Abstract
Abstract. Retrieving time series of atmospheric constituents from ground-based spectrometers often requires different temporal averaging depending on the altitude region in focus. This can lead to several datasets existing for one instrument, which complicates validation and comparisons between instruments. This paper puts forth a possible solution by incorporating the temporal domain into the maximum a posteriori (MAP) retrieval algorithm. The state vector is increased to include measurements spanning a time period, and the temporal correlations between the true atmospheric states are explicitly specified in the a priori uncertainty matrix. This allows the MAP method to effectively select the best temporal smoothing for each altitude, removing the need for several datasets to cover different altitudes. The method is compared to traditional averaging of spectra using a simulated retrieval of water vapour in the mesosphere. The simulations show that the method offers a significant advantage compared to the traditional method, extending the sensitivity an additional 10 km upwards without reducing the temporal resolution at lower altitudes. The method is also tested on the Onsala Space Observatory (OSO) water vapour microwave radiometer confirming the advantages found in the simulation. Additionally, it is shown how the method can interpolate data in time and provide diagnostic values to evaluate the interpolated data.
Highlights
The use of different temporal resolutions is exemplified poral correlations between the true atmospheric states are explicitly specified in the a priori uncertainty matrix
The method is compared to traditional averaging of spechas used a 6 h averaging time in a case study of sudden stratospheric warming (SeeleHanyddHraortologhg, y20a00n) das well as a 24 h tra using a simulated retrieval of water vapour in the mesosphere
The ratio between the required averaging times for high method is tested on the Onsala Space Observatory and low altitudes will in large part be determined by the alti
Summary
In passive atmospheric remote sensing, properties of the atmosphere are determined by analysing the radiation emitted from, and passing through, the atmosphere. The relationship between the measured radiation, y, and the atmospheric properties is described by a forward model, y = F (x), where x, denoted as the state vector, contains the variables to be retrieved. These can include atmospheric variables at different altitudes as well as instrument variables. The regularisation in the time series inversion method is based on the maximum a posteriori (MAP) method, called optimal estimation It uses statistical properties of the measurements and the atmosphere to constrain the solutions (Rodgers, 2000). The sum of a row in the AVK matrix is a measure of the retrieval’s sensitivity to changes in the state vector and is called measurement response (Baron et al, 2002), or measurement sensitivity
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