Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach

Filetype[PDF-993.92 KB]


Details Supporting Files You May Also Like

Details:

  • Journal Title:
    Water Resources Research
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    Climate state can be an important predictor of future hydrologic conditions. In ensemble streamflow forecasting, where historical weather inputs or streamflow observations are used to generate the ensemble, climate index weighting is one way to represent the influence of climate state. Using a climate index, each forecast variable member of the ensemble is selectively weighted to reflect the climate state at the time of the forecast. A new approach to climate index weighting of ensemble forecasts is presented. The method is based on a sampling-resampling approach for Bayesian updating. The original hydrologic ensemble members define a sample drawn from the prior distribution; the relationship between the climate index and the ensemble member forecast variable is used to estimate a likelihood function. Given an observation of the climate index at the time of the forecast, the estimated likelihood function is then used to assign weights to each ensemble member. The weights define the probability of each ensemble member outcome given the observed climate index. The weighted ensemble forecast is then used to estimate the posterior distribution of the forecast variable conditioned on the climate index. The Bayesian climate index weighting approach is easy to apply to hydrologic ensemble forecasts; its parameters do not require calibration with hindcasts, and it adapts to the strength of the relation between climate and the forecast variable, defaulting to equal weighting of ensemble members when no relationship exists. A hydrologic forecasting application illustrates the approach and contrasts it with traditional climate index weighting approaches.
  • Keywords:
  • Source:
    Water Resources Research, 51(9), 7382-7400
  • DOI:
  • ISSN:
    0043-1397
  • Format:
    PDF
  • Publisher:
    American Geophysical Union (AGU)
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA09OAR4310196
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26.1