The invention relates to a device for measuring rain and snow, comprising: a module for collecting water or snow; a module for measuring the level and volume of fluid; an information processing module; a heating module for collecting snow; a frame; and a photovoltaic energy module.

A hail strike recording device is operable to provide quantifiable information about a hail storm event experienced by a roof. The recording device is operable to be installed on a roof and includes a panel component and a mounting assembly. The panel component presents a hail impact zone to sense one or more hail strikes, with the recording device operable to provide recorded data associated with the sensed one or more hail strikes.

A hail strike recording device is operable to provide quantifiable information about a hail storm event experienced by a roof. The recording device is operable to be installed on a roof and includes a panel component and a mounting assembly. The panel component presents a hail impact zone to sense one or more hail strikes, with the recording device operable to provide recorded data associated with the sensed one or more hail strikes.

Computerized method and system for estimating a rain attribute on microwave communications, the estimation being carried out by: obtaining quantized minimum and maximum levels of received signals and transmitted signals over a microwave link during a period; subtracting the quantized maximum level of received signals from the quantized minimum level of transmitted signals to provide a minimal attenuation value; subtracting the quantized minimal level of received signals from the quantized maximal level of transmitted signals to provide a maximal attenuation value; calculating an attenuation difference related to the period by subtracting the minimal attenuation value from the maximal attenuation value; calculating a bias compensated attenuation difference based on the attenuation difference, and bias value related to the microwave link; and calculating the rain attribute, including the average rain during the period, based on the bias compensated attenuation difference.

Computerized method and system for estimating a rain attribute on microwave communications, the estimation being carried out by: obtaining quantized minimum and maximum levels of received signals and transmitted signals over a microwave link during a period; subtracting the quantized maximum level of received signals from the quantized minimum level of transmitted signals to provide a minimal attenuation value; subtracting the quantized minimal level of received signals from the quantized maximal level of transmitted signals to provide a maximal attenuation value; calculating an attenuation difference related to the period by subtracting the minimal attenuation value from the maximal attenuation value; calculating a bias compensated attenuation difference based on the attenuation difference, and bias value related to the microwave link; and calculating the rain attribute, including the average rain during the period, based on the bias compensated attenuation difference.

Snow sensors, mechanisms, and methods for snow sensor reading, assemblies for use with snow sensors, such as snow removal systems and assemblies and related components, as well as component combinations and related methods.

Snow sensors, mechanisms, and methods for snow sensor reading, assemblies for use with snow sensors, such as snow removal systems and assemblies and related components, as well as component combinations and related methods.

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.

A method for estimating confidence bounds for adjusted rainfall values for a set of geo-locations using agricultural data comprises using a server computer system that receives, via a network, agricultural data records that are used to estimate rainfall values for the set of geo-locations. Within the server computer system, rainfall calculation instructions receive digital data including observed radar and rain-gauge agricultural data records. The computer system then aggregates the agricultural data records and creates and stores the agricultural data sets. The agricultural data records are then used to estimate adjusted rainfall values for a set of geo-locations. Rainfall confidence bounds instructions estimate a set of confidence bounds for each of the adjusted rainfall values for the set of geo-locations. The set of confidence bounds provide a range for each of the adjusted rainfall values that represents a particular level of confidence associated with each of the adjusted rainfall values.

A method for estimating confidence bounds for adjusted rainfall values for a set of geo-locations using agricultural data comprises using a server computer system that receives, via a network, agricultural data records that are used to estimate rainfall values for the set of geo-locations. Within the server computer system, rainfall calculation instructions receive digital data including observed radar and rain-gauge agricultural data records. The computer system then aggregates the agricultural data records and creates and stores the agricultural data sets. The agricultural data records are then used to estimate adjusted rainfall values for a set of geo-locations. Rainfall confidence bounds instructions estimate a set of confidence bounds for each of the adjusted rainfall values for the set of geo-locations. The set of confidence bounds provide a range for each of the adjusted rainfall values that represents a particular level of confidence associated with each of the adjusted rainfall values.