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Dorina Surcel and René Laprise

Abstract

Variable-resolution grids are used in global atmospheric models to improve the representation of regional scales over an area of interest: they have reduced computational cost compared to uniform high-resolution grids, and avoid the nesting issues of limited-area models. To address some concerns associated with the stretching and anisotropy of the variable-resolution computational grid, a general convolution filter operator was developed.

The convolution filter that was initially applied in Cartesian geometry in a companion paper is here adapted to cylindrical polar coordinates as an intermediate step toward spherical polar latitude–longitude grids. Both polar grids face the so-called “pole problem” because of the convergence of meridians at the poles.

In this work the authors will present some details related to the adaptation of the filter to cylindrical polar coordinates for both uniform as well as stretched grids. The results show that the developed operator is skillful in removing the extraneous fine scales around the pole, with a computational cost smaller than that of common polar filters. The results on a stretched grid for vector and scalar test functions are satisfactory and the filter’s response can be optimized for different types of test function and noise one wishes to remove.

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Dorina Surcel and René Laprise

Abstract

Global climate models with variable resolution are effective means to represent regional scales over an area of interest while avoiding the nesting issues of limited-area models. The stretched-grid approach provides a dynamical downscaling approach that naturally allows two-way interactions between the regional and global scales of motion. Concentrating the resolution over a subset of the earth’s surface increases computational efficiency and reduces the computational costs compared to global uniform high-resolution models; however, it does not come free of some problems related to the variation of resolution.

To address the issues associated with the stretching and anisotropy of the computational grid, a general convolution filter with a flexible response function is developed. The main feature of this filter is to locally remove scales shorter than a user-prescribed spatially varying length scale. The filtering effectiveness and computational efficiency of the filter can be custom tailored by an appropriate compromise between the filtering response and the width of the convolution stencil. This approach has been tested in one- and two-dimensional Cartesian geometry. It is shown that an effective filter can be obtained using a limited spatial stencil for the convolution to reduce computational cost, and that an adjustable spatially variable and nearly isotropic response can be obtained for application on variable grids.

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Bruce Brasnett and Dorina Surcel Colan

Abstract

Experiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows that the VIIRS and AMSR2 datasets yield analyses with similar global average errors, with the VIIRS analysis performing better during some seasons and the AMSR2 analysis performing better in others. Seasonal cloudiness in some regions diminishes the availability of VIIRS retrievals, resulting in better performance by the AMSR2 analysis. Both datasets were assimilated together along with data from the Advanced Very High Resolution Radiometer (AVHRR), ice data, and in situ data in an updated version of the CMC analysis produced on a 0.1° grid. Verification against independent data shows that the new analysis performed very well, with global average standard deviation consistently better than that of the international Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) real-time system. This analysis is shown to outperform the currently operational CMC SST analysis, with most of the improvement being due to its assimilation of the VIIRS and AMSR2 retrievals and a further small gain being due to changes to the analysis methodology (including higher resolution).

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Gregory C. Smith, Jean-Marc Bélanger, François Roy, Pierre Pellerin, Hal Ritchie, Kristjan Onu, Michel Roch, Ayrton Zadra, Dorina Surcel Colan, Barbara Winter, Juan-Sebastian Fontecilla, and Daniel Deacu

Abstract

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

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