Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics
نویسندگان
چکیده
منابع مشابه
Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging
A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedi...
متن کاملprobabilistic precipitation forecast using post processing of output of ensemble forecasting system
accurate quantitative precipitation forecasts (qpfs) have been always a demanding and challenging job in numerical weather prediction (nwp). the outputs of ensemble prediction systems (epss) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (pqpfs). in this research, different configurations of wrf and mm5 meso-scale models form ...
متن کاملCalibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation∗
Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and underdispersion, and therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy to implement post-processing technique that addresses both forecast bias and underdispersion and takes account of the spread-skill relationship. The tech...
متن کاملProbabilistic Quantitative Precipitation Forecasting using a Two-Stage Spatial Model
Short-range forecasts of precipitation fields are required in a wealth of agricultural, hydrological, ecological and other applications. Forecasts from numerical weather prediction models are often biased and do not provide uncertainty information. Here we present a postprocessing technique for such numerical forecasts that produces correlated probabilistic forecasts of precipitation accumulati...
متن کاملProbabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging
Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts...
متن کاملذخیره 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: Quarterly Journal of the Royal Meteorological Society
سال: 2013
ISSN: 0035-9009
DOI: 10.1002/qj.2183