امکان‌سنجی استفاده از محصولات سنجنده مودیس به‌منظور اقلیم‌شناسی آب قابل‌بارش در ایران

در این پژوهش به اعتبارسنجی محصولات آب قابل‌بارش سنجنده مودیس بر روی ایران پرداخته شده است. به این منظور داده‌های ماهانه سطح سوم مودیس در بازه زمانی 2003 تا 2015 و داده‌های روزانه سطح دوم، طی ماه ژانویه 2004 و ژوئیه 2008 برای محدوده ایران از وب‌سایت مودیس اخذ شد. سپس داده‌های ماهانه با داده‌های era-interim و داده‌های روزانه با داده‌های رادیوسوند مورد مقایسه قرار گرفت. نتایج این پژوهش نشان می‌دهد که در مقیاس ماهانه، ویژگی‌های داده‌های آب قابل‌بارش مودیس در مقایسه با داده‌های era-interim دارای الگوی فضایی خوشه‌ای‌تر، تغییرپذیری مکانی بالاتر، ارتباط فضایی قوی‌تر با داده‌های ارتفاع و میانگین آب قابل‌بارش سالانه مشابه (12.24 میلی‌متر؛ علی‌رغم اختلاف ماهانه آب قابل‌بارش) می‌باشد. این ویژگی‌ها حاکی از آن است که محصولات سنجنده مودیس، جهت اقلیم‌شناسی آب قابل‌بارش در ایران بسیار کارآمد هستند. همچنین مقایسه آب قابل‌بارش مودیس با داده‌های رادیوسوند در شرایط متفاوت جوی انجام پذیرفت. نتایج نشان داد که در شرایط آسمان صاف و همراه با دید افقی بالا، آب قابل‌بارش حاصل از محصولات مودیس و داده‌های رادیوسوند، ارتباط نزدیکی با یکدیگر دارند (ضریب تعیین=73 درصد در ژوئیه 2008). درحالی‌که ضریب تعیین در طی شرایط ابرناکی و دید افقی پایین (کمتر از 10 کیلومتر) در تمامی ایستگاه‌ها به شدت کاهش می‌یابد (ضریب تعیین=0.05 درصد در ژوئیه 2008). با توجه به این نتایج، صحت آب قابل‌بارش مودیس در مقایسه با داده‌های رادیوسوند وابسته به شرایط جوی می‌باشد.

Feasibility of the use of MODIS products for climatology of precipitable water vapor over Iran

Water vapor is the dominant greenhouse gas in the Earth’s atmosphere and, at the same time, highly variable in the atmosphere. Observations of its spatial and temporal variations is a major objective of climate. It is important in several major areas in the atmospheric sciences, on scales from turbulence to synopticscale systems, and including cloud formation and maintenance, radiation and climate. The intent of this paper is to demonstrate the ability of MODIS PWV products for use in monthly and daily variability of climatological scales over Iran. Therefore, the results are presented in two sections. The first section compares the long term (20032015) Monthly mean MODIS Level 3 and ERAInterim PWV data sets. The second section validates the level 2 MODIS PWV products by Radiosonde daily data. For a better comparison of MODIS level 2 PWV products with Radiosonde data, we used data from 10 Radiosonde stations over Iran. We consider the sky conditions (cloudiness and visibility) in our comparison. There are no microwave radiometers (MWR) and Global Positioning System (GPS) sites in Iran hence, in the absence of these data, we used the measurements of Radiosonde and ERAInterim as reference data for the comparison of the MODIS PWV estimates. These data were obtained at monthly and daily scales. In the first section, longterm (2003 2015) spatial and temporal characteristics of monthly mean PWV are investigated over Iran. For this, Level3 MODIS terra (MOD08_M3) products and ERAInterim data were obtained with the 1degree resolution for Iran. In the second section, January (as a month with low values of PWV and unstable atmosphere) of 2004 and July (as a month with high values of PWV and Stable atmosphere) of 2008 were selected for comparison of MODIS daily (MOD05L2) PWV product with Radiosonde data for 10 Radiosonde stations in Iran. The average annual MODIS and ERAInterim PWV data are 12.248 and 12.243 mm, respectively.These values are very close to each other. These values are also close to those derived by Asakereh et al. (2015) from NCEP data reanalysis (about 14.3 mm). Also, Ferencz and Pongra (2008) concluded that the ERAInterim and the MODIS PWV fields are very similar.The maximum and minimum values of PWV for both data sets is observed during July and January, respectively. Tuller (1968) indicated that February and July are the months of highest and lowest precipitable water at most stations. At some, August replaces July, and at a smaller number, January replaces February. Also, our result is the same with the study of Maghrabi Dajani, (2014) over Saudi Arabia. They reported that the lowest PWV values were in December and January, whereas the highest values were in June and July. They pointed that during warm periods, increases in the temperature and height of constant pressure levels result in an increased capacity for water vapor of the air mass, keeping it away from the saturation point and consequently preserving high PWV values. In contrast, in cold periods, the decrease in the height of constant pressure levels, reduce the capacity for water vapor of the air mass and facilitates the condensation process, resulting in a decrease in the amount of PWV. The topography is a key factor in the spatial distribution of PWV. PWV from both data sets has a significant negative relationship with the distribution of topography in all months. This means that the concentration of PWV is high in the highland regions and vice versa. During January 2004, the ranges of errors are in the best case 5.53 mm (Tabriz) and in the worst case (Ahwaz) 16.02 mm. In all stations, the coefficient of determinations are negligible. While in the suitable weather condition, RMSE is decreased in all stations. During July of 2008 at many stations such as Zahedan, Kerman and Esfahan cloud cover and visibility condition have been appropriate, while in Bandar Abbas in all days the visibility was poor (less than 5 KM). It seems that the cloud cover and visibility conditions result in the high coefficient of determinations in Esfahan, Kerman and Zahedan (77, 80 and 66%, respectively) and with high error in Bandar Abbas station. Annual average MODIS PWV and ERAInterim are close to each other (12.24), in addition, MODIS has a higher negative correlation coefficient with topography compared to ERAInterim PWV data. This suggests that MODIS level3 monthly PWV data are valuable for the monthly longterm climatology of PWV over Iran. In daily scale, a comparison of MODIS and radiosonde PWV data in different atmospheric conditions are significantly different. During clear days with appropriate visibility (despite the time lag between two data sets) values of R2 is higher compared to cloudy days with poor visibility. Hence, accuracy of the MODIS PWV data over Iran is strongly dependent on weather conditions.