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Weather

Weather plays a critical role in simulation accuracy, particularly for offshore wind projects. It directly influences key operational parameters such as:

  • Installation duration: Adverse weather can delay activities like jack-up operations or heavy lifts.

  • Asset accessibility: Weather windows determine when tasks can safely and effectively be carried out offshore.

Failing to model realistic weather conditions lead to optimistic projections and inadequate risk assessments — especially during the construction and O&M phases.

Weather options for running your simulation​

When configuring your simulation, you can choose from four weather modeling options: Disabled, Historical, Increment Start Year, and Markov.

Simulation runs weather options The option you select to run your simulation on will determine the output you get. We will now explain these options.

1. Disabled​

This option excludes will run simulations with no weather data.

Use case: Best suited for testing task logic, troubleshooting issues with the weather file, or isolating specific model behaviors without external constraints.

2. Historical​

Uses the first year of your weather file for all simulation runs as the starting point in the weather file.

Use case: When you want to look at variability in the simulation results with the same weather pattern in all simulations, e.g. when you want to look at how much variability comes from the corrective failures in O&M.

Note: For construction simulations that don't have stochastic tasks you will get the same results for every simulation run when you use the Historic option.

3. Increment start year​

Each simulation run begins with a different year from your weather file, starting sequentially from the first year.

Run 1: starts the weather data from year 1 in your weather files (simulation year 1 - weather year 1).

Run 2: starts the weather data from year 2, and so on (simulation year 1 - weather year 2).

If the number of runs exceeds the number of years available from the weather file, the simulation will loop back to the first year in the dataset.

Use Case: Recommended for generating outputs including a wider range of weather scenarios, which means your schedules and weather downtime assessments are more robust. This is the recommended method for construction simulations.

Note: For construction cases set the number of runs to the number of weather years you have in the weather file, as the runs will give the same results for the same weather data when the weather data loops and will clutter your outputs.

4. Markov​

Utilizes our Markov chain model to generate synthetic weather data based on the weather data set provided.

Use case: Beneficial when you want to run more iterations than you have years in your weather data set. For example when your weather set have a few years only.