Estimation of sea ice concentration in the Sea of Okhotsk using PALSAR polarimetric data

2010 ◽  
Author(s):  
Hiroyuki Wakabayashi ◽  
Shoji Sakai
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Sea Ice Concentration ◽  
The Sea Of Okhotsk ◽  
Ice Concentration

scholarly journals DETAILED VALIDATION OF AMSR2 SEA ICE CONCENTRATION DATA USING MODIS DATA IN THE SEA OF OKHOTSK

2020 ◽  
Vol V-3-2020 ◽  
pp. 369-373
Author(s):  
K. Cho ◽  
K. Naoki ◽  
J. Comiso
Keyword(s):  
Global Warming ◽  
Solar Radiation ◽  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Ice Cover ◽  
Passive Microwave ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Sea Of Okhotsk ◽  
Ice Concentration

Abstract. Global warming is one of the most serious problems we are facing in the 21st Century. Sea ice has an important role of reflecting the solar radiation back into space. However, once sea ice started to melt, the ice-free water would absorb the solar radiation and amplify global warming in the Arctic region. Thus, importance of sea ice monitoring is increasing. Since longer wavelength microwave can penetrate clouds, passive microwave radiometers on-board satellites are powerful tools for monitoring the global distribution of sea ice on daily basis. The Advanced Passive Microwave Scanning Radiometer AMSR2 which was launched by JAXA in May 2012 on-board GCOM-W satellite provides brightness temperature data that are used to estimate sea ice concentration, the fundamental parameter that is used to monitor the sea ice cover. JAXA is providing AMSR2 sea ice concentration data, derived using ASMR2 Bootstrap Algorithm as a standard product of AMSR2, as a means to communicate how the sea ice cover is changing. This paper describes the advantages of AMSR2 in calculating sea ice concentration and evaluate the accuracy of the sea ice concentration in the Sea of Okhotsk by comparing the result with simultaneously collected MODIS data. The result suggested that under normal winter condition, the RMSE of the AMSR2 sea ice concentration could be less than 10%.

scholarly journals Sea-ice extent variation along the coast of Hokkaido, Japan: Earth’s lowest-latitude occurrence of sea ice and its relation to changing climate

2011 ◽  
Vol 52 (58) ◽  
pp. 165-168
Author(s):  
Shuhei Takahashi ◽  
Tomofumi Kosugi ◽  
Hiroyuki Enomoto
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Ice Cover ◽  
Meteorological Conditions ◽  
Coast Guard ◽  
Proxy Record ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Japanese Coast ◽  
Ice Concentration

AbstarctThe seasonal duration of sea-ice cover in the region immediately north of Hokkaido in the Sea of Okhotsk was estimated from daily sea-ice concentration maps (1995–2009) produced by the Japanese Coast Guard. The results show that this period is generally well represented by the proxy record of sea-ice duration in the region of Utoro in the Shiretoko peninsula. Comparison of the duration of seasonal sea-ice cover along the coast of Hokkaido with meteorological conditions over the period of sea-ice mapping by the Japanese Coast Guard indicates that all sea ice will disappear from the coast of Hokkaido once air temperature is increased by 2–4˚C over present values by the effects of climate change.

scholarly journals Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model

2021 ◽  
Author(s):  
Yunhe Wang ◽  
Xiaojun Yuan ◽  
Haibo Bi ◽  
Mitchell Bushuk ◽  
Yu Liang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Markov Model ◽  
Bering Sea ◽  
Sea Of Okhotsk ◽  
Regional Model ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
The Sea Of Okhotsk ◽  
The Bering Sea

Abstract. In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Arctic Pacific sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally-varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. Sea ice concentration (SIC) trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions up to 6 month lead times in the Bering Sea and the Sea of Okhotsk. We find that surface radiative fluxes contribute to predictability in the cold season and geopotential height and winds play an indispensable role in the warm-season forecast, contrasting to the thermodynamic processes dominating the pan-Arctic predictability. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.

scholarly journals Forecasting of hazardous marine hydrometeorological phenomena for the regions of oil and gas deposit development on the Sea of Okhotsk Shelf

2021 ◽  
Vol 895 (1) ◽  
pp. 012023
Author(s):  
Yu V Lyubitskiy ◽  
A N Vrazhkin ◽  
P O Kharlamov
Keyword(s):  
Sea Of Okhotsk ◽  
Oil And Gas ◽  
Storm Surges ◽  
Federal State ◽  
Sea Ice Concentration ◽  
Practical Applications ◽  
The Sea Of Okhotsk ◽  
Ice Concentration ◽  
Far Eastern ◽  
Eastern Regional

Abstract A brief description of the marine hazard forecast methods and technology for the Sea of Okhotsk developed in the Federal State Budgetary Institution “ Far Eastern Regional Hydrometeorological Research Institute (FERHRI)” is presented. These methods and technology provide numerical prognostic products for the following parameters: wave height, period and direction; sea ice concentration, thickness, drift and compression; sea level and storm surges. The successful cases of practical applications of these forecasts demonstrate the effectiveness of the developed forecast methods and technology.

scholarly journals Estimation of sea-ice thickness and volume in the Sea of Okhotsk based on ICESat data

2018 ◽  
Vol 59 (76pt2) ◽  
pp. 101-111 ◽  
Author(s):  
Sohey Nihashi ◽  
Nathan T. Kurtz ◽  
Thorsten Markus ◽  
Kay I. Ohshima ◽  
Kazutaka Tateyama ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Heat Budget ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Sea Of Okhotsk ◽  
Ice Volume ◽  
Ice Concentration ◽  
Ice Production ◽  
Hydrostatic Balance

ABSTRACTSea-ice thickness in the Sea of Okhotsk is estimated for 2004–2008 from ICESat derived freeboard under the assumption of hydrostatic balance. Total ice thickness including snow depth (htot) averaged over 2004–2008 is 95 cm. The interannual variability of htot is large; from 77.5 cm (2008) to 110.4 cm (2005). The mode of htot varies from 50–60 cm (2007 and 2008) to 70–80 cm (2005). Ice thickness derived from ICESat data is validated from a comparison with that observed by Electromagnetic Induction Instrument (EM) aboard the icebreaker Soya near Hokkaido, Japan. Annual maps of htot reveal that the spatial distribution of htot is similar every year. Ice volume of 6.3 × 1011 m3 is estimated from the ICESat derived htot and AMSR-E derived ice concentration. A comparison with ice area demonstrates that the ice volume cannot always be represented by the area solely, despite the fact that the area has been used as a proxy of the volume in the Sea of Okhotsk. The ice volume roughly corresponds to that of annual ice production in the major coastal polynyas estimated based on heat budget calculations. This also supports the validity of the estimation of sea-ice thickness and volume using ICESat data.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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