http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Samuel Takele Kenea,이해영,주상원,Miloslav Belorid,이선란,Lev D. Labzovskii,박상훈 한국대기환경학회 2023 Asian Journal of Atmospheric Environment (AJAE) Vol.17 No.1
This study presents an analysis of the atmospheric footprint sensitivities and CO2 enhancements measured at three in situ stations in South Korea (Anmyeondo (AMY), Gosan (JGS), Ulleungdo (ULD)) using the KIM-STILT and WRF-STILT atmospheric transport models. Monthly aggregated footprints for each station were compared between the models for July and December 2020. The footprints revealed major source regions and the sensitivity of atmospheric mole fractions at the receptor to upstream surface fluxes. In July, both models showed similar major source regions for the AMY station, including Korea, the Yellow Sea, and Japan. However, a discrepancy was observed in the Eastern Pacific Ocean, with KIM-STILT showing larger sensitivity compared to WRF-STILT. In December, both models indicated strong sensitivity over Northeast and Eastern China, but KIM-STILT exhibited smaller sensitivities towards Northwestern China and Mongolia compared to WRF-STILT. At station ULD in July, both models exhibited comparable source regions, but a notable difference was found in Southeast China, where KIM-STILT showed stronger sensitivity. For the JGS station, both models agreed on major sources, but WRF-STILT demonstrated stronger sensitivity over North and Northeastern China. Regarding CO2 enhancements, both models generally underestimated the amplitude of CO2 enhancements, especially in July. However, in December, there was better agreement with observed data. The models were able to reproduce the phase of measured ΔCO2 reasonably well despite the underestimation of CO2 amplitudes. The contribution of biospheric CO2 to the observed enhancements, along with fossil-fuel emissions, was highlighted. In specific cases with significant CO2 enhancements, the models provided varying estimates of CO2ff values, particularly in the source regions of Eastern China. The differences in sensitivity estimations emphasize the need for further investigation to understand the underlying factors causing disparities. Overall, this study provides valuable insights into the potential advantages of each model in capturing dispersion patterns in specific regions, highlighting the importance of understanding these differences to improve the accuracy of atmospheric transport models. Further work is necessary to address the observed disparities and enhance our understanding of the transport models in the studied regions.
Samuel Takele Kenea,오영석,구태영,Jae-Sang Rhee,변영화,Lev D. Labzovskii,Shanlan Li 한국기상학회 2019 Asia-Pacific Journal of Atmospheric Sciences Vol.55 No.3
It is evident that evaluating the measurement of greenhouse gases (GHGs) obtained from multi-platform instruments against accurate and precise instrument such as aircraft in-situ is very essential when using remote sensing GHGs results for source/sink estimations with inverse modeling. The results of the inverse models are very sensitive even to small biases in the data (Rayner and O’Brien 2001). In this work, we have evaluated ground-based high resolution Fourier Transform Spectrometer (g-b FTS) and the Greenhouse gases Observing SATellite (GOSAT) column-averaged dry air mole fraction of methane (XCH4) through aircraft in-situ observations over Anmyeondo station (36.538o N, 126.331o E, 30 m above sea level). The impact of the spatial coincidence criteria was assessed by comparing GOSAT data against g-b FTS.We noticed there was no any systematic difference based on the given coincidence criteria. GOSATexhibited a bias ranging from 0.10 to 3.37 ppb, with the standard deviation from 4.92 to 12.54 ppb, against g-b FTS with the spatial coincidence criteria of ±1, ±3, ±5 degrees of latitude and longitude and ± 1 h time window. Data observed during ascent and descent of the aircraft is considered as vertical profiles within an altitude range of 0.2 to a maximum of 9.0 km so that some assumptions were applied for the construction of the profiles below 0.2 and above 9.0 km. In addition, the suitability of aircraft data for evaluation of remote sensing instruments was confirmed based on the assessment of uncertainties. The spatial coincidence criteria is ±1o latitude and ± 2o longitude and for temporal difference is ±1 h of the satellite observation overpass time were applied, whereas g-b FTS data are the mean values measured within ±30 min of the aircraft observation time. Furthermore, the sensitivity differences of the instruments were taken into account.With respect to aircraft, the g-b FTS data were biased by −0.19 ± 0.69%, while GOSAT data were biased by −0.42 ± 0.84%. These results confirm that both g-b FTS and GOSAT are consistent aircraft observations and assure the reliability of the datasets for inverse estimate of CH4.
Samuel Takele Kenea,Haeyoung Lee,Sangwon Joo,Shanlan Li,Lev D. Labzovskii,Chu-Yong Chung,Yeon-Hee Kim 한국대기환경학회 2021 한국대기환경학회 학술대회논문집 Vol.2021 No.10
To comprehend interannual variability of CH₄ and its drivers, we used integrated data from different platforms such as in situ measurements, TROPOMI, and GOSAT retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Korea, ranging from -16.8 to 31.3 ppb yr<SUP>−1</SUP> as captured in situ through 2015-2020. High growth rates were discerned in 2016 (31.3 ppb yr<SUP>−1</SUP> and 13.4 ppb yr<SUP>−1</SUP> from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr<SUP>−1</SUP> and 16.4 ppb yr<SUP>−1</SUP> from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015–2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature. The stable isotopic composition of <SUP>13</SUP>C/<SUP>12</SUP>C in CH₄ (δ<SUP>13</SUP>-CH₄) collected by flask-air sampling at AMY during 2014-2019 supported the soil methane hypothesis. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal that was affected by the variations of soil temperature and soil moisture.
Samuel Takele Kenea,Haeyoung Lee,Sangwon Joo,Shanlan Li,Lev D. Labzovskii,Chu-Yong Chung,Yeon-Hee Kim 한국기상학회 2021 한국기상학회 학술대회 논문집 Vol.2021 No.10
Understanding the temporal variability of atmospheric methane (CH₄) and its potential drivers can advance the progress toward mitigating changes to the climate. To comprehend interannual variability and its drivers, we used integrated data from different platforms such as in situ measurements, TROPOMI, and GOSAT retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Republic of Korea, ranging from -16.8 to 31.3 ppb yr<SUP>-1</SUP> as captured in situ through 2015-2020 and 3.9 to 16.4 ppb yr<SUP>-1</SUP> detected by GOSAT through 2014-2019, respectively. High growth rates were discerned in 2016 (31.3 ppb yr<SUP>-1</SUP> and 13.4 ppb yr<SUP>-1</SUP> from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr<SUP>-1</SUP> and 16.4 ppb yr<SUP>-1</SUP> from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015-2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature according model. The stable isotopic composition of <SUP>13</SUP>C/<SUP>12</SUP>C in CH₄ (δ<SUP>13</SUP>-CH₄) collected by flask-air sampling at AMY during 2014-2019 supported the soil methane hypothesis. The intercept of the Keeling plot for summer and autumn were found to be -53.3‰ and -52.9‰, respectively, which suggested isotopic signature of biogenic emissions. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal. Such changes in the biogenic signal were affected by the variations of soil temperature and soil moisture. The pixel-wise correlation of XCH₄ anomaly with those parameters indicated in the range of 0.5-0.8 with a statistical significance (p<0.05). This implies that the soil-associated drivers are able to exert a large-scale influence on the regional distribution of CH₄ in Korea.