Challenge #21 - Machine Learning to improve the CAMS global air quality forecasts

[EsperanzaCuartero]

Challenge 21 - Machine Learning to improve the CAMS global air quality forecasts

Stream 2 - Machine Learning for weather, climate and atmosphere applications

Goal

Develop an ML algorithm to predict (and correct) the time-varying bias of the global CAMS forecast for surface PM2.5, O3 and NO2 at the locations of air quality monitoring stations.

Mentors and skills

• Mentors: @JohannesFlemming , @miha-at-ecmwf @jerome-barre-ecmwf
• Skills required:
  • Machine Learning
  • Time-series analysis
  • Handling geospatial data
  • Basic understanding of air quality observations and models
  • Python

Note: Challenge is funded by Copernicus. Only nationals from the European Union and ECMWF Member States are eligible to apply (see Terms and Conditions).

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Challenge description

CAMS/ECMWF runs a computer model to predict global air pollution at a spatial resolution of about 40x40km (grid boxes size). While the CAMS model predicts the observed air quality mostly reasonably well errors in the prediction can occur because of the necessary simplification of the CAMS model and the uncertainties in the input data such as the emissions.
The main task is to develop an ML approach to predict the forecast errors at the location of air quality stations in order to correct them as a post-processing step. The observations to be used are hourly observations of surface ozone, NO2 and PM2.5 from about 2000 stations worldwide as provided in the openAQ data repository.

We suggest the following steps towards the solution:

1. Build ML model
   Train the ML model to predict the difference between forecast and station observations using data from a recent previous year (2019 or 2018).
   The input to the correction algorithm can be the CAMS model-forecast of the air quality value (O3, NO2, PM2.5) and forecast meteorological parameters (temperature, wind speed, etc. ).

2. Test performance of ML bias predication with independent data
   Test the performance of the ML model for recent forecasts of the 2020-2021 period. Use basic error statistics such as bias, RMSE and correlation to compare the forecast accuracy of the ML-corrected forecast against the uncorrected forecast.

3. Model error analysis (optional)
   Investigate the importance of the individual predictors and do a spatial analysis to identify patterns that could be used to better understand or improve the forecast model.

[parantak]

Greetings, I am Parantak, a final year CS undergrad. This specific challenge seems interesting and it falls within the domain of my prior endeavors. I'd like to contribute to this.
I have experience in time-series modeling, specifically with financial data. I have effectuated models for time-series classification, long-horizon/short-horizon prediction, anomaly detection, etc. Additionally, I've dealt with geospatial data before for a project in Epidemiology. I'd be obliged if someone could perhaps share an elaborate explanation of the problem statement and perhaps even a brief about the dataset that'll be put into use.

Thanks :D

[EsperanzaCuartero]

Dear Parantak, thank you for your interest. The mentors will answer your questions as soon as possible. Best, Esperanza.

[JohannesFlemming]

Hello Prantak,

Many thanks for your interest in ESoWC. It sound that you are are well suited to tackle the task, given your previous experience.

On of the challenge of the task is dealing with the air quality observations as hosted by openAQ. The data come from different providers, may have gaps and errors in them. So basically it can be "real-life / dirty" data, that need specific attention before the ML processing starts. The CAMS model data for air quality and meteorological variables are gridded data maps provided in netcdf format. Handling of them should be easier. They do not have gaps in space and time.

To get an idea about the model forecast have a look here:
https://www.windy.com/-PM2-5-pm2p5?camsEu,pm2p5,51.443,-0.927,5

The miss-match between the model and the air quality observations is the forecast error. We want you to make it possible to predict the forecast error for each station using meteorological model data and other predictors. Once we have a good predication of the error, we can correct the model forecast and make the users happy. We hope to be able to learn something about the model as such, as we get a better view of the nature of the model error.

My advice for a good proposal would be to demonstrate in a good way that you will be able to handle the openAQ and consider their fragmentation in the process. We also want to get a sound proposal for the procedure to testing your ML approach.

Thanks again,
We are looking forward to your proposal or any further question you may have.

Johannes Flemming

[FedericaCas]

Hi,
I am Federica Casamento, a master's student in Environmental Engineering.
I have a keen interest in climate change studies and I am studying Machine Learning and Python. I have academic experience in time series analysis, extreme value analysis of rainfall and temperature, bias correction and hydrological modelling using HEC-HMS, ERA5-Land dataset, observed data.
All the projects here are very appealing, but I've chosen this challenge, in line with my interests and the topics I would like to prove myself on.
I am drafting some ideas for the proposal and look forward to sharing them with you!

Thanks so much!

[EsperanzaCuartero]

Dear Federica, many thanks for your interest. Looking forward to seeing your ideas.
Best
Esperanza Cuartero

[jwagemann]

Hi,
join us for the ECMWF Summer of Weather Code Ask Me Anything sessions and learn all things ESoWC.

When:

• 17 March 2021 at 4 pm GMT and
• 24 March 2021 at 4 pm GMT

What:

• learn everything about ESoWC - how it works, the challenges this year, some tips for your proposal and listen to ESoWC experiences from previous participants

How: register here.