Spatiotemporal variation of aerosol optical depth based on three-dimensional spatiotemporal interpolation

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

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Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia

Journal of Geophysical Research Atmospheres ◽

10.1029/2011jd016159 ◽

2011 ◽

Vol 116 (D23) ◽

pp. n/a-n/a ◽

Cited By ~ 100

Author(s):

Zhiquan Liu ◽

Quanhua Liu ◽

Hui-Chuan Lin ◽

Craig S. Schwartz ◽

Yen-Huei Lee ◽

...

Keyword(s):

East Asia ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Dust Storm ◽

Three Dimensional ◽

Variational Assimilation

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Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-10425-2013 ◽

2013 ◽

Vol 13 (20) ◽

pp. 10425-10444 ◽

Cited By ~ 47

Author(s):

P. E. Saide ◽

G. R. Carmichael ◽

Z. Liu ◽

C. S. Schwartz ◽

H. C. Lin ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Regional Scale ◽

Three Dimensional ◽

Upper And Lower Bounds ◽

Aerosol Size Distribution ◽

Multi Wavelength ◽

Fine Mode ◽

Sectional Model ◽

Weak Constraints

Abstract. An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM2.5 predictions reduced at over 90% of the stations and AOD errors reduced at 100% of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

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The CAMS reanalysis of atmospheric composition

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-3515-2019 ◽

2019 ◽

Vol 19 (6) ◽

pp. 3515-3556 ◽

Cited By ~ 68

Author(s):

Antje Inness ◽

Melanie Ades ◽

Anna Agustí-Panareda ◽

Jérôme Barré ◽

Anna Benedictow ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Three Dimensional ◽

Chemical Species ◽

Horizontal Resolution ◽

Atmospheric Composition ◽

Reanalysis Dataset ◽

Weather Forecasts ◽

Forecasting System ◽

Total Column

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

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Simultaneous three-dimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth

Journal of Geophysical Research Atmospheres ◽

10.1029/2011jd017383 ◽

2012 ◽

Vol 117 (D13) ◽

pp. n/a-n/a ◽

Cited By ~ 67

Author(s):

Craig S. Schwartz ◽

Zhiquan Liu ◽

Hui-Chuan Lin ◽

Stuart A. McKeen

Keyword(s):

Particulate Matter ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Fine Particulate Matter ◽

Three Dimensional ◽

Variational Assimilation ◽

Fine Particulate

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Improved 1-km-Resolution Hourly Estimates of Aerosol Optical Depth Using Conditional Generative Adversarial Networks

Remote Sensing ◽

10.3390/rs13193834 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3834

Author(s):

Luo Zhang ◽

Peng Liu ◽

Lizhe Wang ◽

Jianbo Liu ◽

Bingze Song ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Network Architecture ◽

Atmospheric Correction ◽

Resolution Enhancement ◽

Three Dimensional ◽

Local Environment ◽

Generative Adversarial Networks ◽

Sampled Data ◽

Generative Adversarial Network

Aerosol Optical Depth (AOD) is a crucial parameter for various environmental and climate studies. Merging multi-sensor AOD products is an effective way to produce AOD products with more spatiotemporal integrity and accuracy. This study proposed a conditional generative adversarial network architecture (AeroCGAN) to improve the estimation of AOD. It first adopted MODIS Multiple Angle Implication of Atmospheric Correction (MAIAC) AOD data to training the initial model, and then transferred the trained model to Himawari data and obtained the estimation of 1-km-resolution, hourly Himawari AOD products. Specifically, the generator adopted an encoder–decoder network for preliminary resolution enhancement. In addition, a three-dimensional convolutional neural network (3D-CNN) was used for environment features extraction and connected to a residual network for improving accuracy. Meanwhile, the sampled data and environment data were designed as conditions of the generator. The spatial distribution feature comparison and quantitative evaluation over an area of the North China Plain during the year 2017 have shown that this approach can better model the distribution of spatial features of AOD data and improve the accuracy of estimation with the help of local environment patterns.

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Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale forecasts

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-12213-2013 ◽

2013 ◽

Vol 13 (5) ◽

pp. 12213-12261 ◽

Cited By ~ 1

Author(s):

P. E. Saide ◽

G. R. Carmichael ◽

Z. Liu ◽

C. S. Schwartz ◽

H. C. Lin ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Regional Scale ◽

Three Dimensional ◽

Upper And Lower Bounds ◽

Aerosol Size Distribution ◽

Multi Wavelength ◽

Fine Mode ◽

Sectional Model ◽

Weak Constraints

Abstract. An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system when WRF-Chem forecasts are performed with a detailed sectional model (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA. A first set of simulations is performed comparing the assimilation impacts of operational MODIS dark target retrievals to observationally constrained ones (i.e. calibrated with AERONET data), the latter ones showing higher error reductions and increased fraction of improved PM2.5 and AOD ground-based monitors. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.

Download Full-text