Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

Rain attenuation can hinder the implementation of quantitative precipitation estimations using X-band weather radar. Numerous studies have been conducted on correcting the attenuation of radar reflectivity by utilizing a dual-polarimetric radar and an arbitrary-oriented microwave link; however, there is a need to optimize the required number of microwave links and their locations. In this study, we tested four attenuation correction methods and proposed a novel algorithm based on the sole use of adjacent multiple microwave links. The attenuation of the X-band radar reflectivity was corrected by performing forward iterations at each link, and the correction coefficients were statistically analyzed to reduce the instability problem. The algorithms of each method were evaluated by studying the cases of convective and stratiform rainfall, and then validated by comparing the corrected reflectivity of the X-band radar with the qualitatively controlled reflectivity of the S-band radar. The new method was as efficient as the conventional method based on the specific differential phase of dual-polarimetric radar. Furthermore, the correction coefficient was more effectively optimized and stabilized using seven microwave links rather than a single link, and no further independent reference data were required. In addition, the attenuation correction also accounted for spatiotemporal differentiation depending on the rainfall type, and could recover the physical structure of the rainfall. The method developed herein can facilitate estimations of quantitative rainfall in developing countries where dual-polarization weather radars are not common. The exploitation of microwave link data is a promising method for rainfall remote sensing.

Section: Attenuation Correction Using Microwave Link Onlysupporting

confidence: 53%

Section: Attenuation Correction Using Microwave Link Onlymentioning

confidence: 99%

Attenuation Correction of X-Band Radar Reflectivity Using Adjacent Multiple Microwave Links

Kim

Kwon

2020

“…The study on the microphysical process of snowfall is mostly based on the analysis of snow PSD. Similar to the numerous studies of raindrop size distribution (DSD) worldwide [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43], the study of snowflake PSD is of great significance for remote sensing and microphysical characterization of snowfall. Disdrometers are devices designed to measure the PSD of falling hydrometeors.…”

Section: Introductionmentioning

confidence: 90%

Snowfall Microphysics Characterized by PARSIVEL Disdrometer Observations in Beijing from 2020 to 2022

Yonghai

Chen

et al. 2022

Accurate snowfall forecasting and quantitative snowfall estimation remain challenging due to the complexity and variability of snow microphysical properties. In this paper, the microphysical characteristics of snowfall in the Yanqing mountainous area of Beijing are investigated by using a Particle Size and Velocity (PARSIVEL) disdrometer. Results show that the high snowfall intensity process has large particle-size distribution (PSD) peak concentration, but the distribution of its spectrum width is much smaller than that of moderate or low snowfall intensity. When the snowfall intensity is high, the corresponding Dm value is smaller and the Nw value is larger. Comparison between the fitted μ−Λ relationship and the relationships of different locations show that there are regional differences. Based on dry snow samples, the Ze−SR relationship fitted in this paper is more consistent with the Ze−SR relationship of dry snow in Nanjing, China. The fitted ρs−Dm relationship of dry snow is close to the relationship in Pyeongchang, Republic of Korea, but the relationship of wet snow shows greatly difference. At last, the paper analyzes the statistics on velocity and diameter distribution of snow particles according to different snowfall intensities.

“…Precipitation can be categorized into short-term, mid-term [4], and long-term precipitation [5] on a time scale [6]. Among these, short-term precipitation (0-2 h) has the most severe impact on residents [7].…”

Section: Introductionmentioning

confidence: 99%

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

Hu,

Yin,

Guo

2024

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.