Mid-to-Long Range Wind Forecast in Brazil Using Numerical Modeling and Neural Networks

Campos, Ricardo M.; Palmeira, Ronaldo M. J.; Pereira, Henrique P. P.; Azevedo, Laura C.

doi:10.3390/wind2020013

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

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

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

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

Mid-to-Long Range Wind Forecast in Brazil Using Numerical Modeling and Neural Networks

2022
By Campos, Ricardo M. ; Palmeira, Ronaldo M. J. ; Pereira, Henrique P. P. ; ...
Source: Wind, 2(2), 221-245

[PDF-19.25 MB]

Select the Download button to view the document

This document is over 5mb in size and cannot be previewed

Details You May Also Like

Details:

Journal Title:

Wind
Personal Author:

Campos, Ricardo M. ; Palmeira, Ronaldo M. J. ; Pereira, Henrique P. P. ; Azevedo, Laura C.

Campos, Ricardo M. ; Palmeira, Ronaldo M. J. ; Pereira, Henrique P. P. ; Azevedo, Laura C. Less -
NOAA Program & Office:

AOML (Atlantic Oceanographic and Meteorological Laboratory) ; OAR (Oceanic and Atmospheric Research)

AOML (Atlantic Oceanographic and Meteorological Laboratory) ; OAR (Oceanic and Atmospheric Research) Less -
Description:

This paper investigated the development of a hybrid model for wind speed forecast, ranging from 1 to 46 days, in the northeast of Brazil. The prediction system was linked to the widely used numerical weather prediction from the ECMWF global ensemble forecast, with neural networks (NNs) trained using local measurements. The focus of this study was on the post-processing of NNs, in terms of data structure, dimensionality, architecture, training strategy, and validation. Multilayer perceptron NNs were constructed using the following inputs: wind components, temperature, humidity, and atmospheric pressure information from ECMWF, as well as latitude, longitude, sin/cos of time, and forecast lead time. The main NN output consisted of the residue of wind speed, i.e., the difference between the arithmetic ensemble mean, derived from ECMWF, and the observations. By preserving the simplicity and small dimension of the NN model, it was possible to build an ensemble of NNs (20 members) that significantly improved the forecasts. The original ECMWF bias of −0.3 to −1.4 m/s has been corrected to values between −0.1 and 0.1 m/s, while also reducing the RMSE in 10 to 30%. The operational implementation is discussed, and a detailed evaluation shows the considerable generalization capability and robustness of the forecast system, with low computational cost.
Source:

Wind, 2(2), 221-245
DOI:

https://doi.org/10.3390/wind2020013
ISSN:

2674-032X
Format:

PDF
Publisher:

MDPI AG
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

[+]

urn:sha-512:dfcb4b03f7cd8ffd499457aef88c2764700dfe7d584eb209ef473616318ca4c5dc7714bf57002eff70e3c8c8973606ecdcb25bf9c5b178d4d30a85696f99def2
Download URL:

https://repository.library.noaa.gov/view/noaa/64259/noaa_64259_DS1.pdf
File Type:

No Additional Files

More +

Mid-to-Long Range Wind Forecast in Brazil Using Numerical Modeling and Neural Networks

Details:

Supporting Files

You May Also Like