Prognostic Meteorological Model Research Articles

Numerical simulation of the radioactive contamination of Ukraine after the Chornobyl disaster: the influence of the input meteorological data on the results uncertainty

In this article we assess the sensitivity of the numerical simulations of the radioactive 137Cs contamination of Ukraine caused by the Chornobyl nuclear power plant accident in 1986 to the input meteorological data. The atmospheric transport, dispersion, and deposition (dry scavenging and rain washout) of the radioactive aerosols was simulated using the CALPUFF dispersion model. The source parameterization of the 137Cs emissions during the active phase of the catastrophe (26 April—May 5 of 1986) was adopted from the previously published literature results. Seventeen different versions/realizations of the input meteorology for CALPUFF simulations were prepared with the regional prognostic meteorological model WRF by combining the available global atmospheric reanalyses for 1986 (NNRP, ERA-Interim, ERA5, CFSR) and the model’s physical parameterizations (microphysics, radiation processes, boundary/surface layer physics). The assessment of the simulation uncertainty was carried out in two different ways. In the first approach, the uncertainty was estimated as the width of the distribution of the calculated 137Cs surface concentrations (adjusted to the logarithmic scale), which were obtained with different versions of the input meteorology. The second approach was based on the statistical comparison of the calculated 137Cs contaminations and the corresponding measured values obtained during a complex assessment of the aftermath of the disaster made at the beginning of 1990s. Two statistical metrics were used: the geometric mean bias and the geometric mean variance. The results of our study demonstrate that even when using somewhat unified meteorological data (atmospheric reanalysis), the results of the radioactive contamination calculations at the same spatial locations can differ by several orders of magnitude. We find that the uncertainty depends not only on the distance to the source of the emissions but also on the physical mechanism (wet or dry deposition) responsible for the formation of the local contamination

Simulating summer mixing heights in California's San Joaquin Valley using the WRF meteorological model with three land surface modules

During the 1990 SARMAP/AUSPEX study a network of rawinsondes was flown at 3- and 6-h intervals to evaluate the meteorological conditions conducive high ozone concentrations in the Central Valley of California. Vertical profiles of air temperature, humidity, and pressure from 9 of the sites were used to determine maximum daily mixing heights in California’s San Joaquin Valley (SJV) during the period August 3–6, 1990 and to evaluate their spatial distribution. The analysis showed a general pattern of increasing mixing heights from north to south within the SJV. But, it also showed that mixing heights were lower in areas in proximity to irrigated agriculture. The WRF prognostic meteorological model was used to simulate mixing heights during this period using three land surface modules (LSM): thermal diffusion (TD), Noah, and Pleim-Xiu (PXU). The maximum daily mixing heights simulated using the TD LSM were generally negatively biased; while those simulated using the Noah and PXU LSMs were positively biased. The differences in simulated mixing heights were attributed to differences in how crop land irrigation water is represented within each LSM. The TD LSM effectively assumes a fixed soil water content, but of sufficient magnitude to reflect latent heat fluxes associated with crop land irrigation. The PXU and Noah LSMs include comprehensive descriptions of the soil water budget. However, soil water may be depleted in the arid summer climate and these LSMs provide no direct means of accounting for water added through irrigation. The LSMs were also sensitive to estimates of ground cover in irrigated areas. For all 9 sites, the root mean square error (RMSE) for maximum daily mixing heights for the TD LSM, the Noah LSM, and the PXU LSM was 269, 350, and 366 m, respectively; and the biases for the 9 sites were − 161, 355, and 437 m, respectively. Using the assumption of well-managed irrigation and leaf area index (LAI) estimates from AVHRR remote sensing data sets, adjustments to the WRF input files were made to update soil water contents and ground cover for irrigated crop land use. Using the Noah LSM with the adjusted files, site-specific mixing height biases were reduced up to a factor of 4 and, for all sites, the RMSE for maximum daily mixing heights were reduced from 350 to 260 m; for the PXU LSM RMSEs were reduced from 366 to 238 m; and for the TD LSM from 269 to 231 m. This analysis illustrates the importance of accounting for the effects of applied irrigation water and ground cover in the simulation of mixing heights during the arid summer within the SJV. Irrigation can influence actual mixing heights and their distribution within a domain. It also illustrates that the choice of LSM is of less importance than setting LSM parametrizations to values appropriate for the modeling domain.

Contributions of traffic and shipping emissions to city-scale NOx and PM2.5 exposure in Hamburg

Abstract We investigated the contribution of road traffic and shipping related emissions of NO2 and PM2.5 to total air quality and annual mean population exposure in Hamburg 2012. For this purpose, we compiled a detailed emission inventory following SNAP categories focusing on the detailed representations of road traffic and shipping emissions. The emission inventory was applied to a global-to-local Chemistry Transport Model (CTM) system to simulate hourly NO2 and PM2.5 concentrations with a horizontal grid resolution of 500 m. To simulate urban-scale pollutant concentrations we used the coupled prognostic meteorological and chemistry transport model TAPM. The comparison of modelled to measured hourly values gives high correlation and small bias at urban and background stations but large underestimations of NO2 and PM2.5 at measurements stations near roads. Simulated contributions of road traffic emissions to annual mean concentrations of NO2 and PM2.5 is highest close to highways with relative contributions of 50% for NO2 and 40% for PM2.5. Nevertheless, the urban domain is widely affected by road traffic, especially in the city centre. Shipping impact focuses on the port and nearby industrial areas with contributions of up to 60% for NO2 and 40% for PM2.5. In residential areas in the north of the port, shipping contributes with up to 20–30% for NO2 and PM2.5. Our simulation resulted in 14% of the population of Hamburg being exposed to hourly NO2 concentration above the hourly limit of 200 μg/m³,

Meteorological and Air Quality Modeling for Hawaii, Puerto Rico, and Virgin Islands.

A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O3, PM2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO2, NO2, PM2.5, and CO and was often related to how well local emissions sources were characterized. SO2 predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.

Modelling smoke distribution in the vicinity of a large and prolonged fire from an open-cut coal mine

The 2014 fire in the Hazelwood open-cut mine of brown coal, located in the State of Victoria (Australia), burned for 45 days. The fire sent dense smoke over the nearby town of Morwell and beyond, resulting in one of the worst air quality incidents in Victoria. Precision air monitoring of PM2.5 (particulate matter 2.5 μm or less in diameter) and carbon monoxide (CO), which has been reported previously, started a few days after the fire at two locations in Morwell and measured PM2.5 levels up to 19 times higher than the 24-h Australian air quality standard. Because of the sparseness of the monitors and the fact that the fire was most intense prior to the start of the air monitoring, it is likely that the smoke concentrations in Morwell were even greater than measured. Thus, the concentration measurements are insufficient in time and space for a comprehensive study of exposure and health impacts due to the smoke. We reconstruct the hourly spatial distributions of smoke (as represented by PM2.5 and CO) in and around Morwell by first developing a rigorous methodology for estimating the fire emissions, and then using them in a high-resolution prognostic meteorological and dispersion model with local wind data assimilation and an appropriate plume rise mechanism. Larger-scale modelling is also conducted to estimate the background concentrations without the mine fire. The model simulates the number of observed exceedances and the observed maximum concentrations exceeding the air quality standards for both PM2.5 and CO within a factor of 2. At the monitor south of Morwell near the fire, the model predicts hourly PM2.5 and CO concentrations as high as 3730 μg m−3 and 58.6 ppm, respectively, in the early phase of the fire; these levels are much higher than those recorded by the subsequent air monitoring. The modelled PM2.5 fields are being used by other researchers to estimate the impact of smoke exposure on health outcomes in the local community.

Surface mining in Western Macedonia, Greece: fugitive dust (PM10) emissions and dispersion

In the operation of large open-pit lignite mines (extracting and handling excavation materials) a series of significant fugitive dust emission sources are recorded. The quantification of the emissions of each source and the investigation of atmospheric dispersion are subjects of great interest for the development and implementation of air quality management systems in the neighbouring residential areas. The study aims to investigate and locate the fugitive dust sources in the surface mines operated in Western Macedonia in NW Greece and quantify their contribution to the total fugitive dust (PM10) emissions produced by the mines' activities. The contribution of each emission source recorded was quantified. An effort was also made to investigate the dispersion of fugitive dust emitted from each individual mine and source activity and assess the contribution of the mining activities to the air quality of the surrounding areas, by using a three-dimensional, nestable, prognostic meteorological, and air pollution model. The results can contribute to the implementation of measures and scenarios for the air quality management in the area.

Comparing estimates from the R-LINE near road dispersion model using model-derived and observation-derived meteorology

Observed meteorological conditions, usually measured at airports or weather monitoring stations, have long provided the only source of meteorology for many Gaussian air pollution dispersion models. This introduces uncertainty and limitations in numerical model estimates, especially for locations of interest far removed from these monitoring stations. Hence, it is advantageous to employ predicted meteorology from a prognostic meteorological model as a substitute. The objective of this study was to compare estimates from the R-LINE near road dispersion model at three inland sites and one coastal site in Connecticut using observation-derived (weather station) and model-derived (Weather Research and Forecasting Model) meteorology. Both the graphical and statistical comparisons indicated less pronounced discrepancies in model estimations in the time periods generally characterized by unstable atmospheric conditions than those characterized by stable atmospheric conditions. There were also more pronounced differences at larger distances from roadways. Comparison of the estimated surface characteristic variables using both the observation-derived and model-derived meteorology displayed similar diurnal trends.

Source apportionment of biogenic contributions to ozone formation over the United States

Vegetation is the leading emitter of volatile organic compounds (VOC), a key ingredient for ozone formation. The contribution of biogenic VOC (BVOC) emissions to regional ozone formation needs better quantification so that air quality regulators can effectively design emission control strategies. One of the key uncertainties for modeling BVOC emissions comes from the estimation of photosynthetically active radiation (PAR) reaching canopy. Satellite insolation retrieval data provide an alternative to prognostic meteorological models for representing the spatial and temporal variations of PAR. In this study, biogenic emission estimates generated with the MEGAN and BEIS biogenic emissions models using satellite or prognostic PAR are used to examine the contribution of BVOC to ozone in the United States. The Comprehensive Air Quality Model with Extensions (CAMx) is applied with Ozone Source Apportionment Technology (OSAT) and brute force zero-out sensitivity runs to quantify the biogenic contributions to ozone formation during May through September 2011. The satellite PAR retrievals are on average lower than modeled PAR and exhibit better agreement with SCAN and SURFRAD network measurements. Using satellite retrievals instead of modeled PAR reduces BEIS and MEGAN estimates of isoprene by an average of 3%–4% and 9%–12%, respectively. The simulations still overestimate observed ground-level isoprene concentrations by a factor of 1.1 for BEIS and 2.6 for MEGAN. The spatial pattern of biogenic ozone contribution diagnosed from OSAT differs from the brute force zero-out sensitivity results, with the former more smoothly distributed and the latter exhibiting peak impacts near metropolitan regions with intense anthropogenic NOx emissions. OSAT tends to apportion less ozone to biogenics as BVOC emissions increase, since that shifts marginal ozone formation toward more NOx-limited conditions. By contrast, zero-out source apportionment of ozone to biogenics increases with BVOC emissions. OSAT simulations with BEIS show that BVOCs typically contribute 10–19% to regional ozone concentrations at nonattainment receptor sites during episode days.

Operational Precise Irrigation for Cotton Cultivation through the Coupling of Meteorological and Crop Growth Models

In this paper, we tested the operational capacity of an interoperable model coupling system for the irrigation scheduling (IMCIS) at an experimental cotton (Gossypium hirsutum L.) field in Northern Greece. IMCIS comprises a meteorological model (TAPM), downscaled at field level, and a water-driven cultivation tool (AquaCrop), to optimize irrigation and enhance crop growth and yield. Both models were evaluated through on-site observations of meteorological variables, soil moisture levels and canopy cover progress. Based on irrigation management (deficit, precise and farmer’s practice) and method (drip and sprinkler), the field was divided into six sub-plots. Prognostic meteorological model results exhibited satisfactory agreement in most parameters affecting ETo, simulating adequately the soil water balance. Precipitation events were fairly predicted, although rainfall depths needed further adjustment. Soil water content levels computed by the crop growth model followed the trend of soil humidity measurements, while the canopy cover patterns and the seed cotton yield were well predicted, especially at the drip irrigated plots. Overall, the system exhibited robustness and good predicting ability for crop water needs, based on local evapotranspiration forecasts and crop phenological stages. The comparison of yield and irrigation levels at all sub-plots revealed that drip irrigation under IMCIS guidance could achieve the same yield levels as traditional farmer’s practice, utilizing approximately 32% less water, thus raising water productivity up to 0.96 kg/m3.

New functionalities developed in the NERIS-TP project regarding meteorological data used by Decision Support Systems

In this paper a description is given of software tools that have been developed during the NERIS-TP project which provide the capability to users of Decision Support Systems (DSSs), such as JRODOS, to calculate their own prognostic meteorological data with the desired spatial and temporal resolution. These tools increase the flexibility of applying the DSSs for any location on the Earth. This is achieved by downloading freely available global meteorological data and downscaling them using the prognostic meteorological model WRF. The results of WRF and/or global data are uploaded to the DSS to calculate the atmospheric dispersion. The above software tools operate in an automated way, in conjunction with the DSS. In addition, data assimilation methodologies have been integrated into the Meteorological Pre-Processor of JRODOS, to correct previously calculated prognostic data on the basis of locally measured meteorological data. These data assimilation methods were successfully tested and their results increase the accuracy of the prognoses of dispersion models.

Reactive puff model SCICHEM: Model enhancements and performance studies

The SCICHEM model incorporates complete gas phase, aqueous and aerosol phase chemistry within a state-of-the-science Gaussian puff model SCIPUFF (Second-order Closure Integrated Puff). The model is a valuable tool that can be used to calculate the impacts of a single source or a small number of sources on downwind ozone and PM2.5. The model has flexible data requirements: it can be run with routine surface and upper air observations or with prognostic meteorological model outputs and source emissions are specified in a simple text format. This paper describes significant advances to the dispersion and chemistry components of the model in the latest release, SCICHEM 3.0. Some of the major advancements include modeling of skewed turbulence for convective boundary layer and updated chemistry schemes (CB05 gas phase chemical mechanism; AERO5 aerosol and aqueous modules). The results from SCICHEM 3.0 are compared with observations from a tracer study as well as aircraft measurements of reactive species in power plant plumes from two field studies. The results with the tracer experiment (Copenhagen study) show that the incorporation of skewed turbulence improves the calculation of tracer dispersion and transport. The comparisons with the Cumberland and Dolet Hills power plume measurements show good correlation between the observed and predicted concentrations of reactive gaseous species at most downwind distances from the source.

Analysis of meteorological parameters for dense gas dispersion using mesoscale models

The current study focuses on characterizing the atmospheric details required for dense gas dispersion analysis resulting from release of cryogenic liquids like LNG. The study investigates the effectiveness of coupling the prognostic MM5 mesoscale model with the CALMET diagnostic model for producing meteorological conditions that is characteristic of dry and arid regions like Qatar, with non-neutral boundary conditions. MM5-CALMET wind fields and temperature data are compared with the meteorological field observations from the Doha International Airport (DIA) on a monthly basis, daily basis and hourly basis to study the effect of different averaging periods. The monthly averages replicate the annual patterns of meteorological parameters very well. However difference in observation and model are observed for wind speed and wind direction variable. The daily averages obtained from the model are in good agreement with the observation for wind speed and temperature. For hourly averaging, the model is found capable of mimicking the temperature of a given location, but not wind speed and direction. The prediction of wind direction parameter using MM5-CALMET is moderate for any averaging period. The sub-optimal performance of wind direction variable is attributed to grid resolution of vertical and horizontal layers of MM5-CALMET model. Additionally a case study is performed to illustrate the effect of variation of meteorological parameters on the lower flammability limits (LFL) resulting from flammable dense gas release of LNG. The case study demonstrates the issues that arise in a risk analysis study when “wrong” meteorological data could be used. The overall study indicates that utilizing the coarsest prognostic meteorological model output in a diagnostic model provides an effective option for generating meteorological inputs for dispersion studies.

Elevated stacks’ pollutants’ dispersion and its contributions to photochemical smog formation in a heavily industrialized area

In this study, the photochemical smog formation over a heavily industrialized area with complex terrain was investigated. A mesoscale prognostic meteorological and air pollution model was used in combination with data, which were collected by in situ and remote monitoring stations. The area of interest is a mountainous basin in north-western Greece. The intensive industrial activity in this area has resulted in effects on the air quality of the area, mainly caused by the lignite-fired power stations and the lignite mining operation. The study focused on the dispersion of ozone production due to primary pollutants’ emissions from the power stations (PSs). Moreover, the investigation of this external sources’ contribution to the ozone concentrations measured in Kozani, the most populated city of the area, is ventured. The results showed a considerable skill of the model in predicting the major mesoscale features affecting the pollutants’ dispersion and concentrations in the area of interest. Numerical simulation data of photochemical pollutants were significantly correlated with meteorology during the simulation period. The correlation reveals the most important factors of ozone production, such as solar radiation, temperature, wind speed and topography. The obtained results have contributed to the verification of distant pollutants’ transfer from industrial sources.

Atmospheric boundary layer top height in South Africa: measurements with lidar and radiosonde compared to three atmospheric models

Abstract. Atmospheric lidar measurements were carried out at Elandsfontein measurement station, on the eastern Highveld approximately 150 km east of Johannesburg in South Africa throughout 2010. The height of the planetary boundary layer (PBL) top was continuously measured using a Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended). High atmospheric variability together with a large surface temperature range and significant seasonal changes in precipitation were observed, which had an impact on the vertical mixing of particulate matter, and hence, on the PBL evolution. The results were compared to radiosondes, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) space-borne lidar measurements and three atmospheric models that followed different approaches to determine the PBL top height. These models included two weather forecast models operated by ECMWF (European Centre for Medium-range Weather Forecasts) and SAWS (South African Weather Service), and one mesoscale prognostic meteorological and air pollution regulatory model TAPM (The Air Pollution Model). The ground-based lidar used in this study was operational for 4935 h during 2010 (49% of the time). The PBL top height was detected 86% of the total measurement time (42% of the total time). Large seasonal and diurnal variations were observed between the different methods utilised. High variation was found when lidar measurements were compared to radiosonde measurements. This could be partially due to the distance between the lidar measurements and the radiosondes, which were 120 km apart. Comparison of lidar measurements to the models indicated that the ECMWF model agreed the best with mean relative difference of 15.4%, while the second best correlation was with the SAWS model with corresponding difference of 20.1%. TAPM was found to have a tendency to underestimate the PBL top height. The wind speeds in the SAWS and TAPM models were strongly underestimated which probably led to underestimation of the vertical wind and turbulence and thus underestimation of the PBL top height. Comparison between ground-based and satellite lidar shows good agreement with a correlation coefficient of 0.88. On average, the daily maximum PBL top height in October (spring) and June (winter) was 2260 m and 1480 m, respectively. To our knowledge, this study is the first long-term study of PBL top heights and PBL growth rates in South Africa.

Evaluation of surface and upper air fine scale WRF meteorological modeling of the May and June 2010 CalNex period in California

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.

Determination of Upwind and Downwind Areas of Seoul, Korea Using Trajectory Analysis

To identify the domains that have the greatest impacts on air quality at the surface, both the upwind and downwind areas of Seoul were determined by season using refined wind fields. Four consecutive days were selected as the study period typical of each season. The mesoscale meteorology of the study period was reproduced by using the MM5 prognostic meteorological model (PSU/NCAR Mesoscale Model) with horizontally nested grids. The gridded meteorological field, which was used on the study area of 242 ㎞×226 ㎞ with grid spacing of 2 ㎞, was generated by using the CALMET diagnostic meteorological model. Upwind and downwind areas of Seoul were determined by calculating 24-hour backward and forward air parcel trajectories, respectively, with u, v, and w velocity vectors. The results showed that the upwind and downwind areas were extended far to the northwest and the southeast as a result of high wind speeds in the spring and winter, while they were restricted on the fringe of Seoul in the summer and fall.

Application of California Puff (CALPUFF) model: a case study for Oman

Oil Refineries surrounding the gulf are the largest source of environmental pollution in the region. Air dispersion models are powerful tools for assessing the consequences of environmental air pollutant concentrations. This study was carried out to investigate the transport and dispersion patterns of SO2 originating from Mina Al-Fahal refinery, in the Sultanate Oman by employing California Puff (CALPUFF) dispersion modeling system. The major goal of this study is to make a comparison of the results produced by this modeling system with a previous study which was conducted for the same area using Industrial Source Complex Short Term (ISCST) model. In order to obtain the meteorological fields of the study area the CALPUFF modeling system was coupled with Weather Research and Forecasting (WRF), a prognostic meteorological model. The results indicated that the performance of the CALPUFF was better than that of ISCST; however, a difference in magnitudes of predicted and measured concentrations of SO2 was found. This difference can be reduced using high-resolution terrain elevation data, site-specific observational meteorological data and buoy data. The complex geography and variable wind regimes played an important role in distribution of SO2 in and around the refinery. The land–sea interaction also influenced the predicted results.

Modelling the Meteorology at the Cabauw Tower for 2005

The meteorology at the Cabauw tower site in the Netherlands has been modelled for 2005 using a local scale prognostic meteorological and air pollution model called TAPM. A number of performance measures have been used to assess model accuracy, including comparison statistics such as root-mean-square error (RMSE) and index of agreement (IOA). Results show that the model performs very well for prediction of wind and temperature at the six tower levels that range from 10 to 200 m above the ground, as well as performing well for radiation and surface fluxes. The model simulation shows almost no bias in mean and standard deviations of wind and temperature at each tower height level, with small RMSE (e.g. RMSE of 1.2 m s−1 for 10-m wind speed, and 1.6°C for 10-m temperature) and high correlation and IOA (e.g. IOA of 0.92 for 10-m wind speed and 0.98 for 10-m temperature). Results for radiation and surface fluxes also show good performance, although some biases were seen for these variables, and possibilities for future model development were identified. An examination of model sensitivity also explored several aspects of the model configuration and input.

Modelling Near-Surface Low Winds over Land under Stable Conditions: Sensitivity Tests, Flux-Gradient Relationships, and Stability Parameters

Low or weak wind-speed conditions, roughly defined as the periods when the mean wind speed at 10 m above the ground is 2 ms−1 or less, are of considerable practical interest. However, they are not readily amenable to treatment within prognostic meteorological models and, consequently, difficult to predict, especially when the ambient stability is strong. In this paper, we apply an E − e prognostic meteorological model to simulate near-surface meteorology and, focusing on low wind speeds, compare the predictions with measurements from two independent datasets. A sensitivity analysis is performed to investigate the possible reasons for the relatively inferior model performance for low winds when the atmosphere is stably stratified. A comprehensive data analysis is carried out to study low wind stable conditions, concentrating on the validity of various forms of flux–gradient relationships for momentum and heat within the framework of the Monin-Obukhov similarity theory, which models employ for calculating surface fluxes. The observed behaviour of various stability parameters, such as the Richardson number, is investigated. The results point to inadequacies of the current flux–gradient relationships, especially regarding momentum, under strongly stable conditions as being a dominant reason for the poor low wind predictions. The modelling issues identified are not just restricted to the present model, but are general in nature. The use of an alternative stability function for momentum under strongly stable conditions is explored. It results in improved model performance for low winds; however, further research is needed to better understand strongly stable flows in the lower atmosphere and to develop methods that can translate that understanding to operational meteorological modelling.

Artificial Neural Networks to reconstruct incomplete satellite data: application to the Mediterranean Sea Surface Temperature

Abstract. Satellite data can be very useful in applications where extensive spatial information is needed, but sometimes missing data due to presence of clouds can affect data quality. In this study a methodology for pre-processing sea surface temperature (SST) data is proposed. The methodology, that processes measures in the visible wavelength, is based on an Artificial Neural Network (ANN) system. The effectiveness of the procedure has been also evaluated comparing results obtained using an interpolation method. After the methodology has been identified, a validation is performed on 3 different episodes representative of SST variability in the Mediterranean sea. The proposed technique can process SST NOAA/AVHRR data to simulate severe storm episodes by means of prognostic meteorological models.