The effect of aerosol representation on cloud microphysical properties in Northeast Asia

This study performed a three-dimensional regional-scale simulation of aerosol and cloud fields using a meso-scale non-hydrostatic model with a bin-based cloud microphysics. The representation of aerosols in the model has been improved to account for more realistic multi-modal size distribution and multiple chemical compositions. Two case studies for shallow stratocumulus over Northeast Asia in March 2005 were conducted with different aerosol conditions to evaluate model performance. Improved condensation nuclei (CN) and cloud condensation nuclei (CCN) are attributable to the newly constructed aerosol size distribution. The simulated results of cloud microphysical properties (cloud droplet effective radius, liquid water path, and optical thickness) with improved CN/CCN number are close to the retrievals from satellite-based observation. The effects of aerosol on the microphysical properties of shallow stratocumulus are investigated by model simulation, in terms of columnar aerosol number concentration. Enhanced aerosol number concentration results in increased liquid water path in humid case, but invariant liquid water path in dry case primarily due to precipitation occurrence. The changes of cloud microphysical properties are more predominant for small aerosol burden than for large aerosol burden with the retarded changes in cloud mass and size due to inactive condensation and collision-coalescence processes. Quantitative evaluation of sensitivity factor between aerosol and cloud microphysical properties indicates a strong aerosol-cloud interaction in Northeast Asian region.     

Retrieval of outgoing longwave radiation from COMS narrowband infrared imagery

Hourly outgoing longwave radiation(OLR) from the geostationary satellite Communication Oceanography Meteorological Satellite(COMS) has been retrieved since June 2010. The COMS OLR retrieval algorithms are based on regression analyses of radiative transfer simulations for spectral functions of COMS infrared channels. This study documents the accuracies of OLRs for future climate applications by making an intercomparison of four OLRs from one single-channel algorithm(OLR12.0using the 12.0 μm channel) and three multiple-channel algorithms(OLR10.8+12.0using the 10.8 and 12.0 μm channels; OLR6.7+10.8using the 6.7 and 10.8 μm channels; and OLR All using the 6.7, 10.8, and 12.0 μm channels). The COMS OLRs from these algorithms were validated with direct measurements of OLR from a broadband radiometer of the Clouds and Earth's Radiant Energy System(CERES) over the full COMS field of view [roughly(50°S–50°N, 70°–170°E)] during April 2011.Validation results show that the root-mean-square errors of COMS OLRs are 5–7 W m-2, which indicates good agreement with CERES OLR over the vast domain. OLR6.7+10.8and OLR All have much smaller errors(～ 6 W m-2) than OLR12.0and OLR10.8+12.0(～ 8 W m-2). Moreover, the small errors of OLR6.7+10.8and OLR All are systematic and can be readily reduced through additional mean bias correction and/or radiance calibration. These results indicate a noteworthy role of the6.7 μm water vapor absorption channel in improving the accuracy of the OLRs. The dependence of the accuracy of COMS OLRs on various surface, atmospheric, and observational conditions is also discussed.     

Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation

Statistical seasonal prediction models for the Arctic sea ice concentration (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-ice regions (of one- or multi-year sea ice). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model.     

Regional Distribution of Perceived Temperatures Estimated by the Human Heat Budget Model （the Klima-Michel Model） in South Korea

The regional distribution of perceived temperatures (PT) for 28 major weather stations in South Korea during the past 22 years (1983–2004) was investigated by employing a human heat budget model, the Klima-Michel model. The frequencies of a cold stress and a heat load by each region were compared. The sensitivity of PT in terms of the input of synoptic meteorological variables were successfully tested. Seogwipo in Jeju Island appears to be the most comfortable city in Korea. Busan also shows a high frequenc...     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

Field Evaluation of the Vertical Bearing Capacity of a Screw Pretensioned Spun High-Strength Concrete Pile

This study has evaluated the vertical bearing capacity by conducting static load tests for noise-free and vibration-free screw pretensioned spun high-strength concrete (PHC) piles installed using two different methods (end-squirting shoe and pre-boring methods). Vertical bearing capacity differences seem to occur due to the displacement of soils near the external circumference of a pile, depending on the installation method. A method by which to evaluate the bearing capacity of screw concrete piles is suggested by considering the equations that already have been used to calculate the bearing capacity of piles. Based on static load tests and analysis, the pile installed using the end-squirting shoe method was assumed to be a bored pile and it was reasonable to use the equation proposed by the Japanese Geotechnical Society. At the same time, the pile installed using the pre-boring method was deemed a low soil displacement pile and so it was reasonable to apply the equations proposed for calculating the bearing capacity of the driven pile suggested by the Architectural Institute of Japan.     

Effects of particle shape on the cushioning mechanics of rock-filled gabions

Acta Geotechnica - Rock-filled gabions are commonly installed in front of reinforced concrete structures to reduce concentrated impact loads induced by rock fall and boulders entrained in debris...