A GNSS-based weather forecasting approach using Nonlinear Auto Regressive Approach with Exogenous Input (NARX)

Rahimi, Zhoobin; Shafri, Helmi Zulhaidi Mohd; Norman, Masayu

doi:10.1016/j.jastp.2018.06.011

Cited by 28 publications

(19 citation statements)

References 27 publications

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“…It is important to have a significantly large training sample in order to ensure that the model achieves viable results [21]. Despite the fact that prior knowledge between input-output variables is not mandatory, the fitting of the results will be more adequate if consistency between the data is found [24].…”

Section: Neural Network Approachmentioning

confidence: 99%

“…Other studies have used meteorological data and artificial intelligence techniques to forecast meteorological parameters related to GNSS, such as the total zenith delay [21], the weighted mean temperature [22] and also the PWV [23]. A nonlinear auto regressive approach with exogenous input (NARX) was used to perform real-time rainfall prediction combining daily rainfall from meteorological stations and GNSS PWV data [24]. This method is appropriated to learn long-term dependencies in a data time series.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

2019

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This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.

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Section: Neural Network Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

2019

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“…Radiosonde can provide water vapor products with high vertical resolution, and the vertical resolution of radiosonde data can be as high as 30 m 7,8 , or even 5 m 9 . However, the spatial–temporal resolutions of the PWV data obtained by this method are low because the distance between the adjacent stations is 200–300 km and the sounding balloon is launched only two to four times a day 10,11 . Such spatial–temporal resolutions can not satisfy the requirements of small- medium scale atmospheric water vapor change and weather prediction.…”

Section: Introductionmentioning

confidence: 99%

“…WVR can provide water vapor products with high temporal resolution, but it has not been widely used because of its expensive equipment and vulnerability to cloud and rainfall 10–13 . Although satellite image products can provide precipitation information with high spatial resolution, these methods are used rarely due to their low accuracy 11,14 .…”

Section: Introductionmentioning

confidence: 99%

Short-term rainfall forecast model based on the improved BP–NN algorithm

Liu

Zhao

Yao

et al. 2019

Sci Rep

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The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP–NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP–NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010–2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP–NN algorithm.

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“…It has been demonstrated that NARX is capable of capturing the dynamics of nonlinear complex systems (Diaconescu, 2008;Chan et al, 2015). Moreover, NARX performs favorably on long-term dependencies (Rahimi et al, 2018). Thus, NARX is particularly useful for time series modeling.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Long-Term Near-Surface Temperature Based on NA-CORDEX Output

Li¹,

Li²,

Zhang³

et al. 2019

J ENVIRON INFORM LET

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Temperature is one of the most important parameters in climate modeling, as it has significant impacts on various geophysical processes such as evaporation and precipitation. Applying multiple climate models for prediction generally outperforms the use of individual climate models, and neural networks perform well at capturing nonlinear relationships, which can provide more reliable temperature projections. In this study, three neural network algorithms, including Multi-layer Perceptron (MLP), Time-lagged Feed-forward Neural Networks (TLFN) and Nonlinear Auto-Regressive Networks with exogenous inputs (NARX), were used to develop data-driven models for predicting daily mean near-surface temperature based on North American Coordinated Regional Downscaling Experiment (NA-CORDEX) output. A case study of Big Trout Lake in Ontario, Canada was carried out to demonstrate the applications and to evaluate the performance of the proposed neural network based models. The results showed that MLP, TLFN, and NARX performed well in generating accurate daily near-surface temperature predictions with the coefficient of determination (R 2) values above 0.84. The three neural network based models had similar performance with no significant difference in terms of root mean square error and R 2. Neural network based climate prediction models outperformed each of the individual regional climate models and generated smoother predicttions with less fluctuation. This study provides a technical basis for generating reliable predictions of daily temperature using neural networks based model.

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A GNSS-based weather forecasting approach using Nonlinear Auto Regressive Approach with Exogenous Input (NARX)

Cited by 28 publications

References 27 publications

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors

Short-term rainfall forecast model based on the improved BP–NN algorithm

Prediction of Long-Term Near-Surface Temperature Based on NA-CORDEX Output

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