scholarly journals Snake Line Performance Applying Single Pixel X-Band MP Radar Data (Case of Mt. Merapi Area, Indonesia)

2019 ◽  
Vol 5 (3) ◽  
pp. 201 ◽  
Author(s):  
Ani Hairani ◽  
Adam Pamudji Rahardjo ◽  
Djoko Legono ◽  
Istiarto Istiarto
Keyword(s):  
Short Duration ◽  
Time Use ◽  
Heavy Rainfall ◽  
Rainfall Intensity ◽  
Radar Data ◽  
Disaster Risk ◽  
Rainfall Data ◽  
High Demand ◽  
X Band ◽  
Single Pixel

The short-duration-rainfall monitoring techniques have become important recently due to the high demand for disaster risk miti­gation. Such techniques produce important information on the rainfall intensity during heavy rainfall in the form of snake line. At the same time, use of X-Band Multi-Parameter Radar (XMP Radar) in rainfall monitoring has increased significantly be­cause of its capacity to cover wide area. An assessment on the snake line performance that was developed based on XMP Radar and ground rainfall monitoring instrument (i.e. Automatic Rainfall Recorder or ARR) has been applied to Mt. Merapi area, Java, Indonesia. Selected rainfall data of November-December 2018 were examined. The assessment used a single pixel of radar data at the location of the ARR. The result shows that rainfall data obtained from XMP Radar are lower than those from ARR. The computed snake line obtained from XMP Radar differs from that from ARR data. The XMP Radar underestimates the warning level by about two level out of four.

scholarly journals A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images

2021 ◽  
Vol 11 (4) ◽  
pp. 1565
Author(s):  
Zhizhong Lu ◽  
Lei Sun ◽  
Ying Zhou
Keyword(s):  
Rainfall Intensity ◽  
Detection Threshold ◽  
Radar Data ◽  
Detection Accuracy ◽  
Intensity Level ◽  
Radar Images ◽  
Wave Parameters ◽  
X Band ◽  
Marine Radar ◽  
Rainfall Detection

Currently, it is a hot research topic to retrieve the wave parameters by using X-band marine radar. However, the rainfall noise usually exists in the collected marine radar images, which seriously interferes with the extraction of the wave parameters. To reduce the influence of rainfall noise, the zero-pixel percentage (ZPP) method is widely used to detect rainfall in radar images, but the detection accuracy is limited, and the selection of the threshold needs to be further studied. Based on the ZPP method, the ratio of zero intensity to echo (RZE) method for rainfall detection is proposed in this paper. The detection threshold is determined by statistical analysis of a large amount of radar data. Additionally, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. In addition, the concept of the occlusion area is proposed. The proposed area and the wave area are used as the rainfall detection area of the radar image, respectively, for experimental research. The data obtained from the Pingtan experimental base in Fujian Province are used to verify the effectiveness of the proposed method. The experimental results show that the detection accuracy of the proposed method is 11.7% higher than that of the ZPP method, and the accuracy of rainfall intensity level retrieval is 84%.

scholarly journals Point-Based Rainfall Intensity Information System in Mt. Merapi Area by X-Band Radar

2019 ◽  
Vol 14 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Santosa Sandy Putra ◽  
Banata Wachid Ridwan ◽  
Kazuki Yamanoi ◽  
Makoto Shimomura ◽  
Sulistiyani ◽  
...  
Keyword(s):  
Satellite Data ◽  
Early Warning Systems ◽  
Radar Data ◽  
Rain Gauge ◽  
Data Grid ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Rain Gauge Data ◽  
X Band ◽  
Gauge Data

An X-band radar was installed in 2014 at Merapi Museum, Yogyakarta, Indonesia, to monitor pyroclastic and rainfall events around Mt. Merapi. This research aims to perform a reliability analysis of the point extracted rainfall data from the aforementioned newly installed radar to improve the performance of the warning system in the future. The radar data was compared with the monitored rain gauge data from Balai Sabo and the IMERG satellite data from NASA and JAXA (The Integrated Multi-satellitE Retrievals for GPM), which had not been done before. All of the rainfall data was compared on an hourly interval. The comparisons were conducted based on 11 locations that correspond to the ground rainfall measurement stations. The locations of the rain gauges are spread around Mt. Merapi area. The point rainfall information was extracted from the radar data grid and the satellite data grid, which were compared with the rain gauge data. The data were then calibrated and adjusted up to the optimum state. Based on January 2017–March 2018 data, it was obtained that the optimum state has a NSF value of 0.41 and R2value of 0.56. As a result, it was determined that the radar can capture around 79% of the hourly rainfall occurrence around Mt. Merapi area during the chosen calibration period, in comparison with the rain gauge data. The radar was also able to capture nearby 40–50% of the heavy rainfall events that pose risks of lahar. In contrast, the radar data performance in detecting drizzling and light rain types were quite precise (55% of cases), although the satellite data could detect slightly better (60% of cases). These results indicate that the radar sensitivity in detecting the extreme rainfall events must receive higher priority in future developments, especially for applications to the existing Mt. Merapi lahar early warning systems.

scholarly journals Comparison between radar and rain gauges data at different distances from radar and correlation existing between the rainfall values in the adjacent pixels

2010 ◽  
Vol 7 (4) ◽  
pp. 5171-5212 ◽  
Author(s):  
S. Sebastianelli ◽  
F. Russo ◽  
F. Napolitano ◽  
L. Baldini
Keyword(s):  
Rainfall Intensity ◽  
National Research Council ◽  
Radar Data ◽  
Sample Volume ◽  
Atmospheric Sciences ◽  
Rain Gauges ◽  
Radar Resolution ◽  
The Difference ◽  
Radar Antenna ◽  
Single Pixel

Abstract. Rainfall intensity data in pixels very far from radar are less correlated than values in pixels near the radar, because at far distances the width of a range-bin is comparable or bigger than the pixel width, so in a pixel there are one or just a few rainfall intensity values. Vice versa, near the radar, there are many radar resolution bins which belong to a single pixel, so great correlation between rainfall intensity values for contiguous pixels is expected. Moreover, the signal returned from precipitation at far distance from radar antenna can be due to a radar sample volume partially or completely filled with mixed phase or ice particles, or can be quite close to the minimum detectable signal. All these phenomena can influence the goodness of rainfall estimates, introducing errors which increase as the distance from radar increases. The objective of this work is to characterize these errors as a function of the distance. For this aim is possible to compare the rainfall data obtained by rain gauges at different distances from radar with rainfall radar data at the same distances, verifying the correlation existing between the rainfall values in the adjacent pixels and how the difference between radar and rain gauges data changes. The radar data utilized in this work have been collected from the CNR–ISAC (Institute of Atmospheric Sciences and Climate of the National Research Council) Polar 55C radar in Rome Tor Vergata during 2008.

Impact of Combined Assimilation of Radar and Rainfall Data on Short-Term Heavy Rainfall Prediction: A Case Study

2020 ◽  
Vol 148 (5) ◽  
pp. 2211-2232 ◽  
Author(s):  
Juanzhen Sun ◽  
Ying Zhang ◽  
Junmei Ban ◽  
Jing-Shan Hong ◽  
Chung-Yi Lin
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Numerical Models ◽  
Radar Data ◽  
Rainfall Data ◽  
Data Sources ◽  
Short Term ◽  
Rainfall Prediction ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.

Comparison of radar and raingauge measurements during heavy rainfall

2005 ◽  
Vol 51 (2) ◽  
pp. 195-201 ◽  
Author(s):  
T. Einfalt ◽  
M. Jessen ◽  
B. Mehlig
Keyword(s):  
Time Series ◽  
Heavy Rainfall ◽  
Radar Data ◽  
Spatial Extent ◽  
Small Scale ◽  
Rainfall Events ◽  
Radar Images ◽  
X Band ◽  
Raingauge Data ◽  
Quality Check

Five heavy small-scale rainfall events in North Rhine-Westphalia (Germany) were investigated with radar and raingauge data. Special attention was paid to quality check and adjustment of radar data. Attenuation effects could be observed on both, C-Band and on X-Band radar. Adjustment of radar data to raingauge values turned out to be very difficult in the vicinity of heavy local rain cells. For the five affected regions the precipitation was quantified in the form of areal time series and cumulated radar images. As further result of this project, the spatial extent of the precipitation fields was identified and compared with radar and raingauge data.

scholarly journals Small-Scale Rainfall Variability Impacts Analyzed by Fully-Distributed Model Using C-Band and X-Band Radar Data

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1273 ◽  
Author(s):  
Igor Paz ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
Bianca Alves de Souza ◽  
Laurent Monier ◽  
...  
Keyword(s):  
Data Quality ◽  
Rainfall Variability ◽  
Drainage System ◽  
Radar Data ◽  
Rainfall Data ◽  
Distributed Model ◽  
Small Scale ◽  
Distributed Models ◽  
X Band ◽  
Spatio Temporal

Recent studies have highlighted the need for high resolution rainfall measurements for better modelling of urban and peri-urban catchment responses. In this work, we used a fully-distributed model called “Multi-Hydro” to study small-scale rainfall variability and its hydrological impacts. The catchment modelled is a semi-urban area located in the southwest region of Paris, an area that has been previously partially validated. At this time, we make some changes to the model, henceforth using its drainage system globally, and we investigate the influence of small-scale rainfall variability by modelling three rainfall events with two different rainfall data inputs: the C-band radar data provided by Météo-France at a 1 km × 1 km × 5 min resolution, and the new X-band radar (recently installed at Ecole des Ponts, France) data at a resolution of 250 m × 250 m × 3.41 min, thereby presenting the gains of better resolution (with the help of Universal Multifractals). Finally, we compare the Multi-Hydro hydrological results with those obtained using an operational semi-distributed model called “Optim Sim” over the same area to revalidate Multi-Hydro modelling, and discuss the model’s limitations and the impacts of data quality and resolution, observing the difficulties associated with semi-distributed models when accounting the spatial variability of weather radar data. This work concludes that it may be useful in future to improve rainfall data acquisition, aiming for better spatio-temporal resolution (now achieved by the weather dual-polarized X-band radars) and data quality when considering small-scale rainfall variability, and to merge deterministic, fully-distributed and stochastic models into a hybrid model which would be capable of taking this small-scale rainfall variability into account.

The Multiple-Vortex Structure of the El Reno, Oklahoma, Tornado on 31 May 2013

2018 ◽  
Vol 146 (8) ◽  
pp. 2483-2502 ◽  
Author(s):  
Howard B. Bluestein ◽  
Kyle J. Thiem ◽  
Jeffrey C. Snyder ◽  
Jana B. Houser
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Rapid Scan ◽  
X Band ◽  
Close Range ◽  
Formation And Evolution ◽  
Using Data ◽  
Secondary Vortices ◽  
The Right ◽  
Gust Front

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.

scholarly journals Design of Optimal Rainfall Monitoring Network Using Radar and Road Networks

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 378
Author(s):  
Taeyong Kwon ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Road Networks ◽  
Monitoring Network ◽  
Conditional Entropy ◽  
Radar Data ◽  
Disaster Risk ◽  
Water Levels ◽  
The Road ◽  
Dam Operation ◽  
A Site ◽  
High Degree

Uncertainty in the rainfall network can lead to mistakes in dam operation. Sudden increases in dam water levels due to rainfall uncertainty are a high disaster risk. In order to prevent these losses, it is necessary to configure an appropriate rainfall network that can effectively reflect the characteristics of the watershed. In this study, conditional entropy was used to calculate the uncertainty of the watershed using rainfall and radar data observed from 2018 to 2019 in the Goesan Dam and Hwacheon Dam watersheds. The results identified radar data suitable for the characteristics of the watershed and proposed a site for an additional rainfall gauge. It is also necessary to select the location of the additional rainfall gauged by limiting the points where smooth movement and installation, for example crossing national borders, are difficult. The proposed site emphasized accessibility and usability by leveraging road information and selecting a radar grid near the road. As a practice result, the uncertainty of precipitation in the Goesan and Hwacheon Dam watersheds could be decreased by 70.0% and 67.9%, respectively, when four and three additional gauge sites were installed without any restriction. When these were installed near to the road, with five and four additional gauge sites, the uncertainty in the Goesan Dam and Hwacheon Dam watersheds were reduced by up to 71.1%. Therefore, due to the high degree of uncertainty, it is necessary to measure precipitation. The operation of the rainfall gauge can provide a smooth site and configure an appropriate monitoring network.

scholarly journals Simulation of X-Band Rainfall Observations from S-Band Radar Data

2006 ◽  
Vol 23 (9) ◽  
pp. 1195-1205 ◽  
Author(s):  
V. Chandrasekar ◽  
S. Lim ◽  
E. Gorgucci
Keyword(s):  
Doppler Radar ◽  
Theoretical Models ◽  
Radar Data ◽  
Shape Distribution ◽  
Microphysical Properties ◽  
Rain Medium ◽  
Microphysical Parameters ◽  
X Band ◽  
State Water ◽  
The Impact

Abstract To design X-band radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. One way to develop that dataset is through theoretical models. This paper presents a methodology to generate realistic range profiles of radar variables at attenuating frequencies, such as X band, for rain medium. Fundamental microphysical properties of precipitation, namely, size and shape distribution information, are used to generate realistic profiles of X band starting with S-band observation. Conditioning the simulation from S band maintains the natural distribution of rainfall microphysical parameters. Data from the Colorado State University’s University of Chicago–Illinois State Water Survey (CHILL) radar and the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-POL) are used to simulate X-band radar variables. Three procedures to simulate the radar variables and sample applications are presented.

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