Improving the precipitation forecasts of the North-American multi model ensemble (NMME) over Sistan basin

Yazdandoost, Farhad; Moradian, Sogol; Zakipour, Mina; Izadi, Ardalan; Bavandpour, Majid

doi:10.1016/j.jhydrol.2020.125263

Cited by 23 publications

(15 citation statements)

References 46 publications

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“…The proposed framework applies (1) NSGA-II (Non dominated Sorting Genetic Algorithm II) with the aim of finding the best solution space of the datasets with assessing the trade-offs between the objectives, (2) ENTROPY theory to find the relative importance of the objectives and (3) TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to choose the best alternative from the available options. The following part elaborates a brief outline of the steps of this framework and complete information can be found in Yazdandoost and Izadi (2016) and Yazdandoost et al. (2020) .…”

Section: Methodsmentioning

confidence: 99%

A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

Yazdandoost

Moradian

Izadi

et al. 2020

Heliyon

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This paper outlines a framework in order to provide a reliable and up-to date local precipitation dataset over Sistan and Baluchestan province, one of the poorly rain gauged areas in Iran. Initially, the accuracy of GPCC data, as the reference dataset, was evaluated. Next, the performance of eight gridded precipitation products (namely, CHIRPS, CMORPH-RAW, ERA5, ERA-Interim, GPM-IMERG, GSMaP-MVK, PERSIANN and TRMM3B42) were compared based on the GPCC observations during 1982–2016 over the study area. The evaluation was done by using eight commonly used statistical and categorical metrics. Then, among the products, the most suitable ones on the basis of their better performance and least time delay in providing data, were utilized as the constituent members of the proposed hybrid dataset. Using several statistical/machine learning approaches (namely, NSGA II, ETROPY and TOPSIS), daily weights of the chosen datasets were estimated, while the correlation coefficient and the estimation error of the data were maximized and minimized, respectively. Finally, the efficiency of the proposed hybrid precipitation dataset was investigated. Results indicate that the developed hybrid dataset (2014-present), using the estimates of the chosen ensemble members (GPM-IMERG, GSMaP-MVK and PERSIANN) and their respective weighting coefficients, provides accurate local daily precipitation data with a spatial resolution of 0.25°, representing the minimum time delay, compared to the other available datasets.

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Section: Methodsmentioning

confidence: 99%

A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

Yazdandoost

Moradian

Izadi

et al. 2020

Heliyon

Self Cite

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“…Sistan and Baluchestan Province in south-east Iran is one of the driest regions in the world and is facing prolonged droughts ( Yazdandoost et al., 2020 ). The prevailing climate of the region is barren and arid, with an average annual rainfall of about 183 mm and an average annual temperature of about 22.4 °C.…”

Section: Data and Case Studymentioning

confidence: 99%

“…The GPCC firstly has been established in 1989 by Germany's National Meteorological Service, the Deutscher Wetterdienst (DWD) on request of the World Meteorological Organization (WMO) ( Yazdandoost et al., 2020 ). The GPCC provides different spatial resolution gridded precipitation data (2.5 ° × 2.5 °, 1.0 ° × 1.0 °, 0.5 ° × 0.5 °, and 0.25 ° × 0.25 ° resolution) based on around 80000 observational stations from several different sources.…”

Section: Data and Case Studymentioning

confidence: 99%

“…Therefore, generating a reference gridded precipitation dataset based on these non-evenly distributed stations across the entire area has hinted to consider the Global Precipitation Climatology Centre (GPCC) as the surrogate reliable reference gridded data for precipitation observations (Azizi et al, 2015;Darand & Zand, 2016;Rezayi et al, 2011). The GPCC firstly has been established in 1989 by Germany's National Meteorological Service, the Deutscher Wetterdienst (DWD) on request of the World Meteorological Organization (WMO) (Yazdandoost et al, 2020). The GPCC provides different spatial resolution gridded precipitation data (2.5° x 2.5°, 1.0° x 1.0°, 0.5° x 0.5°, and 0.25° x 0.25° resolution) based on around 80000 observational stations from several different sources.…”

Section: Observation Datamentioning

confidence: 99%

“…Summary of the four NMME models and their characteristics, used in the study (modified from source:Yazdandoost et al 2020).…”

mentioning

confidence: 99%

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Copula based post-processing for improving the NMME precipitation forecasts

Yazdandoost

Zakipour

Izadi

2021

Heliyon

Self Cite

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Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.

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Rainy season precipitation forecasts in coastal Peru from the North American Multi‐Model Ensemble

Vavrus

Wang

Block

2022

Intl Journal of Climatology

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We assessed operational forecasts of rainy season (January-March [JFM]) precipitation over coastal Peru from seven global climate models (GCMs) in the North American Multi-Model Ensemble (NMME) from 1982 to 2020. Similar to previous representations by GCMs, regional climate models, and atmospheric reanalyses, the NMME models produce excessive precipitation (more than twice the observed values) over all of coastal Peru. Based on a set of commonly used metrics (correlation, mean error, RMSE, and Heidke skill score), there is a wide range in accuracy among the NMME models. As expected, forecast accuracy degrades with increasing lead time from December to July, but the rate of decline varies greatly by region and among models. The cause(s) of differences in model performance remains unclear, but resolution does not appear to be a significant factor. Highest forecast skill is found for southwestern Peru (correlations with observations are significant at all lead times), probably because of its stronger relationship between rainfall and El Niño-Southern Oscillation (ENSO), and lowest skill occurs for west-central Peru (insignificant correlations at all lead times). The distribution of interannual rainy season precipitation in northwestern Peru is extremely positively skewed (skewness >2), making evaluation of forecasts difficult from only a few years that deviate strongly from the mean. Improved correlations with observations across regions are generally found by subsetting multi-model mean forecasts using the better-performing models. Over northwest Peru at short lead times, NMME rainy season precipitation forecasts outperform a simple statistical correlation between JFM rainfall and ENSO, but not at longer leads, while NMME's forecasts for west-central Peru underperform statistical correlations at all lead times. Over southwest Peru, the NMME models are consistently more skilful than a statistical correlation, and these differences become larger with increasing lead time.

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Improving the precipitation forecasts of the North-American multi model ensemble (NMME) over Sistan basin

Cited by 23 publications

References 46 publications

A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region

Copula based post-processing for improving the NMME precipitation forecasts

Rainy season precipitation forecasts in coastal Peru from the North American Multi‐Model Ensemble

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