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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present the encoder-forecaster convolutional long short-term memory (LSTM)deep-learning model that powers Microsoft Weather's operational precipitationnowcasting product. This model takes as input a sequence of weather radarmosaics and deterministically predicts future radar reflectivity at lead timesup to 6 hours. By stacking a large input receptive field along the featuredimension and conditioning the model's forecaster with predictions from thephysics-based High Resolution Rapid Refresh (HRRR) model, we are able tooutperform optical flow and HRRR baselines by 20-25% on multiple metricsaveraged over all lead times.

MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather