Four radar reflectivity diagrams.
The figure shows radar reflectivity at the 3-kilometer height from (a-b) observations and (c-d) the NWP model prediction at 15:20:00 UTC (left) and 15:40:00 UTC (right) on August 24, 2019. The system uses observations from a new-generation weather radar called MP-PAWR located at the center of each panel (red dot) and provides 30-minute forecasts every 30 seconds. The gray shading in (a) and (b) indicates unobservable area by the radar. The gray dotted circles show the distance from the radar (20, 40, and 60 kilometers). Credit: Honda et al. [2022], Figure 11 (modified)
Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Journal of Advances in Modeling Earth Systems

Predicting severe weather is challenging because individual clouds have a small scale of several kilometers and can rapidly develop in 5 to 10 minutes. Observing these storms by conventional radars is difficult, let alone resolving them by Numerical Weather Prediction (NWP) models.

Honda et al. [2022] develop a complete real-time workflow of the big data assimilation (BDA) system which exploits big data from 30-second observations taken by a new-generation weather radar and from a high-resolution NWP model. Using a massive supercomputing system, the BDA system successfully performs 30-minute real-time forecasts refreshed every 30 seconds, which is 120 times more frequently than typical operational NWP systems updated every hour. The BDA system presents an important step for designing next-generation NWP systems to predict rapidly changing severe weather in a warm and humid climate.

Citation: Honda, T., Amemiya, A., Otsuka, S., Lien, G.-Y., Taylor, J., Maejima, Y., et al. (2022). Development of the real-time 30-s-update big data assimilation system for convective rainfall prediction with a phased array weather radar: Description and preliminary evaluation. Journal of Advances in Modeling Earth Systems, 14, e2021MS002823.

—Jiwen Fan, Editor, Journal of Advances in Modeling Earth Systems

Text © 2022. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.