How Big Data on Weather Patterns Can Help Us Respond to the Climate Crisis
Download HEAVY.AI Free, a full-featured version available for use at no cost.GET FREE LICENSE
As the effects of climate change continue to increase in frequency and severity worldwide, it is more important than ever that we can collect and analyze big data on weather patterns.
The rising average temperature across the globe is directly associated with widespread changes in weather patterns. As temperatures continue to rise, extreme weather events are only expected to worsen over time. So the more weather pattern data we can gather and analyze, the better we can understand global warming and improve disaster preparedness.
Weather Forecasting Using Big Data
A significant weather data source is the National Oceanic and Atmospheric Administration’s National Weather Service (NWS), which collects terabytes of climate data. Climate science, a byproduct of the field of weather prediction, relies on advanced earth system models to target long-term trends of big data in weather patterns and ultimately predict, better understand, and develop responses to global warming and climate change scenarios.
The amount of data the NWS collects is in the terabytes. Big data analytics tools, predictive analytics, real-time environmental monitoring systems, remote sensing, Machine Learning, Artificial Intelligence, and the Internet of Things (IoT) work in conjunction to ingest, manage, and analyze vast streams of real-time weather data, spot trends, draw conclusions, make accurate and timely weather forecasts, and make data-driven predictions.
Stores of historical weather observations, such as annual average temperature, total precipitation, and extreme weather indices, are also used for climate and weather predictions and date back to the 19th century. We also have access to natural records of paleoclimates, such as tree rings, coral skeletons, glaciers, fossils, and sediments. These data points are crucial to identifying weather patterns that correlate with climate change.
What Kind of Data does the NWS Collect?
NWS data collection categories are surface, marine, and upper air. National Weather Service climate data includes:
- Surface observations: The Automated Surface Observing Systems program works with NWS, the Federal Aviation Administration, the Department of Defense, and automatic weather stations to support meteorological, hydrological, and climatological research communities. Surface elements observed include dew point, obstructions to vision, precipitation accumulation, pressure, sky condition, temperature, visibility, and wind direction and speed. Big data in weather forecasting is beneficial for aviation purposes.
- Marine observations: Large bodies of water have a significant effect on the weather, making frequent, accurate marine observations a crucial element in accurate weather predictions. NWS’s National Data Buoy Center uses a network of buoys and land-based coastal observing systems to measure air temperature, barometric pressure, wind speed, direction, and gust. This data is then distributed on national and international circuits for use by meteorologists in weather forecasting.
- Upper air observations: Nearly a hundred radiosonde devices (battery-powered telemetry instruments in the form of a 6.6 ft.-wide balloon filled with hydrogen or helium) are released twice a day at specific times every day, reaching as high as 15,000 ft. The attached sensors measure pressure, temperature, and relative humidity profiles and send this data back down to ground receivers via a radio transmitter. This data is primarily used for weather forecasting and research.
The NWS is divided into nine National Centers for Environmental Prediction: the Aviation Weather Center, the Environmental Monitoring Center, the Ocean Prediction Center, the National Hurricane Center, the Space Weather Prediction Center, the Storm Prediction Center, the Weather Prediction Center, NCEP Central Operations, and the Climate Prediction Center. The Climate Prediction Center (CPC) assesses and forecasts the impacts of short-term climate variability. This information enhances disaster preparedness, mitigates losses, and maximizes economic gains.
CPC products cover weekly and seasonal time scales for land, ocean, atmosphere, and stratosphere and produce time-series data and map climate parameters such as degree days, precipitation, snow cover, and temperature. Regions covered include Africa, Asia, Europe, South and Central America, Mexico, the Caribbean, Australia, and New Zealand. National Weather Service GIS data monitoring includes greenhouse gasses, carbon dioxide, arctic sea ice, mountain glaciers, ocean heat, sea levels, spring snow, surface temperatures, incoming sunlight, Arctic, North Atlantic, Southern oscillation, oceanic Niño index, and Pacific-North American Pattern.
Practical Applications in the Field of Disaster Response
The National Weather Service data we’ve covered thus far is an enormous volume to collect, analyze, understand, and leverage for practical use in disaster response. A core goal of the NWS is to create an informed and climate-resilient society. How is climate change big data being used to improve our responses to the events brought on by climate change?
- Drought: Rising global temperatures enhance evaporation, which reduces surface water and dries out soils and vegetation. Climatologists use historical data to understand the likelihood of a drought in a specific region. Knowing when a drought is expected to hit helps us better prepare for mitigating and responding to the effects of drought. Some proactive preparation strategies include artificial precipitation, groundwater recharge, water treatment and recycling, desalination of brackish water, and small-scale water harvesting.
Areas experiencing drought, record high temperatures, and high winds are highly vulnerable to wildfires, so knowing this information will help communities mitigate fire risks.
- Tropical storms: Analyzing historical tropical cyclone reports is as important as collecting and tracking real-time storm data with doppler radar. Historical data such as meteorological statistics, casualties, and damages helps us anticipate the behavior of a developing tropical storm, its likely damage, and proactive strategies to implement ahead of landfall. Some proactive preparation strategies include establishing evacuation routes and transportation, ensuring backup power for flood pump stations, reinforcing flood barriers, and organizing emergency shelters and first-aid responders.
- Sea Level Rise: National Oceanic and Atmospheric Administration’s Sea Level Rise map provides dynamic data visualizations of community-level impacts from coastal flooding due to melting ice sheets and the expansion of warm water. The data used for this comes from local and regional tidal variability.
With this information, emergency management teams can develop proactive flood warning systems, city planners can see predictions for when different coastal regions may be underwater, and the US Department of Agriculture and Farm Service Agency can determine where and when destructive soil erosion and soil salinization may take place.
Data Science in Climate Change
Data science plays a significant role in global warming data analysis. Advanced data science platforms are helping us manage and find patterns within massive weather data sets - patterns that help lay the groundwork for research and strategies.
Data science and analytics help us identify risks and opportunities in our data, extract decision-quality information from massive climate change data sets at scale, interact with geospatial and time-series data sets in a global context, and create striking visualizations that transform tedious, static data into interactive visuals that illuminate actionable insights and inspire real climate change action.
Big data in weather patterns are only as valuable as our ability to understand it. Modern computer technology and analytics tools can help us understand our impact on the planet and pave the way for real change. In other words, the future of climate change is reliant on the future of data science.