Search

We propose an algorithm that combines a pre-processing step applied to the a priori state vector prior to retrievals, with the modified damped Newton method (MDNM), to improve convergence. The initial constraint vector pre-processing step updates the initial state vector prior to the retrievals if the algorithm detects that the initial state vector is far from the true state vector in extreme cases where there are CO emissions. The MDNM uses the Levenberg-Marquardt parameter γ, which ensures a positive Hessian matrix, and a scale factor α, which adjusts the step size to optimize the stability of the convergence. While the algorithm iteratively searches for an optimized solution using observed spectral radiances, MDNM adjusts parameters γ and α to achieve stable convergence. We present simulated retrieval samples to evaluate the performance of our algorithm and comparing it to existing methods. The standard deviation of our retrievals adding random noise was less than 3.8 ppmv. After pre-processing the initial estimate when it was far from the true value, the CO retrieval errors in the boundary layers were within 1.2 ppmv. We tested the MDNM algorithm's performance using GOSAT L1b data with cloud screening. Our preliminary validations comparing the results to TCCON FTS measurements showed that the average bias was less than 1.8 ppm and the correlation coefficient was approximately 0.88, which was larger than for the GOSAT L2 product. [ABSTRACT FROM AUTHOR]

We introduce the path length probability density function (PPDF) method, which is based on an equivalence theorem and parameterizes the aerosol scattering effect by adding four factors to the atmospheric transmittance model. Using simulated observations in the O-A band, we examined the utility of the PPDF-based method to account for the aerosol scattering effect. First, observations were simulated using a forward model under different aerosol conditions; PPDF factors were then retrieved using an optimal estimation method; PPDF factors were used to reconstruct the observations; and finally, simulated true observations and reconstructions were compared. Analysis of the difference between the true observations and reconstructions confirmed the utility of the PPDF-based method. Additionally, the O band was demonstrated to be an efficient observing band for assisting the remote sensing of atmospheric trace gases in the near-infrared band. [ABSTRACT FROM AUTHOR]

Recently, polyolefin thermoplastic elastomers can be obtained directly using ethylene as a single feedstock via α-diimine nickel-catalyzed ethylene chain walking polymerization. Here, a new range of bulky acenaphthene-based α-diimine nickel complexes with hybrid o-phenyl and -diarylmethyl anilines were constructed and applied to ethylene polymerization. All the nickel complexes under the activation of excess Et2AlCl exhibited good activity (level of 106 g mol−1 h−1) and produced polyethylene with high molecular weight (75.6–352.4 kg/mol) as well as proper branching densities (55–77/1000C). All the branched polyethylenes obtained exhibited high strain (704–1097%) and moderate to high stress (7–25 MPa) at break values. Most interestingly, the polyethylene produced by the methoxy-substituted nickel complex exhibited significantly lower molecular weights and branching densities, as well as significantly poorer strain recovery values (48% vs. 78–80%) than those by the other two complexes under the same conditions. [ABSTRACT FROM AUTHOR]

It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH4 trends from three different measurements: the mid–upper tropospheric CH4 (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH4 in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH4 in the atmosphere (XCH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH4 trends in the mid–upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH4 increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH4 in the MUT, as observed by AIRS, are overall smaller than CH4 in MBL and the column-average CH4. During 2009–2011, there was a dip in the increase rate for CH4 in MBL, and the MUT-CH4 increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH4 also reached the minimum during 2009–2011 accordingly, suggesting that the trends of CH4 are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH4 increase rates in the mid–high northern latitude regions is larger than those in the tropics and southern hemisphere. [ABSTRACT FROM AUTHOR]

This article is intentionally published to make erratum to the print version and the online version of Vol.60 (2017) NO. 2 (). Citations of the following publications should be added to the list of Reference for TCCON data use: Wennberg P O, Roehl C M, Wunch D, Toon G C, Blavier J F, Washenfelder R A, Keppel-Aleks G, Allen N T, Ayers J. 2014. TCCON data from Park Falls, Wisconsin, USA, Release GGG2014R0. TCCON data archive, hosted by the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA, http://dx.doi.org/10.14291/tccon.ggg2014.parkfalls01.R0/1149161 Wennberg P O, Wunch D, Roehl C M, Blavier J F, Toon G C, Allen N, Dowell P, Teske K, Martin C, Marti J. 2014. TCCON data from Lamont, Oklahoma, USA, Release GGG2014R0. TCCON data archive, hosted by the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA, http://dx.doi.org/10.14291/tccon.ggg2014.lamont01.R0/1149159 We should have asked for approvals from relevant PIs before publication on the use of TCCON data and cited references by DOIs. We do apologize for the fault and will strictly follow the Data Use Policy in the future. [ABSTRACT FROM AUTHOR]

Understanding how inbreeding affects fitness is biologically important for conservation and pest management. Despite being a worldwide pest of many economically important cruciferous crops, the influence of inbreeding on diamondback moth, Plutella xylostella (L.), populations is currently unknown. Using age-stage-specific life tables, we quantified the inbreeding effects on fitness-related traits and demographic parameters of P. xylostella. Egg hatching rate, survival and fecundity of the inbred line significantly declined compared to those of the outbred line over time. The inbred P. xylostella line showed significantly lower intrinsic rate of increase (r), net reproduction rate (R0), and finite increase rate (λ), and increasing generation time (T). Inbreeding effects vary with developmental stages and the fitness-related traits can be profoundly affected by the duration of inbreeding. Our work provides a foundation for further studies on molecular and genetic bases of the inbreeding depression for P. xylostella. [ABSTRACT FROM AUTHOR]

As one kind of remote sensing image (RSI), Directional Polarimetric Camera (DPC) data are of great significance in atmospheric radiation transfer and climate feedback. The availability of DPC images is often hindered by clouds, and effective cloud detection is the premise of many applications. Conventional threshold-based cloud detection methods are limited in performance and generalization capability. In this paper, we propose an effective learning-based 3D multimodal fusion cloud detection network (MFCD-Net) model. The network is a three-input stream architecture with a 3D-Unet-like encoder-decoder structure to fuse the multiple modalities of reflectance image, polarization image Q, and polarization image U in DPC imagery, with consideration of the angle and spectral information. Furthermore, cross attention is utilized in fusing the polarization features into the spatial-angle-spectral features in the reflectance image to enhance the expression of the fused features. The dataset used in this paper is obtained from the DPC cloud product and the cloud mask product. The proposed MFCD-Net achieved excellent cloud detection performance, with a recognition accuracy of 95.74%, according to the results of the experiments. [ABSTRACT FROM AUTHOR]

Cloud detection is one of the critical tasks in remote sensing image preprocessing. Remote sensing images usually contain multi-dimensional information, which is not utilized entirely in existing deep learning methods. This paper proposes a novel cloud detection algorithm based on multi-scale input and dual-channel attention mechanisms. Firstly, we remodeled the original data to a multi-scale layout in terms of channels and bands. Then, we introduced the dual-channel attention mechanism into the existing semantic segmentation network, to focus on both band information and angle information based on the reconstructed multi-scale data. Finally, a multi-scale fusion strategy was introduced to combine band information and angle information simultaneously. Overall, in the experiments undertaken in this paper, the proposed method achieved a pixel accuracy of 92.66% and a category pixel accuracy of 92.51%. For cloud detection, the proposed method achieved a recall of 97.76% and an F1 of 95.06%. The intersection over union (IoU) of the proposed method was 89.63%. Both in terms of quantitative results and visual effects, the deep learning model we propose is superior to the existing semantic segmentation methods. [ABSTRACT FROM AUTHOR]

This paper presents a rapid regression algorithm for the retrieval of methane (CH) profile from Atmospheric Infrared Sounder (AIRS) based on empirical orthogonal functions (EOF) and its validation. This algorithm was trained using the simulated radiance from an assemble of atmospheric profiles and can be utilized to derive the CH profile rapidly with the input of the AIRS cloud-clear radiance. Validation using hundreds of aircraft profiles demonstrates that the root mean square error (RMSE) is about 1.5 % in the AIRS sensitive region of 359-596 hPa, which is smaller than AIRS-V5 product (except in high latitudes). Comparison with the ground-based solar Fourier transform spectrometry observations showed that the RMSE of the retrieved CH total column amount is less than 3 %. This EOF-based regression method can be easily applied to other thermal infrared sounders for deriving CH and some other gases, and the derived profiles can be used as the first guess for further physical retrieval. [ABSTRACT FROM AUTHOR]

We propose a new method to estimate surface-level particulate matter (PM) concentrations by using satellite-retrieved Aerosol Optical Thickness (AOT). This method considers the distribution and variation of Planetary Boundary Layer (PBL) height and relative humidity (RH) at the regional scale. The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT. By incorporation MODIS AOT, PBL height and RH simulated by RAMS, this method is applied to estimate the surface-level PM concentrations in North China region. The result is evaluated by using 16 ground-based observations deployed in the research region, and the result shows a good agreement between estimated PM concentrations and observations, and the coefficient of determination R is 0.61 between the estimated PM concentrations and the observations. In addition, surface-level PM concentrations are also estimated by using MODIS AOT, ground-based LIDAR observations and RH measurements. A comparison between the two estimated PM concentrations shows that the new method proposed in this paper is better than the traditional method. The coefficient of determination R is improved from 0.32 to 0.62. [ABSTRACT FROM AUTHOR]

The Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite is a spaceborne Fourier transform infrared spectrometer. The study aims to retrieve carbon dioxide (CO2) information (the CO2 profile and column-averaged dry-air mole fraction of XCO2) from June 2018 to December 2019 based on the The National Oceanic and Atmospheric Administration (NOAA)-Unique Combined Atmospheric Processing System (NUCAPS) Cloud-Cleared Radiances (CCRs) via the CrIS. The CCRs products for the CrIS with 2223 channels have been available since 22 May 2018. Characteristics of the CO2 weighting functions inform the choice of multiple channels that are around 15 μm in size that differ by latitude and season to maximize retrieval sensitivity to CO2 and minimize sensitivity to other interfering atmospheric parameters. CO2 was retrieved from these channels using an adopted nonlinear optimization algorithm. The temperature, water vapor, and ozone profiles used in the inversion process were gathered from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Validations of CO2 concentrations as retrieved from CrIS showed the following conclusions: (1) The relative error of the retrieved CO2 concentrations, as compared to Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container (CARIBIC) in situ aircraft measurements, was less than 0.5%, and the root mean square errors (RMSE) were less than 0.7 ppmv (with correlation coefficients of 0.56–0.86); (2) the retrieved XCO2 from June 2018 to December 2019 correlated well with the ground-based Total Carbon Column Observing Network (TCCON) observations, and the differences were within ±0.2 ppmv. Further analysis of the temporal and spatial distribution of the retrieved CO2 at 300 hPa demonstrated a strong seasonal variation of CO2 in 0–60° N in the Northern Hemisphere with the maximum values in June–August and larger amplitudes of seasonal variation in the northeast of Asia and northeastern part of North America. The variations likely occurred due to larger sinks of atmospheric CO2 that are dominated by CO2 uptake in the summer. In the Southern Hemisphere, the CO2 displayed high concentration anomalies in the latitudinal range of 30–60° S in September–November and December–February, which probably occurred due to the lofted smoke plumes from the strong fire seasons in South America and Southern Africa. [ABSTRACT FROM AUTHOR]

• An all fiber near infrared laser heterodyne radiometer (NIR-LHR) is introduced for simultaneous atmospheric remote sensing of CO 2 and CH 4. • A 1 × 2 fiber optical switch is employed as an alternative modulator to traditional chopper, which makes the system more stable and compact. • A novel and precise correlation correction method is developed for the wavenumbers shift corrections. • To validate the ground NIR-LHR results, observations over six months were compared with the GOSAT data results. The performance of an all fibered near-infrared laser heterodyne radiometer (NIR-LHR) is demonstrated in ground-based solar occultation mode. A 1 × 2 fiber optical switch is employed as an alternative modulator to traditional chopper, which makes the system more stable and compact. Two distribute feedback (DFB) lasers centered at 1.6 μm and 1.65 μm, are employed as local oscillators to probe absorption lines of CO 2 and CH 4 , respectively. After data processing and retrieval, column-averaged abundances and the mixing ratio of atmospheric CO 2 and CH 4 are determined based on the measurement of over six months in our laboratory (Hefei, China, 31.9°N 117.16°E). Finally, the results are validated by comparison with the GOSAT results during the same time period. The reported all fibered laser heterodyne radiometer in this manuscript has great potential to be a portable and high spectral resolution instrument for atmospheric remote sensing of multi-component gases. [ABSTRACT FROM AUTHOR]