High-resolution Meteorological Data Research Articles

Exploring intra-urban thermal stress vulnerability within 15-minute city concept: Example of heat waves 2021 in Moscow

Heat vulnerability in big cities is important because of the increase in heat wave frequency and thermal stress that is identified by Urban Heat Island. Our study investigated intra-urban heat vulnerability in Moscow, which strongly influenced by historic context in urban planning, with a focus on local disparities. We considered the vulnerability framework in terms of “exposure,” “sensitivity,” and “adaptive capacity,” and adopted the concept of a 15-minute city to evaluate spatial patterns on example of 2021 heat waves. We used high-resolution meteorological data from the regional meteorological model COSMOCLM and calculated the Physiologically Equivalent Temperature (PET) to assess thermal stress and define exposure. The data from OSM and other open sources were used to assess sensitivity and adaptive capacity through the proximity of green spaces, cooling centers, healthcare, and premium service facilities. The PET varied from 25.3 °C in the outskirts to 30.2 °C in Moscow centre; however, variations in thermal stress did not have adverse effects on the spatial patterns of vulnerability. The vulnerability indicator in the east was six times higher than in more prosperous areas of the center, north and southwest, due to historical development, mainly the transformation from former industrial areas into residential areas.

Data extension of high-resolution wind speed database by fusing meteorological observation and local objective analysis data with POD–LSE

Newly generated high-resolution meteorological reanalysis data have been published over the past few years on short records. Producing high-resolution data in long-term time scales is challenging due to high computational costs and complex weather models. In contrast, urban meteorological observations have been continuously recorded for decades, although at a low spatial resolution. This study proposes a data fusion method using Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE). It fuses meteorological observations and high spatial resolution local objective analysis (LA) data to reproduce a high-resolution and long-term fusion database for urban areas without observations. First, the accuracy of LA data was verified by comparison with meteorological observations. Subsequently, the accuracy of the generated fusion data was investigated by comparison with Doppler lidar observations. Finally, the performance of single- and multi-time LSE in data fusion is discussed. These results indicate that the proposed method generates high-resolution and long-term fusion wind speed data that are similar to the Doppler lidar observations. In addition, the fusion model built using hourly data can accurately generate a highly sampled database at 10-min intervals. The single-time LSE method is recommended to be applied over the multi-time delay method for data fusion.

Advanced Moored Data Buoys for Catching Typhoons in the Western North Pacific

Abstract With the western North Pacific being one of the most active typhoon hotspots, a reliable buoy system is essential for in situ observations during typhoon passages. This study details the development and implementation of two improved buoys designed by National Taiwan University based on the Autonomous Temperature Line Acquisition System. It aims to establish a buoy network for near real-time, high-resolution meteorological and oceanic data collection during typhoon periods in the western North Pacific. The buoy system was distinguished by its reliable design, low power-consumption system, dual satellite and radio communication system, and inductive modem-based data acquisition system. Moreover, the system was proven robust to extreme weather conditions and fishing challenges. From 2015 to 2022, we conducted sea trials in typhoon hotspot regions every summer (normally June to October), demonstrating the buoys' durability and capability to withstand 21 typhoons while collecting valuable scientific data.

A study of particle dry deposition parameterizations in an atmospheric radioactive preparedness model: Application to the Chernobyl case

Parameterization of dry deposition is key for modelling of atmospheric transport and deposition of radioactive particles. Still, very simple parameterizations are often encountered in radioactive preparedness models such as the SNAP model (SNAP=Severe Nuclear Accident Program) of the Norwegian Meteorological Institute. In SNAP a constant dry deposition velocity (=0.2 cm/s) neglecting aerodynamic and surface resistances, is presently used. Therefore, two new dry depositions schemes (the Emerson scheme and the EMEP (European Monitoring and Evaluation Programme) scheme) have been implemented in SNAP to evaluate the benefits of including aerodynamic and surface resistances codes with respect to model prediction skills. The three dry deposition schemes are evaluated using 137Cs total deposition from soil sample data (n = 540) for a 60 km radial zone out from the Chernobyl Nuclear Power Plant (ChNPP) collected during the months after the accident. The present study capitalizes on high resolution meteorological data (2.5 km horizontal resolution), a detailed land-use data set with 273 sub-classes and the hitherto most comprehensive source term description for the Chernobyl accident. Based on our findings it is recommended to replace the present simple SNAP scheme with the Emerson or EMEP dry deposition scheme.

On the energy impact of cool roofs in Dubai

Urban warming can significantly increase the energy demand for building cooling. As temperatures rise, buildings require additional energy for air conditioning, which can lead to higher electricity demand and increased greenhouse gas emissions. This can create a positive feedback loop, as increased energy demand leads to increased waste heat and further exacerbates the urban warming effect. To solve this issue, this study integrates high-resolution meteorological data from the Weather Research and Forecasting coupled with Single Layer Urban Canopy Model (WRF/SLUCM) into CitySim for the first time to evaluate the building energy demands at various building types in Dubai metropolis. Examining how cool materials reduced cooling loads was achievable utilizing modified albedo in Dubai's downtown buildings. An increase of urban albedo from 0.2 to 0.8 significantly lowers the urban heating effect, as well as the building cooling needs. The study shows that for albedo scenario 0.8, Low-rise residential buildings experienced cooling load reductions of about 13.1 kWh/m2 and 6 kWh/m2 for buildings without insulation and those with insulation, respectively, compared to the base case. Additionally, high-rise residential buildings saw cooling load reductions ranging from 7.7 kWh/m2 to 11.7 kWh/m2 for buildings without insulation and a cooling load reduction of 3.6 kWh/m2 to 5.4 kWh/m2 for insulated buildings. This study provides a tool for regulating the energy impact of cool roofs in different urban settings using energy simulation, which could help policymakers control the energy needs in subtropical desert urban environments.

LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations

This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012–2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

Temperature change in the Tianshan Mountains and its external drivers

The Tianshan Mountains area (TMA), located in east Central Asia, has experienced faster temperature rise than the global average rate from the 1960s to the present. This initial estimate is carefully examined using a new dataset of high spatial resolution dynamically downscaled by an updated Weather Research and Forecasting (WRF) regional model. This dataset and other available climate data products show that the WRF model outputs describe temperature changes in TMA consistent with limited available observations (R2 = 0.95 and RMSE = 3.25 °C). The model data confirm that the temperature at TMA has risen at a rate of 0.18 °C/decade, faster than the global mean rate of 0.15 °C/decade. Strong warming rates are also found in spring and summer while the winter is cooling. In addition to revealing these variations of TAM temperatures the model outputs provide high resolution meteorological and climatic data and open the opportunity for studying surface hydrology and ecosystem responses in the region. Possible forcings of the changes of TMA temperature are explored from a global perspective using a set of ten climate indices. Results show that the most impactful processes on TMA temperatures are those associated with the contraction of the arctic sea-ice in recent decades, SST anomalies in the tropical Pacific, e.g., ENSO, SST variations in both the tropical Atlantic and North Atlantic Ocean. The east Asian jet stream also shows a significant impact on TMA temperatures likely by its impact on placing its entrance region over the Central Asia and thus weather disturbances in TMA.

Operational snow-hydrological modeling for Switzerland

The seasonal evolution of snow cover has significant impacts on the hydrological cycle and microclimate in mountainous regions. However, snow processes also play a crucial role in triggering alpine mass movements and flooding, posing risks to people and infrastructure. To mitigate these risks, many countries use operational forecast systems for snow distribution and melt. This paper presents the Swiss Operational Snow-hydrological (OSHD) model system, developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. The OSHD system is a sophisticated snow hydrological model designed specifically for the high-alpine terrain of the Swiss Alps. It leverages exceptional station data and high-resolution meteorological forcing data, as well as various reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, each tailored to specific modeling strategies and applications. For snowmelt runoff forecasting, monitoring snow water resources, and research-grade purposes, the OSHD system employs physics-based modeling chains. For snow climatological assessments, a conceptual model chain is available. We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland. The first dataset comprises a snow water equivalent climatology spanning 1998–2022, with a spatial resolution of 1 km. The second dataset includes snow distribution and snow melt data spanning 2016–2022 at a high spatial resolution of 250 m. To meet the needs of a multi-purpose snow hydrological model framework, the OSHD system employs various strategies for process representation and sub-grid parameterizations at the snow-canopy-atmosphere interface, particularly in complex terrain. Recent and ongoing model developments are aimed at accounting for complex forest snow processes, representing slope and ridge-scale precipitation and snow redistribution processes, as well as improving probabilistic snow forecasts and data assimilation procedures based on remote sensing products.

Streamflow Simulation with High-Resolution WRF Input Variables Based on the CNN-LSTM Hybrid Model and Gamma Test

Streamflow modelling is one of the most important elements for the management of water resources and flood control in the context of future climate change. With the advancement of numerical weather prediction and modern detection technologies, more and more high-resolution hydro-meteorological data can be obtained, while traditional physical hydrological models cannot make full use of them. In this study, a hybrid deep learning approach is proposed for the simulation of daily streamflow in two mountainous catchments of the Daqing River Basin, northern China. Two-dimensional high-resolution (1 km) output data from a WRF model were used as the model input, a convolutional neural network (CNN) model was used to extract the physical and meteorological characteristics of the catchment at a certain time, and the long short-term memory (LSTM) model was applied to simulate the streamflow using the time-series data extracted by the CNN model. To reduce model input noise and avoid overfitting, the Gamma test method was adopted and the correlations between the input variables were checked to select the optimal combination of input variables. The performance of the CNN-LSTM models was acceptable without using the Gamma test (i.e., with all WRF input variables included), with NSE and RMSE values of 0.9298 and 9.0047 m3/s, respectively, in the Fuping catchment, and 0.8330 and 1.1806 m3/s, respectively, in the Zijingguan catchment. However, it was found that the performance of the model could be significantly improved by the use of the Gamma test. Using the best combination of input variables selected by the Gamma test, the NSE of the Fuping catchment increased to 0.9618 and the RMSE decreased to 6.6366 m3/s, and the NSE of the Zijingguan catchment increased to 0.9515 and the RMSE decreased to 0.6366 m3/s. These results demonstrate the feasibility of the CNN-LSTM approach for flood streamflow simulation using WRF-downscaled high-resolution data. By using this approach to assess the potential impacts of climate change on streamflow with the abundant high-resolution meteorological data generated by different climate scenarios, water managers can develop more effective strategies for managing water resources and reducing the risks associated with droughts and floods.

Regional switchgrass carbon sequestration estimates from high‐frequency eddy covariance and Mesonet observations

AbstractMonitoring net ecosystem carbon dioxide (CO2) exchange (NEE) using eddy covariance (EC) flux towers is quite common, but the measurements are valid at the scale of tower footprints. Alternative ways to quantify and extrapolate EC‐measured NEE across potential production areas have not been explored in detail. To address this need, we used NEE measurements from a switchgrass (Panicum virgatum L.) ecosystem and detailed meteorological measurements from the Oklahoma Mesonet and developed empirical relationships for quantifying seasonal (April to October) sums of NEE across potential switchgrass establishment landscapes in Oklahoma, USA. We identified ensemble areas for potential switchgrass expansion regions and created thematic maps of switchgrass productivity using geostatistics and geographic information systems (GIS) routines. The purpose of this study was to explore if model parametrizations based on high temporal frequency meteorological forcing can be used for reliable estimates of NEE for evaluating the source–sink status of carbon. Rectangular hyperbolic light‐response curve and temperature response functions were fitted using EC measurements to estimate gross primary production (GPP) and ecosystem respiration (ER), respectively, on a seasonal scale. Model performance validated by comparing EC‐measured seasonal NEE for 3 yr showed good‐to‐strong agreement (.29 < R2 < .91; p < .05). Additionally, total seasonal NEE estimates were validated with measured biomass data in three additional locations. The estimated seasonal average net ecosystem production (NEP = −NEE) was 3.97 ± 1.92 (SD) Mg C ha−1. However, results based on a simple linear model suggested significant differences in NEP between contrasting climatic years. Overall, the results from this study indicate that this new scaling‐up approach involving high temporal resolution meteorological data may be a helpful tool for assessing spatiotemporal heterogeneity of switchgrass production and the potential of switchgrass fields to sequester carbon in the Southern Great Plains of the United States.

High-resolution European daily soil moisture derived with machine learning (2003–2020)

Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003–2020. Compared to its previous version with a lower spatial resolution (0.25°), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses.

Predicting soil conservation service in the Jinghe River Basin under climate change

As an essential regulating service, soil conservation service is the primary way to suppress soil erosion. Accurate assessment of soil conservation service can provide scientific support for regional ecological protection and is of great significance to regional environmental security. However, because of the lack of future high-resolution meteorological data, there are few researches on ecosystem services prediction, resulting in limited persuasion to guide ecological construction. Therefore, this study uses the WRF model to obtain a high-resolution meteorological dataset (0.05° × 0.05°, 2021–2035, daily) to drive the SWAT model in the Jinghe River basin. Through this method, we introduce the dynamical downscaling method into the research field of ecosystem services to predict the temporal-spatial characteristics of soil conservation service. The results indicate that: first, the dynamical downscaling results of the WRF model can reflect the magnitude, temporal fluctuation, and spatial distribution of regional precipitation and temperature. Second, multi-year average (2021–2035) soil conservation is 9.79 × 108 t (RCP4.5) and 10.98 × 108 t (RCP8.5), which are significantly higher than the 2000–2020 period (4.21 × 108 t). The inter-annual variation of soil conservation service is affected considerably by precipitation, with correlation coefficients of 0.80 (RCP4.5) and 0.81 (RCP8.5). Finally, the spatial distribution of soil conservation service under two scenarios is similar. The research results provide scientific support for ecological construction and scientific management, and the research ideas are also applicable to other ecosystem services prediction at the watershed scale.

Using High-Resolution Climate Models to Identify Climate Change Hotspots in the Middle East: A Case Study of Iran

The adverse effects of climate change will impact all regions around the world, especially Middle Eastern countries, which have prioritized economic growth over environmental protection. However, these impacts are not evenly distributed spatially, and some locations, namely climate change hotspots, will suffer more from climate change consequences. In this study, we identified climate change hotspots over Iran—a developing country in the Middle East that is facing dire economic situations—in order to suggest pragmatic solutions for vulnerable regions. We used a statistical index as a representative of the differences in climatic parameters for the RCP8.5 and RCP4.5 forcing pathways between historical data (1975–2005), near-future data (2030–2060) and far-future data (2070–2100). More specifically, we used downscaled high-resolution (0.25°) meteorological data from five GCMs of the CMIP5 database to calculate the statistical metric. Results indicate that for the far-future period and RCP4.5, regions stretching from the northwest to southeast of Iran, namely the Hotspot Belt, are the most vulnerable areas, while, for RCP8.5, almost the whole country is vulnerable to climate change. The highest and lowest differences in temperature for RCP8.5 in 2070–2100 are observed during summer in the northwestern and central parts and during winter in the northern and northeastern parts. Moreover, the maximum increase and decrease in precipitation are identified over the western parts of Iran during fall and winter, respectively. Overall, western provinces (e.g., Lorestan and Kermanshah), which are mostly reliant on rainfed agriculture and other climate-dependent sectors, will face the highest change in climate in the future. As these regions have less adaptive capacity, they should be prioritized through upstream policy change and special budget allocation from the government to increase their resiliency against climate change.

Within-city variation in ambient carbon monoxide concentrations: Leveraging low-cost monitors in a spatiotemporal modeling framework

Exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes based on human and animal experimental evidence, but current epidemiologic research is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize potential fine-scale variation of CO. This study aimed to develop a high-resolution ambient CO prediction model at the daily scale based on both regulatory agency monitoring data and measurements from calibrated low-cost gas monitors in Baltimore, Maryland. We also evaluated the contribution of three novel parameters to model performance, including high-resolution meteorological data, satellite remote sensing data, and co-pollutant concentrations (PM2.5, NO2, and NOx). The CO model had spatial cross-validation (CV) R² (RMSE) of 0.70 (0.02 parts per million, ppm) and temporal CV R² (RMSE) of 0.61 (0.04 ppm). The predictions revealed spatially resolved CO hotspots associated with population, traffic, and other non-road emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. The three novel parameters did not substantially improve model performance, suggesting that our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiologic research.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

Background:Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.Objectives:To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.Methods:We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (, , and ) concentrations.Results:The CO model had spatial cross-validation (CV) and root-mean-square error (RMSE) of (ppm), respectively; the model had temporal CV and RMSE of 0.61 and , respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.Discussion:The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889

When it Rains, it Pours: Estimating the Spatial Spillover Effect of Rainfall

A large fraction of the world’s poor rely on rain-fed agriculture, which makes them vulnerable to changes in rainfall patterns. In this paper, we examine whether spatial correlation in rainfall results in these households also being vulnerable to an adverse spatial-spillover effect. In particular, we use household-level panel data from India along with high-resolution meteorological data to show how rural household consumption varies with own-region rainfall as well as rainfall in neighboring areas. We find that while greater own rainfall has a positive effect on rural household consumption, greater rainfall in neighboring regions has an adverse spatial-spillover effect. Our results suggest that when this spillover effect is taken into account, the positive impact of own-region rainfall on household consumption falls by 38 percent.

Trends in the Frequency of Water and Heat Stress in Mid-Latitude North America since 1980

The water-energy balance of many mid-latitude watersheds has been changing in recent decades due to global warming. These changes manifest themselves over both long timescales (e.g., hydrologic drought) and short timescales (e.g., agricultural drought) and may be ameliorated or exacerbated by vegetative response. We apply a Budyko framework to assess short-term response to long-term trends in water and heat stress (HS) across mid-latitude North America. Using high-resolution meteorological data and streamflow records, we calculate the frequency of HS every year since 1980 for every gaged watershed with adequate data (n = 1528). We find that HS has become more frequent in most watersheds in the western US, New England, and southeastern Canada. However, we find that HS has become less frequent in the Midwest and the relatively humid eastern US. By assessing the relationship between trends in HS frequency and proximate forcing variables (annual PPT, annual streamflow, minimum and maximum daily temperatures, actual evapotranspiration, and potential evapotranspiration), we find that these trends in HS frequency are primarily driven by meteorological forcings rather than vegetative response. Finally, we contextualize our findings within the Budyko framework, which assumes a landscape in equilibrium with its climate, with the implication that these trends in HS are only likely to be realized after local vegetation has adapted to new meteorological norms.

Testing HYSPLIT Plume Dispersion Model Performance Using Regional Hydrocarbon Monitoring Data during a Gas Well Blowout

A gas well blowout in south central Texas in November 2019 that lasted for 20 days provided a unique opportunity to test the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model’s plume dispersion against hydrocarbon air monitoring data at two nearby state monitoring stations. We estimated daily blowout hydrocarbon emission rates from satellite measurement-based results on methane emissions in conjunction with previously reported composition data of the local hydrocarbon resource. Using highly elevated hydrocarbon mixing ratios observed during several days at the two downwind monitoring stations, we calculated excess abundances above expected local background mixing ratios. Subsequent comparisons to HYSPLIT plume dispersion model outputs, generated using High-Resolution Rapid Refresh (HRRR) or North American Mesoscale (NAM) forecast meteorological input data, showed that the model generally reproduces both the timing and magnitude of the plume in various meteorological conditions. Absolute hydrocarbon mixing ratios could typically be reproduced within a factor of two. However, when lower emission rate estimates provided by the company in charge of the well were used, downwind hydrocarbon observations could not be reproduced. Overall, our results suggest that HYSPLIT, in combination with high-resolution meteorological input data, is a useful tool to accurately forecast chemical plume dispersion and potential human exposure in disaster situations.

Research on Spatial Statistical Downscaling Method of Meteorological Data Applied to Photovoltaic Prediction

Aiming at the low spatial resolution of meteorological data output from a numerical model in photovoltaic power prediction, a geographically weighted statistical downscaling method considers the influence factors such as normalized vegetation index (NDVI), digital elevation model (DEM), slope direction, longitude and latitude is proposed. This method is based on the correlation between meteorological data and NDVI, DEM, slope direction, latitude and longitude, and introduces DEM and local Moran index to improve the regression model, and obtains 100 * 100 m high-resolution meteorological spatial distribution data. Finally, combining the measured data of the study area and the established EOF iterative downscaling method to verify and compare the downscaling results. The results show that the error between the downscaled meteorological data and the measured value is smaller, and the comprehensive downscaling accuracy of the geographically weighted regression method is higher, and the model fitting effect is better. Therefore, this method can effectively improve the influence of errors caused by lower resolution, and provide a more reliable meteorological basis for the prediction of photovoltaic power.

Comparative Analysis on Wave Field Using Wave Measurement and Wave Analysis Data from JMA-MSM

Kim, H.-J.; Cho, W.C., and Moon, Y.-H., 2021. Comparative analysis on wave field using wave measurement and wave analysis data from JMA-MSM. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 181–185. Coconut Creek (Florida), ISSN 0749-0208. Waves are the most important factor in the numerical analysis of coastal hydrodynamics. Wave conditions in coastal areas can be determined in on-site wave measurements or wave numerical analyses. Because the acquisition of spatial wave measurement data in a specific coastal area is limited, the spatial wave data are generally acquired with accurate numerical analyses. In this study, the wave field in a coastal area in South Korea was comparatively analyzed with wave measurements and numerical analyses of wind data acquired with the JMA (Japan Meteorological Agency) Meso-Scale Model (MSM). The high-resolution meteorological data were used for the numerical analysis, and the wave measurement data provided by the Wave Information Network of Korea (WINK) were used for the comparative analysis. Results showed the root mean square errors of 0.3-0.4m for all wave prediction in the East Sea in 2018 and 0.4-0.5m for typhoon KONG-REY in 2018. Therefore, it was concluded that the wave numerical simulations based on the wave data from JMA-MSM overall reproduced the wave field accurately.