A Hybrid Dynamic-Statistical Approach to Link Predictive Understanding to Improve Seasonal Prediction of Rainfall Anomalies at the Regional Scale

Fu, Rong; Erfanian, Amir; Fernando, Nelun

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A Hybrid Dynamic-Statistical Approach to Link Predictive Understanding to Improve Seasonal Prediction of Rainfall Anomalies at the Regional Scale

Supporting Files

2019
By Fu, Rong ; Erfanian, Amir ; Fernando, Nelun
Source Climate Prediction S&T Digest

Details

Journal Title:

Climate Prediction S&T Digest
Personal Author:

Fu, Rong ; Erfanian, Amir ; Fernando, Nelun
NOAA Program & Office:

NWS (National Weather Service) ; OSTI (Office of Science and Technology Integration) ; OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office)
Description:

Seasonal prediction of summer rainfall anomalies over the United State (US) Great Plains (GP) is central for drought early warning and society preparedness. Yet, current dynamic models’ predictions have failed to predict recent extreme droughts in 2011 and 2012 and shown virtually no skills for seasonal prediction of the summer rainfall anomalies (e.g. Quan et al. 2012; Hoerling et al. 2014).
Source:

Climate Prediction S&T Digest
Document Type:

Journal Article
Rights Information:

CC 0
Compliance:

Submitted
Download URL:

https://repository.library.noaa.gov/view/noaa/36781/noaa_36781_DS1.pdf
File Type:

[PDF - 598.15 KB]
Collection(s):

Office of Oceanic and Atmospheric Research (OAR) ; National Weather Service (NWS)
Main Document Checksum:

urn:sha256:0ebcb457cc7868aa7eca484ae69373fadb581baae0fd025922976b471e3925fd

NOAA Annual Climate Diagnostics & Prediction Workshop Index

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