A wearable environmental sensor includes an environmental sensor arranged on a wall surface of a housing including a sealed section, the wall being in contact with an environment, and a protective structure formed around the environmental sensor, wherein the protective structure includes a plurality of ventilating holes, a sensor surface of the environmental sensor is arranged to face an opening of at least one of the ventilating holes, and an attaching part for attaching the environmental sensor to the wall surface comes into contact only with an edge of a sensor substrate of the environmental sensor and with a portion of a back face of the sensor substrate.

An electronic WBGT meter obtains a WBGT index reflecting wind velocity and radiation. A first calculation unit obtains an arbitrary-diameter globe temperature of the electronic WBGT meter with respect to each of specific thermal environments, and a second calculation unit obtains a standard-diameter globe temperature of ISO-compliant WBGT meter with respect to each of the specific thermal environments. A third calculation unit obtains a natural wet-bulb temperature with respect to each of the specific thermal environments. A correlation determination unit determines a correlation of the air temperature, the arbitrary-diameter globe temperature, and the relative humidity with corresponding the standard-diameter globe temperature and the natural wet-bulb temperature. A measurement calculation element storing the correlations is mounted in the electronic WBGT meter under a real thermal environment, and calculates the standard-diameter globe temperature and the natural wet-bulb temperature of ISO-compliant WBGT meter.

An electronic WBGT meter obtains a WBGT index reflecting wind velocity and radiation. A first calculation unit obtains an arbitrary-diameter globe temperature of the electronic WBGT meter with respect to each of specific thermal environments, and a second calculation unit obtains a standard-diameter globe temperature of ISO-compliant WBGT meter with respect to each of the specific thermal environments. A third calculation unit obtains a natural wet-bulb temperature with respect to each of the specific thermal environments. A correlation determination unit determines a correlation of the air temperature, the arbitrary-diameter globe temperature, and the relative humidity with corresponding the standard-diameter globe temperature and the natural wet-bulb temperature. A measurement calculation element storing the correlations is mounted in the electronic WBGT meter under a real thermal environment, and calculates the standard-diameter globe temperature and the natural wet-bulb temperature of ISO-compliant WBGT meter.

A control unit is added to the environmental tester including a thermostat-humidistat container, an air conditioner, a first temperature and humidity sensor for measuring temperature and humidity in the thermostat-humidistat container, a controller for receiving a first signal from the first temperature and humidity sensor to control the air conditioner. The control unit includes a second temperature and humidity sensor configured to measure the temperature and humidity distribution in the thermostat-humidistat container, and an additional unit configured to receive the first signal and set values for the temperature and the humidity from the controller, and receive a second signal from the second temperature and humidity sensor, compute a correction value from the received first signal, second signal, and set values, and send the correction value to the controller.

A wearable device includes a sensor device and a clothes to which the sensor device is attached. The clothes includes a clothes body and an insertion and extraction portion that has an opening formed in the clothes body and enables the sensor device to be inserted into or extracted from the clothes body. The sensor device includes a humidity sensor configured to measure humidity in the clothes body and a housing having a first region exposed to an inside of the clothes body and a second region exposed to an outside of the clothes body, while the sensor device is inserted from the insertion and extraction portion into the clothes body. The humidity sensor is accommodated in the first region of the housing.

Systems for weather sensing and forecasting, and associated devices and methods, are disclosed herein. In some embodiments, a system for predicting a subject's perception of weather conditions is provided. The system can generate an individual profile for the subject, the individual profile including health information of the subject. The system can receive weather data including a first weather condition for a target location. The system can compare the individual profile to a plurality of different user profiles to identify one or more similar user profiles. Each similar user profile can (1) be associated with a user having similar health information as the subject, and (2) include weather perception data indicating how the user perceived a set of second weather conditions. Based on the weather data and the similar user profile(s), the system can generate a prediction of the how the subject will perceive the first weather condition.

Surface modification control stations and methods in a globally distributed array for dynamically adjusting the atmospheric, terrestrial and oceanic properties. The control stations modify the humidity, currents, wind flows and heat removal rate of the surface and facilitate cooling and control of large area of global surface temperatures. This global system is made of arrays of multiple sub-systems that monitor climate and act locally on weather with dynamically generated local forcing & perturbations for guiding in a controlled manner aim at long-term modifications. The machineries are part of a large-scale system consisting of an array of many such machines put across the globe at locations called the control stations. These are then used in a coordinated manner to modify large area weather and the global climate as desired. The energy system installed at a control stations, with multiple machines to change the local parameters of the ocean, these stations are powered using renewable energy (RE) sources including Solar, Ocean Currents, Wind, Waves and Batteries to store energy and provide sufficient power and energy as required and available at all hours. This energy is then used to do directed work using special machines, that can be pumps for seawater to move ocean water either amplifying or changing the currents in various locations and at different depths, in addition it will have machineries for changing the vertical depth profile of the ocean of temperature, salinity and currents. Control stations will also directly use devices such as heat pumps to change the temperatures of local water either at surface or at controlled depths, or modify the humidity and salinity to change the atmospheric and oceanic properties as desired. The system will work in a globally coordinated manner applying artificial intelligence and machine learning algorithms to learn from observations to improve the control characteristics and aim to slow down the rise of global surface temperatures. These systems are used to reduce the temperatures of coral reefs, arctic glaciers and south pacific to control the El Nino oscillations.

Surface modification control stations and methods in a globally distributed array for dynamically adjusting the atmospheric, terrestrial and oceanic properties. The control stations modify the humidity, currents, wind flows and heat removal rate of the surface and facilitate cooling and control of large area of global surface temperatures. This global system is made of arrays of multiple sub-systems that monitor climate and act locally on weather with dynamically generated local forcing & perturbations for guiding in a controlled manner aim at long-term modifications. The machineries are part of a large-scale system consisting of an array of many such machines put across the globe at locations called the control stations. These are then used in a coordinated manner to modify large area weather and the global climate as desired. The energy system installed at a control stations, with multiple machines to change the local parameters of the ocean, these stations are powered using renewable energy (RE) sources including Solar, Ocean Currents, Wind, Waves and Batteries to store energy and provide sufficient power and energy as required and available at all hours. This energy is then used to do directed work using special machines, that can be pumps for seawater to move ocean water either amplifying or changing the currents in various locations and at different depths, in addition it will have machineries for changing the vertical depth profile of the ocean of temperature, salinity and currents. Control stations will also directly use devices such as heat pumps to change the temperatures of local water either at surface or at controlled depths, or modify the humidity and salinity to change the atmospheric and oceanic properties as desired. The system will work in a globally coordinated manner applying artificial intelligence and machine learning algorithms to learn from observations to improve the control characteristics and aim to slow down the rise of global surface temperatures. These systems are used to reduce the temperatures of coral reefs, arctic glaciers and south pacific to control the El Nino oscillations.

A vehicle a sensor system disposed adjacent to a headrest portion of a seat within a passenger compartment of the vehicle. The sensor system includes at least one of an air velocity sensor, an air temperature sensor, a radiant heat flux sensor, a heat flux sensor, or a humidity sensor. The sensor system is positioned near the headrest, facing a forward end of the vehicle, in a position that is not blocked by a head of a passenger seated in the seat. The sensor system provides data related to the air velocity, the air temperature, the radiant heat flux, the heat flux, or the relative humidity, enabling a climate controller to accurately calculate a current Equivalent Homogenous Temperature (EHT) of a passenger seated in the seat. The climate controller may then control a climate system of the vehicle based on the calculated EHT to provide a desired EHT.

Surface modification control stations and methods in a globally distributed array for dynamically adjusting the atmospheric, terrestrial and oceanic properties. The control stations modify the humidity, currents, wind flows and heat removal rate of the surface and facilitate cooling and control of large area of global surface temperatures. This global system is made of arrays of multiple sub-systems that monitor climate and act locally on weather with dynamically generated local forcing & perturbations for guiding in a controlled manner aim at long-term modifications. The machineries are part of a large-scale system consisting of an array of many such machines put across the globe at locations called the control stations. These are then used in a coordinated manner to modify large area weather and the global climate as desired. The energy system installed at a control stations, with multiple machines to change the local parameters of the ocean, these stations are powered using renewable energy (RE) sources including Solar, Ocean Currents, Wind, Waves and Batteries to store energy and provide sufficient power and energy as required and available at all hours. This energy is then used to do directed work using special machines, that can be pumps for seawater to move ocean water either amplifying or changing the currents in various locations and at different depths, in addition it will have machineries for changing the vertical depth profile of the ocean of temperature, salinity and currents. Control stations will also directly use devices such as heat pumps to change the temperatures of local water either at surface or at controlled depths, or modify the humidity and salinity to change the atmospheric and oceanic properties as desired. The system will work in a globally coordinated manner applying artificial intelligence and machine learning algorithms to learn from observations to improve the control characteristics and aim to slow down the rise of global surface temperatures. These systems are used to reduce the temperatures of coral reefs, arctic glaciers and south pacific to control the El Nino oscillations.