ارزیابی کارایی سامانه Gldas در برآورد تابش سطح روزانه در ایران

تابش سطح زمین (ssr) به عنوان بزرگترین منبع انرژی در سطح زمین، از پارامتر های مهم در مطالعات هواشناسی به شمار می‌رود. با توجه به محدودیت های اندازه‌گیری های زمینی تابش ssr و اهمیت آن در مطالعات کشاورزی، استفاده از روش های کم‌هزینه و قابل اعتماد در برآورد تابش در ایران ضرورت دارد. در بیشتر پژوهش‌های انجام شده در‌‌ ایران روش‌های تجربی برآورد تابش ssr مورد بررسی قرار گرفتند که با وجود سادگی، به دلیل در نظر گرفتن تنها تعداد محدودی پارامترهای هواشناسی، گویای دقیقی از تغییرات آن در مقیاس مکانی وسیع با اقلیم‌های گوناگون نیستند. هدف از این پژوهش، ارزیابی تابش ssr استخراج شده از سامانه gldas با استفاده از اندازه‌گیری‌های زمینی در ایران در مقیاس روزانه می‌باشد. بدین منظور تابش ssr برآورد شده توسط سامانه gldas و تابش اندازه گیری شده در 24 ایستگاه تابش‌سنجی برای دوره (20152012) با یکدیگر مقایسه شدند. نتایج تحقیق نشان داد که با ضریب کارایی بالای 88/0، توافق مناسبی بین عملکرد مدل و تابش سطح زمین اندازه‌گیری شده روزانه در ایران وجود دارد. همچنین نشان داده شد که سامانه gldas در شرایط آسمان صاف (ماه‌های گرم سال) نسبت به شرایط ابرناکی (ماه‌های سرد سال)، توانایی بیشتری در برآورد تابش ssr دارد. ارزیابی کارایی مدل در برآورد تابش روزانه سطح زمین در منطقه مورد مطالعه نیز حاکی از این است که سامانه gldas در 71 درصد ایستگاه های مورد بررسی تمایل به کم برآوردگری دارد. همچنین این مدل در ایستگاه های واقع در اقلیم خشک در مقایسه با مناطق نیمه خشک و ساحلی، برآورد بهتری از تابش سطح زمین در منطقه مورد مطالعه ارائه داد.

Assessment of the Performance of the Global Land Data Assimilation System (GLDAS) in Estimating Daily Surface Solar Radiation in Iran

Introduction: Surface Solar Radiation (SSR) as the largest source of landsurface energy is an important parameter in meteorological and climatological studies. Limitations in groundbased measurements have encouraged the users to approach low cost and reliable methods to estimate radiation components, for the regions where the groundbased radiation data are sparse. Different methods have been developed for estimating SSR including empirical models, radiative transfer models, semiempirical models, and models based on satellite and reanalysis products. In most studies in Iran, empirical methods have been investigated. Despite the simplicity of these models, they do not accurately represent SSR variations because of not considering all the parameters affecting radiation variations, at large spatial scales with different climates. The Global Land Data Assimilation System (GLDAS) is a combination of measured and satellite data that uses advanced land surface modeling and data assimilation methods. One of the strengths of this model that makes GLDAS unique is that it has global coverage, high spatialtemporal resolution and is available for free. GLDAS is a terrestrial modeling system uncoupled to the atmosphere. This work was aimed to evaluate SSR derived from GLDAS using ground measurements over Iran from 2012 to 2015 on a daily basis.Materials and Methods: In this study, measured SSR in 24 radiometer stations of Iran from 2012 to 2015 was extracted. Since the measured data are associated with some errors, the quality of the data must be checked and screened before use. In this study, Moradi apos;s proposed method was used to control data quality. The studied areas were classified into three zones of coastal, arid and semiarid climates based on Digital Elevation Model (DEM) and UNESCO climate classification approach. The GLDAS SSR outputs were extracted with a spatial and temporal resolution of 0.25° grid cell and 3hourly from 2012 to 2015. The GLDAS is one of the LDAS projects and has been extended jointly by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP). The purpose of GLDAS is to produce high quality temporal and spatial land surface data. GLDAS drives three land surface models: Mosaic, Noah, and CLM. GLDAS assessments SSR at the land surface using a method and cloud and snow products from the Air Force Weather Agency apos;s (AFWA) Agricultural Meteorology modeling system (AGRMET). Since the GLDAS data are created using the gridded Binary format, the nearest neighborhood interpolation method was used to match these data with groundbased data and GLDAS datasets were generated for station points using CDO software. In this study, GLDAS datasets were compared against measured SSR datasets by four validation metrics. The metrics used are determination coefficient (R2), the mean bias error (MBD), the mean absolute error (MABD), relative mean absolute error (RMABD) and root mean squared error (RMSE).Results and Discussion: Statistical analysis showed that the performance of GLDAS in SSR evaluation is reasonable in Iran with a highefficiency coefficient of 0.88. Also, it was shown that the GLDAS has a higher ability to estimate SSR under clear sky (warm seasons) conditions than cloudy conditions (cold seasons). Similar to the obtained results, TrägerChatterjee et al. (2010); Jia et al. (2013); Boilley and Wild (2015) and Heidary Beni and Yazdanpanah (2017) also showed that the ERA Interim, NCEPDOE, RegCM4 and angstrom model are also more capable of estimating SSR in warm seasons. Seasonal bias variations at three studied areas showed that the most changes occurred in summer and least changes in winter. The highest overestimation was also observed in the coastal areas in summer and the lowest overestimation in the semiarid regions in spring. The evaluation of the GLDAS performance against the site measured SSR data suggests that the GLDAS tends to underestimate in 71% of the studied stations. Moreover, the stations located in the arid region provided a better estimation of SSR as compared with semiarid and coastal locations. These results were compared with those of Boilley and Wald (2015) that showed ERAInterim and MERRA reanalysis models have high uncertainty in areas with tropical humid climates, and in regions with arid climates, models perform better in SSR estimation. Our findings were also in good agreement with their results.Conclusion: GLDAS SSR outputs can be used for agricultural studies. This is due to the facts that arid and semiarid climates are dominant in Iran and the growing season is mostly in the warm season.