Mesoscale Numerical Weather Prediction Research Articles

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing

Accurate precipitation forecasting is one of the most challenging problems in mesoscale Numerical Weather Prediction (NWP) models. The utilization of 3-Dimensional Variational (3DVar) Data Assimilation technique based on automatic weather station (AWS) data can significantly enhance the accuracy of precipitation simulations and forecasts using the Weather Research and Forecasting (WRF) model. However, the impacts of different assimilation frequencies and meteorological variables on the accuracy of precipitation forecasts remain unclear. This study comprehensively evaluated the impact of different assimilation frequencies and meteorological variables on precipitation forecasts, as illustrated by a case study of a squall line event on August 2, 2017, in Beijing. Eight experiments were conducted by assimilating different combinations of meteorological variables (air pressure, temperature, relative humidity, wind speed, and wind direction) at various time intervals (1 h, 3 h, and 6 h). The results indicated that the WRF model roughly simulated the evolution of this event but overestimated the precipitation amount, accompanied by a large deviation in precipitation areas. Assimilating detailed AWS data significantly improves the model performance in precipitation forecasts. The experiment assimilating all variables at a 3 h interval yielded the most accurate forecasts, with the maximum threat score (TS) increasing from 0.02 to 0.56. Higher assimilation frequencies do not guarantee a better performance. In practice, a 3 h assimilation frequency emerges as an optimal choice for AWS data assimilation. The assimilation of various variables enhanced precipitation forecasts to varying degrees, with the optimum results achieved when all variables were assimilated. Wind speed and direction were the most significant factors in dynamically enhancing the simulation of precipitation areas. Relative humidity and temperature influenced the precipitation intensity by affecting the evolution of convective precipitation thermodynamically. The findings of this study can contribute to the development of AWS data assimilation strategies for the WRF-3DVar model, thereby enhancing the precipitation forecast accuracy.

Nowcasting convective activity for the Sahel: A simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature

AbstractFlash flooding from intense rainfall frequently results in major damage and loss of life across Africa. In the Sahel, automatic prediction and warning systems for these events, driven by Mesoscale Convective Systems (MCSs), are limited, and Numerical Weather Prediction (NWP) forecasts continue to have little skill. The ground observation network is also sparse, and very few operational meteorological radars exist to facilitate conventional nowcasting approaches. Focusing on the western Sahel, we present a novel approach for producing probabilistic nowcasts of convective activity out to six hours ahead, using the current location of observed convection. Convective parts of the MCS, associated with extreme and heavy precipitation, are identified from 16 years of Meteosat Second Generation thermal–infrared cloud‐top temperature data, and an offline database of location‐conditioned probabilities calculated. From this database, real‐time nowcasts can be quickly produced with minimal calculation. The nowcasts give the probability of convection occurring within a square neighbourhood surrounding each grid point, accounting for the inherent unpredictability of convection at small scales. Compared to a climatological reference, formal verification approaches show the nowcasts to be skilful at predicting convective activity over the study region, for all times of day and out to the six‐hour lead time considered. The nowcasts are also skilful at capturing extreme 24‐hour rain gauge accumulations over Dakar, Senegal. The nowcast skill peaks in the afternoon, with a minimum in the evening. We find that the optimum neighbourhood size varies with lead time, from 10 km at the nowcast origin to around 100 km at a six‐hour lead time. This simple and skilful nowcasting method could be highly valuable for operational warnings across West Africa and other regions with long‐lived thunderstorms, and help to reduce the impacts from heavy rainfall and flooding.

The impact of aerosol-cloud interaction on mesoscale numerical weather prediction when low-cloud and haze coexist in winter over major polluted regions of China

Compared with climate models, the role of aerosol-cloud interaction (ACI) in mesoscale numerical weather prediction (NWP) models still needs to be better understood, especially in haze regions with relatively high aerosol concentration. Here, we perform two sensitivity experiments with and without ACI (ACI and NO-ACI) in the atmospheric chemistry model GRAPES_Meso5.1/CUACE to investigate the impact of ACI on mesoscale NWP during the low-cloud period in winter 2016 over varying haze regions (severe polluted Jing-Jin-Ji (JJJ), polluted Yangtze River Delta (YRD), and weak polluted Pearl River Delta (PRD)) in China. The study results show that the real-time ACI improves underestimated cloud optical thickness (COT) and cloud water liquid path (CLWP) in haze regions, with the mean bias of simulated COT (CLWP) decreased by 27% (3%), 60% (14%), and 55% (3%) in JJJ, YRD, and PRD, respectively. The increased COT and CLWP lead to a decrease of 6.8, 21, and 13 W m−2 in daytime surface downward shortwave radiation (SDSR) in JJJ, YRD, and PRD, helping to reduce the mean bias of daytime SDSR by 6%, 13%, and 9%. In addition, ACI mitigates the warm bias of temperature at 2 m and dry bias of relative humidity (RH) at 2 m to a certain extent in haze regions, particularly in YRD with the mean absolute bias improved by 13% and 6%. The simulated vertical structure of temperature and RH in the ACI experiment is more consistent with observations than in the NO-ACI experiment. Further investigations find that the ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude over varying haze regions. Higher COT and CLWP, hence more significant meteorology changes due to ACI, occur in YRD, followed by PRD and JJJ. This study demonstrates the importance and complexity of ACI in modifying mesoscale NWP over varying haze regions of China, which promotes the further understanding of ACI in operational NWP models and bridges the gap with climate models.

An adaptive nested dynamic downscaling strategy of wind-field for real-time risk forecast of power transmission systems during tropical cyclones

High winds from tropical cyclones can cause significant damages to power transmission system and lead to widespread power outages resulting in tangible socio-economic losses. Meteorological numerical weather prediction (NWP) models can provide real-time wind-field forecasts, making it possible to conduct real-time risk forecasts of regional-scale power transmission systems in order to inform proactive risk mitigation interventions. However, the operationally employed NWP simulates wind-fields at mesoscales (1∼10km) which is too coarse to capture the wind dynamics of transmission towers (generally covering an area of 25m*25m or less) and conductors over complex terrains. While dynamic downscaling can resolve topographic features into mesoscale wind-field to obtain small-scale (100m∼1km) or micro-scale (10∼100m) wind predictions, it requires practically unattainable computational resources for regional-scale operations in real-time. To address these challenges, we propose an Adaptive Nested Dynamic Downscaling (ANDD) strategy, which (i) spatially considers terrain features, and topology and failure mechanisms of transmission systems; and (ii) temporally adapts to the evolution of an approaching cyclone in real time, enabling the downscaling domains to be reconfigured based on the latest mesoscale NWP. The advantages of the ANDD strategy are illustrated through the power transmission system in Zhejiang Province (105,500 km2), China, during Super Typhoon Lekima of 2019.

Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom.

Preparing the assimilation of the future MTG‐IRS sounder into the mesoscale numerical weather prediction AROME model

AbstractThe infrared sounder (IRS) instrument is an infrared Fourier‐transform spectrometer that will be on board the Meteosat Third Generation series of the future European Organization for the Exploitation of Meteorological Satellite's geostationary satellites. It will measure the radiance emitted by the Earth at the top of the atmosphere using 1,960 channels. The IRS will provide high spatial‐ and temporal‐frequency four‐dimensional information on atmospheric temperature and humidity, winds, clouds, and surfaces, as well as on the chemical composition of the atmosphere. The assimilation of these new observations represents a great challenge and opportunity for the improvement of numerical weather prediction (NWP) forecast skill, especially for mesoscale models such as the Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) at Météo‐France. The objectives of this study are to prepare for the assimilation of the IRS in this system and to evaluate its impact on the forecasts when added to the currently assimilated observations. By using an observing system simulation experiment constructed for a mesoscale NWP model. This observing system simulation experiment framework makes use of synthetic observations of both IRS and the currently assimilated observing systems in AROME, constructed from a known and realistic state of the atmosphere. The latter, called the “nature run”, is derived from a long and uninterrupted forecast of the mesoscale model. These observations were assimilated and evaluated using a 1 hr update cycle three‐dimensional variational data assimilation system over 2‐month periods, one in the summer and one in the winter. This study demonstrates the benefits that can be expected from the assimilation of IRS observations into the AROME NWP system. The assimilation of only 75 channels over oceans increases the total amount of observations used in the AROME three‐dimensional variational data assimilation system by about 50%. The IRS impact in terms of forecast scores was evaluated and compared for the summer and winter periods. The main findings are as follows: (a) over both periods the assimilation of these observations leads to statistically improved forecasts over the whole atmospheric column; (b) for the summer‐season experiment, the forecast ranges up to 48 hr are improved; (c) for the winter‐season experiment, the impact on the forecasts is globally positive but is smaller than the summer period and extends only to 24 hr. Based on these results, it is foreseen that the addition of future IRS observations in the AROME NWP systems will significantly improve mesoscale weather forecasts.

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.

Performance Evaluation and Optimization of the Weather Research and Forecasting (WRF) Model Based on Kunpeng 920

The Weather Research and Forecasting (WRF) model is a mesoscale numerical weather prediction system, which is widely used in major high-performance server platforms. This study focuses on the performance evaluation and optimization of WRF on Huawei’s self-developed kunpeng 920 processor platform, aiming to improve the operational efficiency of WRF. The results of the study show that the scalability of WRF on kunpeng 920 processor is well performed; the performance of WRF on kunpeng 920 processor is improved by 32.6% after invoking the Fast Math Library and Domain Decomposition Core Tile Division optimization. In terms of IO, the main optimizations are parallel IO and asynchronous IO. Eventually, the single output time of WRF is reduced from 37.28 s in serial IO mode to 0.14 s in asynchronous IO mode, and the overall running time is reduced from 1078.80 s to 807.94 s.

Evaluation of High Resolution WRF Solar

The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS.

A coupled turbine-interaction wind farm parameterization in the Weather Research and Forecasting model

The wind farm parameterization in the mesoscale numerical weather prediction (NWP) model is a valuable approach to exploring the performance and atmospheric impact of the large-scale wind farm. However, the sub-grid interactions between turbines are not resolved in the current parameterization method. In this study, we propose an improved wind farm parameterization by coupling a novel sub-grid wind turbine interference model. It is incorporated in the state-of-the-art Weather Research and Forecasting (WRF) model. To verify the model, the coupled model is applied in a real offshore wind farm in China for comparing the model performances with the original parameterization. The findings demonstrate that the proposed approach improves the wind simulation and has minimal sensitivity to horizontal grid resolution, hence strengthening the model’s robustness. Additionally, it is discovered that the sub-grid interaction reduces the turbine-produced turbulence and shortens the wake length by more than 30% compared to the original parameterization. Importantly, the power output prediction of turbine cells is modified, approximately resulting in a 1 % reduction of the entire wind farm relative to the original model. The effects of sub-grid turbine interaction are effectively captured in the proposed model and it is recommended for further wind farm simulations.

Evaluation of simulated CO2 power plant plumes from six high-resolution atmospheric transport models

Abstract. Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations

Abstract. Modeling leading-edge erosion has been a challenging task due to its multidisciplinary nature involving several variables such as weather conditions, blade coating properties, and operational characteristics. While the process of wind turbine blade erosion is often described by engineering models that rely on the well-known Springer model, there is a glaring need for modeling approaches supported by field data. This paper presents a data-driven framework for modeling erosion damage based on blade inspections from several wind farms in northern Europe and mesoscale numerical weather prediction (NWP) models. The outcome of the framework is a machine-learning-based model that can be used to predict and/or forecast leading-edge erosion damage based on weather data/simulations and user-specified wind turbine characteristics. The model is based on feedforward artificial neural networks utilizing ensemble learning for robust training and validation. The model output fits directly into the damage terminology used by industry and can therefore support site-specific planning and scheduling of repairs as well as budgeting of operation and maintenance costs.

Evaluation and Validation of Microscale Atmospheric Modeling With Offline Weather Research and Forecasting Model to Parallelized Large-Eddy Simulation Model Forcing Conditions

Abstract As the rate of urbanization increases, local vegetation is being replaced with man-made materials, causing increasingly adverse impacts on the surface-atmosphere energy balance. These negative effects can be simulated by modeling the urban landscapes in question; however, the main challenges of modeling urban thermal environments are the scale and resolution at which to perform such tasks. Current modeling of urban thermal environments is typically limited to either mesoscale (1 –2000 km) or microscale (<1 km) phenomena. In the present work, an open-source framework for one-way upstream coupled multiscale urban thermal environment simulations is examined and validated. This coupled simulation can provide valuable insights into the flow behavior and energy transport between mesoscale and microscale interactions. The mesoscale to microscale boundary conditions are coupled together using simulated data from the advanced research weather research and forecasting model (WRF-ARW), a mesoscale numerical weather prediction software, and assimilating it into parallelized large-eddy simulation model (PALM), a computational fluid dynamics style (CFD-style) software designed for microscale atmospheric and oceanic flows. The multiscale urban thermal environment simulations are tested for grid sensitivity to variations in model input and control parameters, and then experimentally validated against distributed sensor measurements at the Georgia Institute of Technology (Georgia Tech) campus in Atlanta, GA. Validated microscale atmospheric models with heterogeneous domains can be used to project the thermal benefits of urban heat mitigation strategies (increase use of high-albedo surfaces, tree and vegetation cover, and smart growth practices) and advise building energy usage modeling and policies.

Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting

Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first the irradiance and then converting it to PV power, there are two chief ways in which one can account for the uncertainty embedded in the final PV power forecast. One of those is to produce probabilistic irradiance forecast through, for example, ensemble numerical weather prediction (NWP), and the other is to pass the irradiance forecast through a collection of different irradiance-to-power conversion sequences, which are known as model chains. This work investigates, for the first time, into the question: Whether pairing ensemble NWP with ensemble model chain is better than leveraging any individual method alone? Using data from 14 utility-scale ground-mounted PV plants in Hungary and the state-of-the-art global mesoscale NWP model of the European Centre for Medium-Range Weather Forecasts, it is herein demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, owing to the higher-quality probabilistic forecasts, the point forecast accuracy is also improved substantially through pairing. Overall, the recommended paring strategy achieves a mean-normalized continuous ranked probability score and a root mean square error of 18.4% and 42.1%, respectively.

Evaluation of InSAR Tropospheric Correction by Using Efficient WRF Simulation with ERA5 for Initialization

The delay caused by the troposphere is one of the major sources of errors limiting the accuracy of InSAR measurements. The tropospheric correction of InSAR measurements is important. The Weather Research and Forecasting (WRF) Model is a state-of-the-art mesoscale numerical weather prediction system designed for atmospheric research applications. It can be applied to InSAR tropospheric correction. Its parameters can be altered according to the requirements of the given application. WRF is usually initialized based on 3 h- or 6 h temporal resolution data in InSAR tropospheric correction studies, a lower temporal resolution compared to ERA5 data. A lower time resolution means a longer integration time for WRF to simulate from the initial time to the target time. Initialization with a higher resolution can shorten the integration time of the simulation theoretically and improve its accuracy. However, an evaluation of the effectiveness of ERA5_WRF for InSAR tropospheric correction is lacking. To evaluate the efficiency of WRF tropospheric correction, we used Reanalysis v5 (ERA5) from the European Centre for Medium-Range Weather Forecasts (ECMWF) for initialization to drive the WRF (ERA5_WRF) for efficient applications in InSAR. Three methods based on global atmospheric models—FNL_WRF (tropospheric correction method based on WRF driven by NCEP FNL), Generic Atmospheric Correction Online Service for InSAR (GACOS), and ERA5—were used to evaluate the corrective effects of ERA5_WRF. The reliability of ERA5_WRF in different scenarios with large tropospheric delay was evaluated from the spatial and temporal perspectives by considering seasonal, topographic, and climatic factors. Its applications in the local space showed that ERA5_WRF could adequately correct tropospheric delay. Benefits include its high-quality data sources and the simulation of WRF, and its application in different seasons had proven superior to other methods in terms of the corrective effects of elevation-related and spatially related delays in summer. By analyzing the data sources and downscaling methods of correction methods and weather conditions of cases, ERA5_WRF had superior performance under the condition of large content and hourly variation of tropospheric delay. Furthermore, WRF showed the potential for tropospheric correction when other higher-quality data appear in the future.

Optimization of wind farm portfolios for minimizing overall power fluctuations at selective frequencies – a case study of the Faroe Islands

Abstract. Hourly modeled wind turbine power output time series – modeled based on outputs from the mesoscale numerical weather prediction system Weather Research and Forecasting Model (WRF) – are used to examine the spatial smoothing of various wind farm portfolios located on a complex isolated island group with a surface area of 1400 km2. Power spectral densities (PSDs), hourly step-change functions, and duration curves are generated, and the 5th and 95th percentiles and the standard deviations of the hourly step-change functions are calculated. The spatial smoothing is identified from smaller high-frequency PSD amplitudes, lower hourly fluctuations, and more flat duration curves per installed wind power capacity, compared with single wind turbine outputs. A discussion on the limitation of the spatial smoothing for the region is included, where a smoothing effect is observed for periods of up to 1–2 d, although it is most evident at higher frequencies. By maximizing the smoothing effect, optimal wind farm portfolios are presented with the intention of minimizing overall wind power fluctuations. The focus is mainly on the smoothing effect on the 1–3 h timescale, during which the coherency between wind farm power outputs is expected to be dependent on how the regional weather travels between local sites, thereby making optimizations of wind farm portfolios relevant – in contrast to a focus on either lower or higher frequencies on the scale of days or minutes, respectively, during which wind farm power output time series are expected to be either close to fully coherent due to the same weather conditions covering a small region or not coherent as the turbulences in separate wind farm locations are expected to be uncorrelated. Results show that an optimization of the wind farm capacities at 14 pre-defined wind farm site locations has a minimal improvement on the hourly fluctuations compared with a portfolio with equally weighted wind farm capacities. However, choosing optimized combinations of individual wind farm site locations decreases the 1–3 h fluctuations considerably. For example, selecting a portfolio with four wind farms (out of the 14 pre-defined wind farm site locations) results in 15 % lower 5th and 95th percentiles of the hourly step-change function when choosing optimal wind farm combinations compared with choosing the worst wind farm combinations. For an optimized wind farm portfolio of seven wind farms, this number is 13 %. Optimized wind farm portfolios consist of distant wind farms, while the worst portfolios consist of clustered wind farms.

Aerosol–cloud interaction in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on mesoscale numerical weather prediction under haze pollution conditions in Jing–Jin–Ji in China

Abstract. The representation of aerosol–cloud interaction (ACI) and its impacts in the current climate or weather model remains a challenge, especially for severely polluted regions with high aerosol concentration, which is even more important and worthy of study. Here, ACI is first implemented in the atmospheric chemistry model GRAPES_Meso5.1/CUACE by allowing for real-time aerosol activation in the Thompson cloud microphysics scheme. Two experiments are conducted focusing on a haze pollution case with coexisting high aerosol and stratus cloud over the Jing–Jin–Ji region in China to investigate the impact of ACI on the mesoscale numerical weather prediction (NWP). Study results show that ACI increases cloud droplet number concentration, water mixing ratio, liquid water path (CLWP), and optical thickness (COT), as a result improving the underestimated CLWP and COT (reducing the mean bias by 21 % and 37 %, respectively) over a certain subarea by the model without ACI. A cooling in temperature in the daytime below 950 hPa occurs due to ACI, which can reduce the mean bias of 2 m temperature in the daytime by up to 14 % (∼ 0.6 ∘C) in the subarea with the greatest change in CLWP and COT. The 24 h cumulative precipitation in this subarea corresponding to moderate-rainfall events increases, which can reduce the mean bias by 18 %, depending on the enhanced melting of the snow by more cloud droplets. In other areas or periods with a slight change in CLWP and COT, the impact of ACI on NWP is not significant, suggesting the inhomogeneity of ACI. This study demonstrates the critical role of ACI in the current NWP model over the severely polluted region and the complexity of the ACI effect.

Investigations on offshore wind turbine inflow modelling using numerical weather prediction coupled with local-scale computational fluid dynamics

The computational power available nowadays to industry and research paves the way to increasingly more accurate systems for the wind resource prediction. A promising approach is to support the mesoscale numerical weather prediction (NWP) with high fidelity computational fluid dynamics (CFD). This approach aims at increasing the spatial resolution of the wind prediction by not only accounting for the complex and multiphysics aspects of the atmosphere over a large geographical region, but also including the effects of the fine scale turbulence and the interaction of the wind flow with the sea surface. In this work, we test a set of model setups for both the mesoscale (NWP) and local scale (CFD) simulations employed in a multi-scale modelling framework. The method comprises a one-way coupling interface to define boundary conditions for the local scale simulation (based on the Reynolds Averaged Navier–Stokes equations) using the mesoscale wind given by the NWP system. The wind prediction in an offshore site is compared with LiDAR measurements, testing a set of mesoscale planetary boundary layer schemes, and different model choices for the local scale simulation, which include steady and unsteady approaches for simulation and boundary conditions, different turbulence closure constants, and the effect of the wave motion of the sea surface. The resulting wind is then used for the simulation of a large wind turbine, showing how a realistic wind profile and an ideal exponential law profile lead to different predictions of wind turbine rotor performance and loads.

Development and Application of the SmartAQ High-Resolution Air Quality and Source Apportionment Forecasting System for European Urban Areas

Air pollution forecasting systems are useful tools for the reduction in human health risks and the eventual improvement of atmospheric quality on regional or urban scales. The SmartAQ (Smart Air Quality) forecasting system combines state-of-the-art meteorological and chemical transport models to provide detailed air pollutant concentration predictions at a resolution of 1 × 1 km2 for the urban area of interest for the next few days. The Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model is used to produce meteorological fields and the PMCAMx (Particulate Matter Comprehensive Air quality Model with extensions) chemical transport model for the simulation of air pollution. SmartAQ operates automatically in real time and provides, in its current configuration, a three-day forecast of the concentration of tens of gas-phase air pollutants (NOx, SO2, CO, O3, volatile organic compounds, etc.), the complete aerosol size/composition distribution, and the source contributions for all primary and secondary pollutants. The system simulates the regional air quality in Europe at medium spatial resolution and can focus, using high resolution, on any urban area of the continent. The city of Patras in Greece is used for the first SmartAQ application, taking advantage of the available Patras’ dense low-cost sensor network for PM2.5 (particles smaller than 2.5 μm) concentration measurements. Advantages of SmartAQ include (a) a high horizontal spatial resolution of 1 × 1 km2 for the simulated urban area; (b) advanced treatment of the organic aerosol volatility and chemistry; (c) use of an updated emission inventory that includes not only the traditional sources (industry, transport, agriculture, etc.), but also biomass burning from domestic heating and cooking; (d) forecasting of not only the pollutant concentrations, but also of the sources contributions for each one of them using the Particulate matter Source Apportionment Technology (PSAT) algorithm.