The What, Why & How of Nautical MeteoBase for the Offshore Industry

Metocean conditions affect all phases of offshore projects, so reliable and accurate forecasts are essential.  It helps you address the main concerns and pressures that come with working in offshore: managing costs, keeping projects on track, and ensuring the crew and assets are safe. It may sometimes seem like it’s only a few hours gained here and there through more accurate weather, but over the whole project these add up to real savings. 

For companies working in offshore, Nautical MeteoBase (NMB) is the critical forecast data engine that feeds all marine-related products used by the offshore industry. NMB draws on various sources of atmospheric and oceanographic model forecast data, to deliver accurate weather forecasts.

For critical offshore operations, the weather experts also provide derived elements like risk wind speed and risk wave height. The output data refresh four times each day.

 

The unique technology enables offshore companies to interact with the forecast data in a way that easily integrates into their daily operations. They can accurately plan for weather-related downtime, avoiding unnecessary or last-minute scheduling changes, and reduce operational costs.

Users have visibility of changing conditions plus an inbuilt threshold alarm, leading to improved safety at sea and reduced risk of damage to equipment or environmental disaster. Data is delivered using standard maritime communications systems, minimizing communication cost.

 

What is Nautical MeteoBase (NMB)?

 

NMB a flavor of statistical post processing, which is a technique used by weather experts to enhance and improve their forecasts. It's an umbrella term, describing multiple statistical methodologies, which each have a unique purpose and application.

It works by combining metocean model data and high-resolution environmental data, exploiting the strengths of each. It also applies locally-observed weather from observation networks. Post-processing corrects the quite coarse-scale nature of model output; these corrections are necessary to ensure that local effects are taken into consideration and the most accurate forecast is produced.

"For site-specific forecasts, raw model data is not accurate enough. By applying smart statistical corrections, based on local observations and terrain data, our forecasts gain about 20% in accuracy"

- Wim van den Berg

Senior Meteorological Consultant, WeatherTech Team

 

How the experts combine models for the best forecasts for offshore companies

 

Weather experts improve the forecast by combining data from different weather models, including ECMWF, UKMO, KNMI, and NCEP/NOAA. Each model is given its weight, based on its relative performance. The weights are variable depending on the forecast lead time.

Through model mixing, NMB enhances the strong points of the input models and reduces the weak sides, which improves the accuracy and reliability of the forecast.

The raw NMB data can be improved further through:

 

  • A near-shore post-processing module called Rose, which can incorporate meteorologist expert knowledge to reduce localized model imperfections
  • Automated local calibration using observations with a Kalman filter – an algorithm that reduces systematic errors
  • Manual adjustment by meteorologists interpreting and combining multiple additional data sources, e.g. observations and satellite

 

The NMB output is available on a global 1.0x1.0-degree grid and, for high-precision regional forecasts, on 0.1x0.1 fine-mesh grids. These fine mesh grids are available as standard in highly-used areas but can also be produced for any point in the sea globally on customer request. On request, the weather experts can also run high-resolution SWAN wave forecasts for specific near-shore locations.

 

"Standard metocean models provide a great starting point. However, to improve accuracy, it requires weather experts to combine different models, apply corrections for near-shore locations, and make manual adjustments. This is especially important in areas where there are limited or no observations."

- Maurits Geuze,
Application Domain Expert - Marine

 

What is a Kalman filter?

 

The Kalman filter is a post-processing technique that minimizes systematic errors in model forecasts. It's is a self-learning, recursively combining new measurements with model forecasts.

This allows the filter to learn and apply corrections on-the-fly, reducing the mean error (bias). Because of the dynamic character of the filter, it can adapt to changes in measurements (e.g. seasonal shifts) or changes/upgrades in the model used.

The weather experts can apply a Kalman filter to the significant wave height forecast produced by the Nautical MeteoBase for offshore clients. This requires a live observation data stream between the client and the experts. To use the filter's full potential, the stream should deliver new measurements (via FTP) at a high-frequency stream every 10-60 minutes.

 

How is the Kalman filter applied to waves

 

Step 1 NMB forecast data

First, a check is done to establish whether NMB forecast data for the client offshore location is stored in the database.

 

Step 2 Measurement data

If a live data feed is not available, this needs to be set up by the client. If it has already been up and running, the experts will work on the automatic ingestion of this data into the database.

 

Step 3 Training

Before a Kalman filter can be deployed for a marine client, it needs to be trained over a period of approximately three weeks. If historical measurement and forecast data are both available, a hindcast study can be done – this not only reduces the training period substantially, it also indicates in advance how much the filter can improve the forecast. Any forecasts the client receives during the training period won't have the Kalman filter calibration applied yet.

 

Step 4 Application of the Kalman filter

The training period ensures that the Kalman filter coefficients, which calibrate the NMB forecast, are stabilized. Once that's done, the filter can become operational.

The Kalman filter uses up-to-date measurements, taken just before a new NMB forecast is released. Because of this approach, the filter can further improve the short-range wave height forecast.

Experience shows that coastal locations benefit most from the significant reduction in the mean error of the model forecast. These locations are sometimes not well represented within the NMB, due to factors such as sea-floor levels, currents, and tides. When tides and currents dominate the working conditions for a client in a coastal (shallow water) area, deploying a SWAN domain instead of a Kalman filter is also a serious option, although both work well.

The approach taken to create a detailed and bespoke forecast will ultimately depend on the specific requirements of each client. Working with weather experts, offshore companies can ensure they have access to the right data for their needs.

 

Want to learn more? Visit our Offshore Knowledge Base.

 

Visit Offshore Knowledge Base

Standard metocean models provide a great starting point. However, to improve accuracy, it requires weather experts to combine different models, apply corrections for near-shore locations, and make manual adjustments. This is especially important in areas where there are limited or no observations.

- Maurits Geuze

Application Domain Expert - Marine
MeteoGroup