• Atmosphere
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• v.32 no.4
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• pp.395-409
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• 2022

A new soil moisture initialization scheme is applied to the Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6). It is designed to ingest the microwave soil moisture retrievals from Soil Moisture Active Passive (SMAP) radiometer using the Local Ensemble Transform Kalman Filter (LETKF). In this technical note, we describe the procedure of the newly-adopted initialization scheme, the change of soil moisture states by assimilation, and the forecast skill differences for the surface temperature and precipitation by GloSea6 simulation from two preliminary experiments. Based on a 4-year analysis experiment, the soil moisture from the land-surface model of current operational GloSea6 is found to be drier generally comparing to SMAP observation. LETKF data assimilation shows a tendency toward being wet globally, especially in arid area such as deserts and Tibetan Plateau. Also, it increases soil moisture analysis increments in most soil levels of wetness in land than current operation. The other experiment of GloSea6 forecast with application of the new initialization system for the heat wave case in 2020 summer shows that the memory of soil moisture anomalies obtained by the new initialization system is persistent throughout the entire forecast period of three months. However, averaged forecast improvements are not substantial and mixed over Eurasia during the period of forecast: forecast skill for the precipitation improved slightly but for the surface air temperature rather degraded. Our preliminary results suggest that additional elaborate developments in the soil moisture initialization are still required to improve overall forecast skills.

• Atmosphere
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• v.33 no.5
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.

• Current Optics and Photonics
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• v.4 no.3
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• pp.174-185
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• 2020

Multiscattering occurs when a laser transmits into dense atmosphere targets (e.g. fogs, smoke or clouds), which can cause depolarization effects even though the scattering particles are spherical. In addition, multiscattering effects have additional information about microphysical properties of scatterers. Thus, multiscattering can be utilized to study the microphysical properties of the liquid water cloud. In this paper, a Monte Carlo method was used to simulate multi-scattering transmission properties of Lidar signals in the cloud. The results showed the slope of the degree of linear polarization (SLDLP) can be used to invert the extinction coefficient, and then the cloud effective size (CES) and the liquid water content (LWC) may be easily obtained by using the extinction coefficient and saturation of the degree of linear polarization (SADLP). Based on calculation results, a microphysical properties inversion method for a liquid cloud was presented. An innovative multiscattering polarization Lidar (MSPL) system was constructed to measure the LWC and CES of the liquid cloud, and a new method based on the polarization splitting ratio of the Polarization Beam Splitter (PBS) was developed to calibrate the polarization channels of MSPL. By analyzing the typical observation data of MSPL observation in the northern suburbs of Nanjing, China, the LWC and CES of the liquid water cloud were obtained. Comparisons between the results from the MSPL, MODIS and the Microwave radar data showed that, the microphysical properties of liquid cloud could be retrieved by combining our MSPL and the inversion method.

• Atmosphere
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• v.20 no.3
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• pp.229-238
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• 2010

The 1,000~500 hPa thickness and the $0^{\circ}C$ isotherm at 850 hPa have been used as the traditional predictors for wintertime precipitation-type forecasts. New approaches are taking on added significance as preexistence method of determination for wintertime precipitation-type exhibits more or less prevalent false alarms. Moreover thicknesses and thermodynamic profiles from ordinary upper-air observation were not adequate to monitor the atmospheric structure. In this regard, Microwave radiometric profiler microwave radiometer is useful in wintertime precipitation-type forecasts because radiometric measurements provide soundings at high temporal resolution. In this study, the determination and the predictability of wintertime precipitation-type were examined by using the calculated thicknesses, temperature of 850 hPa (T850) from a microwave radiometer, and surface observation at National Center for Intensive Observation of severe weather (NCIO) located at Haenam, Korea. The critical values for traditional predictors (thickness of 1000~500 hPa and T850) were evaluated and adjusted to Haenam region because snow rarely occurred with a 1000-500 hPa thickness > 5,300 m and T850 > $-10^{\circ}C$. Three thicknesses (e.g., 1,000~850, 1000~700, and 850~700 hPa thickness), T850, surface air temperature, and wet-bulb temperature were also evaluated as the additional predictors. A simple nomogram and a flow chart were finally designed to determine the wintertime precipitation-type using the microwave radiometer. The skill scores for the predictability of precipitation-type determination are considerably improved and the predictors showed the temporal variations in 12 hours before precipitation. We can monitor the hit and run snowfall in winter successful by realtime watch of the predictors, especially in commutes of big cities.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.631-641
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• 2012

In this study, satellite data (MTSAT-1R), a numerical weather prediction model, RDAPS (Regional Data Assimilation and Prediction System) output, ground weather station data, and artificial neural networks were used to improve the accuracy of summer rainfall forecasts. The developed model was applied to the Seoul station to forecast the rainfall at 3, 6, 9, and 12-hour lead times. Also to reflect the different weather conditions during the summer season which is related to the frontal precipitation and the cyclonic precipitation such as Jangma and Typhoon, the neural network models were formed for two different periods of June-July and August-September respectively. The rainfall forecast model was trained during the summer season of 2006 and 2008 and was verified for that of 2009 based on the data availability. The results demonstrated that the model allows us to get the improved rainfall forecasts until lead time of 6 hour, but there is still a large room to improve the rainfall forecast skill.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.382-395
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• 2013

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. However, most researchers provided these users with simple questionnaires and the respondents had to choose one answer among different satisfaction levels. So, the results of this kind of survey have low explanation power and are difficult to use in developing strategy of forecast service. In this study, instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the $2{\times}2$ contingency table, which is a useful tool to evaluate value of forecast, and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China and Seoul, Korea. Moreover, not only the individual satisfaction value of forecast but the user group's satisfaction value was evaluated. As for the result, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group, but in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.200-204
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• 2012

In engineering hydrology, an estimation of precipitation loss is one of the most important issues for successful modeling to forecast flooding or evaluate water resources for both surface and subsurface flows in a watershed. An accurate estimation of precipitation loss is required for successful implementation of rainfall-runoff models. Precipitation loss or hydrological abstraction may be defined as the portion of the precipitation that does not contribute to the direct runoff. It may consist of several loss elements or abstractions of precipitation such as infiltration, depression storage, evaporation or evapotranspiration, and interception. A composite loss rate model that combines four loss rates over time is derived as a lumped form of a continuous time function for a storm event. The composite loss rate model developed is an exponential model similar to Horton's infiltration model, but its parameters have different meanings. In this model, the initial loss rate is related to antecedent precipitation amounts prior to a storm event, and the decay factor of the loss rate is a composite decay of four losses.

• Atmosphere
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• v.30 no.2
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• pp.141-154
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• 2020

Seasonal forecast is growing in demand, as it provides valuable information for decision making and potential to reduce impact on weather events. This study examines how operational climate prediction systems can be reliable, producing the probability forecast in seasonal scale. A reliability diagram was used, which is a tool for the reliability by comparing probabilities with the corresponding observed frequency. It is proposed for a method grading scales of 1-5 based on the reliability diagram to quantify the reliability. Probabilities are derived from ensemble members using hindcast data. The analysis is focused on skill for 2 m temperature and precipitation from climate prediction systems in KMA, UKMO, and ECMWF, NCEP and JMA. Five categorizations are found depending on variables, seasons and regions. The probability forecast for 2 m temperature can be relied on while that for precipitation is reliable only in few regions. The probabilistic skill in KMA and UKMO is comparable with ECMWF, and the reliabilities tend to increase as the ensemble size and hindcast period increasing.

• Atmosphere
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• v.24 no.4
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• pp.533-540
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• 2014

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.