Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Power Stations Based on Dynamic Variable Weight

In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.

An Analysis of the Difference between the Multiple Linear Regression Approach and the Multimodel Ensemble Mean

An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.

Multimodel Ensemble Forecasts for Precipitations in China in 1998

Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the “Climate of the 20th Century Experiment” （20C3M） for the Fourth IPCC Assessment Report. Climate Research Unit （CRU） data were chosen for the observation analysis field. Root mean square （RMS） error and correlation coeffi-cients （R） are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression （MLR） performs best. However, for bias-corrected data, the superensemble based on singular value decomposition （SVD） produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.

Evaluation of summer drought ensemble prediction over the Yellow River basin

Accurately predicting drought a few months in advance is important for drought mitigation and agricultural and water resources management,especially for a river basin like that of the Yellow River in North China.However,summer drought predictability over the Yellow River basin is limited because of the low influence from ENSO and the large interannual variations of the East Asian summer monsoon.To explore the drought predictability from an ensemble prediction perspective,29-year seasonal hindcasts of soil moisture drought,taken directly from several North American multimodel ensemble(NMME)models with different ensemble sizes,were compared with those produced by combining bias-corrected NMME model predictions and variable infiltration capacity(VIC)land surface hydrological model simulations.It was found that the NMME/VIC approach reduced the root-mean-square error from the best NMME raw products by 48%for summer soil moisture drought prediction at the lead-1 season,and increased the correlation significantly.Within the NMME/VIC framework,the multimodel ensemble mean further reduced the error from the best single model by 6%.Compared with the NMME raw forecasts,NMME/VIC had a higher probabilistic drought forecasting skill in terms of a higher Brier skill score and better reliability and resolution of the ensemble.However,the performance of the multimodel grand ensemble was not necessarily better than any single model ensemble,suggesting the need to optimize the ensemble for a more skillful probabilistic drought forecast.

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

Projection of Summer Precipitation over the Yangtze–Huaihe River Basin Using Multimodel Statistical Downscaling Based on Canonical Correlation Analysis

By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combination of empirical orthogonal function and canonical correlation analysis） to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1% 7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multi- model ensemble （SDMME）, the relative error is improved from -15.8% to -1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble （MME） and the downscaling of most individual models. The projections of SDMME reveal that under the RCP （Representative Concentration Pathway） 4.5 scenario, the projected domain-averaged precipitation changes for the early （2016-2035）, middle （2046 2065）, and late （2081-2100） 21st century are 1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of l15°E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods~ with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5°N for both periods.

Has the Prediction of the South China Sea Summer Monsoon Improved Since the Late 1970s?

Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models （CGCMs） from the ENSEMBLES （Ensemble-based Predictions of Climate Changes and Their Impacts） and DEME- TER （Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction） projects, it is found that the prediction of the South China Sea summer monsoon （SCSSM） has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.