Gastvortrag Prof. Dr. Alessandro Fassò (Universität Bergamo) am 26. Oktober 2017
Fr., 27. Oktober 2017
10:00 - 12:00 Uhr (GD 312)
Am 27. Oktober hat Prof. Alessandro Fassò von der Universität Bergamo einen Gastvortrag zum Thema "Statistical harmonization and uncertainty assessment in the comparison of satellite and radiosonde climate variables" gehalten.
Satellite validation is key to ensure the delivery of consistent long-term climate data records for climate and weather studies. Since the agreement of satellite measurements (SAT) with ground-based reference measurements (GND) is an essential quality indicator, one of the major issues to perform a rigorous validation is the quantification of the uncertainty due to the co-location mismatch in time and space between satellite and ground based reference observations.
This mismatch is due to the different sampling of atmosphere carried out by the two instruments (Verholest et al., 2015), which are also quite often based on very different remote sensing techniques. Moreover, satellite and ground based observations are typically collected on very different time and spatial scales. As a consequence, in a SAT-GND comparison we may have horizontal and/or vertical and/or temporal mismatches in smoothing and/or resolution, which are factors of the co-location mismatch uncertainty (hereinafter mismatch uncertainty). In the frame of Horizon 2020 GAIA-CLIM project (www.gaia-clim.eu), this paper considers the mismatch uncertainty in the comparison of the satellite observations obtained by the Infrared Atmospheric Sounding Interferometer (IASI) instrument with the radiosonde observations of the RAOB network.
Along these lines, the present paper focuses on the vertical smoothing mismatch uncertainty of IASI-RAOB profile comparison using a rigorous likelihood approach which takes into account autocorrelation, heteroscedasticity and atmospheric smoothness. In particular the vertical sparseness of RAOB network is assessed by means of a comparison with GRUAN reference products where available.
The proposed technique is a two-step technique. At the first step RAOB profiles are transformed into continuous functions using smoothing weighted splines, which are optimized to much as close as possible to GRUAN reference profiles. In doing this, vertical sparseness uncertainty and processing mismatch uncertainty are assessed. At the second step RAOB are harmonized by considering weighting functions based on the Generalized Extreme Values (GEV) probability density function (pdf) whose parameters are given by a regression on some covariates, which is regularized to take into account atmospheric smoothness. Since the model is highly nonlinear and data are not Gaussian, confidence intervals of parameter estimates and of mismatch uncertainties are assessed by a bootstrap technique.