A method of monitoring a fluid meter is arranged to produce measurements of the overall consumption of an installation. The method includes the steps of: analyzing the overall consumption measurements to identify a mechanism in the installation that operates with operating cycles, each presenting a substantially constant cycle duration (t1−t0) and during each of which the individual consumption (V1−V0) of the mechanism is substantially constant; detecting operating cycles of the mechanism and measuring the individual consumption of the mechanism for each detected operating cycle; detecting abnormal metrological drift of the measuring device as a function of variation over time in the individual consumption.

A method of monitoring a fluid meter is arranged to produce measurements of the overall consumption of an installation. The method includes the steps of: analyzing the overall consumption measurements to identify a mechanism in the installation that operates with operating cycles, each presenting a substantially constant cycle duration (t1−t0) and during each of which the individual consumption (V1−V0) of the mechanism is substantially constant; detecting operating cycles of the mechanism and measuring the individual consumption of the mechanism for each detected operating cycle; detecting abnormal metrological drift of the measuring device as a function of variation over time in the individual consumption.

According to an example aspect of the present invention, there is provided a method in connection with a radiosonde, the method comprising measuring a humidity of the atmosphere at several different altitudes in the atmosphere, measuring a pressure at several different altitudes in the atmosphere or calculating the pressure from an altitude of the radiosonde obtained from GPS or other satellite navigation system, measuring or estimating a temperature of a humidity sensor, and measuring a relative humidity by a capacitor with a polymer insulator, wherein the relative humidity value is corrected based on a correction factor, which is a function of pressure, humidity sensor temperature, and relative humidity, such that the humidity value decreases when pressure decreases.

According to an example aspect of the present invention, there is provided a method in connection with a radiosonde, the method comprising measuring a humidity of the atmosphere at several different altitudes in the atmosphere, measuring a pressure at several different altitudes in the atmosphere or calculating the pressure from an altitude of the radiosonde obtained from GPS or other satellite navigation system, measuring or estimating a temperature of a humidity sensor, and measuring a relative humidity by a capacitor with a polymer insulator, wherein the relative humidity value is corrected based on a correction factor, which is a function of pressure, humidity sensor temperature, and relative humidity, such that the humidity value decreases when pressure decreases.

Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

A method includes detecting, by a pair sensors located at two locations, a radio wave generated from a lightning discharge occurring; for the pair of sensors, determining propagation paths between the discharge and sensors; repeating the same with a second pair of sensors and another lightning discharge; for each path, determine path vectors defined by an attenuation coefficient vector; constructing a matrix with each row comprising the difference between path vectors from sensor pair measurements from the lightning discharge and including a selection entry based on the identification of the sensors to enable the logarithm of sensor calibration factors to be solved for; constructing another vector, where each entry comprises the difference between the logarithms of the sensor amplitudes from a sensor pair measurement adjusted by a logarithm of an offset; inverting a system of linear equations defined by the matrix to solve for attenuation and sensor calibration factors.

A method includes detecting, by a pair sensors located at two locations, a radio wave generated from a lightning discharge occurring; for the pair of sensors, determining propagation paths between the discharge and sensors; repeating the same with a second pair of sensors and another lightning discharge; for each path, determine path vectors defined by an attenuation coefficient vector; constructing a matrix with each row comprising the difference between path vectors from sensor pair measurements from the lightning discharge and including a selection entry based on the identification of the sensors to enable the logarithm of sensor calibration factors to be solved for; constructing another vector, where each entry comprises the difference between the logarithms of the sensor amplitudes from a sensor pair measurement adjusted by a logarithm of an offset; inverting a system of linear equations defined by the matrix to solve for attenuation and sensor calibration factors.