Diurnal variation of aerosol optical depth and PM_2.5 in South Korea: a synthesis from AERONET, satellite (GOCI), KORUS-AQ observation, and the WRF-Chem model

Spatial distribution of diurnal variations of aerosol properties in South Korea, both long term and short term, is studied by using 9 AERONET (AErosol RObotic NETwork) sites from 1999 to 2017 and an additional 10 sites during the KORUS-AQ (Korea–United States Air Quality) field campaign in May and June of 2016. The extent to which the WRF-Chem (Weather Research and Forecasting coupled with Chemistry) model and the GOCI (Geostationary Ocean Color Imager) satellite retrieval can describe these variations is also analyzed. On a daily average, aerosol optical depth (AOD) at 550&thinsp;nm is 0.386 and shows a diurnal variation of 20 to −30&thinsp;% in inland sites, which is larger than the AOD of 0.308 and diurnal variation of ±20&thinsp;% seen in coastal sites. For all the inland and coastal sites, AERONET, GOCI, and WRF-Chem, and observed PM2.5 (particulate matter with aerodynamic diameter less than 2.5&thinsp;µm) data generally show dual peaks for both AOD and PM2.5, one in the morning (often at  ∼ 08:00–10:00&thinsp;KST, Korea Standard Time, especially for PM2.5) and another in the early afternoon ( ∼ 14:00&thinsp;KST, albeit for PM2.5 this peak is smaller and sometimes insignificant). In contrast, Ångström exponent values in all sites are between 1.2 and 1.4 with the exception of the inland rural sites having smaller values near 1.0 during the early morning hours. All inland sites experience a pronounced increase in the Ångström exponent from morning to evening, reflecting an overall decrease in particle size in daytime. To statistically obtain the climatology of diurnal variation of AOD, a minimum requirement of  ∼ 2 years of observation is needed in coastal rural sites, twice as long as that required for the urban sites, which suggests that the diurnal variation of AOD in an urban setting is more distinct and persistent. While Korean GOCI satellite retrievals are able to consistently capture the diurnal variation of AOD (although it has a systematically low bias of 0.04 on average and up to 0.09 in later afternoon hours), WRF-Chem clearly has a deficiency in describing the relative change of peaks and variations between the morning and afternoon, suggesting further studies for the diurnal profile of emissions. Furthermore, the ratio between PM2.5 and AOD in WRF-Chem is persistently larger than the observed counterparts by 30&thinsp;%–50&thinsp;% in different sites, but spatially no consistent diurnal variation pattern of this ratio can be found. Overall, the relatively small diurnal variation of PM2.5 is in high contrast with large AOD diurnal variation, which suggests the large diurnal variation of AOD–PM2.5 relationships (with the PM2.5&thinsp;∕&thinsp;AOD ratio being largest in the early morning, decreasing around noon, and increasing in late afternoon) and, therefore, the need to use AOD from geostationary satellites to constrain either modeling or estimate of surface PM2.5 for air quality application.</p

The solar WIND and suprathermal ion composition investigation on the WIND spacecraft

The Solar Wind and Suprathermal Ion Composition Experiment (SMS) on WIND is designed to determine uniquely the elemental, isotopic, and ionic-charge composition of the solar wind, the temperatures and mean speeds of all major solar-wind ions, from H through Fe, at solar wind speeds ranging from 175 kms −1 (protons) to 1280 kms −1 (Fe +8 ), and the composition, charge states as well as the 3-dimensional distribution functions of suprathermal ions, including interstellar pick-up He + , of energies up to 230 keV/e. The experiment consists of three instruments with a common Data Processing Unit. Each of the three instruments uses electrostatic analysis followed by a time-of-flight and, as required, an energy measurement. The observations made by SMS will make valuable contributions to the ISTP objectives by providing information regarding the composition and energy distribution of matter entering the magnetosphere. In addition SMS results will have an impact on many areas of solar and heliospheric physics, in particular providing important and unique information on: (i) conditions and processes in the region of the corona where the solar wind is accelerated; (ii) the location of the source regions of the solar wind in the corona; (iii) coronal heating processes; (iv) the extent and causes of variations in the composition of the solar atmosphere; (v) plasma processes in the solar wind; (vi) the acceleration of particles in the solar wind; and (vii) the physics of the pick-up process of interstellar He as well as lunar particles in the solar wind, and the isotopic composition of interstellar helium.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43776/1/11214_2004_Article_BF00751327.pd

Constructive cooperative coevolution for large-scale global optimisation

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework