new publications from 2024

Author: nikudarafshi

content/Publications/Papers/Achatz_msgwam.md

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+---
+date: 2024-07-01
+description: " "
+featured_image: "/images/Data_Images/Achatz_msgwam.png"
+title: "MS-GWaM: A Three-Dimensional Transient Gravity Wave Parametrization for Atmospheric Models"
+title2: " "
+---
+## Authors:
+Georg S. Voelker, Gergely Bölöni, Young-Ha Kim, Günther Zängl, and ***Ulrich Achatz***
+
+[Read the full paper here](https://doi.org/10.1175/JAS-D-23-0153.1)
+## Abstract:
+Parameterizations for internal gravity waves in atmospheric models are traditionally subject to a number of simplifications. Most notably, they rely on both neglecting wave propagation and advection in the horizontal direction (single-column assumption) and an instantaneous balance in the vertical direction (steady-state assumption). While these simplifications are well justified to cover some essential dynamic effects and keep the computational effort small, it has been shown that both mechanisms are potentially significant. In particular, the recently introduced Multiscale Gravity Wave Model (MS-GWaM) successfully applied ray-tracing methods in a novel type of transient but columnar internal gravity wave parameterization (MS-GWaM-1D). We extend this concept to a three-dimensional version of the parameterization (MS-GWaM-3D) to simulate subgrid-scale nonorographic internal gravity waves. 
+<!--more-->
+The resulting global wave model—implemented into the weather forecast and climate code Icosahedral Nonhydrostatic (ICON)—contains three-dimensional transient propagation with accurate flux calculations, a latitude-dependent background source, and convectively generated waves. MS-GWaM-3D helps reproduce expected temperature and wind patterns in the mesopause region in the climatological zonal mean state and thus proves a viable internal gravity wave (IGW) parameterization. Analyzing the global wave action budget, we find that horizontal wave propagation is as important as vertical wave propagation. The corresponding wave refraction includes previously missing but well-known effects such as wave refraction into the polar jet streams. On a global scale, three-dimensional wave refraction leads to a horizontal flow-dependent redistribution of waves such that the structures of the zonal mean wave drag and consequently the zonal mean winds are modified.
+
+## Plain Language Summary:
+This study introduces MS-GWaM, a three-dimensional model designed to simulate atmospheric gravity waves generated by mountains. Unlike traditional models that assume steady-state conditions, MS-GWaM accounts for transient, time-dependent effects, providing a more accurate representation of how these waves interact with the atmosphere. The model successfully captures phenomena such as wind reversals at high altitudes caused by wave breaking, which steady-state models often miss. This advancement enhances our understanding of atmospheric dynamics and improves the accuracy of weather and climate predictions.

content/Publications/Papers/Chew_Unstructured_Grids.md

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+---
+date: 2024-08-01
+description: " "
+featured_image: "/images/Data_Images/chew_unstructured.png"
+title: "A Constrained Spectral Approximation of Subgrid-Scale Orography on Unstructured Grids"
+title2: " "
+---
+## Authors:
+***Ray Chew***, Stamen Dolaptchiev, Maja-Sophie Wedel, and ***Ulrich Achatz***
+
+[Read the full paper here](https://doi.org/10.1029/2024MS004361)
+## Abstract:
+The representation of subgrid-scale orography is a challenge in the physical parameterization of orographic gravity-wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non-quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale-aware spectrum of subgrid-scale orography on unstructured geodesic grids.
+<!--more-->
+The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well-known least-squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least-squares spectral analysis in representing the physical energy of a spectrum. Studies involving real-world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work.
+
+## Plain Language Summary:
+Wind flow over terrain has wide-ranging influences on atmospheric processes. Meteorologists want to include this effect in their weather forecasts but encounter computational limitations. For global weather forecasting, terrain features are relatively small and usually not explicitly represented in forecast models. An ongoing research question is how best to represent these small-scale features in forecast models. However, a few difficulties arise: (a) We want to encode as much information about the terrain with as simple a representation as possible. (b) Some forecast models represent the globe with an icosahedron. Therefore, terrain information must be encoded within the triangular or hexagonal cells. (c) The information encoded has to change if the size of the triangles or hexagons changes. In this work, we present a novel method to overcome the three difficulties mentioned above. We can compress terrain information from over 50,000 to below 100 data points that can be used for climate simulations. When the relative effect of the encoded terrain on atmospheric processes is considered, the method has good accuracy despite the severe constraints and data compression, with a 10% error bound. Apart from representing terrain in weather forecasting, this method has potential applications for generic data analysis.
+
+## Key Points:
+1. We introduce a novel scale-aware spectral approximation method for subgrid-scale orographic data on non-quadrilateral geodesic grids
+2. The method yields physically sound results and achieves reasonable error scores when approximating real-world orographic spectra
+3. The method features over two orders of magnitude compression in complexity and has potential applications in generic spectral analyses

content/Publications/Papers/Connelly_Forest.md

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+---
+date: 2024-08-25
+description: " "
+featured_image: "/images/Data_Images/Connelly_forest.png"
+title: "Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection
+"
+title2: " "
+---
+## Authors:
+***David S. Connelly*** and ***Edwin P. Gerber***
+
+[Read the full paper here](https://doi.org/10.1029/2023MS004184)
+## Abstract:
+We train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics-based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network.
+<!--more-->
+Integrations with all three data-driven emulators successfully capture the Quasi-Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1,200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data-driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario.
+
+## Plain Language Summary:
+Parameterizations are reduced-complexity models that estimate the effects of physical processes smaller than what can be resolved by the grid of a weather or climate model. While necessary for realistic simulations, they are a source of uncertainty in climate projections. Recently, machine learning has been used to augment or replace conventional parameterizations of atmospheric gravity waves, a type of motion by which disturbances near the Earth's surface can affect the wind higher up. We compare several machine learning approaches to the gravity wave parameterization problem. In particular, we test neural networks against random and boosted forests, which are built around flowchart-like models called regression trees. We find that boosted forests, though not widely used for climate model parameterization, are especially successful, scoring as well as or better than neural networks on various performance metrics. We then provide proof-of-concept of a novel method to retrain the boosted forest so that it uses its input data more in line with the physics of the system, and show that this technique improves the forest's behavior when used together with an atmospheric model.
+
+## Key Points:
+1. Two kinds of regression forest emulate a gravity wave parameterization offline and online, with boosted forests outperforming random forests
+2. Relative to a neural network benchmark, the boosted forest exhibits similar online skill and ability to generalize to new climates
+3. Feature importance analysis informs a retraining procedure to improve online behavior of data-driven parameterizations

content/Publications/Papers/Gupta_Extratropical.md

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+---
+date: 2024-08-12
+description: " "
+featured_image: "/images/Data_Images/Gupta_extratropics.png"
+title: "Insights on Lateral Gravity Wave Propagation in the Extratropical Stratosphere From 44 Years of ERA5 Data"
+title2: " "
+---
+## Authors:
+***Aman Gupta***, ***Aditi Sheshadri***, ***M. Joan Alexander***, and Thomas Birner
+
+[Read the full paper here](https://doi.org/10.1029/2024GL108541)
+## Abstract:
+The study presents (a) a 44-year wintertime climatology of resolved gravity wave (GW) fluxes and forcing in the extratropical stratosphere using ERA5, and (b) their composite evolution around gradual (final warming) and abrupt (sudden warming) transitions in the wintertime circulation, focusing on lateral fluxes. The transformed Eulerian mean equations are leveraged to provide a glimpse of the importance of GW lateral propagation (i.e., horizontal propagation) toward driving the wintertime stratospheric circulation by analyzing the relative contribution of the vertical versus meridional flux dissipation. 
+<!--more-->
+The relative contribution from lateral propagation is found to be notable, especially in the Austral winter stratosphere where lateral (vertical) momentum flux convergence provides a peak climatological forcing of up to −0.5 (−3.5) m/s/day around 60°S at 40–45 km altitude. Prominent lateral propagation in the wintertime midlatitudes also contributes to the formation of belts of GW activity in both hemispheres.
+
+## Plain Language Summary:
+Atmospheric Gravity Waves (GWs) are atmospheric disturbances created by processes like convection, thunderstorms, flow over topography, etc. These waves can have wavelengths as small as 1 km to as large as 1,000–2,000 km. Most atmospheric GWs are not resolved in coarse-resolution climate models. As a result, they are represented in climate models using parameterizations, which are approximate models that can be subject to various idealizations. One such idealization is the assumption of pure vertical propagation of GWs. In this study, we use multidecadal records from ERA5 reanalysis—which combines a high-resolution model with assimilated observations to produce a close-to-observed state of the atmosphere and resolves some of these GWs—to quantify the impact of this assumption on the mean state of the extratropical stratosphere. This is done by extracting GWs from ERA5 data, computing horizontal momentum fluxes carried by these waves, and comparing the net acceleration/deceleration provided by these fluxes on the peak winter stratospheric circulation and key episodes of abrupt changes in the circulation. Analysis using ERA5 reveals that horizontal propagation of GWs can be notable in the midlatitude stratosphere, highlighting the need to develop GW parameterizations that represent this essential property of atmospheric GWs.
+
+## Key Points:
+1. Climatology of lateral fluxes from ERA5 shows substantial lateral propagation of gravity waves in both hemispheres
+2. Contribution of both lateral and vertical GW fluxes toward zonal wind forcing is the same order of magnitude
+3. Abrupt changes in GW forcing in the upper stratosphere around sudden stratospheric warmings persist even 20 days following the event

content/Publications/Papers/Gupta_ML.md

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+---
+date: 2024-11-13
+description: " "
+featured_image: "/images/Data_Images/Gupta_ml.png"
+title: "Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation"
+title2: " "
+---
+## Authors:
+***Aman Gupta***, ***Aditi Sheshadri***, Sujit Roy, Vishal Gaur, Manil Maskey, and Rahul Ramachandran
+
+[Read the full paper here](https://doi.org/10.48550/arXiv.2406.14775)
+## Abstract:
+Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. 
+<!--more-->
+The most drastic of these approximations is the "single-column approximation" which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.
+
+## Plain Language Summary:
+This study presents data on atmospheric gravity waves (GWs) obtained from a high-resolution global simulation conducted by the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulation, referred to as a "nature run," was performed at a 1 km horizontal resolution over a four-month period. The dataset includes momentum fluxes associated with GWs, which are crucial for understanding their impact on atmospheric dynamics. By providing detailed information on GW momentum fluxes, this dataset aims to enhance the representation of GWs in weather and climate models, leading to improved predictions and a better grasp of atmospheric processes. 

content/Publications/Papers/Gupta_Mom_Flux.md

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+---
+date: 2024-08-21
+description: " "
+featured_image: "/images/Data_Images/Gupta_momflux.png"
+title: "Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System"
+title2: " "
+---
+## Authors:
+***Aman Gupta***, ***Aditi Sheshadri***, and Valentine Anantharaj
+
+[Read the full paper here](https://doi.org/10.1038/s41597-024-03699-x)
+## Abstract:
+Progress in understanding the impact of mesoscale variability, including gravity waves (GWs), on atmospheric circulation is often limited by the availability of global fine-resolution observations and simulated data. This study presents momentum fluxes due to atmospheric GWs extracted from four months of an experimental “nature run", integrated at a 1 km resolution (XNR1K) using the Integrated Forecast System (IFS) model. Helmholtz decomposition is used to compute zonal and meridional flux of vertical momentum from ~1.5 petabytes of data; quantities often emulated by climate model parameterization of GWs.
+<!--more-->
+The fluxes are validated using ERA5 reanalysis, both during the first week after initialization and over the boreal winter period from November 2018 to February 2019. The agreement between reanalysis and IFS demonstrates its capability to generate reliable flux distributions and capture mesoscale dynamic variability in the atmosphere. The dataset could be valuable in advancing our understanding of GW-planetary wave interactions, GW evolution around atmospheric extremes, and as high-quality training data for machine learning (ML) simulation of GWs.
+
+## Plain Language Summary:
+This study presents data on atmospheric gravity waves (GWs) obtained from a high-resolution global simulation conducted by the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulation, referred to as a "nature run," was performed at a 1 km horizontal resolution over a four-month period. The dataset includes momentum fluxes associated with GWs, which are crucial for understanding their impact on atmospheric dynamics. By providing detailed information on GW momentum fluxes, this dataset aims to enhance the representation of GWs in weather and climate models, leading to improved predictions and a better grasp of atmospheric processes.