Abstract: Systems and methods for improving the use of precipitation sensors, such as radar and rain gauges, are described herein. In an embodiment, an agricultural intelligence computer system receives one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations. The system additionally receives one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts, each of which comprising predictions of precipitation at a plurality of lead times. The system identifies a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time. The system uses the plurality of forecast values to generate a probability of precipitation at each of the plurality of locations.

Abstract: In an embodiment, a computer-implemented method of generating and displaying a comprehensive depiction of a weather element comprises: based on archived forecast model and observed data, training a machine learning model; calibrating current forecast data by applying the machine learning model to yield a calibrated forecast probability density function; displaying graphical representation of recently observed data and calibrated forecast probability density.

Abstract: In an embodiment, a computer-implemented method of generating and displaying a comprehensive depiction of a weather element comprises: based on archived forecast model and observed data, training a machine learning model; calibrating current forecast data by applying the machine learning model to yield a calibrated forecast probability density function; displaying graphical representation of recently observed data and calibrated forecast probability density.

Abstract: In an embodiment, a computer-implemented method of generating and displaying a comprehensive depiction of a weather element comprises: based on archived forecast model and observed data, training a machine learning model; calibrating current forecast data by applying the machine learning model to yield a calibrated forecast probability density function; displaying graphical representation of recently observed data and calibrated forecast probability density.

Abstract: In an embodiment, a computer-implemented method of generating and displaying a comprehensive depiction of a weather element comprises: based on archived forecast model and observed data, training a machine learning model; calibrating current forecast data by applying the machine learning model to yield a calibrated forecast probability density function; displaying graphical representation of recently observed data and calibrated forecast probability density.

Abstract: Systems and methods for improving the use of precipitation sensors, such as radar and rain gauges, are described herein. In an embodiment, an agricultural intelligence computer system receives one or more digital precipitation records comprising a plurality of digital data values representing precipitation amount at a plurality of locations. The system additionally receives one or more digital forecast records comprising a plurality of digital data values representing precipitation forecasts, each of which comprising predictions of precipitation at a plurality of lead times. The system identifies a plurality of forecast values for a plurality of locations at a particular time, each of the plurality of forecast values corresponding to a different lead time. The system uses the plurality of forecast values to generate a probability of precipitation at each of the plurality of locations.