Rain Behaviour at Mt. Merapi Area as Observed by XMPR and ARR

2018 ◽  
Vol 881 ◽  
pp. 34-41 ◽  
Author(s):  
Roby Hambali ◽  
Hanggar G. Mawandha ◽  
Djoko Legono ◽  
Rachmad Jayadi ◽  
Satoru Oishi
Keyword(s):  
Communication Technology ◽  
Rain Gauge ◽  
Disaster Mitigation ◽  
Poor Correlation ◽  
Radar Rainfall ◽  
R Ratio ◽  
X Band ◽  
Information And Communication ◽  
Warning Criteria ◽  
Estimated Error

Short duration rainfall information has now become one of many important aspects to support the development of warning criteria for disaster mitigation. Similar importance is also found in the development of warning criteria against the lahar flow disaster at Mt. Merapi area. The rainfall information obtained from the radar observation has also become a new challenge for the last decade in line with the rapid growth of information and communication technology. However, the accuracy of its estimation needs to be evaluated by considering the correlation between radar rainfall and rain gauge rainfall. In case of radar rainfall can be precisely estimated, this information will contribute to generating appropriate warning criteria. This study was carried out as the first attempt to evaluate the rainfall information as performed by the X-Band Multi Parameter Radar (XMPR) that was installed at Mt. Merapi in the mid-August 2015. Several ground rainfall data obtained from Automatic Rainfall Recorder (ARR) have been adopted to analyze the aforesaid radar rainfall information, and estimated errors between the two are presented. Evaluation of the radar estimated error value as a function or range is taken through a Fractional Standard Error (FSE) index that quantifies the differences between ground rainfall measurement (G) and radar rainfall estimation (R), also the G/R ratio characteristics. The result shows there was a poor correlation between radar estimated and rain gauge measured rainfall located over 14 km from radar. Radar bias (M) is suitable for correcting radar rainfall amount, yet inappropriate for fractional values.

scholarly journals Correcting Radar Rainfall Estimates Based on Ground Elevation Function

2019 ◽  
Vol 5 (3) ◽  
pp. 300
Author(s):  
Roby Hambali ◽  
Djoko Legono ◽  
Rachmad Jayadi
Keyword(s):  
Correction Method ◽  
Rain Gauge ◽  
Real Time System ◽  
Radar Rainfall ◽  
Rainfall Condition ◽  
Correction Process ◽  
R Ratio ◽  
X Band ◽  
The Impact ◽  
Rainfall Estimates

X-band radar gives several advantages for quantitative rainfall estimation, involving higher spatial and temporal resolution, also the ability to reduce attenuation effects and hardware calibration errors. However, the estimates error due to attenuation in heavy rainfall condition cannot be avoided. In the mountainous region, the impact of topography is considered to contribute to radar rainfall estimates error. To have more reliable estimated radar rainfall to be used in various applications, a rainfall estimates correction needs to be applied. This paper discusses evaluation and correction techniques for radar rainfall estimates based on ground elevation function. The G/R ratio is used as a primary method in the correction process. The novel approach proposed in this study is the use of correction factor derived from the relationship between Log (G/R) parameter and elevation difference between radar and rain gauge stations. A total of 4590 pairs of rainfall data from X-band MP radar and 15 rain gauge stations in the Mt. Merapi region were used in evaluation and correction process. The results show the correction method based on the elevation function is relatively good in correcting radar rainfall depth with values of Log (G/R) decreased up to 81.1%, particularly for light rainfall (≤ 20 mm/hour) condition. Also, the method is simple to apply in a real-time system.

scholarly journals The New French Operational Polarimetric Radar Rainfall Rate Product

2013 ◽  
Vol 52 (8) ◽  
pp. 1817-1835 ◽  
Author(s):  
Jordi Figueras i Ventura ◽  
Pierre Tabary
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Superior Performance ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Frequency Bands ◽  
Radar Network ◽  
Significant Events ◽  
X Band

AbstractIn 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results.

scholarly journals Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part II: Evaluation and Application

2005 ◽  
Vol 22 (11) ◽  
pp. 1633-1655 ◽  
Author(s):  
S-G. Park ◽  
M. Maki ◽  
K. Iwanami ◽  
V. N. Bringi ◽  
V. Chandrasekar
Keyword(s):  
Drop Size ◽  
Earth Science ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Events ◽  
Differential Reflectivity ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km−1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars.

Improving Spatial Rainfall Estimates at Mt. Merapi Area Using Radar-Rain Gauge Conditional Merging

2019 ◽  
Vol 14 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Roby Hambali ◽  
Djoko Legono ◽  
Rachmad Jayadi ◽  
Satoru Oishi ◽  
◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Early Warning ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Value Distribution ◽  
Rainfall Data ◽  
Warning Systems ◽  
Radar Rainfall ◽  
X Band ◽  
Rainfall Estimates

Rainfall monitoring is important for providing early warning of lahar flow around Mt. Merapi. The X-band multi-parameter radar developed to support these warning systems provides rainfall information with high spatial and temporal resolution. However, this method underestimates the rainfall compared with rain gauge measurements. Herein, we performed conditional radar-rain gauge merging to obtain the optimal rainfall value distribution. By using the cokriging interpolation method, kriged gauge rainfall, and kriged radar rainfall data were obtained, which were then combined with radar rainfall data to yield the adjusted spatial rainfall. Radar-rain gauge conditional merging with cokriging interpolation provided reasonably well-adjusted spatial rainfall pattern.

scholarly journals On precipitation measurements collected by a weather radar and a rain gauge network

2013 ◽  
Vol 13 (3) ◽  
pp. 605-623 ◽  
Author(s):  
S. Sebastianelli ◽  
F. Russo ◽  
F. Napolitano ◽  
L. Baldini
Keyword(s):  
Rain Gauge ◽  
Rainfall Amount ◽  
Rainfall Time Series ◽  
Adjustment Factor ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
R Ratio ◽  
Precipitation Estimation ◽  
Layer Effect ◽  
One Year

Abstract. Many phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The present paper evaluates radar error as a function of the range, in order to correct the rainfall radar estimates. The radar is calibrated utilizing data from the rain gauges. Then, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount is calculated against the slant range. The trend of the G/R ratio shows two behaviours: a concave part due to the melting layer effect close to the radar location and an almost linear, increasing trend at greater distances. A best fitting line is used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology is verified by comparing pairs of rainfall time series that are observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor is investigated at the scale of event, both for convective and stratiform events. The main result is that there is not a univocal range error pattern, as it also depends on the characteristics of the considered event. On the other hand, the adjustment factor tends to stabilize itself for time aggregations of the order of one year or greater.

scholarly journals Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

2018 ◽  
Vol 10 (8) ◽  
pp. 1258 ◽  
Author(s):  
Marios Anagnostou ◽  
Efthymios Nikolopoulos ◽  
John Kalogiros ◽  
Emmanouil Anagnostou ◽  
Francesco Marra ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Rain Gauge ◽  
Storm Events ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Radar Observations ◽  
X Band ◽  
Precipitation Estimation ◽  
Rainfall Estimates

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

scholarly journals Evaluating Polarimetric X-Band Radar Rainfall Estimators during HMT

2010 ◽  
Vol 27 (1) ◽  
pp. 122-134 ◽  
Author(s):  
Sergey Y. Matrosov
Keyword(s):  
Rain Gauge ◽  
Radar Rainfall ◽  
Shape Model ◽  
Drop Shape ◽  
Aspect Ratios ◽  
X Band ◽  
Relative Standard ◽  
The Mean ◽  
Standard Deviations ◽  
Raindrop Size

Abstract Different relations between rainfall rate R and polarimetric X-band radar measurables were evaluated using the radar, disdrometer, and rain gauge measurements conducted during the 4-month-long field experiment. The specific differential phase shift KDP–based estimators generally show less scatter resulting from variability in raindrop size distributions than with the power-based relations. These estimators depend on model assumptions about the drop aspect ratios and are not applicable for lighter rainfalls. The polynomial approximation for the mean drop aspect ratio provides R–KDP relations that result overall in good agreement between the radar retrievals of rainfall accumulations and estimates from surface rain gauges. The accumulation data obtained from power estimators that use reflectivity Zeh and differential reflectivity ZDR measurements generally exhibit greater standard deviations with respect to the gauge measurements. Unlike the phase-based estimators, the power-based estimators have an advantage of being “point” measurements, thus providing continuous quantitative precipitation estimation (QPE) for the whole area of radar coverage. The uncertainty in the drop shape model can result in errors in the attenuation and differential attenuation correction procedures. These errors might provide biases of radar-derived QPE for the estimators that use power measurements. Overall, for all considered estimators, the radar-based total rainfall accumulations showed biases less than 10% (relative to gauges). The standard deviations of radar retrievals were about 23% for the mean Zeh–R relation, 17%–22% for the KDP-based estimators (depending on the drop shape model), and about 20%–32% for different Zeh–ZDR-based estimators. Comparing ZDR-based retrievals of mean mass raindrop size Dm (for Dm > 1 mm) with disdrometer-derived values reveals an about 20%–25% relative standard deviation between these two types of estimates.

scholarly journals Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model

2019 ◽  
Vol 11 (14) ◽  
pp. 1632 ◽  
Author(s):  
Johanna Orellana-Alvear ◽  
Rolando Célleri ◽  
Rütger Rollenbeck ◽  
Jörg Bendix
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Radar Data ◽  
Rain Gauge ◽  
High Mountain ◽  
Radar Rainfall ◽  
Site Specific ◽  
High Attenuation ◽  
X Band ◽  
Precipitation Estimation

Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars.

scholarly journals Analyzing the Application of X-Band Radar for Improving Rainfall Observation and Flood Forecasting in Yeongdong, South Korea

2021 ◽  
Vol 14 (1) ◽  
pp. 43
Author(s):  
Seong-Sim Yoon ◽  
Sang-Hun Lim
Keyword(s):  
South Korea ◽  
Flood Forecasting ◽  
Hydraulic Modeling ◽  
Rain Gauge ◽  
Risk Level ◽  
Radar Rainfall ◽  
Risk Increase ◽  
Flood Level ◽  
X Band ◽  
Rainfall Estimates

The mountainous Yeongdong region of South Korea contains mountains over 1 km. Owing to this topographic blockage, the region has a low-density rain-gauge network, and there is a low-altitude (~1.5 km) observation gap with the nearest large S-band radar. The Korean government installed an X-band dual-polarization radar in 2019 to improve rainfall observations and to prevent hydrological disasters in the Yeongdong region. The present study analyzed rainfall estimates using the newly installed X-band radar to evaluate its hydrological applicability. The rainfall was estimated using a distributed specific differential phase-based technique for a high-resolution 75 m grid. Comparison of the rainfall estimates of the X-band radar and the existing rainfall information showed that the X-band radar was less likely to underestimate rainfall compared to the S-band radar. The accuracy was particularly high within a 10 km observation radius. To evaluate the hydrological applicability of X-band radar rainfall estimates, this study developed a rain-based flood forecasting method—the flow nomograph—for the Samcheok-osib stream, which is vulnerable to heavy rain and resultant floods. This graph represents the flood risk level determined by hydrological–hydraulic modeling with various rainfall scenarios. Rainfall information (X-band radar, S-band radar, ground rain gauge) was applied as input to the flow nomograph to predict the flood level of the stream. Only the X-band radar could accurately predict the actual high-risk increase in the water level for all studied rainfall events.

scholarly journals Attenuation Correction Effects in Rainfall Estimation at X-Band Dual-Polarization Radar: Evaluation with a Dense Rain Gauge Network

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Young-A Oh ◽  
DaeHyung Lee ◽  
Sung-Hwa Jung ◽  
Kyung-Yeub Nam ◽  
GyuWon Lee
Keyword(s):  
Attenuation Correction ◽  
Random Error ◽  
Estimation Method ◽  
Rain Gauge ◽  
Spatial Average ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Gauge Network

The effects of attenuation correction in rainfall estimation with X-band dual-polarization radar were investigated with a dense rain gauge network. The calibration bias in reflectivity (ZH) was corrected using a self-consistency principle. The attenuation correction ofZHand the differential reflectivity (ZDR) were performed by a path integration method. After attenuation correction,ZHandZDRwere significantly improved, and their scatter plots matched well with the theoretical relationship betweenZHandZDR. The comparisons between the radar rainfall estimation and the rain gauge rainfall were investigated using the bulk statistics with different temporal accumulations and spatial averages. The bias significantly improves from 70% to 0% withR(ZH). However, the improvement withR(ZH,ZDR)was relatively small, from 3% to 1%. This indicated that rainfall estimation using a polarimetric variable was more robust at attenuation than was a single polarimetric variable method. The bias did not show improvement in comparisons between the temporal accumulations or the spatial averages in either rainfall estimation method. However, the random error improved from 68% to 25% with different temporal accumulations or spatial averages. This result indicates that temporal accumulation or spatial average (aggregation) is important to reduce random error.

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