A dynamical Gaussian, lognormal, and reverse lognormal Kalman filter

Van Loon, Senne; Fletcher, Steven J.

doi:10.1002/qj.4595

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

i

A dynamical Gaussian, lognormal, and reverse lognormal Kalman filter

2023
By Van Loon, Senne ; Fletcher, Steven J.
Source: Quarterly Journal of the Royal Meteorological Society (2023)

[PDF-2.55 MB]

Details You May Also Like

Details:

Journal Title:

Quarterly Journal of the Royal Meteorological Society
Personal Author:

Van Loon, Senne ; Fletcher, Steven J.

Van Loon, Senne ; Fletcher, Steven J. Less -
NOAA Program & Office:

CIRA (Cooperative Institute for Research in the Atmosphere)
Description:

We derive a generalization of the Kalman filter that allows for non‐Gaussian background and observation errors. The Gaussian assumption is replaced by considering that the errors come from a mixed distribution of Gaussian, lognormal, and reverse lognormal random variables. We detail the derivation for reverse lognormal errors, and extend the results to mixed distributions, where the number of Gaussian, lognormal, and reverse lognormal state variables can dynamically change every analysis time. We robustly test the dynamical mixed Kalman filter on two different systems based on the Lorenz 1963 model, and demonstrate that non‐Gaussian techniques generally improve the analysis skill if the observations are sparse and uncertain, compared to the Gaussian Kalman filter.This article is protected by copyright. All rights reserved.
Keywords:

[+]

Atmospheric Science
Source:

Quarterly Journal of the Royal Meteorological Society (2023)
DOI:

https://doi.org/10.1002/qj.4595
ISSN:

0035-9009;1477-870X;
Publisher:

Wiley
Document Type:

Journal Article
License:

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

CC BY
Compliance:

Library
Main Document Checksum:

[+]

urn:sha256:0499b22f31d87868d02c28136c27c76f426d0e4e11ffa48c04d83ec37bbb294d
Download URL:

https://repository.library.noaa.gov/view/noaa/59099/noaa_59099_DS1.pdf
File Type:

No Additional Files

More +

A dynamical Gaussian, lognormal, and reverse lognormal Kalman filter

Details:

Supporting Files

You May Also Like