Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.

This application relates to an input data generating apparatus for generating forcing data used as input data for a climate change prediction model. In one aspect, the apparatus includes a memory storing instructions and a processor configured to, by executing the instructions, collect new ground type data from land-use harmonization (LUH) data that is restored through history database of the global environment (HYDE) and provided by the coupled model inter-comparison project (CMIP). The processor may also collect existing ground type data calculated by an existing model in a previous phase of the CMIP, generate aggregated ground type data by data-aggregating the new ground type data and the existing ground type data, with priority to the new ground type data. The processor may further generate the forcing data from the aggregated ground type data by performing a distortion correction on a data distortion that occurs during the data aggregation.

This application relates to an input data generating apparatus for generating forcing data used as input data for a climate change prediction model. In one aspect, the apparatus includes a memory storing instructions and a processor configured to, by executing the instructions, collect new ground type data from land-use harmonization (LUH) data that is restored through history database of the global environment (HYDE) and provided by the coupled model inter-comparison project (CMIP). The processor may also collect existing ground type data calculated by an existing model in a previous phase of the CMIP, generate aggregated ground type data by data-aggregating the new ground type data and the existing ground type data, with priority to the new ground type data. The processor may further generate the forcing data from the aggregated ground type data by performing a distortion correction on a data distortion that occurs during the data aggregation.

The system comprises a rainfall monitoring module, a self-calibration and rainfall measurement module, a central processing module, a water level monitoring module, and a drainage module. The rainfall monitoring module is configured to monitor rainfall and send a rainfall signal to the central processing module. The self-calibration and rainfall measurement module is configured to transmit ultrasonic signals and receive calibration echo signals to compute the calibrated flight time, and transmit ultrasonic signals to the water surface in the bucket and receive the measured echo signal reflected by the water surface to obtain the measured flight time under the control of the central processing module. The central processing module is configured to receive the rainfall signal to start the water level monitoring module and the self-calibration and rainfall measurement module, which are used to calculate the rainfall value and output it in a fixed format.

The system comprises a rainfall monitoring module, a self-calibration and rainfall measurement module, a central processing module, a water level monitoring module, and a drainage module. The rainfall monitoring module is configured to monitor rainfall and send a rainfall signal to the central processing module. The self-calibration and rainfall measurement module is configured to transmit ultrasonic signals and receive calibration echo signals to compute the calibrated flight time, and transmit ultrasonic signals to the water surface in the bucket and receive the measured echo signal reflected by the water surface to obtain the measured flight time under the control of the central processing module. The central processing module is configured to receive the rainfall signal to start the water level monitoring module and the self-calibration and rainfall measurement module, which are used to calculate the rainfall value and output it in a fixed format.

A method, computer system, and a computer program product for weather station placement design are provided. Weather data measured at weather stations, current location data regarding current locations of the respective weather stations, and weather forecast data generated by a weather forecast model are received. Forecast performance by the weather forecast model is determined by comparing the weather data to the weather forecast data and so that first weather stations where the weather forecast model had best forecast performance are identified. A weather forecast performance map is generated based on the identified first weather stations. Fractals are generated. The fractals are iteratively matched to the weather forecast performance map to identify a first fractal that most closely matches a layout of the current locations of the first weather stations. A first fractal map that includes the first fractal overlaid on the weather forecast performance map is presented.

An imputation method for surface ultraviolet irradiance based on feasible cloud information and machine learning includes: establishing a deep learning model, wherein the deep learning model is designed to be a two-layered stacking ensemble learning model; constructing a first layer of the deep learning model as combination of multiple fundamental machine learning models; constructing a second layer of the deep learning model as Lasso model, which integrates an output from the first layer to obtain a final retrieval result; matching the surface ultraviolet irradiance with input features comprising cloud and meteorological information according to the temporal and spatial variables; establishing a statistical relationship between the surface ultraviolet irradiance and by training the deep learning model; and estimating the surface ultraviolet irradiance based on the trained deep learning model in regions with missing satellite observations of the surface ultraviolet irradiance.

An imputation method for surface ultraviolet irradiance based on feasible cloud information and machine learning includes: establishing a deep learning model, wherein the deep learning model is designed to be a two-layered stacking ensemble learning model; constructing a first layer of the deep learning model as combination of multiple fundamental machine learning models; constructing a second layer of the deep learning model as Lasso model, which integrates an output from the first layer to obtain a final retrieval result; matching the surface ultraviolet irradiance with input features comprising cloud and meteorological information according to the temporal and spatial variables; establishing a statistical relationship between the surface ultraviolet irradiance and by training the deep learning model; and estimating the surface ultraviolet irradiance based on the trained deep learning model in regions with missing satellite observations of the surface ultraviolet irradiance.

Aspects of the present disclosure describe distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) systems, methods, and structures that advantageously provide rainfall intensity measurements along an entire length of a fiber optic sensor. using existing telecommunications optical fiber—which may be part of a multi-fiber, fiber optic cable—that may simultaneously carry live telecommunications traffic. The DFOS/DAS fiber optic sensing is used to obtain vibration and/or sound data from which rainfall intensity measurements may be made along the entire length of the DFOS/DAS fiber optic sensor.

Aspects of the present disclosure describe distributed fiber optic sensing/distributed acoustic sensing (DFOS/DAS) systems, methods, and structures that advantageously provide rainfall intensity measurements along an entire length of a fiber optic sensor. using existing telecommunications optical fiber—which may be part of a multi-fiber, fiber optic cable—that may simultaneously carry live telecommunications traffic. The DFOS/DAS fiber optic sensing is used to obtain vibration and/or sound data from which rainfall intensity measurements may be made along the entire length of the DFOS/DAS fiber optic sensor.