• Atmosphere
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• v.21 no.2
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• pp.131-142
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• 2011

Spectral solar irradiances were observed using a visible and UV Multi-Filter Rotating Shadowband Radiometer on the rooftop of the Science Building at Yonsei University, Seoul ($37.57^{\circ}N$, $126.98^{\circ}E$, 86 m) during one year period in 2006. 1-min measurements of global(total) and diffuse solar irradiances over the solar zenith angle (SZA) ranges from $20^{\circ}$ to $70^{\circ}$ were used to examine the effects of clouds and total optical depth (TOD) on enhancing four solar irradiance components (broadband 395-955 nm, UV channel 304.5 nm, visible channel 495.2 nm, and infrared channel 869.2 nm) together with the sky camera images for the assessment of cloud conditions at the time of each measurement. The obtained clear-sky irradiance measurements were used for empirical model of clear-sky irradiance with the cosine of the solar zenith angle (SZA) as an independent variable. These developed models produce continuous estimates of global and diffuse solar irradiances for clear sky. Then, the clear-sky irradiances are used to estimate the effects of clouds and TOD on the enhancement of surface solar irradiance as a difference between the measured and the estimated clear-sky values. It was found that the enhancements occur at TODs less than 1.0 (i.e. transmissivity greater than 37%) when solar disk was not obscured or obscured by optically thin clouds. Although the TOD is less than 1.0, the probability of the occurrence for the enhancements shows 50~65% depending on four different solar radiation components with the low UV irradiance. The cumulus types such as stratoculmus and altoculumus were found to produce localized enhancement of broadband global solar irradiance of up to 36.0% at TOD of 0.43 under overcast skies (cloud cover 90%) when direct solar beam was unobstructed through the broken clouds. However, those same type clouds were found to attenuate up to 80% of the incoming global solar irradiance at TOD of about 7.0. The maximum global UV enhancement was only 3.8% which is much lower than those of other three solar components because of the light scattering efficiency of cloud drops. It was shown that the most of the enhancements occurred under cloud cover from 40 to 90%. The broadband global enhancement greater than 20% occurred for SZAs ranging from 28 to $62^{\circ}$. The broadband diffuse irradiance has been increased up to 467.8% (TOD 0.34) by clouds. In the case of channel 869.0 nm, the maximum diffuse enhancement was 609.5%. Thus, it is required to measure irradiance for various cloud conditions in order to obtain climatological values, to trace the differences among cloud types, and to eventually estimate the influence on solar irradiance by cloud characteristics.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.