Many factors influencing the movement of ice such as wind, ice concentration, ice thickness,
roughness, water currents, Coriolis force, bathymetry, artificial and natural obstacles in the area. Current speeds in the Caspian Sea are relatively small and so the main driving force for ice movements is wind. Therefore, main goal of this work was to study wind-ice movement velocities dependence in the region and check how ice concentration and thickness influence on the movement of ice. A high number of measurements and observations was made to describe ice drift in the region, although the data was collected areas and usually not publicly available. In our work, we have used timely consequent optical and SAR satellite images to observe ice movements and its displacement over the area. Wind data for the same period and area was taken from wind models. Ice charts were prepared using visual interpretation of satellite imagery. Ice information (concentration, stage of development, floe size) were stored as vector data in SIGRID3 format. The described data has been correlated and analyzed. The analysis provided in the work can be used for the forecast of short term ice drift on the operational basis and can be the first step for creation of ice drift forecast model for the region of North Caspian Sea. The used data, methods and results of the study are described in this paper.
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Comparison of satellite imagery based ice drift with wind model for the Caspian Sea
1. COMPARISON OF SATELLITE
IMAGERY BASED ICE DRIFT
WITH WIND MODEL FOR THE
CASPIAN SEA
Yevgeniy Kadranov
Anton Sigitov
Sergey Vernyayev
LLP ICEMAN.KZ Kazakhstan
2. ABOUT US
LLP ICEMAN.KZ - Private entity (three
specialists with diverse background) since
2012
Operational support (ice charting and
forecast) for navigation in the Caspian and
seismic operations in NE Greenland
Seal avoidance programs for vessel traffic
Software development and database
management (sea ice monitoring programs)
Design criteria development and engineering
3. INTRODUCTION
Northern Caspian 44N- 47N
Seasonal (November- March) ice cover
Mainly shallow 3-8 m (specific ultra-shallow
ice class vessels for marine traffic - ACV)
Significant O&G activities (Kazakh sector:
Kashagan, Pearls; Russian Sector: Filanovsky,
Korchagin)
Fragile environment (including endangered
fish and seals species)
4. NORTHERN CASPIAN ICE REGIME (LAST 10 YEARS)
Seasonal variability of ice
extent and thickness
depending on winter severity
60 cm max observed ice
thickness of thermally grown
ice
Extensive stable / landfast
area during severe winters –
Mainly mobile during mild
winters
Grounded deformed ice
features as anchor points
2016 is used in the study
(mainly mobile conditions)
5. ICE DRIFT EVENTS
Ice drift happens
Needs forecasting
manage hazards
excess loads on structures
pressure on vessels
pile-ups and ride-ups
facilitate marine and ACV traffic
Monitoring drift of floes with seal
pups
Big gaps between available images -
needs modeling in-between to
maintain full awareness
6. PROCESSING WORKFLOW
Extracted ice displacement from
satellite images
Assigned modelled wind data to
ice drift
Calculated wind-drift
dependency
7. JANUARY – MARCH 2016 IMAGERY
Optical MODIS images (unique season
with less cloudy images)
SAR (TerraSAR-X, Radarsat-2, Risat-1)
provided by KSAT to close Gaps
First experience with Sentinel-1 that was
rare (only one)
8. ICE DRIFT DATA FROM SATELLITE IMAGES
QGIS plugin
Identifying and polygonising the same floes
in consequent images
Calculating vector parameters between
centroids of polygons
Displacement
Direction
Duration
Ice conditions description (concentration
mainly)
9. GFS WIND DATA
GFS set of wind data is averaged
within drift interval
Average wind vector assigned to
the nearest centroid of drift vector
Ice conditions data assigned to the
Start and End of the drift vector
10. OBTAINED WIND-DRIFT DATA
Database record:
Drift (displacement, direction, duration)
Wind (speed, direction, deviation)
Ice condition (start-end concentration, stage of
development, floe size)
Data filtering (records excluded)
Short Duration (<4 h)
Wind spread during averaging (> 60º)
Wind speed (< 5 knots)
Events with the clear obstacles observed
Strange drift-wind behavior
WIND DRIFT
11. WIND DRIFT RATIOS
Good relation for wind-drift directions
Big spread for wind-drift speed data (invalid direct
calculation of drift speed)
Expected drift-wind ratio distribution (2-3% - most
frequent)
Segregation on concentration: ration coefficients tends
to increase with lowering concentrations
12. DRIFT MODEL
Regression analysis has been used to describe drift-wind dependency
Regression coefficients were calculated for different sets of data
A12,A21 smaller than A11,A22
Residual drift variations are significant for low wind speed for MM,HH category
A21 > A12: drift to the left from wind
Drift response ellipse used for visualisation
Higher drift speed (NE-SW direction) for same wind speed
13. MODEL RESULTS AND
UNCERTAINTIES
Comparison of measured
and modelled data based on
regression analysis
Modelled results sometimes
are very close to observed
Obstacles are the main
drawback of modelling
performance
14. DISCUSSION
RESULTS
Data on drift events in North Caspian for
2015-2016 was compiled together
Drift-wind dependencies were analyzed
Concentration, anchor points, and borders of
shores and immobile ice
Model was built based on regression
analysis
IMPROVEMENTS
Enhance model with more historical data
Enhance accuracy with wind data from more
accurate ECMWF
Develop mechanism to incorporate
obstacles into drift analysis
Provide comparison with field data
Describe shortly our activities and where we are from
44-47 although south has seasonal ice cover, all major SAR satellites attend only once in three days
3-8 m – ultrashallow requiring specific fleet and operations set-up with decreasing water level potential introduction of ACVs