CONTENTS
Bias correction is a process used to adjust climate model outputs so they more closely reflect observed real-world data. It corrects for known discrepancies and helps make climate projections more accurate and relevant to specific locations.
Example in context
You're helping a client assess wildfire risk at a site in Portugal. Without bias correction, the climate model overestimates historical temperature trends. Some climate risk intelligence platforms apply correction using satellite and station data, producing a more accurate, localised view of past and future conditions.
Why it matters
All climate models contain inherent biases, which, if left uncorrected, can lead to misleading projections and flawed risk assessments. Bias correction enhances model accuracy by aligning simulated outputs with observed historical data.
This ensures that climate risk indicators reflect real-world conditions and are reliable for decision-making.
EarthScan's (Mitiga's climate intelligence platform) bias correction process uses quantile-based adjustment methods and draws on over 100,000 in situ weather stations, satellite observations, and ERA5 reanalysis datasets.
This rigorous calibration ensures that extreme event frequencies, spatial patterns, and temporal trends are consistent with observed climatology, providing confidence in the platform’s projections and the decisions they inform.
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