Weather radar research has produced numerous radar-based rainfall estimators based on climate, rainfall intensity, a variety of ground-truthing instruments and sensors (e.g., rain gauges, ...disdrometers), and techniques. Although each research direction gives improvement, their collective application in an operational sense still yields uncertainty in rainfall estimation at times. This study aims to explore the concept of implementing Machine Learning (ML) models in optimizing the radar-based rainfall estimations at the bin level from a group of estimator. The Canadian King City C-Band radar was used with a GEONOR T-200B rain gauge (a total of 263 sample points) to establish a group of polarimetric-based rainfall estimators (R(Z), R(Z, ZDR), R(KDP)). The estimators were used to train three ML models, namely Decision Tree, Random Forest, and Gradient Boost, to choose the optimal rainfall estimators based on radar variables (Z, ZDR, KDP). Data from the Canadian Exeter C-Band radar and a Texas Electronics TE525 tipping bucket gauge at a different location were used to verify the ML models and compare their results to the most commonly used Z-R relations. The verification process shows promising results for the ML models, specifically the Gradient Boost model. These encouraging results need to be further explored with more sample points to further refine the ML models.
Tropical cyclone Ockhi (2017) had a very unusual track with unprecedented rapid intensification (RI) and dynamical evolution. During its early phases, a C-band polarimetric Doppler Weather Radar ...(DWR), installed in Thiruvananthapuram, Kerala, continuously monitored it. The present study focuses on the observations and analysis of the extremely tall precipitation features overshooting Troposphere, called hot towers, prior to and during the RI stages of Ockhi. The maximum height of such features within the inner core exceeded 20 km, with the maximum observed areal coverage over 800 km
2
. The peak convective burst (CB) activity was seen 9–12 h prior to the onset of RI of Ockhi. The differences among the Maximum Sustained Wind speed (MSW) and Mean Sea Level Pressure (MSLP) obtained from various best track data sets were in the range 5–25 knots and 1–28 hPa, respectively, with the maximum difference seen during the CB phase. Ockhi also exhibited differential reflectivity enhancements, often collocated with the hot towers. Further, we investigated upper ocean Tropical Cyclone Heat Potential (TCHP), derived from the Global Temperature and Salinity Profile Programme (GTSPP) profiles. During the peak CB activity, the TCHP was around 121 kJ cm
−2
, almost 50 kJ cm
−2
higher than the climatological mean of that area. Significant drop in TCHP was observed in the next 24 h concurrent with the RI. The radar-based observations of the hot towers in conjunction with the TCHP and other relevant ocean parameters could prove to be valuable indicators in predicting the RI phase of the storms and gaining a better insight for estimating the damage potential of the cyclones.
Tropical storm PABUK developed from tropical depression first defined on 31 December 2018 in the lower South China Sea. It made landfall in Pak Phanang, Nakhon Si Thammarat province, southern ...Thailand. PABUK caused heavy rain and flash floods from 3 to 5 January 2019 (D1, D2, and D3) where the total rainfall reached 150–300 mm across 14 provinces of southern Thailand. This paper is aimed to investigate rainstorm properties and rainfall estimation of tropical storm PABUK with weather radar in southern Thailand. The radar data analysis in this study was to extract the radar reflectivity to study rainstorm properties of PABUK over 3 days along southern Thailand derived from the Thunderstorm Identification and Tracking Analysis and Nowcasting (TITAN) algorithm including 5 variables of duration, area, cloud-based height, maximum reflectivity, and speed in the data set. Based on the properties and frequency distribution of 2,557 rainstorms in D1, D2, and D3, rainstorms in D2 and D3 when PABUK made landfall over southern Thailand show a longer lifetime, higher reflectivity, and larger rain-cells as well as it was found efficient in terms of rainfall amount than in D1. In addition, the estimated rainfall using weather radar provides important information of the rainfall distribution for the analysis of the rainstorm as well. These analyses provide a context for interpreting the feasible rainfall estimates based on Z-R relationship during tropical storm PABUK that produced extreme floods in southern Thailand. A Z-R relationship in the form Z = 104R1.5 provided acceptable statistical indicators, making it appropriate for radar estimated rainfall in case studies presented of tropical storm PABUK in southern Thailand. However, the result of this study should be improved to estimate precipitation in case of extremely heavy rainfall in tropical storm occurrence by using radar of southern Thailand and applied for applications of early warning systems.
One option for the improvement of weather radar technology is the use of dual-polarized phased-array radar for weather observations. Several risk factors on this path have been identified and one of ...the most important ones is the existence of significant cross-polar patterns inherent to the phased-array antenna. These antenna patterns induce cross-coupling between returns from the two orthogonal radiation planes, which results in the biases of polarimetric variable estimates. Furthermore, the inductive and capacitive coupling in hardware behind the antenna may exacerbate the cross-coupling effects. This presents a formidable challenge because sufficient cross-polar isolation is difficult to achieve by the antenna hardware alone. Hence, additional approaches are required to reduce the biases due to cross-coupling. One proposed technique is a 180° pulse-to-pulse phase change of signals injected in either the horizontal or vertical ports of the transmission elements. This approach was analyzed for signals processed in the time domain but its effects in the frequency domain have not been investigated. Herein, these effects are considered in the presence of nondepolarizing scatterers.
Reflected waves have been frequently observed via weather radar, even during sunny, cloudless days. Additionally, it is recognized that turbulence and biological scatterers can also dominate the ...scattering process. In previous studies, echo returns of turbulence from clear-air Bragg scattering (CABS) have been used to detect the height of the convective boundary layer (CBL). However, in a dual-frequency antenna multiplexing radar system, through which the dual-wavelength ratio (DWR) identifies the CABS clearly, CABS can be unexpectedly observed not only at the edge of the CBL but also above the CBL, highlighting the need for an expanded set of causes of clear-air echoes. It was further identified that the negative second derivative of horizontal wind speed, which is measured by a coherent Doppler Lidar, is consistent with the variation of the CABS layers’ height above the CBL. These results emphasize the presence of physical processes leading to turbulence in the troposphere, with implications for Bragg scattering studies and the theory of turbulence in general. This study will help the ecology of research better understand the laws of biological activity.
The Australian Black Summer wildfires between September 2019 and January 2020 burnt many parts of eastern Australia including major forests within the Sydney drinking water catchment (SDWC) area, ...almost 16.000 km2. There was great concern on post-fire erosion and water quality hazards to Sydney’s drinking water supply, especially after the heavy rainfall events in February 2020. We developed a rapid and innovative approach to estimate post-fire hillslope erosion using weather radar, remote sensing, Google Earth Engine (GEE), Geographical Information Systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE). The event-based rainfall erosivity was estimated from radar-derived rainfall accumulations for all storm events after the wildfires. Satellite data including Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) were used to estimate the fractional vegetation covers and the RUSLE cover-management factor. The study reveals that the average post-fire erosion rate over SDWC in February 2020 was 4.9 Mg ha−1 month−1, about 30 times higher than the pre-fire erosion and 10 times higher than the average erosion rate at the same period because of the intense storm events and rainfall erosivity with a return period over 40 years. The high post-fire erosion risk areas (up to 23.8 Mg ha−1 month−1) were at sub-catchments near Warragamba Dam which forms Lake Burragorang and supplies drinking water to more than four million people in Sydney. These findings assist in the timely assessment of post-fire erosion and water quality risks and help develop cost-effective fire incident management and mitigation actions for such an area with both significant ecological and drinking water assets. The methodology developed from this study is potentially applicable elsewhere for similar studies as the input datasets (satellite and radar data) and computing platforms (GEE, GIS) are available and accessible worldwide.
Precipitation nowcasting plays a key role in land security and emergency management of natural calamities. A majority of existing deep learning-based techniques realize precipitation nowcasting by ...learning a deep nonlinear function from a single information source, e.g., weather radar. In this study, we propose a novel multimodal semisupervised deep graph learning framework for precipitation nowcasting. Unlike existing studies, different modalities of observation data (including both meteorological and nonmeteorological data) are modeled jointly, thereby benefiting each other. All information is converted into image structures, next, precipitation nowcasting is deemed as a computer vision task to be optimized. To handle areas with unavailable precipitation, we convert all observation information into a graph structure and introduce a semisupervised graph convolutional network with a sequence connect architecture to learn the features of all local areas. With the learned features, precipitation is predicted through a multilayer fully connected regression network. Experiments on real datasets confirm the effectiveness of the proposed method.
A dual-polarized X-band solid-state 1-D electronic scanning "phase-tilt" weather radar (PTWR) and its scanning geometry are presented. In this architecture, the true elevation angle decreases from ...the nominal array tilt angle and the true azimuth angle increases from the requested azimuth as one scans off boresight. Additionally, this scanning geometry induces a canting angle effect, which can be significant especially at higher elevation tilts. The predictions of potential biases in selected polarimetric variables due to this effect are derived. For elevation angles below 10°, where polarimetric measurements are of most value, predicted biases are negligible. The PTWR was deployed in Arlington, TX, USA, for an eight-week period during Spring 2014 collecting data on a number of weather events. The direct proximity (250 m away) of a mechanically scanning magnetron-based radar, employing a dual-polarized parabolic antenna allowed for a qualitative and quantitative data comparison. We find the differences in the observations made by the two radar systems are not attributable to the aforementioned biases, but appear primarily due to differences in sampling volumes of the two radars. We also find that at high-elevation tilts, the coupling of the true elevation angle with the array-relative azimuth scan angle complicates the interpretation of features at a constant altitude such as the melting layer.
X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather ...radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme.
The eruption of the Eyjafjallajökull volcano in April-May 2010 was continuously monitored by the Keflavík C-band weather radar. The Keflavík radar is located at a distance of about 156 km from the ...volcano vent, and it has sensitivity of about -5 dBZ at 2-km range resolution over the volcanic area. The time series of radar volume data, which was available every 5 min, is quantitatively analyzed by using the Volcanic Ash Radar Retrieval (VARR) technique. The latter is a physically based methodology that is applied to estimate ash-fall rate and mass concentration within each radar volume. The VARR methodology is here extended, with respect to the previous formulation, to provide an approximate estimate of both mean particle diameter and airborne tephra particle size distribution under some assumptions. Deposited tephra at ground is also extrapolated together with an estimate of the magma mass flow rate (MFR) at the volcano vent, derived from the implementation of the mass continuity equation in the radar reference system. The VARR-based retrievals are compared with those derived from a direct tephra sampling at the ground, experimentally carried out in terms of ash grain size and loading during the Eyjafjallajökull eruption activity on May 5-7, 2010. VARR-based particle diameter estimates may suggest that a sorting of airborne particles during the downwind transport is taking place without observing aggregation processes during the ash fall. VARR-derived daily ash mass loadings in the period between April 14 and May 10 are also evaluated with respect to integrated ground and model-based data in the Eyjafjallajökull area. VARR-retrieved MFRs are finally compared with corresponding values obtained from analytical 1-D eruption models, using radar-estimated plume height and radio-sounding wind fields. A fairly good agreement is obtained, thus opening the exploitation of weather radar retrievals for volcanic eruption quantitative studies and ash dispersion model initialization.
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