Observation based climatology Martian atmospheric waves perturbation Datasets

Abstract

The Martian atmospheric waves perturbation Datasets (MAWPD) version 2.0 is the first observation-based climatology dataset of Martian atmospheric waves. It contains climatology-gridded temperature, gravity waves, and tides spanning the whole Martian year. MAWPD uses the Data INterpolating Empirical Orthogonal Functions method (DINEOF) reconstruction method for data assimilation with the observational data from the Mars Global Surveyor (MGS), Mars Reconnaissance Orbiter (MRO), Mars Atmosphere and Volatile EvolutioN (MAVEN), Mars Pathfinder (MP), Mars Phoenix Lander (MPL), Mars Exploration Rover (MER) and Mars Express (MEX) temperature retrievals. The dataset includes gridded fields of temperature (Level 1 data) as well as the physical quantities of GWs (Level 2 data, amplitude, and potential energies), SPWs and tides (Level 2 data, amplitude, and phase). The MAWPD, based entirely on multiple reliable observations, provides climatological background atmospheric information of temperature and wave disturbances on Mars. The dataset is not only useful for observation-based scientific studies concerning Martian atmospheric waves, e.g., circulation, dust storms, and wave excitation mechanism, but also for cross-validating with model-based datasets or model results.

The Atmospheric Heating Mechanism over the Tharsis Bulge of Mars and the Impact of Global Dust Storms

“…Our analytical focus narrows to the atmospheric conditions during the winter season of the Northern Hemisphere's 28th Martian year, a period marked by a significant GDS [45,48], alongside the comparative climatology of the 30th Martian year's summer and winter seasons. This latter period, chosen for its absence of GDSs, serves as a climatic baseline, with the adjacent 29th and subsequent 31st Martian years providing additional stability without GDSs for our study.…”

“…Within this framework, we delve into the examination of key atmospheric variables, including pressure; surface and near-surface air temperatures; three-dimensional wind fields; adiabatic heating (sensible, latent, and radiative heating); diabatic heating; advective term; water vapor; and dust mixing ratios. These Our analytical focus narrows to the atmospheric conditions during the winter season of the Northern Hemisphere's 28th Martian year, a period marked by a significant GDS [45,48], alongside the comparative climatology of the 30th Martian year's summer and winter seasons. This latter period, chosen for its absence of GDSs, serves as a climatic baseline, with the adjacent 29th and subsequent 31st Martian years providing additional stability without GDSs for our study.…”

“…Recent advancements in Martian atmospheric data, notably through the Ensemble Mars Atmosphere Reanalysis System (EMARS), have provided unparalleled insights into the Martian atmosphere by integrating observations from various spacecraft and General Circulation Models (GCMs) [44]. These data allow for a detailed analysis of the Martian atmosphere's interaction with topographical features such as the Tharsis Bulge [45]. The significance of topographical diversity in understanding Martian climate dynamics has also been emphasized in recent studies, such as that by Montabone et al (2014) [46].…”

Climatological Gravity Waves in the Middle and Upper Atmosphere of Mars Based on ACS/TGO Observations

“…Each temperature profile was then interpolated at uniform intervals of 0.5 km, to ensure that the data were evenly distributed over the altitude. The temperature perturbations T¢ are due to GWs being obtained by separating the background temperature T ¯from the measured temperature T. This method is widely used for the extraction of atmospheric GW parameters on Earth and Mars (Yiğit et al 2021;He et al 2022;Zhang et al 2023):…”

Analysis of gravity wave activity during stratospheric sudden warmings in the northern hemisphere

“…In recent years, GWs have been studied using different instruments and methods, such as ground-based radar observation, observation from meteorological rockets, radiosonde and satellite observation, and the application of deep learning (Xiao CY and Hu X, 2010;Chang SJ et al, 2019;He Y et al, 2020bHe Y et al, , 2020cXue XH et al, 2020;He Y et al, 2021He Y et al, , 2022aHe Y et al, , 2023Wu Y et al, 2022;Zhang J et al, 2023). Satellite observation is superior to ground observation because the area measured by a satellite is often larger than can be observed by ground instruments, and some satellite observations also provide better temporal and vertical resolution than ground-based systems (Jin S and Feng GP, 2011;Jin S et al, 2013).…”