Linked from

The 11 pages that link to Data assimilation, each with the reason it gives.

• Weather forecastingRelated: It builds the best initial atmospheric state from measurements that vary in coverage and accuracy.

• Numerical weather predictionRelated: It constructs the starting atmospheric state from incomplete, unevenly distributed measurements.

• Chaos theoryRelated: It helps initialize forecasts when errors in state estimates can rapidly grow.

• Digital twinRelated: It incorporates live measurements into the twin’s evolving state.

• Flood forecastingRelated: Better integration of river and weather observations can improve the starting state of forecasts.

• Satellite remote sensingRelated: Assimilation methods incorporate satellite measurements into weather and environmental models despite incomplete coverage.

• Ensemble forecastingRelated: It prepares initial states that ensemble forecasts can perturb and propagate forward.

• NowcastingRelated: Assimilation turns scattered, arriving observations into a coherent picture of current conditions.

• Tropical cyclone forecastingRelated: Assimilation places sparse storm and environmental observations into the model’s starting analysis.

• Tropical cyclone track forecastingRelated: It initializes forecast models with the cyclone and environmental conditions actually observed.

• Marine weather forecastingRelated: Assimilation brings ship, buoy, and satellite measurements into forecast models.