Weather Research and Forecasting Model

We present the High-Elective Resolution Modelling Emission System version 3 ( ), an open source, parallel and stand-alone multiscale atmospheric emission modelling framework that computes gaseous and aerosol emissions for use in atmospheric chemistry models. HERMESv3 is coded in Python and consists of a global_regional module and a bottom_up module that can be either combined or executed separately. In this contribution (Part 1) we describe the global_regional module, a highly customizable emission processing system that calculates emissions from different sources, regions and pollutants on a user-specified global or regional grid. The user can flexibly define combinations of existing up-todate global and regional emission inventories and apply country specific scaling factors and masks. Each emission inventory is individually processed using user-defined vertical, temporal and speciation profiles that allow obtaining emission outputs compatible with multiple chemical mechanisms (e.g. Carbon-Bond 05). The selection and combination of emission inventories and databases is done through detailed configuration files providing the user with a widely applicable framework for designing, choosing and adjusting the emission modelling experiment without modifying the HERMESv3 source code. The generated emission fields have been successfully tested in different atmospheric chemistry models (i.e. CMAQ, WRF-Chem and NMMB-MONARCH) at multiple spatial and temporal resolutions. In a companion article (Part 2) we describe the bottom_up module, which estimates emissions at the source level (e.g. road link) combining state-of-the-art bottom-up methods with local activity and emission factors.

An atmospheric and marine computational forecasting system for Guanabara Bay (GB) was developed to support the Brazilian Sailing Teams in the 2016 Olympic and Paralympic Games. This system, operational since August 2014, is composed of the Weather Research and Forecasting (WRF) and the Regional Ocean Modeling System (ROMS) models, which are both executed daily, yielding 72-h prognostics. The WRF model uses the Global Forecast System (GFS) as the initial and boundary conditions, configured with a three nested-grid scheme. The ocean model is also configured using three nested grids, obtaining atmospheric fields from the implemented WRF and ocean forecasts from CMEMS and TPXO7.2 as tidal forcing. To evaluate the model performances, the atmospheric results were compared with data from two local airports, and the ocean model results were compared with data collected from an acoustic current profiler and tidal prediction series obtained from harmonic constants at four stations located in ...

Introduction

The ongoing desiccation of Lake Urmia in northwestern Iran has transformed its former lakebed into a significant source of airborne dust and salt particles, posing escalating environmental and public health risks. These storms pose serious environmental and health risks by elevating particulate matter concentrations (PM₁₀ and PM₂.₅), degrading air quality, and impairing agricultural productivity. Wind events exceeding 5 m/s can start wind storm mobilization and atmospheric dust generation in arid and semi-arid environments. Wind direction is important for the transport of dust to cities. The east of the Urmia Lake is more affected by wind because of the dominant wind direction in the Urmia Lake basin. This part is more important for risk assessment studies. Classical models, such as the Generalized Extreme Value (GEV) distribution, often fall short of capturing the full complexity of wind extremes under nonstationary conditions. To overcome these limitations, the Simplified Meta-Statistical Extreme Value (SMEV) model is developed and used for the first time, in this study, as a method that integrates both ordinary and extreme wind data into a unified distribution framework. This study aims to estimate return period wind speeds with SMEV and benchmarked against GEV, and evaluate wind direction probabilities for storm prediction. Results will inform regional dust storm risk management and advance extreme value modeling in the Lake Urmia basin.

Materials and Methods

Using three-hourly wind speed and direction data from 2005 to 2024 across four synoptic stations (Tabriz, Maragheh, Bonab, and Shabestar) in the eastern Lake Urmia Basin, SMEV was employed to estimate return period wind speeds and assess directional probabilities. In this research, the CEEMDAN method has been used as a method to remove noise and trends from wind speed data. At the stations, wind events were divided into extreme and ordinary events, based on the wind speed threshold, using the peak-over-threshold (POT) approach by applying the 90th percentile. 5, 10, 20, 50, 100, and 200 periods were chosen for the return period. The model combines a two-parameter Weibull distribution for ordinary winds with annual extreme wind counts to generate composite cumulative distribution functions (CDFs) per dominant direction sector. The bootstrap method was used for SMEV model performance evaluation. The GEV model was used as a benchmark and employed to estimate return period wind speeds, and both models were evaluated using AIC, BIC, FSE, WFSE, and leave-one-out cross-validation (LOO). Additionally, a random forest algorithm was trained to predict the likelihood of wind directions associated with dust transport.

Results and Discussion

SMEV predicted critical wind speeds exceeding 7 m/s with high confidence. In all 4 stations, wind speeds predicted more than 7 m/s, and wind direction analysis revealed over 70% probability of wind-driven dust transport from the southwest and south to the east, toward residential areas. The random forest method has predicted the corresponding wind directions for selected stations east of Lake Urmia. The dominant directions for extreme storm events are southwest and south for short to medium return periods. In longer time periods, the dominance of south and west continues at Shabestar and Maragheh stations, and for Tabriz and Maragheh stations, the dominant direction changes to east. GEV extreme values predicted more than 12 m/s for wind speeds. It shows the GEV overestimated. For Urmia Lake Basin, wind speeds of more than 12 happen rarely and are not common. The SMEV model outperformed the GEV model, providing more stable and realistic estimates of return-level wind speeds, particularly for long recurrence intervals. Error metrics confirmed the superiority of SMEV (FSE = 0.014; WFSE = 20.7) compared to GEV (FSE = 0.081; WFSE = 196), highlighting its improved performance in estimating environmental hazards. The advantage of this method over other classical methods is in distinguishing between extreme and normal events, as well as distinguishing extreme events with the corresponding dominant directions of extreme wind speeds. In addition, the use of a wind speed threshold limit, unlike other statistical methods such as GEV, which only focus on maximum wind speeds in the analysis of extreme events, can provide reliable accuracy for this method in estimating extreme events.

Conclusion

This study focused on the analysis of extreme wind speeds in the eastern part of the Urmia Lake watershed, and using a simplified metastatistical limit value model, was able to provide reliable estimates of strong winds in different return periods. The results showed that speeds exceeding 7 m/s occur with high probability in this area, and this amount is sufficient to initiate the transport of suspended particles and the formation of dust storms in the study area. In conclusion, SMEV demonstrates significant potential for use in regional wind hazard assessments, early warning systems, and dust storm risk mitigation in the Urmia Lake Basin. This model relies solely on wind speed and direction and does not consider other environmental drivers such as soil moisture, land cover, vegetation, or surface roughness that can significantly affect the potential for dust emission. This approach can also help universities, along with other tools, to identify high-risk areas susceptible to dust transport from the dry bed of Lake Urmia. Overall, this model can be used as an effective tool in analyzing climate risks associated with wind and dust storms in the region. In addition, the use of the 90th percentile threshold and 24-hour separation criteria raises statistical assumptions that more extreme events may have been identified, which has increased the accuracy of the model and, on the other hand, has made the model more sensitive to extreme phenomena. However, it is suggested that in future studies, the integration of environmental variables such as relative humidity and precipitation should be considered to improve the SMEV model. Also, combining this model with wind datasets based on satellite images can also improve the spatial representation of wind patterns.

La intercomparación entre resultados de diferentes modelos de dispersión y mediciones de la calidad del aire en condiciones atmosféricas diferentes es una práctica común en la evaluación de dichos modelos. En este estudio se realiza una comparación de resultados de dos modelos de dispersión: el modelo lagrangiano de dispersión de partículas FLEXPART y el modelo lagrangiano de puffs ("bocanadas") CALPUFF (modelo regulatorio), acoplados a los mismos campos meteorológicos producidos por el Modelo de Investigación y Pronóstico Meteorológico (WRF, por sus siglas en inglés). Como caso de estudio se consideró la dispersión atmosférica de emisiones antrópicas de óxidos de nitrógeno (NO x ) (consideradas como un trazador pasivo) durante un caso típico de contaminación severa sobre un área densamente poblada del archipiélago de Guadalupe (Antillas Francesas Occidentales). Se tomaron en cuenta los efectos del terreno complejo y, por supuesto, la influencia costera. Aunque los modelos lagrangianos generalmente brindan mejores resultados de dispersión de la pluma con vientos fuertes, este estudio de caso se realizó en condiciones de vientos alisios débiles dominantes, con el fin de verificar ambos modelos en condiciones atmosféricas no ideales. Como resultado, en comparación con mediciones de concentración de NO x en superficie, FLEXPART muestra una mejor concordancia que CALPUFF. Sin embargo, como modelo regulatorio, CALPUFF sobreestima tanto las concentraciones en superficie como el máximo de FLEXPART, con valores más altos cuando se aplica una resolución horizontal más elevada. Además, se observan diferencias entre los resultados de los modelos en la distribución espacial de NO x en el dominio de resolución horizontal de 1 × 1 km 2 , los cuales muestran isolíneas bastante homogéneas con contornos suaves para CALPUFF frente a isolíneas fragmentadas con contornos irregulares para FLEXPART.

In this study, a four member ensemble of meteorological forcing is generated using the Weather Research and Forecasting (WRF) model in order to simulate a frontal passage event that impacted the Indian River Lagoon (IRL) during March, 2015. The WRF model is run to provide high and low, spatial ( ) and temporal (30 min and 6 h) input wind and pressure fields. The four member ensemble is used to force the Advanced Circulation model (ADCIRC) coupled with Simulating Waves Nearshore (SWAN) and compute the hydrodynamic and wave response. Results indicate that increasing the spatial resolution of the meteorological forcing has a greater impact on the results than increasing the temporal resolution in coastal systems like the IRL where the length scales are smaller than the resolution of the operational meteorological model being used to generate the forecast. Changes in predicted water elevations are due in part to the upwind and downwind behavior of the input wind forcing. The significant wave height is more sensitive to the meteorological forcing, exhibited by greater ensemble spread throughout the simulation. It is important that the land mask, seen by the meteorological model, is representative of the geography of the coastal estuary as resolved by the hydrodynamic model. As long as the temporal resolution of the wind field captures the bulk characteristics of the frontal passage, computational resources should be focused so as to ensure that the meteorological model resolves the spatial complexities, such as the land-water interface, that drive the land use responsible for dynamic downscaling of the winds.

High- and low-resolution sea surface temperature (SST) analysis products are used to initialize the Weather Research and Forecasting (WRF) Model for May 2004 for short-term forecasts over Florida and surrounding waters. Initial and boundary conditions for the simulations were provided by a combination of observations, large-scale model output, and analysis products. The impact of using a 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) SST composite on subsequent evolution of the marine atmospheric boundary layer (MABL) is assessed through simulation comparisons and limited validation. Model results are presented for individual simulations, as well as for aggregates of easterly- and westerly-dominated low-level flows. The simulation comparisons show that the use of MODIS SST composites results in enhanced convergence zones, earlier and more intense horizontal convective rolls, and an increase in precipitation as well as a change in precipitation location. Validation of 10-...

The common methodology in dynamical regional climate downscaling employs a continuous integration of a limited‐area model with a single initialization of the atmospheric fields and frequent updates of lateral boundary conditions based on general circulation model outputs or reanalysis data sets. This study suggests alternative methods that can be more skillful than the traditional one in obtaining high‐resolution climate information. We use the Weather Research and Forecasting (WRF) model with a grid spacing at 36 km over the conterminous U.S. to dynamically downscale the 1‐degree NCEP Global Final Analysis (FNL). We perform three types of experiments for the entire year of 2000: (1) continuous integrations with a single initialization as usually done, (2) consecutive integrations with frequent re‐initializations, and (3) as (1) but with a 3‐D nudging being applied. The simulations are evaluated in a high temporal scale (6‐hourly) by comparison with the 32‐km NCEP North American Reg...

Polarimetric radar variables are simulated from members of the 2013 Center for Analysis and Prediction of Storms (CAPS) Storm-Scale Ensemble Forecasts (SSEF) with varying microphysics (MP) schemes and compared with observations. The polarimetric variables provide information on hydrometeor types and particle size distributions (PSDs), neither of which can be obtained through reflectivity (Z) alone. The polarimetric radar simulator pays close attention to how each MP scheme [including single- (SM) and double-moment (DM) schemes] treats hydrometeor types and PSDs. The recent dual-polarization upgrade to the entire WSR-88D network provides nationwide polarimetric observations, allowing for direct evaluation of the simulated polarimetric variables. Simulations for a mesoscale convective system (MCS) and supercell cases are examined. Five different MP schemes—Thompson, DM Milbrandt and Yau (MY), DM Morrison, WRF DM 6-category (WDM6), and WRF SM 6-category (WSM6)—are used in the ensemble ...

Resumo. O uso de dados solarimétricos representativos é fundamental para uma boa estimativa da produção de energia de longo prazo de uma usina fotovoltaica. Para participação de empreendimentos fotovoltaicos nos leilões de energia do mercado regulado, exige-se, desde 2016, um mínimo de doze meses consecutivos de medição de irradiação solar global no local do empreendimento. Utilizados em conjunto com as estimativas de longo prazo, as medições têm o potencial de reduzir desvios nos dados de modelos baseados em imagens de satélite, reduzindo a incerteza na estimativa da produção de energia. Alguns pontos são cruciais nesse processo, tais como a adequada manutenção da estação solarimétrica na campanha de medição, a escolha de uma fonte de dados de longo prazo de qualidade e a metodologia de ajuste de dados de longo prazo, procedimento que faz uso do período concomitante entre a medição e os dados de modelo para correção de eventuais desvios para o local de interesse. Neste trabalho, avalia-se a importância das medições solarimétricas e as principais metodologias de ajuste presentes na literatura. Apresenta-se diferentes metodologias utilizadas pelas certificadoras participantes do processo de habilitação técnica para os leilões de energia, discutindo-se aspectos relevantes de cada caso. Por fim, com base em recomendações apresentadas na literatura, são apontadas boas práticas e cuidados a serem considerados na obtenção de dados solarimétricos representativos para o local em estudo.

Wind speeds and directions measured in June, July, and August, 2009 at 5 locations separated by up to about 5 km on the Skagit tidal flats (near La Conner, WA) are compared with predictions of a triplenested Weather Research and Forecast (WRF) model with 1.3-km resolution. The model predicts the observed diurnal fluctuations of the wind speeds (bias o 0.4 m s À 1 , root-mean-square error (rmse) o 1 m s À 1 , correlation coefficient r 2 E0.9) and directions (bias o91, rmse o 301, r 2 40.5). The observed and predicted minimum and maximum wind speeds occur in early morning and late afternoon, respectively. Wind speeds and directions are decorrelated over distances shorter than the length scale of the tidal flats (about 10 km). Observed and predicted wind directions are predominantly W and NW on the north flats, and S and SW on the south flats. The spatial and seasonal variability of the winds are investigated using model simulations.

Clouds play a key role in radiation and hence O 3 photochemistry by modulating photolysis rates and lightdependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in O 3 predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface O 3 predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface O 3 concentrations on some days. The average difference in summertime surface O 3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O 3 (MDA8 O 3 ) over the CONUS. This represents up to ∼ 40 % of the total MDA8 O 3 bias under cloudy conditions in the tested model version. Surface O 3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for ∼ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O 3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NO x -limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.

Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in mid-latitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days is still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure, and on the impact of this method on short-term forecasts of GHI for Reunion Island, a tropical island in the South-West Indian Ocean. The multi-layer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and mid troposphere. Ground-based GHI observations at 12 sites on Reunion Island are used to quantify the impact of CWP DA. Over a total of 44 days during austral summer time, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 hours. Root Mean Squared Error and Mean Absolute Error are reduced by 4 % and 3 % respectively.

Clouds play a key role in radiation and hence O 3 photochemistry by modulating photolysis rates and lightdependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in O 3 predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface O 3 predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface O 3 concentrations on some days. The average difference in summertime surface O 3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O 3 (MDA8 O 3 ) over the CONUS. This represents up to ∼ 40 % of the total MDA8 O 3 bias under cloudy conditions in the tested model version. Surface O 3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for ∼ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O 3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NO x -limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.

Mineral dust aerosols often observed over California in winter and spring, associated with long-range transport from Asia and the Sahara, have been linked to enhanced precipitation based on observations. Local anthropogenic pollution, on the other hand, was shown in previous observational and modeling studies to reduce precipitation. Here we incorporate recent developments in ice nucleation parameterizations to link aerosols with ice crystal formation in a spectralbin cloud microphysical model coupled with the Weather Research and Forecasting (WRF) model in order to examine the relative and combined impacts of dust and local pollution particles on cloud properties and precipitation type and intensity. Simulations are carried out for two cloud cases (from the CalWater 2011 field campaign) with contrasting meteorology and cloud dynamics that occurred on 16 February (FEB16) and 2 March (MAR02). In both cases, observations show the presence of dust and biological particles in a relative pristine environment. The simulated cloud microphysical properties and precipitation show reasonable agreement with aircraft and surface measurements. Model sensitivity experiments indicate that in the pristine environment, the dust and biological aerosol layers increase the accumu-lated precipitation by 10-20 % from the Central Valley to the Sierra Nevada for both FEB16 and MAR02 due to a ∼40 % increase in snow formation, validating the observational hypothesis. Model results show that local pollution increases precipitation over the windward slope of the mountains by a few percent due to increased snow formation when dust is present, but reduces precipitation by 5-8 % if dust is removed on FEB16. The effects of local pollution on cloud microphysics and precipitation strongly depend on meteorology, including cloud dynamics and the strength of the Sierra Barrier Jet. This study further underscores the importance of the interactions between local pollution, dust, and environmental conditions for assessing aerosol effects on cold-season precipitation in California.

In this paper a new hybrid Model Output Statistics (MOS), named MOS cascade, is developed to refine the day-ahead forecast of the global horizontal irradiance provided by the Weather Research and Forecast (WRF) model. The proposed approach is based on a sequence of two different MOS. The first, called MOSRH, is a new physically based algorithm, built to correct the treatment of humidity in the WRF radiation schemes. The second, called MOSNN, is based on artificial intelligence techniques and aims to correct the main systematic and learnable errors of the Numerical Weather Prediction output. The 1-day and 2-day forecast accuracies are analyzed via direct comparison with irradiance data measured in two sites, Rome and Lugano. The paper shows that a considerable reduction in error was achieved using MOSRH model and MOS cascade. The differences between the two sites are discussed in details. Finally, the results obtained are compared with the benchmark accuracy reached for the data available for the average climate in Southern Spain and Switzerland.

Linda See1, Jason Ching2, Valery Masson3, Johannes Feddema4, Gerald Mills5, Marina Neophytou6, Michael Foley5, Martin O’Connor5, Grega Milcinski7, Marko Repse7, Christoph Perger1, Martina Duerauer1, Steffen Fritz1 and Benjamin Bechtel8 1 International Institute for Applied Systems Analysis, Laxenburg, Austria, see@iiasa.ac.at 2 Institute for the Environment, University of North Carolina, Chapel Hill NC, USA, jksching@gmail.com 3 Centre National de Recherches Meterologiques, Meteo France, Toulouse, France, valery.masson@meteo.fr 4 Department of Geography, University of Kansas, Lawrence KS, USA, feddema@ku.edu 5 School of Geography, Planning and Environmental Policy, University College Dublin, Dublin, Ireland, gerald.mills@ucd.ie 6 School of Engineering, University of Cyprus, Nicosia, Cyprus, neophytou@ucy.ac.cy 7 Sinergise, Ljubljana, Slovenia, grega.milcinski@sinergise.com 8 Institute of Geography, University of Hamburg, Hamburg Germany, benjamin.bechtel@uni-hamburg.de

Introduction
The capacity of groundwater systems to offer various services depends on their geographically varying properties and is dynamically influenced by ongoing natural and human processes. Because groundwater is often perceived as a private resource (closely connected to land ownership, and in some jurisdictions treated as privately owned), regulation and top–down governance and management are difficult. Governments need to fully assume their role as resource custodians given the common-good aspects of groundwater. The participatory modeling approach included several stakeholder groups from groundwater users, policymakers, environmental groups, and other organizations involved in groundwater management. The scarcity of water resources and ever-increasing demand for these vital resources require identification, quantification, and management of groundwater in a way that prevents overexploitation and consequent economic and environmental damage while satisfying the demand for water from competing sectors. Participatory groundwater management is envisaged to take a significant step in groundwater management at the grassroots level to enable the community and stakeholders to monitor and manage the groundwater as a common pool resource themselves.

Materials and Methods
Marand Plain located in East Azarbaijan province, Iran. There are 502 agricultural wells in the Marand Plain, which are used by 16,000 farmers for agriculture. The total volume of consumed water is 226.82 mm3 and 191.66 mm3 of it is supplied from underground water sources. The participatory system dynamics modeling process includes five main steps: 1- Identifying the problem through participatory workshops: it aims at collectively defining the problem to be solved and the objectives of the model. 2- Formulating a dynamic hypothesis to explain the causes of the problem, leads to the development of a conceptual model or causal loop diagram. 3- Formulation of a system dynamics quantitative simulation model. This step includes the development of decision rules, the quantification of variables, building the stock and flow diagram, and model calibration using parameters to define initial conditions. 4- Ensuring the model is appropriate for the task through model validation. 5- Formulation of potential strategies and the evaluation of the simulated results. It requires the identification of scenarios, i.e., alternative strategies, and the analysis and discussion of the simulated results generated by the model for each scenario over time.

Results and Discussion
The problem statement was created in a qualitative way using the focus group discussion (FGD) method. The key variables of the system (61 variables), the system boundary (including the internal and external variables of closed loops), and the modeling period (20 years) were defined. For formulating the dynamic hypothesis, 9 CLDs were obtained. In the third step, developing the simulation model, all mathematical relationships, behavioral functions, and differential equations were formulated in the form of a stock and flow diagram (SFD). In the fourth step, model reliability tests were conducted in three categories: structural test (including structure verification test and limit condition test), behavioral test (including reference behavior reconstruction test), and sensitivity analysis test. Also, the stakeholders’ feedback test was conducted in a mixed method (quantitative-qualitative). In the policy design and analysis stage, the five general actions in the participatory management of groundwater resources in the Marand Plain, including modifying the cultivation pattern, increasing the irrigation efficiency by implementing modern irrigation networks, decentralizing the water governance and management, creating public institutions for water management (rural production cooperatives), water supply from outside the basin (transfer from the Aras River), were obtained. These actions have the greatest effect on the Marand Plain aquifer reservoir deficit.

Conclusion
The results of our participatory system dynamics simulation show that the balance and restoration of the aquifer are mostly affected by the "reform of the decision-making structure in the governance of groundwater" in the form of institutionalization (empowered farmers association) and decentralization of water management. Other adopted policies will be effective only if these components are present. To get out of the vicious cycle of short-term and unstable policies and decisions in the field of groundwater, fundamental reforms should be made in the structure of governance and groundwater management, and fully equal opportunities should be provided for local communities to participate effectively in the decision-making process. Also, if the process of implementation of laws and decisions is done through the channel of associations of the local community, they will have more executive support. Improving the water use behavior of farmers is the consequence and output of changing the structure and processes of groundwater management and governance.

In this paper, a verification study of the skill and potential economic value of forecasts of ice accretion on wind turbines is presented. The phase of active ice formation on turbine blades has been associated with the strongest wind power production losses in cold climates; however, skillful icing forecasts could permit taking protective measures using anti-icing systems. Coarse-and high-resolution forecasts for the range up to day 3 from global (IFS and GFS) and limitedarea (WRF) models are coupled to the Makkonen icing model. Surface and upper-air observations and icing measurements at turbine hub height at two wind farms in central Europe are used for model verification over two winters. Two case studies contrasting a correct and an incorrect forecast highlight the difficulty of correctly predicting individual icing events. A meaningful assessment of model skill is possible only after bias correction of icing-related parameters and selection of modeldependent optimal thresholds for ice growth rate. The skill of bias-corrected forecasts of freezing and humid conditions is virtually identical for all models. Hourly forecasts of active ice accretion generally show limited skill; however, results strongly suggest the superiority of high-resolution WRF forecasts relative to other model variants. Predictions of the occurrence of icing within a period of 6 h are found to have substantially better accuracy. Probabilistic forecasts of icing that are based on gridpoint neighborhood ensembles show slightly higher potential economic value than forecasts that are based on individual gridpoint values, in particular at low cost-loss ratios, that is, when anti-icing measures are comparatively inexpensive.

This study evaluates the high-resolution climate simulation system CESM/WRF composed of the global climate model, Community Earth System Model (CESM) version 1, and the mesoscale model, Weather Research and Forecasting Model (WRF), for simulating high-resolution climatological temperature and precipitation in the tropics with complex terrain where temperature and precipitation are strongly inhomogeneous. The CESM/WRF climatological annual and seasonal precipitation and temperature simulations for years 1980–1999 at 10 km resolution for Sumatra and nearby regions are evaluated using observations and the global climate reanalysis ERA-Interim (ERA). CESM/WRF simulations at 10 km resolution are also compared with the downscaled reanalysis ERA/WRF at 10 km resolution. Results show that while temperature and precipitation patterns of the original CESM are very different from observations, those for CESM/WRF agree well with observations. Resolution and accuracies of simulations are signifi...

Middle latitude cyclones (dynamic and synoptic meteorology) • Thunderstorm phenomena (mesoscale dynamics) • Air quality • Meteorological decision support systems that can be used by decision makers, planners, and emergency managers charged with protecting communities in the path of potentially adverse weather.

Resumen Se comprueba la habilidad de MM5V3, en el pronóstico de lluvia sobre las provincias habaneras al paso de los huracanes Charley e Iván en el 2004. Para la evaluación de los resultados, se tomaron los registros de lluvia de las estaciones meteorológicas de Batabanó y Casablanca, localizadas en las provincias habaneras. El modelo representa con razonable acierto la ausencia de lluvia, pero cuando hay ocurrencia de precipitación, se sobrevalora la cantidad de lluvia caída; los errores aumentan hacia plazos mayores de 30 horas. La hora de comienzo y la duración, pueden desviarse hasta 12 horas de la realidad. Se concluye que los resultados son aceptables, pero pudieran mejorarse con mallas más finas y representación de los procesos convectivos en forma explícita. Palabras claves MM5V3, pronóstico de lluvia, trayectoria de huracanes

A regional model combination (WRF+ROMS+WW3+SWAN), identified as SPNOA (Numeric Ocean-Atmosphere Forecasting System), was applied for coastal flood forecast in Cuban coastal zones. A module to predict sea level rise by wave setup was added. Nested domains were used, covering the Inter-American Seas, Cuban surrounding waters and shore areas. Several hurricanes were considered as study cases in the experimental work to test the system effectiveness. The following procedures were utilized to evaluate the obtained results: A) WRF output circulation patterns were compared with NOAA re-analyses, and hurricane trajectories with NHC best tracks. B) The records of meteorological stations and buoys were compared with punctual model outputs. C) Storm tide forecast, given by ROMS and wave setup modules at the shore line, were evaluated in comparison with tide gauge records at some points of the Cuba coastal zone, near or located in the flooding areas. Some local testimonies about flooding intens...

A regional model combination (WRF+ROMS+WW3+SWAN), identified as SPNOA (Numeric Ocean-Atmosphere Forecasting System), was applied for coastal flood forecast in Cuban coastal zones. A module to predict sea level rise by wave setup was added. Nested domains were used, covering the Inter-American Seas, Cuban surrounding waters and shore areas. Several hurricanes were considered as study cases in the experimental work to test the system effectiveness. The following procedures were utilized to evaluate the obtained results: A) WRF output circulation patterns were compared with NOAA re-analyses, and hurricane trajectories with NHC best tracks. B) The records of meteorological stations and buoys were compared with punctual model outputs. C) Storm tide forecast, given by ROMS and wave setup modules at the shore line, were evaluated in comparison with tide gauge records at some points of the Cuba coastal zone, near or located in the flooding areas. Some local testimonies about flooding intens...

Warm-season precipitation in the Lower Mississippi River Alluvial Valley (LMRAV) is heavily dominated by the rates of evapotranspiration and surface heat fluxes and is a primary water resource for agriculture. However, the stochastic nature of LMRAV warm-season thunderstorms makes precipitation forecasts challenging. The Weather Research and Forecasting Hydrologic (WRF-Hydro) model, coupled with the multi-parameter Noah land surface (Noah-MP) model, has improved estimates of important warm-season precipitation process. Given the widespread agriculture and dominance of crop and forested landscapes over the region, proper assessment of land use / land cover (LULC) is critical in predicting warm-season precipitation patterns. The objective of this study is to quantify simulated latent heat flux sensitivity (important for warm-season precipitation) to temporally updated LULC datasets. Both the model default and annually updated LULC conditions were used to initialize a 16-year WRF-Hydro simulation from which warm-season latent heat flux estimates were obtained. Annual root mean square difference was computed at each gridpoint. Cluster analysis preprocessed with kernel principal component analysis was used to identify spatial RMSD structures that quantified sensitivity to updated LULC conditions. Results showed the largest impacts occurred directly in the LMRAV and for points slightly east and revealed a meteorological link between these regions.

Global monitoring of Persistent Organic Pollutants (POPs) has allowed the knowledge of levels and distribution around the world as well as the understanding of its transport through the atmosphere. However, there are still some gaps in this regard, especially in some locations, as the case of Great Mendoza, a medium-sized urban area located in the center-west of Argentina. In this work, the WRF/ CALMET/CALPUFF modeling system was used to estimate airborne levels of four families of POPs (PCBs, PBDEs, DDTs and HCB) in the study area. The model was validated from measured data obtained from eleven sites using passive air samplers with polyurethane foam disks (PUFs), subsequently analyzed by GC-ECNI/MS. Considering both sets of data, measured and simulated airborne concentrations, five statistical performance metrics were calculated for each family of POP [Mean bias error, (MBE), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Factor of two (Fa2) and Pearson correlation coefficient (r)]. Results exhibited a good agreement between modeled and measured data, showing that WRF/ CALMET/CALPUFF modeling system predicts POPs airborne concentrations with reasonable accuracy at a local scale. Model output was used to examine the relative source contribution to ground-level concentrations and to assess the spatial variability of the studied POPs in the study area. Source apportionment showed the prevalence of emissions from open burning of municipal solid waste (ranging from 9% to 90%) on the simulated atmospheric concentrations. HCB presented the lowest mean contribution from this activity (37%) but the highest variability (SD ¼ 20%), followed by PCBs (69 ± 9%), and PBDEs (84 ± 4%). The spatial pattern obtained from simulations exhibited that both, lowest and highest levels predicted by the model, occurred in areas where no samples were taken, suggesting that the real gradient in the POPs air concentrations would be much greater than those reflected by measured data. This work highlights the usefulness of the implementation of an atmospheric dispersion model, not only in the study of air quality and exposure levels but also as a tool for the proper design of monitoring networks, taking into account the time and cost that sampling campaigns take, and the conclusions that are intended to be made from the analysis of the obtained data.

Air quality forecast systems need reliable and accurate representations of the planetary boundary layer (PBL) to perform well. An important question is how accurately numerical weather prediction models can reproduce conditions in diverse synoptic flow types. Here, observations from the summer 2014 HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) experimental campaign are used to validate simulations from the Weather Research and Forecasting (WRF) model over the complex, urban terrain of the Greater Athens Area. Three typical atmospheric flow types were identified during the 39day campaign based upon two-day backward trajectories: Continental, Etesians, and Saharan. It is shown the numerical model simulations differ dramatically depending on the PBL scheme, atmospheric dynamics, and meteorological parameter (e.g., 2-m air temperature). Eight PBL schemes from WRF version 3.4 are tested with daily simulations on an inner domain at 1-km grid spacing. Near-surface observations of 2-m air temperature and relative humidity, and 10-m wind speed are collected from multiple meteorological stations. Estimates of the PBL height come from measurements using a multiwavelength Raman lidar, with an adaptive extended Kalman filter technique. Vertical profiles of atmospheric variables are obtained from radiosonde launches, along with PBL heights calculated using bulk Richardson number. Daytime maximum PBL heights ranged from 2.57 km during Etesian flows, to as low as 0.37 km during Saharan flows. The largest differences between model and observations are found with simulated PBL height during Saharan synoptic flows. During the daytime, campaign-averaged nearsurface variables show WRF tended to have a cool, moist bias with higher simulated wind speeds than the observations, especially near the coast. It is determined that nonlocal PBL schemes give the most agreeable solutions when compared with observations.

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April-May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD > 0.25. For this threshold the model BIAS decreases from -0.20 to -0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from -0.49 to -0.08. Similar improvement is also found for the lower AOD thresholds (>0.0 and >0.1), especially for the stations in Europe, the Mediterranean, Sahel, the Middle East, and Arabian Peninsula, which are mostly affected by dust transport during the experimental period. An overprediction of AOD, compared to the original dust-source scheme, is found for some stations in the Sahara desert, the Atlantic Ocean, and the Iberian Peninsula. In total, 124 out of the 170 statistical scores that are calculated indicate improvement of model performance.

Air quality simulations were performed for the Greater Athens Area in very high spatial resolution using the modeling system WRF-CAMx. Sensitivity runs were performed using the SOAP and VBS schemes for organic gas-aerosol partitioning and oxidation. In January 2019, OA-VBS decreased compared to OA-SOAP because of POA reduction. In July 2019, the OA-VBS increased with respect to OA-SOAP as a result of the increase in SOA levels exceeding the decrease in POA ones. The comparison of the WRF-CAMx results against PM 10 , PM 2.5, and OC surface measurements provides the first indications for improved CAMx performance with the VBS scheme.

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April–May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD > 0.25. For this threshold the model BIAS decreases from −0.20 to −0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from −0.49 to −0.08. Similar improvement is also found for the lower AOD thresholds (>0.0 and >0.1), especially for the stations in Eu...

The international experimental campaign Hygroscopic Aerosols to Cloud Droplets (HygrA-CD), organized in the Greater Athens Area (GAA), Greece from 15 May to 22 June 2014, aimed to study the physico-chemical properties of aerosols and their impact on the formation of clouds in the convective Planetary Boundary Layer (PBL). We found that under continental (W-NW-N) and Etesian (NE) synoptic wind flow and with a deep moist PBL (~2-2.5 km height), mixed hygroscopic (anthropogenic, biomass burning and marine) particles arrive over the GAA, and contribute to the formation of convective non-precipitating PBL clouds (with droplets of ~16-20 μm mean diameter) with vertical extent up to 500 m. Under these conditions, high updraft velocities (1-2 m s -1 ) and cloud condensation nuclei (CCN) concentrations (~2000 cm -3 at 1% supersaturation), generated clouds with an estimated cloud droplet number of ~600 cm -3 . Under Saharan wind flow conditions (S-SW) a shallow PBL (b 1-1.2 km height) develops, leading to much higher CCN concentrations (~3500-5000 cm -3 at 1% supersaturation) near the ground; updraft velocities, however, were significantly lower, with an estimated maximum cloud droplet number of ~200 cm -3 and without observed significant PBL cloud formation. The largest contribution to cloud droplet number variance is attributed to the updraft velocity variability, followed by variances in aerosol number concentration.

The Layman fire commenced as a routine fuel reduction burn in southwest Western Australia in October 2010. Late morning on the day following ignition, the fire was more active than fire management officers had expected under the prevailing conditions of wind, temperature and relative humidity in the local eucalypt forest fuels. A convective smoke plume developed over the fire, extending to a height of approximately 4000 m. Traditional measures for assessing fire behaviour gave no indication of the extent of the crown fire that occurred. This paper describes simulations of the fire using the coupled fire-atmosphere model WRF and SFIRE. The simulations show that fire-atmosphere feedback produces a northeast to northwest wind shift and surface convergence occur over the fire just after the fire's buoyant plume grows through the overnight nocturnal layer into the residual mixed layer of the planetary boundary layer (PBL) from the previous day. The simulation results show that the timing of the wind shift and vertical growth of the fire plume closely matched the observed development of the convective smoke plume above the fire.

The coupled atmosphere–fire spread model “WRF-SFIRE” has been used to simulate a fire where extreme fire behavior was observed. Tall flames and a dense convective smoke column were features of the fire as it burned rapidly up the Rocky River gully on Kangaroo Island, South Australia. WRF-SFIRE simulations of the event show a number of interesting dynamical processes resulting from fire–atmosphere feedback, including the following: fire spread was sensitive to small changes in mean wind direction; fire perimeter was affected by wind convergence resulting from interactions between the fire, atmosphere, and local topography; and the fire plume mixed high-momentum air from above a strong subsidence inversion. At 1-min intervals, output from the simulations showed fire spread exhibiting fast and slow pulses. These pulses occurred coincident with the passage of mesoscale convective (Rayleigh–Bénard) cells in the planetary boundary layer. Simulations show that feedback between the fire and...

The coupled fire–atmosphere model consisting of the Weather and Forecasting (WRF) Model coupled with the fire-spread model (SFIRE) module has been used to simulate a bushfire at D’Estrees Bay on Kangaroo Island, South Australia, in December 2007. Initial conditions for the simulations were provided by two global analyses: the GFS operational analysis and ERA-Interim. For each NWP initialization, the simulations were run with and without feedback from the fire to the atmospheric model. The focus of this study was examining how the energy fluxes from the simulated fire modified the local meteorological environment. With feedback enabled, the propagation speed of the sea-breeze frontal line was faster and vertical motion in the frontal zone was enhanced. For one of the initial conditions with feedback on, a vortex developed adjacent to the head fire and remained present for over 5 h of simulation time. The vortex was not present without fire–atmosphere feedback. The results show that t...

Early springtime diurnal variation of vertical wind over the 60 km long Tufan Danda mountain range located in the Far-Western Nepal has been numerically simulated with the application of WRF modeling system. Temporal and spatial distribution of vertical wind over the mountain range has been discussed up to tropospheric height. The study reveals that strong vertical wind gradient prevails throughout the troposphere of the area. The mountain wave excitations from the western edge of the mountain range appear to be propagated beyond the eastern edge of the mountain range enhancing regular bands of up-and downdrafts. The up-and downdrafts may extend beyond the tropospheric height during the late afternoon time in fair weather situations. The speed of the up-and downdrafts over the Tufan Dada may reach more than 180 cm s -1 and 200 cm s -1 , respectively. High speed and great vertical extension of vertical wind over the area can significantly contribute in coupling the upper and lower atmospheric air masses and hence exchange of constituents. Tufan Danda mountain range could be one of the suitable site to monitor exchange of upper and lower atmospheric constituents.

Informasi prakiraan cuaca di wilayah Tangerang penting untuk diketahui karena frekuensi banjir akibat cuaca ekstrim semakin meningkat. Informasi prakiraan cuaca dapat diperoleh dengan memanfaatkan model WRF. Peningkatan akurasi prediksi model WRF dapat dilakukan dengan teknik asimilasi untuk memperbaiki initial condition. Penelitian ini bertujuan untuk mengetahui perbandingan performa asimilasi data satelit cuaca WRF-3DVAR dalam memprediksi curah hujan di wilayah Tangerang pada tanggal 21 Januari 2019. Eksperimen yang dilakukan yaitu asimilasi dengan Himawari-8 kanal IR (DA-IR), Himawari-8 kanal WV (DA-WV), gabungan sensor AMSU-A dan MHS (DA-AM), serta tanpa asimilasi (CTRL). Hasil penelitian menunjukkan bahwa semua eksperimen menghasilkan prediksi yang baik untuk parameter curah hujan, RH, dan kecepatan angin. Namun pada parameter suhu udara, semua eksperimen menghasilkan nilai error yang melebihi nilai toleransi. DA-IR menghasilkan perubahan terbesar pada suhu udara dan RH. Sementara itu, DA-WV memberikan perubahan terbesar pada rasio pencampuran uap air, ketinggian geopotensial, dan kecepatan angin. Secara keseluruhan, DA-IR merupakan eksperimen yang paling baik dibandingkan yang lainnya, baik secara verifikasi titik maupun spasial. Eksperimen asimilasi secara berturut-turut dari performa terbaik adalah DA-IR, DA-WV, dan DA-AM.

The vertical structure of the atmospheric boundary layer (ABL), simulated with the mesoscale model Weather Research and Forecasting (WRF) as well as with its polar optimized version Polar WRF, was compared to tethered balloon soundings and mast observations taken in March and April 2009 from two Arctic fjords in Svalbard. From twelve short (48 h) simulations, the Quasi‐Normal Scale Elimination scheme for the ABL and the NOAH land surface scheme for the surface were found to perform best and were selected for one long (16 day) simulation. The differences in performance of the standard WRF and Polar WRF were marginal. A warm bias, especially near the surface, was found in the modelled temperature profiles related to underestimated temperature inversion strength and depth. The modelled humidity inversions were generally deeper but weaker than the observed, and often occurred independently of temperature inversions. The largest errors in temperature and humidity occurred under high pres...

Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%-161%, 66%-161%, and 65%-149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatialforecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values. SIGNIFICANCE STATEMENT: Heavy rainfall from summer convections is stochastic in terms of intensity and location; therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models' sensitivity to cloud microphysical parameterizations and the feasibility of application to ensemble forecast. Promising results were obtained from simulations of 10 real cases. The cloud microphysical ensemble approach may provide reasonable forecasts of heavy rainfall probability and convincing rainfall spatial distribution, particularly for more organized convection.

ERA-40 reanalyses, and simulations from three regional climate models (RCMs) (ALADIN, LMDZ, and WRF) and from one statistical downscaling model (CDFt) are used to evaluate the uncertainty in downscaling of wind, temperature, and rainfall cumulative distribution functions (CDFs) for eight stations in the French Mediterranean basin over 1991-2000. The uncertainty is quantified using the Cramer-von Mises score (CvM) to measure the "distance" between the simulated and observed CDFs. The ability of the three RCMs and CDF-t to simulate the "climate" variability is quantified with the explained variance, variance ratio and extreme occurrence. The study shows that despite their differences, the three RCMs display very similar performance. In terms of global distributions (i.e. CvM), all models perform better than ERA-40 for both seasons and variables. However, looking at variance criteria, RCMs are not always much better than ERA-40 reanalyses, whereas CDF-t produces accurate results when applied to ERA-40. In a second step, a combined statistical/dynamical downscaling approach has been used, consisting in applying CDF-t to the RCM outputs. It shows that CDF-t applied to the RCM outputs does not necessarily produce better results than those from CDF-t directly applied to ERA-40. It also shows that CDF-t applied to RCMs generally improves the downscaled CDFs and that the "additional" added value of CDF-t applied to the RCMs is independent of the performance of the RCMs in terms of CvM, explained variance, variance ratio and extreme occurrence.

Neste trabalho é analisada a degradação do desempenho de oito módulos fotovoltaicos após seis anos contínuos de operação. Estes módulos foram cuidadosamente caracterizados no ano de 2000, pouco antes de serem incorporados a um sistema fotovoltaico autônomo instalado no Laboratório de Energia Solar da UFRGS. No ano de 2006 novas curvas características foram obtidas, utilizando-se as mesmas metodologia e instrumentação. Os estudos do comportamento e desempenho de módulos fotovoltaicos ao longo de sua vida útil permitem determinar taxas de degradação ao longo do tempo. Estimar estas taxas é importante para determinar a energia que o sistema pode entregar e assim calcular o tempo de retorno do investimento inicial. Os resultados mostraram que diminuição da potência dos módulos foi da ordem de 6 % em comparação com sua potência inicial. Também foram levantadas as curvas para os módulos com deposição de poeira, determinando-se uma queda de potência da ordem de 4 %.

This article performs a systematic study to understand the dependence of the splitting of an initially axisymmetric storm on the various components of the Unified Model (UM) and Weather Research and Forecasting model (WRF). The models are adapted to keep their differences at a minimum. Results at km‐scale grid resolution show that the models differ significantly even under a controlled environment with no surface and radiative forcing. The initial storm in UM splits into two within the first hour. WRF also produces two separate updraughts, but it does not split entirely because of a secondary downdraught that falls just ahead of the original updraught. The cold pool from this downdraught converges with the oncoming winds at lower levels to generate a ring of updraughts connecting the two. UM also shows a similar secondary downdraught, but it is relatively weak. Experiments with the successive reduction in complexity of the microphysics scheme show that the models start to differ wit...

A method to assimilate all-sky radiances from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was developed within the Weather Research and Forecasting (WRF) model's data assimilation (WRFDA) system. The four essential elements are: (1) extending the community radiative transform model's (CRTM) interface to include hydrometeor profiles; (2) using total water Q t as the moisture control variable; (3) using a warm-rain physics scheme for partitioning the Q t increment into individual increments of water vapour, cloud liquid water and rain; and (4) adopting a symmetric observation error model for all-sky radiance assimilation. Compared to a benchmark experiment with no AMSR2 data, the impact of assimilating clear-sky or allsky AMSR2 radiances on the analysis and forecast of Hurricane Sandy (2012) was assessed through analysis/ forecast cycling experiments using WRF and WRFDA's three-dimensional variational (3DVAR) data assimilation scheme. With more cloud/precipitation-affected data being assimilated around tropical cyclone (TC) core areas in the all-sky AMSR2 assimilation experiment, better analyses were obtained in terms of the TC's central sea level pressure (CSLP), warm-core structure and cloud distribution. Substantial ( !20 %) error reduction in track and CSLP forecasts was achieved from both clear-sky and all-sky AMSR2 assimilation experiments, and this improvement was consistent from the analysis time to 72-h forecasts. Moreover, the allsky assimilation experiment consistently yielded better track and CSLP forecasts than the clear-sky did for all forecast lead times, due to a better analysis in the TC core areas. Positive forecast impact from assimilating AMSR2 radiances is also seen when verified against the European Center for Medium-Range Weather Forecasts (ECMWF) analysis and the Stage IV precipitation analysis, with an overall larger positive impact from the all-sky assimilation experiment.

The Advanced Research WRF (ARW) Model is a community atmospheric modeling system, and its development and capabilities are the result of the contributions of a host of individuals and institutions from over the years. The features in ARW Version 4 stem from this generous and collaborative approach and offer the community a state-of-the-art numerical weather prediction model reflecting advancements in meteorology, computational science, and data assimilation. Many people have worked on and provided features and improvements for this latest version, particularly in the areas of physics and dynamics, and the authors thank them for their contributions.

The current work focuses on establishing the parameters that influence the wind’s behavior over the Aegean and Ionian Seas and estimating the wind potential in the region based on long-term historic climate data. Combining a downscaling technique performed with the well-founded WRF-ARW computational algorithm and a number of simultaneous meteorological station time series, an attempt is made to investigate how regional changes may affect low-altitude wind speed distribution at hub height (100 m a.s.l.). The provided time-series coastal data span the entire region of interest from north to south. WRF-ARW v.3.9 is utilized to associate the geostrophic wind distribution obtained from long-term Copernicus ERA5 wind data with the localized wind potential over lower altitudes. Evaluation and correlation of the observational data to the predicted wind climate are performed, and the statistical differences that arise are investigated. High-accuracy wind resource potential maps are thus obta...

The implementation of European Union policies contributed to substantial air pollution reductions in recent years, but atmospheric aerosols remain a key pollutant class with environmental and public health risks. This study develops a novel method for assessing environmental impact and the risk associated with urban atmospheric aerosols. The in-tegrated approach for air quality evaluation and prediction of the effects and risk of cer-tain pollutants is based on Aerosol Optical Depth (AOD) analysis, considering the Aero-sol Robotic Network (AERONET) database. To validate the method, it was applied using monitored air quality data for two cities in Romania, with 13 years (from 2011 to 2023) in one case and 12 years (from 2012 to 2023) in the other. The results demonstrated that an AOD risk index can be developed and utilized for air quality evaluation and prediction, enabling estimation of impacts and risks. In this case, aerosols measured by AERONET (Aerosol Robotic Network) over Cluj-Napoca (2011–2023) were dominated (46%) by a mixture of elemental (EC) and organic carbon (OC), while measurements over Iasi (2012–2023) showed 55% of the EC/OC mixture. The impacts and risks, as calculated by the AOD index for EC, show few significant ones, with an AOD range of 0.88 to 1.05 for Iasi and 0.73 to 0.88 for Cluj-Napoca.

Systematic errors in numerical weather predictions can significantly reduce forecast accuracy. We propose a statistical post-processing method to correct temperature forecasts based on historical deviations between predicted and observed temperatures. Our approach calculates a bias correction coefficient (L^t) from historical data and applies it as an additive adjustment to model outputs. This method improves forecast accuracy while maintaining physical consistency and reproducibility.

Since Indonesia lies in the equatorial region, tropical cyclones (TC) are quite rare. The cyclogenesis phase which is prior to the TC stage usually takes place in the low latitude region like  Indonesia, where the sea surface temperature  is  quite  high  and  hence  favourable  to  induce  low-pressure  areas,  apparently  giving  a  meaningful  impact  as well  as  during  the  TC  stage.  However,  some  tropical  cyclones  ever  occurred  very  close  to  the  Indonesia  region  such  as TC  Vamei  in  2001-2002  and  TC  Cempaka  in  2017.  The  most  recent  tropical  cyclone  in  Indonesia,  namely  TC  Seroja, occurred very close to  Indonesia and hit Nusa Tenggara Timur in 2021. For Seroja itself, the system tends to be unique because it started from a cyclogenesis stage near the equator line and dissipated far in western Australia which survived more  than  one  week  before  landfall.  The  first  intensification  that  was  categorized  into  a  TC  was  just  a  short  moment, which then dissipated &intensified again reaching its strongest intensity over the Indian Ocean. TC Seroja was evaluated in  this  paper  to  understand  the  system  characteristics,  environmental  condition &structure,  especially  for  TC  that  took place  in  the  near  tropical  area.  The  vertical  structure  is  evaluated  as  well  as  the  horizontal  structure  to  understand  the characteristics  of  the  system.  This  paper  revealed  that  the  TC  Seroja,  with  a  typical  cyclogenesis  phase  when  it  struck Indonesia, had the most tremendous impact caused especially by precipitation. The strong environment wind shear is one of the factors that makes TC Seroja unique vertically and horizontally. By this paper, the information during the TC stage in  the  tropics  is  expected  to  be  achieved,  thus,  not  only  the  TC  characteristics  and  structures  during  the  mature  or strongest intensity are acknowledged, but also the first TC intensification stage, especially in the tropical region.