Химический анализ зоопланктона методом лазерно-искровой эмиссионной спектроскопии без использования образцов сравнения

This study deals with the determination of ratios of light metals (Li, Na, K, Mg, and Ca) in zooplankton (Calanus spp.) by calibration free laser-induced breakdown spectroscopy, assuming local thermodynamic equilibrium. The temperature of laser-induced plasma of zooplankton was derived from rotation-vibration bands of CN, and the electron density was calculated by Stark broadening of Mg I 383.23 nm, Li I 610.37 nm, and Ca II 396.85 nm lines. The synthetic spectra calculated with a radiation transport model for the experimental values of T and Ne were used for a selection of analytical atomic lines free from self-absorption. We compared the obtained data with the results of atomic emission and mass spectrometry with inductively coupled plasma. We also discussed the influence of ionization equilibrium on the accuracy of the results. We propose the presented method for direct semi-quantitative determination of Li, Mg, and Ca ratios in zooplankton.

Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.

Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter

Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation.

Investigating the assimilation of CALIPSO global aerosol vertical observations using Four-Dimensional Ensemble Kalman Filter

The aerosol vertical information is critical to quantify the influences of the aerosol on the climate and environment, however large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non‐hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using the four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF). Additionally, a parallel assimilation experiment using the bias-corrected Aerosol Optical Thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations whether including the vertical information on the model performances. The assimilation experiments are successfully performed for a one-month long, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP observed aerosol vertical extinction coefficients and the AOT observations from the MODIS and AErosol RObotic NETwork (AERONET). Our results reveal both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation. This is probably due to the nadir-viewing CALIOP has much sparser coverages than the MODIS. The assimilation efficiencies of CALIOP decrease with the increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation.

The M-1 Radiation Model: Evaluation for Predicting Thermal Radiation From Fires

The M-1 radiation model is a thermal radiation transport model that is derived from a maximum entropy approximation to the radiative transport equation. It involves the solution of four hyperbolic equations for conservation of radiative energy. The M-1 model has similarities to the classical diffusion approximations (like P-1), but is able to better predict directed flux. Consequently, shadowing and long-range transport can be well resolved for a fraction of the cost of methods with exponentially increasing accuracy costs like the method of discrete ordinates and Monte Carlo ray-tracing. The M-1 method is mostly used historically in astronomical radiation transport, but has recently been shown to work for combustion applications of smaller scale. Past work has shown it to give good comparisons to fire problems with length scales of interest. Because of the potential for the model to predict radiation transport more cost-effectively, it is being examined for implementation as an option in our fire codes. We present the theory behind the model. The Eddington factor is used to partition directed and diffuse radiation. It is normally modeled since it is derived from a transcendental functional relationship. We analyze Eddington factor models presented in previous work, and present a new model that we show to be superior in most ways to all the previously presented models. Some 1-dimensional calculations are also shown that illustrate the potential accuracy and challenges with implementing the M-1 model. Such challenges include the specification of boundary conditions and the development of robust solver methods.