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There are a number of sources of weather data, some locations have an abundance while others have little information available. There are normally two main types:
1. Detailed hourly recordings from a nearby weather station, probably showing wind speed and direction, visibility and current. There could well be other factors that are recorded as well, for example the number of lightning strikes.
2. Historic data of channel closures, perhaps recorded by the local pilots and showing when transits were not possible.
These data sources can generally be used effectively to support a model of the weather, but we need to be careful in some areas too:
So we know that there are a variety of different weather statistics but how do we choose the ones that are relevant to the model in question? Firstly, we need to find out which specific aspects of the weather are going to affect operation of the terminal. In most cases we try to characterise the weather by determining the average duration of a weather closure, and the average time between weather closures; normally different by month of the year.
Sometimes we need to use a more sophisticated weather model. An example would be where ships are not able to approach the berth if the wind speed exceeds 25 knots. In addition, loading must be suspended if the wind speed exceeds 30 knots. In this case these factors cannot be input as two independent constraints within the model, otherwise we would end up with a 30 knot disconnection at a time when transits were still allowed. Here we need to build up a model of how the wind speed varies over time and use that in the model’s decision logic instead.
Often we link weather characteristics together and model the correlation between the two. It could be that ships are not allowed to berth if wind speed is above 20 knots, nor are they allowed to berth if the wave height is above 2 metres, but the periods of high waves are quite likely to coincide with periods of high wind.
Accurately modelling the weather patterns that affect a shipping terminal is key for efficient planning and decision making in LNG operations, and will impact hugely on the inventory levels required for smooth operation. Experience has shown us that we can build up different weather models that appear to match the actual data available, but which still allow for different terminal behaviours. In my experience it is definitely worth investing time into detailed weather analysis to get the model right so decision makers have the confidence that their preferred strategies will perform as planned and time and money isn’t wasted.
If you interested in finding out more on how LNG operators can model the weather and a variety of other influencing factors, take a look at Lanner’s LNG Logistics Simulator which generates data driven results across the whole LNG value chain.
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