majid azadi

In many cases, ensemble weather forecasts produced by numerical weather prediction (NWP) models exhibit systematic bias and under-dispersion. Over the past two decades, various ensemble post-processing approaches have been developed to address this issue. These approaches include classical methods such as ensemble model output statistics (EMOS), Bayesian model averaging (BMA), and advanced machine learning-based approaches.In most ensemble post-processing approaches, it is implicitly assumed that there is statistical independence between different forecast margins, such as lead time, location, and meteorological variables. However, this assumption is not valid for realistic forecast application scenarios. End users may be interested in scenarios such as total hydrological basin precipitation, temporal evolution of precipitation, or the interaction of precipitation and temperature, especially when temperatures are close to zero degrees Celsius. Important examples include hydrological applications, air traffic management, and energy forecasting. Such dependencies exist in raw ensemble forecasts, but these dependencies are ignored if standard univariate post-processing methods are applied separately to each margin.In recent years, various multivariate post-processing methods have been proposed. These methods can be categorized into two approaches. The goal of the first approach is to directly model the joint distribution by fitting a specific multivariate probability distribution. This approach is mainly used in low-dimensional problems or when a specific structure is chosen for the application at hand. For example, multivariate models for temperature across space, for wind vectors, and joint models for temperature and wind speed.The second approach is a two-step approach. In the first step, univariate post-processing methods are applied independently to all dimensions, and samples are generated from the resulting probability distributions. In the second step, the multivariate dependencies are recovered by reordering the univariate sample values according to the ranking order structure of a specific multivariate dependence pattern. Mathematically, this is equivalent to using a copula (parametric or nonparametric). Examples include ensemble copula coupling (ECC), Schaake Shuffle, and the Gaussian copula approach.This paper presents multivariate ensemble post-processing of temperature, two meter above ground using the ECC approach. The EMOS method is used for univariate post-processing. The performance of the raw ensemble, EMOS post-processed ensemble, and ECC systems is evaluated using energy score (ES) and variogram score (VS). The ECMWF 51-member ensemble system is used as raw data for the period from January 1, 2018 to December 31, 2023.The results showed that in addition to eliminating the bias of the raw ensemble forecast, the ECC method also preserved the dependence structure between the ensemble members. In contrast, the EMOS method only eliminated the biases without considering the dependence between the ensemble members. Because of its ability to preserve the dependence structure, the ECC method was able to achieve significantly better results than the EMOS method on a variety of metrics, including energy scores and variogram score. This suggests that the ECC method is a valuable tool for ensemble post-processing, and that it should be considered for a wide range of applications.

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).

Although the accuracy of wind-driven wave height simulation largely depends on the quality of the forced wind field, despite the production of high-quality wind products, the wind fields used for ocean waves modeling are more or less subject to bias. Minimizing the impact of wind errors on wave model output is not a long-term operational solution. A more common method is to calibrate the wave model, which is generally implemented by setting certain coefficients in these models. In the present study, the sensitivity of the simulations of the significant wave height to the terms of the wind input and whitecapping dissipation in a third generation wave model has been investigated. For this purpose, 36 different configurations of the wave model have been used. the results of this study allow us to see the response of the SWAN model as a function of physical parameters. Calibration of the SWAN model using the ERA5 forced wind field significantly improved the simulation accuracy in the Oman Sea. For the Oman Sea, it is recommended to use the expressions of wind input and whitecapping dissipation based on the Janssen formulation with the coefficient C_ds=4.0 in the simulation. Considering the error statistics, the same finding was obtained when the model results were compared with the measurements of the wave recorder buoy of the I.R. of Iran Meteorological Organization (IRIMO) in the northern part of the Oman Sea.

Climatology study of fog at Mashhad airportAbstract Low horizontal visibility caused by fog can affect air traffic and in some cases is the main cause of air accidents. Fog is a condition in which water droplets or ice crystals in the air layer near the Earth's surface reduce horizontal visibility to less than 1000 meters. Fog is one of the major causes of flight delays and accidents. In fact, fog is the second hazardous weather event affecting aviation activities (Gultepe et al., 2019). The effects of fog in the aviation industry can cost hundreds of millions of dollars due to flight delays and cancelations (Gultepe et al., 2017). Fog event at Mashhad airport has repeatedly delayed or canceled flights. Therefore, study the climatology of the fog event at this airport helps to better understand this phenomenon and improve fog forecasting. For this purpose, all types of fog events were detected and analyzed using the hourly observation data of METAR during the statistical period from 2001 to 2020 and based on Tardif and Rasmussen (2007) algorithm. Then the fog climate was studied during the period. The results showed that CBL fog is the most common type of fog in terms of frequency with 43.94% of all fog occurrences at Mashhad Airport. Precipitation fog is also the rarest type of fog among fog types with 22.72% of all fog occurrences. Advection fog, which is formed under the influence of the marine environment, was not observed at this airport. Given that radiation fog usually forms at night and usually dissipates after sunrise, this type of fog has been the longest-lasting type of fog at the airport during 16 years of study. Also, the duration of CBL fog was the shortest one compared to other types of fog events.The minimum visibility of radiation and CBL fog events at this airport was lower than other types of fog. Also, precipitation fog had the lowest concentration compared to other types of fog events, which is consistent with the results of Tardif and Rasmussen (2007). The most reports of fog occurrence at Mashhad Airport during the studied years was at midnight and before sunrise, which could be due to the radiation cooling at night and before sunrise, which reaches its maximum (Hoch et al., 2011; Cséplő et al., 2019; Zouzoua et al., 2021; Wærsted et al., 2017). This result is consistent with the study of Cséplő et al. (2019) and Tardif and Rasmussen (2007). Tajbakhsh (2015) has also reported the maximum occurrence of fog at 21, 00 and 03 UTC using 20-years synoptic data at Mashhad Airport. Also, the frequency of fog occurrence decreases rapidly after 03 UTC.The monthly distribution of fog types in Mashhad Airport showed that radiation fog events often occur in winter and CBL fog events often occur from late autumn to mid-spring. Boundary layer cooling is the most important process that causes fog event in spring, while winter fog occurrences can be related to large-scale atmospheric systems (Tardif and Rasmussen, 2007). The maximum monthly frequency of precipitation fog event at this airport is in the winter. The fog events in the autumn can also be related to these weather systems. Since precipitation fog events depend on large-scale factors (Tardif and Rasmussen, 2007), the maximum occurrence of this type of fog is observed in winter. In general, the frequency of fogs in December and January is higher than other months of the year. This result is consistent with Tajbakhsh (2015).In terms of annual changes in fog occurrences, there was no significant trend in the number of fog hours during the studied years (2001-2020). In terms of fog concentration, in all months of the year, the number of semi-dense fog events with minimum visibility of 100 to 500 meters was more than other types. Also, the number of fog events with minimum visibility of 100 meters and then 200 meters had the highest number. It shows the importance of climate investigation and prediction of the fog event at this airport from the point of view of the aviation industry, because dense fog events cause difficulties in the landing and takeoff of airplanes. Keywords: Fog climatology, Fog type, Radiation fog, Precipitation fog, CBL fog.

Numerical weather prediction (NWP) models are not completely accurate and error free, and there is always some uncertainty. The errors in weather forecasting stem from the limitations of human theoretical understanding of the atmosphere and the operational capacity to produce forecasts. It is necessary to make a forecast, along with an estimate of its uncertainty. This is accomplished by creating ensemble systems of weather forecasts differing in the initial conditions or physical formulation of NWP models. There are several methods for post-processing of ensemble forecasting, including Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) that they are more popular because of higher efficiency and accuracy. In this research, first, an 18-member ensemble system is formed, which each member is an independent run of the WRF model with different physical configurations. BMA method was used to estimate the density function of predicting 24-hour cumulative precipitation. Due to some hardware limitations and access to an ensemble system with fewer number and more efficient members, the size of the ensemble system has been reduced to 7 members. Using the BMA method, a weight is assigned to each ensemble member. The size of the ensemble system is reduced by removing the members who had less weight. The probabilistic prediction verification obtained from the 7-member ensemble system in a test period from 15 January 2020 to 15 May 2020 has been checked using reliability diagram. The results show that the probabilistic predictions are sufficiently skilled for 24-hour cumulative precipitation.

The presence of fog reduces the horizontal visibility to less than 1000 meters and disrupts air transport services and can make it impossible for aircraft to land and take off. Climatology of fog can help better diagnosis and prediction of fog. In this study, METAR data from 2011 to 2020 were used to detect fog events at Ardebil Airport and according to the classification algorithm of Tardif and Rasmussen (2007), the types of events were determined. Then the fog climate was studied during the period. The results showed that the most common type of fog at Ardebil Airport is radiation fog and the rarest type is precipitation fog. In terms of duration of fog event, radiation fog was the longest fog event and precipitation fog was the shortest fog event. The highest incidence of fog during the study years was at 3 and 4 GMT. The monthly distribution of radiation fog showed that events often occurred in autumn and early winter (September to December). In terms of annual changes in the occurrence of fog, there was no significant trend in the number of fog hours during the studied years, except for the last three years which had a decreasing trend.

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).

Net profit and its adjustments are among the most valuable information used by investors. This study seeks to answer the question whether company conditions (ambiguity or inability to understand economic information) affect the relationship between forecasting error and adjustment of profit forecasting by management or not? For this purpose, first, the relationship between accruals (abnormal) and error and adjustment of profit forecast, test and then the effect of ambiguity and inability to understand economic information on the above relationship is examined. The research sample includes 91 companies listed on the Tehran Stock Exchange. Findings indicate that there is a positive and significant relationship between working capital accruals and abnormal working capital accruals with management forecast error, but there is a negative relationship between working capital accruals and abnormal working capital accruals wThere is significance. Also, conditions of ambiguity did not affect this relationship, but the inability to understand economic information strengthens this relationship.ith negative forecast and adjustment.

One of the consequences of the increase and accumulation of greenhouse gases in the atmosphere is kown as global warming, which is undoubtedly one of the most important environmental challenges in the world, especially in the Middle East. Given the scarcity of water resources in recent years, the consequences of global warming and climate change in various countries have reached a very worrying level. Carbon dioxide and Methane are known as two of the most important human greenhouse gases in the atmosphere, accounting for 64% and 18%, respectively, of long-lived radiation induction (LLGHGs) (Forster et al, 2007). Methane is considered as the second most important anthropogenic greenhouse gas after Carbon Dioxide.The most important sources of Methane emissions include: biomass incineration, artificial human emissions, wetland emissions, and wastes. Despite the importance of Methane for physical and atmospheric conditions, the spatial distribution of global resources and Methane sinks is not well understood. With the launch of Methane measurement from satellites, knowledge about the global distribution of Methane in the atmosphere greatly increased.The Japanese Greenhouse Gas Satellite (GOSAT) is the only satellite that measures the column mixing ratio of atmospheric Methane. Since the 1990, various global models have been used to simulate CH4 concentrations. High-resolution simulation of CH4 at hourly intervals on Earth, with diverse ecosystems, due to the lack of intensive spatial and temporal measurements and the impossibility of reliable validations for chemical simulations are known as a serious challenge. The main purpose of this study is to understand the performance of the WRF-GHG model in simulation of Methane concentration and validation the results of medium-scale modeling output in total Methane concentration in comparison with GOSAT satellite observations over Iran.

Iran and some area of its surroundings is considered as a study area. This study focuses on two case periods of hot and dry (August 31-2010) and cold and wet (February 1-28, 2010). In order to provide the initial and boundary conditions of meteorological fields, ERA5 reanalysis data were used with a horizontal resolution of 0.25 ° with a time resolution of 6 hours. Different emission input data from three different global greenhouse gas emission databases EDGAR_v5.0 (anthropogenic emissions), GFAS emissions (fire emissions), and datasets (CMS_V01) (wetland emissions) have been used.Preliminary and boundary conditions for the chemical fields taken from atmospheric monitoring service data (CAMS) with a spatial resolution of 0.8º with 137 vertical levels and with 6 hours time resolution. To investigate and quantify the validity of meteorological fields simulated by the WRF-Chem numerical model, a set of observations of selective synoptic stations is used. For validation of CH4 WRF_Chem column concentration and statistical analysis, in the points that include remote sensing data (GOSAT sensor data), is used the set of level 2 products generated by the NIES algorithm. The local transit time of the GOSAT satellite Is approximated around 13: 00_9: 00, so the simulated concentration for this time is applied in the analysis. The first 15 days of the simulation are omitted to take into account the spin-up time. Statistical parameters of mean bias error (MBE), mean absolute error value (MAE), root mean square error (RMSE), and Pearson correlation coefficient (R) in meteorological and chemical variables are studied for validation of numerical simulations and quantification of error levels.

The model has been able to calculate temporal changes in surface temperature, relative humidity, and wind speed to some extent correctly. The general tendency of the model to simulate the observed temperature and relative humidity for the selected time period is evaluated. In general, model values are closer to summer observations than all thirty days in two selective months. The wind speed forecast is often consistent with the wind speed values obtained from the measurements, and in most cases, the wind speed is overestimated at around 1.2 m/s.Statistical evaluations of the WRF-Chem model, together with the GHG gas-phase chemistry mechanism, show the simulation of Methane concentration versus observations by the GOSAT satellite, and the estimation of the average monthly concentration in February and August 2010. The values of MAE, RMSE, RMSE_u, RMSE_s, BIAS and R are calculated equal to 42.92, 46.05, 7.82, 44.60, -24.99 and 0.63, for hot and equal to 12.01, 13.94, 7.09, 11.68, 7.50 and 0.76 ppb, for cold periods respectively. It can be seen that the WRF-Chem model performed better in Methane simulation in cold and wet periods (January) compared to the hot and dry seasons (August).

In this study, the WRF-Chem model was used to simulate meteorological variables and air pollutants (methane greenhouse gas) concentrations in the Middle East-Iran region during the study period of February and August 2010. The sensitivity of the model is considered using the GHG gas-phase chemistry scheme. The main findings of this study are: The model is able to reproduce temporal changes in surface temperature, relative humidity, and wind. The model underestimates the air temperature and relative humidity respectively around, 1/05 ⸰C -5% in the study area (Iran).In simulating of Methane concentration, and examining the results with related GOSAT satellite observations, the model overestimates around15 ppb of the Methane concentrations. The evaluation results show that the WRF-Chem model performs better in the cold season (January) than in the warm season (August). This uncertainty in CH4 simulation can be attributed to a deficiency in various input components of the CH4 emission in different categories. Improving the simulation for the various parameters reported to the model as the primary CH4 emission can generally help to improve the CH4 simulations.

In this study, the results of 24 and 48 hour predictions of mid-scale WRF model with nested slopes with 18 and 6 km separations (implemented in Lorestan meteorology) and with 27 and 9 km separations (implementation) At the Institute of Meteorology and Atmospheric Sciences), without schematic, for a period of 2 months from March 1, 2019 to the end of April 2019 and compared with precipitation observation data for 10 synoptic meteorological stations in Lorestan. For this purpose, 2 * 2 agreement table was used for verification. The results obtained from PC skill score showed that in a period of 24 hours, the ranges of 27, 18 and 9 km in more than 80% of cases were able to occur or not rain. At the provincial level, correctly predict that this score was a minimum of 6% for a range of 6 km. Also, studies performed for a period of 48 hours showed that all slopes in more than 77% of cases showed the accuracy of the occurrence or absence of precipitation. The results of validation in this study have been for days with precipitation and no specific threshold has been considered for precipitation values. It is suggested that in order to improve the relative weakness of the model, validation quantities for specific thresholds (light precipitation, Moderate rainfall and heavy rainfall). The results obtained from the PC skill score showed that in a period of 24 hours, the slopes of 27, 18 and 9 km in more than 80% of cases were able to accurately predict the occurrence or non-occurrence of rainfall in the province. The range of 6 km was minimal and amounted to 67%. Also, studies performed for a period of 48 hours showed that all slopes in more than 77% of cases showed the accuracy of the occurrence or absence of precipitation.* The average quantity of B slope for 4 slopes showed that the number of precipitation forecasts for a period of 24 hours in the surveyed slopes is between 1.39 to 1.49 percent higher than the cases in which precipitation occurred, which indicates that the previous precipitation was higher. The occurrence of precipitation is relative to the occurrence of precipitation. For prediction over a 48-hour period, this quantity is slightly improved and has less error.* The average quantity of TS in the forecast for a period of 24 hours is more than 66% for the ranges of 27, 18 and 9 km, which with the increase of the forecast period to 48 hours, this quantity has improved and in all 4 ranges to more than 72%. it is arrived.* The quantity of FAR in a 24-hour period for all 4 ranges varies from 29 to 38% on average, indicating that only 29 to 38% of the precipitation predictions have not been met. In a period of 48 hours, this quantity has also improved a bit and has reached 21 to 26. The results of quantifying the H collision rate over a 24-hour period indicate that the three slopes of 27, 18 and 9 km had a high ability to predict the occurrence of positive precipitation. Also, in a period of 48 hours, almost all 4 domains had high power.* Examination of the average quantity of F shows that in a 24-hour period for slopes 9, 18 and 27 between 29 to 33% of the cases where no precipitation has occurred, the model has erroneously predicted that this error is 47% for a range of 6 km. Is. In a period of 48 hours, the rate of this error for 4 domains has reached 35 to 50%, which indicates an increase in this type of error with increasing time interval.* Quantity of Pierce PSS skill score in 24-hour period shows 39 to 69% improvement for the studied slopes, among which the 9 and 27 km slopes have better performance. In a period of 48 hours, the value of this quantity has reached 48 to 61%, which, unlike the period of 24 hours, is the best value in the range of 6 km. In order to evaluate the model more accurately, further case studies are suggested in different seasons of the year.* Increase model execution time.* The model should be executed with different schemas.* The results of validation in this study have been for days with precipitation and no specific threshold has been considered for precipitation values. It is suggested that in order to improve the relative weakness of the model, validation quantities for specific thresholds (light precipitation) , Moderate rainfall and heavy rainfall).* The results of this research should be compared with GFS and ECMWF models.

Seasonal forecasting has always been one of the challenges in forecasting Iran's diverse climate. In the last one or two decades, many efforts have been made to develop and improve the climate models of the restricted area and to minimize these challenges, but the problems and challenges still remain. Convective parameterization schemes are always one of the sources of error in regional climate models that have a significant impact on model outputs. Therefore, one of the most important issues in implementing the model is choosing the appropriate convective scheme from the existing schemes. One of the methods of forecasting precipitation in our country is the use of dynamical downscaling by RegCM model. Most of the studies that have been done for this purpose in the country so far have considered single convection schemes for the whole country, the results of which have not shown a significant improvement in rainfall forecasting.

In this study, a relatively new approach was adopted, so that convection schemes were selected appropriate to the climate of the region, and then the final forecast of the entire country by regional integration of each climate zone was presented. In this paper a relatively new perspective of the climatic zones of the regions, was used for optimum configuration of the RegCM4.5 model; The study area in this study is Iran, which includes 25 to 41 degrees north latitude and 47 to 63 degrees east longitude, but the model area ranges from 30 to 70 degrees east longitude and 10 to 55 degrees north latitude. It covers important geographical features, including mountains and seas. In this study, the output of the CFSv2 global climate model originating from November 1 in each year as the boundary condition data has been used and the CRU precipitation data has been used as reanalysis data to test the output of the RegCM model. Because CRU data are averaged monthly, they are suitable for studies that examine monthly averages. CRU data have already been used by various researchers in the country to validate the output of the RegCM model. After selecting the schemes of the planetary boundary layer and surface layer, the selection of the appropriate Cumulus Parametrization Schemas(CPS) was done based on Iran's climatic classification using the Demarten index. This method is the simplest and most common method for climate classification that precipitation and temperature variables are effective in calculating climate index, and precipitation and temperature data have also been used from the CRU database. For this purpose, Iran was first divided into seven very humid, humid, semi-humid, Mediterranean, semi-arid and arid climates based on the Demarten index, and each grid points of the study area were assigned the relevant climate index. The share of each climatic class in zoning was obtained as follows; Arid 32.4%, Semi-Arid 30.1%, Mediterranean 7.6%, Semi-humid 7.6%, Humid 10.5% and Highly humid 11.8%. The study period was 5 rainy seasons 2019-2014 (November to May) that the beginning of each simulation with the initial condition data on the first of November and its end at the end of May (as the end of the rainy season in the country) in each year. The horizontal resolution considered to be 30 km regional model, the planetary boundary layer schemes and the surface layer Holslag and BATS were considered, respectively. Kuo, Grell, Emanuel, Tiedtke and Kain convection schemes were tested during this period to achieve optimal configuration.

In the first stage, mean precipitation and its RMSE from individual and integrated schemas were calculated, but due to the fact that the Emanuel and Kain schemes did not rank higher in any of the model experiments in terms of climatic classes and have more errors than others, theywere removed from the configuration selection process. The results showed that in very humid, humid, semi-humid and humid climates the Tiedtke convection scheme, in the semi-arid regions the Grell scheme and in the arid areas of the Kuo scheme had the least bias compared to other convection schemes. Therefore, the seasonal forecast of the country was presented by combining regional schemas, the average bias of which was calculated at 0.45, 0.79, 1.01 and 0.69 mm in the integrated schemes of Tiedtke, Grell and Kuo, respectively. On the other hand, in addition to calculating the ability of different schemas to predict precipitation using the RMSE index, the area under the ROC curve was also calculated in three classes less than normal (BN), normal (NN) and more than normal (AN) for different climates. For this purpose, in each precipitation layer, the number of schemas that predicted precipitation in different climates and had the largest area under the curve compared to other schemes was extracted. ROC diagrams of different schemas showed that Tiedtke and Grell schemas have the highest ability to predict less than normal, normal and more than normal rainfall classes. The results showed that the regional integrated scheme (TGK: Tiedtke, Grell and Kuo) showed an improvement of 54 to 126% compared to the individual schemas. In general, it can be said that choosing the optimal configuration based on the idea of climate-based convection scheme can increase the performance of the RegCM4.5 regional model in seasonal precipitation forecast in Iran.

Although a study with a regional climatic zones perspective was not found on Iran, but some studies have found the Tiedtke scheme suitable for our country (Alizadeh Choubari et al., 1398), which with the findings of this study in which the Tiedke scheme for four of the six climates used in this study are considered appropriate. On the other hand, Zarrin and Dadashi (1399) used the Grell scheme to study the events of the partial rainfall in Iran by RegCM4 model, which in this study was found to be suitable for semi-arid climate. In addition, it was observed that in the study period of seven months, the most RMSE error occurred in April, which is the month of transition from cold to warm season.

In this study, numerical simulation of two advection and radiation fog events at Ardabil Airport in January 2015 was performed using the Weather Research and Forecasting (WRF) model and SW99 visibility algorithm. Six Planetary Boundary Layer (PBL) schemes including YSU, MYJ, ACM2, MYNN2.5, MYNN3 and QNSE were used to evaluate the sensitivity of fog simulation to the PBL schemes. The results show that the simulation of these two fog events is sensitive to PBL. Also, due to the importance of accurate prediction of 2-m relative humidity, temperature and dew point temperature and 10-m wind speed in fog forecasting, the sensitivity of simulation of these variables to PBL scheme was investigated. The results showed that the simulation of advection fog event was successfully performed using most of PBL schemes mentioned above. YSU, ACM2 and MYNN2.5 schemes performed better in simulation of advection fog. The QNSE scheme was not successful in simulating the advection fog event. In radiant fog simulation, most PBL schemas were not able to simulate the moisture required to form fog at the time of fog occurrence.Most PBL schemes were not able to simulate the moisture required for formation of fog at the time of the radiation fog event. A few hours before the onset of the radiation fog event, the WRF model with most schemes simulated the visibility reduction due to the fog. Generally, QNSE and MYJ schemes performed worse than other schemes in simulating temperature, dew point temperature, relative humidity and wind speed.

In this study, the effect of horizontal and vertical resolution on the precipitation forecast has been investigated. For this purpose, in this study, heavy rainfall occurred in the western region of Iran using GFS data as boundary and initial conditions in the WRF mesoscale numerical forecasting model with two horizontal resolutions and four combinations of position and number of vertical levels has been simulated. The results of this study show that on days when the intensity of precipitation is significant, the results of precipitation simulation improve with increasing horizontal resolution. Also, increasing the resolution of the model in the vertical direction does not necessarily lead to an increase in precipitation forecasting skills and sometimes over-predicted rainfall. Skills of forecasting improved for all rainfall thresholds when the resolution above the melting level was increased. And this is because of better prediction microphysical processes. Also, increasing the resolution of the model in the lower layer of troposphere only in heavy rainfall leads to an increase in forecasting skills. Therefore, the effect of increasing the resolution of the model on forecasting skills is strongly related to the height of added levels and rainfall intensity. In addition, the factor separation methodology shows that with increasing the resolution of the model throughout the troposphere layer, the prediction skill generally decreases in the mean state, and this subject is due to the negative interaction between lower-tropospheric processes and microphysical processes above melting level.

In the present study, using the ERA-Interim reanalysis data for geopotential height, horizontal wind speed and relative vorticity at 300, 200 and 150 hPa levels, the quasi-geostrophic potential vorticity, the quasi-geostrophic potential vorticity gradient, the wave activity and wave activity flux for cyclonic and anticyclonic Rossby wave breaking events that occurred over West Asia during the winter time 1979-2018 were calculated and analyzed. The mechanism of Rossby wave breaking during five days before to five days after break was analyzed. From three to five days before Rossby wave breaking events, the formation and development of wave were initiated. In the five days before anticyclonic breaking, ridge and trough informed with vertical axis potential vorticity penetration across the axis of the trough in the 200hPa about 5PVU. From four days before breaking, the ridge penetrated to north of Europe. It caused to intensify jet and form critical latitudes. Equatorward reflection of ridge and trough caused trough to penetrate to lower latitude with anticyclonic circulation. Anticyclonic circulation reinforcement caused formation of equatorward wave activity flux and divergence of wave activity flux in the two regions of lower latitude. Through the anticyclonic breaking, the NE-SW slope of axis of trough increased and potential vorticity rose to 7PVU. In the breaking days, the weakening of jet was intiated in the upstream of trough on the north of Europe. Through two days after anticyclonic breaking, equatorward wave activity flux in the downstream of trough was weakened and divergnce region of wave activity flux was split into areas in Europe and Mediterranean regions. From three days after breaking, weakening of jet in the downstream of trough was initiated. First the upper part of wave passed from middle latitude and then the lower part passed from subtropical latitude. The process of cyclonic breaking was intiated approximately five days before breaking. Four days before cyclonic breaking, ridge and trough informed with NW-SE axis potential vorticity penetration across the axis of trough in 200hPa about 3PVU. In cyclonic breaking, the formation of the ridge in the midlatitude caused zonal velocity to intensify and the formation of critical latitude over Europe. During three days before wave breaking, the jet in the upstream extended to downstream of trough, the NW-SE slope of the axis of trough increased and potential vorticity rose to its maximam about 6.5PVU in the midlatitude. During these days, poleward reflection of wave activity flux caused the divergence of the wave activity flux in the downstream of trough and cyclonic circulation to increase. Rossby wave breaking mechanism was similar in the different areas of East Atlantic Ocean until West Asia. In these regions, the meridional wave activity flux in the anticyclonic wave breaking was more than in the cyclonic wave breaking. However, equatorward (poleward) wave activity flux in the anticyclonic (cyclonic) wave breaking on the east of Atlantic Ocean and Europe was similar to (twice as many) on the east of Mediterranean and West Asia. Approximately, zonal wave activity flux in wave breaking on the downstream of troughs on East Atlantic Ocean and Europe was twice as many on East Europe and West Asia; just as the wave amplitude on Europe and east of Atlantic Ocean which was higher than on the east of Mediterranean and West Asia. Due to wave breaking, wave activity flux on the east Atlantic and Europe was stronger than on the east Mediterranean and West Asia.

In the present study, using the ERA-INTERIM reanalysis data for geopotential height, horizontal wind speed and relative vorticity at 300, 200, 150, 100 and 50hPa levels, the quasi geostrophic potential vorticity, the quasi geostrophic potential vorticity gradient ,the wave activity and wave activity flux for cyclonic and anticyclonic Rossby wave breaking events that occurred over Europe during the winter time 1979-2018 in the westerly and easterly phase of quasi biennial Oscillation, were calculated and analyzed. The mechanism of Rossby wave breaking during five days before to five days after the wave break were analyzed. The Results show that in the anticyclonic breaking event over west Asia in the QBOe, the poleward displacement of jet in the upstream of trough to upper latitude over the Europe is more consistent than for the QBOw. Whereas in the cyclonic break in the westerly phase, jet on the upstream of trough over the west of Mediterranean sea displace to lower latitude over the Europe more than that pf the easterly phase. Therefore in the anticyclonic wave breaking over the west Asia in the QBOe compared that of to QBOw, the amplitude of the waves increase. The QBOe in the anticyclonic breaking causes increasing altitude on the upstream trough over the Europe and decreasing altitude on the downstream trough over the east Europe and Mediterranean and also causes increasing altitude over the east of Atlantic ocean. In the cyclonic breaking, QBOe causes increasing altitude on upstream of trough over the west of Mediterranean and decreasing altitude on the downstream of trough over the east of Mediterranean region.In the anticyclonic wave breaking on the west Asia and east Mediterranean in the QBOe, anomaly jets velocity and following the formation of critical latitude on north Europe is stronger than the critical latitude in the QBOw. The QBOe causes poleward displacement the jets and critical latitude as compared to that of the QBOw. In the anticyclonic wave breaking over west Asia, formation of extended ridge over Atlantic ocean and Europe causes settlement of the narrow trough on the west Asia. In the QBOe, the jet intensifies over north of Europe and critical latitude on the upstream of trough form stronger, QBOw. Equatorward wave activity flux due to anticyclonic breaking in the QBOe is more than that of the QBOw. Therefore the anticyclonic wave breaking in QBOe is stronger than QBOw.   In the cyclonic waves breaking, jets on the upstream of trough over Europe and jet on the downstream of trough over east Mediterranean are formed across north westerly- south easterly. In the QBOe, jet on the upstream of trough intensifies on the upper latitude as compared to the QBOw. Following this the critical latitude have poleward displacement. In the QBOe, north westerly-south easterly slope of trough is more than QBOw and the trough on the Mediterranean and east Europe has lower altitude compared to that for the QBOw. The poleward wave activity flux due to cyclonic wave breaking is more in QBOe compared to that for the QBOw. Therefore the cyclonic wave breaking is stronger in QBOe compared to that for the QBOw.Whereas in the anticyclonic wave breaking over west Mediterranean in the QBOw compared to that for the QBOe and the meridional gradient of quasi geostrophic potential vorticity is stronger and meridional wave activity flux is more. Therefore the anticyclonic wave breaking over west Mediterranean in the QBOw is stronger compared to that for the QBOe.

Wind energy has been considered as one of the clean energy sources. Due to the variability of wind speed and its effect on wind power plant, wind forecasting methods are of special importance. Fossil fuel consumption has destructive effects on the environment. According to Renewable Energy Policy Network for 21st century(REN21st) in 2014, nearly 20% of the total electricity was generated by wind energy, and the European Wind Energy Association (EWEA) predicts that it reaches 24.4 % in 2030. Wind speed has a great impact on increasing or decreasing air pollution and thus human health. Also it is one of the most important and effective factors in evaporation. Accurate prediction of wind speed is crucial in many social applications such as weather warnings in risk assessment and appropriate decisions in aviation, ship navigation, recreational sailing and agriculture.With the advancement of computers and the ability to perform fast calculations, it became possible to implement weather forecasting models. At first, looking at weather forecasting was only a deterministic forecast, but since numerical weather prediction models include differential equations that approximately describethe physical and dynamic laws of the atmosphere, the answer obtained from the implementation of numerical models is an approximation of the real answer and is always in error. Also, factors such as the existence of errors in the initial boundary values, the inability of the model to take into account all atmospheric processes, the lack of primary data in some areas, the inability of the model to successfully simulate subnet phenomena and the chaotic nature of the atmospheric dynamic system can be mentioned. Hence, forecasting in such a system will be accompanied by high uncertainty. Therefore, to determine how the weather will be in the future, taking into account the mentioned uncertainty, there is an approach called probability forecasting. In this method, by using probabilistic prediction the chance of possible occurrence of future states of the atmosphere is calculated and the uncertainty is quantified and can be obtained more and accurate information. In this way, instead of considering only one model with an initial value, with a physical schema and a dynamic core, it can be created a finite number of prediction models by changing each of these three cases. In this study with the change in the model physics schema, the uncertainty caused by the model physics is considered and each produced prediction is considered a member of the system which is different from others. By applying statistical methods to the members of the ensemble system, a probability distribution function will be obtained in which post-processing is also performed and describes the uncertainty of the future state of the atmosphere and includes sufficient information for the needs of different users.

In this study, probabilistic wind speed predictions are generated after applying two methods of BMA and NGR on the raw output of ensemble system for 24, 48 and 72 hour forecasts and compared with raw probability predictions of the system which are selected democratic voting. The applied ensemble system consists of eight different physical configurations, with changes in the boundary layer scheme of the WRF model with a resolution of 21 kilometers over Iran.GFS forecasts are used for the initial and boundary conditions, and the forecast start time is 12 UTC per day. Observation data of 31 synoptic meteorological stations located in the provincial capitals have been used and the corresponding values of the predictions on these stations have been interpolated by bilinear method. The model run from 1 March to 31 August 2017, and the results from 11 April to 31 August 2017 are considered as the test period. After calculating the forecast errors with different training periods, 30 days are considered as the length of training period for prediction in both BMA and NGR methods.

Verification was performed by Barrier Score(BS), Barrier Skill Score(BSS), reliability diagram, ROC diagram and Economic Value diagram for 10-meter wind speed threshold less than 3 and more than 5,10 and 13 m/s for BMA, NGR and Raw probability prediction of the system in all forecast ages. The results show that BMA and NGR have improved BS, BSS 33% to 46% from Raw probability prediction of the system and the reliability and the resolution have improved 78% to 97% and 12 to 30 times respectively. Reliability diagram and ROC diagrams of NGR and BMA have been also improved significantly. Economic Value diagram shows also that using probabilistic predictions is important to reduce the cost of meteorological hazard. In both BS and Reliability diagram the quality of NGR was better than BMA. In BSS, Roc diagram and Economic Value diagram the both two post-processing methods had the same advantage. The economic value diagram had the best performance at a wind speed threshold of 10 m/s.

The wind is one of the most important and affecting phenomena and is known as one of the significant clean resources of energy. Apart from other atmospheric parameters, the wind has complex behavior and intermittent characteristics. Local phenomena can be accompanied by the wind, which is strong, non-predicted, and damaging.  Weather radars are capable of detecting and displaying storm-related turbulence as well as precipitation in a relatively wide area. This capability can improve the quality of the wind forecast. In this paper, a method is presented and implemented to forecast the probability of strong wind in the next five hours based on the Hidden Markov Model (HMM). The method is expanded to find out the forecast of wind turbine output power and reliability as well. Achieved results show that about 67% of strong winds are correctly forecasted

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.

In this study, deterministic forecasts of 10-meter wind speed for the next 24, 48 and 72 hours have been produced and analyzed over Iran using BMA and EMOS methods for post-processing of raw outputs of the ensemble systems. The main purpose of this article is to compare deterministic forecasts based on these two methods with each raw ensemble members and the mean of the raw ensemble members. The applied ensemble system consists of eight members with different boundary layer schemes in the Weather Research and Forecasting (WRF) model. Other physical schemes remained the same in the ensemble members. For each ensemble member, the 24, 48 and 72-hour forecasts of 10-meter wind speed have ben conducted over Iran, with a horizontal resolution of 21 km. The Global Forecast System (GFS) is used for initial and boundary conditions of forecasts starting at 1200 UTC for each case. Observational data of 31 synoptic meteorological stations located in provincial capitals have been used for model evaluation, in which model outputs are interpolated to the locations of these stations by a bilinear method. The WRF model is run from 1 March to 31 August 2017, but the results from 11 April to 31 August 2017 are considered as the spin-up period. Indeed, after careful examination of the forecast errors using different spin-up periods, the first 30 days of the simulation are considered as the spin-up for both BMA and EMOS methods. Verification is performed by different methods (accuracy: PC, TS and OR; reliability and resolution: FAR, POFD and POD; skill: CSS, HSS, PSS, GSS and Q; statistical errors: RMSE and MAE) for 10-meter wind speed thresholds less than 3 m/s and more than 5, 10 and 15 m/s for both methods for all forecast lead times. Results indicate significant improvements in accuracy scores (300%), reliability and resolution scores (220%), skill scores (340%). Statistical error scores are also reduced by 24%. Furthermore, applying verification for different climatic regions of Iran (cold, semi-arid, hot-dry, hot-humid and moderate-rainy climate) indicates that in all climatic regions, the best performance in terms of RMSE is for BMA and EMOS methods, with the average reduction of error by 21% and 23% ,respectively. Particularly, in hot and humid climates,  these two methods better improve predictions, and hence, are more promisingas they reduce the error by 44% and 46%, respectively.

Precipitation forecasting is an essential tool for optimal management of water resources and flood forecasting. The numerical weather prediction (NWP) models play a major role in weather forecasting. They predict the future state of the weather by mathematical modeling of the atmosphere behavior, based on its current condition. However, the accuracy of the NWP models is still a challenging issue and its improvement is the main goal of the operational prediction centers. In weather forecasting by using NWP models, there are several resources of uncertainty such as intrinsic chaotic behavior of atmosphere dynamic system, errors in observational data and initial conditions which are almost impossible to remove. Ensemble systems are used to quantify these uncertainties. Instead of only one deterministic forecast, an ensemble system is created by several forecasts obtained from perturbation of the initial conditions, physical schemes or dynamical core of the NWP models. Ensemble systems are widely used by the meteorological communities especially for medium-range weather forecasts, short range and even ultra-short range weather prediction. A probabilistic forecasting of flood and extreme precipitation can be produced by an Ensemble Prediction System (EPS). However in practice, ensemble forecasts are generally under-dispersive and thus are not calibrated, especially for meteorological parameters near the ground level. Several statistical methods have been proposed to post-process the EPS outputs. After post-processing the EPS outputs, the biases in both location and dispersion are removed using a historical database of ensemble forecast errors, and then a predictive probability density function (PDF) can be estimated. The most popular ensemble post-processing methods are Bayesian Model Averaging and Ensemble Model Output Statistics. In BMA method, based on the error statistics of each member during a training period, a PDF is first fitted to every ensemble member forecast. Then the predictive PDF is estimated by weighted averaging of members' PDFs. Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity LR was among the first statistical methods that were used to post-process the EPS output. Logistic Regression (LR) was extended to provide a full continuous predictive PDF. In the extended Logistic regression (ELR), the predictions and thresholds are used as additional predictor variables. In this study, an EPS was developed using eight different configurations of the WRF model to produce probabilistic precipitation forecast over Iran. Initial and boundary conditions for WRF were provided from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecasts with a horizontal resolution of 0.5. The BMA and ELR methods were used to calibrate the probabilistic forecasts of rainfall in the fall and winter of 2016-2015 over Iran. The data were separated to two parts of equal periods. The first and second parts of the data were used for training and test respectively. The calibrated probabilistic forecasts were assessed using reliability and Relative Operating Characteristic Curve (ROC) diagrams, ROC Skill Score, Ranked Probability Score (RPS) and Ranked Probability Skill Score RPSS at the thresholds of 0.1, 2.5, 5, 10, 15 and 25 mm. For ensemble probabilistic forecasting, BMA was used as a statistical technique that combines inferences and predictions based on individual ensemble members, so as to yield a more skillful and reliable probabilistic prediction. It was assumed that the forecast PDF of a weather variable y is conditional on the ensemble member forecast f_k: p_k=(y|f_k). The BMA ensemble forecast is essentially an average of forecasts based on individual members weighted by the likelihood that an individual forecasting model is correct given the observations. LR was used as a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predicted is a probability rather than a measurable physical quantity. The mathematical form of the LR equation yields ‘S-shaped’ prediction functions that are strictly bounded on the unit interval (0<p< 1).

In this report, we compare data products from three different algorithms with the reference data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs)  in the Total Carbon Column Observing Network (TCCON), with the 8 selected sites in five years(2011-2015). The algorithms evaluated are NIES, ACOS and SRFP algorithms. These algorithms are focused on retrieving the column abundance of the CO2 to take advantage of the molecular amounts of dry air carbon dioxide (XCO2). To evaluate the products of each algorithm with its equivalent ground observations, statistical indices such as Bias error, root mean square error (RMSE), absolute error (MAE), standard deviation (SD), and Pearson correlation coefficient (CR) were used. By examining the values presented by each algorithm and comparing it with the ground observation values, it can be concluded that the NIES, ACOS, and RemoTeC (SRFP) algorithms have the lowest RMSE, Bias and MAE error respectively. The best agreements with TCCON measurements in the most stations were detected for NIES 02.xx. The SRFP algorithm has a significant difference in estimating CO2 retrieving rates compared to the other two algorithms. So that the lowest correlation values belong to the SRFP algorithm and the highest correlation, values belong to the NIES algorithm.

In this study it has been attempted to simulate the occurrence of radiation and CBL fog during late December 2015 in Orumiyeh, Arak, Bushehr, Zahedan, Hamedan and Shahrekord airports, using the mesoscale WRF model. To simulate radiation and CBL fogs, the output of the WRF model with five different configurations as presented in table 1 was used, and then four visibility calculation algorithms were applied to the output of the model to find the best configuration and visibility calculation algorithm for the prediction of the considered fog events. The WRF model version 3.9.1 with Lambert conformal projection, using two nested domains with 16-km and 4-km grid spacing was used for the simulation. Based on the results of some previous studies (e.g. Lin et al., 2017; Van der Velde et al., 2010; Roman-Casc´on et al., 2012), because of the sensitivity of the fog prediction to cloud microphysics, planetary boundary layer and long-wave radiation schemes, five different configurations of the WRF model with varying physical parametrization schemes were implemented. In the first series of experiments, the five mentioned configurations of the model were run using 28 vertical levels. Then four horizontal visibility calculation algorithms including SW99 (Stoelinga and Warner,1999), FSL (Doran et al., 2009), G2009 (Gultepe et al., 2009) and RUC (Benjamin et al., 2004) were applied on the model output to calculate the horizontal visibility separately for each model output and for six synoptic stations in the domain. It is seen that, since the amount of liquid water content (LWC) of the cloud with all model configurations is zero or nearly zero, the calculated horizontal visibility was much greater than 10 km for all stations. In general, the model showed poor results for the simulation of relative humidity in the boundary layer and thus the occurrence of fog. Since some previous studies (e.g. Yang and Gao, 2016; Philip et al., 2016; Tardif, 2007) have emphasized the importance of high vertical resolution to resolve the main fog formation processes, in the next series of experiments the number of vertical levels in the model was increased from 28 to 32, such that 11 vertical levels were considered from the ground level up to 200 m above ground. The model was then implemented with the same five configurations as in the first experiments. Examining the results revealed that the model found the skill of recognizing moisture and fog in most cases, and predicted 5 out of 6 cases of CBL and 2 out of 4 cases of radiation fogs. For the verification the outputs for the predictions of fog events the calculated visibilities were compared with the verifying observational data and hit rate and equitable threat score were calculated. The evaluation indicated that the configuration 2 with 32 vertical levels combined with SW99 and G2009 algorithms performed better for the prediction of visibility for stations considered here. It can also be said that increasing the number of vertical levels close to the surface is of great importance in improving the quality of fog forecasting, because high vertical resolution is required to realistically represent the vertical structure and magnitude of the radiative cooling in the first few meters of the atmosphere, and thus obtain more accurate forecasts of radiation fog. Using high vertical resolution in simulations, results in an acceptable increase of liquid water content (LWC) and thus improves the accuracy of fog prediction. The results also show that the model's skill to predict CBL fog is higher than that of radiation fog in this case study. By comparing the simulated and observational two meter temperature values, it was observed that the simulated air temperature is overestimated, especially for the radiation fog. Previous researches have also shown a positive bias in predicted air temperature at two-meter height in radiation fog conditions, which is probably due to the inability of the model to simulate the actual radiative cooling associated with fog conditions in the first few meters of the atmosphere (Roman-Casc´on et al., 2019).

In the present study, using the ERA-INTERIM reanalysis data for daily mean of geopotential height, temperature, horizontal wind speed and relative vorticity at 300, 200, 150, 100 and 50 hPa levels, the wave activity and wave activity flux for cyclonic and anticyclonic Rossby wave breaking events that occurred during the easterly and westerly phase of QBO, over Europe for the winter time 1979-2018, were calculated and analyzed. Results showed that during wave breaking events at latitudes around 20-50N are affected by QBO with easterly and westerly phases. As such, the wave breaking events over the Atlantics (east of the Meditranian and west of Asia) during QBOe are weaker (stronger) compared to those during QBOw. The amplitude of the troughs is larger (smaller) during QBOe compared to QBOw and mridional wave activity flux associated with the wave breaking events during the QBOe is smaller (larger) compared to QBOw. While the wave breaking events over the west of the Meditraniean (0-30 N), affected from the tropical jet stream associated with QBO, is different for anticyclonic and cyclonic wave breakings and is described briefly in the following. During the anticyclonic wave breaking events in QBOe, the subtropical jet is shifted over north east of Africa and north of the Arabian Peninsuls and a jet stream is formed at mid latitudes over the south east of Europe. While, in QBOw the subtropical jet stream is intensified over northwest of Africa and is merged with the mid latitude jet stream over southeast of Europe (White et al., 2015). Therefore, in the QBOw, over the Meditranean and west of Europe, the slope of the trough is increased and penetrates to lower altitudes. Consequently, the equatorward wave activity flux caused by anticyclonic wave breaking is increased and the anticyclonic wave breaking is stronger during QBOw compared to QBOe. During the anticyclonic wave breaking events in QBOe, the thermal wind balance is valid near the tropical jet stream associated with QBO over the west of the Indian Ocean and south east of Africa, as secondary circulation is formed that causes the intensification of the subtropical jet stream over north of the Arabian Peninsula and Iran, downstream of the trough. Along with the intensification of the tropical jet stream associated with QBOe over the Atlantics, the subtropical jet stream is shifted to higher latitudes and is intensified. Unlike the trough the altitude of the ridge over the east of the Atlantics is decreased and the zonal wind speed upstream of the trough over the north west of Europe and east of the Atlantics is increased. The Equtorward wave activity flux during the anticyclonic wave breaking events in QBOe is weakened. During the anticyclonic wave beaking events in QBOw, the equatorward shift of the mid latitude jet stream and its merge with the subtropical jet stream downstream of the trough is accompanied with the intensification of the trough over north west of Africa and east of the Mediterranean. Intensification of the jet stream associated with QBOw over the Atalantics causes the subtropical to be shifted equator ward and thus the subtropical jet stream over the east of the Atlantics and north of Africa is intensified and is tilted in the direction NW-SE. During QBOw, along with the penetration of the trough to lower altitudes over Europe, the ridge over the north east of the Atlantics and north of Europe is intensified. Intensification of troughs and ridges causes the intensification of upstream meridional flow and thus the equator ward of wave activity flux associated with anticyclonic wave breakings is increased.  During the cyclonic wave breaking events in QBOe, the subtropical jet is shifted over north east of Africa and north of the Arabian Peninsula and a jet stream is formed at mid latitudes over the Europe in the upstream of the trough. While, in QBOw the midlatitude jet stream is shifted equatorward and is merged with the subtrupical jet stream over northwest of Africa. Therefore, in the QBOe, the slope of the trough is increased and penetrates to lower altitudes, over the Meditranean and East of Europe. Consequently, the poleward wave activity flux caused by the cyclonic wave breaking is increased and the cyclonic wave breaking is stronger during QBOe compared to QBOw. During the cyclonic Wave beaking events in QBOe, intensification of the tropical jet stream associated with QBOe over the Atalantics and south of Africa causes the subtropical jet to be shifted poleward and thus the subtropical jet stream over the west of the Mediterranean and southwest of Europe is intensified.

Introduction Considering the industrial development in recent years, the need for climatological atlas and also daily metrological data have increased and has become important economically. Air temperature is of special importance in our understanding of various natural processes in the nature. Moreover, in order to detect the impact of greenhouse gases on climate change and developing ecological models in various regions, much attention have been given to spatial distribution of temperature. Hence, developing and testing accurate interpolation methods for spatial analysis of temperature is this clear especially over data void regions. In order to successfully transfer information from irregularly distributed observing stations to a regular grid, information about physical characteristics of the region have to be taken into account. To reflect spatially complicated climate patterns at regional scales, climatic dependence on topography must be taken into account when developing reliable climate estimates.