Research on Inversion Algorithm of Aerosol Extinction Coefficient Based on Elman Neural Network

Abstract. In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1 459 200 unique mappings. 75 % randomly selected simulations were used for training and the remaining 25 % for validation. The overall error of estimated aerosol properties for (1) total AOD is −1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is −0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

Download Full-text

Evidence for the predictability of changes in the stratospheric aerosol size following volcanic eruptions of diverse magnitudes using space-based instruments

10.5194/acp-2020-480 ◽

2020 ◽

Author(s):

Larry W. Thomason ◽

Mahesh Kovilakam ◽

Anja Schmidt ◽

Christian von Savigny ◽

Travis Knepp ◽

...

Keyword(s):

Size Distribution ◽

Extinction Coefficient ◽

Volcanic Eruptions ◽

Stratospheric Aerosol ◽

Global Climate Models ◽

Aerosol Size Distribution ◽

Aerosol Layer ◽

Aerosol Extinction ◽

Volcanic Event ◽

Aerosol Extinction Coefficient

Abstract. An analysis of multiwavelength stratospheric aerosol extinction coefficient data from the Stratospheric Aerosol and Gas Experiment II and III/ISS instruments is used to demonstrate a coherent relationship between the perturbation in extinction coefficient in an eruption's main aerosol layer and an apparent change in aerosol size distribution that spans multiple orders of magnitude in the stratospheric impact of a volcanic event. The relationship is measurement-based and does not rely on assumptions about the aerosol size distribution. We note limitations on this analysis including that the presence of significant amounts of ash in the main aerosol layer may significantly modulate these results. Despite this limitation, these findings represent a unique opportunity to verify the performance of interactive aerosol models used in Global Climate Models and Earth System Model and may suggest an avenue for improving aerosol extinction coefficient measurements from single channel observations such the Optical Spectrograph and Infrared Imager System as they rely on a priori assumptions about particle size.

Download Full-text

An evaluation of the SAGE III Version 4 aerosol extinction coefficient and water vapor data products

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-9-22177-2009 ◽

2009 ◽

Vol 9 (5) ◽

pp. 22177-22222

Author(s):

L. W. Thomason ◽

J. R. Moore ◽

M. C. Pitts ◽

J. M. Zawodny ◽

E.-W. Chiou

Keyword(s):

Water Vapor ◽

Extinction Coefficient ◽

Stratospheric Aerosol ◽

Data Sets ◽

Aerosol Extinction ◽

Data Products ◽

Mean Differences ◽

Aerosol Extinction Coefficient ◽

Polar Ozone

Abstract. Herein, we provide an assessment of the data quality of Stratospheric Aerosol and Gas Experiment (SAGE III) Version 4 aerosol extinction coefficient and water vapor data products. The evaluation is based on comparisons with data from four instruments: SAGE II, the Polar Ozone and Aerosol Measurement (POAM III), the Halogen Occultation Experiment (HALOE), and the Microwave Limb Sounder (MLS). Since only about half of the SAGE III channels have a direct comparison with measurements by other instruments, we have employed some empirical techniques to evaluate measurements at some wavelengths. We find that the aerosol extinction coefficient measurements at 449, 520, 755, 869, and 1021 nm are reliable with accuracies and precisions on the order of 10% in the primary aerosol range of 15 to 25 km. We also believe this to be true of the aerosol measurements at 1545 nm though we cannot exclude some positive bias below 15 km. We recommend use of the 385 nm measurements above 16 km where the accuracy is on par with other aerosol channels. The 601 nm measurement is much noisier (~20%) than other channels and we suggest caution in the use of these data. We believe that the 676 nm data are clearly defective particularly above 20 km (accuracy as poor as 50%) and the precision is also low (~30%). We suggest excluding this channel under most circumstances. The SAGE III Version 4 water vapor data product appears to be high quality and is recommended for science applications in the stratosphere below 45 km. In this altitude range, the mean differences with all four corroborative data sets are no bigger than 15% and often less than 10% with exceptional agreement with POAM III and MLS. Above 45 km, it seems likely that SAGE III water vapor values are increasingly too large and should be used cautiously or avoided. We believe that SAGE III meets its preflight goal of 15% accuracy and 10% precision between 15 and 45 km. We do not currently recommend limiting the SAGE III water vapor data utility in the stratosphere by aerosol loading.

Download Full-text

PROCESSING JUMP POINT OF LIDAR DETECTION DATA AND INVERSING THE AEROSOL EXTINCTION COEFFICIENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w9-227-2019 ◽

2019 ◽

Vol XLII-3/W9 ◽

pp. 227-232

Author(s):

H. L. Zhang ◽

H. Zhao ◽

Y. P. Liu ◽

X. K. Wang ◽

C. Shu

Keyword(s):

Atmospheric Aerosols ◽

Extinction Coefficient ◽

Interpolation Method ◽

Linear Interpolation ◽

Mie Scattering ◽

Inversion Method ◽

Aerosol Extinction ◽

Detection Range ◽

Long Time ◽

Aerosol Extinction Coefficient

Abstract. For a long time, the research of the optical properties of atmospheric aerosols has aroused a wide concern in the field of atmospheric and environmental. Many scholars commonly use the Klett method to invert the lidar return signal of Mie scattering. However, there are always some negative values in the detection data of lidar, which have no actual meaning，and which are jump points. The jump points are also called wild value points and abnormal points. The jump points are refered to the detecting points that are significantly different from the surrounding detection points, and which are not consistent with the actual situation. As a result, when the far end point is selected as the boundary value, the inversion error is too large to successfully invert the extinction coefficient profile. These negative points are jump points, which must be removed in the inversion process. In order to solve the problem, a method of processing jump points of detection data of lidar and the inversion method of aerosol extinction coefficient is proposed in this paper. In this method, when there are few jump points, the linear interpolation method is used to process the jump points. When the number of continuous jump points is large, the function fitting method is used to process the jump points. The feasibility and reliability of this method are verified by using actual lidar data. The results show that the extinction coefficient profile can be successfully inverted when different remote boundary values are chosen. The extinction coefficient profile inverted by this method is more continuous and smoother. The effective detection range of lidar is greatly increased using this method. The extinction coefficient profile is more realistic. The extinction coefficient profile inverted by this method is more favorable to further analysis of the properties of atmospheric aerosol. Therefore, this method has great practical application and popularization value.

Download Full-text

Statistical Comparison of Regional-Scale Tropospheric Aerosol Extinction Coefficient across China Based on CALIPSO Data

Aerosol and Air Quality Research ◽

10.4209/aaqr.2017.10.0385 ◽

2018 ◽

Vol 18 (5) ◽

pp. 1351-1359 ◽

Cited By ~ 1

Author(s):

Yujun Qiu ◽

Leiming Zhang ◽

Yongsheng Chen

Keyword(s):

Extinction Coefficient ◽

Regional Scale ◽

Aerosol Extinction ◽

Statistical Comparison ◽

Tropospheric Aerosol ◽

Aerosol Extinction Coefficient

Download Full-text

Evidence for the predictability of changes in the stratospheric aerosol size following volcanic eruptions of diverse magnitudes using space-based instruments

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-1143-2021 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1143-1158 ◽

Cited By ~ 1

Author(s):

Larry W. Thomason ◽

Mahesh Kovilakam ◽

Anja Schmidt ◽

Christian von Savigny ◽

Travis Knepp ◽

...

Keyword(s):

Extinction Coefficient ◽

Climate Models ◽

Single Channel ◽

Volcanic Eruptions ◽

Stratospheric Aerosol ◽

Global Climate Models ◽

Aerosol Size Distribution ◽

Aerosol Layer ◽

Aerosol Extinction ◽

Aerosol Extinction Coefficient

Abstract. An analysis of multiwavelength stratospheric aerosol extinction coefficient data from the Stratospheric Aerosol and Gas Experiment II and III/ISS instruments is used to demonstrate a coherent relationship between the perturbation in extinction coefficient in an eruption's main aerosol layer and the wavelength dependence of that perturbation. This relationship spans multiple orders of magnitude in the aerosol extinction coefficient of stratospheric impact of volcanic events. The relationship is measurement-based and does not rely on assumptions about the aerosol size distribution. We note limitations on this analysis including that the presence of significant amounts of ash in the main sulfuric acid aerosol layer and other factors may significantly modulate these results. Despite these limitations, the findings suggest an avenue for improving aerosol extinction coefficient measurements from single-channel observations such as the Optical Spectrograph and Infrared Imager System as they rely on a prior assumptions about particle size. They may also represent a distinct avenue for the comparison of observations with interactive aerosol models used in global climate models and Earth system models.

Download Full-text