Week 3–4 Prediction of Wintertime CONUS Temperature Using Machine Learning Techniques
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

Week 3–4 Prediction of Wintertime CONUS Temperature Using Machine Learning Techniques

Filetype[PDF-7.56 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed
Details Supporting Files You May Also Like

Details:

  • Journal Title:
    Frontiers in Climate
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    This paper shows that skillful week 3–4 predictions of a large-scale pattern of 2 m temperature over the US can be made based on the Nino3.4 index alone, where skillful is defined to be better than climatology. To find more skillful regression models, this paper explores various machine learning strategies (e.g., ridge regression and lasso), including those trained on observations and on climate model output. It is found that regression models trained on climate model output yield more skillful predictions than regression models trained on observations, presumably because of the larger training sample. Nevertheless, the skill of the best machine learning models are only modestly better than ordinary least squares based on the Nino3.4 index. Importantly, this fact is difficult to infer from the parameters of the machine learning model because very different parameter sets can produce virtually identical predictions. For this reason, attempts to interpret the source of predictability from the machine learning model can be very misleading. The skill of machine learning models also are compared to those of a fully coupled dynamical model, CFSv2. The results depend on the skill measure: for mean square error, the dynamical model is slightly worse than the machine learning models; for correlation skill, the dynamical model is only modestly better than machine learning models or the Nino3.4 index. In summary, the best predictions of the large-scale pattern come from machine learning models trained on long climate simulations, but the skill is only modestly better than predictions based on the Nino3.4 index alone.
  • Keywords:
  • Source:
    Frontiers in Climate, 3
  • DOI:
  • ISSN:
    2624-9553
  • Format:
    PDF
  • Publisher:
    Frontiers Media SA
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA14OAR4310160
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

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

Version 3.27.1