A detailed list of the components available on Orbify related to weather monitoring.
Average humidity
Component Type: Chart
Data Type: Numerical Weather Prediction
Accuracy: Medium
Spatial Resolution: 27830 m
Temporal Resolution: 1 day
Description: The chart represents the average humidity levels over a specified region within a defined time period. The data is derived from the NOAA/GFS0P25 collection, specifically focusing on the relative humidity at 2 meters above ground.
Average humidity
Component Type: Stat
Data Type: Numerical Weather Prediction
Accuracy: Medium
Spatial Resolution: 13 km
Temporal Resolution: 1 day
Description: The Global Forecast Model (GFS) by NOAA is a valuable resource for obtaining specific humidity data at a height of 2 meters above ground level. This model is freely available to the public and is designed to provide accurate and up-to-date meteorological data on a global scale. In addition to specific humidity, the GFS model also provides a wide range of other meteorological parameters that can be useful for weather forecasting and climate analysis.
These parameters include temperature, wind speed and direction, precipitation, cloud cover, and atmospheric pressure, among others. The GFS model is based on advanced numerical weather prediction techniques and incorporates data from a variety of sources, including ground-based weather stations, satellites, and ocean buoys. This allows it to provide highly detailed and accurate forecasts for locations all around the world, with a particular focus on regions that are prone to extreme weather events.
Overall, the specific humidity data provided by the GFS model can be a valuable tool for a range of applications, including agriculture, energy production, and transportation planning. By providing detailed and accurate meteorological data on a global scale, the GFS model is helping to improve our understanding of weather and climate patterns, and to prepare for the impacts of climate change.
Average surface temperature
Component Type: Chart
Data Type: Numerical Weather Prediction
Accuracy: High
Description: GFS temperature data can help us understand the atmospheric conditions in a specific location, providing insights into the potential impacts of weather on human activities and the environment. The availability of this data can enable researchers, policy makers, and the general public to make informed decisions based on the latest weather forecasts and trends.
The Global Forecast Model (GFS) developed by the National Oceanic and Atmospheric Administration (NOAA) is a forecast model that generates numerous meteorological variables, including the temperature measured 2 meters above the Earth's surface. This model provides global forecasts, making it a valuable tool for various applications, such as weather forecasting, climate studies, and research.
Average surface temperature
Component Type: Stat
Data Type: Numerical Weather Prediction
Description: GFS temperature data can help us understand the atmospheric conditions in a specific location, providing insights into the potential impacts of weather on human activities and the environment. The availability of this data can enable researchers, policy makers, and the general public to make informed decisions based on the latest weather forecasts and trends.
The Global Forecast Model (GFS) developed by the National Oceanic and Atmospheric Administration (NOAA) is a forecast model that generates numerous meteorological variables, including the temperature measured 2 meters above the Earth's surface. This model provides global forecasts, making it a valuable tool for various applications, such as weather forecasting, climate studies, and research.
Forest Risk: Drought Risk
Component Type: Layer
Data Type: Optical
Accuracy: Medium
Spatial Resolution: 5 km
Description: The dataset function evaluates drought risks for a specified region over a defined date range. It leverages data from the Sentinel-2 satellite, which is used to calculate the Vegetation Condition Index (VCI). This index serves as an indicator of vegetation health by comparing the current NDVI value of an area to its historical minimum and maximum NDVI values, helping to identify drought conditions.
The function also incorporates precipitation and temperature data in its analysis. Current precipitation levels are juxtaposed against historical data to gauge the severity of the drought conditions. Similarly, temperature data is evaluated to refine the drought risk assessment further.
Another significant aspect of the assessment is the evaluation of the proximity of areas to populated cities. The function considers regions closer to densely populated areas to be at a higher risk during droughts due to the potential strain on water resources. Additionally, the function examines the proximity of areas to major roads and rivers. Regions farther from significant water sources or main roads may face elevated risk levels during droughts because of limited water accessibility and potential challenges in relief operations.
Based on these combined datasets and indices, areas are then classified into three risk categories: low, medium, or high.
Forest Risk: Flood Risk
Component Type: Risk
Data Type: Optical, Radar
Accuracy: Medium
Spatial Resolution: 5 m
Description: The dataset represents flood risks for a given region and time range, derived from multiple Earth Engine sources, including precipitation, population, elevation, slope, and other parameters. It provides a visual categorisation of areas into low, medium, and high flood risk regions.
Conclusion
For more information, check the complete list of components available on Orbify.