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The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach the gray zone of turbulence in the energy-containing range and the lowest model layer is located within the RSL. From this perspective, the RSL has an important implication in atmospheric modeling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s -1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm per month of RMSE). Our findings suggest that the RSL must be properly considered both for better weather and climate simulations and for the application of wind energy and atmospheric dispersion. [ABSTRACT FROM AUTHOR]

Since the Industrial Revolution, the geographical extent of cities has increased around the world. In particular, following three decades of rapid regional economic growth, many Asian megacities have emerged and continue to expand, resulting in inevitable short-term urban redevelopment. In this region, the microclimatic impacts of urban redevelopment have not been extensively investigated using long-term in situ observations. In this study, changes in surface sensible heat exchange, heat storage, and anthropogenic heat emissions that are due to urban residential redevelopment were quantified and analyzed on the basis of a 3-yr micrometeorological record from the Seoul, South Korea, metropolitan area. The results show that, following urban redevelopment of compact high-rise residential buildings, 1) the daily minimum air temperature near the ground surface increased by ~0.6 K; 2) the ratio between surface sensible heat and net radiation increased by from ~9% (summer) to 31% (winter), anthropogenic heat emissions increased by from 7.6 (summer) to 23.6 (spring) W m−2, and daily maximum heat storage ranged from 35.1 (spring) to 54.5 (summer) W m−2; and 3) there was a transition of local circulation with changes in the surface properties of heat sources and roughness. [ABSTRACT FROM AUTHOR]

ABSTRACT Electricity demand is influenced by atmospheric conditions, and, therefore it is important to quantify their relationships suitably for accurate electricity demand forecasting and the implementation of power-saving policies. However, interdependencies and characteristics of covariance among meteorological variables within the same periodicities hinder the quantification of their direct and indirect impacts on electric power load. To investigate the strength of the direct correlation between atmospheric conditions and electric power load, this study harnessed a new partialization analysis method based on a partial phase synchronization index combined with wavelet transformation. The advantage of the proposed method is that it can be used to evaluate the degree of independent contribution of the variables over different spatiotemporal scales. Compared with traditional statistical analyses, this new partialization analysis shows that air temperature is the principal variable associated directly with electricity demand, but that the strength of the relationship varies with season and time scale. Relative humidity and wind speed have strong direct correlations with electricity in summer and winter, respectively. Insolation is directly coupled to the electric power load only on sub-diurnal time scales. This investigation indicates that for accurate forecasting of electricity demand, changes in the coupling strengths of different atmospheric variables should be incorporated into the electric power load forecasting process. The study shows that a partial phase synchronization index, combined with wavelet transformation, is a useful tool that could be used in other studies to assess complex interacting atmospheric oscillations that cannot be assessed properly by traditional approaches. [ABSTRACT FROM AUTHOR]

On 21 September 2010, heavy rainfall with a local maximum of 259 mm d occurred near Seoul, South Korea. We examined the ability of the Weather Research and Forecasting (WRF) model in reproducing this disastrous rainfall event and identified the role of two physical processes: planetary boundary layer (PBL) and microphysics (MPS) processes. The WRF model was forced by 6-hourly National Centers for Environmental Prediction (NCEP) Final analysis (FNL) data for 36 hours form 1200 UTC 20 to 0000 UTC 22 September 2010. Twenty-five experiments were performed, consisting of five different PBL schemes-Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Quasi Normal Scale Elimination (QNSE), Bougeault and Lacarrere (BouLac), and University of Washington (UW)-and five different MPS schemes-WRF Single-Moment 6-class (WSM6), Goddard, Thompson, Milbrandt 2-moments, and Morrison 2-moments. As expected, there was a specific combination of MPS and PBL schemes that showed good skill in forecasting the precipitation. However, there was no specific PBL or MPS scheme that outperformed the others in all aspects. The experiments with the UW PBL or Thompson MPS scheme showed a relatively small amount of precipitation. Analyses form the sensitivity experiments confirmed that the spatial distribution of the simulated precipitation was dominated by the PBL processes, whereas the MPS processes determined the amount of rainfall. It was also found that the temporal evolution of the precipitation was influenced more by the PBL processes than by the MPS processes. [ABSTRACT FROM AUTHOR]

A new hybrid model for forecasting the electric power load several months ahead is proposed. To allow for distinct responses from individual load sectors, this hybrid model, which combines dynamic (i.e., air temperature dependency of power load) and fuzzy time series approaches, is applied separately to the household, public, service, and industrial sectors. The hybrid model is tested using actual load data from the Seoul metropolitan area, and its predictions are compared with those from two typical dynamic models. Our investigation shows that, in the case of four-month forecasting, the proposed model gives the actual monthly power load of every sector with only less than 3% absolute error and satisfactory reduction of forecasting errors compared to other models from previous studies. [ABSTRACT FROM AUTHOR]

The monsoon system is an important natural driver of ecosystem carbon and water exchanges in Asia and is being altered by anthropogenic forcings. This system is accompanied by heavy rainfall and typhoons in the main growing season, thus causing alterations of environmental conditions such as rainfall, wind, and temperature; therefore, it acts as a natural disturbance to forests in Asia. Therefore, degradation of ecosystem service by monsoon activity reinforced by anthropogenic factors in a changing climate is of great concern. In this study, we presented observational evidences for the interplay of terrestrial carbon and water dynamics with the Asian monsoon and their implication in ecosystem modeling. We analyzed 3-year eddy-covariance data at a temperate deciduous forest in Korea. We used wavelet power and coherence spectra to investigate the Asian monsoon system and to determine its impact on the ecosystem. During the study period, our analysis showed strong coupling between ecosystem functioning and temporal variations of monsoon climate. Further scrutiny on the model outputs showed that the model did not accurately reproduce the observed plant phenology and thus ecosystem carbon and water exchanges disturbed by monsoon activities. Our findings suggest that under projected climate scenarios, terrestrial carbon sinks in monsoon Asia will decline if the monsoon disturbance will exceed its natural range of variation and if there is no enhancement in the robustness of the ecosystem in this region. [ABSTRACT FROM AUTHOR]

A simple aerodynamic-variance method is proposed to fill gaps in continuous CO2 flux measurements in rainy conditions, when open-path analysers do not function. The method requires turbulent conditions (friction velocity greater than 0.1ms−1), and uses measurements of mean wind speed, and standard deviations of temperature and CO2 concentration fluctuations to complement, and at times replace, eddy-covariance measurements of friction velocity, sensible heat flux and CO2 flux. Friction velocity is estimated from the mean wind speed with a flux-gradient relationship modified for the roughness sublayer. Since normalised standard deviations do not follow Monin-Obukhov similarity theory in the roughness sublayer, a simple classification scheme according to the scalar turbulence scale was used. This scheme is shown to produce sensible heat and CO2 flux estimates that are well correlated with the measured values. [ABSTRACT FROM AUTHOR]

Results from eight regional climate models (RCMs) participating in the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) initiative of the Global Energy and Water Exchanges (GEWEX) are examined and compared with observations over the Tibetan Plateau (TP). The RCM common domain covers most areas of East Asia with a horizontal resolution of 20–30 km. The model simulation covers a period from April to September in each year between 1991 and 2015. This study explores the RCMs' ability for seasonal climate simulation over the TP, focusing on the summer monsoon climate as part of the LS4P initiative. An intercomparison is made among eight RCMs for precipitation, surface air temperature, mid-troposphere atmospheric circulation, moisture conditions, and surface energy fluxes. It shows that the downscaling characteristics differ significantly between two major RCM types. The RegCM4 models show positive precipitation biases over the entire TP, especially over the south and southeast TP, while the WRF models mostly show both positive and negative precipitation biases over the TP with relatively high spatial correlation between simulated and observed precipitation. The multi-model ensemble mean produces overall smaller precipitation biases than most individual RCMs, with the largest biases over the southeastern TP, and smaller surface air temperature biases over most areas of the TP, especially over the central and southwestern TP. Moreover, the ensemble mean can better reproduce the inter-annual variation of precipitation and surface air temperature than most RCMs with proper magnitude. Sensitivity analyses using RegCM4 with different physics parameterizations show that varying land and cumulus schemes may induce large precipitation differences over the TP by affecting moisture and atmospheric circulation conditions in the lower and upper troposphere, respectively. Moreover, turbulent heat and radiation fluxes differences are associated with the temperature differences between different RegCM4 models. [ABSTRACT FROM AUTHOR]

Background: It is important to quantify changes in CO2 sources and sinks with land use and land cover change. In the last several decades, carbon sources and sinks in East Asia have been altered by intensive land cover changes due to rapid economic growth and related urbanization. To understand impact of urbanization on carbon cycle in the monsoon Asia, we analyze net CO2 exchanges for various land cover types across an urbanization gradient in Korea covering high-rise high-density residential, suburban, cropland, and subtropical forest areas. Results: Our analysis demonstrates that the urban residential and suburban areas are constant CO2 sources throughout the year (2.75 and 1.02 kg C m−2 year−1 at the urban and suburban sites), and the net CO2 emission indicate impacts of urban vegetation that responds to the seasonal progression of the monsoon. However, the total random uncertainties of measurement are much larger in the urban and suburban areas than at the nonurban sites, which can make it challenging to obtain accurate urban flux measurements. The cropland and forest sites are strong carbon sinks because of a double-cropping system and favorable climate conditions during the study period, respectively (− 0.73 and − 0.60 kg C m−2 year−1 at the cropland and forest sites, respectively). The urban area of high population density (15,000 persons km−2) shows a relatively weak CO2 emission rate per capita (0.7 t CO2 year−1 person−1), especially in winter because of a district heating system and smaller traffic volume. The suburban area shows larger net CO2 emissions per capita (4.9 t CO2 year−1 person−1) because of a high traffic volume, despite a smaller building fraction and population density (770 persons km−2). Conclusions: We show that in situ flux observation is challenging because of its larger random uncertainty and this larger uncertainty should be carefully considered in urban studies. Our findings indicate the important role of urban vegetation in the carbon balance and its interaction with the monsoon activity in East Asia. Urban planning in the monsoon Asia must consider interaction on change in the monsoon activity and urban structure and function for sustainable city in a changing climate. [ABSTRACT FROM AUTHOR]

In this study, the WRF-Chem v4.4 model was utilized to evaluate the sensitivity of O 3 simulations with three bottom-up emission inventories (EDGAR-HTAP v2 and v3 and KORUS v5) using surface and aircraft data in East Asia during the Korea-United States Air Quality (KORUS-AQ) campaign period in 2016. All emission inventories were found to reproduce the diurnal variations of O 3 and its main precursor NO 2 as compared to the surface monitor data. However, the spatial distributions of the daily maximum 8 h average (MDA8) O 3 in the model do not completely align with the observations. The model MDA8 O 3 had a negative (positive) bias north (south) of 30° N over China. All simulations underestimated the observed CO by 50 %–60 % over China and South Korea. In the Seoul Metropolitan Area (SMA), EDGAR-HTAP v2 and v3 and KORUS v5 simulated the vertical shapes and diurnal patterns of O 3 and other precursors effectively, but the model underestimated the observed O 3 , CO, and HCHO concentrations. Notably, the model aromatic volatile organic compounds (VOCs) were significantly underestimated with the three bottom-up emission inventories, although the KORUS v5 shows improvements. The model isoprene estimations had a positive bias relative to the observations, suggesting that the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 overestimated isoprene emissions. Additional model simulations were conducted by doubling CO and VOC emissions over China and South Korea to investigate the causes of the model O 3 biases and the effects of the long-range transport on the O 3 over South Korea. The doubled CO and VOC emission simulations improved the model O 3 simulations for the local-emission-dominant case but led to the model O 3 overestimations for the transport-dominant case, which emphasizes the need for accurate representations of the local VOC emissions over South Korea. [ABSTRACT FROM AUTHOR]

For regional weather forecasts and climate predictions, it is important to determine the optimal domain size, location, and top height. A wide model domain can be chosen to avoid noises from lateral boundaries but this can include the Tibetan Plateau and areas of northern Manchuria to the Kamchatka Peninsula in Northeast Asia. This study shows that topographic regions around the Tibetan Plateau and warm pool areas over the Manchuria in an extended model domain may have harmful effects on the accuracy of short‐ to medium‐range regional predictions on the downwind side in spring. The inaccuracy is related to model errors due to steep terrain regions in the Tibetan Plateau and cold bias in the lower stratosphere north of Manchuria. Well‐designed spectral nudging over the eastern flank of the Tibetan Plateau and the use of a higher model top are found to improve regional predictions for Northeast Asia in spring by effectively eliminating errors associated with steep topography and temperature biases in the upper troposphere and lower stratosphere, respectively. Our findings suggest possible ways to mitigate biases due to steep mountains and upper‐level processes in regional modeling. We discuss the role of our method in terms of uncertainties in regional weather forecasts and climate predictions. [ABSTRACT FROM AUTHOR]

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022). [ABSTRACT FROM AUTHOR]

Wind energy plays an important role in the sustainable energy transition towards a low-carbon society. Proper assessment of wind energy resources and accurate wind energy prediction are essential prerequisites for balancing electricity supply and demand. However, these remain challenging, especially for onshore wind farms over complex terrains, owing to the interplay between surface heterogeneities and intermittent turbulent flows in the planetary boundary layer. This study aimed to improve wind characteristic assessment and medium-term wind power forecasts over complex hilly terrain using a numerical weather prediction (NWP) model. The NWP model reproduced the wind speed distribution, duration, and spatio-temporal variabilities of the observed hub-height wind speed at 24 wind turbines in onshore wind farms when incorporating more realistic surface roughness effects, such as the subgrid-scale topography, roughness sublayer, and canopy height. This study also emphasizes the good features for machine learning that represent heterogeneities in the surface roughness elements in the atmospheric model. We showed that medium-term forecasting using the NWP model output and a simple artificial neural network (ANN) improved day-ahead wind power forecasts by 14% in terms of annual normalized mean absolute error. Our results suggest that better parameterizations of surface friction in atmospheric models are important for wind power forecasting and resource assessment using NWP models, especially when combined with machine learning techniques, and shed light on onshore wind power forecasting and wind energy assessment in mountainous regions. [Display omitted] • Onshore wind energy forecasting and assessment are challenging over complex terrain. • Surface friction is properly considered in an atmospheric model and machine learning. • Winds simulation improved with an integration of surface friction over mountain. • Our study improved wind power predictability over complex terrain. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km 2 resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated ( R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed. [ABSTRACT FROM AUTHOR]

Based on observations over an alpine glacier, we investigate the turbulent flux dissimilarity between momentum and sensible heat transfer in a stably stratified katabatic flow. The flux correlation coefficient RF is employed as a measure of variable levels of the flux similarity, which are found to be modulated by the anisotropy of turbulence. In the katabatic wind regime over this glacier, heat is transported more efficiently than momentum. Based on quadrant analysis, the sweep–ejection differences in the flux fraction constitute turbulence characteristics in terms of the velocity aspect ratio, which unravel dissimilar transport of momentum and heat. Moreover, an innovative method is established for connecting quadrant analysis and cospectral analysis, whereby the hyperbolic quadrant hole size is coupled to the frequency underlying the fast Fourier transform. In extending applications of octant analysis, we introduce a hypothetical octant hole, whose size is solicited as a proxy for the amplitude scale of fluctuating fluxes. The contributions to R F can then be quantified for eddy structures that are associated with different amplitude scales. The katabatic flow structures identified from octant analysis differ in their behaviour so helping illuminate the outcome of flux dissimilarity. Exhibited as a statistical behaviour regardless of amplitude scale, along-wind rapid motions of heated air parcels can modify fractional contributions to the heat flux instead of the momentum flux, with reductions in R F related to decreasing heat-flux fractions. Besides, along-wind slow motions of cooled air parcels cannot modify the flux fractions for both momentum and heat. Thus, the flux dissimilarity due to low-speed low-temperature eddies cannot be explicable in terms of the flux fractions alone. These findings are an incipient step towards physical understanding of the turbulent flux dissimilarity for a stably stratified katabatic flow. [ABSTRACT FROM AUTHOR]

Urban surface albedo is an essential biophysical variable in the surface energy balance across all scales, from micro-scale (materials) to the globe, changing with land covers and three-dimensional structures over urban areas. Urban albedos are dynamic over space and time but have not yet been quantified over global scales due to the lack of high-resolution albedo datasets. Here, we combined the direct estimation approach and Landsat surface reflectance product to generate a 30-m-resolution annual surface albedo dataset for 3037 large cities (area > 50 km2) worldwide for the period from 1986 to 2020, allowing spatial patterns and long-term temporal trends to be explored with possible causal drivers, and quantification of the surface radiative forcing from these albedo changes. Evaluation of this new albedo dataset using global urban flux tower-based measurements demonstrates its high accuracy with an overall bias and root-mean-square-error (RMSE) of 0.005 and 0.025, respectively. Analysis of the dataset reveals an overall decreasing trend of albedo during the 35-year evaluation period (1986–2020), which is robust accounting for uncertainties from training sample representativeness, Landsat data uncertainty, seasonal variation, and snow-cover contamination. Our results reveal that urban greening (measured by the positive Normalized Difference Vegetation Index (NDVI) trend) can well explain the total variances in the albedo trend for the 35-year period through two different pathways of tree planting and urban warming-enhanced vegetation growth. The decrease in urban albedo caused a warming effect indicated by positive surface radiative forcing, with a global city-level average surface radiative forcing of 2.76 W·m−2. These findings enhance our understanding of urbanization's impacts on albedo-related biophysical processes and can provide information to quantify urban surface radiation energy and design effective mitigation strategies to reduce urban warming. • High-resolution surface albedo dataset is generated from Landsat for 3037 global major cities. • Most cities have a decreasing albedo trend during the past three decades. • Urban greening explains decreasing albedo trend through two different ways. • Decreased urban albedo incudes a positive surface radiative forcing. [ABSTRACT FROM AUTHOR]

Accurate wind power forecasts help establish efficient power supply plans and stabilize power systems. For long-term forecasts, the outputs of numerical weather prediction (NWP) models are pipelined as inputs for the statistical post-processing model, underscoring the necessity of understanding forecasts simulated from NWP to enhance power prediction accuracy. This study aims to enhance the quality of wind power forecasts in complex terrains, focusing on identifying and processing appropriate wind features of vertical layers simulated by NWP. In complex terrains with significant terrain variability, it is crucial to meticulously analyze and select the optimal vertical layer for each site or turbine individually, as simulated wind speeds at higher vertical layers than those used in previous research could potentially yield stronger correlations. Furthermore, we introduce a data processing technique that integrates wind characteristics across vertical layers, utilizing Principal Component Analysis (PCA). This approach not only provides physically intuitive results but also demonstrates enhanced performance compared to other feature selection methods. By selecting the appropriate vertical layers and applying the proper feature extraction, for wind farms situated in complex terrains in Korea, the annual normalized mean absolute error can be reduced by up to 1.2 %. • In complex terrains, it is essential to carefully select wind features of vertical layers. • A feature extraction method that integrates wind across vertical layers considering physical meaning is introduced. • The proposed method outperforms the feature selection method using a limited subset of vertical layers data. [ABSTRACT FROM AUTHOR]

Using the eddy covariance method, this study reports the one‐year turbulent fluxes of momentum, energy, and CO2, and their seasonal variations over a recently redeveloped high‐rise, high‐population, residential area in the metropolitan city of Seoul, Korea. The study area is affected by the Asian monsoon, which is accompanied by long rain spells and a related mid‐season depression of solar radiation in the summer. Our analysis shows that the urban surface energy balance and turbulence characteristics demonstrate typical urban properties. Unstable conditions dominate all day, and the storage heat flux (night‐time and morning) and sensible heat flux (afternoon) significantly affect the diurnal variations in the urban surface energy balance. Owing to the rough urban surface, the turbulence intensities are higher than those reported previously in other cities. The annual CO2 emission rate is approximately 13.1 kg CO2 m−2⋅year−1 with traffic, which is the major source of CO2 (+2.3 μmol⋅m−2⋅s−1 per 100 vehicles). Ecosystem respiration, including that by vegetation, soil, and humans, becomes dominant in the night‐time (00:00–05:00), thus contributing significantly to the annual CO2 budget. Further analysis indicates a unique coupling of urban surface energy partitioning and CO2 emission rates with the seasonal progression of the Asian monsoon: (a) surface albedo has annual minima in late summer when the sun elevation angle, is relatively higher and the urban surface condition is wetter than in other seasons; (b) the Bowen ratio ranges from 1.7 (summer) to 7.0 (winter); and (c) CO2 emission rates show seasonal variations with the progress of the summer monsoon. [ABSTRACT FROM AUTHOR]

Improvement of fog simulation accuracy was investigated for the fogs that occurred on the south coast of the Korean Peninsula using the WRF (3D) and PAFOG (1D) coupled model. In total, 22 fog cases were simulated and accuracy of the fog simulation was examined based on Critical Success Index, Hit Rate and False Alarm Rate. The performance of the coupled WRF-PAFOG model was better than that of the single WRF model as expected. However, much more significant improvement appeared only when the data from a 300 m meteorological tower was not only used for the initial conditions but also nudged during the simulation. Moreover, a proper prescription of soil moisture was found to be important for accurate fog simulation especially for the fog cases with prior precipitation since efficient moisture supply from the precipitation-soaked soil might have been critical for fog formation. It was also demonstrated that with such optimal coupled model setting, a coastal radiation fog event with prior precipitation could be very realistically simulated: the fog onset and dissipation times matched so well with observation. In detail, radiative cooling at the surface was critical to form a surface inversion layer as the night fell. Then the vapor flux from the precipitation-soaked surface was confined within the inversion layer to form fog. It is suggested that a proper prescription of soil moisture in the model based on observations, if readily available, could be a cost-effective method for improving operational fog forecasting, considering the fact that tall meteorological towers are a rarity in the world. [ABSTRACT FROM AUTHOR]

The dilution effect caused by boundary-layer evolution over land has strong influences on air quality. Accurate and continuous measurements of the boundary-layer height over urban areas are therefore needed for complete air-quality assessments. Commercial ceilometers, in combination with reliable and simple methodologies, can be used to retrieve the mixed-layer height, and represent a means of obtaining information on vertical mixing and atmospheric structure above cities. Here, we evaluate various retrieval algorithms based on the gradient method against high-temporal-resolution radiosonde observations. Based on the results, we propose a simple algorithm by using the gradient method, the correction of background noise and the moving averages, with the minimum number of parameters that need to be adjusted to the local properties and the instrument itself. The algorithm is adjusted for Seoul, Korea, and improves the retrieval performance by reducing high-frequency noise. The algorithm is used to investigate the relationship between the evolution of the daytime mixed-layer height and air pollution under a two-layer mixing model where changes in concentration depend only on the urban boundary-layer growth and air entrainment from the free atmosphere. Using 2 months of ceilometer retrievals of mixed-layer height and air-quality data from across the city, we find strong negative correlations for primary emitted pollutants such as NO2, CO, SO2, and particulate matter smaller than 10 µm, and a modest positive correlation for O3. The results provide insight into the significant influence of urban boundary-layer evolution on Seoul's air quality. [ABSTRACT FROM AUTHOR]

We developed the height correction algorithm based on the Monin–Obukhov similarity theory for application in Weather Research and Forecasting (WRF) model outputs to correct the 10-m height definition and match weather station observations. Thirty simulations were conducted for April 2012 to validate the height correction algorithm against observations such as the automated surface observing system and automatic weather stations. The WRF model simulations were constructed with two nested domains with horizontal grid spacings of 10 and 1 km. The root mean square error of the diagnosed 10-m wind speed after applying the height correction algorithm was consistently reduced compared to the one before applying the algorithm over the innermost domain during the analysis period. The impact of the height correction on diagnosed wind speed was greater when the environment was unstable and the magnitude of the roughness length was large. Evaluation showed that a strong synoptic forcing condition results in a significant reduction in the bias of wind speed. • Developed the height correction algorithm based on Monin–Obukhov similarity theory. • The 10-m height definition in the model is corrected to match the height of observation. • Alleviated positive bias in wind speed of weather research and forecasting model. [ABSTRACT FROM AUTHOR]

Net ecosystem carbon dioxide (CO2) exchange (NEE) is a key parameter for understanding the terrestrial plant ecosystems, but it is difficult to monitor or predict over large areas at fine temporal resolutions. In this research, we estimated the hourly NEE using a combination of the integrated neural network (NN) model with geostationary satellite imagery to overcome the limitations of existing daily polar orbiting satellite-derived carbon flux products. Two sets of satellite imageries (i.e. the meteorological imager (MI) and geostationary ocean colour imager (GOCI) aboard communication, ocean, and meteorological satellite (COMS)) and CO2 flux data derived from eddy covariance measurements were used to verify the feasibility of applying hourly geostationary satellite imagery with an NN-based approach for estimating NEE at high temporal resolutions. For the NN model, the optimum neuronal architecture was established using an NN with one hidden layer that was trained using the Levenberg-Marquardt back propagation algorithm. The hourly NEE values estimated in test period from the NN model using the combined COMS MI and GOCI imagery and ground measurements as model inputs were compared with the eddy covariance NEE values from the measurement tower, which yielded reliable statistical agreement. The hourly NEE results from the NNmodel based on COMS MI and GOCI imagery and ground measurement data had the highest accuracy (RMSE = 2.026 ìmol m-2 s-2, R = 0.975), while the root mean square error (RMSE) and the regression coefficient (R) generated by the NN model based on satellite imagery as the sole input variable were relatively lower (RMSE = 3-230 ìmol m-2 s-2, R = 0.952). Although the simulations for the satellite-only NEE were showed as lower accuracy than the NN model that included all input variables, the hourly variations in NEE also appeared to describe its daily growth and development pattern well, indicating the possibility of deriving hourly-based products from the proposed NN model using geostationary satellite data as inputs. [ABSTRACT FROM AUTHOR]

Aerosol effects on clouds and precipitation account for a large portion of uncertainties in the prediction of the future course of global hydrologic circulations and climate. As a process of a better understanding of interactions between aerosol, clouds and precipitation, simulations are performed for a mixed-phase convective multiple-cloud system over the tropics. Studies on single-cloud systems have shown that aerosol-induced increases in freezing, associated increases in parcel buoyancy and thus the intensity of clouds (or updrafts) are a main mechanism which controls aerosol-cloud-precipitation interactions in convective clouds. However, in the multiple-cloud system that plays much more important roles in global hydrologic circulations and thus climate than single-cloud systems, aerosol effects on condensation play the most important role in aerosol-induced changes in the intensity of clouds and the effects on freezing play a negligible role in those changes. Aerosol-induced enhancement in evaporation intensifies gust fronts and increases the number of subsequently developing clouds, which leads to the substantial increases in condensation and associated intensity of convection. Although aerosol-induced enhancement in freezing takes part in the increases in condensation by inducing stronger convergence around cloud bottom, the increases in condensation are ~one order of magnitude larger than those in freezing. It is found that while aerosol-induced increases in freezing create intermittent extremely heavy precipitation, aerosol-induced increases in evaporation enhance light and medium precipitation in the multiple-cloud system here. This increase in light and medium precipitation makes it possible that cumulative precipitation increases with increasing aerosol concentration, although the increase is small. It is interesting that the altitude of the maximum of the time- and domain-averaged hydrometeor mass densities is quite robust to increases in aerosol concentration. This is because locations of gust fronts and homogeneous freezing do not vary significantly with changing aerosol concentration and this outweighs aerosol effects on hydrometeor size. [ABSTRACT FROM AUTHOR]

Arctic tundra is undergoing a rapid transition due to global warming and will be exposed to snow-free conditions for longer periods under projected climate scenarios. Regional climate modeling is useful for understanding and predicting climate change in the Arctic tundra, however, the lack of in-situ observations of surface energy fluxes and the planetary boundary layer (PBL) structure hinders accurate predictions of local and regional climate around the Arctic. In this study, we investigate the performance of the Polar-optimized version of the Weather Research and Forecasting model (PWRF) in the Arctic tundra on clear days in summer. Based on simultaneous observations of surface fluxes and the PBL structure in Cambridge Bay, Nunavut, Canada, our validation shows that the PWRF simulates a drier environment, leading to a larger Bowen ratio and a warmer atmosphere compared to observations. Further sensitivity analyses indicate that the model biases are mainly from the uncertainties in physical parameters such as surface albedo and emissivity, the solar constant, and the model top height, rather than structural flaws in the model physics. Importantly, the PWRF reproduces the observations more accurately when the observed soil moisture is fed into the simulation. This indicates that there must be improvements in simulations of the land-atmosphere interaction at the Arctic tundra, not only in the accuracy of the initial soil moisture conditions but also in soil hydraulic properties and drainage processes. The mixing diagram analysis also shows that the entrainment process between the PBL and the overlying atmosphere needs to be improved for better weather and climate simulation. Our findings shed light on modeling studies in the Arctic region by disentangling the model error sources from uncertainties by parameters and physics package options. • Arctic tundra is exposed to warmer conditions with a warming global climate. • Polar WRF has bias in simulating climate near the surface on clear summer days. • Polar WRF reproduces the observations accurately with the observed soil moisture. • Regional climate simulation improves with proper numerical design over the Arctic. [ABSTRACT FROM AUTHOR]