Synergistic multi‐wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp‐A

Abstract

.[1] In this paper, synergy refers to a process where the use of multiple satellite observations makes the retrieval more precise than the best individual retrieval. Two general strategies can be used in order to use multi-wavelength observations in an inversion scheme. First, the multi-wavelength observations are merged in the input of the retrieval scheme. This means that the various satellite observations are used simultaneously and that their possible interactions can be exploited by the retrieval scheme. Second, each multi-wavelength observations are used independently to retrieve a same geophysical variable and then, these independent retrievals are combined a posteriori using for example a simple weighted averaging. In this paper, it is shown that the first approach provides better synergy results: The retrieval is better suited to optimize the use of all the information available because they are provided to the algorithm simultaneously. In particular, the retrieval process is able, in this case, to exploit the possible interactions between the various input information. The two retrieval approaches are tested and compared using an application for the retrieval of atmospheric profiles and integrated column quantities (temperature, water vapor, and ozone) using MetOp-A observations from IASI, AMSU-A and MHS instruments. Although real satellite observations are considered in this analysis, the results are dependent on the correlation structure in the training data set (i.e. ECMWF analysis) used to calibrate the retrieval algorithm. However, it can be seen that the infrared and microwave observations have a good synergy for the retrieval of atmospheric temperature, water vapor, and for ozone thanks to an indirect synergy.Citation: Aires, F., O. Aznay, C. Prigent, M. Paul, and F. Bernardo (2012), Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

“…Such ANNs require a target dataset for training. Climate model simulations of the relevant geophysical variable are usually used as the training dataset to facilitate subsequent data assimilation efforts (Aires et al, 2012;Kolassa et al, 2013Kolassa et al, , 2016. However, the downside of this approach is that the resulting fluxes estimated by the ANN often exhibit some of the same biases as the simulations used to train the network (Rodriìguez-Fernández et al, 2015), even if improvements can be achieved such as a more realistic seasonal cycle as it is informed by the seasonal cycle of the remote sensing data (Jiménez et al, 2009).…”

“…This approach has been successfully used for global soil moisture retrieval (Aires et al, 2012;Kolassa et al, , 2016Rodriìguez-Fernández et al, 2015) and surface heat flux retrieval (Jiménez et al, 2009). Such ANNs require a target dataset for training.…”

Synergy between middle infrared and millimeter-wave limb sounding of atmospheric temperature and minor constituents

“…According to the definition given by Aires (2011) and implemented in Aires et al (2012), the synergy factor (SF) of a retrieval scheme using N sources of information (x 1 , x 2 , . .…”

“…Exploitation of potential synergies between two or more data sets from independent soundings can be achieved either by applying a suitable retrieval model involving simultaneous inversion of the observations or by a posteriori combination of the independent retrieval products from individual measurements. Aires et al (2012) compared the two strategies for data fusion in the case of multiwavelength remote sensing observations, demonstrating the better efficiency of the synergistic inversion scheme, with application to the retrieval of atmospheric profiles from IASI, the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) on MetOp-A. The present paper focuses on the performances of a posteriori data fusion methods with the purpose of identifying possible candidates to adequately replace the simultaneous retrieval, which is a demanding approach to the exploitation of complementary capabilities of two more measurement strategies.…”

Importance of interpolation and coincidence errors in data fusion

“…This is an a posteriori method that uses standard retrieval products. With simple implementation requirements, the CDF products are equivalent to those from a simultaneous retrieval, considered to be the most comprehensive way of exploiting different observations of the same quantity (Aires et al, 2012), in spite of a greater computational complexity. However, so far, the data fusion method 25 was mainly applied to measurements performed by the same instrument while sounding the same air sample.…”