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ارزیابی تاثیر روشهای تصحیح اریبی بر مهارت پیشبینی فصلی بارش مدل اقلیمی CFSv2 | ||
| تحقیقات آب و خاک ایران | ||
| دوره 51، شماره 12، اسفند 1399، صفحه 3017-3032 اصل مقاله (1.86 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2020.306717.668680 | ||
| نویسندگان | ||
| فاطمه شعبان پور1؛ جواد بذرافشان* 2؛ شهاب عراقی نژاد1 | ||
| 1گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2دانشیار، گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| روشهای تصحیح اریبی از جمله روشهای آماری متداول برای پسپردازش خروجی مدل‌های اقلیمی هستند. در این تحقیق، تاثیر پنج روش تصحیح اریبی بر مهارت پیشبینی بارش (فصل پاییز) مدل اقلیمی CFSv2 بر مبنای 12 ایستگاه واقع در حوضه آبریز گرگانرود (شمال ایران) مورد ارزیابی قرارگرفته است. روشهای تصحیح اریبی مورد استفاده در این تحقیق شامل دو روش ناپارامتری (نسبت‌گیری خطی(LS) ، نگاشت چندکی تجربی (EQM))، یک روش پارامتری (تبدیل توانی (Ptr)) و دو روش پارامتری مبتنی بر توزیعهای آماری (نگاشت پارامتری چندک (PQM)، نگاشت چندکی پارامتری تعمیم یافته (GPQM)) میباشند. از سنجههای متنوعی برای ارزیابی تاثیر این روشها بر مهارت پیشبینی فصلی بارش استفاده شده است که شامل متوسط اریبی، متوسط ضریب همبستگی پیرسون و همچنین دو سنجه مهارت پیشبینی احتمالاتی شامل امتیازهای مهارتی، ویژگی عملیاتی نسبی (ROCSS) و رتبه احتمال (RPSS) میباشد. نتایج این تحقیق نشان میدهد بیشتر روشهای تصحیح اریبی و در موارد بالایی به‌خوبی توانستند اریبی موجود در پیشبینیها را کاهش دهند. تاثیر استفاده از روشهای مختلف تصحیح اریبی بر مهارت پیشبینی احتمالاتی با استفاده از سنجههای RPSS و ROCSS نیز وابسته به محل و زمان متفاوت است و هر یک از روشها میتوانند این سنجهها را برای محل یا زمانی بهبود دهند و یا تضعیف کنند. از اینرو نتیجه این تحقیق پیشنهاد میکند ارزیابی روشهای مختلف تصحیح اریبی و شناسایی مناسبترین روش با توجه به هدف هر مطالعه میتواند به ارتقاء مهارت پیشبینی فصلی بارش کمک کند. | ||
| کلیدواژه‌ها | ||
| پیش بینی بارش فصلی؛ تصحیح اریبی؛ مهارت؛ ریزمقیاس‌نمایی؛ اقلیم شناسی | ||
| عنوان مقاله [English] | ||
| Evaluation of the Effect of Bias Correction Methods on the Skill of Seasonal Precipitation Forecasts of CFSv2 Climate Model | ||
| نویسندگان [English] | ||
| Fatemeh Shabanpour1؛ Javad Bazrafshan2؛ Shahab Araghinejad1 | ||
| 1Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Associate Professor, Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| چکیده [English] | ||
| Bias correction methods are one of the most common statistical post-processing methods which are utilized on the output of climate models. This study evaluates the effect of five bias correction methods on the skill of seasonal precipitation forecast (fall season) from the CFSv2 climate model based on 12 stations located in Gorganrud basin in Iran. Bias correction methods that have been used in this study consists of two non-parametric methods (Linear Scaling (LS), Empirical Quantile Mapping (EQM)), one parametric method (Power Transformation (Ptr)), and two parametric methods based on the statistical distribution (Parametric Quantile Mapping (PQM), Generalized Parametric Quantile Mapping (GPQM)). Various metrics have been used for evaluating the effects of these methods on the skill of seasonal precipitation forecast which consists of bias, Pearson correlation coefficient, ranked probability skill score (RPSS), and the relative operating curve skill score (ROCSS). The Results of this study revealed that most of bias correction methods decreased the biases of the raw forecasts. The effect of each bias correction method on the RPSS and ROCSS (below and above normal events) scores may vary based on location and time, and each method can improve or worsen these two scores based on location and time. The results of this study suggest that the evaluation of various bias correction methods and distinguishing the most suitable method based on the goal of each study would be helpful in the improvement of seasonal precipitation forecast skill. | ||
| کلیدواژه‌ها [English] | ||
| Seasonal Precipitation Forecast, Bias Correction, Skill, Downscaling, Climatology | ||
| مراجع | ||
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