The present disclosure provides a system that predicts the occurrence and location of a severe weather event including a non-transitory tangible media containing software or firmware encoded thereon for operation by one or more processors that receive a plurality of weather variables, at least one of said weather variables being from radar data from a dual-polarization radar, where the processor (i) generates at least one derived radar variable based on the weather variables, (ii) identifies a geographical region of interest, (iii) validates the presence of the region of interest, (iv) determines whether there is a vertical column of regions of interest, wherein the presence of the vertical column of regions of interest is indicative of the vertical size of the severe weather event and (viii) validates the presence of the vertical columns of regions of interest.

In some examples, a system is configured for determining an estimated altitude of a melting layer, and the system includes a weather radar device configured to transmit radar signals and receive reflected radar signals. In some examples, the system also includes processing circuitry configured to determine the estimated altitude of the melting layer based on the reflected radar signals.

A forensic weather analyzer compares actual meteorological readings with data from multiple weather models. The data is compared and a forensic weather model is selected as the weather model that most closely matches the meteorological readings. The forensic weather model is then used to provide meteorological information pertaining to a weather event such as a hurricane, at a specific location such as a street address.

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.

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.

The present invention is directed to system and method of processing meteorological data. The process comprises receiving a meteorological data corresponding to a geographic region from at least one meteorological data source for a selected time slice, with the meteorological data including radar reflectivity data. The system processes the meteorological data to derive probability of severe hail for points within the geographic region, processes the meteorological data to derive vertically integrated liquid for the points within the geographic region, and processes the meteorological data to derive enhanced echo tops for the points within the geographic region. The system processes the vertically integrated liquid and the enhanced echo tops to derive vertically integrated liquid density for the points within the geographic region and processes the probability of severe hail and the vertically integrated liquid density to derive derived hail index numbers for the points within the geographic region. The system generate data packets of the derived hail index numbers, with each of the derived hail index numbers corresponding to a local geographic point.

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a T.sub.m value of a target point based on GPT3 model, substituting the T.sub.m value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.