• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.31-43
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• 2022

In this study, we estimated the monthly FDSI (Flash Drought Stress Index) for assessing flash drought on South Korea using AMSR2(Advanced Microwave Scanning Radiometer 2) satellite-based soil moisture footprints. We collected the AMSR2 soil moisture and climate-land surface data from April to November 2018 for analyzing the monthly FDSI values. We confirmed that the FDSI values were high at the regions with the high temperature/evapotranspiration while the precipitation is relatively low. Especially, the regions which satisfied an onset of flash drought (FDSI≧0.71) were increased from June. Then, the most of regions suffered by flash drought during the periods (July to August) with the high temperature and evapotranspiration. Additionally, the impacts of landuse and slope degree were evaluated on the monthly FDSI changes. The forest regions that have the steep slope degree showed the relatively higher FDSI values than the others. Thus, our results indicated that the the slope degree has the relatively higher impact on the onset and increasing of flash drought compared to the others.

• Korean Journal of Remote Sensing
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• v.9 no.2
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• pp.51-69
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• 1993

Precipitable Water(PW) are retrieved over the tropical and subtropical Pacific Ocean from TOVS infrared and microwave channel brightness temperature and OLR observations by means of stepwise linear regression. The retrieved TOVS PW fields generated by PW$_{sfc}$(71.1 % of the variance and 0.62 g cm$^{-2}$ standard error over the surface) and PW$_{700500}$(71.7 % and 0.17 g cm$^{-2}$ over the 700 - 500 hPa layer) revealed more evolving synoptic signals over the tropical and subtropical Pacific Ocean. The PW$_{sfc}$ dose not show significantly the TP feature because of the representation of the lower PW for high-level clouds not associated with deep convection. There exists some elusion to trace the TP on the PW$_{sfc}$ field if any supplementary information does not provide. But ECMWF analysis has a general tendency of drying the subtropics and moistening the ITCZ (InterTropical Convergence Zone) and SPCZ(South Pacific Convergence Zone). However, although ECMWF analysis is fairly successful in capturing mean patterms, it is unsuccessful in following active synoptic signal like a tropical plume. Similarly, SMMR-PW does not represent the TP well which consists of the highand middle-level clouds, but PW$_{sfc}$ shows underestimated moistness of TP and does not depict significant signal of TP. In the PW field derived from microwave observations, the TP can not be recognized well. Furthermore, the signature of PW$_{sfc}$ was different from OLR for the TP, which implies the presence of high- and middle-layer thin clouds, but in a closer agreement for deep and active convection areas which contain thick middle- and lower-layer clouds; though OLR represented the cloudiness in the tropics well. In synoptically active regions, it differed from OLR analysis, primarily bacause of actual differences in water vapor and cloud features. The signature of PW$_{sfc}$ was different from OLR for the TP.

• Ocean and Polar Research
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• v.24 no.3
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• pp.255-261
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• 2002

We investigated the distribution of sea ice using Topex/Poseidon (T/P) and ERS-1 .ada. altimeter data in the northwest Weddell Sea, Antarctica, between the area $45-75^{\circ}W\;and\;55-66^{\circ}S$. Using the Geo_Bad_1 flag of the Merged GDR of the T/P, we classified the surface into ocean, land, and sea. Total 257 cycles of altimeter measurements between Oct. 1992 and Sep. 1999 (for nearly 2570 days) were used to analyze the distribution of the Antarctic sea ice. We then calculated the surface area of ice coverage using SUTM20 map projection to monitor the periodic variations. Each year, the maximum and minimum coverage of the sea ice were found in late August and February in the study area, respectively. We also studied the sea ice distribution using ERS-1 altimeter data between $45-75^{\circ}W\;and\;55-81.5^{\circ}S$ to compare with the T/P Using the Valid/Invalid flag of the Ocean Product, we analyzed the sea ice distribution between March and August of 1995, which showed very good coherence with the T/P measurements. Our preliminary results showed that the altimeter measurements can be effectively used to monitor the distribution of the sea ice in the polar region. However, the size of radar footprint, typically 2-6km depending on the roughness of the sea surface, may be too big to monitor the sharp boundary between ice and water/land. If more other altimeter mission data with dense coverage such as Geosat GM are analyzed together, this limitation can be significantly improved. If we also combine other microwave remote sensing data such as radiometer, and SSM/I, the result will be significantly enhanced.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.306-309
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• 2007

Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO) includes global ocean surface heat flux data derived from satellite data and are used in many studies related to air-sea interaction. Recently latent heat flux data version 2 was constructed in J-OFURO. In version 2 many points are improved compared with version 1. A bulk algorithm used for estimation of latent heat flux is changed from Kondo (1975) to COASRE 3.0(Fairall et al., 2005). In version 1 we used NCEP reanalysis data (Reynolds and Smith, 1994) as SST data. However, the temporal resolution of the data is weekly and considerably low. Recently there are many kinds of global SST data because we can obtain SST data using a microwave radiometer sensor such as TRMM/MI and Aqua/AMSR-E. Therefore, we compared many SST products and determined to use Merged satellite and in situ data Global Daily (MGD) SST provided by Japan Meteorological Agency. Since we use wind speed and specific humidity data derived from one DMSP/SSMI sensor in J-OFURO, we obtain two data at most one day. Therefore, there may be large sampling errors for the daily-mean value. In order to escape this problem, multi-satellite data are used in version 2. As a result we could improve temporal resolution from 3-days mean value in version 1 to daily-mean value in version 2. Also we used an Optimum Interpolation method to estimate wind speed and specific humidity data instead of a simple mean method. Finally the data period is extended to 1989-2004. In this presentation we will introduce latent heat flux data version 2 in J-OFURO and comparison results with other surface latent heat flux data such as GSSTF2 and HOAPS etc. Moreover, we will present validation results by using buoy data.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.315-324
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• 1998

The total precipitable water fields derived from HIRS(High Resolution Infrared Radiometer Sounder)and MSU(Microwave Sounding Unit) measurements of TOVS and brightness temperature of SSM/I were used to investigate the evolution of moisture fields for the Typhoon WALT(9407) which after landing in Japan it became tropical depression in Korea-Japan Strait, and FAYE(9503) which was the first tropical storm of 1995 to became a typhoon, respectively. The total precipitable water derived from TOVS observations is delineated according to the evolutions of WALT and FAYE movements because total precipitable water fields of TY WALT(9407) and FAYE9\(9503) were largely controlled by horizontal transport of water vapor over the Northwest Pacific Ocean which dominantly plays an important role in maintaining and accelerating their intensities toward Korea and Japan . These fields demonstrated that two major bands, which imply the rain bands, were locally well-organized and similar to the thick convective cloud features over Japan and the Korean peninsula while WALT and FAYE were approaching away and to. But the values of derived TOVS total precipitable water have shown the underestimate of those of SSM/I total comparatively for two typhoons.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.159-159
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• 2016

북한 지역에서 시작되어 한반도의 금문댐까지 연결되는 임진강은 북한지역의 유출량 미계측으로 인해 유출량 산출에 많은 어려움이 있어왔다. 본 연구에서는 위성 데이터를 활용하여 미계측 유역의 유출량을 추정 할 수 있는 기법을 제시하였다. Satellite-derived Flow Signal (SDF)는 위성 기반 특정 지역의 유출 정보를 제공하며, JAXA의 GCOM-W1 위성에 탑재된 Advanced Microwave Scanning Radiometer 2(AMSR2) 센서에서 산출된다. 본 연구에서는 SDS 뿐 아니라 유출에 크게 관련이 있는 지표 토양수분 데이터와 식생인자를 임진강 유출 값을 예측하기 위한 입력 값으로 활용하였다. 토양수분 데이터는 Metop-A 위성에 탑재된 Advanced Scatterometer(ASCAT) 센서에서 산출되는 데이터를 활용하였으며, 식생데이터는 Aqua 위성에 탑재된 Moderate Resolution Imaging Spectroradiometer(MODIS) 센서에서 측정되는 Normalized Difference Vegetation Index(NDVI) 데이터를 활용하였다. 추가적으로 SDS, 토양수분, NDVI 데이터는 다양한 lag time으로 약 150여개의 입력데이터로 세분화되었다. 150개의 방대한 입력인자는 Partial Mutual Information(PMI) 방법을 통해 소수 중요 인자들로 간추려져 기계 학습 입력인자로 활용되었다. 기계학습에 있어서는 Support Vector Machine(SVM), Artificial Neural Network (ANN) 기법을 활용하였다. SVM, ANN을 통해 모델화된 유출데이터는 금문댐 유출데이터와 비교/분석되었다. SVM 기법 기반의 유출량은 실제 유출량과 0.73의 상관계수를 보여주었고, ANN 기법 기반의 유출량은 0.66의 상관계수를 결과를 나타내었다. 하지만 SVM 기반 유출데이터는 과소 산정 되는 경향을 보였으며, ANN 기법 기반의 유출량은 과대산정되는 결과가 산출되는 한계점이 있음을 파악할 수 있었다.

• 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.

• Journal of the Korean earth science society
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• v.39 no.3
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• pp.228-240
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• 2018

Solar energy is calculated using meteorological (14 station), ceilometer (2 station) and microwave radiometer (MWR, 7 station)) data observed from the Weather Information Service Engine (WISE) on the Seoul metropolitan area. The cloud optical thickness and the cloud fraction are calculated using the back-scattering coefficient (BSC) of the ceilometer and liquid water path of the MWR. The solar energy on the surface is calculated using solar radiation model with cloud fraction from the ceilometer and the MWR. The estimated solar energy is underestimated compared to observations both at Jungnang and Gwanghwamun stations. In linear regression analysis, the slope is less than 0.8 and the bias is negative which is less than $-20W/m^2$. The estimated solar energy using MWR is more improved (i.e., deterministic coefficient (average $R^2=0.8$) and Root Mean Square Error (average $RMSE=110W/m^2$)) than when using ceilometer. The monthly cloud fraction and solar energy calculated by ceilometer is greater than 0.09 and lower than $50W/m^2$ compared to MWR. While there is a difference depending on the locations, RMSE of estimated solar radiation is large over $50W/m^2$ in July and September compared to other months. As a result, the estimation of a daily accumulated solar radiation shows the highest correlation at Gwanghwamun ($R^2=0.80$, RMSE=2.87 MJ/day) station and the lowest correlation at Gooro ($R^2=0.63$, RMSE=4.77 MJ/day) station.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.65-73
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• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• Atmosphere
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• v.27 no.3
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• pp.359-370
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• 2017

In this study, the oceanic Total Precipitable Water (TPW) retrieval algorithm at 16 km altitude of High Altitude Long Endurance Unmanned Aerial Vehicle (HALE UAV) is described. Empirical equation based on Wentz method (1995) that uses the 18.7 and 22.235 GHz channels is developed using the simulated brightness temperature and SeeBor training dataset. To do radiative simulation, Satellite Data Simulator Unit (SDSU) Radiative Transfer Model (RTM) is used. The data of 60% (523) and 40% (349) in the SeeBor training dataset are used to develop and validate the TPW retrieval algorithm, respectively. The range of coefficients for the TPW retrieval at the altitude of 3~18 km with 3 km interval were 153.69~199.87 (${\alpha}$), 54.330~58.468 (${\beta}$), and 84.519~93.484 (${\gamma}$). The bias and RMSE at each altitude were found to be about $-0.81kg\;m^{-2}$ and $2.17kg\;m^{-2}$, respectively. Correlation coefficients were more than 0.9. Radiosonde observation has been generally operated over land. To validate the accuracy of the oceanic TPW retrieval algorithm, observation data from the Korea Meteorological Administration (KMA) Gisang 1 research vessel about six clear sky cases representing spring, autumn, and summer season is used. Difference between retrieved and observed TPW at 16 km altitude were in the range of $0.53{\sim}1.87kg\;m^{-2}$, which is reasonable for most applications. Difference in TPW between retrieval and observation at each altitude (3~15 km) is also presented. Differences of TPW at altitudes more than 6 km were $0.3{\sim}1.9kg\;m^{-2}$. Retrieved TPW at 3 km altitude was smaller than upper level with a difference of $-0.25{\sim}0.75kg\;m^{-2}$ compared to the observed TPW.