Weather Radar Research Articles

An early warning approach for the rapid identification of extreme weather disasters based on phased array dual polarization radar cooperative network data.

In recent years, X-band phase array dual polarization weather radar technology has matured. The cooperative networking data from X-band phase array dual polarization weather radar have many advantages compared with traditional methods, namely, high spatial and temporal resolution (approximately 70 seconds in one scan, 30 m in radial distance resolution), wide coverages that can compensate for the observation blind spots, and data fusion technology that is used in the observation overlap area to ensure that the observed precipitation data have spatial continuity. Based on the above radar systems, this study proposes an improved hail and lightning weather disaster rapid identification and early warning algorithm. The improved thunderstorm identification, tracking, analysis, and nowcasting (TITAN) algorithm is used to quickly identify three-dimensional strong convective storm cells. Large sample observation experiment data are used to invert the localized hail index (HDR) to identify the hail position. The fuzzy logic method is used to comprehensively determine the probability of lightning occurrence. The comparative analysis experiment shows that, compared with the live observation data from the ground-based automatic station, the hail and lightning disaster weather warning algorithm developed by this study can increase warning times by approximately 7 minutes over the traditional algorithm, and its critical success index (CSI), false alarm ratio (FAR) and omission alarm ratio (OAR) scores are better than those of the traditional method. The average root mean square error (ARMSE) for identifying hail and lightning locations by this improved method is also significantly better than that of traditional methods. We show that our method can provide probabilistic predictions that improve hail and lightning identification, improve the precision of early warning and support operational utility at higher resolutions and with greater lead times that traditional methods struggle to achieve.

Evaluasi Spasial Estimasi Curah Hujan pada Radar Cuaca Menggunakan Metode Z-R Marshall-Palmer di Wilayah Jawa Barat

Rainfall is one of the weather parameters that affect various sectors. High rainfall intensity can trigger hydrometeorological disasters, so rainfall observation data is vital to monitor rainfall conditions in an area. An automatic rain gauge is an instrument that measures rainfall at an observation point, but the instrument has reasonably low coverage and has yet to reach the entire region. Weather radar is a remote sensing instrument capable of spatially estimating rainfall. Weather radar data can be used to estimate rainfall using the Marshall-Palmer Z-R method. The application of the method can be an alternative for areas that do not have rainfall observation equipment. However, the estimation needs to be evaluated to improve the accuracy of the estimation value. Based on the evaluation, the highest coefficient of determination was 0.92, and the lowest was 0.67. The lowest RMSE value was 2.40, the highest was 6.76, the highest ME value was 16.59, and the lowest was 5.93; the highest bias was 12.90, and the lowest was 5.30. The study results show that the weather radar can operate according to the specifications of the maximum observation distance of up to 220 KM, but the farther the observation distance to a point, the higher the performance of rainfall estimation accuracy.

Features of Atmospheric Dynamics During Tropical Cyclone AMPHAN Observed Using ST Radar and Doppler Weather Radar

Features of Atmospheric Dynamics During Tropical Cyclone AMPHAN Observed Using ST Radar and Doppler Weather Radar

On the Radial-Based Estimation of Colored Noise Power in Weather Radars

On the Radial-Based Estimation of Colored Noise Power in Weather Radars

Doppler Velocity Recovery and Dealiasing Algorithm for Multi-PRT Scans in Weather Radars

Doppler Velocity Recovery and Dealiasing Algorithm for Multi-PRT Scans in Weather Radars

An Animal Migration Forecast Model with Weather Radar and Meteorological Data

An Animal Migration Forecast Model with Weather Radar and Meteorological Data

Adopting a Neuro-Fuzzy Logic Method for Fall Armyworm Detection and Monitoring Using C-Band Polarimetric Doppler Weather Radar With Field Verification

Adopting a Neuro-Fuzzy Logic Method for Fall Armyworm Detection and Monitoring Using C-Band Polarimetric Doppler Weather Radar With Field Verification

Counter-Aliasing Is Better Than De-Aliasing: Application to Doppler Weather Radar With Aperiodic Pulse Train

Counter-Aliasing Is Better Than De-Aliasing: Application to Doppler Weather Radar With Aperiodic Pulse Train

Warn-on-Forecast System: From Vision to Reality

Abstract In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction. Significance Statement The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Phase and Amplitude Correction for Adaptive Beamforming of Phased Array Weather Radar

Phase and Amplitude Correction for Adaptive Beamforming of Phased Array Weather Radar

Improved Joint Phase–Attenuation Estimation With Adaptive and High-Resolution Empirical Coefficient Conditioning for Polarimetric Weather Radars

Improved Joint Phase–Attenuation Estimation With Adaptive and High-Resolution Empirical Coefficient Conditioning for Polarimetric Weather Radars

Phased Array Weather Radar Architectures for Doppler Estimation with Space Time Processing

Phased Array Weather Radar Architectures for Doppler Estimation with Space Time Processing

Аномальный грозовой процесс на территории Ставропольского края

The paper analyzes the dynamics of such anomalous weather phenomenon as a thunderstorm. The thunderstorm process that took place in the North Caucasus on June 27, 2023 is considered. The thunderstorm process under consideration differed significantly from the statistical average, both in duration and in lightning parameters. The analysis was carried out using the example of the Stavropol Krai, although anomalous thunderstorms on June 27, 2023 also occurred in other regions of the North Caucasus. The work used the lightning direction network of the Federal State Budgetary Institution VGI with spatially separated sensors LS8000 and LS7002. To determine the characteristics of thunderstorm activity, the work used long-term data from the specified lightning direction network. Studies of thunderstorm activity were carried out in conjunction with the characteristics of convective clouds determined by MRL-5 weather radars. The Federal State Budgetary Institution VGI has organized the uptake, processing, archiving and transmission to the ASPD network of Roshydromet of radar information from all MRL-5 weather radars of the anti-hail services of the North Caucasus. During the day, about 30,000 lightning strikes were recorded by VGI in this territory. Of these, 3,713 were ground discharges. Of the registered 3,713 ground-based lightning discharges in the Stavropol Krai, negative lightning amounted to 2,901, and the positive one – 812. The share of negative lightning discharges from the total number of ground-based lightning discharges in the specified territory was 78%. The largest number of negative lightning (more than 70%) had values up to 20 kA. More than 40% of positive lightning discharges had values from 21 to 40 kA. The average value of currents of negative polarity was -18.6 kA, and that of positive polarity was +53.2 kA.

Ground Clutter and Noise Mitigation Based on Range–Doppler Spectral Processing for Polarimetric Weather Radar

Ground Clutter and Noise Mitigation Based on Range–Doppler Spectral Processing for Polarimetric Weather Radar

The Application of the New Generation Weather Radar in Disaster Prevention and Reduction Work

The Application of the New Generation Weather Radar in Disaster Prevention and Reduction Work

Doppler Velocity De-Aliasing Based on Lag-1 Cross-Correlation for Dual-Polarization Weather Radar

Doppler Velocity De-Aliasing Based on Lag-1 Cross-Correlation for Dual-Polarization Weather Radar

Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning

Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning

Assessing Quantitative Precipitation Estimation Methods Based on the Fusion of Weather Radar and Rain-Gauge Data

Assessing Quantitative Precipitation Estimation Methods Based on the Fusion of Weather Radar and Rain-Gauge Data

Quantitative Precipitation Estimation using X-Band Radar for Orographic Rainfall in the San Francisco Bay Area

In the San Francisco Bay Area, precipitation occurs in the wintertime, mostly as rain. Wintertime rainfall can be further classified into cold or stratiform rain with a typical radar bright band signature and warm orographic rain with absence of a radar bright band. Vertical Pointing S-Band profiler radar and disdrometer measurements from two of NOAA’s Hydrometeorology Testbed (HMT) sites in California are used to study the differences in microphysical properties between these two types of rain and their implications in radar rainfall estimation. A methodology has been developed to discriminate non bright band (NBB) rainfall from bright band (BB) rainfall using reflectivity (Z) and differential reflectivity (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR</sub> ) computed from disdrometer data. Delineating the two rainfall types in this way allowed for an algorithm to be applied to the radar scans to identify rainfall types and apply appropriate reflectivity based and specific differential phase (K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DP</sub> ) based rainfall estimators. Recently, a gap-filling X-Band weather radar with dual-polarization capabilities was deployed in the San Francisco Bay Area in Santa Rosa to aid in weather monitoring and provide high resolution Quantitative Precipitation Estimation (QPE) products. When applied to real radar observations, this method shows great potential for improving the QPE compared to traditional operational products which more often tend to underestimate rainfall in the California coastal region.

Time-Altitude Variation of 30-Second-Update Full Volume Scan Data for Summer Convective Storms Observed With X-Band Dual Polarized Phased Array Weather Radar

Time-Altitude Variation of 30-Second-Update Full Volume Scan Data for Summer Convective Storms Observed With X-Band Dual Polarized Phased Array Weather Radar