Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Williams, Timothy; Korosov, Anton; Rampal, Pierre; Ólason, Einar

doi:10.5194/tc-2019-154

Cited by 4 publications

(6 citation statements)

References 49 publications

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“…This issue is more important for the models trained with buoy observations due to the longer period used for training these models. Moreover, TOPAZ4 does not reproduce the recent acceleration of sea ice drift, as already reported by Xie et al (2017), and the bias of TOPAZ4 sea ice drift speed has changed during the studied period (Fig. S6 in the Supplement).…”

Section: Discussionmentioning

confidence: 57%

“…Though the TOPAZ4 system provides forecasts with hourly time steps, the forecasts with daily outputs were used here due to the 24 h span of SAR observations. Previous studies have reported that the speed of sea ice drift is overestimated in the TOPAZ4 system compared to buoy observations from the IABP (Sakov et al, 2012;Xie et al, 2017).…”

Section: Predictor Variablesmentioning

confidence: 84%

“…Short-term sea ice drift forecasts are operationally produced by numerical prediction systems but are affected by biases despite the numerous efforts to improve the models (Hebert et al, 2015;Schweiger and Zhang, 2015;Rabatel et al, 2018;Williams et al, 2019). Hebert et al (2015) evaluated sea ice drift speed forecasts from the U.S. Navy's Arctic Cap Nowcast/Forecast System (ACNFS).…”

Section: Introductionmentioning

confidence: 99%

“…These forecasts outperform a climatological reference for all lead times (up to 9 d). Sea ice drift forecasts from the neXtSIM-F system have been evaluated by Rabatel et al (2018) and Williams et al (2019), and root mean square errors of about 3 and 4 km d −1 have been reported for lead times of 1 and 4 d, respectively (Williams et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Calibration of sea ice drift forecasts using random forest algorithms

Palerme

Müller

2021

The Cryosphere

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Abstract. Developing accurate sea ice drift forecasts is essential to support the decision-making of maritime end-users operating in the Arctic. In this study, two calibration methods have been developed for improving 10 d sea ice drift forecasts from an operational sea ice prediction system (TOPAZ4). The methods are based on random forest models (supervised machine learning) which were trained using target variables either from drifting buoy or synthetic-aperture radar (SAR) observations. Depending on the calibration method, the mean absolute error is reduced, on average, between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift. Overall, the algorithms trained with buoy observations have the best performances when the forecasts are evaluated using drifting buoys as reference. However, there is a large spatial variability in these results, and the models trained with buoy observations have particularly poor performances for predicting the speed of sea ice drift near the Greenland and Russian coastlines compared to the models trained with SAR observations.

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Section: Discussionmentioning

confidence: 57%

Section: Predictor Variablesmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Calibration of sea ice drift forecasts using random forest algorithms

Palerme

Müller

2021

The Cryosphere

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“…A CC of 0.58 between SMOS-SMAP-derived ice thickness and the ship observations was estimated during the period of October 5 to November 4, 2015, in the Beaufort and Chukchi seas [55]. The values of bias (-0.12 m) and RMSE (0.26 m) from November to December 2018 from the comparison between the Ocean and Sea Ice Satellite Application Facility (OSISAF) and SMOS ice thickness was reported [56]. Therefore, our results show excellent agreement with those estimated by the previous studies.…”

Section: Validation Of Smap Sit and Smos Sirmentioning

confidence: 93%

Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations

Kim

Kwon

et al. 2019

Remote Sensing

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The variations in the Arctic sea ice thickness (SIT) due to climate change have both positive and negative effects on commercial human activities, the ecosystem, and the Earth’s environment. Satellite microwave remote sensing based on microwave reflection signals reflected by the sea ice surface has been playing an essential role in monitoring and analyzing the Arctic SIT and sea ice concentration (SIC) during the past decades. Recently, passive microwave satellites incorporating an L-band radiometer, such as soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP), have been used for analyzing sea ice characteristics, in addition to land and ocean research. In this study, we present a novel method to estimate thin SIT and sea ice roughness (SIR) using a conversion relationship between them, from the SMAP and SMOS data. Methodologically, the SMAP SIR is retrieved. The SMAP thin SIT and SMOS SIR are estimated using a conversion relationship between thin SIT data from SMOS data and SMAP-derived SIR, which is obtained from the spatial and temporal collocation of the SMOS thin SIT and the SIR retrieved from SMAP. Our results for the Arctic sea ice during December for four consecutive years from 2015 to 2018, show high accuracy (bias = −2.268 cm, root mean square error (RMSE) = 15.919 cm, and correlation coefficient (CC) = 0.414) between the SMOS-provided thin SIT and SMAP-derived SIT, and good agreement (bias = 0.03 cm, RMSE = 0.228 cm, and CC = 0.496) between the SMOS-estimated SIR and SMAP-retrieved SIR. Consequently, our study could be effectively used for monitoring and analyzing the variation in the Arctic sea ice.

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Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion

Cheng¹,

Aydoğdu

Rampal

et al. 2020

Oceans

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We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.

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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Cited by 4 publications

References 49 publications

Calibration of sea ice drift forecasts using random forest algorithms

Calibration of sea ice drift forecasts using random forest algorithms

Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations

Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion

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