One or more processors may detect that an extreme event that occurred in a first region. One or more processors may determine that the extreme event creates a drift in a probability distribution that is an output of a model associated with the first region. One or more processors may retrain the model associated with the first region using data associated with the extreme event. One or more processors may identify a second region similar to the first region according to a defined correlation threshold. One or more processors may generate data representing potential extreme scenarios for the second region based on the detected extreme event in the first region. One or more processors may retrain a model associated with the second region based on the generated data representing potential extreme scenarios.

The present disclosure provides a method for calibrating daily precipitation forecast by using a Bernoulli-Gamma-Gaussian distribution, including the following steps: acquiring daily raw forecast data and observed data; using a Bernoulli distribution to perform precipitation occurrence analysis; using a Gamma distribution to perform precipitation amount analysis on the data that precipitation occurs; using a Gaussian distribution to perform normal transformation on the raw forecast data and the observed data according to the analysis results of the Bernoulli distribution and the Gamma distribution, and obtaining corresponding normalized variables; constructing a bivariate joint normal distribution; constructing a conditional probability distribution of a predictand; and determining whether a forecast to be calibrated is that a precipitation event occurs, determining a conditional probability distribution parameter of the predictand, then randomly sampling the conditional probability distribution of the predictand, and finally obtaining the calibrated forecast by means of inverse normal quantile transform.

The present disclosure provides a method for calibrating daily precipitation forecast by using a Bernoulli-Gamma-Gaussian distribution, including the following steps: acquiring daily raw forecast data and observed data; using a Bernoulli distribution to perform precipitation occurrence analysis; using a Gamma distribution to perform precipitation amount analysis on the data that precipitation occurs; using a Gaussian distribution to perform normal transformation on the raw forecast data and the observed data according to the analysis results of the Bernoulli distribution and the Gamma distribution, and obtaining corresponding normalized variables; constructing a bivariate joint normal distribution; constructing a conditional probability distribution of a predictand; and determining whether a forecast to be calibrated is that a precipitation event occurs, determining a conditional probability distribution parameter of the predictand, then randomly sampling the conditional probability distribution of the predictand, and finally obtaining the calibrated forecast by means of inverse normal quantile transform.

A method involves determining an estimated position of a mobile device within a region. Atmospheric data measurement stations are identified within the region. A geographical anomaly is identified within the region that physically intervenes between the mobile device and a first atmospheric data measurement station. Based on a positional relationship between the mobile device, the geographical anomaly, and the first atmospheric data measurement station, it is determined that atmospheric pressure measurements collected at the first atmospheric data measurement station should be conditionally used for determining a reference pressure estimate. The reference pressure estimate is determined using a plurality of atmospheric pressure measurements collected at the atmospheric data measurement stations and conditionally using the atmospheric pressure measurements collected at the first atmospheric data measurement station. An estimated altitude of the mobile device is determined using a measurement of atmospheric pressure at the mobile device and the reference pressure estimate.

A method involves determining an estimated position of a mobile device within a region. Atmospheric data measurement stations are identified within the region. A geographical anomaly is identified within the region that physically intervenes between the mobile device and a first atmospheric data measurement station. Based on a positional relationship between the mobile device, the geographical anomaly, and the first atmospheric data measurement station, it is determined that atmospheric pressure measurements collected at the first atmospheric data measurement station should be conditionally used for determining a reference pressure estimate. The reference pressure estimate is determined using a plurality of atmospheric pressure measurements collected at the atmospheric data measurement stations and conditionally using the atmospheric pressure measurements collected at the first atmospheric data measurement station. An estimated altitude of the mobile device is determined using a measurement of atmospheric pressure at the mobile device and the reference pressure estimate.

Field calibration of a pressure device involves collecting simultaneous pressure data or pressure and temperature data at two devices for multiple time points. Pressure differences between pairs of simultaneous data points of the collected pressure data are calculated. A model is fitted to the pressure differences and the temperatures and/or pressures, and model parameters are used to correct measurements from the second device. Alternatively, a pressure gradient is estimated for a region that encompasses the two devices for each time point. A distance is determined between the two devices. A pressure gradient difference is determined between the two devices for each time point. A pressure difference offset is obtained for one of the pairs of simultaneous data points for each time point. An average pressure difference offset is determined between the two devices and is used to correct measurements from one of the devices.

Field calibration of a pressure device involves collecting simultaneous pressure data or pressure and temperature data at two devices for multiple time points. Pressure differences between pairs of simultaneous data points of the collected pressure data are calculated. A model is fitted to the pressure differences and the temperatures and/or pressures, and model parameters are used to correct measurements from the second device. Alternatively, a pressure gradient is estimated for a region that encompasses the two devices for each time point. A distance is determined between the two devices. A pressure gradient difference is determined between the two devices for each time point. A pressure difference offset is obtained for one of the pairs of simultaneous data points for each time point. An average pressure difference offset is determined between the two devices and is used to correct measurements from one of the devices.

Disclosed therein are an inspection apparatus for a thermo-hygrometer based on phase change and methods for controlling and inspecting the same which can simultaneously inspect a thermometer and a hygrometer through a simple method using a phase change of reactants. The thermo-hygrometer based on phase change includes: reactants of at least two kinds; a plurality of cylinder type cells; a reactor body having a temperature sensor hole and a plurality of cell holes; a flow pipe in which a first gas circulates by a circulation pump; a relative humidity chamber which has a relative humidity sensor disposed therein; and a temperature control unit which is mounted below the reactor body, wherein when a phase change of a first reactant is induced, the relative humidity sensor is inspected based on the relative humidity of the first gas, and the temperature sensor is inspected based on the phase change temperature of the first reactant.

Disclosed therein are an inspection apparatus for a thermo-hygrometer based on phase change and methods for controlling and inspecting the same which can simultaneously inspect a thermometer and a hygrometer through a simple method using a phase change of reactants. The thermo-hygrometer based on phase change includes: reactants of at least two kinds; a plurality of cylinder type cells; a reactor body having a temperature sensor hole and a plurality of cell holes; a flow pipe in which a first gas circulates by a circulation pump; a relative humidity chamber which has a relative humidity sensor disposed therein; and a temperature control unit which is mounted below the reactor body, wherein when a phase change of a first reactant is induced, the relative humidity sensor is inspected based on the relative humidity of the first gas, and the temperature sensor is inspected based on the phase change temperature of the first reactant.

A system and method for computing radar based precipitation estimates using inverse distance weighting is provided. In an embodiment, an agricultural intelligence computer system receives first electronic digital data comprising a first plurality of values representing precipitation gauge measurements at a plurality of gauge locations. The agricultural intelligence computer system obtains second electronic digital data comprising a second plurality of values representing radar based precipitation estimates at the plurality of gauge locations. For each radar based precipitation estimate value at the plurality of gauge locations, the agricultural intelligence computer identifies one or more corresponding precipitation gauge measurement values, computes a gauge radar differential value for the radar based precipitation estimate based, at least in part, on one or more corresponding precipitation gauge measurement values and the radar based precipitation estimate value, and stores the gauge radar differential value with location data identifying a corresponding location of the plurality of gauge locations. The agricultural intelligence computer system then obtains a particular radar based precipitation estimate at a non-gauge location. The agricultural intelligence computer system determines that one or more particular gauge radar differential values at one or more particular gauge locations correspond to the particular radar based precipitation estimate at the non-gauge location and computes a particular radar based precipitation estimate error at the non-gauge location based, at least in part, on the one or more particular gauge radar differential values at the one or more particular gauge locations and one or more distances between the non-gauge location and the one or more particular gauge locations.