Characteristic analysis of dual-polarization weather radar echoes of clear air and precipitation in the Mount Everest region

2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Lei Wang ◽  
Yueqing Li ◽  
Xiangde Xu ◽  
Hao Wang
Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1671
Author(s):  
Lei Wang ◽  
Yueqing Li ◽  
Xiangde Xu

This paper introduces the X-band weather radar dual-polarization parameters of isolated convective cell precipitation and meso/microscale snowfall on Mount Everest and presents the first precipitation observations based on dual-polarization weather radar in this area. Compared with the Chengdu Plain, Mount Everest experienced convective precipitation on smaller horizontal and vertical scales with a narrower Zdr probability density spectrum (uniformly distributed around approximately 0). The Zh profile on Mount Everest displayed two peaks, unlike that over the plains, and the precipitation at the strong convective core was denser. Furthermore, during winter snowfall on the northern slope of Mount Everest, when the boundary layer exhibited sufficient water vapor and dynamic uplift, due to the low boundary layer temperature (<0 °C), water vapor produced stratiform clouds in the middle and lower layers (approximately 1.5 km above ground level (AGL)). Water vapor condensation at 1.5–2.5 km AGL led to latent heat release, which increased the temperature of regional stratiform clouds with increasing height. Consequently, the temperature at the stratiform cloud top height (2.5 km AGL) unexpectedly exceeded 0 °C. Additionally, the −20 °C isotherm was at approximately 4 km AGL, indicating that the middle- and upper-layer atmospheric temperatures remained low. Therefore, thermal instability occurred between the stratiform cloud top height and the middle/upper atmosphere, forming convective motion. These findings confirm the occurrence of elevated winter snowfall convection above Mount Everest and may have certain reference value for retrieving raindrop size distributions, quantitatively estimating precipitation, and parameterizing cloud microphysical processes in numerical prediction models for the Qinghai-Tibetan Plateau.


Author(s):  
Precious Jatau ◽  
Valery Melnikov ◽  
Tian-You Yu

AbstractThe S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.


2021 ◽  
Vol 1812 (1) ◽  
pp. 012016
Author(s):  
Qian Zhang ◽  
Peng Zhang ◽  
Yi Zhang ◽  
Yi Zheng ◽  
Haiwen Wei ◽  
...  

2021 ◽  
pp. 105852
Author(s):  
M. Montopoli ◽  
E. Picciotti ◽  
L. Baldini ◽  
S. Di Fabio ◽  
F.S. Marzano ◽  
...  

2009 ◽  
Vol 105 (3-4) ◽  
pp. 121-132 ◽  
Author(s):  
Adolfo Vicente Magaldi ◽  
Joan Bech ◽  
Jerónimo Lorente
Keyword(s):  

Author(s):  
Joaquin Cuomo ◽  
V. Chandrasekar

AbstractNowcasting based on weather radar uses the current and past observations to make estimations of future radar echoes. There are many types of operationally deployed nowcasting systems, but none of them are currently based on deep learning, despite it being an active area of research in the last few years. This paper explores deep learning models as alternatives to current methods by proposing different architectures and comparing them against some operational nowcasting systems. The methods proposed here, harnessing residual convolutional encoder-decoder architectures, reach a level of performance expected of current systems and in certain scenarios can even outperform them. Finally, some of the potential drawbacks of using deep learning are analyzed. No decay in the performance on a different geographical area from where the models were trained was found. No edge or checkerboard artifact, common in convolutional operations, was found that affects the nowcasting metrics.


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