GIS and Weather: Common Types of GIS Weather Data
- Jan 27
- 5 min read
Weather data is inherently spatial. Every forecast, observation, and climate statistic exists somewhere on Earth, changes over time, and interacts with terrain, land use, and infrastructure. Because of that, Geographic Information Systems (GIS) play a central role in turning weather data into something usable for planning, operations, and decision-making.
At the same time, weather data does not arrive “GIS-ready.” Modern weather models produce large, multidimensional datasets designed for scientific accuracy and computational efficiency, not for direct use in mapping software. Bridging the gap between meteorological data and GIS workflows is where experience matters.
This article outlines the most common types of GIS weather data, how they are structured, and how they are typically used in real-world applications.
What Is GIS Weather Data?
GIS weather data refers to meteorological information that has been structured, processed, or derived so it can be analyzed spatially. This includes data from forecast models, observations, reanalysis products, and derived layers built on top of those sources.
A key distinction is that meteorological data structures and GIS data structures are not the same thing. Weather models are optimized for physics, time evolution, and numerical computation. GIS systems are optimized for spatial relationships, overlays, and querying across geographic features. Moving weather data into a GIS environment requires deliberate decisions about format, resolution, projection, and interpretation.
Common sources of GIS weather data include:
Numerical weather prediction models
Remote sensing and in-situ observations
Reanalysis datasets that blend models and observations
Derived or impact-based products such as indices and thresholds
Choosing the Right GIS Weather Data Type
Not every decision requires the same type of weather data. Selecting the appropriate data type depends on the problem being solved and the constraints of the workflow.
Key considerations include:
Matching data type to the decision need
Scale, resolution, and update frequency
Compatibility with existing workflows
Performance and storage tradeoffs
In some cases, a simplified derived layer is more useful than a complex multidimensional dataset. In others, access to raw model output is essential.
Raster-Based Weather Data in GIS
Raster data is the most common way weather information is represented spatially. In a raster model, the world is divided into a regular grid, and each grid cell contains a value such as temperature, precipitation, wind speed, or soil moisture.
It is important to separate the raster data model from the file formats used to store weather data. Formats such as NetCDF, GRIB, GRIB2, Zarr, and BIN are not rasters themselves; they are container formats designed to hold multidimensional scientific data. Within those containers are gridded fields that can be mapped to raster structures for GIS use.
Weather data stored this way often includes additional dimensions beyond latitude and longitude, such as time, forecast lead, ensemble member, or vertical level. Converting these datasets into GIS-ready rasters requires selecting the appropriate dimensions, variables, and coordinate systems.
Common rasterized weather variables include:
Surface temperature and humidity
Precipitation totals and rates
Wind speed and direction
Snow depth and soil moisture
Typical use cases include forecast visualization, spatial analysis across regions, and exposure or risk mapping tied to infrastructure, assets, or populations.
Vector Weather Data
While weather itself is continuous, vector data is often used to represent boundaries, zones, and discrete features associated with weather impacts. Vector data stores information as points, lines, or polygons rather than grid cells.
Common vector formats used in weather-related GIS workflows include shapefiles, GeoJSON, and GeoPackage (which can store vector data, raster tiles, vector tiles, and attribute tables). These formats are well suited for representing warning areas, impact zones, and administrative boundaries.
Examples of vectorized weather data include:
Severe weather warning polygons
Floodplain or inundation boundaries
Storm tracks or point-based observations
Vector data is frequently used alongside raster weather layers to support alerting, reporting, and operational decision-making, especially when actions are tied to specific jurisdictions or assets.
Time-Enabled and Multidimensional Weather Data
Time is not an optional attribute in weather data; it is fundamental. Most weather datasets are time-enabled and often multidimensional, combining space, time, and additional axes such as ensemble members or atmospheric levels.
Traditional GIS systems were not originally designed to handle large multidimensional datasets, which can make direct use of weather data challenging. Decisions must be made about whether to represent time as individual layers, time-enabled rasters, or aggregated products.
Common use cases for time-enabled weather data include:
Analyzing forecast evolution over time
Identifying trends or anomalies
Comparing multiple scenarios or ensemble outcomes
Handling these datasets correctly requires an understanding of both GIS capabilities and meteorological context.
Derived and Modeled GIS Weather Layers
Many of the most useful GIS weather products are not raw model outputs, but derived layers built to answer specific questions. These layers translate meteorological variables into applied metrics that align with operational or planning needs.
Examples of derived GIS weather layers include:
Threshold exceedance maps (e.g., temperature above a critical value)
Weather or climate indices
Composite risk or impact scores
Creating these layers involves algorithm development, validation, and careful interpretation of the underlying data. The result is a product that is easier to use in GIS workflows while retaining meteorological meaning.
Common GIS Workflows for Weather Data
Turning raw weather data into GIS-ready products typically involves several processing steps:
Ingesting raw model output
Reprojection and resampling
Temporal aggregation
Spatial aggregation and zonal statistics
Algorithm development for applied thresholds
Exporting to GIS-friendly formats
Each step introduces choices that affect accuracy, performance, and interpretability. Small decisions. such as how data is resampled or aggregated, can have large downstream impacts.
Why Weather Expertise Matters in GIS
Weather data is deceptively easy to map. A dataset can look visually correct in GIS while being meteorologically misleading or outright wrong. This is especially true when weather data is handled without sufficient domain knowledge.
Common pitfalls include misinterpreting forecast lead times, averaging variables that should not be averaged, or applying thresholds without physical context. These errors can be difficult to detect visually and often only become apparent when decisions fail in practice.
Accurate GIS weather analysis depends on understanding how the data was generated, what assumptions it contains, and how it should (and should not) be used.
GIS Weather Data Summary
GIS is a powerful tool for working with weather data, but it is not a substitute for meteorological understanding. Raster and vector data, time-enabled datasets, and derived layers each play a role in turning complex weather information into actionable insight.
The difference between a map and a decision-ready product lies in how weather data is interpreted, processed, and applied. At Adiabat, GIS and weather expertise are combined, and the result is analysis that is not just spatially correct, but scientifically sound and operationally useful.
Oh hello, thank you for reading.
We are Adiabat, your go-to weather and geospatial professionals.
Data-driven. Independent. Woman-owned.
