سرمد قادر

In the warm seasons of the year, some storms occur that are difficult and complicated to predict. These strong winds that are usually accompanied by dust are known as Haboob in some countries. In the last decade, some of the storms that have occurred over Tehran area, which have caused numerous damages, have included the mechanism of the Haboob event. In this study, a method is introduced for the feasibility of predicting the potential of storms with a down burst structure, that provides defined warning levels for this phenomenon. In the this method, by using a post-processing on the output of a numerical weather prediction model, according to the dynamics and thermodynamic conditions of the weather, a level of warnings is issued for the potential of storm events with down burst mechanism. In the present work, an ensemble forecasting system developed for the WRF model, is used to provide short term predictions of such storms over Tehran area. Five different thermodynamic indices were calculated for the grid points and the process of calculating the potential of a storm event with a down burst structure was carried out by considering the temperature near the earth's surface, the thermodynamic conditions of the atmosphere, the vertical profile of relative humidity, and also checking the presence of dynamic conditions for air ascent. If the thermodynamic conditions and the instabilities of the atmosphere identified by the relevant indicators are conducive and the temperature of the earth's surface and the vertical profile of the relative humidity are appropriate, then the potential of a storm with a down burst structure can be considered probable. The humidity conditions were considered in such a way that the lower levels of the atmosphere have low relative humidity and the higher levels have more relative humidity so that the probability of evaporation of rain before it reaches the earth's surface is high. By combining these conditions for the output of the numerical model, in all of the time steps, three warning levels of the model output for the potential of a storm with a down burst mechanism were presented in the form of yellow, orange and red color zones. Four cases of strong winds and storm, as well as the famous storm that occurred on June the second, 2014, were investigated for the city of Tehran. Various forecasting maps of the output of model run include the mean sea level pressure, the thickness of the layer between the levels of 500 and 1000 hPa, wind speed, the relative vorticity of the level of 500 hPa, geopotential heights of some levels, thermodynamic indices, relative humidity in some pressure levels and the skew-T diagram was prepared at the grid points to analyze the dynamics, thermodynamic and synoptic conditions of the atmosphere. According to the information of the Meteorological Organization of Iran, winds with speeds between 80 to 120 kilometers per hour have been recorded in the meteorological stations of Tehran on these dates. In all these cases, the recorded wind speeds is significantly higher than the direct prediction of numerical weather forecasting models. In fact, it could not be recognized and predicted only by the output of these models. The maps for forecasting the warning level of the feasibility of a storm with a down burst mechanism in all cases investigated in this work during the calculations related to the probability of the occurrence are presented in this study. It seems that the presented method is able to predict the potential of the occurrence of strong winds and storms with down burst structure for the city and province of Tehran.

Several numerical methods are employed to solve the linearized shallow-water equations describing the propagation of Poincaré waves within a one-dimensional finite domain. An analytical solution to the problem, set off by a discontinuous step like elevation, is known and allows to assess the accuracy and robustness of each method and in particular, their ability to capture the traveling discontinuities without generating spurious oscillations.
    The present work examines and applies the central and non-central compact finite difference schemes for the numerical solution of the governing equations of Poincaré waves. Undoubtedly, the central and non-central compact spatial discretization methods have higher numerical accuracy than the central second-order method, and in places where there is an exact solution, the compact methods have shown that these methods are stable under various applied boundary conditions and three-diagonal and five-diagonal forms can be used according to possible limitations. The fourth-order central compact, the third-order and the fifth-order non central compact methods are employed to carry out the spatial differencing of the governing equations and a fourth-order Runge-Kutta method is used for the temporal discretization. The Runge-Kutta time discretization method of the fourth order is a four-step method. In each step, a value for an assumed function is calculated in an intermediate time step, and in the next step, in the same time step, this value is modified.
    In this research, first, the one-dimensional advection equation, which has an analytical solution, is discretized using the above methods, and the performance and numerical accuracy of the methods are measured. Then, the governing equations of Poincaré waves are numerically solved using the mentioned methods and the results are compared for two initial conditions with discontinuous points. The initial condition of the step function is a smooth condition that produces spurious oscillations but the initial condition of the hyperbolic tangent is a sloping condition in the corners, which produces less oscillations. Finally, the numerical solutions of the central and non-central compact methods are compared with each other and the results are analyzed.
    The central and non-central compact methods work well in detecting and identifying the traveling discontinuities. Among the used methods, the non-central compact method of the fifth order has better performance. Moreover, it has a lower error and a higher numerical accuracy. However, with the increase of grid points, the computational cost of this method increases drastically because the fifth-order non-central compact method is a five-point method, and the matrix of their coefficients forms a five-diagonal matrix which has a great impact on the computational time.

Numerous numerical experiments have been performed to cloud model seeding over the last two decades. Silver iodide nucleation has been parameterized using different methods in these studies. The results of these studies indicate that cloud seeding can change the distribution of precipitation in most cases. Moreover, most of these numerical simulations have been used only in the field of convective cloud seeding and are incapable of complete simulation of atmospheric conditions. For this purpose, the governing equations should be parameterized in three dimensions for the general case and be used in the appropriate model.In this study, the effect of cloud seeding, whether increasing or decreasing in rainfall, has been studied. For this purpose, the WRF numerical model has been developed to simulate the cloud seeding. Since, it is virtually impossible to repeat experiments under similar meteorological conditions, a model that can simulate the effect of cloud seeding on microphysical processes and precipitation could avoid many speculations or inaccurate estimates.The basic hypothesis of cloud seeding is based on the physical principle that at sub-freezing temperatures the equilibrium vapor pressure relative to ice is lower than the equilibrium vapor pressure relative to liquid water. Therefore, the saturated environment with 100% relative humidity relative to water (RHW = 100%) will be supersaturated relative to ice at temperatures below zero degrees Celsius (Pruppacher and Klett, 2010). As a result, in a cloud that is saturated with liquid water and composed of supercooled cloud water droplets, ice particles grow rapidly to form larger and heavier drops which could be fall as rain drops. In that environment, tiny, supercooled cloud droplets either grow in upward motion or evaporate to provide vapour for ice to grow. Therefore, in the cloud seeding with silver iodide, ice particles are expected to be produced and grow in the cold part of the cloud, and the liquid water of the cloud will be transformed into ice phase species more quickly.The operational cloud seeding project has been carried out in the northwest area of Iran. At the time of operational project, the seeding target area was under the influence of the eastern Mediterranean low pressure center, this trough has caused the formation of divergence in its downstream in the upper levels of the atmosphere in the target area and has led to the formation of severe upward movements. Stable and thick clouds have formed in the area. Under the above mentioned environmental conditions, 44 pyropatrons of 4% silver iodide were fired at the target area by a seeding aircraft. Silver iodide particles measuring 0.1 to 1 mm are very effective in freezing nuclei. In this study, the effect of seeding is coded based on the model of Meyers et. al (1995) and Seto et. al (2011) by applying the seeding conditions into the Morrison scheme code within the WRF model and changing the number density and mixing ratio of cloud ice due to the silver iodide injected into the atmosphere.By simulating the effect of cloud seeding, meteorological quantities, including precipitation under seeding conditions, are estimated by changing the Morrison microphysical scheme in the WRF model. The WRF numerical model was also run in control mode (without applying cloud seeding relations). By comparing the output rainfall of the numerical model in seeding mode with the output rainfall of the numerical model in control mode, the amount of cloud seeding effect was determined.The results showed that the changes resulting from seeding in the studied cloud seeding operation were not favorable in all stations, and in some cases, the decrease in precipitation was seen 2 hours after seeding. This decrease in some stations, such as Maragheh, Tabriz, Sahand, and Khoy, starts from seeding time and continues until the end. But in a station like Sarab, although the rainfall decreases slightly at the beginning of cloud seeding, over time, it increases to 7% after two hours. While seeding in Parsabad, and Ahar stations resulted in precipitation enhancement by 3%, 9%, and 27% two hours after seeding, respectively.

Today we are witnessing a multitude of destructive natural meteorological and hydrological phenomena that cause more financial and environmental losses to human life. One of the atmospheric phenomena that can have a direct impact on flight safety, transportation, structures, energy and many other aspects of human life is wind gust. The aim of this study was to investigate the temporal and spatial distribution of wind gust in Iran over a period of 15 years and to evaluate an experimental method called WPD to predict this phenomenon using the output of the WRF model. For this purpose, the data recorded in 32 synoptic stations between 2004 and 2018 were studied. The results showed that the number of wind gusts occurred in the southeast and northwest Iran was much higher than other regions, while the frequency of convective wind gusts has been higher in the western half of Iran. In general, the frequency of wind gust had an increasing trend during the studied period and reached its maximum in 2018. Moreover, most convective wind gust reports have been related to spring. The highest number of wind gust reports with 67% belonged to the first half of the year. However, only 13% of the reports belonged to the autumn. Most of the wind gusts were reported between 12:00 and 18:00 local standard time (0800 to 1400 UTC). Among several wind gust forecasting methods, the relationship used in the WRF post processing system (WPD) was selected and its performance in Iran was evaluated. The results of the method on 885 non-convective wind gust indicated the optimal performance of the method for forecasting wind gust in Iran (RMSE=3.23, MAE=2.83, MSE=13.4 and R=0.71).

Calculating the Q vector and preparing its output products can be very effective for meteorology and weather forecasting. The vector and its convergence determine the upward motions of the atmosphere, so maps involving the vector and its convergence at different pressure levels, along with other meteorological maps, for weather forecasts and related warnings, increases the accuracy of this process. In order for the omega equation to have a more appropriate description and analysis, and also to combine sentences in the equation that neutralize each other to some extent, the Q vector has been defined and replaced in the equation. Thus, convergence and divergence for the Q vector show the ascending and descending motions of the atmosphere, respectively. In this study, Q vector values and their convergence were calculated at different pressure levels and the accuracy of the results was studied for two incoming meteorological systems with heavy rainfall. A period of time includes February 25 and 26, 2020, during which regions in the west of the Iran and the southern slopes of the Alborz mountain range received significant rainfall. The second period is July 13 and 14, 2020, with relatively heavy rainfall in parts of the north of the Iran.To perform the calculation, global GFS model output data with a horizontal resolution of 0.5 degrees and 3-hour forecast time step, temperature values and geopotential height at grid points were extracted. the Q vector and its convergence were calculated using the central second-order finite difference method and the numerical noise arising from high spatial resolution was reduced using a discrete spatial filter. Convergence maps of the Q vector representing the upward motion were analyzed by the accumulated precipitation, ground surface information, satellite images, and synoptic maps. The results of vector calculation and its convergence had considerable accuracy and consistency with the time and place of heavy rainfall leading to flooding in the two cases studied. In the first case, the precipitation system was imported from the west of the Iran with a dynamic active trough, and there was a good humidity flux from lower altitudes, which caused heavy rainfall, especially in Ilam province and areas of the southern slopes of the Alborz mountain range, including Tehran province. Studying of synoptic maps, cumulative precipitation and analyzes performed and their comparison with the results of Q vector calculation and its convergence in different forecasting hours, shows the appropriate accuracy of the Q vector calculation. The convergence of Q vector, corresponds to the hours and areas of heavy rainfall in the west of the country and the southern slopes of the Alborz mountain range. In the second case, the existing conditions from the analysis of synoptic maps indicate that the influence of the high pressure system from the north to the shores of the Caspian Sea, which is a common cause of mechanically ascending due to precipitation in this region, does not have a strong presence and being located at the cold output of the jet, the presence of the trough in the middle of the atmosphere in the northwest of the Iran and the flux of moisture from the eastern Mediterranean have caused instability and precipitation. These rains caused floods and damage in areas of the northern slopes of the Alborz mountain range, especially in the western and eastern heights of Gilan province. Despite the differences in precipitation forecasts by GFS and ARPEGE models for this time period, the studies show a significant temporal and spatial agreement between the results of Q vector calculation and its convergence with the analysis of synoptic maps and land surface information.The results showed that the Q vector results for the western regions and along the Zagros mountain range at the level of 500 hPa, for the southern areas of the Alborz mountain range at the level of 700 hPa and for Gilan province and the southern shores of the Caspian Sea at the level of 850 hPa, was more consistent with the cumulative precipitation measurements and synoptic analyzes. Considering more cases continuously and operationally, can provide the better estimate the conditions of forecasting by Q vector and its accuracy, according to the specific conditions of each region, such as altitude, moisture resources, location of roughness and mountain ranges and other factors.

Convective storms are the most atmospheric hazards that can cause significant dangers to sectors such as agriculture, industry, aviation industry and urban and rural facilities in different parts of the world. These storms are often accompanied by thunderstorms, hails, floods or severe winds. Severe dust from some convective phenomena has also been identified as air and environmental pollutant. Therefore, studing their various aspects has provided the basis for many atmospheric studies in different parts of the world.Accurate and timely forecasting of convective storms is still challenging in operational weather forecasting centers. Given that conventionally configured numerical models did not accurately predict this phenomenon in most cases, their nowcasting using satellite data is of great importance. Meanwhile, geostationary satellites have been recognized as a useful tool for identifying areas with potential convection. In this paper, an attempt is made to provide an algorithm using the Meteosat8 data to identify areas prone to the convection initiation and to improve the issuance of timely warnings in a very short time. This work has been done by studying the convective events during the day and night hours of spring and summer of 1397 in Tehran province. The present algorithm is based on  selecting 22 fields using the brightness temperature of infrared channels, the reflectance of visible and near-infrared channels and the differences and their 15 and 30 minute trends. The preprocessing performed on the satellite data are radiometric and geometric corrections. To do this, after calculating the radiance of individual channels from the digital counts, the brightness temperature of the infrared and reflectance of the visible and near infrared channels are calculated. Also to calculate the trend of the fields between two time steps, a spatial low-pass filter using the box-averaging method has been used. Large-Scale atmospheric pattern analysis of the ten strongest dust convective storm events in Tehran in the last decade shows that in most of them, at least five days before the storm, a ridge of geopotential height at the 500 hPa level developed on the area. On the day of the storm, the passage of the geopotential trough and the cold front of the surface destroyed the atmospheric stability and released the energy necessary for the development of the cumulonimbus cloud and the occurrence of the storm. Finally results show that using this algorithm to nowcasting the convective initiation, the probability of detection and correct alarm rate are 62 and 79 percent, respectively.

Atmospheric currents, known as winds, are among the most important fields of study in different disciplines of science. One of the most important characteristics of wind is gustiness. Wind gust, among many other characteristics of the wind field, is studied extensively due to severe impacts that it may have on many aspects of human socio-economic activities. There are several models to predict wind gust speed. The results of these models always contain random and systematic errors that reduce the accuracy of predictions due to the lack of topographic resolution as well as the deficiencies of different physical schemes in the models. Consequently, post-processing is the most important process in the course of simulation and prediction using different types of models. Artificial neural network is one of the available tools that may be used to reduce errors of models by matching their outputs and observations.     The aim of this study was to evaluate the performance of two models and artificial neural network in forecasting wind gust in Iran. First, a study was designed to examine two methods of the non-convective wind gusts forecasting, i.e., the UK Meteorological Office (MOA) and WRF post-process diagnostic of wind gusts (WPD) performances. To investigate the performace of two methods, 1880 cases of non-convective wind gust observations of 32 synoptic stations in Iran, between 2013 and 2018, were studied. Four RMSE, MAE, MSE and R were used to measure the performace of those two methods. The results for WPD and MOA were 3.89, 3.07, 15.2, 0.66 and 4.37, 3.43, 19.1, 0.55, respectively. The results showed that the WPD method performed better than the MOA method. To post-process the wind gust forecasts with an artificial neural network, a feedforward multilayer perceptron with the back-propagation learning algorithm was designed. The model had a hybrid structure with a sigmoid activation function for the hidden layer and a linear transfer function in the output layer. Three training algorithms were used in the implementation of the model. Various combinations of normalized output variables of the WRF were used as input for network training and the target was observational wind gust speed. Seventy percent of the data were used for training, fifteen percent for testing and fifteen percent for validation.     The results showed that the best way to combine the input parameters is to use 10m wind, sea level pressure, temperature and relative humidity resulting from the output of the WRF model and the wind gust speed resulting from both methods mentioned above. Also, the best algorithm for neural network training was the Levenberg-Marquardt algorithm. Finally, the implemented artificial neural network was able to improve the results of both wind gust speed prediction methods (WPD and MOA). Due to the relatively higher accuracy of the WPD method compared with MOA method in predicting the wind gust speed in Iran, the artificial neural network that assumed the prediction of this method as input, was more accurate than MOA method (RMSE, MAE, MSE and R were 2.05, 1.6, 4.21, 0.83 and 2.37, 1.86, 5.2, 0.77, respectively).

Evaluation of the performance of the parameterization schemes used in the WRF model is assessed for precipitation over northwest Iran at a 5 km by 5 km grid. Simulations are performed for a winter day. A step-wise decision approach is followed, beginning with seven simulations for the various Cumulus schemes and then four microphysics schemes; after that, 36 different configurations of the model’s PBL, Long-wave, Short-wave, and Land Surface schemes were tested. Root-Mean-Squared-Error chooses the best performing scheme at each step. The concluding scheme set consists of the Lin et al. microphysics scheme, the MYJ PBL scheme, the Dudhia scheme for shortwave, the RRTM for longwave radiation, Eta Similarity option for the Land Surface scheme, and without cumulus scheme. In this study, combinations of schemas that have the most and least effect on the distribution of simulated precipitation data were also identified. For this purpose, 36 configurations are adopted for longwave radiation, shortwave radiation, and boundary layer schemes in 12 groups of three with rotation of shortwave radiation and 12 groups of three with rotation of longwave schema, and in 9 groups of 4 with rotation of the boundary layer scheme was divided, and simulated precipitation variance and sd was calculated for these 33 groups. Thus, among the configurations created, the configurations that have the most and the least effect in estimating the six-hour rainfall and their scattering were identified. The results of the scatter study of each group of precipitation estimated data calculated by variance showed that the change in the choice of the boundary layer scheme when longwave radiation scheme is the RRTM scheme and short wave radiation scheme is the Goddard scheme, can bring the results closer to observation. Changes in the choice of shortwave radiation when longwave radiation is CAM and boundary layer scheme is the MYJ scheme has the least effect on precipitation estimation. This indicates the variability of selecting the most effective schema in precipitation prediction and can be influential in choosing the configuration in ensemble precipitation.

Clear air turbulence (CAT) forecasting got its start in the World War II era, when scientists and individuals in the field of aviation first began attempting to correlate observed clear air turbulence events with large scale synoptic features. Although there is a large spectrum of eddy sizes in the atmosphere, from macro scale to micro scale, aircraft bumpiness is felt mainly for a range of eddy sizes between about 100 m and 1 km; larger eddies cause only slow variations in the flight path while the effect of very small eddies is integrated over the surface of the aircraft.This study is two case study and comparison of the report of CAT of center, east and south east crossings (Sector 4, 5 and 6) of the results of the IRIAF IT, Communication and Electronic Administration. Regional weather predictions are carried out using an ensemble forecasting system development for the WRF model. Perturbed initial conditions and different choices of physical parameterizations are used to generate ensemble members. In addition, the initial and lateral boundary conditions are taken from the global forecast system (GFS). For each member of the ensemble system, two computational domains of the nesting area are used with spatial resolutions of 27,000 meters, 9,000 meters. Case study of the predicted clear air turbulence indicates the proper performance of the predicted meteorological parameters.

Increasing population growth and consequently the development of urban areas can profoundly affect climate events and thus intensify phenomena such as heat stress. Given the expected effects of this phenomenon on human health, it is very important to provide mitigating operational solutions to control future conditions. Therefore, the present study was conducted with the aim of simulating the effect of urban planning solutions on dynamic processes in the urban environment and at the local scale in Tehran city using the WRF mid-scale numerical model. Simulations were performed using 4 nested domains with a two-way interactive nesting procedure. The study used a simple Single-Layer Urban Canopy Model and a more advanced multi-layered approach called Multi‐layer urban canopy (BEP). The results of the simulations, after comparing the two urban schemes with a sensitivity measurement for different strategies, showed that the surface reflectance change scenario has the greatest impact on the land surface compared to the two scenarios of increasing urban green areas and reducing building density. Due to Tehranchr('39')s specific topographic location and high overall temperature in this region, Tehran is relatively vulnerable to heat stress. Compared to the intensity of 5.5 °C for base mode, applying control measures can reduce the intensity of UHI up to 3 °C when using bright colors with high reflectivity for the ceiling and 1 ° C by replacing impermeable surfaces with natural vegetation in urban areas of Tehran.

Visibility is one of the most important optical characteristics of the atmosphere. Prediction of visibility is essential for air pollution, air traffic, flight safety, road traffic and shipping. Visibility reduction may be caused by different reasons. Fog is one of the most common reasons of visibility reduction, i.e. the droplets of water suspended in the atmosphere reduce the visibility to less than 1 km. Precipitation may also reduce visibility. Prediction of visibility in NWP models is usually accomplished by using the relationship between visibility and liquid water content, temperature, relative humidity. Purpose of the present work is to predict visibility during fog and precipitation over Tehran area in January 11th, 2014 and March 7th, 2013. Different algorithms including UPP1, AFWA, FSL and SW99 have been experimented to predict visibility.. Predicted visibility has been compared to observations, including Synoptic and METAR data in Imam Khomeini and Mehrabad airport.  The  WRF version 3.8.1 has been used to simulate precipitation and fog. In this simulation model configuration defined in Lambert uniform space. The model consist three nested domains. First domain was a 27-km grid model (83×65), surrounding a 9-km grid model (112×94) which was surrounding a 3-km grid model (112×97). Center of all domains was at longitude 51° and 44chr('39') and latitude 36° and 5chr('39') which is located almost at center of Tehran. All domains had 40 vertical layers and model top was located at 100hPa. The out puts of 3-km domain is used for visibility estimation. Initial and boundary conditions were set by using FNL data which is 1°×1° degree grid data. This data is available every 6 hours. Simulations were in 36 hours and first 12 hours was the spin up time. Results show that most of these algorithms can partly predict visibility reduction. The FSL algorithm works better than the other methods in fog situation and SW99 works better in snow situation. Comparing results shows that the visibility reduction during snow is more reliable than during fog. There were some errors in model predictions some of them were due to visibility algorithms, because the coefficients of these algorithms were driven in other parts of earth. The other errors were systematic errors of WRF. Predictions of temperature had warm bias and also there were positive bias in prediction of relative humidity.

The clear air turbulence (CAT) is one of the atmospheric phenomena that can endanger the flights, and may creat restrictions in the flight surveillance. CAT can be defined as all turbulences in the free atmosphere (about >10000m AGL) of interest in aerospace operations that is not in or adjacent to visible convective activity. As Iran is located in the gate of southwest Asia and Eastern Europe, investigation of the occurrence of the CAT in Iran is essential. To this end, the CAT indices such as Richardson number, vertical wind shear and Dutton's index are calculated via post processing of outputs of the WRF model. Using the Global Forecast System (GFS) data as input to the Weather Research and Forecasting (WRF) model, prediction of the CAT is provided for a 24-hour lead time. The GFS data with 0.5 degree horizontal resolution are used as the initial and boundary conditions for running the WRF model, while reports of pilots and aviation maps are used to evaluate the model performance. The WRF model was run with a three nested domains with horizontal resolutions of 18, 6 and 2 km, respectively. The CAT is diagnosed for two flight routes over Iran area: Tehran to Ardabil on 9 March 2018 and Ahwaz to Tehran on 24 April 2018. Both of routes are embedded in the Alborz and Zagros mountains. Results indicate that the CAT can be better predicted using the Dutton when the YSU planetary boundary layer scheme, the KF Cumulus schemes and Lin and Kessler microphysics schemes are used in WRF model setup. Prediction of the CAT based on wind shear index is better achieved when the MYJ planetary boundary layer scheme, the KF Cumulus scheme and the WSM3 are employed in WRF model. Based on the Richardson index, the CAT is better predicted using the MYJ and YSU planetary boundary layer schemes, the KF cumulus scheme and the WSM3 and Lin microphysics schemes. Based on the results of the evaluations, for the horizontal resolution of 18 km, the best indices for the weak to moderate CAT are Richardson number index, vertical wind shear index and Dutton's index for the Tehran to Ardabil flight route, and Richardson number index, Dutton's index and vertical wind shear index for the Ahwaz to Tehran flight route. The CAT in these routes is accompanied with the upper-tropospheric (200 to 300 hPa) jet streams and troughs and ridges.

Considerable decay of wave energy along the wave trajectory over muddy beds makes a different wave generation/transformation in comparison with sandy/rocky environments. The role of soft mud to dissipate waves has been recently implemented to SWAN wave model. A new dispersion relation obtained from a two-layer viscous model is implemented in the wave-simulation model, SWAN-mud, to consider wave decay in coastal areas due to the presence of fluid mud deposits. Significant wave heights over gentle muddy slopes are usually overestimated using default SWAN model setup, while it is expected that using SWAN-mud setup, improves the correlation between model simulations and real field measurements. However, considering the viscous assumption of mud behavior in developing new dispersion relation, the model results are highly dependent on the assumed mud rheology and mud behavior in reality.The north-western part of the Persian Gulf is covered by mud deposits originated mainly from the Arvand River catchment area. Mud deposits up to 20 meters thickness are observed at the very shallow coast of Deylam Bay, which implies high rates of wave dissipation in the area; something which should be taken in to consideration for wave climate estimations. A set of 37-days field measurements in 2007 is available for model performance validation. This research aims to adopt various facilities implemented in SWAN wave model to regenerate the wave measurements at a mid-depth station in Deylam Bay, over the muddy bed of Northern Persian Gulf with acceptable accuracy. A number of about 30 model configurations are considered for wave generation over the mud coast of Deylam Bay. Input wind data are adopted from ERA5 product of ECMWF global model. WRF model is adopted to improve input wind data accuracy as well. The focus of this study is to develop a proper hindcast and forecast system for predicting wave characteristics in the north-western of Persian Gulf, including the mud induced wave dissipation in the areas covered with soft muddy deposits. The developed model considering mud-induced wave dissipation is validated against available field data. Based on the achieved results, the ordinary SWAN model setup is not capable to well estimate all the storm events in the period of measurements. However, considering the mud-induced dissipation, implemented in SWAN-mud model, the developed model is capable to capture all the events with different combinations of wave properties in the study area. The final model predictions favorably agree with the field survey data in the study area.

The Urban Heat Island (UHI) describes the temperature difference between urban and rural temperatures. Finding urban heat island mitigation strategies is of great importance, given expected influences on human health and air quality. This study presents numerical simulations over a summer period to investigate the impact of urban heat island control measures on Tehran urban air quality. The WRF-Chem Chemical Mesoscale Model is used to investigate the effect of increasing urban vegetation and highly reflective surfaces on the concentration of primary pollutants (CO, NO) as well as secondary pollutants (O3) in urban canyons. In order to account for the heterogeneity of urban areas, a multi-layered urban canopy model is coupled with WRF-Chem. Using this canopy model at its broad range requires introducing several urban user classes in WRF-Chem. Tehran metropolis is considered to simulate designed experiments in the summer of 2016. The selected reduction measures in the simulations are able to reduce the urban temperature by about 1-3 degrees Celsius and average daily ozone concentration by 5 to 10 percent. The modeling results also presented secondary negative effects on urban air quality, which is strongly related to the reduction of vertical mixing in the urban boundary layer. The simulation results show a 1 to 20% increase in the primary pollutants (NO and CO). Despite the daily average decrease in ozone concentration, highly reflective surfaces due to severe short-wavelength radiation that accelerates photochemical reactions can lead to an increase in the peak ozone concentration by up to 9% at noon hours.

Significant emission of heat from human activities and overheating of synthetic surfaces over natural surfaces leads to urban heat island formation. The average annual temperature in central areas of a large city is at least about 1-3 ° C above the surrounding area. On calm nights, city centers can experience temperatures as high as 12 ° C. In addition to the health problems caused by rising temperatures, the effect of increasing rates of photochemical reactions, which in turn worsens indoor air quality, is also of particular importance (Oke, 1982). Specific measures such as the use of green roofs or facades and materials with high reflectivity are able to reduce the intensity of the urban heat island. The purpose of this study is to use the WRF-Chem model, coupled with urban parameterization schemes, to investigate the dynamical and chemical processes when applying conventional reduction strategies. The study area is the urban area of ​​Tehran as one of the most polluted cities in Iran.

To show the three-dimensional structure of urban areas, the urban parameterization plan was used along with the Noha land surface model. In order to show the heterogeneity of urban land levels and to use urban modeling with its full expansion, the main urban class (1) in the WRF / Chem model was divided into 3 subclasses (31-33). The range of the model for the internal nest was 103 by 79 network cells with a horizontal resolution of 1.33 km. In order to identify morphological features for each class of Tehran urban area, the characteristics of roads and buildings (building height, street width, albedo level, vegetation, etc.) as well as thermodynamic properties and roughness characteristics within the model were updated. The simulation period was July 17 to July 23, 2016, a period of thermal stress in Tehran that could be considered as a special period for future weather conditions in Tehran. The basic mode (control) was simulated along with urban planning strategies such as increasing urban vegetation (park), increasing building surface whiteness (albedo), and changing building density (density) for further study.

During the study of simulation sensitivity, different meteorological and pollution parameters of the simulated air in the default mode were compared with the average observations of the three urban metering stations. The correlation between simulations and observations, except for CO, was greater than 0.5, and the model performed well in reproducing various parameters. Comparing the results of simulations with observational data, it can be stated that the model generally simulates the hourly changes of meteorological variables well, but more or less estimates the concentration of air pollutants during the simulation period (Grossman and Sobert and Clark, 2013). One reason for the comparison method is that simulation outputs are extracted at the beginning of each hour, while measurements are reported as average or average daily (Akbari et al., 2001). To investigate the effect of different reduction strategies, the effect of each strategy on the concentration of different pollutants was simulated. On average, the air temperature decreases by 3.37 degrees Celsius and 1.7 degrees Celsius, respectively, for the albedo and park scenario. In relation to the primary pollutants CO, SO2 or NOx, the positive effect of reduced temperature is reversed. This was particularly the case for a scenario in which the whiteness of the roof and walls of buildings increased (the relative increase in primary contaminants by up to 20%). An increase in ozone concentration of up to 9% for the Albido scenario was found around 1300 hours, which could be due to a further increase in ceiling and wall surface whitening from 0.2 to 0.7 (Takabayashi and Moriyama, 2007), which is 67% higher than the increase in use. It was done by two other studies (Taha, 2008 and 1997-A) and on the other hand, the maximum decrease in air temperature in Tehran urban area was 2.170 C, which is about 0.83 33 2.83 33 C lower than the decrease. The temperature was reported by Taha (2008).

Simulation experiments were designed and studied to evaluate urban thermal island control strategies that could have an adverse effect on urban air quality. The selected measures showed positive and negative effects on the concentration and dispersion of pollutants. Albido's strategies and urban vegetation are able to improve air quality, followed by a daily decrease in the average concentration of ozone. Also, lowering the temperature has a significant effect on the dynamic structure of the urban boundary layer. Reduction of turbulent kinetic energy (TKE) due to lower temperatures leads to a decrease in turbulence mixing rate and a decrease in the height of the mixing layer, which leads to higher surface concentrations of primary contaminants. This case study provides simulation for a city in certain climatic conditions, because for cities with different sizes, locations, population densities, emission conditions, or different meteorological conditions, similar actions may have different effects on air quality. In urban planning, the social effects of parks on increasing the well-being of citizens should also be considered

Reliable wave estimation in complex body of waters from geographical point of view is a matter of high importance. Main straits in the world, such as the Strait of Hormuz are major referent for this issue. Marine activities such as coastal management and ship routing, navigation, maintenance, and installation of offshore infrastructure are all greatly dependent on reliable wave estimations. Predicting waves in a region requires a well-developed wave model that can account for the shallow water wave mechanisms like their generation, propagation, and dissipation. On the other hand, reliable input wind data is a pre-requisite for realistic wave estimation, while the winds over such environments are highly affected by the land features around the straits. The wind data accuracy is also dependent on horizontal resolution of the models to capture the meso-scale dominant phenomena in the interest region. This study aims to develop a wave model employing SWAN wave model, in order to improve wave estimations over the Strait of Hormuz, where is highly affected by geographical complexity. Initially, the simulation is carried out for more than one month from January to May for period of 40 days of 2011. The main parameters of the model were assigned based on a comprehensive sensitivity analysis study and the model performance was verified based on the available archive field data for two stations Jask and Larak in the eastern part and western part of Strait of Hormuz, respectively. The numerical modeling activities are conducted to adopt two sources of surface wind data. The first adopted dataset, ECMWF’s hourly ERA5 product, is based on the presence of meso-scale locally convective phenomena such as land-seas breeze which is dominant in a water body like the Persian Gulf. ERA5 reanalysis dataset, which is the best available wind source, misses these features (because of low resolution) and in turn, when it is used to force the wave models, may result in predicting less accurate wave fields. For improving wind data accuracy, the second dataset, i.e., the high resolution meso-scale atmospheric model WRF was adopted to generate a more realistic wind field. This model reflects the meso-scale phenomena and using it to force the wave model, reflects more accurate wave fields over study area. Different model resolutions are also tested and the result showed that reducing the horizontal resolution for wind field improves the result. The final model results show a significant improvement in wave estimations in the middle of the Strait of Hormuz for coupling wave model SWAN and using WRF wind data. For Larak station, the RMSE decreases 10 percent in comparison to ERA5 wave data and CC (Corolation Coefficient) get to about 0.75. For Jask staion in eastern part of Strait of Hurmoz, ERA5 wave data CC is about 0.9 which is the best performance.

The chaotic nature of the atmosphere, inexact equations of motions, and gaps in specifying the initial state of the atmosphere result in some uncertainties in the weather forecasting. One of the operational methods that has been employed to overcome this difficulty and improve the accuracy of weather forecasts is ensemble forecasting which gains popularity as a technique in numerical weather prediction due to its improved forecast results. There are some different approaches to develope an ensemble forecasting system including using multi-numerical weather prediction models with different numerics, utilizing different physical parametrization schemes, and perturbing initial and boundary conditions. The aim of the present work is to develope a multi-physics ensemble forecasting system for the Weather Research and Forecasting (WRF) model and to evaluate its performance in forecasting of precipitation as a low predictability parameter. To construct the ensemble members, seven microphysics, two planetary boundary layers (PBL) and six cumulus parameterization schemes are employed. These choices are used to generate 84 memebers of the ensemble system. For running the model, a three-nested domain consisting of 36, 12, and 4 km spatial resolution was used to simulate the precipitation for 12 days during 2015-2016 winter in central region of Iran. The one degree FNL data of National Center for Environmental Prediction (NCEP) are utilized as the initial condition for running WRF. The model outputs are evaluated against observations using some standard metrics of forecasting verification including categorical, continuous and probabilistic indices. The observed 24-h accumulated precipitation measured at 132 synoptic stations of national meteorological organization of Iran was used for verification of simulations. In this study, the general performance of the different members of ensemble system is discussed. The members employing MYJ planetary boundary layer scheme, KF and GF cumulus schemes and WSM6, Goddard and New Thompson schemes are found to be appropriate more than the others for accumulated precipitation simulation in the study region. Furthermore, a multi categorical verification using three threshold of 1, 10, and 50 mm/day showed the suitable appraisal of the total performance of ensemble system. Moreover, increasing in ensemble size results in better outputs. However, it seems that expanding the ensemble members might be limited at an optimum number. It means that the skill of ensemble system doesn’t change significantly when the ensemble members are more than the optimum number. In addition, the results show that using a cumulus parametrization scheme for the spatial resolution of the finest domain of this study (i.e., 4 km) is useful to improve the precipitation simulations. It is worth to mention that this finding is in agreement with reports of other studies.

Due to the approximately spherical nature of the earth and the complex nature of atmospheric and oceanic flows, numerical solution of corresponding governing equations requires using an appropriate coordinate system on the spherical geometry. All spherical grids defined for the spherical surface or shell, have their own advantages and disadvantages generally. The Yin–Yang grid belongs to the family of overset grids. This coordinate is composed of two grid components named Yin and Yang with partial overlapping at their boundaries. Some advantages of the Yin–Yang grids are as follows: 1- Yin and Yang grid components are both orthogonal and based on the conventional latitude–longitude grid; 2- The singular points are absent; 3- The metric factors of the both grid components are analytically known; 4- The grid lengths are uniform approximately; 5- It requires less grid points than for the conventional latitude–longitude grid; 6- We can adapt the existing latitude–longitude discretizations and codes for the use with the Yin–Yang grids. In this research, three types of the Yin–Yang grid are compared: the rectangular (basic), modified and modified with identical components. It is worth noting that the Yin–Ying grid with identical components is introduced for the first time in the present study. The central second-order finite difference scheme is applied to solve the two-dimensional advection equation on three types of Yin–Yang grid for a well-known test case. In addition, the fourth-order Runge–Kutta method is used to advance the governing equation in time. Results show that using the Yin–Ying grids to solve the advection equation is highly effective in reducing the computational cost compared to the conventional latitude–longitude grid. However, the use of rectangular and modified Yin–Yang grids entails a lower computational cost than the modified Yin–Yang grid with identical components. In addition, global errors are computed using the absolute, square and infinite norms. By calculating the errors using these norms, it can be seen that there is a slight increase in the errors in all three grids compared to the conventional latitude–longitude grid. Another point to note is a little higher accuracy of modified Yin–Yang grid with identical components relative to rectangular and modified Yin–Yang grids in the same resolution; though, the higher accuracy is associated with a relative increase in computational cost. In the considered algorithm, reduction of the accuracy in using Yin-Yang grids is likely due to interpolation. However, interpolation is an essential part of numerical solving process for various oceanic and atmospheric equations on Yin-Yang grids in spherical geometry.

A 3D current and water level forecasting system is developed for the whole Persian Gulf in this study, in order to offer a reasonable response for the needs to provide a better understanding of coastal and gulf-scale hydrodynamic processes in this important body of water. There are a couple of research attempts published during the past decades on the hydrodynamics and circulation of the Persian Gulf; however, most of them were concentrated on the coastal and relatively shallow water areas and presented reasonable results. Hence, this study aims to improve model performance in deep water areas while the accuracy of tidal and wind-driven current parameters in shallow water results is acceptable. The most important driving forces, including tides and surface winds, are taken into consideration in simulations, in order to provide relatively accurate estimations of hydrodynamic parameters in the Persian Gulf.     For water level and current three-dimensional simulations, FVCOM numerical open-source model is applied and run for some time periods in which field observations are available for both current specifications and water levels in the Persian Gulf. The open boundary data are adopted from OTPS global model and the input wind field data are applied from WRF wind modeling over the whole body of water. The model results were calibrated for a number of parameters selected in an extensive sensitivity analysis program and optimum values are selected for the under-study parameters. A comprehensive set of field measurements is collected, whose main objective is to provide sufficient and reliable input data for current simulations in the Persian Gulf in both deep and shallow areas. The collected survey parameters are mainly focused on: vertical profiling of current speed and direction; mid‐depth current speed and direction measurements; tidal (water level) measurements; and wind measurements. The data covers a wide range of spatial distribution in the Persian Gulf, including near-shore and offshore areas as well as a wide range of water depth values.     In this study, the obtained water level and current model results are verified against collected field observations, both in shallow and deep water areas and near-shore and offshore regions. Consequently, the optimum settings for obtaining accurate model results in both shallow and deep water areas are reported. The results of this research are of great help to understand the hydrodynamics of the Persian Gulf and provide a basis for more accurate estimations of forecasted current and water level parameters over the study area.

In this paper, marine meteorological and oceanic characteristics of Persian Gulf and Oman Sea were simulated using coupled numerical weather prediction model WRF and numerical oceanic model ROMS. At first, WRF model was operated over the sea and its results were analyzed and verified, and then numerical model ROMS operated and assessed on the sea by considering meteorological forcing. The major phenomena for analyzing WRF model was Indian Ocean summer Monsoon. For the model ROMS, transferring seawater form Indian Ocean to Oman Sea and Persian Gulf was studied. Oceanic numerical model was operated for a 7 years period and its results were compared by satellite images. The results show that considering interaction of atmosphere and ocean (by coupling) lead to more real results.

Due to the approximately spherical nature of the atmosphere, oceans and other layers of the Earth and the complex nature of atmospheric and oceanic flows, numerical solution of their governing equations requires using an appropriate coordinate on the spherical geometry. All spherical grids defined for the spherical surface or shell, generally have their own advantages and disadvantages. In general, it can be said that there is no spherical grid which has all the following characteristics: 1- The grid is orthogonal; 2- There is no singularity; 3- There is no grid convergence problem; and defined over entire spherical surface. Thus, we have to discard one of these incompatible conditions. An overset grid is a type of grid that divides a spherical surface into subregions. Yin–Yang grid belongs to the family of overset grids. This coordinate is composed of two grid components named Yin and Yang with partial overlapping at their boundaries. Some of the advantages of the Yin–Yang grids are as follows: 1- Yin and Yang grid components are both orthogonal and based on the conventional latitude–longitude grid; 2- The singular points are absent; 3- The metric factors of the both grid components are analytically known; 4- The grid lengths are uniform approximately; 5- It requires less grid points than the conventional latitude–longitude grid; and 6- We can adapt the available latitude–longitude discretization and codes for the use with the Yin–Yang grids. In addition, we have to use interpolation for setting boundary conditions for the two grid components. The interpolation scheme that has been used in this study is bilinear. In this research, a type of the Yin–Yang grid called the rectangular (basic) is applied to solve the two-dimensional advection equation for a well-known test case using the fourth-order compact MacCormack scheme. Furthere, the fourth-order Runge–Kutta method is used for time stepping. Results show that using the Yin–Ying grids to solve the advection equation is highly effective in reducing the computational cost compared to the conventional latitude–longitude grid, however the use of rectangular Yin–Yang grid entails a lower accuracy than the conventional latitude–longitude grid. In this numerical test, global errors are computed using the absolute-value, Euclidean and maximum norms. By calculating the errors using these norms, there is an order of magnitude increase in the errors in rectangular Yin-Yang grid compared to the conventional latitude–longitude grid. This increase in error can come from the inevitable interpolation process involved in the Yin-Yang grid.

Reliable and sufficient information of 10-m wind and temperature fields over open seas and near coastlines is a necessary and important data that has impact on many marine activities. Assimilation of NWP models can be used to assess an estimation of these fields. This study reports the performance of the weather research and forecasting (WRF) model to hindcast 10-mwind and temperature fields that were evaluated under two different physical options of planetary boundary layer (PBL) and surface layer (SL) for an area over the Gulf of Oman and the Arabian Sea. The case study includes 16 simulations of 8 different days from WRF model version 3.7.1. The WRF model is configured with two nests. Parent nest has 0.3 degree and the inner nest has 0.1 degree horizontal grid resolution. The grid spacing of the inner domain is almost 11-km. The Lat-Lon (latitude-longitude) method is used as the map projection method. For all domains and all runs 39 terrain following vertical levels are set. The validation of the simulated fields is done considering two observational datasets (the weather stations for 10-m wind and 2-m temperature and satellite instruments just for 10-m wind). Near-surface observations of 2-m temperature and 10-m wind speed and direction are collected from 55 weather stations, located within the chosen area. The measurements from satellite instruments have become an important source of data in the regions that in-situ observations are sparse like seas and oceans, hence observations from two different scatterometers (ASCAT and OSCAT) are also used to evaluate 10-m wind simulations. Moreover, in order to better understand the model performance for different choices of the physical schemes, sensitivity of the model has been investigated. There is plenty of choices for the combination of parameterization schemes available for WRF model; for the current study two configurations are taken from other’s previous published research works. The physical parameterization that used in this study are Revised MM5 and Monin-Obukhov for surface layer and MRF and MYJ for planetary boundary layer. These choices are used to create two different configurations called Phys1 and Phys2. Comparison between winds from satellite scatterometer and simulated winds show very little difference and hence good agreement with observations. Acceptable accuracy has been obtained from statistical analyses. These analyses demonstrate that the maximum average RMSE of wind field is 2.39 m/s, based on results of comparing with ASCAT data and it is 2.37 m/s, based on results of comparing with OSCAT data. The analyses also show that simulation of wind fields have better results over offshore regions than coastlines weather stations. The outcome shows that the simulated 10-m ‎wind‎ present acceptable general skills over the sea. The validation of 2-m temperature presents that the model has a proper estimation about temperature field over the coasts and near coastal station within the simulation domain. The maximum average RMSE of temperature field is 2.6 degrees of centigrade. Finally, without any justification to run WRF for longer periods from a quantitative and qualitative assessment of the results, it can be concluded that for the WRF model has an acceptable performance to simulate 10-m wind and 2-m temperature over the Gulf of Oman and the Arabian Sea. It should be noted that to verify these results for longer periods more similar experiments must be tested.

In this work‎, ‎sensitivity and performance of the Weather Research and Forecasting (WRF) model for surface wind field simulations are evaluated under several initial and‎ boundary conditions‎, ‎along with‎ ‎different planetary boundary layer (PBL) schemes during several dates over the Persian Gulf region‎. Since there are differences between production approaches and development periods of analysis and reanalysis data (namely‎, ‎assimilation system) on the one hand‎, ‎and‎ differences in applied method for PBL parameterization by any scheme on the other hand‎, ‎this paper aims to identify a suitable set up among the whole configurations which‎ are under examination‎. ‎To this end‎, ‎three datasets (two reanalyses and one analysis) including‎, ‎ERA-Interim‎, ‎NCEP-R2‎, ‎and NCEP-FNL and six PBL‎ schemes (two local and four nonlocal) including ACM2‎, ‎BouLac‎, ‎MYJ‎, ‎MYNN‎, ‎QNSE‎, ‎and YSU accompanied by their relevant surface-layer schemes are used‎. To assess the WRF model wind simulations, available observational wind data including 22 synoptic weather stations located in the region and‎ observations of QuikSCAT and ASCAT satellites are employed‎. Findings of this study indicate that when the wind simulations are compared with synoptic weather stations observations‎, ‎irrespective of the type of PBL scheme‎, ‎ERA-Interim and‎ NCEP-FNL datasets exhibit better performance in comparison with the NCEP-R2 and ‎when PBL schemes are also considered‎, ‎results show that combination of YSU scheme and‎ ERA-Interim reanalysis data leads to a better estimate of wind speed and combination of YSU and NCEP-FNL data generates less error for wind direction‎. Moreover‎, ‎comparison of model wind simulations and observations of QuikSCAT and ASCAT satellites shows that there are no substantial differences between various‎ configurations‎. ‎However‎, ‎using YSU and ACM2 scheme‎s, ‎WRF model generates speed and direction of the wind close to the observations of QuikSCAT‎. Although all tests have almost similar results as ASCAT satellite observations‎, ‎YSU scheme estimates are slightly better than other schemes. Overall, the results of this study revealed that the major difference between WRF wind simulations and measured winds arises from the choice of initial conditions data and it does not depend on different PBL schemes. Consequently, changing initial and boundary conditions data has a noticeable impact on the model wind results. Thus, in future studies, emphasis must be more on reanalysis and analysis datasets and the option of WRF PBL parameterization schemes should be the second priority. Due to the fairly good similarity of the model surface wind with QuikSCAT and ASCAT observations, the choice of WRF model simulations as offshore wind database can be a valid available alternative instead of QuikSCAT and ASCAT wind, particularly when meeting their limitation in spatial resolution (swath data) or temporal sampling.

In this study, using the HYCOM, the role of bed friction in the salinity front was studied. A resolution of 0.05° horizontally and 29 hybrid layers were used for the simulation, and the simulation area includes the entire Persian Gulf and most parts of the Oman-Sea (up to 59.9°E). The model uses the initial conditions of the WOA13 data with a resolution of 0.25°, the sponge boundary condition in the east of the Oman Sea with an e-folding time of 78 day and a buffer zone of 50 km. The CFSV2 Atmospheric Forcing (0.2°) uses 1-hourly data with baro-tropic and baro-clinic time-steps of 15,120 seconds respectively. The model was integrated from 2011 to 2015, and the results of 2015 were selected for discussion. The results indicate that eddies are formed along the salinity front. The salinity front appears to be prone to baroclinic instability that mainly appears in the summer months in the form of cyclonic-eddies (more saline center) and anti-cyclonic eddies (less saline center), which peak in August. In this instability, in addition to seasonal changes and density stratification, the role of friction is important. Spectral analysis of water characteristics in the salinity front shows the time scales of eddies, which varies from several hours to about three-months. Once the drag coefficient is halved and then doubled. The results indicate that the advance of the salinity front into the Persian Gulf has an inverse relationship with the drag- coefficient. Also, in lower friction runs, anticyclone-eddies is not observed, but in higher friction mode, both cyclonic and anti-cyclonic eddies were observed.

This study evaluates the application of Atmosphere-Land Surface Interaction System (ALSIS) scheme in simulating the streamflow in Karkheh river basin. The Climate Forecast System Reanalysis (CFSR) data for the period 1982-2011 are used as atmospheric forcing data and sub-grid scale heterogeneity of the land-surface is represented by soil-vegetation mosaics. The cascade of linear reservoirs model is used for modelling the base flow and a routing model, linked to the land surface scheme, is used for modelling river discharge. The comparison of simulated and observed streamflow in six hydrometric stations over Karkheh basin reveals the model ability in simulating the monthly streamflow. Moreover, the model has a good ability in simulating the monthly regime of water balance components, spatial distribution of long-term average of components and their relationships.

Determination of affected area by seeding agents, the so-called target area, is an essential requirement for evaluation of cloud seeding projects. The most conservative and credible estimates of seeding effects were obtained from control matches drawn from outside the operational target within 2 hours of the time that each unit was seeded initially (DeFelice et al., 2014). A coupled modeling system consisting of the mesoscale WRF model and the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT), provides capability to simulate the transportation and dispersion of seeding materials and to characterize target area on the map.
This study is devoted to sensitivity analysis of simulated dispersion patterns to several parameters including different configuration based on physical parameterizations used in WRF model, horizontal and temporal resolution of WRF and spatial resolution of HYSPLIT, to determine the most probable dispersion patterns.
Since temperature and wind parameters are the most important parameters in cloud seeding operations, they are measured instantaneously at 1-second intervals at the flight height of the airplane during each flight and therefore, they are very valuable data to assess the performance of the WRF model in simulating these fields. Hence, at first the WRF model outputs such as temperature and wind are validated by data measured by the airplane. Results indicate that there is an acceptable agreement between field data and WRF outputs that are going to be used as input data for dispersion model.
In this study, eight configurations of the WRF model based on different physical parameterization schemes are used for 34 flights in cloud seeding project in 2015 and HYSPLIT model is run by these types of input data and resulting target area are compared on the map. Then, HYSPLIT model is run for four selected seeding operations according to three temporal and two horizontal resolutions of input data in addition to three spatial resolutions of HYSPLIT model and the transport of seeding plumes is characterized on the geographical map.
The results indicate that dispersion model is sensitive to all mentioned parameters. Also, in most cases, dispersion model results at the flight height of cloud seeding aircraft are significantly influenced by the input data provided by the WRF model. In addition, the dispersion model results are less sensitive to other parameters. Furthermore, when the spatial resolution of the HYSPLIT model is close to the horizontal resolution of the input meteorological data provided by the WRF model, affected area of seeding agents is more integrated and therefore there is a greater degree of certainty in determining the target area.