## 4 Towards Enhanced Global-Scale Local-Relevant Monitoring and Forecasting
### Relevance for Atmospheric Composition  
Satellite retrievals of gases and aerosols in the global atmosphere carry the imprint of the surface fluxes of these compounds. The latter can therefore be inferred from the former by inversion of the atmospheric transport and of the atmospheric chemistry within a Bayesian approach. In recent years, Earth observations from space-based instruments have played a key role in improving our understanding of surface processes driving air composition, like for air quality [314].  
The need to complement or evaluate the diverse bottom-up estimations of these fluxes by a top-down approach that would be inclusive of all source and sink processes has dramatically increased the interest in inverse modelling over the last decade (see, e.g., item 59 in SBSTA 2017, [[https://unfccc.int/sites/default/files/resource/docs/2017/sbsta/eng/07.pdf](https://unfccc.int/sites/default/files/resource/docs/2017/sbsta/eng/07.pdf)]([https://unfccc.int/sites/default/files/resource/docs/2017/sbsta/eng/07.pdf](https://unfccc.int/sites/default/files/resource/docs/2017/sbsta/eng/07.pdf))). In some cases, like for greenhouse gases, surface flux estimation has become a primary motivation for some satellite programmes, like NASA's Orbiting Carbon Observatory. A step further consists in optimizing some key parameters of Earth surface models based on inferred surface fluxes. For carbon monoxide, such an optimization could target fire emission factors to resolve the long-lasting mismatch in the peak fire month in Africa between models and satellite observations [315]. For ammonia, it could improve the representation of fertilisers spreading practices in inventories [316]. For methane, it could allow wetland models to better represent the response to extreme events [317]. For carbon dioxide, it could facilitate the sophistication of carbon processes representation outside the peak growing season, such as leaf flushing [318]. For aerosols, it could improve binding energy or other parameters in mineral dust emission models [319].  
However, in practice, detecting enhanced or depleted plumes from space does not imply that the associated surface sources and sinks can be quantified to sufficiently useful accuracy and localization. The modesty of many plume signals when averaged vertically (particularly in the case of long-lived tracers), their fuzzy boundary within their background and with other plumes, joined to systematic errors (for given atmospheric or surface conditions) in satellite retrievals, the coarse resolution of EO systems, and in atmospheric transport and chemistry models, have slowed down progress and keep inverse modelling for Earth observation as a major scientific challenge. Some of the solutions found so far are trivial: an empirical bias correction of the retrievals before or within the inversion systems; a rise in computer resources allocated to chemistry-transport models in inversion systems in order to increase horizontal and vertical numerical resolutions. More robust solutions require dedicated spectroscopic or transport-process studies. For chemistry, measurements of new tracers that could serve as proxies may be necessary. In the next decade, satellite imagers for greenhouse gases and pollutants are expected and will bring more emphasis on flux estimation at the local scale for usage outside the scientific community. They will likely require optimizing local meteorological variables together with surface fluxes. Similarly, there is a growing need to merge information from various sensors for a given species, or for species emitted by common processes, like fire or fossil fuel burning. Properly quantifying the uncertainty of each data-stream, of the correlations between these, and of underlying models is prerequisite for these developments.