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Supplement of Atmos. Chem. Phys., 20, 12171232, 2020 https://doi.org/10.5194/acp2012172020supplement Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.Supplement
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Identify the system or phenomenon you want to model.
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Gather observational data relevant to the system being modeled.
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Select an appropriate modeling framework or software suitable for observationally constrained modeling.
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Input the observational data into the model and apply the defined constraints.
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Run the model to generate outputs, ensuring they align with the observational data.
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Observationally constrained modeling refers to the process of creating models that are validated and adjusted based on observed data. It integrates empirical observations to ensure that the model predictions are consistent with the real-world measurements.
Typically, researchers and organizations involved in fields such as environmental science, climate research, and engineering may be required to file observationally constrained modeling. This may also include regulatory bodies and companies that need to demonstrate compliance with environmental standards.
To fill out observationally constrained modeling, one should gather relevant observational data, select appropriate modeling techniques, and accurately document the process, including any assumptions made. The model should be calibrated using the observational data and results should be reported with clear methodologies.
The purpose of observationally constrained modeling is to ensure that models reflect real-world conditions accurately, enhance predictive capabilities, and support decision-making by providing a more accurate understanding of complex systems.
The information that must be reported includes the objectives of the modeling, the data sources used, the methodologies employed, the results obtained, any limitations of the model, and recommendations based on the findings.
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