Evaluation of bias-correction methods for ensemble streamflow volume forecasts

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Evaluation of bias-correction methods for ensemble streamflow volume forecasts

Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three biascorrection methods for ensemble streamflow volume forecasts. Al...

متن کامل

Evaluation of bias-correction methods for streamflow forecasts

Evaluation of bias-correction methods for ensemble streamflow volume forecasts T. Hashino, A. A. Bradley, and S. S. Schwartz University of Wisconsin, Department of Atmospheric and Ocean Sciences, Madison, Wisconsin, USA The University of Iowa, IIHR – Hydroscience & Engineering, Iowa City, Iowa, USA Center for Urban Environmental Research and Education, UMBC, Baltimore, Maryland, USA Received: 1...

متن کامل

Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction

Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical ...

متن کامل

A comparative study of quantitative mapping methods for bias correction of ERA5 reanalysis precipitation data

This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected syno...

متن کامل

Comparison of data-driven methods for downscaling ensemble weather forecasts

This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. 5 Given the coarse resolution (about 200-km grid spacing) of the MR...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Hydrology and Earth System Sciences

سال: 2007

ISSN: 1607-7938

DOI: 10.5194/hess-11-939-2007