تحلیل تاثیر ابرناکی بر روی بازتابندگی و دمای روشنایی بدست آمده از داده های ماهواره ای متئوست در ایران مقاله

در این مطالعه، چگونگی تغییرات بازتابندگی و دمای روشنایی بدست آمده از مشاهدات داده های ماهواره ای، مورد بررسی قرار گرفته است. برای انجام این مطالعه از دو مجموعه داده ماهواره ای و مشاهداتی استفاده شده است. داده های مشاهداتی شامل داده های بارش 6 ساعته در طول ساعات روز (ساعت 06 تا 12 گرینویچ) و داده های ماهواره ای نیز شامل داده های سطح 5/1 از تصویربردار چرخان پیشرفته مریی و فروسرخ (SEVIRI) بر روی نسل دوم ماهواره های متیوست (MSG) می باشند. این داده ها برای موقعیت 399 ایستگاه هواشناسی کشور ایران برای 26 روز استخراج و بررسی شده اند. سپس روند تغییرات بازتابندگی و دمای روشنایی در اثر ابرناکی بررسی شده و میزان همبستگی بین بازتابندگی و دمای روشنایی کانال های مختلف با یکدیگر و همچنین با بارش، محاسبه شده است. نتایج نشان می-دهد کانال های مریی همبستگی مثبت و کانال های فروسرخ همبستگی منفی با بارش دارند. در بین 11 کانال بررسی شده ی سنجنده SEVIRI، بیشترین همبستگی بارش به ترتیب با کانال های VIS0.8 ،VIS0.6، IR3.9 و IR8.7 می باشد. عمده تغییرات میانگین بازتابندگی و دمای روشنایی در این کانال ها بسیار متفاوت بوده و کمترین همپوشانی را با یکدیگر دارند. بنابراین پتانسیل تمییز شرایط بارشی از غیربارشی و نشان دادن تاثیر ابرناکی را دارا می باشند. به همین جهت این کمیت ها بعنوان ورودی مدل ماشین بردار پشتیبان انتخاب گردیدند. مدل طراحی شده با دقت 85% توانایی تفکیک مناطق با ابرهای بارشی از غیر بارشی را داراست.

Introduction 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 methods 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. Results and discussion 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. Conclusion 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.