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.

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

15. THANK YOU FOR
ATTENTION