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구름방울 활성화 과정 모수화 방법에 따른 구름 형성의 민감도 실험
김아현(Ah-Hyun Kim),염성수(Seong Soo Yum),장동영(Dong Yeong Chang) 한국기상학회 2018 대기 Vol.28 No.2
Cloud droplet activation process is well described by Köhler theory and several parameterizations based on Köhler theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the Köhler equation, i.e., osmotic coefficient method (OSM) and κ-Köhler method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.
기상 관측선 기상 1호에서 관측한 황해의 에어로졸과 구름응결핵 수농도 특성 연구
박민수(Minsu Park),염성수(Seong Soo Yum),김나진(Najin Kim),차주완(Joo Wan Cha),류상범(Sang Boom Ryoo) 한국기상학회 2016 대기 Vol.26 No.2
Total number concentration of aerosols larger than 10 nm (N<SUB>CN10</SUB>), 3 nm (N<SUB>CN3</SUB><SUB></SUB>), and cloud condensation nuclei (N<SUB>CCN</SUB>) were measured during four different ship cruises over the Yellow Sea. Average values of N<SUB>CN10</SUB> and N<SUB>CCN</SUB> at 0.6% supersaturation were 6914 and 3353 cm<SUP>−3</SUP>, respectively, and the minimum value of NCN10 was 2000 cm<SUP>−3</SUP>, suggesting significant anthropogenic influence even at relatively clean marine environment. Although NCN10 and N<SUB>CN3</SUB><SUB></SUB> increased near the coast due to anthropogenic influence, N<SUB>CCN</SUB> was relatively constant and therefore N<SUB>CCN</SUB>/N<SUB>CN10</SUB> ratio tended to decrease, suggesting that coastal aerosols were relatively less hygroscopic. In general N<SUB>CN10</SUB>, N<SUB>CN3</SUB>, and N<SUB>CCN</SUB> during the cruises seemed to be significantly influenced by wet scavenging effects (e.g. fog) and boundary layer height variation. Only one new particle formation (NPF) event was observed during the measurement period. Interestingly, the NPF event occurred during a dust storm event and spatial scale of the NPF event was estimated to be larger than 100 km. These results demonstrate that aerosol and CCN concentration over the Yellow Sea can vary due to various different factors.
Takahashi 구름모형에서의 얼음입자 충돌효율 개선
이한아(Hannah Lee),염성수(Seong Soo Yum) 한국기상학회 2012 대기 Vol.22 No.1
The collision efficiency data for collision between fraupel of hail particles and cloud drops that take into account the differences of particle density are applied to the Takahashi cloud medel. The original setting assumes that graupel or hail collision efficiency is the same as that of the cloud drops of the same volume. The Takahashi cloud model is run with the new collision efficiency data and the results are compared with those with the original. As an initial condition, a thermodynamic profile that can initiate strong convection is provided. Three different CCN concentration values and therefore initial cloud drop spectra are prescribed that represent martitime (CCN concentration = 300 cm-³), continental (1000 cm-³) and extreme continental (5000 cm-³) air masses to examine the aerosol effects on cloud and precipitation development. Increase of CCN concentration causes cloud drop sizes to decrease and cloud drop concentrations to increase. However, the concentration of ice particles decreases with the increase of CCN concentration because small drops are difficult to freeze. These general trends are well captured by both medel runs (one with the new collision efficiency data and the other with the original) but there are significant differences: with the new data, the development of cloud and raindrop formation are delayed by (1) decrease of ice collision efficiency, (2) decrease if latent heat from riming process and (3) decrease of ice crystals generated by ice multiplication. These results indicate that the model run model with the original collision efficiency data overestimates precipitation rates.
기상청 지구시스템모델에서의 구름입자 수농도 모수화 방법 개선
이한아(Hannah Lee),염성수(Seong Soo Yum),심성보(Sungbo Shim),부경온(Kyung-On Boo),조천호(ChunHo Cho) 한국기상학회 2014 대기 Vol.24 No.1
In the Korea Meteorological Administration earth system model (HadGEM2-AO), cloud drop number concentration is determined from aerosol number concentration according to the observed relationship between aerosol and cloud drop number concentrations. However, the observational dataset used for establishing the relationship was obtained from limited regions of the earth and therefore may not be representative of the entire earth. Here we reestablished the relationship between aerosol and cloud drop number concentrations based on a composite of observational dataset obtained from many different regions around the world that includes the original dataset. The new relationship tends to provide lower cloud drop number concentration for aerosol number concentration < 600 cm<SUP>?3</SUP> and the opposite for > 600 cm<SUP>?3</SUP>. This new empirical relationship was applied to the KMA earth system model and the historical run (1861~2005) is made again. Here only the 30 year (1861~1890) averages from the runs with the new and the original relationships between aerosol and cloud drop number concentrations (newHIST and HIST, respectively) were compared. For this early period aerosol number concentrations were generally lower than 600 cm<SUP>?3</SUP> and therefore cloud drop number concentrations were generally lower but cloud drop effective radii were larger for newHIST than for HIST. The results from the complete historical run with the new relationship are expected to show more significant differences from the original historical run.
인천국제공항의 안개 특성에 따른 안개 안정 지수 FSI(Fog Stability Index) 개발 및 검증
송윤영(Yunyoung Song),염성수(Seong Soo Yum) 한국기상학회 2013 대기 Vol.23 No.4
The original Fog Stability Index (FSI) is formulated as FSI = 2(T?T<SUB>d</SUB>) + 2(T?T<SUB>850</SUB>) + WS<SUB>850</SUB>, where T?T<SUB>d</SUB> is dew point deficit (temperature-dew point temperature), T?T<SUB>850</SUB> is atmospheric stability measure (temperature-temperature at 850 hPa altitude) and WS<SUB>850</SUB> is wind speed at 850 hPa altitude. As a way to improve fog prediction at Incheon International Airport (IIA), we develop the modified FSI for IIA, using the meteorological data at IIA for two years from June 2011 to May 2013, the first one year for development and the second one year for validation. The relative contribution of the three parameters of the modified FSI is 9: 1: 0, indicating that WS<SUB>850</SUB> is found to be a non-contributing factor for fog formation at IIA. The critical success index (CSI) of the modified FSI is 0.68. Further development is made to consider the fact that fogs at IIA are highly influenced by advection of moisture from the Yellow Sea. One added parameter after statistical evaluation of the several candidate parameters is the dew point deficit at a buoy over the Yellow Sea. The relative contribution of the four parameters (including the new one) of the newly developed FSI is 10: 2: 0.5: 6.4. The CSI of the new FSI is 0.50. Since the developmental period of one year is too short, the FSI should be refined more as the data are accumulated more.