Weighting of NMME temperature and precipitation forecasts across Europe

Slater, Louise; Villarini, Gabriele; Bradley, A. Allen

doi:10.1016/j.jhydrol.2017.07.029

Cited by 35 publications

(28 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al: Significant spatial patterns from the GCM seasonal forecasts of global precipitation GCMs generate a vast amount of high-dimensional forecast data, including retrospective forecasts of past climate and real-time forecasts (Kirtman et al, 2014;Saha et al, 2014;Jia et al, 2015). Due to the complexity of atmospheric processes and model physics, the predictive performance of GCM forecasts is not uniform, but varies considerably across the globe (Yuan et al, 2013;Tian et al, 2017;Zhao et al, 2018). Therefore, interpreting and understanding the predictive performance is a critical issue in the applications of GCM forecasts (Doblas-Reyes et al, 2013;Saha et al, 2014;Jia et al, 2015;Hudson et al, 2017;Wang et al, 2019a).…”

Section: Introductionmentioning

confidence: 99%

“…Yuan et al, 2011;Saha et al, 2014;Crochemore et al, 2016;Hudson et al, 2017;Zhao et al, 2017a). Compared to PIT that requires a diagnostic plot and CRPS that relies on numerical integration, anomaly correlation is conceptually simple, easy to implement, and also robust to missing and censored values (Yuan et al, 2011;Luo et al, 2013;Slater et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Spatial plotting with latitude and longitude has been extensively used to handle the dimensionality for the verification of GCM forecasts (Kirtman et al, 2014;Hudson et al, 2017;Slater et al, 2017). The fact that forecasts are commonly generated by GCMs as grid-based data makes spatial plotting a particular tool of choice for verification (Merryfield et al, 2013;Saha et al, 2014;Jia et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Zhao

Zhang

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Zhao

Zhang

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The anomaly correlation that indicates how well large (small) values of forecasts correspond to large (small) values of observations is one of the most popular metrics [e.g., Yuan et al, 2011;Saha et al, 2014;Crochemore et al, 2016;Hudson et al, 2017;Zhao et al, 2017]. Compared to PIT that requires a diagnostic plot and CRPS that relies on numerical integration, anomaly correlation is conceptually simple, easy to implement, and also robust to missing and censored values [Yuan et al, 2011;Luo et al, 2014;Slater et al, 2017].…”

mentioning

confidence: 99%

“…Spatial plotting with latitude and longitude has been extensively used to handle the dimensionality for the verification of GCM forecasts [Kirtman et al, 2014;Hudson et al, 2017;Slater et al, 2017]. The fact that forecasts are commonly generated by GCMs as grid-based data makes spatial plotting a particular tool of choice for verification [Merryfield et al, 2013;Saha et al, 2014;Jia et al, 2015].…”

mentioning

confidence: 99%

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Zhao¹,

Zhang²,

Zhang³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Fully-coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate center exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least one of the ten sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

show abstract

Improved multi‐model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical‐statistical approach during fall and winter seasons

Najafi

Robertson

Bavani

et al. 2021

Intl Journal of Climatology

View full text Add to dashboard Cite

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° 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.

show abstract

Weighting of NMME temperature and precipitation forecasts across Europe

Cited by 35 publications

References 53 publications

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Improved multi‐model ensemble forecasts of Iran's precipitation and temperature using a hybrid dynamical‐statistical approach during fall and winter seasons

Contact Info

Product

Resources

About