Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

Wei, Chih-Chiang; Hsu, Chen-Chia

doi:10.3390/rs12142203

Cited by 8 publications

(5 citation statements)

References 57 publications

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“…Specifically, XGBoost is approximately 68% faster than the RF model. Additionally, XG-Boost, which has displayed competitive performance in previous experiments [30], [31], is 58.8 faster than the ANN. Moreover, XGBoost outperformed the other methods in SM estimation.…”

Section: F Computational Efficiencymentioning

confidence: 90%

“…Among the traditional ML methods, RFs are popular and widely employed, and they are powerful tools for ML regression [19], [20], [27], [28], [29]. XGBoost exhibits superior performance and provides several advantages, such as a fast speed, easy parameterization scheme, and high robustness [30], [31]; thus, it is chosen here. Advanced ANNs are also commonly used ML techniques and have performed well in previous ML-based CYGNSS SM studies [17], [18], [25], and for these reasons, ANNs are employed as well.…”

Section: A Evaluation Criteria and Methodsmentioning

confidence: 99%

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An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Jia

Jin

Yan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 × 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm 3 /cm 3 and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm 3 /cm 3 and Manuscript

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Section: F Computational Efficiencymentioning

confidence: 90%

Section: A Evaluation Criteria and Methodsmentioning

confidence: 99%

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Jia

Jin

Yan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The relationship between radar reflectivity (Z) and precipitation (R) also plays an important role in predicting precipitation in the literature. Many experiments directly used the ZR formula (Z = aR b ) derived from the data to predict rainfall rates according to the Z values [16,17], dating back to the Marshall-Palmer formula [18] (Z = 200 R 1.6 where Z is in mm 6 /m 3 and R is in mm/h) which links radar reflectivity and precipitation rate. Although the formula was intensively used, it comes with arguably uncertainty, as the values of the parameters are usually estimated through an empirical approach, based on the comparison of radar and rain gauge.…”

Section: Introductionmentioning

confidence: 99%

Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Ma¹,

Cui²,

Jiang³

2022

Computers, Materials &Amp; Continua

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Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method of deep learning and the ZR relationship, and compared it with a traditional ZR equation, a ZR equation with its parameters estimated by the least square method, and a pure deep learning model. The experimental results show that our combined model performs much better than the equation-based ZR formula and has the similar performance with a pure deep learning nowcasting model, both for all level precipitation and heavy ones only.

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“…Therefore, XGB has been used by various authors such as Chakraborty and Alajali [ 57 ] and Yuan et al [ 58 ]. Wei and Hsu [ 59 ] addressed the rainfall retrieval problem for quantitative precipitation estimation. The feasibility of rain retrievals was examined from linear regressions, support vector regressions, and XGB models.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Wei

Hsu

2021

Sensors

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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

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Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

Cited by 8 publications

References 57 publications

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

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