Aggregated data from the Rover Environmental Monitoring Station (REMS) onboard the Mars Science Laboratory (MSL). Seasonal and diurnally fitted wave modes for the observed pressure. Raw data retrieved from NASA PDS archive (https://atmos.nmsu.edu/PDS/data/mslrem_1001/)

This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a “reanalysis”).

The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA’s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA’s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites

https://pds-geosciences.wustl.edu/missions/odyssey/themis.html, https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html

The model into which the observations are assimilated is the UK version of Laboratoire de Météorologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).

The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.

Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:

Variables and attributes 0 lon: FLOAT(72) = FLOAT(lon) 0 long_name: longitude 1 units: degrees_east 1 lat: FLOAT(36) = FLOAT(lat) 0 long_name: latitude 1 units: degrees_north 2 sigma: FLOAT(25) = FLOAT(sigma) 0 long_name: sigma 1 units: sigma_level = p/ps 3 soil: FLOAT(18) = FLOAT(soil) 0 long_name: soil levels (i.e. levels below the surface to represent thermal variations) 1 units: none 4 time: FLOAT(360) = FLOAT(time) 0 long_name: model time 1 units: days since 00:00:00 (the beginning of the file) 5 controle: FLOAT(100) = FLOAT(lentable) 0 long_name: Table of run parameters 1 description: MGCM run 5.000 6 Ls: FLOAT(360) = FLOAT(time) 0 Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox) 1 units: deg 7 tsurf: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: Surface temperature 1 units: K 8 ps: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: surface pressure 1 units: Pa 9 co2ice: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: co2 ice thickness (column mass density) 1 units: kg.m-2 10 fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux) 1 units: W.m-2 11 fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: fluxsurf_sw (surface visible radiative flux) 1 units: W.m-2 12 temp: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time) 0 Physics_diagnostic: temperature 1 units: K 13 u: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time) 0 Physics_diagnostic: Zonal (east-west) wind 1 units: m.s-1 14 v: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time) 0 Physics_diagnostic: Meridional (north-south) wind 1 units: m.s-1 15 rho: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time) 0 Physics_diagnostic: density 1 units: kg.m-3 16 udrag: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: Drag velocity 1 units: m/s 17 udragt: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: Threshold velocity for dust lifting 1 units: m/s 18 aerosol: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time) 0 Physics_diagnostic: dust opacity considering layer thickness 1 units: SI (opacity/m) 19 taudustvis: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: Dust optical depth 1 units: SI 20 q01: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time) 0 Physics_diagnostic: mix. ratio 1 units: kg/kg 21 dqsdevtot: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: dust devil lift rate 1 units: kg.m-2.s-1 22 dqsstrtot: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: near surface wind stress dust lifting rate 1 units: kg.m-2.s-1 23 dqssedtot: FLOAT(72,36,360) = FLOAT(lon,lat,time) 0 Physics_diagnostic: dust sedimentation rate 1 units: kg.m-2.s-1

This archive contains the data used to generate Figures 1-3 in the paper "Detection of a bolide in Jupiter's atmosphere with Juno UVS" by Giles et al. (2021), submitted to Geophysical Research Letters. The original Juno UVS data is available at the PDS Atmospheres Node (https://pds-atmospheres.nmsu.edu/PDS/data/jnouvs_3001/).

This dataset contains organized CSVs of derived environmental measurements from the Mars 2020 Perseverance Rover’s Mars Environmental Dynamics Analyzer (MEDA), covering all available data from Sols 1–1379, archived by NASA’s Planetary Data System (PDS) and organized for easy analysis and machine learning workflows.

What is MEDA? The Mars Environmental Dynamics Analyzer (MEDA) aboard the Perseverance rover measures: - Air temperature - Ground temperature - Pressure - Relative humidity - Wind speed and direction - Ultraviolet radiation - Dust and sky brightness

These measurements help characterize daily and seasonal weather cycles at Jezero Crater, supporting Mars science and future mission planning.

What Are Derived MEDA Data? The MEDA dataset is divided into Raw, Partially Processed, and Derived products: - Raw: Unprocessed instrument output (engineering data). - Partially Processed: Calibrated data in engineering units. - Derived: Higher-level products with data reductions and calculations applied to produce physically meaningful measurements, removing outliers, converting units, and aligning time-series for analysis.

This dataset contains only derived data, which: - Uses the highest-level available MEDA data products. - Is directly usable for scientific analysis, time series forecasting, and ML pipelines without extensive preprocessing. - Provides environmental context for research, robotics planning, and mission operations.

For more information visit: NASA MEDA Data Overview

This archive contains the data used to generate Figures 1-6 in the paper "Possible Transient Luminous Events observed in Jupiter's upper atmosphere" by Giles et al. (2020), submitted to JGR Planets. The original Juno UVS data is available at the PDS Atmospheres Node (https://pds-atmospheres.nmsu.edu/PDS/data/jnouvs_3001/). The images in Figure 7 were obtained from the JunoCam archive at the PDS Imaging Node (https://pds-imaging.jpl.nasa.gov/data/juno/). The image is Figure 8 was obtained from the WFCJ archive at MAST (https://archive.stsci.edu/hlsp/wfcj).

The Jornada Long Term Ecological Research (LTER) project focuses on changes in the distribution of soil resources as an index of the impact of vegetation change--desertification--on semiarid lands. Specifically, we hypothesize that a relatively homogeneous pattern of soil nutrients is found in areas of grassland. Various factors leading to vegetation change alter the distribution of soil resources, leading to the invasion and persistence of shrubs and the development of a patchy distribution of soil nutrients. Nutrient-rich areas that develop under shrub canopies are known as "islands of fertility," while soil resources are lost from the adjacent inter-shrub spaces by wind and water erosion. These changes in soil resources have importance consequences for ecosystem function, linking the ecosystem processes in deserts to changes in the global environment (Schlesinger et al. l990).

Similar changes in vegetation and soils have occurred over large areas of the Chihuahuan desert and in other areas of the world, where semiarid grasslands have been replaced by shrubland vegetation. The Jornada Experimental Range is a designated Man and the Biosphere (MAB) Reserve, which allows comparative studies at other MAB Reserves in the Chihuahuan Desert at Big Bend National Park (Texas) and the Mapimi Biosphere Reserve in Mexico. In addition, we believe that studies at the Jornada LTER program can be used to infer the causes and consequences of desertification worldwide.

Field research at the Jornada LTER is conducted in various habitat types found within New Mexico State University's Chihuahuan Desert Rangeland Research Center (25,900 ha) and the adjacent lands of the USDA Jornada Experimental Range (78,266 ha). These lands, which form the Jornada del Muerto Basin in southern New Mexico, are found at the northern end of the Chihuahuan desert (MAP- 60Kb), which extends from southcentral New Mexico, USA to the state of Zacatecas, Mexico, comprising 36% of North American Desert land (MacMahon and Wagner l985).

This information was obtained in part from the Jornada LTER home page at "http://jornada-www.nmsu.edu/".

Jornada is part of the Long Term Ecological Research Network, which is funded by the National Science Foundation, and accessible at www.lternet.edu. Data are one of the most valuable products of the LTER program. The goal of the Network is to provide fast, effective, and open access to LTER data. Over 2000 ecological datasets are part of a network-wide information system designed to facilitate data exchange and integration to meet the needs of ecological scientists.