Xavier Calbet

Global Land Surface Emissivity Retrieved From Satellite Ultraspectral IR Measurements

Ultraspectral resolution infrared (IR) radiances obtained from nadir observations provide information about the atmosphere, surface, aerosols, and clouds. Surface spectral emissivity (SSE) and surface skin temperature from current and future operational satellites can and will reveal critical information about the Earths ecosystem and land-surface-type properties, which might be utilized as a means of long-term monitoring of the Earths environment and global climate change. In this study, fast radiative transfer models applied to the atmosphere under all weather conditions are used for atmospheric profile and surface or cloud parameter retrieval from ultraspectral and/or hyperspectral spaceborne IR soundings. An inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral IR sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface or cloud microphysical parameters. This inversion scheme has been applied to the Infrared Atmospheric Sounding Interferometer (IASI). Rapidly produced SSE is initially evaluated through quality control checks on the retrievals of other impacted surface and atmospheric parameters. Initial validation of retrieved emissivity spectra is conducted with Namib and Kalahari desert laboratory measurements. Seasonal products of global land SSE and surface skin temperature retrieved with IASI are presented to demonstrate seasonal variation of SSE.

Nonlinear Statistical Retrieval of Atmospheric Profiles From MetOp-IASI and MTG-IRS Infrared Sounding Data

This paper evaluates nonlinear retrieval methods to derive atmospheric properties from hyperspectral infrared sounding spectra, with emphasis on the retrieval of temperature, humidity, and ozone atmospheric profiles. We concentrate on the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-A satellite data for the future Meteosat Third Generation Infrared Sounder (MTG-IRS). The methods proposed in this work are compared in terms of both accuracy and speed with the current MTG-IRS L2 processing concept, which processes MetOp-IASI and proxy MTG-IRS data. The official chain consists of a principal component extraction, typically referred to as empirical orthogonal functions (EOF) and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering: 1) other linear feature extraction methods instead of EOF, such as partial least squares; and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks and kernel ridge regression as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models yield better results than linear retrieval for both MetOp-IASI and MTG-IRS synthetic and real data. Averaged gains throughout the column of +1.8 K and +2.2 K are obtained for temperature profile estimation from MetOp-IASI and IRS data, respectively. Similar gains are obtained for the estimation of dew point temperatures. In both variables, these improvements are more noticeable in lower atmospheric layers. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. The nonlinear statistical approach is successfully compared to optimal estimation (OE) in terms of accuracy, bias and computational cost. These results confirm the potential of statistical nonlinear inversion techniques for the retrieval of atmospheric profiles.

A geometrical derivation of the Boltzmann factor

We show that the Boltzmann factor has a geometrical origin, which follows from the microcanonical ensemble. The Maxwell–Boltzmann distribution or the wealth distribution in human society are some direct applications of this interpretation.

Derivation of the Maxwellian distribution from the microcanonical ensemble

The origin of the Boltzmann factor is revisited. An alternative derivation from the microcanonical picture is given. The Maxwellian distribution in a one-dimensional ideal gas is obtained by following this derivation. We also note other possible applications such as the wealth distribution in human society.

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 desert areas. For the identification of anomalies, we observed that the confidence intervals provided by the KRR may help in discarding big errors. High detection accuracy (around 90%) is achieved by a support vector machine, which largely outperforms standard linear and nonlinear classifiers.

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 a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial–spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors.

Nonlinear retrieval of atmospheric profiles from MetOp-IASI and MTG-IRS data

This paper evaluates the potential use of nonlinear retrieval methods to derive cloud, surface and atmospheric properties from hyperspectral MetOp-IASI and MTG-IRS spectra. The methods are compared in terms of both accuracy and speed with the current IASI and IRS L2 PPFP implementation, which consists of a principal component extraction, typically referred as to Empirical Orthogonal Functions (EOF), and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering 1) other linear feature extraction methods instead of EOF, such as (orthonormalized) partial least squares, and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks (NN) and kernel ridge regression (KRR) as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models outperform the linear retrieval both in the presence of noise and noise-free settings, and for both IASI and IRS synthetic and real data. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. These results confirm the validity of the proposed approach for retrieval of atmospheric profiles.

Validation of the IASI temperature and water vapor profile retrievals by correlative radiosondes

The METOP-A satellite Infrared Atmospheric Sounding Interferometer (IASI) Level 2 products comprise retrievals of vertical profiles of temperature and water vapor. The L2 data were validated through assessment of their error covariances and biases using radiosonde data for the reference. The radiosonde data set includes dedicated launches as well as the ones performed at regular synoptic times at Lindenberg station (Germany). For optimal error estimate the linear statistical Validation Assessment Model (VAM) was used. The model establishes relation between the compared satellite and reference measurements based on their relations to the true atmospheric state. The VAM utilizes IASI averaging kernels and statistical characteristics of the ensembles of the reference data to allow for finite vertical resolution of the retrievals and spatial and temporal non-coincidence. For temperature retrievals expected and assessed errors are in good agreement; error variances/rms of a single FOV retrieval are 1K between 800 - 300 mb with an increase to ~1K in tropopause and ~2K at the surface, possibly due to wrong surface parameters and undetected clouds/haze. Bias against radiosondes oscillates within ±0 5K . between 950 - 100 mb. As for water vapor, its highly variable complex spatial structure does not allow assessment of retrieval errors with the same degree of accuracy as for temperature. Error variances/rms of a single FOV relative humidity retrieval are between 10 - 13% RH in the 800 - 300 mb range.

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-dimensional input-output spaces efficiently. We give empirical evidence of models performance on Infrared Atmospheric Sounding Interferometer (IASI) simulated data. Kernel regression model largely improves previous least squares linear regression model quantitatively, with an average reduction of 25% in mean-square error.