April 2018

Analysis of Long-term Environmental Load Factor in the Kara Sea Region using Meteorological and Marine Numerical Modeling Products

05 April, 2018

Hyun-ju Lee (Deputy Senior Surveyor, Future Technology Research Team)

1. Introduction

It is essential that an understanding of the various meteorological and marine environment conditions are incorporated into the design and production of offshore plant substructures or upper platforms in the Arctic environment. Particularly important are the major environmental load factors that can directly affect the installation and operation of plant structures, and the reanalysis of data from meteorological and marine numerical modeling based on observation data, which provides useful information for realistic design and operation conditions.

   - Temperature (air/ocean), wind speed
   - Wave height, wave period, wave length / current (speed)
   - Sea ice thickness, sea ice concentration

While this information can not completely prevent unexpected damage caused by the environmental load factors, it is possible to optimize the design for offshore plant in the Arctic, if sufficient review of the environmental load factor in the region has been conducted.

2. Background of Dataset Selection

To accurately estimate the environmental load factor conditions in the Arctic, it is necessary to use continuous observation data for the targeted area. However, it is not possible to obtain long-term observation data because of the limitations of the distinct characteristics in arctic and marine region.

The numerical forecast model is a kind of program which calculates the numerically physical process and dynamic phenomenon by parameterization using a super computer. The European model, which is currently the operational weather forecast model in KMA and known to have the highest level of accuracy, shares over 90% similarity.

Furthermore, the reanalysis data is simulated from past weather conditions with increased accuracy through the assimilation of observation data (radar, sounding, ship/buoy/aircraft report, radiosonde, pilot balloon, dropsonde, satellite) which is based on the latest numerical forecast model. The reanalysis data is used in the field of weather forecasting and relative research to improve the performance of climate analysis and numerical prediction.

2-1(E).png

[Flowchart for Operation of Numerical Forecast Model (2010, KMA)]

3. Scope and Region for Study

KR targeted the Kara Sea as a region for study and identified and collected long-term continuous data over more than 10 years from NCAR (National Center for Atmosphere Research) / UCAR (University Corporation for Atmospheric Research) and RDA (Research Data Archive). The reanalysis data for environmental load factors was analyzed through the algorithm developed by KR.

ECMWF Reanalysis Data(European Centre for

Medium Range Weather

Forecasts)

The comprehensive reanalysis of climate data using observation data from numerical predictions of global atmosphere/ocean/land coupled model. It is used as initial condition for weather forecasting in various global forecast systems and research institutes.

CMEMS Reanalysis Data
(Copernicus Marine

Environment Monitoring

Services)

The Arctic/ocean physics reanalysis data from the TOPAZ4 system running at MET Norway, with improved accuracy through the use of HYCOMS coupled to a sea ice model and a 100-members to assimilate both in situ and satellite data form different sensors.

CAWCR Reanalysis Data
(Centre for Australian

Weather and Climate

Research)

The ocean wave reanalysis data, using the Wave Watch III wave model interspersed with NCEP CFSR hourly winds and daily sea ice.

[The Environment Load Factors and Region (Kara sea) for Study]

4. Results

The trends of each of the factors were analyzed for daily/seasonal/annual changes for a period of thirteen years from 2000 to 2012. The minimum (1%), medium (50%) and maximum (99%) values of the factors were also obtained daily to determine the range of variability in the target region (Kara Sea).

(1) Temperature (2m above the sea level)

	Lowest Minimum	Highest Maximum
Temp.	233.68K(-39.47℃)	289.22k (+16.07℃)
Date	11th Jan. 2004	6th Aug. 2009
Diff.	55.54 ℃

(2) Wind (10m above the sea level)

	Lowest Minimum	Highest Maximum
Speed	0.14 m/s	28.5 m/s
Date	17th Feb. 2006	22th Neb. 2000
Diff.	28.36 m/s

(3) Wave Height

	Lowest Minimum	Highest Maximum
Height	-	6.87 m
Date	-	26th Jan. 2007
Diff.	6.87 m

(4) Wave Period

	Lowest Minimum	Highest Maximum
Period	1.65 s	14.66 s
Date	-	23th Nob. 2006
Diff.	13.01 s

(5) Wave Length

	Lowest Minimum	Highest Maximum
Length	4 m	336 m
Date	-	3th Nob. 2006
Diff.	332 m

(6) Current (Speed)

	Lowest Minimum	Highest Maximum
Speed	0.0 m/s	0.61 m/s
Date	-	24th Jul. 2010
Diff.	0.61 m/s

(7) Sea Ice Fraction (Percentage of presence of sea ice in single grid)

	Lowest Minimum	Highest Maximum
Frac- tion	0.0 %	100 %
Date	-	-
Diff.	100 %

(8) Sea Ice Thickness

	Lowest Minimum	Highest Maximum
Thick- ness	0.0 m	2.58 m
Date	-	4th Jul. 2008
Diff.	2.58 m

The various factors that affect the installation and operation of offshore plant structures can be identified, and incorporating analytical data from the last 50 years will provide a useful basis for optimized design. In addition, this observational-based numerical data can be applied not only to offshore plant structures but also to the design and operation of various structures on land and ocean.

Reference
- ERA REPORT SERIES No.1 Version 2.0, B. Paul et al., 2011
- Data Services Research Data Archive (RDA) CISL/NCAR, Steven Worley & Doug Schuster,

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