Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

Ahn, Jihye; Hong, Sungwook; Cho, Jaeil; Lee, Yang-Won; Lee, Ho-Sang

doi:10.3390/rs6065520

Cited by 17 publications

(10 citation statements)

References 47 publications

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“…In order to model response of TWS to evapotranspiration and temperature changes, instantaneous moisture flux (equivalent to evapotranspiration [29]) and land surface air temperature (LSAT) fields were retrieved from ERA-Interim data system. Additionally, precipitation (P) minus evaporation (E), also known as net-precipitation, derived from the atmospheric water budget [24] was used to further characterize TWS response to atmospheric moisture conditions over the region, while soil moisture, also obtained from ERA-Interim was used to model land surface moisture conditions.…”

Section: Era-interim Data Systemmentioning

confidence: 99%

A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

Ferreira

Andam-Akorful

Dannouf

et al. 2019

Water

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Remotely sensed terrestrial water storage changes (TWSC) from the past Gravity Recovery and Climate Experiment (GRACE) mission cover a relatively short period (≈15 years). This short span presents challenges for long-term studies (e.g., drought assessment) in data-poor regions like West Africa (WA). Thus, we developed a Nonlinear Autoregressive model with eXogenous input (NARX) neural network to backcast GRACE-derived TWSC series to 1979 over WA. We trained the network to simulate TWSC based on its relationship with rainfall, evaporation, surface temperature, net-precipitation, soil moisture, and climate indices. The reconstructed TWSC series, upon validation, indicate high skill performance with a root-mean-square error (RMSE) of 11.83 mm/month and coefficient correlation of 0.89. The validation was performed considering only 15% of the available TWSC data not used to train the network. More so, we used the total water content changes (TWCC) synthesized from Noah driven global land data assimilation system in a simulation under the same condition as the GRACE data. The results based on this simulation show the feasibility of the NARX networks in hindcasting TWCC with RMSE of 8.06 mm/month and correlation coefficient of 0.88. The NARX network proved robust to adequately reconstruct GRACE-derived TWSC estimates back to 1979.

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Section: Era-interim Data Systemmentioning

confidence: 99%

A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

Ferreira

Andam-Akorful

Dannouf

et al. 2019

Water

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“…Sea ice extent has displayed an important decline in the last decades (Cavalieri and Parkinson, 2012), as it can be observed with remote sensing data, such as passive microwave data, which have been acquired since 1978. Another important source of information on sea ice cover is model predictions which come from deterministic models (Hunke et al, 2017;Rousset et al, 2015;Weaver et al, 2001), based on dynamical and thermodynamic equations evolving in synergy inside a modelling framework, or from statistical models, based on statistical tools such as simple and multiple regression analysis (Ahn et al, 2014;Drobot, 2007;Pavlova et al, 2014) to explain an expected sea ice parameter value (e.g. sea ice extent, sea ice area, sea ice concentration, sea ice thickness).…”

Section: Methodsmentioning

confidence: 99%

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

2019

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Abstract. A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were completed using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From this research, reliable information helping to understand sea ice evolution in space and in time is available to stakeholders. However, no research has, until present, assessed the evolution of sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters of the Hudson Bay system in northeast Canada. Grid-cell-scale models are based on the generalized beta distribution and generated at a weekly temporal resolution. Results showed coherence with the Canadian Ice Service 1981–2010 Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice-free season duration, for any given location in the simulation domain. Results obtained in this project pave the way towards various analyses on sea ice concentration spatio-temporal distribution patterns that would gain in terms of information content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.

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“…Sea ice extent has displayed an important decline in the last decades (Cavalieri and Parkinson, 2012;Markus et al, 2009;Meier et al, 2007;Cavalieri, 2002, 2008;Parkinson et al, 1999), as it can be observed with remote sensing images acquired since 1978. Another important source of information on the sea ice cover are model predictions which come from deterministic models (Hunke et al, 2017;Rousset et al, 2015;Weaver et al, 2001), based on dynamical and thermodynamic equations evolving in synergy inside a modelling framework, or from statistical models, based on statistical tools such as simple and multiple regression analysis (Ahn et al, 2014;Drobot, 2007;Pavlova et al, 2014) to explain an expected sea ice parameter value.…”

Section: Methodsmentioning

confidence: 99%

IcePAC – a Probabilistic Tool to Study Sea Ice Spatiotemporal Dynamic: Application to the Hudson Bay area, Northeastern Canada

Gignac

Bernier

Chokmani

2018

Preprint

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Abstract. A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were realized using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From these research, reliable information helping to understand sea ice evolution in space and in time are available to stakeholders. However, no research has, as of today, assessed the evolution of the sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from EUMETSAT OSI-409 product, to study the sea ice spatiotemporal behaviour in the waters of the Hudson Bay System in Northeastern Canada. Pixel scale models are based on the generalized Beta distribution and generated at a weekly temporal resolution. Results showed coherent with the Canadian Ice Service 1980–2010 Sea Ice Climatic Atlas average freeze-up and meltdown dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice free season duration, for any given location on the simulation domain. This innovative research opens a path towards various analyses on sea ice dynamics that would gain in informative content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool.

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Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

Cited by 17 publications

References 47 publications

A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

IcePAC – a Probabilistic Tool to Study Sea Ice Spatiotemporal Dynamic: Application to the Hudson Bay area, Northeastern Canada

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