Advances in Multi-Source Remote Sensing, Space-Based Observations, Data Assimilation and Deep Learning for Localized Extreme Weather: Understanding Remote Sensing

Dear Colleagues,

Localized extreme weather events, ranging from severe convective storms and heavy precipitation to heatwaves and flash floods, present some of the most challenging prediction problems in meteorology. Accurately forecasting the initiation, evolution, and local impact of these events is critical for mitigating risks to society and the economy. While high-resolution numerical weather prediction (NWP) models have made significant strides, challenges remain in resolving the detailed structure of these local-scale phenomena due to initial condition uncertainties and imperfect physical parameterizations.

This Special Issue aims to highlight state-of-the-art advances in bridging the gap between remote sensing observations and the prediction of localized extreme weather. We seek to explore how the synergy of next-generation satellite imagery (e.g., GOES-R, FY-4), weather radars, and ground-based profilers can be leveraged through advanced data assimilation and emerging machine learning techniques. By focusing on the integration of these technologies into the forecasting value chain, this issue aligns with the journal's scope of applying remote sensing technology to understand Earth system processes.

The Issue encourages contributions that address the full forecasting value chain. We welcome original research articles and comprehensive reviews that utilize advanced physical modelling, data-driven methods, or hybrid approaches to enhance prediction accuracy for local extremes.

Suggested topics include, but are not limited to, the following:

1. Dynamics of local extremes: Utilization of remote sensing data to characterize the lifecycle and structure of localized weather systems (e.g., mesoscale convective systems, fog, urban heat islands).
2. Advanced data assimilation: Assimilation of high-frequency satellite radiances, lightning data, and radar reflectivity into high-resolution models.
3. AI/ML for nowcasting: Deep learning architectures for satellite/radar extrapolation and short-term severe weather warning.
4. Forecast post-processing and downscaling: Advanced machine learning methods for bias correction, resolution enhancement, and grid-to-point mapping for station-level precision.
5. Multi-source data fusion: Integration of satellite, ground-based, and NWP data for improved environmental monitoring and situational awareness.
6. Extreme event analysis: Detection and tracking of severe weather phenomena using intelligent pattern recognition.

Prof. Dr. Chuang Zhang
Prof. Dr. Zhengxia Zou
Dr. Xiaoling Zhang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.