Articles | Volume 9, issue 4
https://doi.org/10.5194/tc-9-1735-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-9-1735-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
31 Aug 2015
Research article |
| 31 Aug 2015
Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems
P. G. Posey
CORRESPONDING AUTHOR
Naval Research Laboratory, Stennis Space Center, MS, USA
E. J. Metzger
Naval Research Laboratory, Stennis Space Center, MS, USA
A. J. Wallcraft
Naval Research Laboratory, Stennis Space Center, MS, USA
D. A. Hebert
Naval Research Laboratory, Stennis Space Center, MS, USA
R. A. Allard
Naval Research Laboratory, Stennis Space Center, MS, USA
O. M. Smedstad
Vencore Services and Solutions, Inc., Stennis Space Center, MS, USA
M. W. Phelps
Jacobs Technology Inc., Stennis Space Center, MS, USA
F. Fetterer
National Snow and Ice Data Center, Boulder, CO, USA
J. S. Stewart
J. Scott Stewart of Exploratory Thinking, Longmont, CO, USA
W. N. Meier
NASA Goddard Space Flight Center, Greenbelt, MD, USA
S. R. Helfrich
US National Ice Center, Suitland, MD, USA
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Cited
39 citations as recorded by crossref.
- The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX W. Copeland et al. 10.3389/fmars.2024.1400479
- Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS Y. Liu et al. 10.3390/rs12101582
- Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
- PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction D. Zhang et al. 10.3390/app14083278
- Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system S. Fritzner et al. 10.5194/tc-13-491-2019
- Sea Ice Properties in High‐Resolution Sea Ice Models J. Zhang 10.1029/2020JC016686
- A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology Q. Zheng et al. 10.3390/rs14122889
- Toward Optimization of Rheology in Sea Ice Models through Data Assimilation J. Stroh et al. 10.1175/JTECH-D-18-0239.1
- From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234
- Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM) E. Fiedler et al. 10.5194/tc-16-61-2022
- Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system R. Allard et al. 10.1016/j.asr.2017.12.030
- Validation metrics for ice edge position forecasts A. Melsom et al. 10.5194/os-15-615-2019
- Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System X. Liang et al. 10.1029/2019JC015073
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Evaluation of sea-ice thickness reanalysis data from the coupled ocean-sea-ice data assimilation system TOPAZ4 Y. Xiu et al. 10.1017/jog.2020.110
- A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results S. Ren et al. 10.5194/gmd-14-1101-2021
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- Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals W. Meier & J. Stewart 10.3390/rs12142197
- Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model K. Wang et al. 10.5194/tc-17-4487-2023
- Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data J. Rusin et al. 10.1017/aog.2024.6
- Operational Implementation of Sea Ice Concentration Estimates From the AMSR2 Sensor W. Meier et al. 10.1109/JSTARS.2017.2693120
- Application of Radar Image Fusion Method to Near-Field Sea Ice Warning for Autonomous Ships in the Polar Region T. Hsieh et al. 10.3390/jmse10030421
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic M. Serreze & W. Meier 10.1111/nyas.13856
- Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model A. Schweiger & J. Zhang 10.1002/2015JC011273
- Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1 Y. Batrak et al. 10.5194/gmd-11-3347-2018
- The Impact of an Intense Cyclone on Short‐Term Sea Ice Loss in a Fully Coupled Atmosphere‐Ocean‐Ice Model D. Stern et al. 10.1029/2019GL085580
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- An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport T. Bilge et al. 10.3390/jmse10020265
- Near-real-time Arctic sea ice thickness and volume from CryoSat-2 R. Tilling et al. 10.5194/tc-10-2003-2016
- Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020 S. Cheng et al. 10.5194/tc-17-1735-2023
- Multiweek Prediction Skill Assessment of Arctic Sea Ice Variability in the CFSv2 Y. Liu et al. 10.1175/WAF-D-18-0046.1
- Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis J. Xie et al. 10.5194/tc-12-3671-2018
- Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts F. Zhao et al. 10.5194/gmd-17-6867-2024
- Wave Evolution in Off‐Ice Wind Conditions J. Gemmrich et al. 10.1029/2018JC013793
- Predictability of the Arctic sea ice edge H. Goessling et al. 10.1002/2015GL067232
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Landfast Ice and Coastal Wave Exposure in Northern Alaska L. Hošeková et al. 10.1029/2021GL095103
- Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. 10.3389/fmars.2019.00429
38 citations as recorded by crossref.
- The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX W. Copeland et al. 10.3389/fmars.2024.1400479
- Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS Y. Liu et al. 10.3390/rs12101582
- Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
- PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction D. Zhang et al. 10.3390/app14083278
- Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system S. Fritzner et al. 10.5194/tc-13-491-2019
- Sea Ice Properties in High‐Resolution Sea Ice Models J. Zhang 10.1029/2020JC016686
- A Mid- and Long-Term Arctic Sea Ice Concentration Prediction Model Based on Deep Learning Technology Q. Zheng et al. 10.3390/rs14122889
- Toward Optimization of Rheology in Sea Ice Models through Data Assimilation J. Stroh et al. 10.1175/JTECH-D-18-0239.1
- From Observation to Information and Users: The Copernicus Marine Service Perspective P. Le Traon et al. 10.3389/fmars.2019.00234
- Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM) E. Fiedler et al. 10.5194/tc-16-61-2022
- Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system R. Allard et al. 10.1016/j.asr.2017.12.030
- Validation metrics for ice edge position forecasts A. Melsom et al. 10.5194/os-15-615-2019
- Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System X. Liang et al. 10.1029/2019JC015073
- Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Y. Zhang et al. 10.1029/2023GL105672
- Evaluation of sea-ice thickness reanalysis data from the coupled ocean-sea-ice data assimilation system TOPAZ4 Y. Xiu et al. 10.1017/jog.2020.110
- A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description and preliminary results S. Ren et al. 10.5194/gmd-14-1101-2021
- Calibrating a Viscoelastic Sea Ice Model for Wave Propagation in the Arctic Fall Marginal Ice Zone S. Cheng et al. 10.1002/2017JC013275
- Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals W. Meier & J. Stewart 10.3390/rs12142197
- Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model K. Wang et al. 10.5194/tc-17-4487-2023
- Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data J. Rusin et al. 10.1017/aog.2024.6
- Operational Implementation of Sea Ice Concentration Estimates From the AMSR2 Sensor W. Meier et al. 10.1109/JSTARS.2017.2693120
- Application of Radar Image Fusion Method to Near-Field Sea Ice Warning for Autonomous Ships in the Polar Region T. Hsieh et al. 10.3390/jmse10030421
- Exploring non-Gaussian sea ice characteristics via observing system simulation experiments C. Riedel & J. Anderson 10.5194/tc-18-2875-2024
- The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic M. Serreze & W. Meier 10.1111/nyas.13856
- Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model A. Schweiger & J. Zhang 10.1002/2015JC011273
- Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1 Y. Batrak et al. 10.5194/gmd-11-3347-2018
- The Impact of an Intense Cyclone on Short‐Term Sea Ice Loss in a Fully Coupled Atmosphere‐Ocean‐Ice Model D. Stern et al. 10.1029/2019GL085580
- Impacts of synoptic-scale cyclones on Arctic sea-ice concentration: a systematic analysis E. Schreiber & M. Serreze 10.1017/aog.2020.23
- An Evaluation of the Performance of Sea Ice Thickness Forecasts to Support Arctic Marine Transport T. Bilge et al. 10.3390/jmse10020265
- Near-real-time Arctic sea ice thickness and volume from CryoSat-2 R. Tilling et al. 10.5194/tc-10-2003-2016
- Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020 S. Cheng et al. 10.5194/tc-17-1735-2023
- Multiweek Prediction Skill Assessment of Arctic Sea Ice Variability in the CFSv2 Y. Liu et al. 10.1175/WAF-D-18-0046.1
- Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis J. Xie et al. 10.5194/tc-12-3671-2018
- Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts F. Zhao et al. 10.5194/gmd-17-6867-2024
- Wave Evolution in Off‐Ice Wind Conditions J. Gemmrich et al. 10.1029/2018JC013793
- Predictability of the Arctic sea ice edge H. Goessling et al. 10.1002/2015GL067232
- Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. 10.5194/os-19-1375-2023
- Landfast Ice and Coastal Wave Exposure in Northern Alaska L. Hošeková et al. 10.1029/2021GL095103
Saved (final revised paper)
Latest update: 15 Nov 2024
Short summary
This study presents the improvement in the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. A method of blending ice concentration observations from AMSR2 along with a sea ice mask has been developed, resulting in an ice concentration product with high spatial resolution. A significant improvement in the ice edge location has been shown in the operational system assimilating this new product.
This study presents the improvement in the US Navy's operational sea ice forecast systems gained...
