Climate and weather data refers to any information about the weather and the climate of a defined area. The data may measure atmospheric, temperature, wind, or geographical patterns.
Weather refers to a short-term forecast of an hour, a day, or a few weeks. Climate, in contrast, is the long-term (seasonal, yearly, decades-long) patterns in an area.
Many organizations, both private and governmental, collect climate and weather data from various sensors: airport observation stations, satellites, radar, and more.
Common columns and attributes present in climate and weather data sets are temperature, air pressure, precipitation, and air currents. This data (particularly the temperatures) frequently appears as lists of daily highs and lows with mean averages over time.
Additional attributes will be added based on the specific need that the data set addresses. For example, marine vessels need to track ocean currents while farmers need historical crop yield data.
People in all kinds of industries use climate and weather data: The uses of the data can be remarkably wide.
Obvious users of weather and climate data are those in the energy sector. Land management services use the data to track plant and animal populations and plan controlled burns or other sustainability measures.
Outdoors enthusiasts also rely on weather and climate data to safely engage in their hunting, fishing, camping, hiking, etc. hobbies.
Less obviously are the emergency services providers and disease specialists. Emergency services use weather data to plan for natural disasters while disease specialists use it to track disease outbreaks and hopefully eliminate them from a region.
Other users of this data include any company with shipping, logistics, and storage needs. Marketing teams and managers also use the data to plan marketing strategies and analyze seasonal sales and footfall data.
The best way to test this data is to check historical data to see if conclusions match predictions.
In addition, check the sensors used to measure the data. For example, plane observation posts don’t produce images as detailed satellite do, particularly when near a body of water or urban area.
The most important factors to use to vet a climate/weather data set are the type, location, and number of data sensors. As noted above, some types of sensors are more reliable than others, especially the further away from population centers or bodies of water they are.
In addition, the more sensors the better: with more sensors, you or the data vendor can compare the results of the sensors to increase accuracy.
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Accurately predicting whether a hurricane will undergo rapid intensification – where wind speeds increase by 35 mph (56 kph) or more within 24 hours – is incredibly difficult. But researchers led by scientists at NASA’s Jet Propulsion Laboratory in Southern California have used machine learning to develop an experimental computer model that promises to greatly improve the accuracy of detecting rapid-intensification events.
NASA: A Machine-Learning Assist to Predicting Hurricane Intensity
Sustainable Platform’s dataset – ‘Carbon Risk, Transition Risk, Physical Risk, Stranded Asset Risk, Paris Climate Alignment at 1.5, 2, 3 and 4 Degree C for 18,000+ companies’ provides Climate and Weather Data, Environmental Data and that can be used in Hedge Fund Management, Supplier Risk and
Storm Glass’s dataset – ‘Storm Glass Tide API – Global Coverage – Forecasts & Historical Data’ provides Climate and Weather Data and that can be used in and Smart Ship
Storm Glass’s dataset – ‘Storm Glass Global Weather API – Marine & Terrestrial Weather – High Resolution (10 Day Forecasts & Historical)’ provides Climate and Weather Data and that can be used in and Smart Ship