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Recent Advances in Precipitation Radar

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Dear Colleagues,

There have been many natural disasters caused by high-impact weather such as torrential rainfall, hail, flood, tornado, snow storm and tropical cyclone as climate change progresses around the world. Weather radars (Doppler, polarimetric, phased array etc.) have been crucial instruments to monitor chaff diffusion, precipitation and winds, and to forecast high-impact weather systems with higher spatial and temporal resolution than other remote sensing equipment. Polarimetric capabilities help to understand the microphysical characteristics of precipitation systems and improve radar quantitative precipitation estimation/forecasting. The newly developed and advanced analyses of radar precipitation included with hydrometeo classification and wind field retrieval for QPE/QPF are of special interest for this Special Issue.

The goal of this Special Issue is share the recent achievements in various applications using operational or research radar data (e.g., field observation campaign, rainfall estimation, chaff diffusion in clear sky, nowcasting of precipitation, microphysical features of precipitation systems, hydrological modeling and forecasting in severe weather using Doppler radar and polarimetric radar. We encourage contributions on the current state-of-the-art in the field, including challenges and discussions toward the better utilization of radar data.

We invite manuscripts on the following topics:

Field observation campaign;
Radar data quality control;
Quantitative precipitation estimation;
Wind field retrieval and analyses;
Short-term forecast of precipitation;
Assimilation of radar data into NWP;
Orographic/topographic precipitation;
Hydrological applications using weather radar;
High-impact weather such as hail, tornado, typhoon and lightning;
Atmospheric diffusion by chaff experiments;
Torrential rainfall and nowcasting;
Hydrometeo classification in clouds and precipitation.

Prof. Dr. Dong-In Lee
Guest Editor

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Keywords

weather radar quality control algorithms
quantitative precipitation estimation and forecasting
microphysical characteristics of precipitation
polarimetric and phased array radar applications
field observational campaign of high-impact weather
development mechanism of frontal systems and tropical cyclones
radar wind field retrieval and analyses
chaff diffusion analyses by radars
radar nowcasting

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20 pages, 5369 KiB

Open AccessArticle

METEO-DLNet: Quantitative Precipitation Nowcasting Net Based on Meteorological Features and Deep Learning

by Jianping Hu, Bo Yin and Chaoqun Guo

Remote Sens. 2024, 16(6), 1063; https://doi.org/10.3390/rs16061063 - 17 Mar 2024

Viewed by 673

Abstract

Precipitation prediction plays a crucial role in people’s daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms based on radar echo extrapolation for short-term precipitation forecasting. However, there are inadequately addressed issues with radar echo extrapolation methods based on deep learning, particularly when considering the inherent meteorological characteristics of precipitation on spatial and temporal scales. Additionally, traditional forecasting methods face challenges in handling local images that deviate from the overall trend. To address these problems, we propose the METEO-DLNet short-term precipitation prediction network based on meteorological features and deep learning. Experimental results demonstrate that the Meteo-LSTM of METEO-DLNet, utilizing spatial attention and differential attention, adequately learns the influence of meteorological features on spatial and temporal scales. The fusion mechanism, combining self-attention and gating mechanisms, resolves the divergence between local images and the overall trend. Quantitative and qualitative experiments show that METEO-DLNet outperforms current mainstream deep learning precipitation prediction models in natural spatiotemporal sequence problems. Full article

(This article belongs to the Special Issue Recent Advances in Precipitation Radar)

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25 pages, 21681 KiB

Open AccessArticle

An Evaluation of Simulated Cloud Microphysical Characteristics of Three Mei-Yu Rainfall Systems in Taiwan by a Cloud-Resolving Model Using Dual-Polarimetric Radar Observations

by Chung-Chieh Wang, Yu-Han Chen, Yu-Yao Lan and Wei-Yu Chang

Remote Sens. 2023, 15(19), 4651; https://doi.org/10.3390/rs15194651 - 22 Sep 2023

Viewed by 610

Abstract

This study selected three heavy-rainfall events of different types in Taiwan’s Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model’s capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a mesoscale convective system (MCS) embedded in southwesterly flow, and a local convection near the front in southern Taiwan during the South-West Monsoon Experiment (SoWMEX) in 2008, chosen mainly because of the availability of the S-band polarimetric (S-Pol) radar observations, and especially the particle identification results. The simulations using the Cloud-Resolving Storm Simulator (CReSS) could reproduce all three corresponding rainfall systems at roughly the correct time and location, including their kinematic structures such as system-relative flows with minor differences, although the cells appeared to be coarser and wider than the S-Pol observations. The double-moment cold-rain microphysics scheme of the model could also capture the general distributions of hydrometeors, such as heavy rainfall below the updraft core with lighter rainfall farther away below the melting level, and graupel and mixed-phase particles in the upper part of the updraft with snow and ice crystals in stratiform areas between updrafts above the melting level. Near the melting level, the coexistence of rain and snow corresponds to wet snow in the observations. Differences in cloud characteristics in the events are also reflected in the model results to some extent. Overall, the model’s performance in the simulation of hydrometeors exhibits good agreement with the observation and appears reasonable. Full article

(This article belongs to the Special Issue Recent Advances in Precipitation Radar)

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