Chemical Data Assimilation (CDA)
Satellite evaluation and validation are necessary, but sampling issues often make practical application problematic. This site presents a chemical data assimilation system (AutoChem) that aims to aid constituent evaluation/validation by using the multivariate technique of data assimilation cast in Lagrangian coordinates. An advantage of assimilation is that it propagates information from data-rich regions to data-poor regions. Data assimilation also offers a mathematical framework to check and quantify the chemical consistency of multispecies observations with one another and with photochemical theory through the use of objective skill scores. That is, the analysis can examine both the consistency between different instruments observing the same constituent, and the photochemical self-consistency between multiconstituent observations and photochemical theory. The latter issue has not figured highly in previous satellite instrument evaluation.
Chemical data assimilation can ingest observations from multiple platforms to prepare global stratospheric multiconstituent analyses. These analyses can then be sampled in the same way as the instruments to be validated and compared with the observations to be validated using such tools as scatter diagrams, comparing probability distribution functions (quantile-quantile plots), and skill scores. The skill scores quantify various aspects of bias between observations and photochemical theory. Since the typical magnitude of these scores is known from previous assimilation studies they should be useful in highlighting any instrument biases that may be present in the observations to be validated.
The observations to be validated could be processed similarly to the validating observations to produce global analyses which are then intercompared with the same tools. Additionally, chemical data assimilation can add value to observations by providing synoptic analyses for scientific studies in the same way that meteorological data are used to produce meteorological analyses that are then used in scientific studies.
A particular use of this methodology is for the validation of constituents with a marked diurnal cycle (such as NO2) and those for which there is only a small amount of poorly matched data.
Why Use Assimilation?
The question could be asked, 'How does using chemical data assimilation differ from using reverse domain filling (RDF) isentropic trajectories where ozone depletion is computed along trajectories using aircraft and satellite data or other 2D and 3D photochemical models?'
Chemical data assimilation combines the observational information available from measurements with the theoretical information encapsulated into a deterministic model of atmospheric chemistry, together with the associated uncertainties of each. Specifically assimilation uses:
- Constituent observations (in this case observations of many constituents simultaneously).
- Observational uncertainty.
- Theoretical knowledge to propagate information from the observations available to other species and locations and evaluate chemical self consistency.
- Provide an uncertainty on the analysis produced that accounts for observational uncertainty, temporal and spatial representativeness uncertainty, and theoretical (model) uncertainty, i.e. assimilation requires a specification of the error statistics of the observations and the underlying model.
The basic elements of the analysis system infrastructure are described here.
We use a PDF analysis to help decide which data to assimilate .