[The Application of the L-Curve Method in the Retrieval of Temperature Profiles Using Ground-Based Hyper-Spectral Infrared Radiance].

The thermodynamic profiles of Planetary Boundary Layer could be retrieved by using ground-based hyper-spectral infrared radiance. The AERIoe algorithm has a better performance at the dependency of initial profiles than the "onion peeling" method which was originally applied in the Atmospheric Emitted Radiance Interferometer. The regularization parameter is the key to the AERIoe algorithm, and the strategy for choosing the regularization parameter in the retrieval algorithm is based on the empirical method, which requires too much time for computation while the empirical method needs many iteration steps. A L-curve criterion is proposed to calculate the regularization parameter in AERIoe algorithm. The L-curve criterion is based on a log-log plot of corresponding values of the residual and solution norms, and the optimal regularization parameter corresponds to a point on the curve near the "corner" of the L-shaped region. Therefore, the L-curve criterion has better theoretical basis than the traditional empirical method. The result of retrieval experiment using the observed data collected at the SGP site of the year 2011 shows that, the L-curve method has a good performance in terms of stability, convergence and accuracy of the retrieval. Compared with empirical method, L-curve algorithm converges more quickly which saves much computation time when retrieving the temperature profiles. When considering the retrieval accuracy, the L-curve method has a better behavior at the middle and top of the boundary layer, with an improvement of 0.2 K of RMSE at the altitude of 1～3 km than the empirical method. Therefore, the L-curve algorithm has a better performance compared with the empirical method when choosing the regularization parameter in the retrieval of temperature profiles using the ground-based hyper-spectral infrared radiance.

[Research on the Noise Reduction with Hyper-Resolution Infrared Spectrum Based on Improved PCV Method].

The noise reduction with observed high resolution infrared radiance is crucial to improve the accuracy and stability of the retrieval of thermodynamic profiles. When applying the principal component analysis noise filter algorithm to the observed radiance, the optimal number k of principal components that used in the algorithm was mostly calculated with the statistical and empirical method. The percent cumulative variance method is one of the statistical methods that have been commonly used to calculate k, however, the threshold of the percent cumulative variance was determined subjectively and arbitrarily, which limits the application of this method. While the empirical method need the real-time Noise-Equivalent Spectral Radiance (NESR) to normalize non uniform noise in the observed data, but the real-time NESR needs the raw data of complex spectrum which is not easy to obtain in most cases. Aiming at the solving the problems above, a PCA noise filter based on the Improved PCV algorithm is proposed, of which the threshold is determined by iteratively calculating the difference between the simulated and reconstructed spectrum using different principal components, whereby k is determined such that the PCV is larger than the threshold. The new method solves the problem of arbitrary of the determination of k, and at the same time it doesn't need the real-time NESR to normalize the observed radiance. First, the impact of normalization on the noise reduction is analyzed using physical retrieval of temperature profiles; the result shows that the impact is very small, which less than the impact of calculation error of k is caused by normalization on the retrieval of temperature profiles. Then, the noise reduction of the representative radiance data which covers four quarters of 2011 shows that, the RMSE of the retrieved temperature profile using the Improved PCV method is improved by 0.1 K compared to the factor indicator function method when the real-time NESR is not available, and it is almost the same with the latter when the normalization is done. Under the condition that the NESR is not available, the method proposed in this article could objectively and reasonably reduce the noise level of the ground-based high resolution infrared radiance.

[Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (2): Retrieval of Cloud Effective Radius and Water Path].

The cloud microphysical properties such as cloud effective radius and cloud water path are fundamental properties for understanding the cloud formation, radiative impacts and interactions with aerosol and precipitation. The downwelling infrared radiance spectra is studied here to retrieve microphysical properties of clouds. The sensitivity of the downwelling radiance spectra and cloud emissivity spectra to the liquid cloud and ice cloud effective radius and optical depth is analyzed. The look-up-tables are established for optically thin clouds (cloud optical depth less than 6) that rely on parameters of the slopes and differences of the emissivity spectra. These parameters include the difference in the emissivity between 862.1 and 934.9 cm(-1), the difference in the emissivity between 1 900.1 and 2 170.1 cm(-1), the slope of the cloud emissivity and the radiation between 900 and 1 000 cm(-1), the slope of the cloud emissivity and the radiation between 1 100 and 1 200 cm(-1). The look-up-tables are constrained by the incorporation of mean ozone band transmissivity between 1 050 and 1 060 cm(-1). Cloud effective radius and optical depth can be obtained with by least squares fitting between observed and modeled above-mentioned multiple spectral parameters. The cloud water path can then be derived from the experiential relationship. The inversion results are compared with the ARM baseline cloud microphysics product (MICROBASE). It is shown that, the cloud effective radius is roughly in the same order of magnitude while the water paths derived from both method are of large differences especially for the liquid cloud path. The algorithm proposed in this paper is efficient for retrieving microphysical properties of thin clouds with cloud optical depth less than 6.

[Research on Cloud Phase Detemination Using Infrared Emissivity Spectrum Data (1): Cloud Phase Determination].

As a key factor in the climate model, cloud phase is an important prerequisite to performing cloud property retrievals from remote sensor measurements. The ability to infer cloud phase using cloud emissivity spectra is investigated by numerical simulations. It is shown that for emissivity below 0.95, several spectral features such as the slopes, the ratios and the differences of the emissivity are consistent with the variation of cloud phase in some spectral regions. Specifically, these features include the slope of the cloud emissivity between 800 and 900 cm(-1), the slope of the cloud emissivity between 900 and 1 000 cm(-1), the difference in the mean emissivity between above-mentioned two regions, the ratio of the emissivity at 862.1 cm(-1) to the emissivity at 989.8 cm(-1), the difference in the emissivity between 862.1 and 989.8 cm(-1), the ratio of the emissivity at 1 900.1 cm-1 to the emissivity at 2 029.3 cm-1, the ratio of the mean emissivity for far-infrared region to the emissivity at 900 cm(-1). A cloud phase classifier is proposed based on support vector machines (SVM). A series of simulations including various cloud patterns are performed. The RBF kernel function parameters and the penalty factor of SVM are selected by using the genetic algorithm. The phase determination algorithm is applied for collecting data from the AERI at the SGP site. The results from the ground-based multisensor cloud phase classifier proposed by Shupe are used to validate the phase determination algorithm. It is found the two results are consistent in general. 30% clouds are indicated as opaque due to its high emissivity. The cloud with small lidar's depolarization is misclassified as clear sky by the Shupe method. It can be concluded that the proposed algorithm considering the spectral information (spectral slopes, ratios and differences) is efficient for cloud phase determination of thin cloud.