ارزیابی تاثیر روش‌های تصحیح اریبی بر مهارت پیش‌بینی فصلی بارش مدل اقلیمی CFSv2

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 دانشیار، 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


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 Shabanpour 1
  • Javad Bazrafshan 2
  • Shahab Araghinejad 1
1 Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Associate 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
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