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Stratospheric Gravity waves in AIRS observations and high-resolution models

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Atmospheric gravity waves vary hugely in scale; with horizontal wavelengths ranging from a few to thousands of km. Typically, gravity waves are smaller than model grid-size and as a result, their effects are parametrised instead of being explicitly resolved. However, recent computational and scientific advancements have allowed for the development of higher resolution global-scale models. These models have horizontal resolutions of order a few km with around 1km vertical resolution in the stratosphere. At such scales, it should in principle be possible to accurately simulate the majority of GWs without relying on parametrisation.In this work, we use data from three models from the DYAMOND Initiative (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains). Specifically, IFS (Integrated Forecast System – produced by ECMWF) at 4km horizontal resolution, ICON (Icosahedral NonHydrostatic) at 5km horizontal resolution and GEOS (Goddard Earth Observing System model) at 3km horizontal resolution. All models are initialised with the same initial conditions and are free running for 40 days. We then compare the properties of resolved gravity waves with observations from the AIRS instrument (Atmospheric InfraRed Sounder) onboard NASA’s Aqua satellite. Importantly, we note that the AIRS observations are limited by the ‘observational filter’, wherein each observing system can only `see' a limited portion of the full GW spectrum. To account for this, an important step in this work is in resampling the model atmospheres as though viewed by the AIRS instrument.We compare the representation of resolved waves in the three models and AIRS observations across 40-days in Austral winter. We use a recently developed machine learning wave identification method to separate gravity waves in the dataset and determine gravity wave occurrence frequencies. Next, we use spectral analysis to estimate gravity wave amplitudes, wavelengths and calculate momentum fluxes and the intermittency of gravity waves. This work provides an essential evaluation of the accuracy of current gravity wave modelling capabilities.
Title: Stratospheric Gravity waves in AIRS observations and high-resolution models
Description:
Atmospheric gravity waves vary hugely in scale; with horizontal wavelengths ranging from a few to thousands of km.
Typically, gravity waves are smaller than model grid-size and as a result, their effects are parametrised instead of being explicitly resolved.
However, recent computational and scientific advancements have allowed for the development of higher resolution global-scale models.
These models have horizontal resolutions of order a few km with around 1km vertical resolution in the stratosphere.
At such scales, it should in principle be possible to accurately simulate the majority of GWs without relying on parametrisation.
In this work, we use data from three models from the DYAMOND Initiative (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains).
Specifically, IFS (Integrated Forecast System – produced by ECMWF) at 4km horizontal resolution, ICON (Icosahedral NonHydrostatic) at 5km horizontal resolution and GEOS (Goddard Earth Observing System model) at 3km horizontal resolution.
All models are initialised with the same initial conditions and are free running for 40 days.
We then compare the properties of resolved gravity waves with observations from the AIRS instrument (Atmospheric InfraRed Sounder) onboard NASA’s Aqua satellite.
Importantly, we note that the AIRS observations are limited by the ‘observational filter’, wherein each observing system can only `see' a limited portion of the full GW spectrum.
To account for this, an important step in this work is in resampling the model atmospheres as though viewed by the AIRS instrument.
We compare the representation of resolved waves in the three models and AIRS observations across 40-days in Austral winter.
We use a recently developed machine learning wave identification method to separate gravity waves in the dataset and determine gravity wave occurrence frequencies.
Next, we use spectral analysis to estimate gravity wave amplitudes, wavelengths and calculate momentum fluxes and the intermittency of gravity waves.
This work provides an essential evaluation of the accuracy of current gravity wave modelling capabilities.

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