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.

Provided are a device for measuring the amount of snowfall and a method of controlling the same, the device including a bottom plate, a graduated ruler extending upward from the bottom plate, an image capturing unit configured to capture an image of the graduated ruler and an upper portion of snow deposited on the bottom plate by using an image capturing device, a measurement unit configured to measure the amount of snowfall based on information on the captured image, and a movement unit configured to move the device for measuring the amount of snowfall so that a position of the bottom plate moves from a first position to a second position.

Provided are a device for measuring the amount of snowfall and a method of controlling the same, the device including a bottom plate, a graduated ruler extending upward from the bottom plate, an image capturing unit configured to capture an image of the graduated ruler and an upper portion of snow deposited on the bottom plate by using an image capturing device, a measurement unit configured to measure the amount of snowfall based on information on the captured image, and a movement unit configured to move the device for measuring the amount of snowfall so that a position of the bottom plate moves from a first position to a second position.

The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

A method for monitoring precipitation of magnetospheric particles includes detecting charged magnetospheric particles by a particles detector, processing the detection data to associate a respective estimate or measurement of kinetic energy with the detected magnetospheric particles, obtaining a first count value N.sub.H associated with a relatively higher estimate or measurement of kinetic energy, obtaining a second count value N.sub.L associated with a relatively lower estimate or measurement of kinetic energy, detecting a relative variation of the second count value N.sub.L with respect to the first count value N.sub.H, determining that an impulsive event of precipitation of charged magnetospheric particles (MPP event) in the magnetosphere occurred, assigning to the MPP event geomagnetic longitude and time, defining one or more groups of MPP events occurred in a time range at a same geomagnetic longitude, and identifying a group of MPP events indicative of an activity of terrestrial origin.

A method for monitoring precipitation of magnetospheric particles includes detecting charged magnetospheric particles by a particles detector, processing the detection data to associate a respective estimate or measurement of kinetic energy with the detected magnetospheric particles, obtaining a first count value N.sub.H associated with a relatively higher estimate or measurement of kinetic energy, obtaining a second count value N.sub.L associated with a relatively lower estimate or measurement of kinetic energy, detecting a relative variation of the second count value N.sub.L with respect to the first count value N.sub.H, determining that an impulsive event of precipitation of charged magnetospheric particles (MPP event) in the magnetosphere occurred, assigning to the MPP event geomagnetic longitude and time, defining one or more groups of MPP events occurred in a time range at a same geomagnetic longitude, and identifying a group of MPP events indicative of an activity of terrestrial origin.

Embodiments generally relate to a method for selecting hybrid variables. The method comprises sampling at least one interaction effect structure of at least one multivariable dataset, sampling at least one hybrid variable for each sampled interaction effect structure, calculating a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria, labeling each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria, training a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, applying the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria, and retaining only hybrid variables with a likelihood value that exceeds a decision criteria. The training of the machine learning model is performed using the labeled sampled hybrid variables.

A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm's impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm's impact.

A computer-based method for identifying ice storm risk across a geographical extent includes receiving, at a computer-based ice storm risk calculation system, historical data regarding a plurality of past ice storms. The historical data includes, for each respective one of the plurality of past ice storms, data about the size of the geographical region that was impacted by the ice storm, the thickness of ice that accumulated from the ice storm, and qualitative data (e.g., written observations in new reports, etc.) reflecting human observations of the ice storm's impact. The method further includes calculating an ice storm severity index based, in part, on the size of the geographical region that was impacted by the ice storm and the thickness of the accumulated ice that resulted from the ice storm, and validating the calculated ice storm index with the qualitative data reflecting the human observations of the ice storm's impact.

A weather-detecting device (100) can include a substrate (102) and a detection region (106) exposed to an environment within which the weather-detecting device (100) is situated when in use. An array (110) of heating elements (112) can be mounted at a first side of the substrate (102), with at least one surface of each heating element (112) in the array (110) being positioned within the detection region (106). A controller can be electrically coupled to the array (110) of heating elements (112), and the controller can individually address each heating element (112) in the array (110) to selectively pass electrical current through each heating element (112).