Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

Qian, Siwei; Chen, Jie; Li, Xiangquan; Xu, Chong‐Yu; Chen, Hua; Wu, Xushu

doi:10.1002/joc.6216

Cited by 18 publications

(10 citation statements)

References 62 publications

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“…(2) PCC measures the degree of linear correlation between forecast ensemble means and observations:

italicPCC goodbreak= \frac{\sum_{k = 1}^{K} ((), {\overset{true¯}{f}}_{k} goodbreak- \overset{f}{true}) ((), o_{k} goodbreak- \overset{o}{true})}{\sqrt{\sum_{k = 1}^{K} {({\overset{f}{true}}_{k} - \overset{true¯}{f})}^{2}} \sqrt{\sum_{k = 1}^{K} {(o_{k} - \overset{true¯}{o})}^{2}}}

where

\overset{f}{true}

represents the mean value of ensemble means and

\overset{o}{true}

represents the mean value of observations. PCC varies from −1 to 1, and its value is 1 when there is a perfect linear correlation between forecasts and observations (Kolachian & Saghafian, 2018; Qian et al, 2019; Slater et al, 2016; Yuan, 2016; Zhang et al, 2016; Zhao et al, 2018).…”

Section: Verification Of Ensemble Hydroclimatic Forecastsmentioning

confidence: 99%

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Huang

Zhao

2022

WIREs Water

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Predictive performance is one of the most important issues for practical applications of ensemble hydroclimatic forecasts. While different forecasting studies tend to use different combinations of verification metrics and diagnostic plots, this paper presents a literature survey of popular verification metrics, diagnostic plots and their corresponding forecast attributes. Twenty metrics to quantify predictive performance of ensemble, deterministic and categorical forecasts are detailed; six types of diagnostic plots to visualize the relationship between ensemble forecasts and observations are presented; and the correspondence relationships of verification metrics and diagnostic plots with ten forecast attributes are illustrated. Numerical experiments are devised for raw ensemble precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) to illustrate the verification metrics, diagnostic plots and forecast attributes. The results suggest that verification metrics that pay attention to some similar attributes can be related to one another. Meanwhile, the metrics are not necessarily in full accordance as they tend to reflect different attributes. Time series plots visualize forecasts over a period of interest and graphically illustrate forecast bias, reliability and skill. Quantile range plots provide an overall diagnosis for the association of ensemble forecasts with observations. Overall, verification metrics can be illustrated by diagnostic plots for individual grid cells and also be screened by spatial plots at regional and global scales. The survey presented in this article can serve as a roadmap for the selection of verification metrics, diagnostic plots and forecast attributes for verifying ensemble hydroclimatic forecasts. This article is categorized under: Science of Water > Hydrological Processes

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“…(2) PCC measures the degree of linear correlation between forecast ensemble means and observations:

italicPCC goodbreak= \frac{\sum_{k = 1}^{K} ((), {\overset{true¯}{f}}_{k} goodbreak- \overset{f}{true}) ((), o_{k} goodbreak- \overset{o}{true})}{\sqrt{\sum_{k = 1}^{K} {({\overset{f}{true}}_{k} - \overset{true¯}{f})}^{2}} \sqrt{\sum_{k = 1}^{K} {(o_{k} - \overset{true¯}{o})}^{2}}}

where

\overset{f}{true}

represents the mean value of ensemble means and

\overset{o}{true}

Section: Verification Of Ensemble Hydroclimatic Forecastsmentioning

confidence: 99%

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Huang

Zhao

2022

WIREs Water

View full text Add to dashboard Cite

show abstract

“…Hervieux (2017) proposed an anomaly prediction assessment method based on NMME for large Marine ecosystems off the coast of the USA and Canada, with a leveled approach to monthly SST to improve the overall prediction [4]. Qian (2020) compares the prediction effect of the statistical model of SST and the dynamic global circulation model on the seasonal precipitation in the Yangtze River Basin, finding that the statistical model had higher prediction performance, especially in long time span [5]. Dias (2019) adopts the inverse linear statistical model to forecast the sea temperature and sea temperature changes in the North Pacific Ocean, which is better than NMME in seasonal forecasting ability [6].…”

Section: Related Workmentioning

confidence: 99%

RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction

Zhu

Liu

et al. 2021

J Wireless Com Network

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For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predict SST variation in a long time span. By studying the Grey systematic relationship between historical SST data, the model obtains the development law of temperature variation, and further introduces different greenhouse gas emission scenarios in the future as the indexes coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper. The accuracy of the model is as high as 0.99984. The model predicts that the regional SST could reach a maximum of $$15.3\,^{\circ }{\mathrm {C}}$$ 15.3 ∘ C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness.

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“…Hervieux (2019) proposed an anomaly prediction assessment method based on NMME for large Marine ecosystems off the coast of the United States and Canada, with a leveled approach to monthly SST to improve the overall prediction [4]. Qian (2020) compares the prediction effect of the statistical model of SST and the dynamic global circulation model on the seasonal precipitation in the Yangtze River Basin, finding that the statistical model had higher prediction performance, especially in long time span [5]. Dias (2019) adopts the inverse linear statistical model to forecast the sea temperature and sea temperature changes in the North Pacific Ocean, which is better than NMME in seasonal forecasting ability [6].…”

Section: Related Workmentioning

confidence: 99%

Long-term SST Prediction Based on RSST-ARGM

Zhu¹,

Liu²,

Liu³

et al. 2021

Preprint

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For the purpose of exploring the long-term variation of regional SST, this paper studies the historical SST in local sea areas and the emission pattern of greenhouse gases and proposes a gray model of regional SST based on atmospheric reflection which can be used to predict SST variation in a long time span. By studying the grey systematic relationship between historical SST data, the model obtains the development law of temperature change, and furtherintroduces different future greenhouse gas emission scenarios as the index coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper, and its accuracy is as high as 0.99984. The model predicts that the local SST could reach a maximum of 15.3°C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness.

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Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

Cited by 18 publications

References 62 publications

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction

Long-term SST Prediction Based on RSST-ARGM

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