Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models

Abstract

Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intraannual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model

“…In order to verify the accuracy of the multivariations LSTM model more over the traditional single-variation LSTM model, the single-variation LSTM (1) [15] model (using the runoff volume of the previous time period to predict the runoff volume of the next moment) and the multivariations LSTM (2) [29], model (using the rainfall of the previous time period to predict the runoff volume of the next moment) were selected as comparison models. Moreover, the single-variation EEMD-LSTM (1) [16] model and the EEMD-LSTM multivariations model (the EEMD-LSTM (2) model, which is a multivariations LSTM model with the main variables reconstructed by EEMD decomposition, and the EEMD-LSTM (3) model, which has the model input variables added to the remaining meteorological factors as secondary variables for runoff prediction) were selected as comparison models to verify the effectiveness of the EEMD method. The input and output settings of each model are shown in Table 4.…”

“…LSTM (1) Historical runoff None runoff runoff LSTM (2) Historical rainfall, runoff None rainfall runoff EEMD-LSTM (1) Historical runoff EEMD IMF 1 -IMF n , R IMF 1 -IMF n , R EEMD-LSTM (2) Historical rainfall, runoff EEMD, K-means K 1 -K d runoff EEMD-LSTM (3) Historical meteorological factors, runoff EEMD, K-means K 1 -K d , meteorological factors runoff Table 5. Comparison of simulation results of various models.…”

“…Accurate and reliable runoff simulation and prediction are significant for water management in irrigated paddy areas and agricultural non-pointsource pollution prevention and control. However, the runoff formation process is relatively complex and can be influenced by many factors, such as rainfall and topography, which gives the runoff series the characteristics of nonlinearity and nonstationarity [1]. The runoff yield and concentration of irrigated paddy areas are affected by human activities, especially by agricultural management measures.…”

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

“…With the development of artificial intelligence prediction model, it has been widely used in precipitation forecasting research in recent years. Chang et al [11] established a wavelet analysis combined with artificial neural network (ANN) model to predict 30-year non-linear and unsteady precipitation signals in a certain area. The results show that this method has higher forecasting precision than the traditional method.…”

Multi-temporal runoff-sediment discharge relationships in the source region of the Yellow River

“…Furthermore, runoff and sediment systems are highly complex, and they are influenced by various factors, such that the time series or multi-temporal component series of runoff and sediment discharge may be nonstationary (Chang et al, 2017). Nevertheless, previous studies on the time series of hydrological variables have usually assumed that the time series are stationary, and they have thereby constructed steady-state models.…”