A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

Hu, Yijia; Zhong, Zhong; Zhu, Yimin; Yao, Huajian

doi:10.1007/s00704-017-2094-9

Cited by 7 publications

(8 citation statements)

References 41 publications

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“…More efficient models are therefore desired (Belkin and Niyogi, 2003;Weinberger and Saul, 2006). Therefore, the idea of combining dynamical and statistical methods to improve weather and climate prediction has been developed in many studies (Huang et al, 1993;Yu et al, 2014a, b). By introducing genetic algorithms (GAs), Zhang et al (2006) inverted and reconstructed a new dynamical-statistical forecast model of the tropical Pacific SST field using historic statistical data (Zhang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

et al. 2018

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Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

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Section: Introductionmentioning

confidence: 99%

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

et al. 2018

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“…Earth's Future showed that spectral nudging is crucial to alleviate the dry bias over the Plains and the Midwest, especially during the warm season (Hu et al, 2018). We find this is the case not only in the spring but also in the summer (not shown).…”

Section: 1029/2018ef000956mentioning

confidence: 58%

“…That said, WGN and WCB increase the magnitude of the wet bias from the ESMs in the Southern Great Plains but overall are still much more accurate than their ESM counterpart. Previous regional modeling studies also showed that spectral nudging is crucial to alleviate the dry bias over the Plains and the Midwest, especially during the warm season (Hu et al, ). We find this is the case not only in the spring but also in the summer (not shown).…”

Section: Resultsmentioning

confidence: 95%

“…Extensive past research in dynamic downscaling focused on added value relative to ESMs (e.g., Hu et al, ; E. K. Chang, Ma, et al, ; W. Chang, Stein, et al, ; W. Chang et al, ). In this section, we discuss how the ESMs and RCMs in this study capture extreme and seasonal precipitation compared to observations.…”

Section: Resultsmentioning

confidence: 99%

“…Extensive past research in dynamic downscaling focused on added value relative to ESMs (e.g., Hu et al, 2018;E. K. Chang, Ma, et al, 2016;W.…”

Section: Spatial Added Valuementioning

confidence: 99%

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Analyses for High‐Resolution Projections Through the End of the 21st Century for Precipitation Extremes Over the United States

et al. 2018

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This study compares an ensemble of dynamically downscaled projections of extreme daily precipitation over the contiguous United States (CONUS). With a grid spacing of 12 km and a domain that encompasses most of North America, we use the Weather Research and Forecast model as a regional climate model. We incorporate initial and boundary conditions from three different Coupled Model Intercomparison Project Phase 5 Earth system models (ESMs). We focus on precipitation extremes in the future climate (2045–2054 and 2085–2094) by comparing a business‐as‐usual high‐emissions scenario to emissions from a historical period (1995–2004). In the historical period, the Weather Research and Forecasting‐downscaled simulations result in significant improvements over the ESMs for precipitation extremes. In the analyses of future climate, there is a large increase in the projected frequency of extreme precipitation events over the entire CONUS and a decrease in median precipitation days. Moreover, most regions show an increase in the number of dry days in the future scenarios. The magnitude of extreme precipitation events is projected to increase in the CONUS at all temperatures above freezing. The strongest precipitation events will increase in intensity, primarily because of a shift in precipitation distribution due to the increase in near‐surface air temperatures (Clausius‐Claperyon relationship). However, these events are also affected by changes in some atmospheric dynamical factors, such as stronger low level jet, especially over the Midwestern United States. In addition, compared to those calculated by the ESMs, the downscaled projections show important regional differences in the frequency and magnitude of extreme precipitation events.

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Spatiotemporal variations of precipitation concentration and their potential links to drought in mainland China

Guo

Wang

Jirigala

et al. 2020

Journal of Cleaner Production

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A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

Cited by 7 publications

References 41 publications

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Analyses for High‐Resolution Projections Through the End of the 21st Century for Precipitation Extremes Over the United States

Spatiotemporal variations of precipitation concentration and their potential links to drought in mainland China

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