Improved mechanisms for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, atmospheric liquid water content, or precipitable water and other atmospheric-based phenomena, for example presence and intensity of fog, based upon these sensor readings, predicting future precipitation and atmospheric-based phenomena, and estimating effects of the atmospheric-based phenomena on visibility, for example by calculating runway visible range (RVR) estimates and forecasts based on the atmospheric-based phenomena.

Methods, systems, and computer programs are presented for flood-risk analysis and mapping. One method includes operations for presenting, in a graphical user interface (GUI), options for calculating a flood risk map, and receiving, via the GUI, input identifying a geographical region and a weather scenario. Further, the method includes operations for dividing the geographical region into cells; calculating, utilizing a hydrological model, an inflow and an outflow of water between cells in the geographical region based on the weather scenario; and calculating, utilizing a hydraulic model, water depth in each cell based on the weather scenario and the inflow and outflow of water between cells. The flood risk map, generated based on the calculated water depth in each cell, shows the probability that each cell in the geographical region will be inundated with water under the weather scenario. The flood risk map is presented in the GUI.

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.

The system as described collects and utilizes weather data sensor information in order to rapidly collect and update weather forecasts using real-time weather data collected at high rates of frequency, and use this collected high frequency weather data to rapidly correct and update the weather forecasts generated by the system.

Provided are a prediction value correction method and apparatus. The prediction value correction method includes steps of: (a) determining a prediction condition to be predicted; (b) receiving past prediction values and past measurement values according to the determined prediction condition; (c) filtering the past prediction values and the past measurement values by using an H-infinity filter to obtain an output value for a final time point; (d) estimating a future bias for a date and time point to be predicted by using the output value of the H-infinity filter; and (e) correcting a future prediction value for the date and time point to be predicted by using the estimated future bias to obtain a corrected future prediction value for the date and time point to be predicted.

Disclosed is an environmental sensor apparatus in which a plurality of light shielding plates including an insertion space portion providing an insertion space of the circuit board and a guide portion guiding the insertion of the circuit board to the inner surface of the insertion space portion are stacked, and a circuit board is mounted inside a light shielding portion in which the plurality of light shielding plates are stacked.

A method and a system for monitoring the spraying of sunlight reflecting particles into the stratosphere to address global warming using smartphones are provided herein. The method may include the following steps: spraying, using a plurality of airplanes flying through a plurality of different geographical locations, sunlight reflecting particles, in accordance with a spraying scheme; sampling using sensors of a plurality of smartphones located in a plurality of different geographical locations, a plurality of atmospheric measurements; training, using a computer processor, and based on the plurality of atmospheric measurements, a model configured to predict a trend in a change of temperature in consequence of the spraying of the sunlight reflecting particles; and repeatedly updating the spraying scheme based on the model.

This invention addresses the problem of Global Warming, expressed as the environmental condition of unintended and imperceptible levels of Vapor Pressure Deficit, (VPD) in Nursing Homes and Hospitals and Psychiatric Facilities. The invention teaches an art form which addresses Global Warming as expressed by Vapor Pressure deficit and resistance to medication. The invention identifies the ideal conditions for fungal and bacteria growth and in particular a new highly resistant fatal form of Candida Fungus, referred to as Candida auris (C. auris). Existing HVAC technology does not address this problem, since it is novel in that it identifies a unique interaction between Global Warming with the problem of resistances to medication and the neurological causes of Suicide. The invention is also novel and unobvious in that it teaches an art form indicating that certain levels of imperceptible VPD require continued HVAC, A/C dehumidification and temperature reduction even throughout tepid temperatures when such equipment may be turned off. As well as teaches an art form to alert medical staff and administration as to when these conditions are occurring and help plan treatments during periods of favorable ambient indoor and outdoor environmental conditions.

A predictive real time and prospective environmental analysis and display system accessible by one or more client computing devices through a network to depict on the display surface of a computing device a graphical representation of a geographic environment which can be delimited into one or more two or three-dimensional zones in which visual indicators provide predicted current or prospective airflow speed or direction values associated with the geographic environment.

An ocean-onto-land drought (OTLD) identification and propagation mechanism analysis method includes: identifying a new drought event in a global range with data of a historical data and data of a future test; extracting a 3D space-time cube (STC) of the drought event, quantifying spatiotemporal characteristics of the global OTLD, and searching a landfalling hotspot; projecting an OTLD in a future period in combination with different tests, and detecting an anthropogenic signal in an index change of the OTLD in the historical period and in the future period; and analyzing, with moisture transport during the OTLD as a reference, an occurrence mechanism of the OTLD in the historical period and an intensification mechanism of the OTLD in the future period; and clarifying a primary physical factor during the OTLD, and assessing a synthetic risk of a OTLD-affected region with a machine learning method.