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

Based on the U.S. Weather Research and Forecasting (WRF) numerical model, this study has developed the Northwest Mesoscale Numerical Prediction Service and Experimental System (NW-MNPS). Surface and sounding data assimilation has been introduced for this system. Effects of model vertical layers and land-use data replacement have been assessed. A year-long forecast validation and analysis have been performed. The following results have been obtained: (1) Data assimilation can improve the performance of regional numerical forecasting. (2) Compared to simulations with 40 vertical layers, simulations with 55 vertical layers are more accurate. The average absolute error and root-mean-square error of the 48 h surface element forecast decrease. The analysis of threat score (TS) and equitable threat score (ETS) shows that there are higher TS and ETS values for various precipitation intense levels, in particular for heavy rainfall when comparing a 55-vertical-layer test with a 40-vertical-layer test. (3) Updating the database to include vegetation coverage can more accurately reflect actual surface conditions. The updated land-use data reduce prediction errors in all domains of the NW-MNPS.

Development and Evaluation of a WRF-Based Mesoscale Numerical Weather Prediction System in Northwestern China