1,721,188 research outputs found
Mechanisms for the Decadal Change of the MJO Teleconnection over the Northern Hemisphere in Boreal Winter
Full text linkDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)This thesis suggests dynamical mechanisms for the decadal change of the MJO teleconnection pattern over the Northern Hemisphere during boreal winter by investigating the influence of the decadal change of background states in the mid-latitude and tropical forcing on MJO teleconnection pattern and interpreting this relationship.
Thus, it is important to understand how these changes influence on MJO teleconnection pattern to represent the MJO teleconnection pattern caused by changes of background states and MJO activities over the tropics. In boreal winter, OLR variance with intraseasonal variability variance is strengthened over the Maritime Continent and weakened over the southern Indian Ocean and the western Pacific. This is consistent with the results of the MJO amplitude at each MJO phase based on OMI. These changes of MJO activity are associated with the La Nina-like change of background states in tropics. There is wet and warm anomaly over the Maritime Continent and the western Pacific, and ascending motion over the Maritime Continent, whereas dry, cold and descending anomaly over the central and the eastern Pacific. The location of MJO teleconnection patterns can be determined by seasonal mean upper-level zonal wind acted as a waveguide. The obvious change of the jet stream is found, especially over the eastern Pacific. Over this region, stationary Rossby wavenumber is expanded east and northward in the recent period than that in the past period. This change modifies the location and intensity of the teleconnection patterns based on the analysis of zonal wavenumber.
Based on the model experiments, the decadal change in the background states, especially zonal wind at the upper level, leads to strengthening the intensity of the MJO teleconnection pattern over the jet exit region but does not modify the pattern itself. On the other hand, the tropical heating modifies the teleconnection patterns over the North Pacific and North America, and these changes are similar to the observed difference maps between the two periods. Thus, the results indicate that the decadal change of the MJO teleconnection pattern is caused by the change of tropical diabatic heating rather than the change of zonal wind in the mid-latitude as background states. These changes are associated with the background states are changed into La Nina-like pattern in the recent period. This leads to moisture and SST increase over the warm pool region. In addition, the ascending motion of the vertical circulation in the background states strengthens over the eastern region of Maritime Continent, and then the convective anomaly becomes stronger over this region in the recent period than in the past period. It is thought that these background changes including the intensification of the vertical circulation lead to the enhancement of the MJO teleconnection pattern and its teleconnectivity over the upstream regions. Furthermore, there is close relationship of the MJO teleconnection patterns between P1 and El Nino years or P2 and La Nina years.clos
IMPACTS OF BIOMASS BURNING AND LARGE-SCALE TRANSPORT ON THE SOUTHEAST ASIAN HAZE
Full text linkDepartment of Urban and Environmental Engineering
(Environmental Science and Engineering)Biomass burning has significant impacts on regional air quality and climate in Southeast Asia. This study examines the impacts of biomass burning on the large-scale transport of aerosols and haze events using observational analysis and numerical model simulations. The spatiotemporal variation of observed aerosols shows significant correlations, positively with the emission induced by fire and negatively with the removal by precipitation both in seasonal and inter-annual timescale. Particularly, the variation of aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) is primarily affected by El Niño Southern Oscillation (ENSO), leading to a substantial year-to-year variation. The aerosol reanalysis data from the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2) reveals that the aerosols emitted from combustion such as organic carbon and sulfate are the main contributors to the total AOD variation in this region. Organic carbon accounts for over 60 % of total AOD amounts, being highly correlated with the biomass burning, while sulfate also serves as a significant source for the background aerosol concentration. The impacts of aerosols on meteorology and the local air quality have been further investigated using the Weather Research and Forecasting/Chemistry (WRF-Chem) model simulations for June 2013 and September 2015. Overall, the model simulation can capture the most of observed spatial and temporal variations of aerosol appeared in MODIS and MERRA-2, although it tends to underestimate AOD for the both tested cases. The model sensitivity experiments show that both aerosol direct and indirect effects have significant impacts on meteorology and local air quality. The direct impact of aerosols tends to reduce the incoming shortwave radiation at the surface, thereby decreasing surface temperature and the planetary boundary layer (PBL) height. Because of decreasing the PBL height and stabilizing lower atmosphere, the aerosol direct effect tends to increase near-surface concentration of atmospheric trace gases such as NOx, CO and O3. The indirect impact of aerosols also contributes to decrease the shortwave radiation through enhanced activation of cloud condensation nuclei particularly over the ocean. The near-surface concentration of trace gases tends to increase also by the aerosol indirect impact near the emission source except O3, which actually decreases. In case of AOD and PM2.5, both aerosol effects have significant impacts in which the direct effect increases AOD and PM2.5 whereas aerosol indirect effect decreases AOD and PM2.5. Although the direct and indirect feedbacks on aerosol mass concentrations are subject to uncertainties, this work demonstrates the significance role of aerosol feedback for real-time air quality forecasting under haze conditions.ope
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Full text linkDepartment of Urban and Environmental Engineering (Environmental Science and Engineering )Nitrogen dioxide (NO2) is a tracer of Nitrogen oxides (NOx) and it has been very used for tracking NOx. In-situ measurements is very helpful of determining atmospheric concentrations. OMI tropospheric NO2 column retrieval is also used for estimating NOx in atmosphere. Even though we have such useful instrument and in-situ measurement, there still remains a limitation of spatio-temporal inhomogeneous which means that it is hard to understand a three-dimensional transport mechanism of atmospheric transport of air pollutants in detail. Trans-boundary anthropogenic pollutants from a local or transported has been becoming more serious issues in context of politics and scientific basis. We employed a lagrangian particle dispersion model to get better understanding of 3-dimensional atmospheric transport mechanism.
At this point, to overcome these limitations, we conduct a multi-year model simulation to further understand the long-range and trans-boundary transport, and to better understand the 3-dimensional transport mechanism, we estimate the factors affecting to the transport and carry out sensitivity tests for each factor respectively through Source-Receptor Relationship (SRR). We initially estimated emissions, meteorological conditions and decay time as major factors, and investigate a phased experiment, to control the unexpected experimental results. As results, the factors originally set have an effect, but they have not impact drastically on transport mechanism. However, we could confirm that the cause was a significant contribution to the wind field change in the synoptic weather condition, and atmospheric transport is mainly dependent on meteorological conditions rather than emissions. Of course, the weather conditions are relatively dominant, but the effect of emissions is not that much of small. In other words, long-range trans-boundary transport in the East Asia are comprised of the complicated interactions particularly.clos
Development of a Coupled Data Assimilation System in the Fully Coupled Model and Its Implications for Seamless Prediction
No full textDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)clos
Improvement of Air Quality Forecasts based on Aerosol Data Assimilation with Satellite and Ground Observations
Full text linkDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)This study develops a 3DVAR data assimilation and forecasting system that simultaneously assimilates aerosol optical depth (AOD) from Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites and ground-based PM10 and PM2.5 observations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The simulation domain covers Northeast Asia, and the assimilation and forecast skill is evaluated for two periods, one is Korea-US Air Quality (KORUS-AQ) intensive observing period and the other is April of 2017. In evaluating the data assimilation performance, the assimilated surface PM concentrations exhibit higher consistency with the observed data by showing increased correlations for PM10 and PM2.5 from the no assimilation run. The data assimilation also shows beneficial impacts on the air quality forecasts over South Korea until 24 hours from the initialized states. A couple of deficiencies are also found in the data assimilation and forecast system. They show pronounced seasonal dependence in the forecast skill, suggesting an important role of the seasonal changes in regional atmospheric circulation patterns. The forecast accuracy becomes improved than the background model statement, which is most of the skill improvement comes from the surface PM assimilation, showing a practically useful level of skill until 12 hours from the initial state. The seasonal forecast becomes lower in spring and winter when the ambient aerosol concentration is higher than in other seasons. The forecasts also show much degradation as the forecast lead time increases due to systematic model biases. When a simple statistical bias correction is applied to the forecast outputs by adjusting the mean and variance of the forecast outputs to those in the observed distribution, the forecast maintains the skill at a practically useful level for more than a day. For a categorical forecast, the skill score has increased by more than 10 % on average and 37 % for high-concentration events, respectively. Additionally, the analysis of the impact of the data assimilation component for the 3DVAR has been done. Each is a data assimilation method compared to EnKF, the observing system experiments (OSE), and the adaptation of Four-Dimensional Data Assimilation (FDDA) for the constraining meteorology. EnKF method showed comparable or less data assimilation skills because of the spread problem, OSE showed the partial data assimilation effect to the different types of variables and the slight improvement of the forecast accuracy. The FDDA simulation showed that the constraining meteorology can affect the atmospheric aerosol directly by the wind modification, and in this study, the PM result has been improved because of the FDDA option.ope
Remote Influences of ENSO and IOD on the Interannual Variability of the West Antarctic Sea Ice
Full text linkDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)This study focused on remote influences of El Ni??o???Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) on the interannual variability of the West Antarctic sea ice. The sea ice of the West Antarctic has a large variability and is linked to tropical conditions such as ENSO and IOD. The sea ice of the Antarctic is important. According to previous studies, the sea ice extent of the Antarctic is 20% greater than in the Arctic, thus could result in dramatic changes to planetary albedo. Also, feedbacks sea ice change and ocean temperature and salinity play a role in determining the stability of Antarctica???s massive ice shelves and its melting directly associated with global sea-level-rise. ENSO and IOD are well known phenomena that impact on the Antarctic sea ice, but which one has a higher impact is not revealed due to strong correlation (=0.61). For this reason, this study figures out 1) How ENSO and IOD impact the Antarctic and 2) Both of them, which one has more powerful effects? To classify the effects of ENSO and IOD, linear and partial regression analysis and Geophysical Fluid Dynamics Laboratory (GFDL) model experiment is conducted. In the results of linear regression analysis, the teleconnection pattern between ENSO and IOD is not significantly different. On the other hand, using a partial regression analysis, remote influences induced by the ENSO and IOD is kind of different. For example, ENSO is dominant to develop the atmospheric circulation in the Amundsen-Bellingshausen Sea (ABS). Meanwhile, IOD is contributed to form a low-pressure nearby the date line and high-pressure on the interior of ABS. This finding implies that both ENSO and IOD impact the sea ice of the West Antarctic, especially, ENSO in the Pacific Ocean has the greatest effect.clos
Understanding of organic aerosol processes in South Korea based on observations and chemical transport model outputs
No full textDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)clos
Data Assimilation of Remote Sensing Soil Moisture Retrievals with Local Ensemble Transform Kalman Filter Scheme
Full text linkDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)This thesis proposes a development of land data assimilation system to produce realistic land surface states, which is performed with diverse remote sensing retrievals using advanced land surface model (LSM) and data assimilation techniques. Remote sensed soil moisture retrievals with high-temporal and ???spatial resolution is recently available. For instance, the Advanced Scatterometer (ASCAT), a C-band active microwave remote sensing instrument, and the NASA Soil Moisture Active Passive (SMAP) L-band passive remote sensing retrievals provide global near-surface soil moisture condition in real-time. Bias corrected observation datasets are used in the assimilation based on cumulative distribution function fitting because there is a large discrepancy of soil moisture contents between each retrieval and LSM offline simulation by difference sensed layer depth and algorithms, and characteristics of model physics. This study performs the soil moisture data assimilation using these bias corrected satellite retrievals with Local Ensemble Transform Kalman Filter (LETKF) scheme. The impact of the soil moisture assimilation is evaluated with ground based in-situ soil moisture measurement network over the globe.
The result reveals that each satellite retrieval provides significant added value in the data assimilation. The impact of the assimilation tends to be better improved when active and passive satellite retrievals are simultaneously used. The temporal correlation of the assimilated soil moisture increases surface soil moisture skills by ??R???0.12 over the continental U.S., and the improvement at root-zone is ??R???0.1. The result is explained by ???Assimilation Gain???, where the quality of assimilated satellite data and the number of assimilated observations strongly contribute to the skill improvement of assimilated soil moisture estimates. The skill improvement through the multi-retrieval assimilation is mostly significant in transitional climate regime where land-atmosphere interaction is strong, and the impact of soil moisture initialization is clearly shown in the forecast model. Furthermore, the skill improvement is also significant in other validated regions (e.g. western Europe, and central Tibetan Plateau). The magnitude of the skill improvement through the assimilation is large when the quality of satellite retrievals tends to be better than that of the open loop. The assimilated soil moisture estimates are widely used in understanding land surface physical process of hydrology cycle and the land-atmosphere interaction. Furthermore, the realistic land surface condition gives the better information in land surface monitoring system.clos
Improvement of Air Quality Forecasts using Aerosol Data Assimilation and Artificial Intelligence
No full textDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)This thesis proposes the development of advanced aerosol data assimilation (DA) system combined with artificial intelligence (AI) to improve air quality forecasts in South Korea. The aerosol DA system could significantly improve aerosol forecasting skills by reducing the uncertainty of initial condition (IC). This study developed the aerosol data assimilation system based on the Weather Research and Forecasting model with chemistry (WRF-Chem). There are two main approaches in DA: one is the variational method (e.g. 3D-VAR, 4D-VAR) and the other is the ensemble method (e.g. EnKF). The advantages of EnKF are flow-dependent background error covariance which is important in fast-developing air quality system and considering the nonlinearity of background states through ensemble forecast. However, EnKF has a limitation of sampling problem due to inadequate ensemble size. Without enough ensemble spread, EnKF exhibits poor performance. Although previous studies employed inflation and covariance localization to alleviate the sampling problem in chemical data assimilation, it is still deficient, as the ensemble spread is smaller than the prior root mean square error. This study suggested two new effective methods for increasing model background error in the EnKF aerosol data assimilation: the Multiphysics approach and perturbation to prognostic variables. Both methods improved the quality of surface PM analysis substantially. And the EnKF experiment which incorporates both uncertainty in model physics and prognostic variables, demonstrates the best performance, resulting in a larger ensemble spread.
Unlike climate models, emission inventory is a large part of model error in chemical transport models. Bottom-up emission inventory from detailed industrial statistical data has some limitations such as slow updates and missing values, due to its diverse emission sources and complex calculation processes. However, a timely update of the emission inventories is an essential prerequisite for an acceptable air quality forecast. Therefore, optimized emissions from a top-down approach using EnKF could be timely updated and lead to improvements in the forecasting of air quality.
Furthermore, the data assimilation has some limitations regarding linear assumption, Gaussian hypothesis, and unbiased assumption. I propose a novel approach that improves the DA system by applying an artificial intelligence algorithm that does not require the above assumptions and can consider nonlinearity. By replacing the AOD observation operator in the DA system with AI algorithms, the performance of AOD data assimilation showed significant benefits. By combining data assimilation and machine learning, the effectiveness of DA using satellite AOD retrievals on air quality simulations can be maximized. Overall, WRF-Chem and the improved aerosol data assimilation system provide a framework for air quality forecasting and emissions constraint that can be used to enhance our understanding of the interactions between air quality, climate change, and human health.clos
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Full text linkDepartment of Urban and Environmental Engineering (Environmental Science and Engineering )This thesis proposes a comprehensive view of teleconnection between the El Ni??o and Southern Oscillation (ENSO) and the Indian Ocean (IO). This study aims to 1) better understanding of dynamics underlying ENSO-IO teleconnection, 2) investigation of possible strengthening mechanisms of ENSO-IO teleconnection with multi-decadal climate change, 3) identification of ENSO-related predictability sources in dynamical seasonal prediction and future prospection.
From these perspectives, this dissertation consists of following chapters. Chapter 1 summarizes current issues respect to ENSO teleconnection and research objectives. Chapter 2 investigates dynamics underlying ENSO teleconnection to regional precipitation around the IO. There are clear evidences in strengthening relationship of the ENSO-IO teleconnection since 1990s. Hence dynamics of strengthening ENSO-IO teleconnections are assessed with respect to decadal change of zonal sea surface temperature (SST) gradient in the equatorial Pacific Ocean. Chapter 3 assessed prediction skill and predictability sources of rainfall around the IO using state-of-the-art seasonal prediction system. The ENSO-IO teleconnection in the coupled models and their prospects with future scenarios are also investigated from the fifth phase of the Coupled Model Intercomparison Project (CMIP5).
From these synthetic examinations for the ENSO-IO teleconnection, this thesis suggests that a better understanding of regional climate around the IO responses to ENSO with a seasonal evolution mechanism, which are intensified dynamical connection since the 1990s. By unveiling the dynamical mechanism underlying their decadal change, better seasonal prediction skills are expected with the ENSO-related precursors. Dynamical ensemble predictions from the North American Multi-model Ensemble show better prediction skills due to the decadal change of the ENSO-IO teleconnection. This study could contribute to better mitigations and adaptations of extreme climate variability such as drought and flood.clos
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