Geoscience & Climate
Data at planetary scale.
With a PhD in Atmospheric Sciences and an MSc in Geophysics & Climatology, Michel brings deep expertise in weather, climate, and ocean systems — combined with modern data science methods to extract insight from complex geophysical datasets.
This work spans research collaboration, data analysis, teaching, and the development of open-source computational tools for geoscience — with a focus on scientific modelling and open, reproducible research outputs.
Discuss your project →Sample climate visualisations — illustrating the type of geoscience analyses M2Lab CSDS can deliver for research and academic clients.
Temperature Anomaly
Annual anomaly relative to 1981–2010 baseline (°C) — sample data
Multi-variable Climate Analysis
Sea surface temperature, air temperature, and precipitation index — sample annual cycle
Skills & expertise
Atmospheric Sciences
- ›Numerical weather prediction (WRF, ECMWF)
- ›Atmospheric dynamics and thermodynamics
- ›Mesoscale meteorology
- ›Weather pattern analysis
- ›Extreme weather events
Climate Science
- ›Climate variability and change
- ›Climate model analysis (CMIP)
- ›Downscaling (statistical & dynamical)
- ›Temperature and precipitation trends
- ›Climate indices (NAO, ENSO, AMO)
Oceanography
- ›Sea surface temperature analysis
- ›Ocean–atmosphere interactions
- ›Marine climate variability
- ›Coastal and regional oceanography
Data & Methods
- ›Python (xarray, netCDF4, cartopy)
- ›R for climate statistics
- ›Time series analysis of geophysical data
- ›Spatial data analysis and visualisation
- ›Reanalysis data (ERA5, NCEP, MERRA)
Research highlight
Completed research programme: NORINDIA, 2012–2015.
Climate and water resources in India
NORINDIA · Regional climate modelling and hydrology · 2012–2015
NORINDIA was a research programme on India’s future climate and water resources. The work used the WRF model for dynamical downscaling, producing higher-resolution regional climate information for India than global climate models can provide.
This regional climate information supported research on monsoon variability and snow and glacier processes, and was combined with hydrological modelling to examine implications for regional hydrology — connecting atmospheric modelling with water-resource research.
WRF Resources
Archival reference for past WRF teaching and tools.
WRF (Weather Research and Forecasting) is a numerical weather model developed by NCAR. Since 2012, Mesquita has taught WRF across Asia, Europe, and Latin America, training more than 1,600 participants. Read more in this BAMS paper co-authored with Dr. Peter Walton (University of Oxford) and Yarker Consulting.
We developed e-WRF — one of the first virtualised versions of WRF, now available as a Docker container. If you are interested in WRF training or e-WRF, get in touch.