Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Leibniz Institute of Atmospheric Physics, University of Rostock, Schlossstraße 6, 18225 Kühlungsborn, Germany
Related authors
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Preprint under review for GMD
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Juliana Jaen, Toralf Renkwitz, Jorge L. Chau, Maosheng He, Peter Hoffmann, Yosuke Yamazaki, Christoph Jacobi, Masaki Tsutsumi, Vivien Matthias, and Chris Hall
Ann. Geophys., 40, 23–35, https://doi.org/10.5194/angeo-40-23-2022, https://doi.org/10.5194/angeo-40-23-2022, 2022
Short summary
Short summary
To study long-term trends in the mesosphere and lower thermosphere (70–100 km), we established two summer length definitions and analyzed the variability over the years (2004–2020). After the analysis, we found significant trends in the summer beginning of one definition. Furthermore, we were able to extend one of the time series up to 31 years and obtained evidence of non-uniform trends and periodicities similar to those known for the quasi-biennial oscillation and El Niño–Southern Oscillation.
Tarique A. Siddiqui, Astrid Maute, Nick Pedatella, Yosuke Yamazaki, Hermann Lühr, and Claudia Stolle
Ann. Geophys., 36, 1545–1562, https://doi.org/10.5194/angeo-36-1545-2018, https://doi.org/10.5194/angeo-36-1545-2018, 2018
Short summary
Short summary
Extreme meteorological events such as SSWs induce variabilities in the ionosphere by modulating the atmospheric tides, and these variabilities can be comparable to a moderate geomagnetic storm. The equatorial electrojet (EEJ) is a narrow ribbon of current flowing over the dip equator in the ionosphere and is particularly sensitive to tidal changes. In this study, we use ground-magnetic measurements to investigate the semidiurnal solar and lunar tidal variabilities of the EEJ during SSWs.
Olawale Bolaji, Oluwafisayo Owolabi, Elijah Falayi, Emmanuel Jimoh, Afolabi Kotoye, Olumide Odeyemi, Babatunde Rabiu, Patricia Doherty, Endawoke Yizengaw, Yosuke Yamazaki, Jacob Adeniyi, Rafiat Kaka, and Kehinde Onanuga
Ann. Geophys., 35, 123–132, https://doi.org/10.5194/angeo-35-123-2017, https://doi.org/10.5194/angeo-35-123-2017, 2017
Short summary
Short summary
Movement of plasma to higher latitudes by EIA is known to relate to eastward electric field/EEJ and thermospheric meridional neutral wind. Experiments from GPS measurements that unveil thermospheric meridional neutral wind effect on plasma transportation in the F region are very few compared with electric field/EEJ. This work includes examples of thermospheric meridional neutral wind effects on GPS TEC measurements and their roles in transportation of plasma compared to electric field/EEJ.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Preprint under review for GMD
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Juliana Jaen, Toralf Renkwitz, Jorge L. Chau, Maosheng He, Peter Hoffmann, Yosuke Yamazaki, Christoph Jacobi, Masaki Tsutsumi, Vivien Matthias, and Chris Hall
Ann. Geophys., 40, 23–35, https://doi.org/10.5194/angeo-40-23-2022, https://doi.org/10.5194/angeo-40-23-2022, 2022
Short summary
Short summary
To study long-term trends in the mesosphere and lower thermosphere (70–100 km), we established two summer length definitions and analyzed the variability over the years (2004–2020). After the analysis, we found significant trends in the summer beginning of one definition. Furthermore, we were able to extend one of the time series up to 31 years and obtained evidence of non-uniform trends and periodicities similar to those known for the quasi-biennial oscillation and El Niño–Southern Oscillation.
Tarique A. Siddiqui, Astrid Maute, Nick Pedatella, Yosuke Yamazaki, Hermann Lühr, and Claudia Stolle
Ann. Geophys., 36, 1545–1562, https://doi.org/10.5194/angeo-36-1545-2018, https://doi.org/10.5194/angeo-36-1545-2018, 2018
Short summary
Short summary
Extreme meteorological events such as SSWs induce variabilities in the ionosphere by modulating the atmospheric tides, and these variabilities can be comparable to a moderate geomagnetic storm. The equatorial electrojet (EEJ) is a narrow ribbon of current flowing over the dip equator in the ionosphere and is particularly sensitive to tidal changes. In this study, we use ground-magnetic measurements to investigate the semidiurnal solar and lunar tidal variabilities of the EEJ during SSWs.
Olawale Bolaji, Oluwafisayo Owolabi, Elijah Falayi, Emmanuel Jimoh, Afolabi Kotoye, Olumide Odeyemi, Babatunde Rabiu, Patricia Doherty, Endawoke Yizengaw, Yosuke Yamazaki, Jacob Adeniyi, Rafiat Kaka, and Kehinde Onanuga
Ann. Geophys., 35, 123–132, https://doi.org/10.5194/angeo-35-123-2017, https://doi.org/10.5194/angeo-35-123-2017, 2017
Short summary
Short summary
Movement of plasma to higher latitudes by EIA is known to relate to eastward electric field/EEJ and thermospheric meridional neutral wind. Experiments from GPS measurements that unveil thermospheric meridional neutral wind effect on plasma transportation in the F region are very few compared with electric field/EEJ. This work includes examples of thermospheric meridional neutral wind effects on GPS TEC measurements and their roles in transportation of plasma compared to electric field/EEJ.
Related subject area
Atmospheric sciences
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Orbital-Radar v1.0.0: A tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
The MESSy DWARF (based on MESSy v2.55.2)
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
The CHIMERE chemistry-transport model v2023r1
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
Short summary
A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Short summary
Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
Short summary
Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
Short summary
Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Short summary
This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Short summary
Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
Short summary
Short summary
This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
Short summary
Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
Short summary
Short summary
The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
Short summary
Short summary
Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
Short summary
Short summary
Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
Short summary
Short summary
Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
Short summary
Short summary
Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
Short summary
Short summary
RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Short summary
We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary
Short summary
We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
Short summary
Short summary
Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
Short summary
Short summary
Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
Short summary
AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
Short summary
Short summary
Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Short summary
Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Short summary
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
Short summary
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Short summary
A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
Short summary
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
Short summary
Short summary
Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
Short summary
Short summary
We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
Short summary
Short summary
This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Short summary
Short summary
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
Short summary
Short summary
TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
Short summary
Short summary
Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
Short summary
Short summary
We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
Short summary
Short summary
A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
Short summary
Short summary
The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
Short summary
Short summary
In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
Short summary
Short summary
A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
Short summary
Short summary
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
Short summary
Short summary
A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Cited articles
Akmaev, R., Fuller-Rowell, T., Wu, F., Forbes, J., Zhang, X., Anghel, A.,
Iredell, M., Moorthi, S., and Juang, H.-M.: Tidal variability in the lower
thermosphere: Comparison of Whole Atmosphere Model (WAM) simulations with
observations from TIMED, Geophys. Res. Lett., 35, L03810, https://doi.org/10.1029/2007GL032584,
2008. a, b
Alexander, S. P. and Shepherd, M. G.: Planetary wave activity in the polar lower stratosphere, Atmos. Chem. Phys., 10, 707–718, https://doi.org/10.5194/acp-10-707-2010, 2010. a
Baldwin, M. P., Ayarzagüena, B., Birner, T., Butchart, N., Butler, A. H.,
Charlton-Perez, A. J., Domeisen, D. I., Garfinkel, C. I., Garny, H., Gerber,
E. P., Hegglin, M. I., Langematz, U., and Pedatella, N. M.: Sudden stratospheric warmings, Rev. Geophys., 59,
e2020RG000708, https://doi.org/10.1029/2020RG000708, 2021. a
Black, R. X., and McDaniel, B. A.: The dynamics of Northern Hemisphere stratospheric final warming events, J. Atmos. Sci., 64, 2932–2946, https://doi.org/10.1175/JAS3981.1, 2007. a
Butler, A. H., Seidel, D. J., Hardiman, S. C., Butchart, N., Birner, T., and
Match, A.: Defining sudden stratospheric warmings, B. Am. Meteorol. Soc., 96,
1913–1928, https://doi.org/10.1175/BAMS-D-13-00173.1, 2015. a
Chandran, A., Garcia, R., Collins, R., and Chang, L.: Secondary planetary waves
in the middle and upper atmosphere following the stratospheric sudden warming
event of January 2012, Geophys. Res. Lett., 40, 1861–1867,
https://doi.org/10.1002/grl.50373, 2013. a
Chang, L. C., Palo, S. E., and Liu, H.-L.: Short-term variability in the
migrating diurnal tide caused by interactions with the quasi 2 day wave, J.
Geophys. Res.-Atmos., 116, D12112, https://doi.org/10.1029/2010JD014996, 2011. a
Davis, R. N., Chen, Y.-W., Miyahara, S., and Mitchell, N. J.: The climatology, propagation and excitation of ultra-fast Kelvin waves as observed by meteor radar, Aura MLS, TRMM and in the Kyushu-GCM, Atmos. Chem. Phys., 12, 1865–1879, https://doi.org/10.5194/acp-12-1865-2012, 2012. a
Day, K. A., Hibbins, R. E., and Mitchell, N. J.: Aura MLS observations of the westward-propagating s=1, 16-day planetary wave in the stratosphere, mesosphere and lower thermosphere, Atmos. Chem. Phys., 11, 4149–4161, https://doi.org/10.5194/acp-11-4149-2011, 2011. a
Espy, P., Hibbins, R., Riggin, D., and Fritts, D.: Mesospheric planetary waves
over Antarctica during 2002, Geophys. Res. Lett., 32, L21804,
https://doi.org/10.1029/2005GL023886, 2005. a
Fan, Y., Huang, C. M., Zhang, S. D., Huang, K. M., and Gong, Y.: Long-Term
Study of Quasi-16-day Waves Based on ERA5 Reanalysis Data and EOS MLS
Observations From 2005 to 2020, J. Geophys. Res.-Space, 127,
e2021JA030030, https://doi.org/10.1029/2021JA030030, 2022. a, b
Farge, M.: Wavelet transforms and their applications to turbulence,
Annu. Rev. Fluid Mech., 24, 395–458,
https://doi.org/10.1146/annurev.fl.24.010192.002143, 1992. a
Fejer, B., Olson, M., Chau, J., Stolle, C., Lühr, H., Goncharenko, L.,
Yumoto, K., and Nagatsuma, T.: Lunar-dependent equatorial ionospheric
electrodynamic effects during sudden stratospheric warmings, J. Geophys.
Res.-Space, 115, A00G03, https://doi.org/10.1029/2010JA015273, 2010. a
Forbes, J., Hagan, M., Miyahara, S., Vial, F., Manson, A., Meek, C., and
Portnyagin, Y. I.: Quasi 16-day oscillation in the mesosphere and lower
thermosphere, J. Geophys. Res.-Atmos., 100, 9149–9163,
https://doi.org/10.1029/94JD02157, 1995a. a
Forbes, J., Zhang, X., Palo, S., Russell, J., Mertens, C., and Mlynczak, M.:
Tidal variability in the ionospheric dynamo region, J. Geophys. Res.-Space, 113, A02310, https://doi.org/10.1029/2007JA012737, 2008. a
Forbes, J. M.: Atmospheric tides: 1. Model description and results for the
solar diurnal component, J. Geophys. Res.-Space, 87, 5222–5240,
https://doi.org/10.1029/JA087iA07p05222, 1982a. a
Forbes, J. M.: Atmospheric tide: 2. The solar and lunar semidiurnal components,
J. Geophys. Res.-Space, 87, 5241–5252,
https://doi.org/10.1029/JA087iA07p05241, 1982b. a
Forbes, J. M.: Middle atmosphere tides, J. Atmos. Terr. Phys., 46, 1049–1067,
https://doi.org/10.1016/0021-9169(84)90008-4, 1984. a
Forbes, J. M.: Tidal and planetary waves, The Upper Mesosphere and Lower
Thermosphere: A Review of Experiment and Theory, Geophys. Monogr. Ser., 87,
67–87, https://doi.org/10.1029/GM087p0067, 1995. a
Forbes, J. M. and Zhang, X.: Quasi-10-day wave in the atmosphere, J. Geophys.
Res.-Atmos., 120, 11–079, https://doi.org/10.1002/2015JD023327, 2015. a, b
Forbes, J. M. and Zhang, X.: The quasi-6 day wave and its interactions with
solar tides, J. Geophys. Res.-Space, 122, 4764–4776,
https://doi.org/10.1002/2017JA023954, 2017. a
Forbes, J. M., Zhang, X., Palo, S. E., Russell, J., Mertens, C. J., and
Mlynczak, M.: Kelvin waves in stratosphere, mesosphere and lower thermosphere
temperatures as observed by TIMED/SABER during 2002–2006, Earth Planet. Space, 61, 447–453, https://doi.org/10.1186/BF03353161, 2009. a
Forbes, J. M., Zhang, X., Maute, A., and Hagan, M. E.: Zonally symmetric
oscillations of the thermosphere at planetary wave periods, J.
Geophys. Res.-Space, 123, 4110–4128,
https://doi.org/10.1002/2018JA025258, 2018. a
Frigo, M. and Johnson, S. G.: FFTW: An adaptive software architecture for the
FFT, in: Proceedings of the 1998 IEEE International Conference on Acoustics,
Speech and Signal Processing, ICASSP'98 (Cat. No. 98CH36181), IEEE, vol. 3,
1381–1384,https://doi.org/10.1109/ICASSP.1998.681704, 1998. a
Fritts, D. C. and Alexander, M. J.: Gravity wave dynamics and effects in the
middle atmosphere, Rev. Geophys., 41, 1003, https://doi.org/10.1029/2001RG000106,
2003. a
Fuller-Rowell, T., Wu, F., Akmaev, R., Fang, T.-W., and Araujo-Pradere, E.: A
whole atmosphere model simulation of the impact of a sudden stratospheric
warming on thermosphere dynamics and electrodynamics, J. Geophys. Res.-Space, 115, A00G08, https://doi.org/10.1029/2010JA015524, 2010. a
Fuller-Rowell, T., Wang, H., Akmaev, R., Wu, F., Fang, T.-W., Iredell, M., and
Richmond, A.: Forecasting the dynamic and electrodynamic response to the
January 2009 sudden stratospheric warming, Geophys. Res. Lett., 38, L13102,
https://doi.org/10.1029/2011GL047732, 2011. a
Gan, Q., Oberheide, J., and Pedatella, N. M.: Sources, sinks, and propagation
characteristics of the quasi 6-day wave and its impact on the residual mean
circulation, J. Geophys. Res.-Atmos., 123, 9152–9170,
https://doi.org/10.1029/2018JD028553, 2018. a
Gan, Q., Eastes, R. W., Burns, A. G., Wang, W., Qian, L., Solomon, S. C.,
Codrescu, M. V., and McClintock, W. E.: New observations of large-scale waves
coupling with the ionosphere made by the GOLD Mission: Quasi-16-day wave
signatures in the F-region OI 135.6-nm nightglow during sudden stratospheric
warmings, J. Geophys. Res.-Space, 125, e2020JA027880,
https://doi.org/10.1029/2020JA027880, 2020. a
Gan, Q., Oberheide, J., Goncharenko, L., Qian, L., Yue, J., Wang, W.,
McClintock, W. E., and Eastes, R. W.: GOLD Synoptic Observations of
Quasi-6-Day Wave Modulations of Post-Sunset Equatorial Ionization Anomaly
During the September 2019 Antarctic Sudden Stratospheric Warming, Geophys.
Res. Lett., 50, e2023GL103386, https://doi.org/10.1029/2023GL103386,
2023. a
Gasperini, F.: SD WACCM-X v2.1, Climate Data Gateway
at NCAR [data set], https://doi.org/10.26024/5b58-nc53, 2019. a
Gasperini, F., Forbes, J., Doornbos, E., and Bruinsma, S.: Wave coupling
between the lower and middle thermosphere as viewed from TIMED and GOCE, J.
Geophys. Res.-Space, 120, 5788–5804, https://doi.org/10.1002/2015JA021300,
2015. a
Gasperini, F., Forbes, J. M., Doornbos, E. N., and Bruinsma, S. L.: Kelvin wave
coupling from TIMED and GOCE: Inter/intra-annual variability and solar
activity effects, J. Atmos. Sol.-Terr. Phys., 171,
176–187, https://doi.org/10.1016/j.jastp.2017.08.034, 2018. a
Gasperini, F., Liu, H., and McInerney, J.: Preliminary evidence of
Madden-Julian Oscillation effects on ultrafast tropical waves in the
thermosphere, J. Geophys. Res.-Space, 125, e2019JA027649,
https://doi.org/10.1029/2019JA027649, 2020. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewcz, M., and Zhao, B.: The
modern-era retrospective analysis for research and applications, version 2
(MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Goncharenko, L., Chau, J., Liu, H.-L., and Coster, A.: Unexpected connections
between the stratosphere and ionosphere, Geophys. Res. Lett., 37, L10101,
https://doi.org/10.1029/2010GL043125, 2010a. a
Goncharenko, L., Coster, A., Chau, J., and Valladares, C.: Impact of sudden
stratospheric warmings on equatorial ionization anomaly, J. Geophys. Res.-Space, 115, A00G07, https://doi.org/10.1029/2010JA015400, 2010b. a
Goncharenko, L. P., Harvey, V. L., Greer, K. R., Zhang, S.-R., and Coster,
A. J.: Longitudinally dependent low-latitude ionospheric disturbances linked
to the Antarctic sudden stratospheric warming of September 2019, J. Geophys.
Res.-Space, 125, e2020JA028199, https://doi.org/10.1029/2020JA028199, 2020. a
Goncharenko, L. P., Harvey, V. L., Liu, H., and Pedatella, N. M.: Sudden
Stratospheric Warming Impacts on the Ionosphere–Thermosphere System: A
Review of Recent Progress, Ionosphere Dynamics and Applications,
369–400, https://doi.org/10.1002/9781119815617.ch16, 2021. a
Gu, S.-Y., Li, T., Dou, X., Wu, Q., Mlynczak, M., and Russell Iii, J.:
Observations of quasi-two-day wave by TIMED/SABER and TIMED/TIDI, J. Geophys.
Res.-Atmos., 118, 1624–1639, https://doi.org/10.1002/jgrd.50191, 2013. a
Gu, S.-Y., Dou, X., Lei, J., Li, T., Luan, X., Wan, W., and Russell III, J.:
Ionospheric response to the ultrafast Kelvin wave in the MLT region, J.
Geophys. Res.-Space, 119, 1369–1380, https://doi.org/10.1002/2013JA019086,
2014. a
Gu, S.-Y., Liu, H.-L., Dou, X., and Li, T.: Influence of the sudden stratospheric warming on quasi-2-day waves, Atmos. Chem. Phys., 16, 4885–4896, https://doi.org/10.5194/acp-16-4885-2016, 2016. a
Gu, S.-Y., Dou, X.-K., Yang, C.-Y., Jia, M., Huang, K.-M., Huang, C.-M., and
Zhang, S.-D.: Climatology and anomaly of the quasi-two-day wave behaviors
during 2003–2018 austral summer periods, J. Geophys. Res.-Space, 124, 544–556, https://doi.org/10.1029/2018JA026047, 2019. a
Gu, S.-Y., Teng, C.-K.-M., Li, N., Jia, M., Li, G., Xie, H., Ding, Z., and Dou,
X.: Multivariate analysis on the ionospheric responses to planetary waves
during the 2019 Antarctic SSW event, J. Geophys. Res.-Space, 126,
e2020JA028588, https://doi.org/10.1029/2020JA028588, 2021. a
Hagan, M. and Forbes, J.: Migrating and nonmigrating diurnal tides in the
middle and upper atmosphere excited by tropospheric latent heat release, J.
Geophys. Res.-Atmos, 107, ACL–6, https://doi.org/10.1029/2001JD001236, 2002. a
Harada, Y., Goto, A., Hasegawa, H., Fujikawa, N., Naoe, H., and Hirooka, T.: A
major stratospheric sudden warming event in January 2009, J. Atmos. Sci., 67,
2052–2069, https://doi.org/10.1175/2009JAS3320.1, 2010. a
He, M., Chau, J. L., Forbes, J. M., Thorsen, D., Li, G., Siddiqui, T. A.,
Yamazaki, Y., and Hocking, W. K.: Quasi-10-day wave and semidiurnal tide
nonlinear interactions during the Southern Hemispheric SSW 2019 observed in
the Northern Hemispheric mesosphere, Geophys. Res. Lett., 47,
e2020GL091453, https://doi.org/10.1029/2020GL091453, 2020a. a
He, M., Yamazaki, Y., Hoffmann, P., Hall, C. M., Tsutsumi, M., Li, G., and
Chau, J. L.: Zonal Wave Number Diagnosis of Rossby Wave-Like Oscillations
Using Paired Ground-Based Radars, J. Geophys. Res.-Atmos., 125,
e2019JD031 599, https://doi.org/10.1029/2019JD031599, 2020b. a
He, M., Chau, J. L., Forbes, J. M., Zhang, X., Englert, C. R., Harding, B. J.,
Immel, T. J., Lima, L. M., Bhaskar Rao, S. V., Ratnam, M. V., Li, G., Harlander, J. M., Marr, K. D., and Makela, J. J.:
Quasi-2-day wave in low-latitude atmospheric winds as viewed from the ground
and space during January–March, 2020, Geophys. Res. Lett., 48,
e2021GL093466, https://doi.org/10.1029/2021GL093466, 2021. a
Hibbins, R., Espy, P. J., Orsolini, Y., Limpasuvan, V., and Barnes, R.:
SuperDARN observations of semidiurnal tidal variability in the MLT and the
response to sudden stratospheric warming events, J. Geophys.
Res.-Atmos., 124, 4862–4872, https://doi.org/10.1029/2018JD030157, 2019. a
Hirooka, T. and Hirota, I.: Normal mode Rossby waves observed in the upper
stratosphere. Part II: Second antisymmetric and symmetric modes of zonal
wavenumbers 1 and 2, J. Atmos. Sci., 42, 536–548,
https://doi.org/10.1175/1520-0469(1985)042<0536:NMRWOI>2.0.CO;2, 1985. a
Hirota, I. and Hirooka, T.: Normal mode Rossby waves observed in the upper
stratosphere. Part I: First symmetric modes of zonal wavenumbers 1 and 2, J.
Atmos. Sci., 41, 1253–1267,
https://doi.org/10.1175/1520-0469(1984)041<1253:NMRWOI>2.0.CO;2, 1984. a
Holton, J. R. and Lindzen, R. S.: A note on “Kelvin” waves in the
atmosphere, Mon. Weather Rev., 96, 385–386,
https://doi.org/10.1175/1520-0493(1968)096<0385:ANOKWI>2.0.CO;2, 1968. a
Huang, C., Li, W., Zhang, S., Chen, G., Huang, K., and Gong, Y.: Investigation
of dominant traveling 10-day wave components using long-term MERRA-2
database, Earth Planet. Space, 73, 1–12,
https://doi.org/10.1186/s40623-021-01410-7, 2021. a
Immel, T., Sagawa, E., England, S., Henderson, S., Hagan, M., Mende, S., Frey,
H., Swenson, C., and Paxton, L.: Control of equatorial ionospheric morphology
by atmospheric tides, Geophys. Res. Lett., 33, L15108, https://doi.org/10.1029/2006GL026161,
2006. a
Jin, H., Miyoshi, Y., Fujiwara, H., Shinagawa, H., Terada, K., Terada, N.,
Ishii, M., Otsuka, Y., and Saito, A.: Vertical connection from the
tropospheric activities to the ionospheric longitudinal structure simulated
by a new Earth's whole atmosphere-ionosphere coupled model, J. Geophys. Res.-Space, 116, A01316, https://doi.org/10.1029/2010JA015925, 2011. a
Jin, H., Miyoshi, Y., Pancheva, D., Mukhtarov, P., Fujiwara, H., and Shinagawa,
H.: Response of migrating tides to the stratospheric sudden warming in 2009
and their effects on the ionosphere studied by a whole atmosphere-ionosphere
model GAIA with COSMIC and TIMED/SABER observations, J. Geophys. Res.-Space, 117, A10323, https://doi.org/10.1029/2012JA017650, 2012. a, b, c
Kasahara, A.: Normal modes of ultralong waves in the atmosphere, Mon. Weather
Rev., 104, 669–690, https://doi.org/10.1175/1520-0493(1976)104<0669:NMOUWI>2.0.CO;2,
1976. a
Kasahara, A. and Puri, K.: Spectral representation of three-dimensional global
data by expansion in normal mode functions, Mon. Weather Rev., 109,
37–51, https://doi.org/10.1175/1520-0493(1981)109<0037:SROTDG>2.0.CO;2, 1981. a
Kikuchi, K.: An introduction to combined Fourier–wavelet transform and its
application to convectively coupled equatorial waves, Clim. Dynam., 43,
1339–1356, https://doi.org/10.1007/s00382-013-1949-8, 2014. a, b, c
Kikuchi, K. and Wang, B.: Spatiotemporal wavelet transform and the multiscale
behavior of the Madden–Julian oscillation, J. Climate, 23, 3814–3834,
https://doi.org/10.1175/2010JCLI2693.1, 2010. a, b
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis:
General specifications and basic characteristics, J. Meteorol. Soc. Jpn.
Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015. a
Laštovička, J.: Forcing of the ionosphere by waves from below, J.
Atmos. Sol.-Terr. Phys., 68, 479–497, https://doi.org/10.1016/j.jastp.2005.01.018,
2006. a
Lee, W., Song, I.-S., Kim, J.-H., Kim, Y. H., Jeong, S.-H., Eswaraiah, S., and
Murphy, D.: The observation and SD-WACCM simulation of planetary wave
activity in the middle atmosphere during the 2019 Southern Hemispheric sudden
stratospheric warming, J. Geophys. Res.-Space, 126, e2020JA029094,
https://doi.org/10.1029/2020JA029094, 2021. a
Lieberman, R., Riggin, D., Ortland, D., Oberheide, J., and Siskind, D.: Global
observations and modeling of nonmigrating diurnal tides generated by
tide-planetary wave interactions, J. Geophys. Res.-Atmos., 120,
11419–11437, https://doi.org/10.1002/2015JD023739, 2015. a
Lieberman, R. S. and Riggin, D.: High resolution Doppler imager observations of
Kelvin waves in the equatorial mesosphere and lower thermosphere, J. Geophys.
Res.-Atmos., 102, 26117–26130, https://doi.org/10.1029/96JD02902, 1997. a
Lim, E.-P., Hendon, H. H., Butler, A. H., Garreaud, R. D., Polichtchouk, I.,
Shepherd, T. G., Scaife, A., Comer, R., Coy, L., Newman, P. A., hompson, D. W. J., and Nakamuara, H.: The
2019 Antarctic sudden stratospheric warming, SPARC Newsletter, 54, 10–13,
https://www.sparc-climate.org/wp-content/uploads/sites/5/2017/12/SPARCnewsletter_Jan2020_WEB.pdf (last access: 18 August 2023),
2020. a
Lim, E.-P., Hendon, H. H., Butler, A. H., Thompson, D. W., Lawrence, Z. D.,
Scaife, A. A., Shepherd, T. G., Polichtchouk, I., Nakamura, H., Kobayashi,
C., Comer, R., Coy, L., Dowdy, A., Garreaud, R. D., Newman, P. A., and Wang, G.: The 2019 Southern Hemisphere stratospheric polar vortex weakening
and its impacts, B. Am. Meteorol. Soc., 102, E1150–E1171,
https://doi.org/10.1175/BAMS-D-20-0112.1, 2021. a
Lin, J., Lin, C., Rajesh, P., Yue, J., Lin, C., and Matsuo, T.: Local-time and
vertical characteristics of quasi-6-day oscillation in the ionosphere during
the 2019 Antarctic sudden stratospheric warming, Geophys. Res. Lett., 47,
e2020GL090345, https://doi.org/10.1029/2020GL090345, 2020. a
Lindzen, R. S. and Chapman, S.: Atmospheric tides, Space Sci. Rev., 10, 3–188,
https://doi.org/10.1007/BF00171584, 1969. a
Liu, G., England, S. L., and Janches, D.: Quasi two-, three-, and six-day
planetary-scale wave oscillations in the upper atmosphere observed by
TIMED/SABER over ∼17 years during 2002–2018, J. Geophys.
Res.-Space, 124, 9462–9474, https://doi.org/10.1029/2019JA026918, 2019. a
Liu, G., Lieberman, R. S., Harvey, V. L., Pedatella, N. M., Oberheide, J.,
Hibbins, R. E., Espy, P. J., and Janches, D.: Tidal variations in the
mesosphere and lower thermosphere before, during, and after the 2009 sudden
stratospheric warming, J. Geophys. Res.-Space, 126,
e2020JA028827, https://doi.org/10.1029/2020JA028827, 2021. a
Liu, G., Janches, D., Ma, J., Lieberman, R. S., Stober, G., Moffat-Griffin, T.,
Mitchell, N. J., Kim, J.-H., Lee, C., and Murphy, D. J.: Mesosphere and lower
thermosphere winds and tidal variations during the 2019 Antarctic Sudden
Stratospheric Warming, J. Geophys. Res.-Space, 127,
e2021JA030177, https://doi.org/10.1029/2021JA030177, 2022. a
Liu, H.-L.: Variability and predictability of the space environment as related
to lower atmosphere forcing, Space Weather, 14, 634–658,
https://doi.org/10.1002/2016SW001450, 2016. a
Liu, H.-L., Talaat, E., Roble, R., Lieberman, R., Riggin, D., and Yee, J.-H.:
The 6.5-day wave and its seasonal variability in the middle and upper
atmosphere, J. Geophys. Res.-Atmos., 109, D21112, https://doi.org/10.1029/2004JD004795,
2004. a
Liu, H.-L., McInerney, J., Santos, S., Lauritzen, P., Taylor, M., and
Pedatella, N.: Gravity waves simulated by high-resolution whole atmosphere
community climate model, Geophys. Res. Lett., 41, 9106–9112,
https://doi.org/10.1002/2014GL062468, 2014. a
Liu, H.-L., Bardeen, C. G., Foster, B. T., Lauritzen, P., Liu, J., Lu, G.,
Marsh, D. R., Maute, A., McInerney, J. M., Pedatella, N. M., Qian, L., Richmond, A. D., Roble, R. G., Solomon, S. C., Vitt, F. M., and Wang, W.:
Development and validation of the Whole Atmosphere Community Climate Model
with thermosphere and ionosphere extension (WACCM-X 2.0), J. Adv. Model.
Earth Sy., 10, 381–402, https://doi.org/10.1002/2017MS001232, 2018. a
Longuet-Higgins, M. S.: The eigenfunctions of Laplace's tidal equation over a
sphere, Philos. T. R. Soc. A, 262, 511–607,
https://doi.org/10.1098/rsta.1968.0003, 1968. a
Luo, J., Ma, Z., Gong, Y., Zhang, S., Xiao, Q., Huang, C., and Huang, K.:
Record-Strong Eastward Propagating 4-Day Wave in the Southern Hemisphere
Observed During the 2019 Antarctic Sudden Stratospheric Warming, Geophys.
Res. Lett., 50, e2022GL102704, https://doi.org/10.1029/2022GL102704, 2023. a
Ma, Z., Gong, Y., Zhang, S., Zhou, Q., Huang, C., Huang, K., Luo, J., Yu, Y.,
and Li, G.: Study of a Quasi 4-Day Oscillation During the 2018/2019 SSW Over
Mohe, China, J. Geophys. Res.-Space, 125, e2019JA027687,
https://doi.org/10.1029/2019JA027687, 2020. a
Ma, Z., Gong, Y., Zhang, S., Xiao, Q., Xue, J., Huang, C., and Huang, K.:
Understanding the Excitation of Quasi-6-Day Waves in Both Hemispheres During
the September 2019 Antarctic SSW, J. Geophys. Res.-Atmos., 127,
e2021JD035984, https://doi.org/10.1029/2021JD035984, 2022. a
Madden, R. A.: Large-scale, free Rossby waves in the atmosphere – An update,
Tellus A, 59, 571–590, https://doi.org/10.1111/j.1600-0870.2007.00257.x, 2007. a, b
Mallat, S.: A wavelet tour of signal processing, Elsevier,
https://doi.org/10.1016/B978-0-12-466606-1.X5000-4, 1999. a, b
Manney, G. L., Schwartz, M. J., Krüger, K., Santee, M. L., Pawson, S., Lee,
J. N., Daffer, W. H., Fuller, R. A., and Livesey, N. J.: Aura Microwave Limb
Sounder observations of dynamics and transport during the record-breaking
2009 Arctic stratospheric major warming, Geophys. Res. Lett., 36, L12815,
https://doi.org/10.1029/2009GL038586, 2009. a
Marques, C. A. F., Marta-Almeida, M., and Castanheira, J. M.: Three-dimensional normal mode functions: open-access tools for their computation in isobaric coordinates (p-3DNMF.v1), Geosci. Model Dev., 13, 2763–2781, https://doi.org/10.5194/gmd-13-2763-2020, 2020. a
Matsuno, T.: Quasi-geostrophic motions in the equatorial area, J. Meteorol.
Soc. Jpn. Ser. II, 44, 25–43, https://doi.org/10.2151/jmsj1965.44.1_25, 1966. a
Matthias, V., Hoffmann, P., Rapp, M., and Baumgarten, G.: Composite analysis
of the temporal development of waves in the polar MLT region during
stratospheric warmings, J. Atmos. Sol.-Terr.
Phys., 90, 86–96, https://doi.org/10.1016/j.jastp.2012.04.004, 2012. a
Matthias, V., Stober, G., Kozlovsky, A., Lester, M., Belova, E., and Kero, J.: Vertical structure of the Arctic spring transition in the middle atmosphere, J. Geophys. Res.-Atmos., 126, e2020JD034353, https://doi.org/10.1029/2020JD034353, 2021. a
Maute, A.: Thermosphere-ionosphere-electrodynamics general circulation model
for the ionospheric connection explorer: TIEGCM-ICON, Space Sci. Rev.,
212, 523–551, https://doi.org/10.1007/s11214-017-0330-3, 2017. a
McDonald, A., Hibbins, R., and Jarvis, M.: Properties of the quasi 16 day wave
derived from EOS MLS observations, J. Geophys. Res.-Atmos., 116, D06112,
https://doi.org/10.1029/2010JD014719, 2011. a
Mechoso, C. R. and Hartmann, D. L.: An observational study of traveling
planetary waves in the Southern Hemisphere, J. Atmos. Sci., 39, 1921–1935,
https://doi.org/10.1175/1520-0469(1982)039<1921:AOSOTP>2.0.CO;2, 1982. a
Meyer, C. K. and Forbes, J.: A 6.5-day westward propagating planetary wave:
Origin and characteristics, J. Geophys. Res.-Atmos.,
102, 26173–26178, https://doi.org/10.1029/97JD01464, 1997. a
Meyers, S. D., Kelly, B. G., and O'Brien, J. J.: An introduction to wavelet
analysis in oceanography and meteorology: With application to the dispersion
of Yanai waves, Mon. Weather Rev., 121, 2858–2866,
https://doi.org/10.1175/1520-0493(1993)121<2858:AITWAI>2.0.CO;2, 1993. a
Mitra, G., Guharay, A., Batista, P. P., and Buriti, R.: Impact of the September
2019 Minor Sudden Stratospheric Warming on the Low-Latitude Middle
Atmospheric Planetary Wave Dynamics, J. Geophys. Res.-Atmos., 127,
e2021JD035538, https://doi.org/10.1029/2021JD035538, 2022. a
Miyoshi, Y.: Temporal variation of nonmigrating diurnal tide and its relation
with the moist convective activity, Geophys. Res. Lett., 33, L11815,
https://doi.org/10.1029/2006GL026072, 2006. a
Miyoshi, Y. and Fujiwara, H.: Day-to-day variations of migrating diurnal tide
simulated by a GCM from the ground surface to the exobase, Geophys. Res.
Lett., 30, 1789, https://doi.org/10.1029/2003GL017695, 2003. a
Miyoshi, Y. and Fujiwara, H.: Excitation mechanism of intraseasonal oscillation
in the equatorial mesosphere and lower thermosphere, J. Geophys. Res.-Atmos., 111, D14108, https://doi.org/10.1029/2005JD006993, 2006. a
Miyoshi, Y. and Fujiwara, H.: Gravity waves in the thermosphere simulated by a
general circulation model, J. Geophys. Res.-Atmos., 113, D01101,
https://doi.org/10.1029/2007JD008874, 2008. a
Miyoshi, Y. and Yamazaki, Y.: Excitation mechanism of ionospheric 6-day
oscillation during the 2019 September sudden stratospheric warming event, J.
Geophys. Res.-Space, 125, e2020JA028283,
https://doi.org/10.1029/2020JA028283, 2020. a, b
Miyoshi, Y., Pancheva, D., Mukhtarov, P., Jin, H., Fujiwara, H., and Shinagawa,
H.: Excitation mechanism of non-migrating tides, J. Atmos. Sol.-Terr. Phys.,
156, 24–36, https://doi.org/10.1016/j.jastp.2017.02.012, 2017. a
Moldwin, M.: An introduction to space weather, Cambridge University Press,
https://doi.org/10.1017/CBO9780511801365, 2022. a
Moudden, Y. and Forbes, J.: Quasi-two-day wave structure, interannual
variability, and tidal interactions during the 2002–2011 decade, J. Geophys.
Res.-Atmos., 119, 2241–2260, https://doi.org/10.1002/2013JD020563, 2014. a
Mukhtarov, P., Andonov, B., Borries, C., Pancheva, D., and Jakowski, N.:
Forcing of the ionosphere from above and below during the Arctic winter of
2005/2006, J. Atmos. Sol.-Terr. Phys., 72, 193–205,
https://doi.org/10.1016/j.jastp.2009.11.008, 2010. a
Noguchi, S., Kuroda, Y., Kodera, K., and Watanabe, S.: Robust enhancement of
tropical convective activity by the 2019 Antarctic sudden stratospheric
warming, Geophys. Res. Lett., 47, e2020GL088743,
https://doi.org/10.1029/2020GL088743, 2020. a
Oberheide, J., Forbes, J., Häusler, K., Wu, Q., and Bruinsma, S.:
Tropospheric tides from 80 to 400 km: Propagation, interannual variability,
and solar cycle effects, J. Geophys. Res.-Atmos., 114, D00I05,
https://doi.org/10.1029/2009JD012388, 2009. a
Oberheide, J., Forbes, J., Zhang, X., and Bruinsma, S.: Climatology of upward
propagating diurnal and semidiurnal tides in the thermosphere, J. Geophys.
Res.-Space, 116, D00I05, https://doi.org/10.1029/2011JA016784, 2011. a
Palo, S., Forbes, J., Zhang, X., Russell III, J., and Mlynczak, M.: An eastward
propagating two-day wave: Evidence for nonlinear planetary wave and tidal
coupling in the mesosphere and lower thermosphere, Geophys. Res. Lett., 34, L07807,
https://doi.org/10.1029/2006GL027728, 2007. a
Pancheva, D. and Mukhtarov, P.: Strong evidence for the tidal control on the
longitudinal structure of the ionospheric F-region, Geophys. Res. Lett., 37, L14105,
https://doi.org/10.1029/2010GL044039, 2010. a
Pancheva, D., Mukhtarov, P., and Siskind, D. E.: The quasi-6-day waves in
NOGAPS-ALPHA forecast model and their climatology in MLS/Aura measurements
(2005–2014), J. Atmos. Sol.-Terr. Phys., 181, 19–37,
https://doi.org/10.1016/j.jastp.2018.10.008, 2018. a
Pedatella, N., Liu, H.-L., and Hagan, M.: Day-to-day migrating and nonmigrating
tidal variability due to the six-day planetary wave, J. Geophys. Res.-Space, 117, A06301, https://doi.org/10.1029/2012JA017581, 2012a. a
Pedatella, N., Liu, H.-L., Richmond, A., Maute, A., and Fang, T.-W.:
Simulations of solar and lunar tidal variability in the mesosphere and lower
thermosphere during sudden stratosphere warmings and their influence on the
low-latitude ionosphere, J. Geophys. Res.-Space, 117, A08326,
https://doi.org/10.1029/2012JA017792, 2012b. a
Pedatella, N., Liu, H.-L., Sassi, F., Lei, J., Chau, J., and Zhang, X.:
Ionosphere variability during the 2009 SSW: Influence of the lunar
semidiurnal tide and mechanisms producing electron density variability, J.
Geophys. Res.-Space, 119, 3828–3843, https://doi.org/10.1002/2014JA019849,
2014. a, b
Pedatella, N., Chau, J., Schmidt, H., Goncharenko, L., Stolle, C., Hocke, K.,
Harvey, V., Funke, B., and Siddiqui, T.: How Sudden stratospheric warmings
affect the whole atmosphere, EOS, https://doi.org/10.1029/2018EO092441, 2018. a
Pfister, L.: Baroclinic instability of easterly jets with applications to the
summer mesosphere, J. Atmos. Sci., 42, 313–330,
https://doi.org/10.1175/1520-0469(1985)042<0313:BIOEJW>2.0.CO;2, 1985. a
Pogoreltsev, A., Fedulina, I., Mitchell, N., Muller, H., Luo, Y., Meek, C., and
Manson, A.: Global free oscillations of the atmosphere and secondary
planetary waves in the mesosphere and lower thermosphere region during
August/September time conditions, J. Geophys. Res.-Atmos., 107,
ACL–24, https://doi.org/10.1029/2001JD001535, 2002. a
Qin, Y., Gu, S.-Y., and Dou, X.: A New Mechanism for the Generation of
Quasi-6-Day and Quasi-10-Day Waves During the 2019 Antarctic Sudden
Stratospheric Warming, J. Geophys. Res.-Atmos., 126, e2021JD035568,
https://doi.org/10.1029/2021JD035568, 2021a. a
Qin, Y., Gu, S.-Y., Dou, X., Teng, C.-K.-M., and Li, H.: On the Westward
Quasi-8-Day Planetary Waves in the Middle Atmosphere During Arctic Sudden
Stratospheric Warmings, J. Geophys. Res.-Atmos., 126,
e2021JD035071, https://doi.org/10.1029/2021JD035071, 2021b. a
Qin, Y., Gu, S.-Y., Teng, C.-K.-M., Dou, X.-K., Yu, Y., and Li, N.:
Comprehensive study of the climatology of the quasi-6-day wave in the MLT
region based on Aura/MLS observations and SD-WACCM-X simulations, J. Geophys.
Res.-Space, 126, e2020JA028454, https://doi.org/10.1029/2020JA028454,
2021c. a
Qin, Y., Gu, S.-Y., Dou, X., Teng, C.-K.-M., and Yang, Z.: Secondary 12-Day
Planetary Wave in the Mesospheric Water Vapor During the 2016/2017 Unusual
Canadian Stratospheric Warming, Geophys. Res. Lett., 49,
e2021GL097024, https://doi.org/10.1029/2021GL097024, 2022a. a
Qin, Y., Gu, S.-Y., Dou, X., Teng, C.-K.-M., Yang, Z., and Sun, R.: Southern
Hemisphere Response to the Secondary Planetary Waves Generated During the
Arctic Sudden Stratospheric Final Warmings: Influence of the Quasi-Biennial
Oscillation, J. Geophys. Res.-Atmos., 127, e2022JD037730,
https://doi.org/10.1029/2022JD037730, 2022b. a, b
Rao, J., Garfinkel, C. I., White, I. P., and Schwartz, C.: The Southern
Hemisphere minor sudden stratospheric warming in September 2019 and its
predictions in S2S models, J. Geophys. Res.-Atmos., 125,
e2020JD032723, https://doi.org/10.1029/2020JD032723, 2020. a
Safieddine, S., Bouillon, M., Paracho, A.-c., Jumelet, J., Tence, F., Pazmino,
A., Goutail, F., Wespes, C., Bekki, S., Boynard, A., Hadji-Lazaro, J., Coheur, P.-F., Hurtmans, D., and Clerbaux, C.: Antarctic ozone
enhancement during the 2019 sudden stratospheric warming event, Geophys. Res.
Lett., 47, e2020GL087810, https://doi.org/10.1029/2020GL087810, 2020. a
Sakazaki, T. and Hamilton, K.: An array of ringing global free modes discovered
in tropical surface pressure data, J. Atmos. Sci., 77,
2519–2539, https://doi.org/10.1175/JAS-D-20-0053.1, 2020. a
Salby, M. L.: The 2-day wave in the middle atmosphere: Observations and theory,
J. Geophys. Res.-Oceans, 86, 9654–9660, https://doi.org/10.1029/JC086iC10p09654,
1981a. a
Salby, M. L.: Rossby normal modes in nonuniform background configurations. Part
I: Simple fields, J. Atmos. Sci., 38, 1803–1826,
https://doi.org/10.1175/1520-0469(1981)038<1803:RNMINB>2.0.CO;2, 1981b. a
Salby, M. L.: Rossby normal modes in nonuniform background configurations. Part
II. Equinox and solstice conditions, J. Atmos. Sci., 38, 1827–1840,
https://doi.org/10.1175/1520-0469(1981)038<1827:RNMINB>2.0.CO;2, 1981c. a, b
Salby, M. L.: Survey of planetary-scale traveling waves: The state of theory
and observations, Rev. Geophys., 22, 209–236, https://doi.org/10.1029/RG022i002p00209,
1984. a
Salby, M. L. and Callaghan, P. F.: Seasonal amplification of the 2-day wave:
Relationship between normal mode and instability, J. Atmos. Sci., 58,
1858–1869, https://doi.org/10.1175/1520-0469(2001)058<1858:SAOTDW>2.0.CO;2, 2001. a
Sassi, F., Garcia, R., and Hoppel, K.: Large-scale Rossby normal modes during
some recent Northern Hemisphere winters, J. Atmos. Sci., 69, 820–839,
https://doi.org/10.1175/JAS-D-11-0103.1, 2012. a
Sassi, F., Liu, H.-L., Ma, J., and Garcia, R. R.: The lower thermosphere during
the Northern Hemisphere winter of 2009: A modeling study using high-altitude
data assimilation products in WACCM-X, J. Geophys. Res.-Atmos., 118,
8954–8968, https://doi.org/10.1002/jgrd.50632, 2013. a
Sassi, F., Liu, H.-L., and Emmert, J. T.: Traveling planetary-scale waves in
the lower thermosphere: Effects on neutral density and composition during
solar minimum conditions, J. Geophys. Res.-Space, 121, 1780–1801,
https://doi.org/10.1002/2015JA022082, 2016. a
Schunk, R. and Sojka, J. J.: Ionosphere-thermosphere space weather issues, J.
Atmos. Terr. Phys., 58, 1527–1574, https://doi.org/10.1016/0021-9169(96)00029-3, 1996. a
Siddiqui, T.: WACCM-X simulations – 2009 SSW, Mendeley Data V1 [data set], https://doi.org/10.17632/47pnw8pgmk.1, 2020. a
Siddiqui, T., Maute, A., and Pedatella, N.: On the importance of interactive
ozone chemistry in Earth-system models for studying mesophere-lower
thermosphere tidal changes during sudden stratospheric warmings, J. Geophys.
Res.-Space, 124, 10690–10707, https://doi.org/10.1029/2019JA027193, 2019. a
Siddiqui, T., Yamazaki, Y., Stolle, C., Maute, A., Laštovička, J.,
Edemskiy, I., Mošna, Z., and Sivakandan, M.: Understanding the total
electron content variability over Europe during 2009 and 2019 SSWs, J.
Geophys. Res.-Space, 126, e2020JA028751,
https://doi.org/10.1029/2020JA028751, 2021. a, b
Siddiqui, T. A., Maute, A., Pedatella, N., Yamazaki, Y., Lühr, H., and Stolle, C.: On the variability of the semidiurnal solar and lunar tides of the equatorial electrojet during sudden stratospheric warmings, Ann. Geophys., 36, 1545–1562, https://doi.org/10.5194/angeo-36-1545-2018, 2018. a
Siddiqui, T. A., Chau, J. L., Stolle, C., and Yamazaki, Y.: Migrating solar
diurnal tidal variability during Northern and Southern Hemisphere Sudden
Stratospheric Warmings, Earth Planet. Space, 74, 1–17,
https://doi.org/10.1186/s40623-022-01661-y, 2022. a, b
Smith, A. K.: Global dynamics of the MLT, Surv. Geophys., 33,
1177–1230, https://doi.org/10.1007/s10712-012-9196-9, 2012. a
Sobhkhiz-Miandehi, S., Yamazaki, Y., Arras, C., Miyoshi, Y., and Shinagawa, H.:
Comparison of the tidal signatures in sporadic E and vertical ion convergence
rate, using FORMOSAT-3/COSMIC radio occultation observations and GAIA model,
Earth Planet. Space, 74, 1–13, https://doi.org/10.1186/s40623-022-01637-y, 2022. a
Sridharan, S., Sathishkumar, S., and Gurubaran, S.: Variabilities of mesospheric tides and equatorial electrojet strength during major stratospheric warming events, Ann. Geophys., 27, 4125–4130, https://doi.org/10.5194/angeo-27-4125-2009, 2009. a
Stening, R., Forbes, J., Hagan, M., and Richmond, A.: Experiments with a lunar
atmospheric tidal model, J. Geophys. Res.-Atmos., 102,
13465–13471, https://doi.org/10.1029/97JD00778, 1997. a
Stober, G., Baumgarten, K., McCormack, J. P., Brown, P., and Czarnecki, J.: Comparative study between ground-based observations and NAVGEM-HA analysis data in the mesosphere and lower thermosphere region, Atmos. Chem. Phys., 20, 11979–12010, https://doi.org/10.5194/acp-20-11979-2020, 2020. a
Torrence, C: Torrence & Compo Wavelet Analysis Software, GitHub [code], https://github.com/ct6502/wavelets, last access: 18 August 2023. a
Wang, H., Akmaev, R., Fang, T.-W., Fuller-Rowell, T., Wu, F., Maruyama, N., and
Iredell, M.: First forecast of a sudden stratospheric warming with a coupled
whole-atmosphere/ionosphere model IDEA, J. Geophys. Res.-Space, 119,
2079–2089, https://doi.org/10.1002/2013JA019481, 2014. a
Wang, J. C., Palo, S. E., Forbes, J., Marino, J., Moffat-Griffin, T., and
Mitchell, N.: Unusual quasi 10-day planetary wave activity and the
ionospheric response during the 2019 Southern Hemisphere sudden stratospheric
warming, J. Geophys. Res.-Space, 126, e2021JA029286,
https://doi.org/10.1029/2021JA029286, 2021a. a
Wang, J. C., Palo, S. E., Liu, H.-L., and Siskind, D.: Day-to-Day Variability
of Diurnal Tide in the Mesosphere and Lower Thermosphere Driven From Below,
J. Geophys. Res.-Space, 126, e2019JA027759,
https://doi.org/10.1029/2019JA027759, 2021b. a
Wargan, K., Weir, B., Manney, G. L., Cohn, S. E., and Livesey, N. J.: The
anomalous 2019 Antarctic ozone hole in the GEOS Constituent Data Assimilation
System with MLS observations, J. Geophys. Res.-Atmos., 125,
e2020JD033335, https://doi.org/10.1029/2020JD033335, 2020. a
Wells, D. E., Vaníček, P., and Pagiatakis, S. D.: Least squares
spectral analysis revisited, Tech. rep., Department of Surveying Engineering,
University of New Brunswick Fredericton, N.B., Canada,
https://gge.ext.unb.ca/Pubs/TR84.pdf (last access: 18 August 2023), 1985. a
Wheeler, M. and Kiladis, G. N.: Convectively coupled equatorial waves: Analysis
of clouds and temperature in the wavenumber–frequency domain, J. Atmos.
Sci., 56, 374–399, https://doi.org/10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2,
1999. a
Wu, D. L., Hays, P., Skinner, W., Marshall, A., Burrage, M., Lieberman, R., and
Ortland, D.: Observations of the quasi 2-day wave from the High Resolution
Doppler Imager on UARS, Geophys. Res. Lett., 20, 2853–2856,
https://doi.org/10.1029/93GL03008, 1993. a
Wu, D. L., Hays, P., and Skinner, W.: Observations of the 5-day wave in the
mesosphere and lower thermosphere, Geophys. Res. Lett., 21, 2733–2736,
https://doi.org/10.1029/94GL02660, 1994. a
Wu, D. L., Hays, P. B., and Skinner, W. R.: A least squares method for spectral
analysis of space-time series, J. Atmos. Sci., 52,
3501–3511, https://doi.org/10.1175/1520-0469(1995)052<3501:ALSMFS>2.0.CO;2, 1995. a
Xiong, J., Wan, W., Ding, F., Liu, L., Ning, B., and Niu, X.: Coupling between
mesosphere and ionosphere over Beijing through semidiurnal tides during the
2009 sudden stratospheric warming, J. Geophys. Res.-Space, 118, 2511–2521, https://doi.org/10.1002/jgra.50280, 2013. a
Yamazaki, Y.: Quasi-6-day wave effects on the equatorial ionization anomaly
over a solar cycle, J. Geophys. Res.-Space, 123, 9881–9892,
https://doi.org/10.1029/2018JA026014, 2018. a
Yamazaki, Y.: Matlab and Python software to compute Fourier- wavelet spectra
(fourierwavelet v1.1) using longitude-time data for studying global-scale
atmospheric waves, Zenodo [code], https://doi.org/10.5281/zenodo.8033686, 2023. a
Yamazaki, Y. and Yasunobu, M.: Simulation data from GAIA (Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy) for the September 2019 sudden stratospheric warming event, GFZ Data Services [data set], https://doi.org/10.5880/GFZ.2.3.2020.004, 2020. a
Yamazaki, Y. and Matthias, V.: Large-amplitude quasi-10-day waves in the middle
atmosphere during final warmings, J. Geophys. Res.-Atmos., 124,
9874–9892, https://doi.org/10.1029/2019JD030634, 2019. a, b, c, d
Yamazaki, Y., Matthias, V., Miyoshi, Y., Stolle, C., Siddiqui, T.,
Kervalishvili, G., Laštovička, J., Kozubek, M., Ward, W.,
Themens, D. R., Kristoffersen, S., and Alken, P.: September 2019 Antarctic sudden stratospheric
warming: Quasi-6-day wave burst and ionospheric effects, Geophys. Res. Lett.,
47, e2019GL086577, https://doi.org/10.1029/2019GL086577, 2020a. a, b, c
Yamazaki, Y., Miyoshi, Y., Xiong, C., Stolle, C., Soares, G., and Yoshikawa,
A.: Whole atmosphere model simulations of ultrafast Kelvin wave effects in
the ionosphere and thermosphere, J. Geophys. Res.-Space, 125,
e2020JA027939, https://doi.org/10.1029/2020JA027939, 2020b. a
Yamazaki, Y., Matthias, V., and Miyoshi, Y.: Quasi-4-Day Wave: Atmospheric
Manifestation of the First Symmetric Rossby Normal Mode of Zonal Wavenumber
2, J. Geophys. Res.-Atmos., 126, e2021JD034855,
https://doi.org/10.1029/2021JD034855, 2021. a, b
Yamazaki, Y., Harding, B. J., Qiu, L., Stolle, C., Siddiqui, T., Miyoshi, Y.,
Englert, C. R., and England, S.: Monthly climatologies of zonal-mean and
tidal winds in the thermosphere as observed by ICON/MIGHTI during April
2020–March 2022, Earth Space Sci., 10, e2023EA002962,
https://doi.org/10.1029/2023EA002962, 2023. a
Yano, J.-I. and Jakubiak, B.: Wavelet-based verification of the quantitative
precipitation forecast, Dynam. Atmos. Oceans, 74, 14–29,
https://doi.org/10.1016/j.dynatmoce.2016.02.001, 2016. a
Yano, J.-I., Moncrieff, M. W., and Wu, X.: Wavelet analysis of simulated
tropical convective cloud systems. Part II: Decomposition of convective-scale
and mesoscale structure, J. Atmos. Sci., 58, 868–876,
https://doi.org/10.1175/1520-0469(2001)058<0868:WAOSTC>2.0.CO;2, 2001a. a
Yano, J.-I., Moncrieff, M. W., Wu, X., and Yamada, M.: Wavelet analysis of
simulated tropical convective cloud systems. Part I: Basic analysis, J. Atmos. Sci., 58, 850–867,
https://doi.org/10.1175/1520-0469(2001)058<0850:WAOSTC>2.0.CO;2, 2001b. a
Yano, J.-I., Bechtold, P., Redelsperger, J.-L., and Guichard, F.:
Wavelet-compressed representation of deep moist convection, Mon. Weather
Rev., 132, 1472–1486,
https://doi.org/10.1175/1520-0493(2004)132<1472:WRODMC>2.0.CO;2, 2004. a
Yiğit, E. and Medvedev, A. S.: Internal wave coupling processes in
Earth’s atmosphere, Adv. Space Res., 55, 983–1003,
https://doi.org/10.1016/j.asr.2014.11.020, 2015.
a
Yin, S., Ma, Z., Gong, Y., Zhang, S., and Li, G.: Response of quasi-10-day
waves in the MLT region to the sudden stratospheric warming in March 2020,
Adv. Space Res., 71, 298–305, https://doi.org/10.1016/j.asr.2022.10.054, 2022. a
Yu, F. R., Huang, K. M., Zhang, S. D., Huang, C. M., Yi, F., Gong, Y., Wang,
R., Li, G., and Ning, B.: Quasi 10-and 16-day wave activities observed
through meteor radar and MST radar during stratospheric final warming in 2015
spring, J. Geophys. Res.-Atmos., 124, 6040–6056,
https://doi.org/10.1029/2019JD030630, 2019. a
Yue, J., Liu, H.-L., and Chang, L. C.: Numerical investigation of the quasi 2
day wave in the mesosphere and lower thermosphere, J. Geophys. Res.-Atmos., 117, D05111, https://doi.org/10.1029/2011JD016574, 2012. a
Yue, X., Schreiner, W. S., Lei, J., Rocken, C., Hunt, D. C., Kuo, Y.-H., and
Wan, W.: Global ionospheric response observed by COSMIC satellites during the
January 2009 stratospheric sudden warming event, J. Geophys. Res.-Space, 115, A00G09, https://doi.org/10.1029/2010JA015466, 2010. a
Žagar, N., Kasahara, A., Terasaki, K., Tribbia, J., and Tanaka, H.: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community, Geosci. Model Dev., 8, 1169–1195, https://doi.org/10.5194/gmd-8-1169-2015, 2015. a
Zhang, X. and Forbes, J. M.: Lunar tide in the thermosphere and weakening of
the northern polar vortex, Geophys. Res. Lett., 41, 8201–8207,
https://doi.org/10.1002/2014GL062103, 2014. a
Zhang, X., Forbes, J. M., Hagan, M. E., Russell III, J. M., Palo, S. E.,
Mertens, C. J., and Mlynczak, M. G.: Monthly tidal temperatures 20–120 km
from TIMED/SABER, J. Geophys. Res.-Space, 111, A10S08,
https://doi.org/10.1029/2005JA011504, 2006. a
Zhao, Y., Taylor, M. J., Pautet, P.-D., Moffat-Griffin, T., Hervig, M. E.,
Murphy, D. J., French, W., Liu, H.-L., Pendleton Jr, W. R., and Russell III,
J.: Investigating an unusually large 28-day oscillation in mesospheric
temperature over Antarctica using ground-based and satellite measurements, J.
Geophys. Res.-Atmos., 124, 8576–8593, https://doi.org/10.1029/2019JD030286,
2019. a
Zhou, X., Yue, X., Yu, Y., and Hu, L.: Day-To-Day Variability of the MLT DE3
Using Joint Analysis on Observations From TIDI-TIMED and a Meteor Radar
Meridian Chain, J. Geophys. Res.-Atmos., 127,
e2021JD035794, https://doi.org/10.1029/2021JD035794, 2022. a
Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate...