Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian, Wei; Lei, Yi; Liu, Wei; Huang, Wei; Ma, Guangyi; Zhang, Yonghong

doi:10.1186/s13677-020-00167-w

Cited by 14 publications

(15 citation statements)

References 47 publications

(47 reference statements)

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“…By expanding the scale of the reflectivity data and applying the 2D‐CNN networks as the QPE models, it is possible to learn the precipitation characteristics in reflectivity data. Nevertheless, the larger‐scale reflectivity data contains many signals unrelated to precipitation, and the accuracy of the rain gauge data is difficult to encompass large areas of reflectivity (Tian et al., 2020; Yo et al., 2021). Therefore, we propose RM‐1DCNN for weather radar QPE based on 1D‐CNN.…”

Section: Methodsmentioning

confidence: 99%

“…The radar QPE method based on the PTP framework is limited to the further improvement of estimation accuracy. On the basis of the convolutional neural networks (CNN), some researchers (Tian et al., 2020; Yo et al., 2021) estimated the precipitation of ground stations by using two‐dimensional radar echo as input. The CNNs can effectively extract the characteristics of precipitation from the reflectivity data, but the precision of the experimental results is not high due to the difference of reflectivity data size and the large‐scale two‐dimensional data contains many signals unrelated to precipitation.…”

Section: Introductionmentioning

confidence: 99%

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Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network

Zhang

Chen

Tian

2021

Earth and Space Science

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Hydrological and flood forecasting programs require accurate estimation of precipitation data. In particular, accurate, high-resolution and real-time quantitative precipitation estimation (QPE) plays an important role during severe convective rainfall events in localized regions (Cole & Moore, 2008;Jian et al., 2013). Rain gauges and radar are the two most widely used rainfall sensors, providing a direct and indirect measurements of rainfall intensity, respectively. In order to obtain the spatial distribution of precipitation from available gauges that are usually unevenly distributed, spatial interpolation techniques are required. The Kriging-based method is the most widely used rain gauge interpolation method (Jin & Heap, 2011), which is used to estimate the continuous attribute values of the true value at an unknown position. The methods based on the Kriging have the basic assumption of Gaussianness, and the fitting of the variogram model is complicated. To relax the Gauss hypothesis and make the interpolation method more flexible, some researchers have attempted it from different perspectives. Based on an artificial neural network (ANN), Sivapragasam et al. (2010) used data from 18 rain gauges to interpolate the average monthly precipitation in the Tasmilabarani Basin, India. The results suggested that ANN is a powerful alternative to the traditional Kriging spatial interpolation method. Verdin et al. ( 2016) compared two interpolation methods, the Ordinary Kriging (OK) and the K-Nearest Neighbor (K-NN) local polynomials, and fused them with precipitation data from the mountains of Central America and Colombia. Experimental results illustrate that these fusion methods improve precipitation estimation based on satellite data and increase the ability to capture extreme precipitation. To obtain the variogram of the OK method with better adaptability in daily precipitation interpolation, Q. Wang et al. ( 2016) compared and analyzed the four commonly used variograms

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network

Zhang

Chen

Tian

2021

Earth and Space Science

Self Cite

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“…For instance, Bonnet et al [17] applied Video Prediction Deep Learning algorithms for precipitation nowcasting and short-term forecasting, i.e., to predict the future sequence of reflectivity images for up to 1-h lead time for São Paulo, Brazil. Tian et al [18] developed two models based on the Back Propagation Neural Networks and Convolutional Neural Networks, respectively. Both approaches outperformed the traditional Z-R relation for QPE.…”

Section: Introductionmentioning

confidence: 99%

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Vogl

Laux

Bialas³

et al. 2022

Water

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Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.

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“…[28], the stationary wavelet transform (SWT) method is used to extract the frequency reflectivity and rainfall information in the wavelet domain, so as to obtain the estimated value of rainfall. In recent years, more popular deep learning methods are used for rainfall retrieval, such as [29,30]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [29], convolutional neural networks and back propagation neural networks are used to improve the accuracy of precipitation retrieval. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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

Lu¹,

Sun²,

Zhou³

2021

Applied Sciences

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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%.

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Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Cited by 14 publications

References 47 publications

Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network

Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

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

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