Remote Sensing in Climate Change Modelling Training Course

Introduction

Climate change is one of the most pressing global challenges of our time, demanding robust scientific understanding and effective mitigation and adaptation strategies. Climate Change Modelling relies heavily on comprehensive and consistent observational data to understand past and present climate dynamics, project future scenarios, and validate complex Earth System Models (ESMs). While traditional ground-based observations provide valuable point data, they are often spatially sparse and lack the continuous, global coverage necessary for large-scale climate studies. Remote Sensing from satellite, airborne, and increasingly, drone platforms has revolutionized climate science by providing an unprecedented wealth of data on various Essential Climate Variables (ECVs) across the atmosphere, land, and oceans. From monitoring changes in polar ice sheets and sea levels to tracking deforestation, land surface temperature, and atmospheric greenhouse gas concentrations, remote sensing offers a synoptic, repetitive, and objective view of the Earth's climate system. This continuous stream of data is critical for calibrating and validating climate models, identifying long-term trends, detecting climate anomalies, and refining our understanding of the physical processes driving climate change. Without proficiency in handling and integrating these vast and diverse remote sensing datasets, climate scientists, modelers, and environmental researchers would be limited in their ability to develop accurate climate projections and robust climate change impact assessments. Many professionals in the climate community understand the theoretical importance of remote sensing but lack the practical skills to effectively integrate this data into their modeling and analysis workflows.

Conversely, mastering Remote Sensing in Climate Change Modelling empowers professionals to leverage the full potential of Earth observation data to enhance climate model accuracy, validate projections, and conduct cutting-edge research on climate variability and change. This specialized skill set is crucial for transforming raw sensor data into powerful insights that directly support climate action, policy development, and resilience planning. Our intensive 5-day "Remote Sensing in Climate Change Modelling" training course is meticulously designed to equip climate scientists, climate modelers, environmental researchers, meteorologists, oceanographers, GIS professionals, and data scientists with the essential theoretical knowledge and practical, hands-on skills required to confidently utilize remote sensing data for understanding, modeling, and predicting climate change and its impacts.

Duration

5 Days

Target Audience

The "Remote Sensing in Climate Change Modelling" training course is ideal for a broad range of professionals and researchers involved in climate science, climate modeling, environmental monitoring, and policy development related to climate change. This includes:

  • Climate Scientists and Researchers: Seeking to integrate observational data into their modeling efforts.
  • Climate Modelers: Looking to improve model initialization, validation, and calibration using remote sensing data.
  • Environmental Scientists: Focused on understanding climate impacts on ecosystems and natural resources.
  • Meteorologists and Atmospheric Scientists: Studying atmospheric composition, temperature, and precipitation patterns.
  • Oceanographers: Analyzing sea surface temperature, sea level rise, and ocean currents related to climate change.
  • Hydrologists: Monitoring water resources, snow and ice, and drought in a changing climate.
  • GIS Professionals and Data Analysts: Working with large geospatial datasets for climate applications.
  • Policy Makers and Planners: Requiring data-driven insights for climate change adaptation and mitigation strategies.
  • Graduate Students and Academics: Pursuing research in climate science, Earth system science, and remote sensing.
  • Anyone involved in understanding, predicting, or responding to climate change and its impacts.

Course Objectives

Upon successful completion of the "Remote Sensing in Climate Change Modelling" training course, participants will be able to:

  • Understand the fundamental role of remote sensing in climate change research and modeling.
  • Identify and access key Essential Climate Variables (ECVs) derived from satellite remote sensing.
  • Perform pre-processing and quality control on remote sensing datasets for climate studies.
  • Utilize remote sensing data for monitoring long-term trends in climate indicators (e.g., temperature, sea level, ice extent).
  • Apply remote sensing data for the validation and calibration of climate models.
  • Integrate remote sensing observations into climate change impact assessments across various sectors.
  • Leverage cloud-based platforms for handling and analyzing large climate-related remote sensing datasets.
  • Critically evaluate the strengths, limitations, and uncertainties of remote sensing data in climate change modeling.

 Course Modules

Module 1: Foundations of Climate Change and Remote Sensing's Role

  • Introduction to the Earth's climate system and key climate change concepts.
  • Definition and significance of Essential Climate Variables (ECVs).
  • Why remote sensing is indispensable for climate change monitoring and modeling.
  • Overview of satellite missions and sensor types providing climate-relevant data (e.g., MODIS, VIIRS, Landsat, Sentinel, ICESat, GRACE).
  • Challenges in using remote sensing for long-term climate trends (e.g., sensor drift, data continuity).

Module 2: Atmospheric ECVs from Remote Sensing

  • Monitoring atmospheric temperature and humidity profiles using microwave sounders and infrared sensors.
  • Remote sensing of atmospheric composition: Greenhouse gases (CO2, CH4), aerosols, ozone.
  • Satellite-derived precipitation estimates and their role in hydrological cycle monitoring.
  • Cloud properties from space: Cloud cover, cloud top temperature, cloud type.
  • Data sources and pre-processing for atmospheric ECVs (e.g., AIRS, OCO-2, GPM, CERES).

Module 3: Land Surface ECVs from Remote Sensing

  • Land Surface Temperature (LST) and its role in energy balance and urban heat islands.
  • Vegetation dynamics: NDVI, EVI, and their application in monitoring phenology, drought, and carbon uptake.
  • Land cover and land use change mapping for carbon cycle assessment and climate impact studies.
  • Soil moisture estimation from microwave remote sensing (e.g., SMAP, SMOS).
  • Fires: Detection, burned area mapping, and emission estimation from satellite data.

Module 4: Ocean and Cryosphere ECVs from Remote Sensing

  • Sea Surface Temperature (SST) and its role in ocean-atmosphere interactions and marine heatwaves.
  • Sea Level Rise: Measuring ocean altimetry and its long-term trends (e.g., TOPEX/Poseidon, Jason, Sentinel-6).
  • Ocean Color: Chlorophyll-a and primary productivity as indicators of ocean health and carbon sequestration.
  • Cryosphere monitoring: Sea ice extent and thickness, glacier mass balance, ice sheet dynamics (e.g., ICESat, CryoSat).
  • Ocean salinity (e.g., Aquarius, SMAP) and ocean heat content (from altimetry).

Module 5: Remote Sensing Data for Climate Model Initialization and Forcing

  • How remote sensing data provides initial conditions for climate models.
  • Using satellite-derived land surface parameters as boundary conditions for models.
  • Prescribing or assimilating atmospheric composition and aerosols in climate models.
  • Importance of consistent, long-term remote sensing records for reanalysis products.
  • Challenges and opportunities in data assimilation for climate modeling.

Module 6: Climate Model Validation and Evaluation with Remote Sensing

  • The crucial role of observational data in evaluating climate model performance.
  • Comparing climate model outputs (e.g., temperature, precipitation, ice extent) with satellite observations.
  • Identifying model biases and strengths using remote sensing data.
  • Using time series analysis of remote sensing data to validate climate model projections of trends and variability.
  • Uncertainty quantification in remote sensing data and its implications for model validation.

Module 7: Applied Remote Sensing for Climate Change Impact Assessment

  • Assessing climate change impacts on agriculture: Crop yield monitoring, drought assessment.
  • Monitoring climate-induced changes in water resources: Glacier melt, lake level changes, extreme precipitation.
  • Remote sensing for climate change adaptation planning (e.g., coastal vulnerability, urban resilience).
  • Mapping and quantifying ecosystem shifts and biodiversity impacts due to climate change.
  • Using remote sensing for disaster risk reduction in the context of extreme weather events.

Module 8: Cloud Computing, Big Data, and Future Directions

  • Leveraging cloud-based platforms for large-scale climate data analysis (e.g., Google Earth Engine, Microsoft Planetary Computer).
  • Processing and analyzing massive remote sensing archives for long-term climate trends.
  • Introduction to Machine Learning and Artificial Intelligence in climate data analysis and modeling.
  • Emerging satellite missions and technologies for climate monitoring (e.g., new ECV missions, L-band SAR for biomass).
  • Interdisciplinary approaches and the future of remote sensing in climate science.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Remote Sensing In Climate Change Modelling Training Course in North Macedonia
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