Solution to the Meteorological environment in the Arctic

The best solution for reduce the uncertainties in the Arctic is to predict the destructive weather precisely and timely. As discussed above, currently both the data collection and the prediction models are not satisfactory, then another technique, hindcast, has turned out to be a good solution.

Metocean conditions vary widely from region to region in the Arctic, so site specific information is necessary for the researchers. Unfavorably, data collected for pure scientific reasons are seldom sufficient, especially for use in design and operations of offshore structures and ships in the Arctic. Now it already turned out to be a big challenge to collect the needed data. DNV (2008) has concluded that measured data is seldom available for more than 30 years (Gulf of Mexico, North Sea, Indonesia). Fortunately hindcast data in some areas, can be used to improve extreme weather prediction, which can cover up to 50 years and longer.

Contrast to forecast, which is based on considerable data or observations, hindcast is an efficient and useful technique to reproduce nature when there are not enough observations (DNV, 2008). Wikipedia (2013) defines the hindcast as a way of testing an existing model, which is performed through inputting known or closely estimated data into the model to see how well the output matches the known results. Another name of hindcasting, backtesting, might be a better explanation of its true meaning. One sound example cited by DNV (2008) is to utilize knowledge of the physical processes which causes wind from differences in air pressure and waves from the winds. Essentially most of the weather forecasts were performed based on air pressure which has been proved with acceptable accuracy and reliability for decades. That is why it is possible to produce weather maps that could date back to the early 1950’s or even longer. Though this way huge number of points over large areas can be located, and data record with span of 40 – 50 years could be found. This philosophy goes well when it is used for calculating currents.

DNV (2008) has proposed that hindcast is recommendable to be developed as a technology because it creates significant data both in time and space, but there still some points need to be paid attention such as:

• All hindcast models need to be validated and verified before they are used for prediction;

• Verified models should have no incorrect model code or programming errors etc.

• Validated models are expected to supply a satisfactory description of nature;

• Comparison between computed and observed conditions should be taken to verify the validation of the models;

• Shorter observational records are preferred to be used for validation check rather than required for calculation, Figure 3.13 illustrates how the shorter records have been used for the validation of hindcast data in the Barents Sea.

Fig. 3.13 Shorter records of waves and currents in the Barents Sea that can be used for hindcast validation.

DNV, (2008)

Two distinctive features can be found when comparing hindcast in the North Sea and in the Barents Sea:

• Historic weather maps for the North Sea are more accurate than the Barents Sea;

• Verification of data are more extensive in North Sea than in the Barents Sea.

Due to the above facts, it is reasonable to say that hindcast data in the North Sea can be employed with more confidence than that for the Barents Sea.

The hindcast condition of the Arctic region can be discussed with the example of hindcast for the Barents Sea as of spring 2008:

• A hindcast database giving wind and waves every 75 km for the time window between year1955 to year 2007 is available so far;

• This data archive shows that the Barents Sea has a “milder” wind and wave condition than that in the North Sea, which might be different with most people’s expectation;

• A new Norwegian hindcast database with more records covering wind and waves for every 10 km for the Barents and North Seas for the years 1955 – 2002 will be available soon, which can benefit offshore operation to high extent. The hindcast database which both covers longer time windows and intensive observations are expected. Figure 3.14 shows an example of winds from this archive for the North, Norwegian and Barents Seas by the Norwegian Meteorological Institute. The archive could list wind, temperature and water content at 40 different heights above the sea surface in addition to waves;

• Some other sources such as European Centre for Medium-Range Weather Forecasts, Fugro Ocean or US Navy can be referred to.

Figure 3.14 Example of coverage of the New Norwegian hindcast archive for wind and wave.

Courtesy to Norwegian Meteorological Institute (met.no)

To avoid or mitigate the negative influence caused by the extreme weather in the Arctic, one of the best solutions is to make accurate predictions, which can both enhance efficiency and safety level of operation. As mentioned above, this mission needs support of considerable data and optimized models. Currently, the records in the Barents Sea are neither enough in the quantity, nor with the good quality, or in other words, with larger uncertainties comparing with the data in the North Sea. So when the data in the Barents Sea are to be employed, this factor must be taken into consideration.

So the conclusion can be drawn up that weather forecasts are less reliable in the Barents Sea than in the further south region such as the North Sea, because of two main reasons:

fewer meteorological stations at sea and the occurrence of small scale atmospheric phenomena which are difficult to forecast. Then it is fair that the probability of failing or disrupted operations in the Barents Sea is higher than its south neighbors.

Based on lots of experience both successful and failed, DNV (2008) believes that it is a challenge to predict extreme ice and metocean conditions that will occur only once every 100 years or more, because it is truly small probability case. Only few valuable record can be referred, because just from the last century, human started systematic research for the Arctic, which compares with the period of 100 years, it is not long enough for supply rich data. Statistical data and approaches can be taken as design basis, while for operations the weather forecasts is the only access to be trusted. Reliable prediction of ice and metocean extreme values requires statistically reliable data and proper statistical models and methods.

The statistical data base can be established and enriched in this way: after a data base has been established which covers all the needed information, the data will be subjected to statistical analysis to estimate values as per desired safety levels; for the practical employment, even 30 – 50 years may be insufficient for rigorous statistical determination and uncertainty, both from data and from statistical approaches, will adhere to the estimates at the 10^-2 and 10^-4 levels (DNV, 2008). The standards mentioned above do not give little guidance on how to perform the statistical analysis and deal with the uncertainties. In some international standard it is only stated that interpretation by specialists is usually needed.

DNV (2008) has proposed some points for collection and application of the data base. To compensate the regional climate varieties and to secure the same level of reliability in different regional climates it is critical to evaluate the needs and requirements that are placed on the data bases. These considerations should include but not limit to:

• The records are long enough to catch seasonal and inter-annual variability;

• Ensure the relevant and right type of data are collected;

• The observational uncertainty connected to the different measurement techniques has not been neglected and kept as low as possible;

• That weather forecasting is secured with the right spatial and temporal coverage of weather observations;

• That processing and analysis approaches and tools are transparent, verified, documented well and have widespread acceptability.

So next mission for the researchers is to pay more efforts on collection of more data covering more regions and lasting for longer period, through international cooperation this mission can be performed with higher efficiency and reliability.