Iran Meteorological Organization Research Articles

Comparison of Long Short-Term Memory and Convolutional Neural Network Models for Emergency Department Patients’ Arrival Daily Forecasting

Background: One of the most critical challenges in the emergency department (ED) is overcrowding, which creates negative consequences for patients and staff. Therefore, predicting the rate of patients entering the ED can help manage resources in this department effectively. Objectives: According to the time of data collection, we intended to predict the volume of patient admissions to the ED in epidemic conditions, such as COVID-19 and non-epidemic. In addition, we planned to compare the performance of the LSTM and CNN models. Methods: The collected data consists of three main categories. The first category pertains to air pollutants, provided by the Tehran air quality control organization. The second type relates to data from the Iran Meteorological Organization, and the third category includes the number of patients admitted to the ED of a hospital in Tehran. We also incorporated binary indicators for epidemic and non-epidemic conditions in the dataset. The data collection period spans from February 2018 to March 2022. We employed the Dickey-Fuller test to assess the stationarity of the data. After preprocessing, we independently developed long short-term memory (LSTM) neural network and convolutional neural network (CNN) models, considering various time windows of previous days. Keras and Tensorflow libraries in Python, along with the Google Colab environment, were utilized to execute the models. Results: The LSTM model exhibited the lowest root mean square error (RMSE) and mean absolute error (MAE) with a time window of the last seven days, while the CNN model outperformed the LSTM model with a time window of the previous 13 days. Additionally, the CNN model required less execution time than the LSTM model. Conclusions: In conclusion, deep learning algorithms prove suitable for analyzing multivariate time series data. The CNN model demonstrated the lowest prediction error.

Identification of Patterns and Relationships of Jet Stream Position with Flood-Prone Precipitation in Iran during 2006–2019

Jet streams are atmospheric phenomena that operate on a synoptic scale and can intensify the descending/ascending conditions of the air at the lower levels of the atmosphere. This study aimed to identify the patterns and location of the jet stream in southwest Asia during the days of widespread rainfall in Iran based on two criteria: “highest frequency of stations involved” and “maximum cumulative amount on the day of peak rainfall”. For this purpose, the daily precipitation data for 42 synoptic stations in Iran during the period 2006–2019 from the Meteorological Organization of Iran, the daily data at 500 hPa Geopotential Height (HGT), and U and V wind components at 500 and 300 hPa from NCEP/NCAR were gathered. Synoptic patterns were obtained based on daily precipitation data, daily maps at HGT 500 hPa, and U and V wind components at 500 and 300 hPa. The analysis of patterns showed that the position of precipitation cores is associated with the position and extension of jet stream centers at 300 hPa in winter, spring, and autumn. The main position of jet stream cores during flood-causing rainfall at 300 hPa was over the northern part of Saudi Arabia, the Mesopotamia basin, and southern Iran. This position seems to have provided the conditions for the convergence of the earth’s surface and the divergence of the atmosphere for the easy passage of moisture from the Red Sea, Aden Sea, and the Persian Gulf, and in the second rank, the Mediterranean Sea and the Arabian Sea.

Impacts of climate change on occupational health indicators in the three climatic regions of Iran

ABSTRACT Climate change has increased the exposure risk of workers to occupational health risk factors and diseases. This study aims to investigate the impacts of climate change on the occupational health indicators at the workplaces in Iran. This study was conducted during 2021 in three climatic regions of Iran. Required data was collected from Health Deputies of Medical Universities and Iran Meteorological Organization. Stepwise linear regression model used for data analysis and predictions were done based on three scenarios of SSP1–2.6, SSP3–7.0, and SSP5–8.5 during the period of 2021–2100. This indicated 2.6 and 2.9 times higher percentage of workers who were exposed to heat stress and Ultra Violet (UV) radiation, respectively, in the provinces understudy. This study suggests a holistic approach to address potential impacts of climate change on workers’ health and safety that would benefit in making decisions on climate-related planning and developing the adaptation strategies at workplaces.

Spatio-Temporal Evolution of the Thermo-Hygrometric Index (THI) during Cold Seasons: A Trend Analysis Study in Iran

Global warming can cause deep and extensive changes in the Earth’s climate and changes in the time and place of climatic phenomena. The present trend analysis study assesses cold stress using the thermo-hygrometric index (THI) in the two seasons of autumn and winter in outdoor environments in Iran. The data related to the average of the two variables of daily air temperature and relative humidity from 60 synoptic meteorological stations for a statistical period of 30 years were obtained from the Iranian Meteorological Organization. The THI index was calculated for autumn and winter, and the level of thermal discomfort was determined for each station. The Mann–Kendall statistical test with the help of Minitab ver17.1.0 software was also used to investigate the changes in air temperature, relative humidity and THI index. The THI for autumn increased in 68% of the stations, and this increasing trend is statistically significant in 51% of these stations. The THI for winter increased in 83% of the stations, and this increasing trend is statistically significant in 51% of these stations. In autumn, 53% of the stations were in the range of thermal discomfort, and in winter only 5% were in the range of thermal comfort. The decreasing trend in THI in some climatic types, along with the increasing trend in air temperature, can indicate the decrease in relative humidity in the monitored stations during the studied years. It is recommended to know the temporal and spatial distribution and the probability of occurrence of cold stress through the use of THI in order to adopt preventive measures and policies in the outdoors in Iran.

Spatial analysis of bioclimatic patterns over Iranian cities as an important step in sustainable development

• In this study determined the bioclimatic component loading patterns over Iran. • The results showed that the first Varimax-rotated loading pattern accounts for 51.82% of the total variances. • This study presented novel insights into the identification of bioclimatic regions in Iran. • Cluster analysis demonstrated that Iran can be divided into five bioclimatic regions based on PCA. • Spatial distribution of bioclimatic zones showing geographical dependency. The main objective of this paper is to classify bioclimatic regions in Iran for optimal environmental planning. Mean daily meteorological data of 155 well-distributed synoptic stations during 1995–2017 were extracted from Iran's Meteorological Organization. Percentage of occurrence of each thermal sensation classes was determined over all studied stations using five bioclimatic indices including Perceived Temperature (PT), Physiological Equivalent Temperature (PET), Predicted Mean Vote (PMV), Standard Effective Temperature (SET) and Universal Thermal Climate Index (UTCI). An R-mode Principal Component Analysis (PCA) was applied to a N × M (155 × 40) matrix of N = 155 stations and M = 40 total thermal thresholds based on all used indices during the considered time period. According to the results of PCA, the five loading PCs that approximately account for 98.5% of the total variance were selected for further analysis. In addition, Ward method was used for Cluster Analysis (CA) to divide the stations into different bioclimatic groups. The results illustrated that the first Varimax-rotated loading pattern which accounts for 51.82% of total variance characterized Northwest of Iran with more than 0.8 PC scores. Furthermore, cluster analysis demonstrated that Iran can be divided into five bioclimatic regions based on the R-mode PCA.

Feasibility study on utilizing oscillating wave surge converters (OWSCs) in nearshore regions, case study: Along the southeastern coast of Iran in Oman sea

Considering the need for energy basket diversification and reducing fossil fuel consumption to control global warming, making renewable energies an appropriate solution. Wave energy is one of the most notable solutions offering the highest energy density among renewable energies. However, this energy source is not yet extensively developed. Finding an appropriate location to install the interested Wave Energy Converters (WECs) requires exquisite consideration. This study introduced a methodology through a case study to assess the use of OWSCs in nearshore regions. The southeast coasts of Iran along the Oman Sea were investigated as the case study, is the country's only oceanic coast and offers a higher potential for wave energy extraction. Therefore, to achieve this aim, the wave characteristics data were obtained from the Iranian National Institute for Oceanography and Atmospheric Science (INIO). The data was validated based on two buoys implemented by the Iran Meteorological Organization. By overlapping four key variables (average wave power, wave direction, distance from shoreline, and bathymetry) in annual and seasonal intervals via the geographic information system (GIS) program, five points were identified as optimal. Next, the distribution of wave height and period as well as angle distribution of wave power were analyzed for five considered points. Therefore, two points with appropriate distributions were selected with the following specifications an average annual wave power of 2–3.5 kW/m, wave direction arriving from the south-southwest, a depth of below 40 m, and coastal distance less than 5 km. In summer, due to the Indian Ocean monsoon, the potential to harness wave energy significantly increases.

Impact of the COVID-19 Outbreak on Air Pollutants, in 2020 Compared to the Same Period in 2019 in Qom, Iran

Introduction: The present study aimed to investigate the change of air pollutants in 2020 in Qom compared to the same period in 2019 in five scenarios.
 Materials and Methods: The hourly air quality data was obtained from air quality monitoring stations of Qom Environmental Protection Organization (EPO). The meteorological parameters were obtained from Iranian Meteorological Organization website. The data were analyzed using Excel, SPSS, and WRPLOT view.
 Results: In the first month of the COVID-19 crisis, NO2, SO2, and CO decreased by 26.4, 39, and 0.2 µg/m3 compared to same period in 2019, respectively; however, PM2.5 and O3 increased by 7.1 and 2.3 µg/m3, respectively. In Iranian Nowruz holidays, an increase of 2.9 µg/m3 in O3 mean concentration and a decrease of 8.1, 23.8, 22.8, and 0.2 µg/m3 in mean concentration of PM10, NO2, SO2, and CO were experienced. The prevailing wind direction during the 2020 in each scenario was from the west of Qom city.
 Conclusion: Gaseous pollutants decreased during the crisis, but particulate pollutants increased slightly compared to the same period in 2019. The lockdown may have had the most impact in decreasing pollutants. A slight increase in wind speed from the west could be a factor in increasing particles. This crisis provided an opportunity to assess the role of policies, such as traffic reduction plans or discarding worn-out cars or urban management to improve air quality.

Consistency assessment between summer simmer index and other heat stress indices (WBGT and Humidex) in Iran's climates

This study aimed to assess the applicability of summer simmer index (SSI) in different climates of Iran and compare it with other common heat stress indices including WBGT and Humidex. The daily mean air temperature and relative humidity were obtained from the Meteorological Organization of Iran for the 60-year period between 1957 and 2016. Statistical tests including Pearson correlation coefficient and linear regression tests were used to consistency assessment between Summer Simmer Index and wet-bulb globe temperature (WBGT) and Humidex indices. The results of the present study showed that the SSI is significantly correlated with both WBGT and Humidex indices (R2 > 0.932, p < 0.001). In addition, the obtained correlation between SSI and Humidex was higher than correlation between SSI and WBGT in all the climates. Moreover, SSI in the Mediterranean region shows less correlation with both WBGT and Humidex indices (R2 = 0.932 and 0.964 for WBGT and Humidex, respectively). The highly correlation coefficients were observed between SSI and WBGT and Humidex indices, and also between SSI and other environmental parameters; moreover interpreting of SSI is easy, so it can be suggested that SSI can be used as an alternative index for WBGT or Humidex in assessment of heat stress in different climates of Iran.

Geographical and Meteorological Evaluations of COVID-19 Spread in Iran

Since late 2019 many people all over the world have become infected and have died due to coronavirus. There have been many general studies about the spread of the virus. In this study, new and accumulated confirmed cases (NCC and ACC), new and accumulated recovered cases (NRC and ARC), and new and accumulated deaths (ND and AD) were evaluated by geographical properties, meteorological parameters and air particulate matters between 3 April 2020 and 11 June 2020 within 15 provinces in Iran. Meteorological parameters, air particulate matters and COVID-19 data were collected from Iran Meteorological Organization, the Environmental Protection Agency and Aftabnews website, respectively. The results of the study show that provinces in dry lands (i.e., Kerman and South Khorasan) not only had low admission of NCC, ACC, ARC and AD but also presented lower rates of NCC, ACC and AD per 105 population. Air temperature showed positive and significant correlation with the number of COVID-19 cases. This is because of hot outdoor air especially in costal and equatorial regions that forces people to stay in closed environments with no ventilation and with closed-cycle air conditioners. Maximum air pressure was found to be the most frequent (66%) and significant parameter correlating with health outcomes associated with COVID-19. The most engaged province in this study was Khuzestan, while provinces in dry lands (i.e., Kerman and South Khorasan) showed low number of health endpoints associated with COVID-19. The highest rate of accumulated and new recovered cases per 105 population were also found in Khuzestan and Kerman provinces. North Khorasan also showed the worst rate of N&amp;ARC/105 population. Therefore, air temperature, dry lands and population were the most important factors for the control of coronavirus spread.

New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This study aims to compare four empirical approaches to climate classification (Thornthwaite and Mather, De Martonne, the Extended De Martonne, and the IRIMO (I.R. of Iran Meteorological Organization)) with Ward’s hierarchical agglomerative clustering applied to the climate of Iran. The dataset used in this study comprises maximum and minimum temperatures and precipitation data of 356 weather stations extracted from IRIMO’s databases. Thirty-five synoptic weather stations are selected among 356 stations. These stations are selected regarding the best uniform distribution, elevation, windward and leeward sides of the mountain ranges, and availability of a continuous 50-year data (1966–2015). Compared with the other three empirical reference methods of climate classification, the Thornthwaite and Mather method emphasizes the role of water bodies and air masses in determining the climate type of a region. Highlighting these two factors is identified as the main advantage of this method over the other three. This advantage is the most noticeable for the highlands/mountainous regions, in the vicinity of the Zagros Mountains, and in the western regions of Iran. As a case in point, while in the De Martonne and the Extended De Martonne methods, the Zagros storm cell is climatically classified similar to patchy areas in Caspian Sea coastal zone, this cell is correctly identified as a separate zone in the Thornthwaite and Mather method. The results also reveal that the clusters obtained from Ward’s algorithm are comparable to those of empirical climate classifications, particularly Thornthwaite and Mather method.

Modeling behaviour of wet and dry days in Iran from the perspective of Markov chains

The current study aims to model the behaviour of wet and dry days in Iran using Markov Chain Models. To this end, data related to daily precipitation of 44 synoptic stations for a 25-years interval (1991-2015) was obtained from Iran Meteorological Organization. Then, the Markov features of dry and wet days of Iran including stationary probabilities of dry and wet days occurrence, the expected length of dry periods, the expected length of wet periods, dry-wet spells cycle, return periods for dry or wet episodes and finally, the possibility of occurrence of the continuity of dry days for 5, 10, 15, 20, 25 and 30 days were calculated for all the synoptic stations in a seasonal scale. The results showed that there is the occurrence of dry short continuities (5 and 10 days) in three seasons of autumn, winter and spring with different possibilities all over Iran. However, the possibility of occurrence of long-term dry continuities (more than 20 days) is variable in terms of season and place so that in winter, no possibility of occurrence of this type of continuities is obvious in the northern half of Iran. As in autumn and spring those are the end and beginning of long-term stability conditions of the atmosphere in the upper atmosphere levels of Iran, the possibility of periodical occurrence of 30-days dry days, particularly in the southern half of Iran increases. In addition, the expected return periods for dry days is almost steady for every part of Iran and is in the range between 1 and 2 days. However, the number of return days to a precipitation period does not follow this rule and varies for every part of Iran so that from 2.15 days in autumn to 79 days in spring is variable, pointing to the climate diversity of Iran.

Intense extreme hydro-climatic events take a toll on society

In March 2019, the Iranian meteorological organization warned of the formation of several dense precipitation systems throughout the country. This was followed by a chain of storm events that ended up with three major floods with heavy damages, including at least 78 fatalities. Reportedly, within the first 48 h of the storm, the cumulative rainfall reached about 400 mm. Soon after, 23 provinces of Iran received a severe storm warning and an imminent flood alert as the second wave of rainfall was forming in the region. What was striking about these events was that, according to the historical data, the local rainfall rarely caused such extreme floods, indicating the dominant role of human alteration to natural cycles in these damaging flood events. Extreme hydro-climatic events are unavoidable, yet damages by such events are, to some extent, preventable. With proper, efficient, and timely decision making, the threat of natural hazards can be reduced or avoided. On the other hand, hasty and short-sighted decisions can be costly in the face of a natural catastrophe. This study evaluates the 2019 flood events in Iran and assesses the potential causes that amplified the damages, in search for clues to prevent future flood losses.

Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran.

Drought is a natural phenomenon that can significantly impacts on water resources studies, agricultural and environmental societies around the world, hence, accurate spatio-temporal monitoring of drought is very important. In this research, a comparative analysis of a newly developed precipitation dataset, SM2RAIN-ASCAT (which is based on bottom-up approach), with 40 ground-measured Iranian Meteorological Organization (IMO) precipitation data are performed to estimate the precipitation and monitor the drought events over diverse climate regions of Iran. The SPI index, as a widely used drought index, at the temporal resolution ranging from one month to one year is used to this aim, and the outputs are analyzed based on the statistical and categorical metrics. Results indicated that the highest correlation coefficient (CC) and lowest root mean square error (RMSE) between SM2RAIN-ASCAT and in situ observations are found at 10-day and monthly time scales. Analyzing both datasets using FAR and POD indices in the mid and long-term time scales indicated that the SM2RAIN-ASCAT has a good performance in detecting rainy days. This product overestimate the precipitation values in extra-arid regions, while in humid and per-humid climate areas it tends to underestimation. Moreover, assessing the reliability of this product for drought monitoring showed that the SPI at 1, 3 and 6month time scales are in good agreement with ground-based observations over different climate regions of Iran. At these temporal resolutions, the CC value between SPIs calculated based on in situ observations and SM2RAIN-ASCAT is higher than 0.7 in more than 75% of the meteorological stations. The efficiency of SM2RAIN-ASCAT in detecting drought periods in extra-arid and arid zones is relatively better than that of in humid and per-humid climates. In addition, the performance of this product for capturing wet periods in extra-arid to semi-arid regions is better than that of in Mediterranean and humid zones. Overall, the outcomes of this study demonstrated that SM2RAIN-ASCAT, despite poor performance in estimating precipitation in some regions, can be considered as a complementary to ground-gauge observations or an appropriate alternative dataset for drought analysis, especially in arid and semi-arid regions which include most parts of the world.

Spatial and temporal analysis of mean and frequency variations of dry spells in Iran

For probabilistic modeling of variations in averages and frequencies of length of dry spells in Iran and obtaining a set of suitable patterns in this relation, the data on daily precipitation from 41 synoptic meteorological stations for a 30-year period (1989-2018) were obtained from the Iran Meteorological Organization. A dry spell is defined as a sequence of consecutive dry days with precipitation equal to or less than 0.1 mm per day. In order to evaluate the probabilistic distribution of the mean length and frequency of short (1-10 days), medium (11-20 days), and long dry spells (21-30 days), a first-order Markov chain model, a second-order Markov chain model, an exponential distribution model, and a negative binomial distribution were used. The results showed that there were longer dry spells at more southern latitudes and shorter dry spells at more northern latitudes in Iran. At an annual scale, the longest dry spell in southeastern Iran was 351 days and the longest in northern Iran was 37 days. Simulation of seasonal variations in mean and frequency of different lengths of dry spells using the four probabilistic models showed that the exponential and the negative binomial distributions gave the closest approximations to the empirical mean of the length of dry spells at the studied stations. However, these estimates were relatively simulated well only for mean length of dry spells that lasted less than 10 days and were not simulated well at all for dry spells with lengths of more than 10 days. Moreover, considering that the average frequencies of medium dry spells (11-20 days) and long dry spells (21-30 days) indicated the length and severity of dry spells in different parts of Iran, it was observed that the exponential and negative binomial distributions provided closer approximations to the empirical averages compared to the other two models. In general, we can conclude that the selected models performed poorly in simulating the average length of dry spells but performed better in simulating the average number of short, medium, and long dry spells.

Evaluation of satellite/reanalysis precipitation products over Iran

ABSTRACT This study aims to evaluate the satellite- and model-based precipitation products against a relatively dense station network of Iran Meteorological Organization (IRIMO) from January 2015 to June 2018 at daily timescale over Iran. The evaluations are carried out based on continuous and categorical statistical metrics. First, we employed daily satellite soil moisture (SM) observations from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for estimating rainfall through the use of the relatively new SM2RAIN approach (SM2R-AMSR2). Next, we validated rainfall estimations from satellite/reanalysis products including version 05 daily Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) Early (IMERG-DE), Late (IMERG-DL), and Final Daily (IMERG-DF) Runs and the European Centre for Medium-Range Weather Forecasts’ ReAnalysis Interim (ERA-Interim), ECMWF ReAnalysis 5th Generation (ERA5) and SM2R-AMSR2. Based on the probability density function (PDF), all satellite and model products capture more (less) precipitation events than the reference data set for precipitation under (upper) 5.0 mm day−1 threshold. Regarding the mean absolute error (MAE) and root-mean-square error (RMSE) quantities, the ERA5 product has the lowest error relative to other products. Also, the threat score (TS) indicates that this product has the highest accuracy. ERA-Interim and IMERG-DF had roughly similar performances. The results reveal the overall superiority of ERA5 data over other products as it has the highest correlation with observation and the best discrimination between occurrence and non-occurrence of the precipitation events. Also, the results of spatial correlations show that ERAs have a higher agreement with in situ observation when compare to satellite products, over the north to northwest of Iran, where rain is formed mainly by synoptic-scale systems.

Determination of maize agronomic calendar Based on its climatic Feasibility in south Iran

Maize is the third most consumed grain in the world , which is planted in warm climate . The purpose of this study is to compile the agronomic calendar of the 704 single_cross cultivar of maize and to determine the coducive areas for planting it in south regions of Iran based on the temporal needs using phenology information in its different growing stages in three agricultural research farms of Zarghan,Darab , Rodan and Arzoie during 2001-2017. Daily weather statistics presenting the temperature(1996-2016) of 61 Synoptic stations from Meteorological Organization of Iran . Then The relationship between the 15-day mean temperature and the altitude in the linear regression is calculated and maps of the first and second planting times in south Iran was plotted in the GIS. The plant growth degree days and the number of days in every phenology phase are calculated and the occurrence of exposure to growth inhibiting temperatures for all of the stations studied. Map of suitable areas Based on the supply of heat needs integrated with the land use,slope,height layers and the final map are plotted. The results of this study show that In 73% of the study area, thermal conditions are provided for corn cultivation based on the determined planting time;but only 23% area of study(91846.59km2) are suitable of cultivating corn in the study area due to topographic limitations and unsuitable cultivated lands.highly suitable, suitable and moderately suitable areas are 12%, 8% ,3%, respectively. Farmers have the best crop calendars and locations using these maps.

Relations Between Durability of Spring Frosts and North Advection on Omega Blocking Over Iran

Blocking of atmospheric systems may lead to slowing down and even stopping the movement of western wind waves. This condition prolongs the warm days in catchment areas in front of the wave and induces continuation of cold and frost days in catchment areas behind the wave. The effect of cold and frost on the crops, especially in spring, is undeniable. Therefore, analyzing the blocking systems, named as omega, is important in Iran’s spring frost duration in southern middle latitudes. The daily minimum temperature data of 69 meteorological stations were collected from the Iranian Meteorological Organization from March 20, 1968, to June 21, 2014, in order to determine the effects of omega blocking systems on duration of the spring frost waves in Iran (equivalent to spring season in the Iranian calendar). By adjusting the tables of daily temperature distribution and plotting the frequency variation diagrams, 114 synoptic waves lasting 3–25 days were identified. Four durable waves (> 12 days) and ten low-durability waves (< 12 days) were identified by dividing the 114 waves into two groups (47 durable waves and 67 low-durability waves) and designing the atmospheric circulation pattern for the middle eastern region based on the air maps of 500 hPa. The results showed that blocking patterns usually occur in the areas of central Europe to the Black Sea basin and cause advective cold air from Scandinavian and central Russia basins to Iran, thereby resulting in durable frosts during spring.

Energy performance of building integrated photovoltaic high-rise building: Case study, Tehran, Iran

Solar energy in the building can reduce energy consumption in this sector1. This research aims to design a high-rise office building using electricity power generation by photovoltaic panels in the building (BIPV1Building-integrated photovoltaics 1 Since 1880.3 Photovoltaic1), which work in a combination of Facades. The objectives for the BIPV design were at the first step to provide at least 20% monthly required lighting electricity for the critical month, which is June and to reduce thermal energy consumption. In this research, the feasibility of the application of the systems was evaluated for the first time with Honeybee plugins in the Rhinoceros software. These plugins are based on the EnergyPlus engine. To achieve the aim with the specified PV3, 830 modules each 2 m2 are required. This BIPV system can provide 51.3% of lighting electricity in February with the maximum electricity production by this system. At the next step, Energy plus weather data (Epw) file is validated with the I.R. Iran meteorological organization (IRIMO) data. The results of the PV system are validated by the experimental data from a PV station in Mashhad (Iran). The produce electricity by this system is 354734.7 kWh/year. The amount of electricity Supplied by BIPV can reduce GHG by the amount of 87 tons of CO2 per year. Reduced thermal energy consumption is validated by ASHRAE 140-2017.

Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index (MSPI) can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm (MLP‐ICA) have been investigated for the first time in multivariate drought studies. For this purpose, two semi‐arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization (IRIMO), during the period of 1988–2017. MSPI was calculated in 5‐time windows of the multivariate drought, including MSPI3–6 (drought in perspectives of soil moisture and surface hydrology simultaneously), MSPI6–12 (hydrological and agricultural droughts simultaneously), MSPI3–12 (soil moisture, surface hydrology, and agricultural droughts simultaneously), MSPI12–24 (drought in perspectives of agriculture and groundwater simultaneously), and MSPI24–48 (socio‐economical droughts). The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows (MSPI12–24 and MSPI24–48) had better predictions than the smaller ones (MSPI3–6, MSPI6–12, and MSPI3–12). Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously (MSPI3–6). The hybrid MLP‐ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP’s accuracy by 28.5%, which is a significant improvement. According to the evaluations (RMSE = 0.20; MAE = 0.15; R = 0.95), the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas.

Effect of Dust Storms on Non-Accidental, Cardiovascular, and Respiratory Mortality: A Case of Dezful City in Iran.

Background:Despite the fact that Iran has been exposed to severe dust storms during the past 2 decades, few studies have investigated the health effects of these events in Iran. This study was conducted to assess the association between dust storms and daily non-accidental, cardiovascular, and respiratory mortality in Dezful City (Khuzestan Province, Iran) during 2014 to 2019.Methods:In this study, mortality, meteorological, and climatological data were obtained from the Dezful University of Medical Sciences, Iranian Meteorological Organization, and Department of Environment in Khuzestan Province, respectively. Days of dust storm were identified based on the daily concentration threshold of particulate matter with an aerodynamic diameter of less than 10 μm (PM10) according to Hoffmanns҆ definition, and then an ecological time-series was used to estimate the short-term effects of dust storms on daily mortality. Statistical analysis was performed using a distributed lag linear model (DLM) and a distributed lag non-linear model (DLNM) packages by R software and the study results were reported as excess mortality.Results:During the study period, 15 223 deaths were recorded, and 139 dust storms occurred in Dezful city. In addition, there was statistically significant excess risk of mortality due to dust storms in Dezful City (mortality in the group under 15 years of age, lag4: 34.17% and 15-64 years of age groups, lag5: 32.19%, lag6: 3.28%), also dust storms had statistically significant effects on respiratory mortality (lag6: 5.49%).Conclusion:The findings of the current study indicate that dust storms increase the risk of mortality with some lags. An evidence-based early warning system may be able to aware the people of the health effects of dust storms.