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As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an ‘archetype-in-neighbourhood’ framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014–2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (Tex, °C), and (2) estimated indoor cooling temperature set-point (Tin, °C) of the city’ housing stock during the same period. Through a k-fold (k = 4) validation, the current regression model (2014–17) was evaluated to have an overall coefficient of determination R2=0.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071–2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design.

The current study investigated the impact of the Weather Research and Forecasting (WRF) (a) surface layer (SL), (b) land surface and (c) planetary boundary layer (PBL) physics on the short-term simulation of the meteorological and human thermal comfort conditions during 15 heat waves identified between 2004 and 2013 in the southeast Mediterranean and Balkan Peninsula. Four widely used PBL-SL schemes (YSU-MM5, MYJ-Eta, ACM2-Revised MM5, YSU-Revised MM5) and land surface models (LSMs; Noah, Noah-MP, CLM4, RUC) were tested. Comparisons with ground-based observations in 60 measuring sites showed that the WRF model is characterized by a cold bias leading to the underestimation of the extreme heat stress conditions over the entire study region. The MYJ-Eta experiment simulated greater sensible heat fluxes compared to the other PBL-SL scenarios due to the high surface heat exchange coefficient provided by the Eta SL scheme. This contributed to the high MYJ-Eta-modeled daytime 2-m air temperature (T2) and physiologically equivalent temperature (PET) values that were close to the observations. The ACM2 PBL algorithm produced significantly higher PBL heights compared to rest of the tested PBL parameterizations reflecting the strong vertical mixing generated in this scheme. This contributed to the most accurate ACM2-Revised MM5-modeled T2 and PET results in overall. The use of the RUC LSM strengthened significantly the modeled sensible heat fluxes contributing directly to the satisfactory replication of the observed T2 and PET values. However, the RUC-induced enhanced sensible heating relative to the latent heating is may related to a possible underestimation of the soil moisture, as indicated by the notable dry biases during the RUC experiment. Thus, the most accurate T2 and PET results provided by the RUC model may arise from the wrong reason.

The community multiscale air quality (CMAQ) model was used to forecast air quality over the Pearl River Delta region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with two different meteorological models, i.e., the global/regional assimilation and prediction system (GRAPES) and the fifth-generation mesoscale model (MM5), was assessed by comparison with observational data. The effects of meteorological factors and physicochemical processes on the forecast results were discussed through process analysis. The results showed that both models exhibited good performance but that of GRAPES-CMAQ was better. GRAPES was superior in predicting the overall variation tendencies of meteorological fields, but it showed large deviations in atmospheric pressure and wind speed. This contributed to the higher correlation coefficients of the pollutants with GRAPES-CMAQ but with greater deviations. The underestimations of nitrate and ammonium salt contributed to the underestimations of both particulate matter and extinction coefficients. Source emissions made the only positive contributions to surface layer SO2, CO, and NO. It was found that O3 originated primarily from horizontal and vertical transport and that its consumption was predominantly via chemical processes. Conversely, NO2 was found derived primarily from chemical production.

Two surface layer parameterization schemes along with five planetary boundary layer (PBL) schemes in the Weather Research and Forecasting model (WRF) are analyzed in order to evaluate the performance of the WRF model in simulating the surface variables and turbulent fluxes over an Indian sub-continent region. These surface layer schemes are based on the fifth-generation Pennsylvania State University—National Center for Atmospheric Research Mesoscale Model (MM5) parameterization; (a) Old MM5 scheme having Businger-Dyer similarity functions and (b) revised MM5 scheme utilizing the functions that are valid for full ranges of atmospheric stabilities. The study suggests that each PBL scheme can reproduce the diurnal variation of 2 m temperature, momentum flux and sensible heat flux irrespective of the surface layer scheme used for the simulations. However, a comparison of model-simulated values of surface variables and turbulent fluxes with observed values suggests that each PBL scheme is found to systematically over-estimate the nocturnal 2 m temperature and 10 m wind speed with both the revised and old schemes during stable conditions.

A methodology for the estimation of the emission rate of 137Cs by the Lagrangian Particle Dispersion Model (LPDM) with the use of monitored 137Cs concentrations around a nuclear power plant has been developed. This method has been employed with the MM5 meteorological model in the 600 km × 600 km model domain with the horizontal grid scale of 3 km × 3 km centered at the Fukushima nuclear power plant to estimate 137Cs emission rate for the accidental period from 00 UTC 12 March to 00 UTC 6 April 2011. The Lagrangian Particles are released continuously with the rate of one particle per minute at the first level modelled, about 15 m above the power plant site. The presently developed method was able to simulate quite reasonably the estimated 137Cs emission rate compared with other studies, suggesting the potential usefulness of the present method for the estimation of the emission rate from the accidental power plant without detailed inventories of reactors and fuel assemblies and spent fuels. The advantage of this method is not so complicated but can be applied only based on one-time forward LPDM simulation with monitored concentrations around the power plant, in contrast to other inverse models. It was also found that continuously monitored radionuclides concentrations from possibly many sites located in all directions around the power plant are required to get accurate continuous emission rates from the accident power plant. The current methodology can also be used to verify the previous version of radionuclides emissions used among other modeling groups for the cases of intermittent or discontinuous samplings.

One of the major components of the hydrological cycle, reference evapotranspiration (ET0) represents the maximum amount of water transferred from the land surface to the atmosphere. Vital to quantifying crop water needs, accurate predictions of ET0 are particularly critical in arid regions, where they allow for informed water resources management adjustments through changes to agricultural irrigation rates and scheduling. Drawing upon 84 meteorological stations in northwest China, spatiotemporal variations in present-day ET0 were investigated. Support vector regression (SVR), Extreme learning machine (ELM), and Multivariate adaptive regression spline (MARS) — three machine learning (ML) techniques — served to establish relationships between historical ET0 and the Coordinated Regional Climate Downscaling Experiment – East Asia (CORDEX-EA), drawn from the output data sets of each of three regional climate models (RCM): Weather Research and Forecasting (WRF), Regional Climate Model version 4.0 (RegCM4) and the Mesoscale Model version 5 (MM5). The ML-RCM combinations were calibrated and validated with separate batches (66:34, respectively) of historical ET0 data, and their respective performance and level of uncertainty were assessed statistically. In the historical period (1960–2017) ET0 declined by −0.15, −0.75, and − 0.42 mm y−1 in north Xinjiang, south Xinjiang, and Qinghai region, respectively, and increased in the Hexi Corridor by 0.5 mm y−1. For all four regions, the MARS-WRF and MARS-MM5 combinations performed well and showed greater predictive accuracy than either ELM-WRF or ELM-MM5 combinations. Performances in predicting future (2035–2050) ET0 from CORDEX-EA outputs based on regional climate predictions RCP 4.5 and RCP 8.5 scenarios, depended to a greater extent on the RCM outputs that were selected, rather than the modeling methods. Future ET0 predicted from RCMs generally exhibit increasing trends, and more significantly under the RCP 8.5 scenario. The representation and characterization ability of RCMs to future climate change is crucial for future ET0 projection. Uncertainty analysis, achieved by employing multiple RCMs to predict future ET0, is highly recommended. Knowledge of trends in future ET0 can help guide the management of agricultural irrigation in oases and support decision-makers engaged in water resources management in the future.

Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.

The quantitative assessment of change in water availability and appropriate water resources management are needed for corresponding adaptation. However, there are large uncertainties in climate change impact assessment on water resources. In this sense, the aims of this study are to suggest the uncertainty assessment method for climate change impact assessment and to investigate the uncertainty characteristics for high and low flow between downscaling methods and hydrological models. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods (SPP), and 2 hydrological models (HYM) were applied on the Chungju dam basin, Korea. The results of uncertainty analysis showed that RCM has the largest sources of uncertainty in 1-day maximum dam inflow (about 40.7%), while HYM has the largest sources of uncertainty in 30-days minimum dam inflow (about 41.5%). In other words, high flow was mainly effected by RCM and low flow by HYM. The proposed methodology in this study can be used to quantify the uncertainties caused by RCM, SPP, and HYM.

In this work, the application of the online-coupled Weather Research and Forecasting model with chemistry (WRF/Chem) version 3.3.1 is evaluated over East Asia for January, April, July, and October 2005 and compared with results from a previous application of an offline model system, i.e., the Mesoscale Model and Community Multiple Air Quality modeling system (MM5/CMAQ). The evaluation of WRF/Chem is performed using multiple observational datasets from satellites and surface networks in mainland China, Hong Kong, Taiwan, and Japan. WRF/Chem simulates well specific humidity (Q2) and downward longwave and shortwave radiation (GLW and GSW) with normalized mean biases (NMBs) within 24%, but shows moderate to large biases for temperature at 2-m (T2) (NMBs of −9.8% to 75.6%) and precipitation (NMBs of 11.4–92.7%) for some months, and wind speed at 10-m (WS10) (NMBs of 66.5–101%), for all months, indicating some limitations in the YSU planetary boundary layer scheme, the Purdue Lin cloud microphysics, and the Grell–Devenyi ensemble scheme. WRF/Chem can simulate the column abundances of gases reasonably well with NMBs within 30% for most months but moderately to significantly underpredicts the surface concentrations of major species at all sites in nearly all months with NMBs of −72% to −53.8% for CO, −99.4% to −61.7% for NOx, −84.2% to −44.5% for SO2, −63.9% to −25.2% for PM2.5, and −68.9% to 33.3% for PM10, and aerosol optical depth in all months except for October with NMBs of −38.7% to −16.2%. The model significantly overpredicts surface concentrations of O3 at most sites in nearly all months with NMBs of up to 160.3% and NO 3 - at the Tsinghua site in all months. Possible reasons for large underpredictions include underestimations in the anthropogenic emissions of CO, SO2, and primary aerosol, inappropriate vertical distributions of emissions of SO2 and NO2, uncertainties in upper boundary conditions (e.g., for O3 and CO), missing or inaccurate model representations (e.g., secondary organic aerosol formation, gas/particle partitioning, dust emissions, dry and wet deposition), and inaccurate meteorological fields (e.g., overpredictions in WS10 and precipitation, but underpredictions in T2), as well as the large uncertainties in satellite retrievals (e.g., for column SO2). Comparing to MM5, WRF generally gives worse performance in meteorological predictions, in particular, T2, WS10, GSW, GLW, and cloud fraction in all months, as well as Q2 and precipitation in January and October, due to limitations in the above physics schemes or parameterizations. Comparing to CMAQ, WRF/Chem performs better for surface CO, O3, and PM10 concentrations at most sites in most months, column CO and SO2 abundances, and AOD. It, however, gives poorer performance for surface NOx concentrations at most sites in most months, surface SO2 concentrations at all sites in all months, and column NO2 abundances in January and April. WRF/Chem also gives lower concentrations of most secondary PM and black carbon. Those differences in results are attributed to differences in simulated meteorology, gas-phase chemistry, aerosol thermodynamic and dynamic treatments, dust and sea salt emissions, and wet and dry deposition treatments in both models.

The present study focuses on the predictions and verification of these predictions of solar energy using ground-based solar measurements from the Hellenic Network for Solar Energy and the National Observatory of Athens network, as well as solar radiation operational forecasts provided by the MM5 mesoscale model. The evaluation was carried out independently for the different networks, for two forecast horizons (1 and 2 days ahead), for the seasons of the year, for varying solar elevation, for the indicative energy potential of the area, and for four classes of cloud cover based on the calculated clearness index (k t ): CS (clear sky), SC (scattered clouds), BC (broken clouds) and OC (overcast). The seasonal dependence presented relative rRMSE (Root Mean Square Error) values ranging from 15% (summer) to 60% (winter), while the solar elevation dependence revealed a high effectiveness and reliability near local noon (rRMSE ∼30%). An increment of the errors with cloudiness was also observed. For CS with mean GHI (global horizontal irradiance) ∼ 650 W/m 2 the errors are 8%, for SC 20% and for BC and OC the errors were greater (>40%) but correspond to much lower radiation levels ( 2 ) of consequently lower energy potential impact. The total energy potential for each ground station ranges from 1.5 to 1.9 MWh/m 2 , while the mean monthly forecast error was found to be consistently below 10%.

Global solar radiation is the driving force in hydrological cycle especially for evapotranspiration (ET) and is quite infrequently measured. This has led to the reliance on indirect techniques of estimation for data scarce regions. This study presents an improved technique that uses information from a numerical weather prediction (NWP) model (National Centre for Atmospheric Research NCAR's Mesoscale Meteorological model version 5 MM5), for the determination of a cloud cover index (CI), a major factor in the attenuation of the incident solar radiation. The cloud cover index (CI) together with the atmospheric transmission factor (KT) and output from a global clear sky solar radiation were then used for the estimation of global solar radiation for the Brue catchment located in the southwest of England. The results clearly show an improvement in the estimated global solar radiation in comparison to the prevailing approaches.

In this work the impact of topography on the distribution and the amount of precipitation in Epirus, NW Greece is investigated, during the cold period of the year. A precipitation event with typical wintertime characteristics is considered. In the selected case, extended and heavy precipitation over Epirus is caused by a low pressure system centred northwest of Epirus and moving southeastwards, generating southerly winds over the Ionian Sea. The case is simulated by applying the numerical meteorological model MM5 in a high resolution grid (2 × 2km). Various sensitivity tests are performed, searching for the model initialization time and the convective parameterization scheme (CPS) leading to the best simulation of the observed event. Then, the model is applied again incorporating modified data of topography, in order to study the differentiations in the fields of the most important meteorological parameters, which are directly related with precipitation. The topography is modified with two ways: either by reducing the height of the mountains (type RT) or by elevating the terrain height (type ET). The results show that in the RT type, the precipitation field appears more homogeneous, with weaker maxima on the windward sides and above the mountain ridges. As a consequence, less rain falls in these areas and humid air passes easier to the lee sides reducing rain shadows. In the ET type, rain increases in the windward coastal zone of Epirus while it is reduced in the inland areas where less humid air is available. Generally, the modification of topography changes the flows near the surface, causing a displacement of the convergence zones and thus a new distribution of vertical velocities, which, combined with the new water vapour field, has a direct impact on precipitation.

Accurately simulating the transport and fate of reduced nitrogen (NHx = ammonia (NH3) + ammonium (NH4+))- and sulfur-containing compounds emitted from agricultural activities represents a major challenge in agricultural air quality modeling. In this study, the Community Multiscale Air Quality (CMAQ) modeling system is further developed and improved by implementing 22 ammonia (NH3)/hydrogen sulfide (H2S) related gas-phase reactions and adjusting a few key parameters (e.g., emission potential) for bi-directional exchange of NH3 fluxes. Several simulations are conducted over the eastern U.S. domain at a 12-km horizontal resolution for January and July 2002 to examine the impacts of those improved treatments on air quality. The 5th generation mesoscale model (MM5) and CMAQ predict an overall satisfactory and consistent performance with previous modeling studies, especially for 2-m temperature, 2-m relative humidity, ozone (O3), and fine particulate matter (PM2.5). High model biases exist for precipitation in July and also dry/wet depositions. The updated model treatments contribute to O3, NHx, and PM2.5 by up to 0.4 ppb, 1.0 μg m−3, and 1.0 μg m−3 in January, respectively, and reduce O3 by up to 0.8 ppb and contribute to NHx and PM2.5 by up to 1.2 and 1.1 μg m−3 in July, respectively. The spatial distributions of O3 in both months and sulfur dioxide (SO2) in January are mainly affected by inline dry deposition velocity calculation. The spatial distributions of SO2 and sulfate (SO42−) in July are affected by both inline dry deposition velocity and NH3/H2S reactions. The variation trends of NH3, NHx, ammonium nitrate (NH4NO3), PM2.5 and total nitrogen (TN) are predominated by bi-directional exchange of NH3 fluxes. Uncertainties of NH3 emission potentials and empirical constants used in the bi-directional exchange scheme may significantly affect the concentrations of NHx and PM2.5, indicating that a more accurate and explicit treatment for those parameters should be considered in the future work.

An adequate simulation of the wind in the near surface marine environment is important for a large range of applications, including energy assessments. In this work, wind speed simulated by different models is evaluated through comparison with observational data. Three experiments using two open mesoscale models (MM5 and WRF) under different types of simulation mode were considered together with two institutional meteorological models (IFS and Aladin). Observational data measured over land in meteorological stations, in the near coast by marine buoys and in the open sea by altimetry, were used for the comparisons. Results show that although IFS is the model with the best results, the three experiments using WRF and MM5 are also of good quality, especially in the coast and open ocean.The added value of using a mesoscale model over global, lower resolution ones, usually used as driver, is investigated for the different environments (land, coast and open ocean) and the three experiments. Using the Brier skill score, it is found that there is added value in the case of simulations over land and in the coast, but not for the open ocean.

The average wind speed and wind power density of Taiwan had been evaluated at 10 m, 30 m and 50 m by simulation of mesoscale numerical weather prediction model (MM5). The results showed that wind energy potential of this area is excellent. Taiwan has offered funds to encourage the founding of offshore wind farms in this area. The purpose of this study is to make a high resolution wind energy assessment for the offshore area of Taiwan west coast and Penghu archipelago by using WAsP. The result of this study has been used to the relative financial planning of offshore wind farm projects in Taiwan. The basic inputs of WAsP include wind weather data and terrain data. The wind weather data was from a monitoring station located on a remote island, Tongi, because that all of weather stations in the area of Taiwan west coast are affected by urbanization. SRTM was selected to be used as terrain data and downloaded from CGIAR-CSI for voids problem. The coverage of considered terrain area in this assessment work is about 300 km × 400 km that made some difficulties to run wind energy assessment of the whole area with a high resolution of 100 m. So the interested area of this study is divided into 19 areas for the wind energy assessment and mapping. The assessment results show the Changhua area has best wind energy potential in the area of Taiwan west coast which power density is above 1000 W/m2 height and the areas of Penghu archipelago are above 1300 W. These results are higher than the expected from NWP. 180 of 3 MW wind turbines were used in the study of micro sitting in the Changhua area. The type and number of the wind turbines and the layout of the wind farm is similar to the prior study of Taipower Company for demonstrating the reliability of this study. The assessment result of average net annual energy production (AEP) of the wind farm is about 11.3 GWh that is very close to the prior study. The terrain effect is also studied. The average net annual energy production will decrease about 0.7 GWh if the wind turbines were moved eastward 3600 m closer to the coast because of terrain effect. As the same reason, the average net annual energy production would be increased to 11.392 GWh if the wind farm is moved westward 3600 m away from the coast.

The objective of this study was to evaluate three meteorological models (MM5, WRF and TRAMPER) by comparing the calculated meteorological parameters with observations over the Po Valley area (Italy) for 2005. The analysis shows that MM5 and WRF perform with similar quality, with advantages of WRF at following high resolution time patterns and better scores of MM5 at reproducing annual averages, noticed for precipitation and relative humidity calculations. Results from the TRAMPER model also reflect very well surface meteorological measurements, they may be however driven by data assimilation applied in this modelling system and thus strongly influenced by very local effects. This feature of TRAMPER may lead as well to large uncertainties in reproducing other parameters important for air quality modelling such as planetary boundary layer (PBL) heights, friction velocity (u*) values or stability conditions.

Summary This study investigated the limitations associated with using dynamically-downscaled, bias-corrected reanalysis data (i.e. regional reanalysis data) to predict hydrologic behavior of low-relief rainfall driven systems using an integrated surface/groundwater model. Four different sets of global reanalysis data (NCEP/NCAR-R1, NCEP-DOE-R2, ERA40, and 20CR) that were previously downscaled using two RCMs (MM5 and RSM) were obtained, bias-corrected on a daily basis using the CDF-mapping approach, and used to drive an integrated hydrologic model (INTB) that was previously calibrated and verified for the Tampa Bay region. All raw dynamically-downscaled reanalysis datasets accurately estimated the annual cycle of daily maximum and minimum temperature, except the NCEP/NCAR R1+MM5 data which consistently underestimated daily maximum temperature. All raw regional reanalysis precipitation data significantly overestimated precipitation, particularly for the dry season. Bias-correction using the CDF-mapping approach effectively removed biases in the temporal mean and standard deviation of both the daily precipitation and temperature predictions. Biases in the mean monthly and mean annual precipitation totals were removed by CDF-mapping on a daily basis, but the standard deviation of the monthly and annual precipitation totals were not accurately reproduced. Furthermore inaccuracies in actual daily precipitation time series aggregated into monthly and annual rainfall total time series that showed significant and temporally persistent errors. Precipitation timing errors produced by regional reanalysis data were propagated and enhanced by non-linear streamflow generation, groundwater flow and storage processes in the hydrologic model and produced significant errors in both actual and mean daily, monthly and annual streamflow and groundwater level predictions. These results show that improvement in large-scale reanalysis products and regional climate models may be required before dynamically downscaled bias-corrected reanalysis data can be used as a surrogate for observational data in hydrologic model applications for low-relief, rainfall driven systems.

Summary A Regional Climate Model (RCM), MM5 (the Fifth Generation Pennsylvania State University/National Center for Atmospheric Research mesoscale model), is used to simulate summer precipitation in Central Alberta. MM5 was set up with a one-way, three-domain nested framework, with domain resolutions of 27, 9, and 3 km, respectively, and forced with ERA-Interim reanalysis data of ECMWF (European Centre for Medium-Range Weather Forecasts). The objective is to develop high resolution, grid-based Intensity–Duration–Frequency (IDF) curves based on the simulated annual maximums of precipitation (AMP) data for durations ranging from 15-min to 24-h. The performance of MM5 was assessed in terms of simulated rainfall intensity, precipitable water, and 2-m air temperature. Next, the grid-based IDF curves derived from MM5 were compared to IDF curves derived from six RCMs of the North American Regional Climate Change Assessment Program (NARCCAP) set up with 50-km grids, driven with NCEP-DOE (National Centers for Environmental Prediction-Department of Energy) Reanalysis II data, and regional IDF curves derived from observed rain gauge data (RG-IDF). The analyzed results indicate that 6-h simulated precipitable water and 2-m temperature agree well with the ERA-Interim reanalysis data. However, compared to RG-IDF curves, IDF curves based on simulated precipitation data of MM5 are overestimated especially for IDF curves of 2-year return period. In contract, IDF curves developed from NARCCAP data suffer from under-estimation and differ more from RG-IDF curves than the MM5 IDF curves. The over-estimation of IDF curves of MM5 was corrected by a quantile-based, bias correction method. By dynamically downscale the ERA-Interim and after bias correction, it is possible to develop IDF curves useful for regions with limited or no rain gauge data. This estimation process can be further extended to predict future grid-based IDF curves subjected to possible climate change impacts based on climate change projections of GCMs (general circulation models) of IPCC (Intergovernmental Panel on Climate Change).

Prognostic meteorological models such as Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) are often used to supply inputs for retrospective air quality modeling done to support ozone and PM2.5 emission control demonstrations. In this study, multiple configurations of the WRF model are applied at 4 km grid resolution and compared to routine meteorological measurements and special study measurements taken in California during May–June 2010. One configuration is routinely used by US EPA to generate meteorological inputs for regulatory air quality modeling and another that is used by research scientists for evaluating meteorology and air quality. Mixing layer heights estimated from airborne High Spectral Resolution Lidar (HSRL) measurements of aerosol backscatter are compared with WRF modeled planetary boundary layer (PBL) height estimates. Both WRF configurations generally capture the variability in HSRL mixing height between days, hour-to-hour, and between surface features such as terrain and land–water interfaces. Fractional bias over all flights and both model configurations range from −38% to 32% and fractional error ranges from 22% to 58%. Surface and upper level measurements of temperature, water mixing ratio, and winds are generally well characterized by both WRF model configurations, often more closely matching surface observations than the input analysis data (12-NAM). The WRF model generally captures orographic and mesoscale meteorological features in the central Valley (bifurcation of wind flow from the San Francisco bay into the Sacramento and San Joaquin valleys) and Los Angeles air basin (ocean-land flows) during this summer period.

Air quality simulations were conducted using MM5/CMAQ modeling platform to study the intricacies introduced by photochemical reactions during the dispersion of urban pollution plume of Phoenix metropolis. The simulation days included the sole ozone episode recorded during 1996–2005, which violated the previous 1-h ozone standard (0.12 ppm). The modeling results suggest that the Phoenix urban plume can be described in terms of “inert passive dispersion” and “chemically active dispersion”. The former is exemplified by the CO distribution and takes the form of a Gaussian-like plume, for which the source is located at the ground level of the urban core or a freeway. The passive dispersion, nevertheless, is directly subjected to heterogeneities of topography and flow patterns, and hence cannot be strictly Gaussian. The case of active dispersion is much more complicated, and leads to a different plume shape, depending on the chemical reactivity of pollutant species. Secondary pollutants such as ozone and its precursors cause the plume core to have its maximum concentration far downwind of the urban area. Chemical species such as VOCs, which are directly emitted from a source as well as transformed by other primary pollutants, form a plume that qualitatively resembles a transition from an inert plume (CO) to a highly reactive plume (NO x ).

Ground-level ozone is a secondary pollutant that causes serious health and environmental damage. One of the main challenges in controlling ground-level ozone is to identify the categories of ozone precursor species that contribute the most to ozone levels, and the regions from which they are emitted. In this study, we apply a photochemical model source apportionment tool to identify the contributions of various precursors to seasonal ozone levels in Hong Kong (HK) and the Pearl River Delta (PRD) region, according to their source regions and categories. We establish a full precursor contribution matrix for the ozone concentrations in all cities within the HK/PRD region. This matrix can be used to rank the relative importance of precursors by source region/category for each city. It can also be used to show how the precursor emissions from any city/category affect ozone levels in neighboring cities. The results show substantial seasonal variation in the source contributions from the HK/PRD region. During ozone episodes, precursors from the region accounted for 68–80% of ozone concentrations in July and 35–55% in October, but only 19–32% in January and April. For all of the cities in the PRD, the top three precursor categories contributing to ozone episodes were mobile, point and area sources. Biogenic and point sources were more important in July than in October. In contrast, the ranking of contributing source regions showed considerable variation across cities and seasons, implying substantial variations in the ways in which precursors from one city affect ozone levels in neighboring cities. Our results show that source contribution and impact analysis can be very useful for evaluating the ozone control strategies of local governments. For the PRD region, a local reduction in mobile, area and point sources, together with regional collaborations on pollution control, are of particular importance in effectively reducing episodic ozone concentrations.

This study investigates the simulated present and past climate of southern South America through the use of the PSU/NCAR MM5 mesoscale model forced by Princeton GFDL global atmosphere-ocean model data. This approach is taken to obtain climate data with sufficiently high resolution to resolve the steep mountain ranges of the southern Andes, which generate large amounts of orographic precipitation. Results indicated that the region experienced northward shifted low level westerlies at and before 9000 years before present and shifts in upper level winds associated with the midlatitude jet culminated about 9000 years ago. A simple energy balance model indicates that the lower equilibrium line altitudes in the southern Andes during the Last Glacial maximum persisted into the early Holocene, resulting in an expansion of the area receiving net snow accumulation, particularly in eastern parts of the Andes mountains. Changes in simulated winds and precipitation in the lee of the southern Andes are consistent with Laguna Potrok Aike climate proxies.

This study presents a description of the emission, transport, dispersion, and deposition of heavy metals contained in atmospheric aerosols emitted from a large industrial complex in southern Spain using the HYSPLIT model coupled with high- (MM5) and low-resolution (GDAS) meteorological simulations. The dispersion model was configured to simulate eight size fractions ( 17 μm) of metals based on direct measurements taken at the industrial emission stacks. Twelve stacks in four plants were studied and the stacks showed considerable differences for both emission fluxes and size ranges of metals. We model the dispersion of six major metals; Cr, Co, Ni, La, Zn, and Mo, which represent 77% of the total mass of the 43 measured elements. The prediction shows that the modeled industrial emissions produce an enrichment of heavy metals by a factor of 2–5 for local receptor sites when compared to urban and rural background areas in Spain. The HYSPLIT predictions based on the meteorological fields from MM5 show reasonable consistence with the temporal evolution of concentrations of Cr, Co, and Ni observed at three sites downwind of the industrial area. The magnitude of concentrations of metals at two receptors was underestimated for both MM5 (by a factor of 2–3) and GDAS (by a factor of 4–5) meteorological runs. The model prediction shows that heavy metal pollution from industrial emissions in this area is dominated by the ultra-fine (

A few tropical-like cyclones have developed over the Mediterranean Sea during the last decades according to the inventory of images provided by Meteosat satellite. These extreme small-scale warm-core storms, also called “medicanes”, operate on the thermodynamical disequilibrium between the sea and the atmosphere, and sometimes attain hurricane intensity and threaten the islands and coastal regions. Despite their small size, mesoscale model runs at moderate horizontal resolutions (7.5 km) made with MM5 are able to simulate the formation of a subsynoptic cyclone and the general trajectory of the disturbance, and for most of the cases a warm-core axi-symmetrical structure becomes evident in the simulations. The timing and precise details of the storm trajectories are shown to be more problematic when compared against the satellite images available for the events. It is hypothesized that the small size of the systems and the crucial role of moist microphysics, deep convection and boundary layer parameterizations are the main factors behind these errors. On the other hand, a sensitivity analysis examining the role of the sea surface heat fluxes is conducted: latent and sensible heat fluxes from the Mediterranean are switched off at the beginning of the simulations to explore the effects of these factors on the medicane trajectories and deepening rate. Results show different roles of the surface heat fluxes on medicane properties (intensification and track) depending on their magnitude and spatial distribution over the Mediterranean Sea. In this way, three distinct patterns have been identified using a database of twelve events.

Air pollution, which is a global environmental problem, has been the hot research area among the scientists in the geoscience community. Air pollution simulation is of low-efficiency caused by the computation-intensive models, such as MM5 or WRF, and the complicated and unfriendly user interface. These issues are addressed in this paper by integrating computational grid and virtual geographic environment (VGE). The computational grid is employed to improve the computation efficiency of air pollution models. The VGE is used as a straightforward and easy to use interface to navigate the air pollution modeling workflow and improve the operational efficiencies of the models with respect to initiation, computation, and output visualization. On the aspect of technique implementation, this paper designs a framework and addresses the methodologies of the integration of computational grid and VGE. The prototype system, which integrates the computation grid of the Chinese University of Hong Kong (CUGrid) and a VGE to facilitate air pollution simulation based on the Mesoscale Model Version 5 (MM5), was developed. Based on the prototype system, a case was tested and the results indicate that the efficiencies of air pollution simulation on the model computation and workflow operation based on MM5 are increased significantly. This success also proves the reasonability of our general contribution of integrating computational grid and VGE to facilitate air pollution simulation.

This paper couples the meteorological Mesoscale Model (MM5) with the non-steady state CALPUFF modelling system in a short range dispersion context to assess regulatory compliance with air quality standards in a coastal urban area with complex terrain. For this purpose, an inventory of CO, NO x , and PM 10 emissions from an industrial complex, highway and quarrying activity was developed using emission factors reported by the US Environmental Protection Agency (USEPA) and the European Environment Agency (EEA). Multiple emission scenarios were then simulated to test the representativeness of the obtained emission factors using statistical analysis of predictions against year-round field measurements. At the validated emission rates, CALPUFF simulations showed an acceptable ability to predict the upper end of observed concentrations and reproduced field measurements at several locations. Statistical analysis revealed that sources are likely to be emitting at conservative estimates (worst-case efficiency of control equipment and fuel quality), with total CO, NO x and PM 10 emissions reaching 913 g s −1 (19% by highway and 81% by industries), 1266 g s −1 (63% by highway and 37% by industries) and 2970 g s −1 (93% by industries and 7% by quarries), respectively.

Vertical profiles of temperature, water vapor and wind field were traditionally obtained from radiosonde observations for the analysis of mesoscale phenomena. The scarce amount of radiosonde data available and the inadequate temporal and spatial resolution, motivated the development of prognostic soundings derived from Numerical Weather Prediction (NWP) models. Their use should be subject to previous quality verification of model outputs. This work is focused on the evaluation of the NCAR/Penn State Fifth Generation Mesoscale Model MM5 outputs under snow precipitation conditions in the Sierra de Guadarrama (Madrid). A comparison of forecasted vertical profiles of temperature, water vapor density and wind field with those observed by the Madrid–Barajas Airport rawinsonde was carried out. The results showed that the MM5 vertical profiles were able to represent thermodynamic conditions during winter precipitation events. Correlation coefficients of temperature and water vapor were close to 1 at both times. Wind field forecast was also accurate since wind direction and wind speed correlation coefficients were statistically significant for a 95% confidence. It was also found that MM5 simulations remained accurate after 24 h from the initialization.

The performance of cumulus parameterization schemes in the short range prediction of rainfall during onset phase of the Indian Southwest Monsoon (ISM) was evaluated using Fifth-Generation Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5). MM5 model was used to predict rainfall at 30 km resolution up to 72 h over the Indian subcontinent for each day during the period 1–30 June 2002, which corresponds to the onset phase of the ISM. Experiments were performed with 5 different cumulus parameterization schemes of Anthes–Kuo (AK), Grell (GR), Betts–Miller (BM), Kain–Fritsch (KF) and Kain–Fritsch2 (KF2). Rainfall prediction assessment was made over five zones through comparison with corresponding APHRODITE gridded precipitation data and for selected location with station observations by analyzing the statistical parameters of correlation coefficient, mean absolute error and Hanssen–Kuipers score. Monthly mean zone-wise rainfall was well predicted by GR and AK schemes up to 48 hours and slight overestimation beyond. GR scheme predicted higher rainfall over west coast, central parts of India and low rainfall over southeast peninsula. BM and KF schemes showed overestimation with prediction of rainfall over dry southeast peninsula. All the schemes underestimated the coefficient of variability (CV) over all the five zones. AK and GR schemes had the mean and CV nearer to the APHRODITE observations, with AK scheme slightly better than GR scheme over Zones 1, 2 and 3 while GR scheme had the best agreement over Zones 4 and 5. GR scheme had also shown higher CC values and lower MAE over most of the zones up to 72 h, while BM had the least predictability with lower CC and HK scores and higher MAE over most of the zones. Over Western Ghats, the uncertainty limits could be higher than shown due to dominant heavy rains. Of the ten stations selected for verification, GR scheme had shown better prediction with significant positive CC values at nine of the ten stations and consistently lower MAE values and higher HK scores. Further analysis has shown that GR scheme predicted higher grid-scale and nighttime rainfall agreeing with earlier studies concerning monsoon rainfall. All other schemes predicted the features contrarily with higher convective and daytime rainfall. GR scheme alone was found to have provided the best prediction considering the mean monthly, daily zone-wise and station rainfall predictions. The present study concludes that GR cumulus parameterization scheme is the most suitable at 30 km resolution.

The present work aims at studying the local to regional atmospheric pollution transport and transformation processes over Bulgaria and at tracking and characterizing the main pathways and processes that lead to atmospheric composition formation in the region. The US EPA Models-3 system is chosen as a modeling tool. As the NCEP Global Analysis Data with 1 degree resolution is used as meteorological background, the MM5 and CMAQ nesting capabilities are applied for downscaling the simulations to a 9 km resolution over Balkans and 3 km over Bulgaria. The TNO emission inventory is used as emission input. Special pre-processing procedures are created for introducing temporal profiles and speciation of the emissions. The study is based on a large number of numerical simulations carried out day by day for the years 2000-2007 and four emission scenarios-with all the emissions and with biogenic emissions, emissions from energetics and road transport excluded. Results from the numerical simulations concerning the main features of the atmospheric composition in Bulgaria and the contribution of the different emission categories are demonstrated in the paper. Some results from the CMAQ ''Integrated Process Rate Analysis'' are also given.

Correlation between heat flux over the Indian Ocean and rainfalls in coastal Thailand by using the Pennsylvania State University/National Center for Atmospheric Research mesoscale model: MM5 is the method to study monsoon onset in Thailand. The study areas are over the Indian Ocean from Latitude 30°S to 30°N and Longitude 40°E to 120°E. The used data were during May to October since the years 1996 to 2000, and also covered the years where El Nino and La Nina were present. When the heat flux over the Southeast Indian Ocean was increased about100 w/m2, this persisted for approximately five consecutive days before the average onset date of the 15th May in Thailand. Once the recorded increase in heat flux of five days had passed, it was then observed that after a period of eleven days this caused the onset of rainfalls at nine Thai meteorological stations. From the observations recorded the results showed a correlation between the development of heat flux and rainfalls. During the normal years (1996, 1999 and 2000) the correlation of the heat flux with the rainfalls data at the twelve primary meteorological stations in Thailand were computed by linear regression analysis and the best of these resulted in coefficient of determination(R2)= 0.782. The results (correlation between heat flux which obtained from MM5 model and amounts of rainfalls from weather observation stations) of this study is possible to be utilize by the Thai Meteorological Department to predict the onset day of southwest monsoon rainfall, mainly for agricultural activities more accurately procedure.

EPA's Community Multiscale Air Quality (CMAQ) model was used to investigate the influence of lateral boundary conditions (LBCs) on ozone simulation. Meteorological fields used to drive the model were from the fifth generation Mesoscale Model (MM5). Emission files were prepared from the Sparse Matrix Operating Kernel for Emissions (SMOKE) model. Realtime Air Quality Modeling System (RAQMS) model with assimilated satellite observations were used as the LBCs for the CMAQ. CMAQ simulations with RAQMS LBCs and predefined LBCs were compared with INTEX Ozonesonde Network Study (IONS) ozonesonde data and Ozone Monitoring Instrument (OMI) satellite measurements. CMAQ forced with RAQMS LBCs could reasonably reproduce the vertical profiles of ozone mixing ratio. It was revealed that the influence of LBCs on ozone simulations is significant in the upper troposphere, moderate in the middle troposphere, and small in the lower troposphere. CMAQ model outputs provided a unique opportunity to evaluate the quality of the current air quality model and would help mission designers to better design future mission.

This study describes the implementation of a one way coupled high resolution numerical weather and river runoff forecasting system within the Perl Object Environment (POE) framework and presents its application and performance analysis for the Alpine catchment of the Ammer River located in southern Germany. The simulation system employs the hydrological water balance model WaSiM-ETH run at 100 m x 100 m grid resolution one way coupled with the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution. The state and event driven forecasting system implements the input data download, input data provision via SOAP based WEB service and the run of the hydrology model with observed and with predicted NWP meteorology fields. It applies a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. The simulation system has been setup and designed for flood forecasting in the alpine environment. It is run operationally as well as in extended time slice experiments for all episodes with highest observed runoff in the period 01.10.2005-30.09.2010. The system application demonstrates the great potential of the POE based system in networking, distributed computing as well in the setup of various experiments. The river runoff simulation results show high correlation with observed runoff when driven with precipitation interpolated from station observations. The performance of the forecast shows limitations resulting from deficient timing and amount of the predicted rainfall in the complex mountainous area. Forecast skills were improved after application of a lagged ensemble prediction system.

The performance of the updated Air Quality Forecasting System (AQFS) using Weather Research and Forecasting (WRF v.3.1) and the U.S. EPA's Models-3/Community Multiscale Air Quality (CMAQ v4.6) with emphasis on PM 10 (Particulate Matter with aerodynamic diameters less than 10 μm) forecast is evaluated over the Seoul Metropolitan Area (SMA) for 2010. The simulations of the updated and old forecasting systems are compared with air quality and meteorological measurements in the modeling domain. The results of the analysis show that the updated forecasts of daily PM 10 can reproduce the magnitude and temporal variation of the observations. The time variations of forecasted PM 10 are in good agreement with the observations with the range of Index of Agreement (IOA) over 0.7. The forecasted concentrations of daily PM 10 are underestimated in all forecasting regions with a range of Normalized Mean Bias (NMB) from −10.76% in the Seoul Metropolitan to −21.29% in the North Gyeonggi province. The discrepancy can be attributed to uncertainties in emissions, forecasted meteorology and models. Even with persistent uncertainties in emission data, the PM 10 forecasts from the updated system with emission inventories of INTEX-B for year 2006 in Asia, as well as CAPSS supplementing fugitive dust and biomass emissions for year 2007 in Korea, perform better than those of the old system, which consists of MM5 (v4.7) and CMAQ (v.4.3) and the emission data from TRACE-P for 2000 in Asia and CAPSS for 2003 in Korea. It is also demonstrated that the forecasting system is effective to detect the onset time of the episode and peak value of PM 10 in advance which is mainly caused by the long-range transport of aerosols from eastern China to the SMA. The Probability of Detection (POD) for the “C” category of Air Quality Index (AQI), which indicates a health risk for the sensitive group, improves to over 60% by applying the bias-adjustment of hybrid forecast (HF).

Haze has been one of the major air pollution problems in Chinese cities, and the southern Hebei area has attracted particular attention because of its high frequency of haze weather and the rapid deterioration in visibility in recent years. This study is aimed at understanding the characteristics and sources of the serious haze pollution in the southern Hebei area using the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ). The haze frequencies in the cities of southern Hebei, including Shijiazhuang, Xingtai, and five other urban centers in the surrounding regions, are analyzed for a ten-year period from 2001 to 2010, which shows a very similar and clear seasonal variation. The contributions of the local and regional anthropogenic emissions to the concentration of fine particulate matter (PM 2.5 , particles with an aerodiameter of less than or equal to 2.5 μm) and the light extinction coefficient ( B ext ) are estimated by conducting simulations of seven scenarios. The results show that approximately 65% of the PM 2.5 in Shijiazhuang and Xingtai originated from the local emissions of the southern Hebei area, followed by Shanxi Province and the northern area of Hebei (13.8% and 7.3% to Shijiazhuang and 10.4% and 5.2% to Xingtai, respectively). The contributions of the emissions from the local area, Shanxi Province and the northern Hebei area to B ext are approximately 59.4%, 13.8% and 6.8% for Shijiazhuang and 58.2%, 10.1% and 5.0% for Xingtai, respectively. Moreover, an analysis of a typical pollution episode indicates that the contributions from the Shandong and Henan provinces are also significant. Further investigations are still required because of the complexity of the haze pollution over the southern Hebei area.

Climate change alone influences future air pollution levels through modifications of gas-phase chemistry, transport, removal, and natural emissions. Hence, the goal of this study is to determine at what extent concentrations of air pollutants respond to changes over the Iberian Peninsula under a climate change scenario. The methodology includes the use of the regional modeling system MM5 (regional climate model version)-CHIMERE for two nested domains covering Europe and the Iberian Peninsula. Two time slices driven by ECHO-G global circulation model covering from 1991 to 2010 and 2071 to 2100 under the SRES A2 scenario have been compared. Climate change influences the concentrations of both gas-phase pollutants and aerosols through changes in temperature, precipitation, mixing height, transport, humidity, and oxidant levels. The trends of variation of ozone (changes up to 5 ppb, +10% increase during summertime) and aerosols over southwestern Europe are influenced by the higher mean temperature modeled for the future climate (up to +5.4 K), since it favors the formation of secondary gas-phase products. It also enhances sulphates (+2 μg m−3) and secondary organic aerosols (SOA) (+2.5 μg m−3 under SRES A2 scenario) and contributes to the decomposition of ammonium nitrate, remaining in the gas phase. Further, the 17% percent decrease of precipitation modeled for 2071–2100 has a strong effect in the frequency of the washout and therefore in the levels of natural aerosols: the concentrations of aerosols decrease with increasing precipitation as wet deposition provides the main aerosol sink.

To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

The performance of regional climate simulations is evaluated for the Great Lakes region. Three 10-year (1990–1999) current-climate simulations are performed using the MM5 regional climate model (RCM) with 36-km horizontal resolution. The simulations employed identical configuration and physical parameterizations, but different lateral boundary conditions and sea-surface temperatures derived from the NCEP Global Reanalysis and output from the CCSM3 and GISS general circulation models (GCMs). The simulation results are compared to the North American Regional Reanalysis (NARR). The three RCM simulations appeared to be more accurate in winter and least accurate in summer, and more accurate aloft than near the surface. The reanalysis-constrained simulation adequately captured the spatial distribution and seasonal cycle of the observed surface-air temperature and precipitation, but it produced consistently across all seasons a cold bias that is generally larger over the lakes than over land and a wet bias due to an overestimation of non-convective precipitation. The simulated seasonal cycle of moisture–flux convergence over the region was in very good agreement with NARR. The two GCM-driven runs adequately simulated the spatial and seasonal variation of temperature, but overestimated cold-season precipitation and underestimated summer precipitation, reversing the observed annual precipitation cycle. The GISS-driven run failed to simulate the prevailing low-level flow and moisture convergence patterns. All three RCM simulations successfully captured the impact of the Great Lakes on the region's climate, especially on winter precipitation, a significant improvement over coarse-resolution GCM simulations over the region.

The performance of a triple-nested mesoscale atmospheric model (MM5) implemented in the Online Nuclear Emergency Response System (ONERS) at Kalpakkam on southeast coast of India is evaluated. Real-time atmospheric model predictions are used to compute radiological plume dispersion in the mesoscale ranges using Lagrangian particle models. About 280 days falling in dry and wet weather and distributed in 2006, 2007, 2008 and 2009 years are considered. About 25 upper air, 100 surface station data including radiosonde, GPS sonde, micrometeorological tower and automated weather stations are used for model evaluation. Results indicate that model could reproduce the synoptic pressure, geopotential heights, winds and precipitation patterns in the coarse domain as well as the fine scale features of the atmospheric circulation in the inner fine domain. Model diagnosis with observations shows correlation (r), mean absolute error (MAE) and bias as (0.685, 1.87 C, 1.28 C) for temperature, (0.93, 1.55 hPa, 0.113 hpa) for pressure, (0.56,15 m, 0.53 m) for geopotential, (0.55, 12%, −10.5%) for humidity and (0.45, 2.3 m s−1, 1.70 m s−1) for wind speed indicating appreciable performance in the lower atmosphere for both dry and wet weather events. Model error in wind speed/direction reduced with height and slightly increased for temperature and humidity. Model performance is relatively better for dry weather cases than for the rainfall events. Also simulations from high resolution domain-3 are found to be better with relatively lower error metrics than those over coarse domains 1 and 2.

In Part I of this work, an ensemble prediction system (EPS) based on different combinations of model physical parameterizations was compared against another ensemble based on perturbing initial and boundary conditions through the Potential Vorticity (PV) field. This comparison was done for western Mediterranean cyclonic situations associated with high-impact weather phenomena such as heavy rain and showed a better performance of the PV-perturbed ensemble over the more traditional multiphysics approach. The current study extends the comparison to another ensemble based on perturbing initial and boundary conditions through the PV field but guided by the MM5 adjoint derived sensitivity zones (PV-adjoint) instead of by the three-dimensional PV features showing intense values and gradients as was done in Part I (PV-gradient). The PV-adjoint and PV-gradient EPSs perturb specific areas of the cyclonic development using a PV error climatology that typifies PV errors in the initial and boundary conditions to provide the appropriate error range. The non-hydrostatic MM5 mesoscale model nested in the ECMWF forecast fields is used to provide all predictions. For the studied cases, 19 cyclonic events associated with heavy rain, the verification results show that both PV-perturbed are skillful, the PV-gradient being the best. Therefore, for our testbed, the extra computational cost of running the MM5 adjoint model does not provide a significant ensemble skill improvement.

In a future climate scenario forest fire activity over Portugal will substantially increase and consequently area burned and forest fire emissions to the atmosphere are also expected to increase. This study investigated the impact of future forest fire emissions on air quality over Portugal under the IPCC SRES A2 scenario. Reference and future climate change scenarios were simulated using the MM5/CHIMERE air quality modelling system, which was applied over Europe and over Portugal, using nesting capabilities. The initial and boundary conditions were provided by the HadAM3P model simulations for the reference and the future climate. The forest fire emissions were estimated using a methodology, which included the selection of emission factors for each pollutant, burning efficiency, fuel loads and the predicted area burned. These emissions were added to the simulation grid using specific parameterizations for their vertical distribution. Modelling results for Portugal pointed out that future forest fire activity will increase the O3 concentrations of almost 23 μg m−3 by 2100 but a decrease of approximately 6 μg m−3 is detected close to the main forest fire locations. Future forest fire emissions will also impact the PM10 concentrations over Portugal with increases reaching 20 μg m−3 along the Northern coastal region in July. The highest increases are estimated over the north and centre of Portugal where the area burned projections in future climate are higher. Fil: Carvalho, A.. Universidade de Aveiro; Portugal Fil: Monteiro, A.. Universidade de Aveiro; Portugal Fil: Flannigan, M.. University of Alberta; Canadá Fil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina Fil: Miranda, A. I.. Universidade de Aveiro; Portugal Fil: Borrego, C.. Universidade de Aveiro; Portugal

This study provided an insight into the complex phenomenon of acid deposition over Dhaka division of Bangladesh. The 10– day HYSPLIT back trajectories showed that majority of air masses arriving to Bangladesh were originated from the west. The gridded emissions (0.055° or ~5.5 km) for Dhaka division was prepared for 2006 by updating a preliminary emission inventory (EI) available for 2000 and by conducting a new EI for two major source categories, brick kilns and traffic. In Dhaka division as of 2006, the brick kilns contributed the largest SO 2 emission (about 70%), while residential emissions had the highest share of CO and PM 10 (over 60%) and substantial NMVOC (about 40%). Emission rates of SO 2 and NO x in the dry season of 355 t/d and 183 t/d, respectively, were higher than the corresponding rates in the wet season of 60 t/d and 146 t/d, which was mainly due to the operation of brick kilns in the dry season. The acid deposition was simulated, using CAMx–MM5 model, for December and June to represent the dry and wet seasons, respectively. Model performance was reasonable considering the simulated spatial distribution of acid deposition with simulated wind and precipitation fields. The model results for SO 2 and NO x concentrations were in the same ranges of the limited monitoring data available. However, the acid deposition simulation was still largely experimental due to the lack of acid deposition monitoring data for the model evaluation. The preliminary acid deposition simulation results suggested that the nitrogen wet deposition was the major contributor over the Dhaka division that could be the reason of exceeding the critical load for the local ecosystem. Further research is still required to refine the emission inventory and to gather the monitoring data to confirm the modeling results.

Vertical profiles of particle number concentration, potential temperature and relative humidity were measured in the Po Valley using an optical particle counter and a portable meteorological station attached to a tethered balloon. The field campaign covered the period 2006–2008, providing an extended dataset of vertical profiles in both stable and convective boundary-layer conditions. These vertical profiles were used to estimate an experimentally retrieved mixing height (MH). The MM5 meteorological model was also used to simulate the atmospheric dispersion characteristics for the same period, using a variety of different boundary-layer and land surface parameterization schemes (Medium-Range Forecast; high-resolution Blackadar; Gayno–Seaman; and Pleim–Chang). The model simulated MHs were compared among themselves, and then with that measured from balloon soundings. MRF parameterization represented the best compromise solution to simulate increasing MHs in the Po Valley. The MM5 simulations showed the regional character of meteorological forcing on PM ground-concentrations in the Po Valley.

We evaluate the simulations of SO2 and sulfate using the Community Multiscale Air Quality model (CMAQ) version 4.6 with the observations over the United States in 2002. MM5 was used for meteorological simulations. While the general seasonal cycles of SO2 and sulfate are simulated well by the model, we find significant systematic biases in the summer. The model low bias in sulfate is considerably more severe than the model bias in SO2. Both ACM and RADM schemes are used in the model to test the sensitivities of simulated sulfate to cloud processing. We carry out detailed modeling analysis and diagnostics for July 2002. Compared to satellite observations of cloud liquid water path, CMAQ cloud modules greatly overestimates convective (sub-grid) precipitating clouds, leading to large overestimation of sulfate wet scavenging. Limiting convective precipitating cloud fraction in the cloud modules to

Tropospheric ozone (O(3)) is considered one of the most important air pollutants affecting human health. The role of peri-urban vegetation in modifying O(3) concentrations has been analyzed in the Madrid region (Spain) using the V200603par-rc1 version of the CHIMERE air quality model. The 3.7 version of the MM5 meteorological model was used to provide meteorological input data to the CHIMERE. The emissions were derived from the EMEP database for 2003. Land use data and the stomatal conductance model included in CHIMERE were modified according to the latest information available for the study area. Two cases were considered for the period April-September 2003: (1) actual land use and (2) a fictitious scenario where El Pardo peri-urban forest was converted to bare-soil. The results show that El Pardo forest constitutes a sink of O(3) since removing this green area increased O(3) levels over the modified area and over down-wind surrounding areas.

A system of thunderstorms approached the Slovakia and Hungary in the late evening hours of 25 June 2008, causing extensive damage and peak wind gusts up to 40 m/s. This study examines the macro- and mesosynoptic conditions for the windstorm using soundings, analyses, and forecasts of numerical models (ALADIN, ECMWF). A derecho-like character of the event is discussed. Meteosat Second Generation imagery and convective indices inferred from satellite and model data are used to assess the humidity distribution and the conditional instability of the thunderstorm environment. An intrusion of the environmental dry air into the convective system and intensification of downdrafts is considered to be one of the reasons for the damaging winds observed at some areas. This is supported by the radar imagery showing a sudden drop of radar reflectivity and creation of line echo wave patterns and bow echoes. A numerical simulation provided by the non-hydrostatic MM5 model indicated the development of meso-γ scale vortices embedded in the convective system. The genesis and a possible role of such vortices in creating rear-inflow jets and intensifying the low level winds are investigated with the help of the vorticity equation and several other diagnostic parameters. In addition, the effect of various physical parameterisations on the forecast of the windstorm is evaluated.

Short-term prediction of wind variations as a function of height using the PSU/NCAR MM5 model with an optimum horizontal resolution of 3 km, and assimilation of meso-network measurements of PRWONAM, has been carried out for the southern peninsular region of India. The studies were made within the framework of the PRWONAM programme. The observed wind data used here are from the MST radar at Gadanki and GP Sondes at a few locations (SHAR, Bangalore, Gadanki, Chennai, Arakkonam, Cochin) in the peninsular region. During the southwest monsoon season, the wind fields showed coherent pattern in the vertical unlike during the spring season. Comparisons in wind variations between predictions and observations for Gadanki revealed an average bias of 3.84 m s −1 in the predicted mean wind above 8 km during the spring season, with 75% (63%) of instantaneous predictions falling in ±5 m s −1 error bounds with (without) bias correction. The percentage within these limits below 8 km altitude is 90%.

The rapid growth of energy consumption in conjunction with economic development during past decades in East Asia, especially China, caused severe air pollution problems at local and regional scales. Understanding of the meteorological conditions for air pollution is essential to the understanding of the formation mechanism of air pollutants and the development of effective emission control strategies to reduce air pollution. In this paper, the Fifth Generation National Center for Atmospheric Research (NCAR)/Pennsylvania State University (PSU) Mesoscale Model (MM5) modeling system is applied to simulate meteorological fields during selected six 1-month periods in 2007/2008 over a triple-nested modeling domain covering East Asia, the eastern China, and Shandong Province at horizontal grid resolutions of 36-, 12-, and 4-km, respectively. MM5 generally reproduces well the observations in the eastern China but performs worse in the western China and northeastern China. Largest biases occur in 2-m temperatures (T2) and wind speed and wind direction at 10-m in haze months (i.e., winter) and daily mean precipitation (Precip) in non-haze months (i.e., summer), due to limitations of the model in simulating snow cover and convective precipitation. Meteorological predictions agree more closely with observations at urban sites than those at the coastal and mountain sites where the model performance deteriorates because of complex terrains, influences of urban heat island effect and land/sea breezes, and higher elevations. Model results at 12-km in Shandong Province show an overall better performance than those at 4- or 36-km while the results at 4-km show worst performance due to inaccurate land use and the model’s incapability in simulating meteorological processes at a fine scale.

This paper describes the details of the Atmospheric Model Evaluation Tool (AMET) v1.1 created by scientists in the Atmospheric Modeling and Analysis Division (AMAD) of the U.S. Environmental Protection Agency (EPA). AMET was first developed to evaluate the performance of the 5th Generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological model output and was later extended to include capabilities for evaluating output data from the Community Multiscale Air Quality (CMAQ) model as well. AMET is designed to leverage several open-source software packages that are used in combination to 1) pair the modeled and observed values in time and space, 2) store these paired datasets in an easily accessible and searchable database and 3) access and analyze these data using a statistical package. Through this process, AMET is able to provide a convenient method for evaluating meteorological and air quality model predictions. The use of a searchable, relational database allows the entire dataset to be quickly subset into only those data that are of the most interest for the current analysis, a process that is often tedious and time consuming without the use of a database. In addition to common summary statistics (e.g. RMSE, bias, and correlation), several of the many analysis features available in AMET include scatter plots, time series plots, box plot and spatial plots as part of operational model evaluation. Additionally, several unique analysis functions are also available in AMET, and the system provides a framework within which users may extend the current functionality for their own custom analyses. While AMET was designed to work specifically with MM5, WRF and CMAQ model output, it could easily be modified to work with output data from other meteorological and air quality models.

In the frame of this paper a heat wave that resulted in record high temperatures in Athens Greece is analysed. Namely the analysis focuses in the ability of three widely used planetary boundary layer parameterisations to reproduce the heat wave temperatures. The simulations were performed with the Pennsylvania State University – National Center for Atmospheric Research MM5 model that is also used for operational weather forecasting at the National Observatory of Athens since 2002. The 2-m temperature at a grid increment of 1-km over the highly complex terrain of the Athens Area is statistically verified against the available surface station observations. The results of the analysis showed that the two nonlocal schemes, namely Blackadar and MRF succeeded much better than the local scheme ETA to reproduce the heat wave 2-m temperature although they considerably underestimated the maximum observed temperatures. In addition, it was found that the model grid points characterised by “urban” land-use provided better statistical verification results, with reduced cold bias. Further, in order to study the role of the initial skin temperature conditions on the 2-m temperature forecasts, satellite observed skin temperatures have been used to initialise the high resolution simulations. This sensitivity test showed that when using the satellite observed skin temperature in the model initial conditions, the simulation of the 2-m temperature is positively affected during the early stages of the simulation while later on the model physical parameterisations are decisive for the time evolution of temperature.

Surface ozone concentrations at Istanbul during a summer episode in June 2008 were simulated using a high resolution and urban scale modeling system coupling MM5 and CMAQ models with a recently developed anthropogenic emission inventory for the region. Two sets of base runs were performed in order to investigate for the first time the impact of biogenic emissions on ozone concentrations in the Greater Istanbul Area (GIA). The first simulation was performed using only the anthropogenic emissions whereas the second simulation was performed using both anthropogenic and biogenic emissions. Biogenic NMVOC emissions were comparable with anthropogenic NMVOC emissions in terms of magnitude. The inclusion of biogenic emissions significantly improved the performance of the model, particularly in reproducing the low night time values as well as the temporal variation of ozone concentrations. Terpene emissions contributed significantly to the destruction of the ozone during nighttime. Biogenic NMVOCs emissions enhanced ozone concentrations in the downwind regions of CIA up to 25 ppb. The VOC/NOx ratio almost doubled due to the addition of biogenic NMVOCs. Anthropogenic NOx and NMVOCs were perturbed by +/-30% in another set of simulations to quantify the sensitivity of ozone concentrations to the precursor emissions in the region. The sensitivity runs, as along with the model-calculated ozone-to-reactive nitrogen ratios, pointed NOx-sensitive chemistry, particularly in the downwind areas. On the other hand, urban parts of the city responded more to changes in NOx due to very high anthropogenic emissions. (C) 2010 Elsevier B.V. All rights reserved.