Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

Rostosky, Philip; Spreen, Gunnar; Gerland, Sebastian; Huntemann, Marcus; Mech, Mario

doi:10.1029/2019jc015465

Cited by 12 publications

(4 citation statements)

References 78 publications

(140 reference statements)

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“…The assumed correlation lengths are taken from Rostosky et al. (2020) based on ice core data, where the FYI values are in agreement with values reported by Tucker et al. (1992).…”

Section: Methodssupporting

confidence: 56%

“…Note that the parameterization for ice assumes scattering at spheres and calculates an effective permittivity (for FYI done assuming randomly oriented needles), even though the dielectric mixture models assumes randomly oriented ellipsoids for FYI. The assumed correlation lengths are taken from Rostosky et al (2020) based on ice core data, where the FYI values are in agreement with values reported by Tucker et al (1992).…”

Section: Ice Parametersmentioning

confidence: 63%

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Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Rückert,

Huntemann,

Tonboe

et al. 2023

Earth and Space Science

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Monitoring surface and atmospheric parameters—like water vapor—is challenging in the Arctic, despite the daily Arctic‐wide coverage of spaceborne microwave radiometer data. This is mainly due to the difficulties in characterizing the sea ice surface emission: sea ice and snow microwave emission is high and highly variable. There are very few data sets combining relevant in situ measurements with co‐located remote sensing data, which further complicates the development of accurate retrieval algorithms. Here, we present a multi‐parameter retrieval based on the inversion of a forward model for both, atmosphere and surface, for non‐melting conditions. The model consists of a layered microwave emission model of snow and ice. Since snow scattering and emission effects, as well as temperature gradients, are taken into account, a high variability in brightness temperatures can be simulated. For ocean regions and the atmosphere existing parameterized forward models are used. By using optimal estimation, the forward model can be inverted allowing for the simultaneous and consistent retrieval of nine variables: integrated water vapor, liquid water path, sea ice concentration, multi‐year ice fraction, snow depth, snow‐ice interface temperature and snow‐air interface temperature as well as sea‐surface temperature and wind speed (over open ocean). In addition, the method provides retrieval uncertainty estimates for each retrieved parameter. To evaluate the forward model as well as the retrieval, we use the extensive data sets acquired during the year‐long Arctic expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (2019–2020) as a reference.

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“…The assumed correlation lengths are taken from Rostosky et al. (2020) based on ice core data, where the FYI values are in agreement with values reported by Tucker et al. (1992).…”

Section: Methodssupporting

confidence: 56%

Section: Ice Parametersmentioning

confidence: 63%

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Rückert,

Huntemann,

Tonboe

et al. 2023

Earth and Space Science

Self Cite

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“…The satellite-based sea ice pathways were determined with a drift analysis system called IceTrack. The system traces sea ice forward in time using a combination of satellite-derived, low-resolution drift products (Krumpen et al, 2019Belter et al, 2021;Wilson et al, 2021 (Rozman et al, 2011;Krumpen et al, 2013).…”

Section: Lagrangian Sea Ice Trackingmentioning

confidence: 99%

MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

et al. 2021

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Abstract. We combine satellite data products to provide a first and general overview of the physical sea ice conditions along the drift of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a comparison with previous years (2005–2006 to 2018–2019). We find that the MOSAiC drift was around 20 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents–Kara–Laptev sea region between January and March. In winter (October–April), satellite observations show that the sea ice in the vicinity of the Central Observatory (CO; 50 km radius) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea ice concentration, lead frequency and snow thickness during winter months were close to the long-term mean with little variability. With the onset of spring and decreasing distance to the Fram Strait, variability in ice concentration and lead activity increased. In addition, the frequency and strength of deformation events (divergence, convergence and shear) were higher during summer than during winter. Overall, we find that sea ice conditions observed within 5 km distance of the CO are representative for the wider (50 and 100 km) surroundings. An exception is the ice thickness; here we find that sea ice within 50 km radius of the CO was thinner than sea ice within a 100 km radius by a small but consistent factor (4 %) for successive monthly averages. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

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“…The observation error standard deviation, which follows those found in Zhang et al (2018), is 15% of the true values of SIC and 0.1 m for SIT. The observation error standard deviation is 10% of the true values of SNWD (Rostosky et al, 2020) and 1.5 • C for SIST (Hall et al, 2015). The locations for all synthetic observation types were based on 10-second CryoSat-2 locations, which provides a realistic observational network for testing (Fig.…”

Section: Perfect Model Ossesmentioning

confidence: 99%

Exploring Non-Gaussian Sea Ice Characteristics via Observing System Simulation Experiments

Riedel

Anderson

2023

Preprint

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Abstract. The Arctic is warming at a faster rate compared to the globe on average, commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and gathered attention recently due to the decline in summer sea ice extent. Data assimilation (DA) is the act of combining observations with prior forecasts to obtain a more accurate model state. Sea ice poses a unique challenge for DA because sea ice variables have bounded distributions, leading to non-Gaussian distributions. The non-Gaussian nature violates Gaussian assumptions built into DA algorithms. This study configures different observing system simulated experiments (OSSEs) to find the optimal sea ice and snow observation subset for assimilation to produce the most accurate analyses and forecasts. Findings indicate that not assimilating sea ice concentration observations while assimilating snow depth observation produced the best sea ice and snow forecasts. A simplified DA experiment helped demonstrate that the DA solution is biased when assimilating sea ice concentration observations. The biased DA solution is related to the observation error distribution being a truncated normal distribution and the assumed observation likelihood is normal for the DA method. Additional OSSEs show that using a non-parametric DA method does not alleviate the non-Gaussian effects of the sea ice concentration observations, and assimilating sea ice surface temperatures have a positive impact on snow updates. Lastly, it is shown that perturbed sea ice model parameters, used to create additional ensemble spread in the free forecasts, lead to a year-long negative snow volume bias.

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Modeling the Microwave Emission of Snow on Arctic Sea Ice for Estimating the Uncertainty of Satellite Retrievals

Cited by 12 publications

References 78 publications

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

Exploring Non-Gaussian Sea Ice Characteristics via Observing System Simulation Experiments

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