English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This study investigates a cross-variable ozone data assimilation(DA) method based on an ensemble Kalman filter (EnKF) that has been used inthe companion study to improve ozone forecasts over Beijing and surroundingareas. The main purpose is to delve into the impacts of the cross-variableadjustment of nitrogen oxide (NOx) emissions on the nitrogen dioxide(NO2) forecasts over this region during the 2008 Beijing Olympic Games.A mixed effect on the NO2 forecasts was observed through application ofthe cross-variable assimilation approach in the real-data assimilation (RDA)experiments. The method improved the NO2 forecasts over almost half ofthe urban sites with reductions of the root mean square errors (RMSEs) by15–36 % in contrast to big increases of the RMSEs over other urbanstations by 56–239 %. Over the urban stations with negative DA impacts,improvement of the NO2 forecasts (with 7 % reduction of the RMSEs) wasnoticed at night and in the morning versus significant deterioration during daytime(with 190 % increase of the RMSEs), suggesting that the negative data assimilation impactsmainly occurred during daytime. Ideal-data assimilation (IDA) experimentswith a box model and the same cross-variable assimilation method confirmedthe mixed effects found in the RDA experiments. In the same way, NOxemission estimation was improved at night and in the morning even under largebiases in the prior emission, while it deteriorated during daytime (except forthe case of minor errors in the prior emission). The mixed effects observedin the cross-variable data assimilation, i.e., positive data assimilation impacts on NO2 forecastsover some urban sites, negative data assimilation impacts over the other urban sites, andweak data assimilation impacts over suburban sites, highlighted the limitations of the EnKFunder strong nonlinear relationships between chemical variables. Under strongnonlinearity between daytime ozone concentrations and NOx emissionsuncertainties (with large biases in the a priori emission), the EnKF may comeup with inefficient or wrong adjustments to NOx emissions. The presentfindings reveal that bias correction is essential for the application of theEnKF in dealing with the data assimilation problem over strong nonlinear system

atmospheric chemistry and physics

Limitations of ozone data assimilation with adjustment of NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; emissions: mixed effects on NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; forecasts over Beijing and surrounding areas