0000000001317131
AUTHOR
Xavier Calbet
Can turbulence within the field of view cause significant biases in radiative transfer modeling at the 183 GHz band?
The hypothesis whether turbulence within the passive microwave sounders field of view can cause significant biases in radiative transfer modeling at the 183 GHz water vapor absorption band is tested. A novel method to calculate the effects of turbulence in radiative transfer modeling is presented. It is shown that the turbulent nature of water vapor in the atmosphere can be a critical component of radiative transfer modeling in this band. Radiative transfer simulations are performed comparing a uniform field with a turbulent one. These comparisons show frequency dependent biases which can be up to several kelvin in brightness temperature. These biases can match experimentally observe…
Improved Statistically Based Retrievals via Spatial-Spectral Data Compression for IASI Data
In this paper, we analyze the effect of spatial and spectral compression on the performance of statistically based retrieval. Although the quality of the information is not com- pletely preserved during the coding process, experiments reveal that a certain amount of compression may yield a positive impact on the accuracy of retrievals. We unveil two strategies, both with interesting benefits: either to apply a very high compression, which still maintains the same retrieval performance as that obtained for uncompressed data; or to apply a moderate to high compression, which improves the performance. As a second contribution of this paper, we focus on the origins of these benefits. On the one…
Statistical atmospheric parameter retrieval largely benefits from spatial-spectral image compression
The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield…
Kernel-based retrieval of atmospheric profiles from IASI data
This paper proposes the use of kernel ridge regression (KRR) to derive surface and atmospheric properties from hyperspectral infrared sounding spectra. We focus on the retrieval of temperature and humidity atmospheric profiles from Infrared Atmospheric Sounding Interferometer (MetOp-IASI) data, and provide confidence maps on the predictions. In addition, we propose a scheme for the identification of anomalies by supervised classification of discrepancies with the ECMWF estimates. For the retrieval, we observed that KRR clearly outperformed linear regression. Looking at the confidence maps, we observed that big discrepancies are mainly due to the presence of clouds and low emissivities in de…
Nonlinear statistical retrieval of surface emissivity from IASI data
Emissivity is one of the most important parameters to improve the determination of the troposphere properties (thermodynamic properties, aerosols and trace gases concentration) and it is essential to estimate the radiative budget. With the second generation of infrared sounders, we can estimate emissivity spectra at high spectral resolution, which gives us a global view and long-term monitoring of continental surfaces. Statistically, this is an ill-posed retrieval problem, with as many output variables as inputs. We here propose nonlinear multi-output statistical regression based on kernel methods to estimate spectral emissivity given the radiances. Kernel methods can cope with high-dimensi…
IASI dataset v1
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series measures the infrared spectrum with high resolution. The ground footprint resolution of the instruments is 12 km at nadir, and a spectral resolution of 0.25cm −1 in the spectrum between 645 cm −1 and 2760 cm −1 . This results in 8461 spectral samples covering 2200km scan-swath with 60 points per line. IASI is an ideal instrument for monitoring different physical/chemical parameters in the atmosphere e.g. temperature, humidity and trace gases such as ozone. Energy from different altitudes returns a different spectral shift. In this way atmospheric profiles can be obtained and these provides important …