JRAero: the Japanese Reanalysis for Aerosol v1.0

Abstract

Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a twodimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1 • × 1.1 • ). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations.Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under-and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts.AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0.90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

A 3-D evaluation of the MACC reanalysis dust product over Europe, northern Africa and Middle East using CALIOP/CALIPSO dust satellite observations

“…Apart from being a potent assimilation tool it is shown here that LIVAS constitutes an ideal observational dataset for the evaluation of climate model simulations and reanalysis datasets, and the need for more studies towards this direction is acknowledged. It is suggested that dust products from more recent reanalysis projects such as the CAMS interim reanalysis (CAMSiRA) (Flemming et al, 2017), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (Gelaro et al, 2017) and the Japanese Reanalysis for Aerosol (JRAero) (Yumimoto et al, 2017) should be evaluated in a similar way (2-D or 3-D evaluation depending on the availability of dust profile data).…”

“…The amount of dust emitted into the atmosphere depends on surface wind speed and on factors such as soil texture, soil moisture and vegetation cover (IPCC, 2013). Dust is also produced locally from anthropogenic activities (e.g., manufacturing, construction, mining, agricultural activities, herding livestock, off-road vehicles and warfare) (Zender et al, 2004). On a global scale, it has been estimated that natural sources account for ∼ 75 % and anthropogenic sources for ∼ 25 % of the dust emissions (Ginoux et al, 2012).…”

Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM): comparison with measurements and model simulations

“…Model difficulties in reproducing these atmospheric dust properties are largely associated with their inability to accurately simulate important dust processes, such as dust emission, transport, and deposition (e.g. Ginoux et al, 2001;Shao, 2001;Zender et al, 2003;Huneeus et al, 2011;Kok et al, 2017). Dust aerosols are emitted from source regions such as the Saharan, Middle East, and Asian deserts and are deposited after they are transported for thousands of kilometres (Duce et al, 1980;Prospero et al, 1981;Weinzierl et al, 2017).…”

An updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018

“…Meanwhile, assimilation of AOD and aerosol observations are required to further improve the representation of primary aerosol emissions and concentrations (e.g. Yumimoto et al, 2017). Simultaneous assimilation of trace gas and aerosol observations would be a powerful approach to fully represent aerosol-gas interactions in the data assimilation framework, which would improve both trace gas and aerosol data assimilation analysis.…”