Which data sources are used to initialize numerical weather prediction models?

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Data assimilation uses a wide mix of observations to estimate the current state of the atmosphere for model initialization. No single data source provides a complete picture: surface observations give near-surface conditions, radiosondes add vertical profiles of temperature, moisture, and wind, satellites extend coverage globally and reveal information about temperature, humidity, winds, and cloud properties in upper levels, radar adds details on precipitation structure and, with Doppler capabilities, wind fields in storms, aircraft measurements fill in upper-level data over flight routes, and buoys supply sea-surface conditions that influence the boundary layer and air-sea interactions. Reanalysis data offer a consistent, long-term background that helps stabilize the initial state when observations are sparse or noisy. By blending all these sources through data assimilation, meteorologists produce a more accurate and physically consistent initial condition than would be possible with any single data type, which is why the comprehensive set of data sources is used for initialization. Relying on surface observations alone would miss the vertical structure and many crucial atmospheric features that drive forecast evolution.