High-Resolution Simulation of Polar Lows over Norwegian and Barents Seas Using the COSMO-CLM and ICON Models for the 2019–2020 Cold Season

Revokatova, A. P.; Nikitin, Michail; Rivin, G. S.; Rozinkina, I. A.; Nikitin, A. E.; Tatarinovich, Ekaterina

doi:10.3390/atmos12020137

Cited by 10 publications

(10 citation statements)

References 36 publications

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“…The PL case study was documented by Revokatova et al. (2021) as well. PLs are small, but intense mesoscale cyclones forming in cold air masses over the ocean with a maximum extent of 600 km diameter (Rojo et al., 2015; Stoll et al., 2018).…”

Section: Results: Data Assimilation Diagnosticsmentioning

confidence: 88%

“…The AA background valid at 26 November 2019 12 UTC, and the result of a 3 hr forecast initiated from the 09 UTC analysis has been investigated. The PL case study was documented by Revokatova et al (2021) as well. PLs are small, but intense mesoscale cyclones forming in cold air masses over the ocean with a maximum extent of 600 km diameter (Rojo et al, 2015;Stoll et al, 2018).…”

Section: Model Wind Variabilitymentioning

confidence: 99%

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Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

Mile

Azad

Marseille

2022

Geophysical Research Letters

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The wind profiles measured by the Aeolus satellite mission are unique and valuable wind products for numerical weather prediction. However, just like any other remote sensing measurements, it has a footprint extending over a number of model grid cells, especially in high-resolution mesoscale models. The spatial representation of Aeolus Rayleigh-clear observations has been assessed in a mesoscale limited-area model called AROME-Arctic by using a footprint observation operator. The footprint representation provides small, but consistent reduction in the standard deviation of background departures and an observing system experiment comparing the default and the footprint operator based on Aeolus data assimilation showed a small positive impact on AROME-Arctic forecasts. Plain Language SummaryThe Aeolus satellite mission provides unique and pioneering wind measurements in a global distribution that helps to unveil the wind structures of sparsely observed regions like the Arctic. These observations are also very useful for improving weather prediction, however, the use of Aeolus data is usually conservative and imperfect. In this study, the added value and advanced use of Aeolus wind observations are presented and discussed in an Arctic weather prediction model.

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Section: Results: Data Assimilation Diagnosticsmentioning

confidence: 88%

Section: Model Wind Variabilitymentioning

confidence: 99%

Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

Mile

Azad

Marseille

2022

Geophysical Research Letters

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“…Indeed, many studies found large discrepancies between different observed gridded datasets despite that they are all based on weather stations (Prein & Gobiet, 2017; Zittis et al, 2017). This is especially true in low‐density populated polar (Revokatova et al, 2021) and mountainous regions where weather stations are sparsely distributed. In addition, weather stations are affected by precipitation undercatch (Gao, Chen, & Jiang, 2020; Neff, 1977; Pollock et al, 2018), which is even more severe for snow measurements in winter (Fortin et al, 2008; Gao, Chen, & Jiang, 2020; Hughes et al, 2020; Rasmussen et al, 2012).…”

Section: Methodology and Principles Behind Cprcmsmentioning

confidence: 99%

“…For more than a decade, CPRCMs have been used to simulate regional surface wind over complex terrain (Jiménez et al, 2010), offshore (Jiménez et al, 2015), and during storms (Schaaf & Feser, 2018; Platonov & Kislov, 2020) and polar lows (Revokatova et al, 2021). CPRCMs were deemed quite useful to identify regions with high wind energy potential (Carvalho et al, 2012) and where observational coverage could be improved (Jiménez et al, 2010).…”

Section: Cprcm Benefits For Impact Studiesmentioning

confidence: 99%

Convection‐permitting modeling with regional climate models: Latest developments and next steps

Lucas‐Picher

Argüeso

Brisson

et al. 2021

WIREs Climate Change

134

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Approximately 10 years ago, convection-permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1-4 km) decadal-long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal ($1 km; $1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.

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“…Spherical icosahedral grid is often used as the basic framework of atmospheric models, such as the ICosahedral Nonhydrostatic model (ICON) [1-3], Models for Predication Across Scales (MPAS) [4], the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) [5], DYNAMICO [6], Ocean-Land-Atmosphere Model (OLAM) [7,8], etc. These models are wieldy used for polar lows simulation [9], monsoon simulation [10], ocean tides dissipation [11], etc. Recently, the Spectral Radiation Transport Model for Aerosol Species coupled with NICAM (NICAM-SPRINTARS) [12] has been developed for studying and simulating atmosphere-aerosols interactions and their effects on atmospheric pollution and climate change.…”

Section: Introductionmentioning

confidence: 99%

An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

et al. 2021

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The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation.

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High-Resolution Simulation of Polar Lows over Norwegian and Barents Seas Using the COSMO-CLM and ICON Models for the 2019–2020 Cold Season

Cited by 10 publications

References 36 publications

Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

Convection‐permitting modeling with regional climate models: Latest developments and next steps

An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

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