MOOC-AI: Week - 5

Topic 5a: Part 1 - Introduction to Monitoring the Atmosphere
- Some of today’s most important environmental concerns relate to the composition of the atmosphere. The increasing concentration of greenhouse gases and the cooling effect of aerosols are prominent drivers of a changing climate, but the extent of their impact is often still uncertain.
  
  At the Earth’s surface, aerosols, ozone and other reactive gases such as nitrogen dioxide determine the quality of the air around us, affecting human health and life expectancy, the health of ecosystems and the fabric of the built environment. Ozone distributions in the stratosphere influence the amount of ultraviolet radiation reaching the surface. Dust, sand, smoke and volcanic aerosols affect the safe operation of transport systems and the availability of power from solar generation, the formation of clouds and rainfall, and the remote sensing by satellite of land, ocean and atmosphere.
  
  To address these environmental concerns we need data and processed information. The Copernicus Atmosphere Monitoring Service (CAMS) has been developed to meet these needs, to support policymakers, business and citizens with enhanced atmospheric environmental information.
  
  CAMS provides consistent and quality-controlled information related to air pollution, and heath, solar energy, greenhouse gases and climate forcing – everywhere in the world.
  
  Over the last few decades, research by the World Health Organization has established that air pollution is responsible for a broad range of adverse health effects. This evidence, together with environmental protection policies, has stimulated the development of air quality monitoring and forecast services in Europe.
  
  Like weather forecasts, air quality forecasts are based on specialized Numerical Weather Prediction models, ingesting observations of trace gases and particles, whilst also taking into account natural and anthropogenic emissions. CAMS delivers air quality forecasts every day.
  
  Atmosphere monitoring from space
  
  There are various key satellites which play a role in providing the data that we use for monitoring the atmosphere.
  
  In Europe, EUMETSAT operates the Metop-A, -B and -C satellites, which circle the globe via the poles and continuously collect data from an altitude of 817km. Aboard the Metop satellites the Global Ozone Monitoring Experiment-2 (GOME-2) measures vertical profiles of ozone and other constituents of the atmosphere, such as Nitrogen Dioxide (NO2) and SO2. Also, the IASI instrument on the Metop satellite observes ozone, carbon monoxide (CO) and methane (CH4).
  
  The next generation of Metop satellites – known as Metop-Second Generation will bring Metop’s valuable contributions into the 2040s, by operating in three successive pairs and carrying enhanced and new instruments, as well as the Copernicus Sentinel-5 instrument. The Sentinel-5 instrument will be exploited in combination with the IASI-NG infrared sounder on board the Metop-SG satellites, to observe the signature of trace gases across the spectrum, from the UV to the thermal infrared.
  
  In geostationary orbit, the Meteosat satellites provide imagery for the early detection of fast developing severe weather and can be used to characterise aerosols in the atmosphere including volcanic ash and dust.The next generation of Meteosat satellites, Meteosat Third Generation (MTG), will be active from the end of 2022 until the 2040s and carry the Copernicus Sentinel-4 instrument to monitor air quality over Europe every hour at high spatial resolution.Sentinel-4 will work with the new Infrared Sounder (IRS) and imager (FCI) on MTG.
  
  Sentinel-5P is the first Copernicus satellite dedicated to monitoring the atmosphere. The satellite carries the state-of-the-art Tropomi instrument to map a multitude of trace gases such as nitrogen dioxide, ozone, formaldehyde, sulphur dioxide, methane, carbon monoxide and aerosols – all of which affect the air, health, climate.
Topic 5a: Part 2 - Introduction to Monitoring the Atmosphere - The Added Value of Satellites
- Dr Federico Fierli from EUMETSAT introduces how we can used satellites to monitor the Atmosphere and why this is important for citizens.
  
  VIDEO
Topic 5a: Part 3 - Introduction to Monitoring the Atmosphere - ML for Weather Modelling
- Numerical weather prediction (NWP) was first implemented in the 1920s, but it was not until the 1950s when it produced accurate results, and this was because of the introduction of computer simulations.
  
  There are now many state-of-the art NWP models that are used for weather forecasting. However, these models have the challenging job of representing our complex Earth system. In this video, Peter Duben explains how machine learning can help with this challenge and improve NWP.
Topic 5b: Part 1 - Effects of Air Quality on Human Health and the Environment
- Poor air quality is a very serious environmental risk to public health around the world. According to the World Health Organization (WHO), there are 4.2 million deaths every year as a result of exposure to ambient air pollution and 91% of the world’s population live in places where air quality exceeds WHO guidelines.
  
  As well as air pollution, the depletion of the ozone layer has also posed a risk to human health due to increasing the UV levels leading to more cases of skin cancer, cataracts and impaired immune systems.
  
  Bad air quality also has negative effects on our environment, for example, acid rain can travel hundreds of kilometres causing damage to trees and the pollution of soils and water bodies such as lakes and rivers, harming wildlife and ecosystems.
  
  Sentinel-5P is the first Copernicus mission dedicated to monitoring our atmosphere. The satellite carries the state-of-the-art TROPOMI instrument to map a multitude of trace gases such as nitrogen dioxide, ozone, formaldehyde, sulphur dioxide, methane, carbon monoxide and aerosols – all of which affect the air we breathe and therefore our health, and our climate. Information from the mission is used through CAMS for air quality forecast and for decision-making. The TROPOMI instrument is a spectrometer that maps the global atmosphere with a resolution as high as 7km x 3.5km. At this resolution, air pollution over cities can be detected.
  
  Satellite remote sensing, such as from Sentinel-5p, provides information with the required spatial coverage to monitor global air quality. Together with the application of ML algorithms, scientists are able to extract implicit relationships among variables in training datasets to address atmospheric inversion problems.
  
  In this video, Fabio Del Frate explains about the damaging effects of air pollution, and how we can effectively use satellite remote sensing and artificial intelligence to set up services that can, for example, inform citizens of risks to their health from ambient air pollution and UV.
Topic 5b: Part 2 - Tracking Air Quality
- Air quality has attracted considerable scientific interest over the last few decades because it both directly and indirectly affects many social and economic activities, but above all it adversely affects human health and wellbeing.
  
  Tools have been developed to track air quality so that citizens and decision makers have access to real-time air quality data.
  
  An example of this is Plume Labs, who have created products to help citizens reduce their air pollution exposure with air pollution forecasts down to the street level and Flow, a smart personal air quality tracker. Their air quality tracking technology works by gathering the latest pollution levels from over 12,000 monitoring stations and 80 public agencies around the world, and local specifics such as geographical features, land use, population density, and human activities. They then use AI algorithms and atmospheric models to turn this massive amount of data into hourly forecasts, maps, and more. Generating truly real-time data, and predictions up to seven days in advance.
  
  Jupyter Notebook Task: AI for Ozone Concentration Estimation
  
  For this Jupyter Notebook task, you will have a look at the process of training a neural network based on Sentinel-5P/TROPOMI data in order to predict total column ozone concentration.
  
  You can view the video for this tutorial both here on FutureLearn as well as at the top of the Jupyter Notebook.
  
  For this Jupyter Notebook workflow tutorial please follow this link to go to the WEkEO Jupyter Hub or click on the link in the ‘See Also’ section below.
Topic 5c - Methane Retrievals Using Sentinel-5P
- Methane is a powerful greenhouse gas and is one of the most significant drivers of global warming. It has an impact on climate change 84 times greater than CO2 (per unit mass, over 20 years). It is also a precursor to the formation of tropospheric ozone, which can have a number of indirect effects on agriculture productivity, crop yields and human health.
  
  Methane is one of the key targets of Sentinel-5P’s Tropospheric Monitoring Instrument (TROPOMI).
  
  Current methods of tracking methane perform well but have a large computation cost. Scientists are using Radiative Transfer Model (RTM) simulations, which allow faster and less computationally demanding retrievals.
  
  In this video, Jennifer Adams explains how AI was used to learn the physics of RTMs for greenhouse gases, in particular for methane retrievals from S5p.
  
  Jupyter Notebook Task: Methane Retrievals Using Sentinel-5P data
  
  In this Jupyter Notebook task, you will use physics-based machine learning for Copernicus Sentinel-5P methane retrievals. A neural network will be trained to learn the physics of Radiative Transfer Models (RTMs) for Sentinel-5p methane retrievals, allowing for faster retrievals of methane.
  
  You can view the video for this tutorial both here on FutureLearn as well as at the top of the Jupyter Notebook.
  
  For this Jupyter Notebook workflow tutorial please follow this link to go to the WEkEO Jupyter Hub or click on the link in the ‘See Also’ section below.
  
  If you have any questions, comments or would just like to discuss this Notebook with your fellow learners, please write in the discussion below.
Topic 5d - Estimating Precipitation
- The prevailing element in the Earth’s water cycle is precipitation – both liquid and solid. This plays a crucial role in the hydrological budget. If we have a better understanding of the water cycle – it will have a positive impact in many different fields, such as: climatology, weather forecasting, agriculture, fishing and flooding early warning systems.
  
  Despite its importance, precipitation is one of the most difficult geophysical parameters to be quantified, particularly in regions where ground measurements are sparse, or do not exist (such as in the ocean, remote locations or developing countries)
  
  AI applied to data collected by meteorological satellites can provide a significant improvement to precipitation monitoring in these regions. These are characterized by frequent temporal sampling capabilities over large areas. Areas covered by networks of rain gauges or weather radars can also benefit from satellite precipitation estimation to compensate for the usual irregular spatial distribution of rain gauges or the orography problems that could affect weather radars.
Topic 5e - Estimating Emissions During the COVID-19 Pandemic
- The area of estimating emissions has developed quickly in the context of the COVID-19 Pandemic. AI can be used to infer emissions, or even ‘potential’ emissions – for example what would have been observed, what energy use would have been in the absence of lockdown measures.
  
  To do this, different types of activity statistics are available, such as: mobility or traffic counts, flight counts and energy network fluxes.
  
  There has been a recent CAMS study using machine learning, (specifically gradient boosting) to study the impact of Covid on emission reductions in relation to electricity demand.
Topic 5f - ML for More Accurate Weather Forecasts: Part 1 - WeatherBench
- Accurate weather forecasting is becoming increasingly more crucial to local and national crisis management planning and climate adaptation decision-making, as the frequency of extreme weather events increases due to climate change.
  
  Currently, weather forecasts are generated using physics-based computer simulations, which are generally effective, but perform poorly when it comes to the prediction of extreme events, and they are also computationally expensive. Data driven approaches, such as deep learning, directly learn from the best available observations and could potentially produce better forecasts.
  
  To look into this, WeatherBench was set up in order to evaluate deep learning models for global medium-range (i.e. several days to two weeks) weather forecasting.
  
  In this video, Peter Dueben explains how WeatherBench works and what data is behind it.
Topic 5f - ML For More Accurate Weather Forecasts: Part 2 - MET Norway
- The Norwegian Meteorological Institute (MET Norway) delivers reliable and accurate weather predictions, from hours to several days into the future, and focuses on Norway, the European Arctic and adjacent areas.
  
  Their weather models accurately cover a resolution of 20 kilometres and above, so, in order to fill in gaps in the data, they use crowd-sourced in situ weather observations. This is used to improve the accuracy of their weather prediction model and therefore allows them to provide more accurate forecasts.
  
  In Norway, these in-situ observations are taken from Netatmo smart home weather sensors that are widely owned by the general population. Netatmo’s station density is roughly 50 times greater than MET Norway’s, resulting in temperature forecasts being able to update every hour, compared to the current six-hour frequency.
End of Week 5

Topic 5a: Part 1 - Introduction to Monitoring the Atmosphere

Atmosphere monitoring from space

Topic 5a: Part 2 - Introduction to Monitoring the Atmosphere - The Added Value of Satellites

Topic 5a: Part 3 - Introduction to Monitoring the Atmosphere - ML for Weather Modelling

Topic 5b: Part 1 - Effects of Air Quality on Human Health and the Environment

Topic 5b: Part 2 - Tracking Air Quality

Jupyter Notebook Task: AI for Ozone Concentration Estimation

Topic 5c - Methane Retrievals Using Sentinel-5P

Jupyter Notebook Task: Methane Retrievals Using Sentinel-5P data

Topic 5d - Estimating Precipitation

Topic 5e - Estimating Emissions During the COVID-19 Pandemic

Topic 5f - ML for More Accurate Weather Forecasts: Part 1 - WeatherBench

Topic 5f - ML For More Accurate Weather Forecasts: Part 2 - MET Norway

End of Week 5