Polarimetric Radar for Automotive Self-Localization

Many nontornadic supercell storms have times when they appear to be moving toward tornadogenesis, including the development of a strong low-level vortex, but never end up producing a tornado. These tornadogenesis failure (TGF) episodes can be a substantial challenge to operational meteorologists. In this study, a sample of 32 pre-tornadic and 36 pre-TGF supercells is examined in the 30 min pre-tornadogenesis or pre-TGF period to explore the feasibility of using polarimetric radar metrics to highlight storms with larger tornadogenesis potential in the near-term. Overall the results indicate few strong distinguishers of pre-tornadic storms. Differential reflectivity (ZDR) arc size and intensity were the most promising metrics examined, with ZDR arc size potentially exhibiting large enough differences between the two storm subsets to be operationally useful. Change in the radar metrics leading up to tornadogenesis or TGF did not exhibit large differences, though most findings were consistent with hypotheses based on prior findings in the literature.

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What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

Atmosphere ◽

10.3390/atmos11040362 ◽

2020 ◽

Vol 11 (4) ◽

pp. 362 ◽

Cited By ~ 1

Author(s):

Alexander V. Ryzhkov ◽

Jeffrey Snyder ◽

Jacob T. Carlin ◽

Alexander Khain ◽

Mark Pinsky

Keyword(s):

Cloud Model ◽

Weather Prediction ◽

Primary Source ◽

Polarimetric Radar ◽

Microphysical Process ◽

Weather Radars ◽

Cloud Models ◽

Cloud Modeling ◽

Bin Microphysics ◽

The Impact

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

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Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization

Remote Sensing ◽

10.3390/rs11121436 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1436 ◽

Cited By ~ 1

Author(s):

Skripniková ◽

Řezáčová

Keyword(s):

Comparative Analysis ◽

Heavy Rain ◽

Radar Data ◽

Severe Weather ◽

Detection Methods ◽

Polarimetric Radar ◽

Dual Polarization ◽

Rain Events ◽

Single Polarization ◽

Strong Agreement

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

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