A climatology Martian atmospheric waves dataset based only on observations

Based on observation data of multiple spacecrafts, we managed to build the Martian atmospheric waves perturbation Datasets (MAWPD) version 2.0, which is the first observation-based climatology dataset of Martian atmospheric waves.
Published in Astronomy
A climatology Martian atmospheric waves dataset based only on observations
Like

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

What the dataset contains?

It contains climatology-gridded temperature, gravity waves, and tides spanning the whole Martian year, including 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).

Why the dataset is important?

The dataset is important due to two main reasons: 1. It is meaningful to study Martian atmospheric waves as a whole but there are currently no datasets that directly show atmospheric fluctuations; 2. Observation-based 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.

Why it is meaningful to study Martian atmospheric waves as a whole?

The importance of gravity waves (GWs), stationary planetary waves (SPWs), and tides for the atmospheres of Mars are now universally recognized. In some cases, these waves act together to modify the atmosphere, e.g., GWs and zonally modulated thermal tides jointly contributed to the significant wave activity over the Martian tropics. In other cases, they interact with each other, e.g., nonmigrating tides are believed to be partly excited by the interaction between the migrating tides and SPWs. Consequently, a study of these waves as a whole may better explain most of the atmospheric phenomena on Mars, which needs a dataset with a comprehensive estimate of the state and temporal evolution of the atmospheric waves.

Why the observation-based dataset is important?

The observation-based dataset is a dataset based only on observation data, without combining it with model output data. It serves as a proxy for the observations used and is important to observation-based scientific studies and cross-validating with model output.

Single spacecraft data is not always enough to study a certain atmospheric wave on Mars, let alone to study waves as a whole, and thus multiple data sources are in need for observation-based researches.

However, no observation-based dataset for Martian atmospheric waves has been established so far partly due to the scattered and discontinuous observation data that is not sufficient to build the dataset, either in time or space and the data scarcity had caused the difficulty in wave discrimination in the past. In addition, it is complex and time-consuming to gather temperature data of multiple spacecrafts together, get super-observation according to the weighting of data, reconstruct data with high accuracy, conduct error analysis and finally calculate the atmospheric tides, and that is why we do this. We have completed most of the data collection and processing and build an observation-based dataset, so that the researchers could save a lot of time when conducting studies concerning Martian atmospheric waves.

How we got this dataset?

The Martian atmospheric waves perturbation Datasets (MAWPD) is based on the grids established by Martian atmospheric temperature retrievals from instruments on multiple orbiters and landers, while the DINEOF reconstruction method is used to fill in the missing data of grids. The reconstructed grids are then used to calculate all the waves including tides, GWs, and SPWs. Generally, this can be divided into following steps:

Firstly, we consulted, cited, and collected the open-source data of multiple spacecrafts, i.e., the Mars Global Surveyor (MGS), the 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).

Dataset creation process. Based on the input data (third column) collected by instruments (second column) of spacecrafts (first column), the output data (final column) is reconstructed using DINEOF method. The spacecrafts used include the orbiters (MRO, MGS, MEX, and MAVEN) and the landers (MP, MPL, MER1, and MER2). They are connected to the detection instruments (MCS, TES, RO, SPICAM, IUVS, ASI/MET, and IMU) they carry by polylines. The arrows connected to each instrument point to the data type (limb viewing data, nadir viewing data, radio occultation data, solar occultation data, stellar occultation data, and entry accelerometers data) they provide. The output data (MAWPD) is the reconstructed climatology gridded temperature data (Level 1 data) and the derived gravity waves, tides, and stationary planetary waves (Level 2 data).

Then, we delete the second half (Ls 180-360) of the MY 25 and 28 to filter out global dust storm activity. In our present work to present interannual variability in a merged climatology, results are mainly affected by 25 and 28 years of global dust storms, and to a lesser degree by regional dust storms that occur in autumn and winter every year. To improve the issue, regional dust storms are preserved due to its relatively slight impacts compared to the global dust storm and that it can happen throughout the year, so it is difficult to remove and does not make much sense to results after removal. In addition, wave activities during regional dust storms are meaningful to study so the removal of the global dust storms could also make sure that regional dust storm effects were not confounding.

Thirdly, the super observation is created to accomplish the weighting of data. Considering the different accuracy, coverage and numbers of different data, we must weight them for use or there would be no point using the data with fewer profiles. Thus, we create the super observations for each grid. The super observation, assumed to be at the grid center, is the weighted average of all types of inner observation. The larger the observation error, the smaller the weight of the observation.

Then, the Data INterpolating Empirical Orthogonal Functions (DINEOF) method, which ensures that the temporal information is not lost in the reconstruction of data without any a priori information, is used in the MAWPD to fill the missing data. The DINEOF had been successfully used as the basis of datasets on Earth to fill the missing. Due to the scarcity of observation coverage on Mars, there would still be some uncovered grids after conducting super observation and the DINEOF is one of the best methods at present to fill them correctly.

Based on the reconstructed temperature retrievals, we calculate gravity waves, tides, and stationary planetary waves in Martian atmosphere. So far, the calculation of the database has been completed, then we save the data in ‘nc’ format and complete the construction of the MAWPD.

Conclusion

Although it is recognized that the atmospheric waves are all propagating perturbations, with various wave parameters (e.g., amplitude, wavelength, frequency...), in the atmospheric fluid. Most observation-based researches on Martian atmosphere paid attention to only a certain of wave, and studied waves separately. Because of the great difference between different fluctuations, there is no problem in doing so. But we still suggest a study of these waves as a whole may better explain most of the atmospheric phenomena on Mars, since these waves were proved to act together and interact with each other sometimes. It is thus necessary to build a dataset with a comprehensive estimate of the state and temporal evolution of the atmospheric waves to sustain the relevant researches, and that is why we built the MAWPD.

The dataset, based entirely on multiple reliable observations, provides climatological background atmospheric information of temperature and wave disturbances on Mars. Due to the reliability and scarcity of observations relative to models, the climatological atmospheric information of MAWPD is not only useful for observation-based scientific studies concerning Martian atmospheric waves, e.g., circulation, dust storms, and the wave excitation mechanism on Mars, but also for cross-validating with model-based datasets or model results.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Astronomy, Cosmology and Space Sciences
Physical Sciences > Physics and Astronomy > Astronomy, Cosmology and Space Sciences

Related Collections

With collections, you can get published faster and increase your visibility.

Ecological data for tracking biological diversity and environmental change

This collection presents data contributions addressing topics in biodiversity and ecology.

Publishing Model: Open Access

Deadline: Jan 31, 2024

Remote sensing data for changes in land use

This Collection comprises a series of articles presenting data on changes to land use in urban areas, farmland, forests, and natural environments, as determined using remote sensing techniques.

Publishing Model: Open Access

Deadline: Jan 31, 2024