干旱区生态环境知识资源中心(Arid): Improved multi-model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical-statistical approach during fall and winter seasons

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DOI	10.1002/joc.7148
	Improved multi-model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical-statistical approach during fall and winter seasons
	Najafi, Husain; Robertson, Andrew W.; Massah Bavani, Ali R.; Irannejad, Parviz; Wanders, Niko; Wood, Eric F.
通讯作者	Najafi, H ; Bavani, AMR (corresponding author), Univ Tehran, Dept Irrigat & Drainage Engn, Aburaihan Campus, Tehran, Iran.
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
ISSN	0899-8418
EISSN	1097-0088
出版年	2021
英文摘要	Skillful seasonal climate forecasts can support decision making in water resources management and agricultural planning. In arid and semi-arid regions, tailoring reliable forecasts has the potential to improve water management by using key hydroclimate variables months in advance. This article analyses and compares the performance of two common approaches (empirical and hybrid dynamical-statistical) in seasonal climate forecasting over a drought-prone area located in Southwest Asia including Iran. Empirical models are framed as a baseline skill that hybrid models need to outperform. Both approaches provide probabilistic forecasts of precipitation and temperature using canonical correlation analysis to provide forecasts at 0.25 degrees resolution. Empirical models are developed based on the large-scale observed atmosphere-ocean patterns for forecasting using antecedent climate anomalies as predictors, while the hybrid approach makes use of model output statistics to correct systematic errors in dynamical climate model forecast outputs. Eight state-of-the-art dynamical models from the North American Multi-Model Ensemble project are analysed. Individual models with the highest goodness index are weighted to develop seven different hybrid dynamical-statistical Multi-model Ensembles. In this study, (October-December) and (January-February) are considered as target seasons which are the most important periods within the water year for water resource allocation to the agriculture sector. The results show that the hybrid approach has improved performance compared to the raw general circulation models and purely empirical models, and that the performance of the hybrid models is season-dependent. Seasonal forecasts of precipitation (temperature) have a higher skill in OND (JFM). In addition, in most cases, Multi-model Ensemble (MME) is more skillful than the empirical models and outperforms individual dynamical models. However, the best individual model might be as skillful as the MME given the target season and region of interest.
英文关键词	hybrid Iran multi-model ensemble North American Multi-Model Ensemble (NMME) Seasonal climate forecasting
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000652004400001
WOS关键词	SOUTHWEST ASIA PRECIPITATION ; NORTH-ATLANTIC OSCILLATION ; NINO-SOUTHERN-OSCILLATION ; MADDEN-JULIAN OSCILLATION ; METEOROLOGICAL DROUGHT ; CLIMATE FORECASTS ; ZAGROS MOUNTAINS ; RISK-MANAGEMENT ; SOIL-MOISTURE ; ENSO
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352242
作者单位	[Najafi, Husain] UFZ Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Leipzig, Germany; [Najafi, Husain; Massah Bavani, Ali R.] Univ Tehran, Dept Irrigat & Drainage Engn, Aburaihan Campus, Tehran, Iran; [Robertson, Andrew W.] Columbia Univ, Int Res Inst Climate & Soc IRI, Palisades, NY USA; [Irannejad, Parviz] Univ Tehran, Inst Geophys, Dept Space Phys, Tehran, Iran; [Wanders, Niko] Univ Utrecht, Dept Phys Geog, Utrecht, Netherlands; [Wood, Eric F.] Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA
推荐引用方式 GB/T 7714	Najafi, Husain,Robertson, Andrew W.,Massah Bavani, Ali R.,et al. Improved multi-model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical-statistical approach during fall and winter seasons[J],2021.
APA	Najafi, Husain,Robertson, Andrew W.,Massah Bavani, Ali R.,Irannejad, Parviz,Wanders, Niko,&Wood, Eric F..(2021).Improved multi-model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical-statistical approach during fall and winter seasons.INTERNATIONAL JOURNAL OF CLIMATOLOGY.
MLA	Najafi, Husain,et al."Improved multi-model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical-statistical approach during fall and winter seasons".INTERNATIONAL JOURNAL OF CLIMATOLOGY (2021).