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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Observation based climatology Martian atmospheric waves perturbation Datasets
Jie Zhang1, Qianqian Ji1, Zheng Sheng2
1College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China.
Scientific Data
|January 3, 2023
Summary
The Martian Atmospheric Waves Perturbation Datasets (MAWPD) v2.0 offers the first observation-based Martian atmospheric wave climatology. This dataset provides crucial temperature, gravity wave, and tide data for Mars research.
Area of Science:
- Planetary Science
- Atmospheric Science
- Aeronomy
Background:
- Understanding Martian atmospheric dynamics is crucial for planetary exploration.
- Previous datasets lacked comprehensive, observation-based climatologies of atmospheric waves.
Purpose of the Study:
- To introduce the Martian Atmospheric Waves Perturbation Datasets (MAWPD) version 2.0.
- To provide a gridded climatology of Martian atmospheric waves derived from multiple observational sources.
Main Methods:
- Data assimilation using the Data INterpolating Empirical Orthogonal Functions (DINEOF) method.
- Utilized observational temperature data from multiple Mars missions (MGS, MRO, MAVEN, MP, MPL, MER, MEX).
- Generated Level 1 (temperature) and Level 2 (gravity waves, solar-planetary waves, tides) data products.
Main Results:
- MAWPD v2.0 is the first observation-based climatology of Martian atmospheric waves.
- The dataset includes gridded temperature, gravity wave (amplitude, potential energies), solar-planetary waves, and tides (amplitude, phase) for the entire Martian year.
- Provides reliable climatological background atmospheric information based on multiple observations.
Conclusions:
- MAWPD v2.0 serves as a valuable resource for observation-based studies of Martian atmospheric phenomena.
- The dataset is essential for understanding Martian circulation, dust storms, and wave excitation mechanisms.
- Facilitates cross-validation with model-based datasets and results.
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