Marc Bocquet
Biographic Data
| ID | 6818111 |
|---|---|
| NAME | Marc Bocquet |
| GIVEN NAMES | Marc |
| FAMILY NAME | Bocquet |
| SIGNATURE | BOCQUET M |
| AFFILIATIONS | École nationale des ponts et chaussées |
| ORCID | 0000-0003-2675-0347 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Learning physically interpretable deep networks from reanalysis data for medium-term regional PM 2.5 forecasts
Medium-term regional forecasts of fine particulate matter (PM 2.5 ) are critical to mitigate its hazardous environmental effects. Deep learning (DL) has recently emerged as a promising technique to improve traditional forecasts based on chemistry-transport models (CTMs). However, challenges remain in (1) representing the complex physics of the PM 2.5 pollution processes including the emission, transport, and chemical conversion of the pollutants,…
Data assimilation in the geosciences
We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of cl…
No prominent works on this page.
Data assimilation in the geosciences
We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of cl…
Learning physically interpretable deep networks from reanalysis data for medium-term regional PM 2.5 forecasts
Medium-term regional forecasts of fine particulate matter (PM 2.5 ) are critical to mitigate its hazardous environmental effects. Deep learning (DL) has recently emerged as a promising technique to improve traditional forecasts based on chemistry-transport models (CTMs). However, challenges remain in (1) representing the complex physics of the PM 2.5 pollution processes including the emission, transport, and chemical conversion of the pollutants,…
Atmospheric and Environmental Gas Dynamics (2 works) · Computer Science (2 works) · Geography (2 works) · Meteorology (2 works) · Air Quality and Health Impacts (1 works) · Air Quality Monitoring and Forecasting (1 works) · Artificial Intelligence (1 works) · Assimilation (phonology (1 works) · Baseline (sea (1 works) · Chemistry (1 works)