ارزیابی کارایی روش‌های پس پردازش و اصلاح اریبی بر پیش‌بینی‌های ماهانه بارش و دما در حوضه کارون

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 گروه عمران آب، دانشکده عمران معماری و هنر، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران

3 استادیار، گروه مهندسی عمران، دانشگاه آزاد اسلامی، واحد اسلامشهر، اسلامشهر، ایران.

چکیده

پیش‌بینی‌ مناسب بارش و دما با افق یک‌ماهه می‌تواند فرصتی استثنایی برای برنامه‌ریزی منابع آب و مقابله با سیل و خشکسالی در اختیار مدیران قرار دهد. اعمال روش‌های پس‌پردازش و اصلاح اریبی مناسب می‌تواند کارایی پیش‌بینی‌های عددی هواشناسی را تا حد قابل قبولی ارتقا بخشد. در این تحقیق ضمن ارزیابی پیش‌بینی‌های خام بارش و دمایS2S مرکز ECMWF در یکی از حوضه‌های آبریز مهم کشور، روش‌های متنوعی برای پس‌پردازش و اصلاح اریبی این پیش‌بینی‌ها مورد استفاده قرار گرفت و نتایج با معیارهای ارزیابی مختلف مقایسه گردید. تکنیک‌های نگاشت چندک(QM)، میانگین‌گیری مدل بیزین(BMA)، رگرسیون بردار پشتیبان(SVR)، رابطه تجربی اصلاح اریبی دما و روش‌های ترکیبی بر روی پیش‌بینی‌ها اعمال شد که از بین آن‌ها روش BMA هم در بهبود پیش‌بینی‌های دما و هم بارش اندکی مؤثرتر از سایر روش‌ها عمل نمود. در حالت خام، پیش‌بینی‌های بارش و دما تنها در 2 یا 3 ماه سال قابل استفاده ارزیابی شدند ولی اعمال روش‌های پس‌پردازش توانست دقت پیش‌بینی‌های بارش را در نیمی از ماه‌ها، به‌ویژه ماه‌های پرباران تا حد قابل قبولی ارتقا دهد و اعمال روش ترکیبی معادله تجربی-میانگین مدل بیزین در 10 ماه از سال با پیش‌بینی‌هایی بهتر از تخمین دمای ماه آتی با استفاده از آمار بلندمدت همراه بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of Post-Processing and Bias Correction of Monthly Precipitation and Temperature Forecasts in Karun Basin

نویسندگان [English]

  • Roya Kolachian 1
  • Bahram Saghafian 2
  • Saber Moazami 3
1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Water Engineering, Faculty of Civil Engineering, Architecture and Art, Islamic Azad University, Science and Research Branch, Tehran, Iran
3 Assistant Professor, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
چکیده [English]

Efficient forecast of precipitation and temperature with a one-month horizon can provide managers with an exceptional opportunity to plan water resources and deal with floods and droughts. The application of proper post-processing and bias correction methods can much improve the accuracy of these predictions. In this study, the S2S (Sub seasonal to Seasonal) precipitation and temperature forecasts of ECMWF were evaluated in one of the important basins of Iran. A variety of methods were used for post-processing and bias correction of these predictions, and the results were compared with different evaluation criteria. Quantile mapping (QM), Bayesian model averaging (BMA), Support vector regression (SVR), an Empirical equation for bias correction of temperature, and some hybrid methods were applied to forecasts. The BMA outperformed the other methods in improving both temperature and precipitation forecasts. Raw precipitation and temperature forecasts were only applicable in 2 or 3 months of the year, but post-processing methods were able to accurately improve precipitation in half of the months, especially rainy months. The hybrid of empirical equation-BMA in 10 months of the year was led to better results than the estimate of the next month's temperature using climatological data.

کلیدواژه‌ها [English]

  • Post-processing
  • bias correction
  • Bayesian model averaging
  • Quantile Mapping
  • Support vector regression
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