scholarly journals An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation

2016 ◽  
Vol 48 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Che-Hao Chang ◽  
Shiang-Jen Wu ◽  
Chih-Tsung Hsu ◽  
Jhih-Cyuan Shen ◽  
Ho-Cheng Lien
Keyword(s):  
Temporal Variation ◽  
Evaluation Framework ◽  
Spatial And Temporal Variation ◽  
Cluster Number ◽  
Data Set ◽  
Rainfall Characteristics ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Grid Based

This study proposes an evaluation framework to identify the optimal raingauge network in a watershed using grid-based quantitative precipitation estimation (QPE) with high spatial and temporal resolution. The proposed evaluation framework is based on comparison of the spatial and temporal variation in rainfall characteristics (i.e. rainfall depth and storm pattern) from the gauged data compared with those from QPE. The proposed framework first utilizes cluster analysis to separate raingauges into various clusters based on the locations and rainfall characteristics. Then, a cross-validation algorithm is used to identify the influential raingauge in each cluster based on evaluating performance of fitting weighted spatiotemporal semivariograms of rainfall characteristics from the gauged rainfall to the QPE data. Thus, the influential raingauges for a specific cluster number form the representative network. The optimal raingauge network is the one corresponding to the best fitness performance among the representative networks considered. The study area and data set are the hourly rainfall from 26 raingauges and 1,336 QPE grids for 10 typhoons in the Wu River watershed located in central Taiwan. The proposed evaluation framework suggests that a 10-gauge network is the optimal and can describe a good spatial and temporal variation in the rain field similar to the grid-based QPE from two additional typhoon events.

scholarly journals Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

2020 ◽  
Vol 21 (7) ◽  
pp. 1605-1620
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Haonan Chen ◽  
Dongming Hu ◽  
Peiling Fu ◽  
...  
Keyword(s):  
Variational Approach ◽  
Optimization Approach ◽  
Radar Rainfall ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Differential Reflectivity ◽  
The Impact

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

Big commercial microwave link data: Detecting rain events with deep learning

2020 ◽  
Author(s):  
Julius Polz ◽  
Christian Chwala ◽  
Maximilian Graf ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Rain Rate ◽  
Data Sets ◽  
Data Set ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Signal Fluctuation ◽  
Rain Events ◽  
Microwave Link ◽  
Good Agreement

<p>Commercial microwave links (CMLs) can be used for quantitative precipitation estimation. The measurement technique is based on the exploitation of the close to linear relationship between the attenuation of the signal level by rainfall and the path averaged rain rate. At a temporal resolution of one minute, the signal level of almost 4000 CMLs distributed all over Germany is being recorded since August 2017, resulting in one of the biggest CML data sets available for scientific purposes. A crucial step for retrieving rainfall information from this large CML data set is to accurately detect rainy periods in the time-series, a process which is hampered by strong signal fluctuations, occasionally occurring even when there is no rain. In our study, we evaluate the performance of convolutional neural networks (CNNs) to distinguish between rainy and non-rainy signal fluctuations by recognizing their specific patterns. CNNs make use of many layers and local connections of neurons to recognize patterns independent of their location in the time-series. We designed a custom CNN architecture consisting of a feature extraction and classification part with 20 layers of neurons and 1.4 x 10<sup>5</sup> trainable parameters. To train the model and validate the results we refer to the gauge-adjusted radar product RADOLAN-RW, provided by the German meteorological service. Despite not being an absolute truth, it provides robust information about rain events at the CML locations at an hourly time resolution. With only 400 CMLs used for training and 3504 for validation, we find that CNNs can learn to recognize different signal fluctuation patterns and generalize well to sensors and time periods not used for training. Overall we find a good agreement between the CML and weather radar derived rainfall information by detecting on average 87 % of all rainy and 91 % of all non-rainy periods.</p>

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 Deep Learning for Polarimetric Radar Quantitative Precipitation Estimation during Landfalling Typhoons in South China

2021 ◽  
Vol 13 (16) ◽  
pp. 3157
Author(s):  
Yonghua Zhang ◽  
Shuoben Bi ◽  
Liping Liu ◽  
Haonan Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China ◽  
Rainfall Intensity ◽  
Spatiotemporal Variability ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Radar Observations ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation

Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of “dual-polarization radar observations—surface rainfall (DPO—SR)” were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), QPENetV3 performs the best.

scholarly journals A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Wenxin Wu ◽  
Haibo Zou ◽  
Jiusheng Shan ◽  
Shanshan Wu
Keyword(s):  
Mean Squared Error ◽  
The Other ◽  
Intense Rainfall ◽  
Rainfall Events ◽  
Quantitative Precipitation Estimation ◽  
Squared Error ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Radar Echo ◽  
Short Time

Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). The new Z-R relationship is then applied to derive the RQPE over the middle and lower reaches of Yangtze River for two short-time intense rainfall cases in summer (2200 UTC 1 June 2016 and 2200 UTC 18 June 2016) and one stratiform rainfall case in winter (0000 UTC 15 December 2017), and then the comparative analyses between the RQPE and the RQPEs derived by the other two methods (the fixed Z-R relationship and the dynamical Z-R relationship based on radar reflectivity classification) are accomplished. The results show that the RQPE from the new Z-R relationship is much closer to the observation than those from the other two methods because the new method simultaneously considers the echo intensity (reflecting the size and concentration of hydrometers to a certain extent) and the echo-top height (reflecting the updraft to a certain extent). Two statistics of 720 rainfall events in summer (April to June 2017) and 50 rainfall events in winter (December 2017) over the same region show that the correlation coefficient (root-mean-squared error and relative error) between RQPE derived by the new Z-R relationship and observation is significantly increased (decreased) compared to the other two Z-R relationships. Besides, the new Z-R relationship is also good at estimating rainfall with different intensities as compared to the other two methods, especially for the intense rainfall.

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