We developed a new aerosol satellite retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used as an a priori estimate instead of spatially and temporally constant values. This method was demonstrated using observation of the Advanced Himawari Imager onboard the Japan Meteorological Agency's geostationary satellite Himawari-8. Overall, the retrieval results incorporated strengths of the observation and the model and complemented their respective weaknesses, showing spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than an a priori model forecast by adding satellite information. Further, the satellite retrieval accuracy was improved by introducing the model forecast instead of the constant a priori estimates. By using the assimilated forecast for an a priori estimate, information from previous observations can be propagated to future retrievals, leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model are incorporated cyclically to effectively estimate the optimum field of aerosol.

Aerosols have a fundamental influence on the energy budget of the earth's
climate system through the scattering and absorption of solar radiation. The
fifth assessment report of the Intergovernmental Panel on Climate Change
(IPCC, 2014) stated that radiative forcing of the total aerosol effect in the
atmosphere, including cloud adjustments due to aerosols, is

In satellite aerosol remote sensing, not all aerosol properties can be
accurately detected by satellite sensors, as there are more unknown aerosol
parameters (e.g., particle size distributions, vertical density
distribution, shape, refractive index) than the actual information obtained
by the sensors. Most studies use assumptions or information about aerosol
parameters and limit the number of parameters retrieved. For example,
Higurashi and Nakajima (1999) and Fukuda et al. (2013) assumed fixed
complex refractive indices (1.5–0.005

Aerosol data assimilation methods using satellite data have also been developed to obtain better initial conditions for the aerosol transport model. The aerosol data assimilation study was first developed with low earth orbit (LEO) satellites (Benedetti et al., 2009; Saide et al., 2013; Dai et al., 2014; Rubin et al., 2016; Yumimoto et al., 2015). In recent years, assimilation studies have been extended to using geostationary satellites with large spatial coverage and fine observation frequencies (Saide et al., 2014; Lee et al., 2016; Yumimoto et al., 2016, 2018; Dai et al., 2019; Jin et al., 2019).

Due to the development of such assimilation studies, the satellite data have contributed to improving aerosol forecast simulations. However, no studies have utilized assimilated model forecast as an a priori estimate of the retrieval. Since satellite sensors cannot accurately detect all parameters and unrealistic assumptions of aerosol parameters are a major cause of retrieval errors as mentioned above, adding the model information is expected to improve the retrieval accuracy. Therefore, in this study, we utilize the forecast of an aerosol transport model for a priori estimates of the retrieval. This allows the aerosol information in the aerosol transport model to be used for retrieval. By using the assimilated forecast, information from previous satellite observations can be propagated to future satellite retrievals through the aerosol transport model.

The sections in this study are organized as follows: Sect. 2 explains the retrieval methodology in detail. Section 3.1 presents the results of application to the Advanced Himawari Imager (AHI) onboard Himawari-8. Section 3.2 describes the validation of the estimations using ground observations, and Sect. 3.3 tests the worst-case scenario. Finally, Sect. 4 summarizes our findings.

The aerosol retrieval algorithm in this study is based on Yoshida et al. (2018). As an a priori estimate of the retrieval, the algorithm introduces aerosol forecast from a transport model that has assimilated previous satellite observations. Given the general applicability of the retrieval algorithm by Yoshida et al. (2018), the methodology explained in this section can also be applied to various sensors. Here, we demonstrate the algorithm using the Himawari-8/AHI whose assimilation system is operationally available. The AHI has six observation bands from visible to near-infrared wavelength ranges and observes the top-of-atmosphere (TOA) radiance at a resolution of 0.5–2.0 km over Asia and Oceania at 10 min intervals (Bessho et al., 2016).

Flowchart of data processing for aerosol retrieval at time

Figure 1 depicts an overview of the algorithm, showing the process of using
forecast data for a priori estimates of the retrieval. In the original
retrieval process, the Level-2 (L2) aerosol optical thickness at 500 nm
(

The L3

Forecast of aerosol transport model used for retrieval at time

Mean (upper) and standard deviation (lower) of

In the new retrieval process, we retrieve the L2 aerosol properties
(

To introduce a more realistic a priori estimate and covariances into the
retrieval process, we employ the forecast from the aerosol assimilation
system instead of the constants. The model forecast includes the total
aerosol optical thickness at 500 and 870 nm and the absorption aerosol
optical thickness at 500 nm derived from the modeled volume concentration
and extinction cross section of each aerosol component (Yumimoto et al.,
2017). We assign an a priori estimate

The assimilation system uses an ensemble method to calculate the background
error covariance matrix (Yumimoto et al., 2018). In the method, the ensemble
was collected from forecast values within

Aerosol optical thickness at 500 nm

Same as Fig. 4, except for the case at 05:00 UTC on 7 May 2017.

We applied the methodology described in Sect. 2 to the Himawari-8/AHI. We
retrieved

Frequency distribution of

Figures 4 and 5 compare the retrieval results from the new algorithm using

We conducted a preliminary validation of our method by comparing the
retrieved

Aerosol optical thickness at 500 nm

Figure 6 compares the

For the

Frequency distribution of

Frequency distribution of

Frequency distribution of

Frequency distribution of the difference between

For the

For the

We also investigated the cause of the possible large deviation between the
retrieved parameters from the new algorithm and the ground observation.
Figures 8, 9, and 10 show the validation results of

Same as Fig. 4, except for using the forecast on 27 April 2018 as an a priori estimate.

We have shown that the new retrieval algorithm using the forecast of an
aerosol transport model improves the retrieval accuracy. However, in order
to use this algorithm constantly (such as in an operational system), the
effects of the model forecast (a priori estimate) that deviate from reality
must be examined because the model forecast may miss an aerosol event.
Therefore, we conducted a sensitivity test to investigate the impact on the
retrieval results of using unrealistic forecast as an a priori estimate. Figure 12 shows the retrieval results on the same day as in Fig. 4, except for
using the forecast on another day (27 April 2018) as an a priori estimate of
the retrieval (Fig. 12d). If only

We developed a new satellite aerosol retrieval algorithm combining a
numerical aerosol forecast. In the retrieval algorithm, the short-term
forecast from an aerosol data assimilation system was used for an a priori
estimate instead of spatially and temporally constant values. This is the
first study that utilizes the assimilated model forecast of aerosol as an a priori estimate of the satellite retrieval. We applied this new algorithm to
the Himawari-8/AHI and confirmed that the aerosol parameters detectable by
satellite sensors were retrieved more accurately (RMSE of 0.290 for

We assume that the aerosol model is an external mixture of fine and coarse
particles (

Regarding each aerosol size, we use a monomodal lognormal volume size
(

Figure B1 shows the relations of the final retrieval parameters

The relations of

Himawari-8/AHI aerosol data are available from the JAXA Himawari Monitor (2020) site:

MY developed the retrieval code and analyzed the data with significant conceptual input from KY and critical technical support from TMN, MK, and HM. KY and TYT prepared the assimilated forecast data for test cases and long time validations, respectively. MY prepared the paper with contributions from all co-authors.

The authors declare that they have no conflict of interest.

The authors are grateful to the Open CLASTER project for allowing us to use the RSTAR package for this research. We would like to thank the AERONET project and its staff for establishing and maintaining the AERONET sites considered in this investigation. We also thank Haruma Ishida for providing the cloud detection algorithm (CLAUDIA). Finally, we appreciate the valuable discussions and support provided by Takashi Maki, Tsuyoshi Sekiyama, Makiko Hashimoto, and Teruyuki Nakajima.

This research has been supported by the JSPS Grants-in-Aid for Scientific Research (grant no. JP16H02946) and the JAXA Earth Observation Priority Research (grant no. PI.ER2GCF212).

This paper was edited by Stelios Kazadzis and reviewed by Alexander Kokhanovsky and two anonymous referees.