measuring weather data and particularly, but not exclusively, to apparatuses, methods, and systems for sensing hydrometeors (e.g., rain) and measuring hydrometeor characteristics (e.g., volume, rate, size distribution, etc.).

Provided herein is technology relating to measuring weather data and particularly, but not exclusively, to apparatuses, methods, and systems for sensing hydrometeors (e.g., rain) and measuring hydrometeor characteristics (e.g., volume, rate, size distribution, etc.).

Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.

Implementations are described herein for predicting a future climate condition in an agricultural area. In various implementations, a teleconnection model may be applied to a dataset of remote climate conditions such as water surface temperatures to identify one or more of the most influential remote climate conditions on the future climate condition in the agricultural area. A trained machine learning model may be applied to the one or more most influential remote climate conditions and to historical climate data for the agricultural area to generate data indicative of the predicted future climate condition. Based on the data indicative of the predicted future climate condition, one or more output components may be caused to render output that conveys the predicted future climate condition.