• Development and Reproduction
• /
• v.7 no.1
• /
• pp.23-28
• /
• 2003

The seasonal reproductive cycle of the female honeycomb grouper, Epinephelus merra, inhabiting Chuuk was examined by histological observations of the ovaries. The gonadosomatic index (GSI) began to increase in February and peaked in March. Histological observations revealed many oocytes laden with yolk in the ovaries from March to April. From June to January, the ovaries were occupied by immature oocytes. These results suggest that the reproductive season of E. merra in Chuuk is from March through April.

• Journal of the Korean Solar Energy Society
• /
• v.35 no.2
• /
• pp.33-41
• /
• 2015

A feasibility study to estimate annual energy production of an offshore wind farm was performed using MERRA reanalysis data. Two well known commercial codes commonly used to wind farm design and power prediction were used. Three years of MERRA data were used to predict annual energy predictions of the offshore wind farm close to Copenhagen from 2011 to 2013. The availability of the wind farm was calculated from the power output data available online. It was found from the study that the MERRA reanalysis data with commercial codes could be used to fairly accurately predict the annual energy production from offshore wind farms when a meteorological mast is not available.

• Atmosphere
• /
• v.30 no.1
• /
• pp.91-102
• /
• 2020

MERRA-2 ozone and atmospheric data are utilized to test the usefulness of reanalysis-based tracer transport analysis for ozone in the tropical tropopause layer (TTL). Transport and mixing processes related to the seasonal variation of TTL ozone are examined using the tracer transport equation based on the transformed Eulerian mean, and the results are compared to previously proposed values from model analyses. The analysis shows that the seasonal variability of TTL ozone is mainly determined by two processes: vertical mean transport and horizontal eddy mixing of ozone, with different contributions in the Northern and Southern Hemispheres. The horizontal eddy mixing process explains the major portion of the seasonal cycle in the northern TTL, while the vertical mean transport dominates in the southern TTL. The Asian summer monsoon likely contributes to this observed difference. The ozone variability and related processes in MERRA-2 reanalysis show qualitatively similar features with satellite- and model-based analyses, and it provides advantages of fine-scale analyses. However, it still shows significant quantitative biases in ozone budget analysis.

• Journal of Wind Energy
• /
• v.4 no.1
• /
• pp.39-45
• /
• 2013

Massive offshore wind projects of have recently been driven in full gear on the Western Offshore of Korea including the 2.5 GW West-Southern Offshore Wind Project of the Ministry of Trade, Industry and Energy, and the 5 GW Offshore Wind Project of the Jeollanamdo Provincial Government. On this timely occasion, this study performed a general wind resource assessment on the Western Offshore by using the MERRA reanalysis data of temporal-spatial resolution and accuracy greatly improved comparing to conventional reanalysis data. It is hard to consider that wind resources on the Western Sea are excellent, since analysis results indicated the average wind speed of 6.29 ± 0.39 m/s at 50 m above sea level, and average wind power density of 307 ± 53 W/m². Therefore, it is considered that activities shall be performed for guarantee economic profits from factor other than wind resources when developing an offshore wind project on the Western Offshore.

• Journal of the Korean Solar Energy Society
• /
• v.34 no.2
• /
• pp.82-90
• /
• 2014

The MERRA reanalysis data provided online by NASA was applied to predict the annual energy productions of two largest wind farms in Korea. The two wind farms, Gangwon wind farm and Yeongyang wind farm, are located on complex terrain. For the prediction, a commercial CFD program, WindSim, was used. The annual energy productions of the two wind farms were obtained for three separate years of MERRA data from June 2007 to May 2012, and the results were compared with the measured values listed in the CDM reports of the two wind farms. As the result, the prediction errors of six comparisons were within 9 percent when the availabilities of the wind farms were assumed to be 100 percent. Although further investigations are necessary, the MERRA reanalysis data seem useful tentatively to predict adjacent wind resources when measurement data are not available.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.24 no.4
• /
• pp.65-81
• /
• 2021

In this study, soil moisture and evapotranspiration were calculated throughout South Korea using the Korea Land Data Assimilation System(KLDAS) of the Korea-Land Surface Information System(K-LIS) built on the basis of the Land Information System (LIS). The hydrometeorological data sets used to drive K-LIS and build KLDAS are MERRA-2(Modern-Era Retrospective analysis for Research and Applications, version 2) GDAS(Global Data Assimilation System) and ASOS(Automated Synoptic Observing System) data. Since ASOS is a point-based observation, it was converted into grid data with a spatial resolution of 0.125° for the application of KLDAS(ASOS-S, ASOS-Spatial). After comparing the hydrometeorological data sets applied to KLDAS against the ground-based observation, the mean of R² ASOS-S, MERRA-2, and GDAS were analyzed as temperature(0.994, 0.967, 0.975), pressure(0.995, 0.940, 0.942), humidity (0.993, 0.895, 0.915), and rainfall(0.897, 0.682, 0.695), respectively. For the hydrologic output comparisons, the mean of R² was ASOS-S(0.493), MERRA-2(0.56) and GDAS (0.488) in soil moisture, and the mean of R² was analyzed as ASOS-S(0.473), MERRA-2(0.43) and GDAS(0.615) in evapotranspiration. MERRA-2 and GDAS are quality-controlled data sets using multiple satellite and ground observation data, whereas ASOS-S is grid data using observation data from 103 points. Therefore, it is concluded that the accuracy is lowered due to the error from the distance difference between the observation data. If the more ASOS observation are secured and applied in the future, the less error due to the gridding will be expected with the increased accuracy.

• Journal of Korea Water Resources Association
• /
• v.56 no.9
• /
• pp.543-551
• /
• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Journal of the Korean Solar Energy Society
• /
• v.36 no.2
• /
• pp.9-17
• /
• 2016

In order to examine how accurately the wind farm design software, WindPRO and Meteodyn WT, predict annual energy production (AEP), an investigation was carried out for Seongsan wind farm of Jeju Island. The one-year wind data was measured from wind sensors on met masts of Susan and Sumang which are 2.3 km, and 18 km away from Seongsan wind farm, respectively. MERRA (Modern-Era Retrospective Analysis for Research and Applications) reanalysis data was also analyzed for the same period of time. The real AEP data came from SCADA system of Seongsan wind farm, which was compare with AEP data predicted by WindPRO and Meteodyn WT. As a result, AEP predicted by Meteodyn WT was lower than that by WindPRO. The analysis of using wind data from met masts led to the conclusion that AEP prediction by CFD software, Meteodyn WT, is not always more accurate than that by linear program software, WindPRO. However, when MERRA reanalysis data was used, Meteodyn WT predicted AEP more accurately than WindPRO.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.213-225
• /
• 2018

In order to access the accuracy of the gridded daily Advanced Scatterometer (hereafter DASCAT) ocean surface wind data in the surrounding of Korea, the DASCAT was compared with the wind data from buoys. In addition, the reanalysis data for wind at 10 m provided by European Centre for Medium-Range Weather Forecasts (ECMWF, hereafter ECMWF), National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR, hereafter NCEP), Modern Era Retrospective-analysis for Research and Applications-2 (MERRA-2, hereafter MERRA) were compared and analyzed. As a result, the RMSE of DASCAT for the actual wind speed is about 3 m/s. The zonal components of wind of buoys and the DASCAT have strong correlation more than 0.8 and the meridional components of wind them have lower correlation than that of zonal wind and are the lowest in the Yellow Sea (r=0.7). When the actual wind speed is below 10 m/s, the EMCWF has the highest accuracy, followed by DASCAT, MERRA, and NCEP. However, under the wind speed more than 10 m/s, DASCAT shows the highest accuracy. In the nature of error according to the wind direction, when the zonal wind is strong, all dataset has the error of more than $70^{\circ}$ on the average. On the other hand, the RMSE of wind direction was recorded $50^{\circ}$ under the strong meridional winds. ECMWF shows the highest accuracy in these results. The RMSE of the wind speed according to the wind direction varied depending on the actual wind direction. Especially, MERRA has the highest RMSE under the westerly and southerly wind condition, while the NCEP has the highest RMSE under the easterly and northerly wind condition.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.641-645
• /
• 2017

This study was conducted to compare three agro-climatic indices among 22 agro-climatic zones in Northeast China area. Meteorological data produced by NASA (MERRA-2) was used to calculate growing degree days (GDD), frost free period (FFP), and growth season length (GSL) at this study sites. The three indices did not differ among 6 years (2011-2016). However, they showed statistical spatial difference among agro-climatic zones. The GDD ranged between $531.7^{\circ}C{\cdot}day$ (zone 22) and $1650.6^{\circ}C{\cdot}day$ (zone 1). The range of the FFP was from 141.5 day (zone 22) to 241.7 day (zone 1). And the GSL showed spatial distribution between 125.1 day (zone 22) and 217.9 day (zone 1).