According to one embodiment, there is provided a weather radar apparatus including means for generating observation data related to a weather phenomenon by processing a radar signal received by an antenna, and information processing means for processing the observation data. The information processing means includes means for executing recognition processing to recognize a rain area which arises as the weather phenomenon, based on the observation data, means for generating threat information for calculating a degree of threat of severe rain to a target point, based on a recognition result of the recognition processing, and means for specifying a predetermined function for calculating the degree of threat, applying the threat information as a parameter to the function, and calculating the degree of threat.

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. The system determines that the probability of precipitation at a particular location is lower than a stored threshold value and, in response, store data identifying the particular location as having received no precipitation.

The present invention is a method and system for generating an area of interest for unmanned aerial vehicle (UAV) missions. Using radar and weather data, a mission area may be generated for flights which will maximize efficiency by pre-generating flight paths based on atmospheric and other data. The UAV may include artificial intelligence (AI) capabilities for processing imaging and other sensed data. Post-processing of the data may include additional AI training and processing.

The distinguishing of rain echoes from ground echoes is performed by an analysis of the attenuation of the radar echoes, a radar echo being classed as a rain echo if its attenuation on a logarithm scale as a function of distance fluctuates around an affine straight line according to a given statistical law.

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. The system determines that the probability of precipitation at a particular location is lower than a stored threshold value and, in response, store data identifying the particular location as having received no precipitation.

The purpose is to reliably calculate a water vapor amount at a given position. A water vapor observing apparatus may include a transmitting part (which may also be referred to as a transmitter circuitry) configured to transmit a first transmission wave and a second transmission wave having different frequencies, a receiving part (which may also be referred to as a receiver circuitry) configured to receive, as reception waves, reflection waves caused by the transmission waves reflected on and returned from a ground surface portion or a water surface after passing through water vapor, and an arithmetic processor configured to calculate an amount of the water vapor in a passing area of the transmission waves based on first reception information generated from a first reception wave obtained from the first transmission wave, and second reception information generated from a second reception wave obtained from the second transmission wave.

The present invention is directed to system and method of processing meteorological data. The system receives meteorological data corresponding to a geographic region for a storm event. A hail data indicator pair comprising a first hail data indicator and a second hail data indicator is selected, with hail data indicators being meteorological data which directly or indirectly indicates hail activity.

In some examples, a system includes a weather radar device configured to transmit radar signals, receive first reflected radar signals at a first time, and receive second reflected radar signals at a second time. In some examples, the system also includes processing circuitry configured to determine a first magnitude of reflectivity based on the first reflected radar signals and determine a second magnitude of reflectivity based on the second reflected radar signals. In some examples, the processing circuitry is also configured to determine a temporal variance in reflectivity magnitudes based on determining a difference in reflectivity between the first magnitude and the second magnitude. In some examples, the processing circuitry is further configured to determine a presence of ice crystals based on the first magnitude of reflectivity, the second magnitude of reflectivity, and the temporal variance in reflectivity magnitudes.

A method for forecasting flood, the method including: calibrating the hydrological model by using an objective function that is a sum of squared difference between the observed streamflow and the corresponding forecasted streamflow at each lead time to obtain the optimized hydrological model; using the optimized hydrological model to forecast floods; and evaluating forecasting performance of the optimized hydrological model. The method improves the forecasting accuracy and provides forecasting results at various lead times.

A method and system for estimating uncertainties in radar based precipitation estimates is provided. In an embodiment, gauge measurements at one or more gauge locations are received by an agricultural intelligence computer system. The agricultural intelligence computer system obtains precipitation estimates for the one or more gauge locations that correspond to the gauge measurements and computes the differences between the precipitation estimates and the gauge measurements. Using the precipitation estimates and the computed differences, the agricultural intelligence computer system then models a dependence of the uncertainty in the precipitation estimates on the value of the precipitation estimates. When the agricultural intelligence computer system receives precipitation estimates for a location where gauge measurements are unavailable, the agricultural intelligence computer identifies an uncertainty for the precipitation estimate based on the value of the precipitation estimate and the model of the dependence of the uncertainty on the precipitation estimate values.