BAYESIAN PROCESSOR OF OUTPUT FOR PROBABILISTIC FORECASTING OF PRECIPITATION OCCURRENCE By

نویسندگان

  • Coire J. Maranzano
  • Roman Krzysztofowicz
چکیده

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

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 کامل

Probabilistic Weather Forecasting in R

Abstract This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the compo...

متن کامل

Probabilistic View of Occurrence of Large Earthquakes in Iran

In this research seismicity parameters, repeat times and occurrence probability of large earthquakes are estimated for 35 seismic lineaments in Persian plateau and the surrounding area. 628 earthquakes of historical time and present century with MW>5.5 were used for further data analysis. A probabilistic model is used for forecasting future large earthquake occurrences in each chosen lineament....

متن کامل

A Soft-Input Soft-Output Target Detection Algorithm for Passive Radar

Abstract: This paper proposes a novel scheme for multi-static passive radar processing, based on soft-input soft-output processing and Bayesian sparse estimation. In this scheme, each receiver estimates the probability of target presence based on its received signal and the prior information received from a central processor. The resulting posterior target probabilities are transmitted to the c...

متن کامل

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

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

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006