Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite

Kikuchi, Maki; Murakami, Hiroshi; Suzuki, Kentaroh; Nagao, Taro; Higurashi, Akiko

doi:10.1109/tgrs.2018.2800060

Cited by 102 publications

(70 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To overcome temporal resolution limitations, there were several attempts to retrieve AOD using first-generation meteorological geostationary satellites such as the Geostationary Operational Environmental Satellite (GOES), the Geostationary Meteorological Satellite (GMS), and the Multifunction Transport Satellite (MTSAT), but they showed worse accuracy than those of LEO sensors due to the wider and fewer visible channels with coarser spatial resolution, which make it difficult to distinguish aerosol types (Kim et al, 2008;Knapp et al, 2002;Urm and Sohn, 2005;Wang et al, 2003;Yoon et al, 2007). As the specifications of recently launched geostationary Earth orbit (GEO) sensors, such as the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI) over East Asia and the Advanced Baseline Imager (ABI) over the United States, are approaching those of current LEO sensors, aerosol optical properties can be retrieved with an accuracy as high as that of LEO sensors, and at much higher temporal resolutions, from a few minutes to an hour during daylight hours (Chen et al, 2018;Choi et al, 2016Choi et al, , 2018Daisaku, 2016;Kikuchi et al, 2018;Lee et al, 2010;Lim et al, 2018;Zhang et al, 2018). This breakthrough in temporal resolution of GEO aerosol data enables us to monitor highly variable aerosol conditions and improve air quality forecasting, particularly for PM, with data assimilation (Jeon et al, 2016;Lee et al, 2016;Pang et al, 2018;Park et al, 2014;Saide et al, 2014) or machine learning (Park et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign

et al. 2019

View full text Add to dashboard Cite

Recently launched multichannel geostationary Earth orbit (GEO) satellite sensors, such as the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI), provide aerosol products over East Asia with high accuracy, which enables the monitoring of rapid diurnal variations and the transboundary transport of aerosols. Most aerosol studies to date have used low Earth orbit (LEO) satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR), with a maximum of one or two overpass daylight times per day from midlatitudes to low latitudes. Thus, the demand for new GEO observations with high temporal resolution and improved accuracy has been significant. In this study the latest versions of aerosol optical depth (AOD) products from three LEO sensors -MODIS (Dark Target, Deep Blue, and MAIAC), MISR, and the Visible/Infrared Imager Radiometer Suite (VIIRS), along with two GEO sensors (GOCI and AHI), are validated, compared, and integrated for a period during the Korea-United States Air Quality Study (KORUS-AQ) field campaign from 1 May to 12 June 2016 over East Asia. The AOD products analyzed here generally have high accuracy with high R (0.84-0.93) and low RMSE (0.12-0.17), but their error characteristics differ according to the use of several different surface-reflectance estimation methods. Highaccuracy near-real-time GOCI and AHI measurements facilitate the detection of rapid AOD changes, such as smoke aerosol transport from Russia to Japan on 18-21 May 2016, heavy pollution transport from China to the Korean Peninsula on 25 May 2016, and local emission transport from the Seoul Metropolitan Area to the Yellow Sea in South Korea on 5 June 2016. These high-temporal-resolution GEO measurements result in more representative daily AOD values and make a greater contribution to a combined daily AOD product assembled by median value selection with a 0.5 • × 0.5 • grid resolution. The combined AOD is spatially continuous and has a greater number of pixels with high accuracy (fraction within expected error range of 0.61) than individual products. This study characterizes aerosol measurements from LEO and GEO satellites currently in operation over East Asia, and the results presented here can be used to evaluate satellite measurement bias and air quality models.

show abstract

Section: Introductionmentioning

confidence: 99%

Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In this study, we focus on assimilating the latest version of the Himawari‐8 Level 3 hourly merged AOTs at 500 nm (Kikuchi et al, ). This data set contains as many AOT retrievals as possible with a horizontal resolution of 0.05° × 0.05°, which are derived from a set of AOTs with strict cloud screening within the preceding 1 hr.…”

Section: Observational Data and Operatormentioning

confidence: 99%

“…During this preprocessing, we aggregate the original Himawari‐8 gridded AOTs to a model horizontal resolution of 2° × 2° using the mean value of all the data points in the aggregation cell and assign a missing value for the grid where the number of effective Himawari‐8 observations is less than 320, which represents 20% of the maximum possible number of 0.05° × 0.05° Himawari‐8 observations in a 2° × 2° grid. The threshold value of 20% is applied to avoid representing coarse grid values with limited observations filled only within a portion of the coarse grid (Kikuchi et al, ; Schutgens et al, ). The gridded total observation error variances (

σ_{o}^{2}

) are estimated as the sum of the instrumental error variance (

σ_{i}^{2}

) and sample error variance (

σ_{s}^{2}

), or the so‐called spatial representative error variance (Zhang et al, ).…”

Section: Observational Data and Operatormentioning

confidence: 99%

“…During regridding, the σ i and σ s values are calculated as the mean of the retrieval uncertainties given in the official Himawari‐8 AOT product and the standard deviation of the AOTs, respectively. The detailed algorithm to estimate the retrieval uncertainty of the Himawari‐8 hourly AOT is given in Kikuchi et al ().…”

Section: Observational Data and Operatormentioning

confidence: 99%

See 1 more Smart Citation

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

Dai

Cheng

Suzuki

et al. 2019

J Adv Model Earth Syst

Self Cite

View full text Add to dashboard Cite

The next‐generation geostationary satellite Himawari‐8 has a much higher observation frequency of the aerosol field than polar‐orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one‐monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari‐8‐retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non‐hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM‐SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four‐dimensional LETKF (4D‐LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari‐8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)‐ and AErosol RObotic NETwork (AERONET)‐retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root‐mean‐square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari‐8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D‐LETKF experiments are generally not so different, but the computational load of the 4D‐LETKF is much lighter than that of the LETKF.

show abstract

“…However, satellite AOD products are defined at relatively coarse spatial resolution. For example, AOD products have been developed using data from Total Ozone Mapping Spectrometer (TOMS) at 1 • resolution [14], Polarization and Directionality of Earth Reflectances (POLDER) at 1/6 • resolution [15], Multi-angle Imaging SpectroRadiometer (MISR) at 17.6 km resolution [16], Medium Resolution Imaging Spectrometer (MERIS) at 10 km resolution [17], Visible Infrared Imaging Radiometer Suite (VIIRS) at 6 km resolution [18], Himawari-8 Geostationary at 0.05 • resolution [19], and MODIS at 10 km, 3 km [9,20] and 1 km resolution [21]. Coarse resolution products are less suitable for studying aerosols in urban environments where aerosol distributions may change at finer spatial scales due to factors including spatial variations in building and transport infrastructure and human population densities [22][23][24].The recently launched Landsat-8 and Sentinel-2 satellites can be used to derive AOD data at 30 m and 10 m resolution respectively and so have the potential for urban aerosol monitoring.…”

mentioning

confidence: 99%

Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

Roy

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

show abstract

Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite

Cited by 102 publications

References 31 publications

Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign

Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

Contact Info

Product

Resources

About