جستجوی مقالات مرتبط با کلیدواژه "brightness temperature" در نشریات گروه "جغرافیا"

Determining the cloudiness and the spatial and temporal characteristics of clouds is essential in forecasting the weather as well as climate studies. Studies show that changes in cloud cover negatively affect daily temperatures. (Dai, et al. 1999; Karl, et al. 1993). Accurate information about the physical and radiative properties of clouds is essential to determine the role of clouds in the climate system (Forster, et al. 2007). Data and To this study, two sets of satellite and observational data were used. Observational data include 6-hour rainfall data during daylight hours (06 to 12 GMT) for 26 days in January, April, October and, November 2018 from 399 meteorological stations in Iran. Satellite data also includes 1.5 level data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. The SEVIRI has 12 channels for measuring electromagnetic radiation. The radiance of three channels at visible and very-near infrared wavelengths (VIS0.6, VIS0.8, and NIR1.6) converts to reflectance. The radiance of eight channels from near-infrared to thermal infrared wavelengths (IR3.9, WV6.2, WV7.3, IR8.7, IR9.7, IR10.8, IR12.0, and IR13.4) converts to brightness temperature. These channels have 3*3 km spatial resolution at nadir. All of these channels have a temporal resolution of 15 minutes. Since 15-minute satellite data and 6-hour rainfall data are available, the minimum, maximum, and average of reflectance values (for channels 1 to 3) and brightness temperatures (for channels 4 to 11) have been calculated during these 6 hours and their correlation with precipitation has been analyzed.Changes in the 6-hour mean values of reflections and brightness temperatures for 90% of the data were investigated for rain and no rain conditions, separately. The results show that the mean of reflectance in rain conditions is higher than no-rain conditions. And the mean of brightness temperature in rain conditions is less than no-rain conditions for each channel.The study of the correlation between channels and precipitation shows a high correlation between VIS0.6 and VIS0.8 channels. The NIR1.6 channel has very poor communication with other channels, but this channel is important for identifying cloud ice particles. Channel IR3.9 correlates relatively poorly with channels VIS0.6, VIS0.8, NIR1.6 and, WV6.2, but shows a good correlation with other channels. WV6.2 and WV7.3 channels, which show the amount of humidity at different levels of the atmosphere, have a very high correlation of 0.91%. The WV7.3 channel correlates better with other channels than the WV6.2 channel. IR channels indicate ground, sea, and cloud temperatures, while WV6.2 indicates air temperature near the clouds. Therefore, this poor correlation is acceptable. Channel 7 to 11 are highly correlated with each other.The reflectivity of VIS0.6 and VIS0.8 channels has a positive correlation with precipitation and consequently cloudiness. Increasing the cloudiness increases the reflectivity. Because the reflection in these channels indicates the optical thickness of the cloud and the amount of water in the cloud. Therefore, the thicker the cloud, the greater its reflectivity. The NIR1.6 does not show much correlation with precipitation and is close to zero. Areas of rain clouds with a high optical thickness (high reflectivity VIS0.6) and large effective particle radius (low reflectance NIR1.6), with higher rainfall, compared to cloud areas with a low optical thickness (low reflectivity VIS0.6) and particle radius Small effect (high reflectivity NIR1.6) are specified. Infrared and water vapor channels have a negative correlation with precipitation. So more cloudiness leads to lower brightness temperature. The mean brightness temperature in the IR3.9 channel is the best indicator in this channel to detect the presence of clouds. Because it has a high correlation with precipitation, and also its difference in rain and no-rain conditions is more significant. The correlation between precipitation and the average 6-hour brightness temperature in all 5 channels is better than the minimum and maximum of 6-hour brightness temperature. Negative correlation also emphasizes that precipitation is inversely related to brightness temperature. In all channels, the mean difference of these parameters in the 6-hour mean brightness temperature mode has the best distinction between rain and no-rain conditions.Among the minimum, maximum and, average 6-hour reflectance in VIS0.6 and VIS0.8 channels, the highest correlation with precipitation is related to the 6-hour average reflectance in both channels and is about 0.44. As a result, they are the best channels to show the cloudy effect. The NIR1.6 does not have a good correlation with precipitation and cannot distinguish between rain and no-rain conditions. Therefore, the use of this channel for cloud detection is not recommended. Since the IR3.9 channel shows the structure of the cloud top well and is sensitive to particle size. Therefore, the average brightness temperature in this channel is a good indicator for detecting the amount of cloudiness. Also, among infrared channels, the IR3.9 channel has the highest correlation of -0.33 with precipitation.Among the water vapor channels, the best indicator for detecting the amount of cloudiness is the minimum 6-hour brightness temperature of the WV7.3 channel. Channels IR8.7, IR9.7, IR10.8, IR12.0 and, IR13.4, which mainly represent the cloud top temperature, show relatively similar correlations with precipitation, while they are highly correlated with each other. The negative correlation in infrared channels means that with decreasing brightness temperature in these channels, cloudiness and precipitation increase and vice versa. In these channels, the average 6-hour brightness temperature is a better indicator of the amount of cloudiness.Since the major changes of VIS0.6 channel in rain conditions are in the range of 26.7-44.7% and in no-rain conditions are in the range of 53.3-69.3% and about VIS0.8 channel are 32.7-49.6% and 58.3-73.9%, separately. Therefore, rain and no-rain conditions in VIS0.6 and VIS0.8 have the least overlap, so separating them will be easier. Among the infrared channels, only the WV6.2 have overlap in rain and no-rain conditions. So that the range of changes in rain and no-rain condition is 226-234 and 229-237 degrees Kelvin, Respectively. Therefore, its separation will be difficult. The rest of the infrared channels have slightly overlap.

Differentiating agricultural areas which are not covered by vegetation from bare lands as well as identifying bare lands from urban areas in medium spatial resolution images, e.g. Landsat imagery, are usually difficult and erroneous tasks which lead to the inaccurate classification results. Therefore, this study aims to present a new approach to increase the accuracy of the classification. For this purpose, different scenarios were applied based on different input attributes. The input attributes comprised of spectral bands, textural attributes, i.e. grey level co-occurrence matrix (GLCM), and two types of indices including spatial and thermal attributes proposed in this study. Three classification methods, maximum likelihood (ML), artificial neural networks (ANN), and support vector machine (SVM) embedded with different kernels, were applied to examine different scenarios. The results showed that SVM algorithm embedded with Radial basis function (RBF) results in better accuracy, with overall accuracy of 98.81% and Kappa coefficient of 98.25%, when all types of input attributes were combined together. Finally, the variable importance analysis by random forest feature selection indicated that the proposed indices played an important role to execute more efficient classification by SVM.

Overshooting tops (OTs) are the product of deep convective storm updraft cores of sufficient strength to rise above the storms general equilibrium level in the tropopause region and penetrate into the lower stratosphere. Thunderstorms with OTs frequently produce hazardous weather such as heavy rainfall, damaging winds, large hail, and tornadoes.OTs are most identified in visible channel imagery as having a lumpy texture. However, they are only identifiable during the day and also most easily observed during early morning or late afternoon hours when the sun angle is low and the shadows on the cloud anvil cast by the overshooting tops are well pronounced. One of the most commonly used methods for detecting OTs is based upon the BTDs between the 6.2 μm and 10.8 μm (WV–IRW) channels. This BTD is positive above deep convective clouds and is related to the presence of water vapor above the cloud tops. Warmer temperatures in the lower stratospheric water vapor result in greater observed WV–IRW BTD values. The BTD of the ozone channel (9.7 μm) and the IRW channel also show a positive signature for cloud tops above 11 km. The signal in the BTD between the ozone and IRW channels is even more significant than the signal in the BTD between theWV and IRW channel near the tropopause.Thus, this BTD could be a better indicator of deep convective activity. The BTD between the carbon dioxide (13.4 μm) and the IRW channel is a good indicator of the height of the opaque clouds. The reason is that with higher cloud tops, the absorption effect of CO2 becomes smaller, producing a BTD of the CO2 and IRW channel that is close to 0 or positive, in the case of very deep convective clouds.
Material and In this study the convective system and its related overshooting tops that occurred in 27 march 2007 were studied using High Rate SEVIRI Level 1.5 Image Data from Second Generation Meteosat satellite. The data is transmitted as High Rate transmissions in 12 spectral channels. Level 1.5 image data corresponds to the geolocated and radiometrically pre-processed image data, with a spatial resolution (pixel size) of 3 x 3 km at nadir and a temporal resolution of 15 minutes. Preprocesses such as calibration, geo-referencing and brightness temperature calculation weredone using Envi software. Then, RGB color composites were used for monitoring convection as follow: Red: Cloud depth and amount of cloud water and ice, provided by the visible reflectance at 0.6 μm. Green: Cloud particle size and phase, approximated by the 1.6 μm solar reflectance component. Blue: Temperature, provided by the 10.8 μm channel. Overshooting tops weredetected using visible reflectance at 0.6 μm. Three OT detection methods, WV–IRW, CO2–IRW, O3-IRW brightness temperature difference (BTD), which use combinations of SEVIRI channels in the form of brightness temperature differences, have been tested and compared with OT detection in visible images. Then, the most appropriate BTD threshold was determined in any methods and areas identified as OT was compared in different methods. Finally, CAPE, low level jet, air flow pattern, relative and specific humidity Maps and graphs were presented and interpreted to understanding OTs occurrence conditions. To do this, all data was collected from ECMWF with resolution of 0.125˚*0.125˚ latitude/longitude.
Results showed that most of theOTs (but not all of them) wereformed in the region where the IR brightness temperature was lower than 215K. Using lower brightness temperature difference threshold ( or for CO2–IRW and WV–IRW and K for O3–IRW) led to exaggeration of OT number and extension in all three methods however they could identify all OTs. While using higher threshold (4 K for CO2–IRW and WV–IRW and 13 K for O3–IRW) led to the method failed to identify many of OTs. Thus, it was tried to determine the optimized thresholds that succeed to identify OTs as much as possible and have the least false detection. Finally, the thresholds weredetermined as follow: 3.1 for CO2-IRW, 3.5 for WV-IRW and 12 for O3-IRW. By these thresholds some OTs have not been detected and some pixels have been detected as OT falsely in all three methods, which was due to low spatial resolution. CO2-IRW BTD represent the best results because of fewer missed and false detections. So, while SEVIRI image and these methods have not enough to detectOTs precisely, they are very useful to assess temporal and spatial as well as their occurrence conditions.
Assessment of OTs occurrence conditions in 27 march 2007 revealed that there was a low pressure in west of Iran surrounded by some high pressureat low levels of atmosphere in occurrence day. High pressure in north of the Caspian Sea, north of the Mediterranean Sea and northeast of Africa was advected moisture air mass of the Caspian Sea, the Mediterranean Sea and the Red Sea to the low pressure in west of Iran. Also, there was a weak high pressure in the Arabian Sea which was advected warm and moisture air mass from the Arabian Sea, the Persian Gulf and the Red Sea to the low pressure in west of Iran. Low level jet in the study area has accelerated advection of warm and moisture air mass to this low pressure. Thus, air flow pattern resulted in moisture convergence from all resources in the region (the Persian Gulf, the Arabian Sea, the Red Sea,the Caspian Sea and the Mediterranean Sea) which has major role in the Mesoscale convective system (MCS) and OTs occurrence. So that special and relative humidity had reached 8-12 gr/kg and 100% respectively. Also, the special and relative humidity was high to tropopause and had reached 0.2 gr/kg and 90-100% in 200 hPa respectively. The maximum of the convective available potential energy (CAPE) was about 1200 Jkg-1 to 1500 Jkg-1 at the time of system formation Maximum of the lifted index was observed in the Red Sea convergence zone with value about -3 to -6,which induced to deep convection in this day.
In this study the convective system and its related overshooting tops occurred in 27 March 2007 was studied using High Rate SEVIRI Level 1.5 Image Data. Preprocesses such as calibration, geo-referencing and brightness temperature calculation weredone using Envi software. Then, RGB color composites were used for monitoring convection.Three OT detection methods, WV–IRW, CO2–IRW, O3-IRW brightness temperature difference (BTD) have been tested and compared with OT detection in visible images. Then, the most appropriate BTD threshold was determined in any methods and areas identified as OT was compared in different methods. CO2-IRW BTD represent the best results because of fewer missed and false detections. While SEVIRI image and these methods have not enough to detect OTs precisely, they are very useful to assess temporal and spatial as well as their occurrence conditions.
Low level jet in the study area has advected warm and moisture air mass in occurrence and previous day. Air flow pattern resulted in moisture convergence from all resources in the region (the Persian Gulf, the Arab Sea, the Red Sea, and the Mediterranean Sea) which has major role in the Mesoscale convective system (MCS) and OTs occurrence.The maximum of the convective available potential energy (CAPE) was about 1200 Jkg-1 to 1500 Jkg-1 at the time of system formation.Maximum of the lifted index was observed in the Red Sea convergence zone with value about -3 to -6. It revealed that low atmosphere was also instable in study daywhich induced to deep convection in this day.

Mountains are the main sources of turbulence and change in the shape of atmospheric flows, and they can cause airflow upward as well as clouds formation and rain through productive mechanisms such as upslope condensation and convection. They also have an important effect on regional and world precipitation turbulence (Banta, 1990; Barros & Lettenmaier, 1994), and can cause severe incidents such as destructive floods (Pastor, Gomez, & Estrella, 2010).
Previous researchers have done numerous studies on mountainous region weather and climate, and cause of precipitation phenomenon using different methods such as numerical modeling of airflow and satellite images. Using RegCM model, Insel, Christopher, Poulsen and Ehlers (2009) have studied the effect of Andes Mountains on convection, precipitation and humidity transformation in South America. They showed that Andes Mountains have lots of effects on humidity transfer between Amazon basin and central Andes, deep convention processes and precipitation across South America through low–level jet (LLJ) and topographical blocking from Pacific Ocean.
Zagros mountain range located in west of Iran plateau is among vast mountain ranges that locates in path of zonory flows with its south-north expansion and can affect those flows.
Therefore, the present study aims to investigate different factors affecting mesoscal convective systems from Zagros heights, and analyze their life cycle dynamic conditions using brightness temperature threshold, area expansion and RegCM4 numerical modeling.
Material and This study was done in an area of about 220000 km2 in west of Iran including Kermanshah, Kurdistan, Hamadan, Khuzestan, Lorestan, Kohgiloyeh and Boyer Ahmad, Ilam, and cheharmahal and Bakhtiari provinces. Using satellite images obtained from infrared band of Meteosat geostationary satellite, GOES and GMS, the mesoscal convective systems and their life cycle were identified.
Regarding that Inoue, Vila, Rajendran, Hhamada, Wu and Machado (2009) proved if we use one colder or warmer threshold, both initiation and dissipation phases may not indicate the life cycle, in this study, brightness temperature threshold of 224 K (Volasco & Fritsch, 1987) and 243 K (Machado, 1998) were used for identifying and analyzing systems life cycle. The researchers have tried to choose some days for this study over which mesoscal convective systems have been made and spent their life cycle without merger or split.
By transfering these systems to GIS environment using area expansion index (∆E) whose validation and viability have been confirmed by Vila, Machado, Laurent, and Velasco (2008) its life cycle was verified (eq. 1).
∆E=(1 δA)/(A δt) (1)
A = the system area in a given time (Tir

Convective systems cause many hazards in Iran. On the other hands, convective precipitation is the major proportion of total precipitation in southwest of Iran, and has important role on providing water resources. Thus, it is important to assess their climatology characteristics. In this research, spatial and temporal distribution of Mesoscale Convective Systems (MCSs) assessed over southwest of Iran by the use of Global merged IR brightness temperature images obtained from NCEP/NWS and synoptic stations data. MCSs was detected on the basis of temperature, maximum area and duration thresholds (228 k, 10000 km2 and 3 hours respectively) in cases rainfall was more than 10mm during 6 hours and also convective phenomena recorded at least in three stations. A total number of 189 systems were detected during 2001-2005. The analysis revealed that the most of MCSs (54 cases) occurred in December. MCSs initiation location influenced by topography but windward slope of Zagros has not played important role in MCSs formation. In general, they were most predominant across northeast of study area with a decreasing southwestward gradient, that follows topography, but this pattern was different in cold and warm months, so that we have observed the most accordance in warm months and the least accordance in cold months.

Snow, as one of the basic factors of water supply, plays an important role in water resources management, especially in areas with cold winters and warm summers. The data obtained from snow gauges as well as temperature and precipitation time series data are generally being used to develop experimental models in order to estimate the spatial and temporal distribution of snow in watersheds. However, when reliable snow or other necessary climatic data records do not exist, using proper substitutes becomes essential. Hence, the snow cover area (SCA) derived from satellite images can be used as a representative of the amount of snow in a basin. Moreover, Remote Sensing (RS) is a useful tool in identifying snow and calculating SCA in mountainous regions with low accessibility and deficiency of snow gauges. Accordingly, the SCA time series data can then be used as input dataset in flow forecasting by hydrologic models. This paper aims to study the snow cover area of Shahcheraghi Dam basin in order to collect the necessary input data for developing dam inflow forecasting models. The basin is located in the north of Semnan province, Iran. The area of the basin is 1373km2 and the annual precipitation and mean temperature of the basin are 124mm and 12°c, respectively. Since there is no active snow gauges within the basin and also there is only one weather station with reliable temperature records in the region, NOAA satellite images have been used for defining the SCA. In this paper snow cover area detection in Shahcheraghi dam basin has been studied using NOAA-AVHRR images in a 22-year period from 1986 to 2007. In order to improve the precision of calculated monthly SCAs, an image per 10 days was processed (3 images per month). The highest value of SCA among the three calculated values in each month is selected as the final SCA data of the month. Since during this period of time two different sensors of AVHRR-2 and AVHRR-3 have recorded data in different spectral bands, it is necessary to use different algorithms in separating snow from other phenomena including cloud and land cover. By employing the differences between the spectral characteristics of snow compared with other phenomena, the snow covered area can be separated. Therefore, two threshold algorithms are used to separate SCAs. These algorithms are based on grouped conditions of comparing albedo of bands 1 and 2 and brightness temperature values of thermal bands. The most significant difference between the conditions in these methods is using the albedo of band 3A (1. μm) in AVHRR-3. On the other hand, it is necessary to evaluate the numerical difference among the snow separation methods as they may significantly affect the statistic parameters of the time series. Moreover, two trend detection methods are used to examine whether significant trends in the time series exist. The hypothesis-based linear regression and non-parametric Mann-Kendall methods are applied to the maximum annual SCA data. Based on the NOAA-AVHRR image properties, snow cover area is detected by the aforementioned threshold algorithms. The results show that the maximum amount of SCA occurs in January. Generally the snow settlement in the basin is from December to April while there is no record of snow from May to September, which is due to the abrupt air temperature rise in spring. Furthermore, the difference between the snow separation methods is analyzed by comparing two successive images of the basin, taken by different sensors on 5th November 2003. One of the images contains channel 3B which includes thermal infrared band and the other contains channel 3A that scans near infrared wavelengths. Accordingly, the SCA of AVHRR-3 sensor which contains channels 3A has been calculated 4% more than the SCA of AVHRR-2 which records channel 3B. Moreover, the result of applying trend detection tests shows that the SCA time series has no evident linear or monotonic trend. The trend analysis on the SCA dataset has demonstrated that no significant statistic trend exists in the SCA time series. Moreover, the difference between calculated values of the SCA derived from two different AVHRR-2 and AVHRR-3 sensors does not affect the reliability of the SCA dataset, considering the area of the basin. Hence, as a representative of the snow in Shahcheraghi basin, it is possible to consider the calculated snow cover area data as an appropriate input for hydrologic flow forecasting models.