Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

Abstract A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

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Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

Atmosphere ◽

10.3390/atmos10060306 ◽

2019 ◽

Vol 10 (6) ◽

pp. 306 ◽

Cited By ~ 1

Author(s):

Dominique Faure ◽

Guy Delrieu ◽

Nicolas Gaussiat

Keyword(s):

Ground Level ◽

Radar Data ◽

Rain Gauge ◽

Altitudinal Gradients ◽

French Alps ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Radar Data Processing

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

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The Results of Applying Different Methodologies to 10 Years of Quantitative Precipitation Estimation in Catalonia Using Weather Radar

Geomatics ◽

10.3390/geomatics1030020 ◽

2021 ◽

Vol 1 (3) ◽

pp. 347-368

Author(s):

Tomeu Rigo ◽

Maria Carmen Llasat ◽

Laura Esbrí

Keyword(s):

Quality Control ◽

Weather Radar ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Radar Network ◽

Daily Data ◽

Precipitation Estimation ◽

Weather Stations ◽

Point Data ◽

Single Polarization

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

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A Neural-Network Quality Control scheme for improved Quantitative Precipitation Estimation accuracy on the UK weather radar network

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0120.1 ◽

2021 ◽

Author(s):

Nawal Husnoo ◽

Timothy Darlington ◽

Sebastián Torres ◽

David Warde

Keyword(s):

Neural Network ◽

Quality Control ◽

Weather Radar ◽

Radar Data ◽

Superior Performance ◽

Ground Clutter ◽

Quantitative Precipitation Estimation ◽

Radar Network ◽

Precipitation Estimation ◽

The Uk

AbstractIn this work, we present a new Quantitative-Precipitation-Estimation (QPE) quality-control (QC) algorithm for the UK weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground clutter mitigation (using CLEAN-AP) and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated “truth” with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data.

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A Case Study of Radar-Derived Quantitative Precipitation Estimation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.4541 ◽

2013 ◽

Vol 726-731 ◽

pp. 4541-4546 ◽

Cited By ~ 1

Author(s):

Li Li Yang ◽

Yi Yang ◽

You Cun Qi ◽

Xue Xing Qiu ◽

Zhong Qiang Gong

Keyword(s):

Doppler Radar ◽

Heavy Precipitation ◽

Radar Data ◽

Convective Precipitation ◽

Precipitation Process ◽

Fuzzy Logic Algorithm ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation

The convective and stratiform precipitations have different precipitation mechanisms. Different reflectivityrainfall rate (ZR) relations should be used for them. A heavy precipitation process on 22nd July, 2009(UTC) in Anhui Province is analyzed with Hefei Doppler radar and 269 rain gauges. First, the type of precipitation is obtained by a fuzzy logic algorithm with radar data. Then the reflectivity values are converted to rainfall rates using an adaptive Z-R relation according to different rain types. It is tested with the case and showed significant improvements over the current operational Z-R QPE when compared with gauges. Results also show that the precipitation process is caused by stratiform and convective precipitation; the rain estimated from radar corresponds well with cloud classification.

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Use of dual-polarization weather radar quantitative precipitation estimation for climatology

10.5194/hess-2019-624 ◽

2020 ◽

Author(s):

Tanel Voormansik ◽

Roberto Cremonini ◽

Piia Post ◽

Dmitri Moisseev

Keyword(s):

Weather Radar ◽

Early Warning Systems ◽

Dual Polarization ◽

Climate Conditions ◽

Rain Gauges ◽

Weather Radars ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Reflectivity Data ◽

First Time

Abstract. Accurate, timely and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitations, traditional rain gauges networks often miss precipitation maxima, due to density limitations and high spatial variability of rainfall field. Despite several limitations like attenuation or partial beam-blockings, the use of C-band weather radar has become operational in most of European weather services. Traditionally, weather radar-based quantitative precipitation estimation (QPE) are derived by horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome a number of shortcomings of the legacy horizontal reflectivity based estimation. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; those ones are located in Estonia and in Italy, in very different climate conditions and environments. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs accumulations for 1 hour, 24 hours and one month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances.

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Attenuation correction effects on quantitative precipitation estimation (QPE) at Sidoarjo weather radar

10.1117/12.2617901 ◽

2021 ◽

Author(s):

Laode Bangsawan ◽

Retnadi H. Jatmiko ◽

Emilya Nurjani

Keyword(s):

Attenuation Correction ◽

Weather Radar ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation

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Generation and Verification of Rainfall Estimates from 10-Yr Volumetric Weather Radar Measurements

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0166.1 ◽

2016 ◽

Vol 17 (4) ◽

pp. 1223-1242 ◽

Cited By ~ 20

Author(s):

Edouard Goudenhoofdt ◽

Laurent Delobbe

Keyword(s):

Mean Field ◽

Weather Radar ◽

Rain Gauge ◽

Absolute Difference ◽

Precipitation Regime ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Field Bias ◽

Rain Rates

Abstract Volumetric measurements from a C-band weather radar in Belgium are reprocessed over the years 2005–14 to improve the quantitative precipitation estimation (QPE). The data quality is controlled using static clutter and beam blockage maps and clutter identification based on vertical gradients, horizontal texture, and satellite observations. A new QPE is obtained using stratiform–convective classification, a 40-min averaged vertical profile of reflectivity (VPR), a brightband identification, and a specific transformation to rain rates for each precipitation regime. The rain rates are interpolated on a 500-m Cartesian grid, linearly accumulated, and combined with hourly rain gauge measurements using mean field bias or kriging with external drift (KED). The algorithms have been fine-tuned on 13 cases with various meteorological situations. A detailed validation against independent daily rain gauge measurements reveals the importance of VPR correction. A 10-yr verification shows a significant improvement of the new QPE, especially at short and long range, with roughly 50% increase in coverage. Adding the KED allows average improvements of 38%, 35%, and 80% for the mean absolute difference, the multiplicative error spread, and the fraction of good estimates, respectively. The benefit is higher in widespread situations and increases when considering higher rainfall amounts. The mitigation of radar artifacts is clearly visible on 10-yr statistics, including mean annual totals, probabilities to exceed 10 mm, and maxima for hourly and daily accumulation. The correlation of mean totals with rain gauges increases from 0.54 to 0.66 with the new QPE and to 0.8 adding KED.

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The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

Remote Sensing ◽

10.3390/rs13030351 ◽

2021 ◽

Vol 13 (3) ◽

pp. 351

Author(s):

Zbyněk Sokol ◽

Jan Szturc ◽

Johanna Orellana-Alvear ◽

Jana Popová ◽

Anna Jurczyk ◽

...

Keyword(s):

Temporal Resolution ◽

Initial Conditions ◽

Flash Flood ◽

Weather Radar ◽

Radar Data ◽

High Temporal Resolution ◽

Quantitative Precipitation Estimation ◽

Current State ◽

Precipitation Estimation ◽

Full Capacity

Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. In this paper, a comprehensive review through a categorization of radar-related topics aims to provide a general picture of the current state of radar research. First, the importance and impact of the high temporal resolution of weather radar is discussed, followed by the description of quantitative precipitation estimation strategies. Afterwards, the use of radar data in rainfall nowcasting as well as its role in preparation of initial conditions for numerical weather predictions by assimilation is reviewed. Furthermore, the value of radar data in rainfall-runoff models with a focus on flash flood forecasting is documented. Finally, based on this review, conclusions of the most relevant challenges that need to be addressed and recommendations for further research are presented. This review paper supports the exploitation of radar data in its full capacity by providing key insights regarding the possibilities of including radar data in hydrological and meteorological applications.

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Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

Advances in Meteorology ◽

10.1155/2019/6542410 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17

Author(s):

Ju-Young Shin ◽

Yonghun Ro ◽

Joo-Wan Cha ◽

Kyu-Rang Kim ◽

Jong-Chul Ha

Keyword(s):

Machine Learning ◽

Random Forest ◽

South Korea ◽

Extreme Learning Machine ◽

Radar Data ◽

Machine Learning Algorithms ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Learning Machine ◽

Estimation Models

Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model. The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station. Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea. The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events. The extreme learning machine is considered the best of the algorithms used based on evaluation criteria.

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