An Operational Weather Radar-Based Quantitative Precipitation Estimation and its Application in Catchment Water Resources Modeling

2011 ◽  
Vol 10 (1) ◽  
pp. 8-24 ◽  
Author(s):  
Xin He ◽  
Flemming Vejen ◽  
Simon Stisen ◽  
Torben O. Sonnenborg ◽  
Karsten H. Jensen.
Keyword(s):  
Water Resources ◽  
Weather Radar ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

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

2021 ◽  
Vol 893 (1) ◽  
pp. 012054
Author(s):  
M F Handoyo ◽  
M P Hadi ◽  
S Suprayogi
Keyword(s):  
Attenuation Correction ◽  
Estimation Error ◽  
Weather Radar ◽  
Radar Data ◽  
Signal Interference ◽  
Active System ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

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).

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

Geomatics ◽  
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.

scholarly journals The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China

2021 ◽  
Vol 13 (23) ◽  
pp. 4956
Author(s):  
Linye Song ◽  
Shangfeng Chen ◽  
Yun Li ◽  
Duo Qi ◽  
Jiankun Wu ◽  
...  
Keyword(s):  
South China ◽  
Weather Radar ◽  
Absolute Error ◽  
Rain Gauge ◽  
Observation Data ◽  
Matching Method ◽  
Study Region ◽  
Surface Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.

scholarly journals Challenges in Estimating Quantitative Precipitation Estimation (QPE) Using Weather Radar Observation Over the Mountainous Country of Nepal

2017 ◽  
Vol 21 ◽  
pp. 50-59
Author(s):  
Shakti P.C. ◽  
Masayuki Maki
Keyword(s):  
Research Work ◽  
Weather Radar ◽  
Radar Observation ◽  
Spatial And Temporal Variation ◽  
Accurate Information ◽  
Mountain Range ◽  
Mountain Areas ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Mountainous Country

South Asian country Nepal characterizes a complex mountain range in this world. The country’s population density is increasing along with rapid growth of population especially over mountainous cities, southern hills and the Terai. On the other hand, a number of fatal natural calamities, such as flash flooding and landslides raised by clutter intensive rainfall, have been increasing since the last decade. To deal with such water hydro meteorological disasters, accurate information on spatial and temporal variation of rainfall distribution is very important. In Nepal, the amount of rainfall has been obtained from limited rain gauge networks, which may leads to many errors in making a Quantitative Precipitation Estimation (QPE). Weather radar observations have recently been highlighted as an alternative option for estimating the spatial and temporal distribution of precipitation across specified time intervals. However, estimating rainfall from radar observation has its own challenges, especially over a mountainous country like Nepal.Another mountainous country Japan is well known for using weather radar observation to make QPE product. Different types of weather radar have been used to record, monitor and forecast precipitation in Japan for both operational and research purposes. A high level research work has also been done on this field. The high spatial and temporal (250-m and 1-min) QPE product obtained from the radar observation is available for the public. It shows good harmony with ground data in the flat and mountain areas of Japan. Though Nepal and Japan are located in different regions, both countries represent complex mountain regions and have been facing natural disaster caused by extreme rainfall. In Nepal, weather radar observation for estimating precipitation amounts has not started on an operational basis till date. Hence, sharing knowledge and skills from Japan’s research on weather radar observation would play a key role to achieve the radar based QPE product in Nepal. Therefore, we discuss about the challenge in obtaining QPE product, considering an example of the progress of weather radar system in Japan. It is believed that any discussion on it will be a reference for weather radar deployment and its QPE product in Nepal in coming days.  HYDRO Nepal JournalJournal of Water Energy and EnvironmentIssue: 21, July, 2017Page: 50-59Upload Date: July 18, 2017

scholarly journals Assimilation of weather radar and binary ubiquitous sensor measurements for quantitative precipitation estimation

2015 ◽  
Vol 17 (4) ◽  
pp. 598-613 ◽  
Author(s):  
David J. Hill
Keyword(s):  
Weather Radar ◽  
Simulation Studies ◽  
Heterogeneous Sensors ◽  
Intensity Measurements ◽  
Quantitative Precipitation Estimation ◽  
Markov Random ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Temporal Measurement ◽  
Quantitative Precipitation Estimates

Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.

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

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.

The recommended location of rain gauge stations based on radar

2020 ◽  
Author(s):  
Taeyong Kwon ◽  
Sanghoo Yoon
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
Nuclear Power ◽  
Conditional Entropy ◽  
Rain Gauge ◽  
Entropy Theory ◽  
High Entropy ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Precipitation Estimates

<p>The characteristics of the watershed are important to reduce hydrologic disasters, such as the risk of dam flooding. In other words, quantitative precipitation estimation(QPE) is important to manage water resources in large regions. Both radar and rain gauged data are used to improve QPE. This study is dealt with suggesting the best location of additional rain gauged stations to be installed in order to improve QPE as entropy theory. Conditional entropy is used to quantitatively evaluate the location of additional gauged stations to be installed given the existing rainfall network. Because radar produces high-resolution precipitation estimates, it can be used to identify the high entropy points to reduce rainfall uncertainty. The data were collected from May 2018 to August 2019 in the Bukhan river dam basin. Road networks were also considered for the establishment for a practical approach.</p><p> </p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD</p><p>(No. 2018-Tech-20)</p>

scholarly journals Quantitative precipitation estimation with weather radar using a data- and information-based approach

2019 ◽  
Author(s):  
Malte Neuper ◽  
Uwe Ehret
Keyword(s):  
Information Content ◽  
Rain Rate ◽  
Empirical Relation ◽  
Weather Radar ◽  
Circulation Pattern ◽  
Rain Gauge ◽  
Information Theoretic ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Synoptic Circulation

Abstract. In this study we propose and demonstrate a data-driven approach in an information-theoretic framework to estimate precipitation quantitatively. In this context predictive relations are expressed by empirical discrete probability distributions directly derived from data instead of fitting and applying deterministic functions as it is standard operational practice. Applying a probabilistic relation has the benefit to provide joint statements about rain rate and the related estimation uncertainty. The information-theoretic framework furthermore allows integration of any kind of data considered useful and explicitly considers the uncertain nature of quantitative precipitation estimation (QPE). With this framework we investigate the information gains and losses associated with various data and practices typically applied in QPE. To this end we conduct six experiments using four years of data from six laser optical disdrometers, two micro rain radars MRR regular rain gauges, weather radar reflectivity and other operationally available meteorological data from existing stations. Each experiment addresses a typical question related to QPE: First, we measure the information about ground rainfall contained in various operationally available predictors. Here weather radar proves to be the single most important source of information, which can be further improved when distinguishing radar reflectivity – ground rainfall relationships (Z-R relations) by season and prevailing synoptic circulation pattern. Second, we investigate the effect of data sample size on QPE uncertainty using different data based predictive models. It shows that the combination of reflectivity and month of the year as a double predictor model is the best trade-off between robustness of the model and information gain. Third, we investigate the information content in spatial position by learning and applying site-specific Z-R relations. The related information gains are only moderate and especially lower than when distinguishing Z-R relations according to time of the year or synoptic circulation pattern. Fourth, we measure the information loss when fitting and using a deterministic Z-R relation, as it is standard practice in operational radar based QPE applying e.g. the standard Marshal-Palmer relation, instead of using the empirical relation derived directly from the data. It shows that while the deterministic function captures the overall shape of the empirical relation quite well, it introduces additional 60% of uncertainty when estimating rain rate. Fifth, we investigate how much information is gained along the radar observation path, starting with reflectivity measured by radar at height, continuing with the reflectivity measured by a MRR along a vertical profile in the atmosphere and ending with the reflectivity observed by a disdrometer directly at the ground. The results reveal that considerable additional information is gained by using observations from lower elevations by avoiding information losses caused by ongoing microphysical precipitation processes from cloud height to ground. This underlines both the importance of vertical corrections for accurate QPE and of the required MRR observations. In the sixth experiment we evaluate the information content of radar data only, rain gauge data only and a combination of both as a function of the distance between the target and predictor rain gauge. The results show that station-only QPE outperforms radar-only QPE up to a distance of 7 to 8 km and that radar-and-gauge QPE performs best, even compared to radar-based models applying season or circulation pattern.

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