An introduction to multivariate probabilistic forecast evaluation

Probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic forecasts follows naturally with this,because evaluation is the key to improving the forecasts.Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist,we find that there is a need for an introduction with some practical application.In particular,many forecast scenarios in energy systems are inher-ently multivariate,and while univariate evaluation methods are well understood and documented,only limited and scattered work has been done on their multivariate counterparts.This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods,primarily scoring rules,as well as practical sections that explain how these methods can be calculated and estimated.In three case studies featuring simple autore-gressive models,stochastic differential equations and real wind power data,we implement,apply and discuss the logarithmic score,the continuous ranked probability score and the variogram score for forecasting problems of varying dimension.Finally,the advantages and disadvantages of the three scoring rules are highlighted,and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.

Evaluation of the subseasonal forecast skill of surface soil moisture in the S2S database

Based on the reforecasts from ve models of the Subseasonal to Seasonal(S2S)Prediction project,the S2S prediction skill of surface soil moisture(SM)over East Asia during May September is evaluated against ERA-Interim.Results show that good prediction skill of SM is generally 510 forecast days prior over southern and northeastern China in the majority of models.Over the Tibetan Plateau and northwestern China,only the ECMWF model has good prediction skill 20 days in advance.Generally,better prediction skill tends to appear over wet regions rather than dry regions.In terms of the seasonal variation of SM prediction skill,some diffierences are noticed among the models,but most of them show good prediction skill during September.Furthermore,the significant positive correlation between the prediction skill of SM and ENSO index indicates modulation by ENSO of the S2S prediction of SM.When there is an El Nino(a La Nina)event,the SM prediction skill over eastern China tends to be high(low).Through evaluation of the S2S prediction skill of SM in these models,it is found that the prediction skill of SM is lower than that of most atmospheric variables in S2S forecasts.Therefore,more attention needs to be given to the S2S forecasting of land processes.

Preface to the Special Issue on the"Forecast and Evaluation of Meteorological Disasters"(FEMD)

Meteorological disasters usually exert huge impacts on the development of both human society and the economy. According to statistics from the United Nations International Strategy for Disaster Reduction, the annual mean economic loss caused by meteorological disasters accounts for 3%-6% of the total amount of global GDP. China is a country that has been one of the most severely influenced by natural disasters.

Spatial and temporal synthesized probability gain for middle and long-term earthquake forecast and its preliminary application

The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.

Establishment and verification of labor demand estimation model in planting industry

In view of the poor precision of the theoretical model of labor demand estimation,it is difficult to estimate and predict the actual production problems accurately.Based on the actual production conditions and the relationship between the degree of mechanization of planting and the demand of labor force,this study established an estimation model for the labor demand of planting industry considering the factors of planting structure and mechanization degree.In order to ensure high reliability of data,the method of checking out abnormal data was adopted to obtain the cultivated land area index when the mechanization degree is from 0 to 100%.Taking Suihua region(Heilongjiang Province,China)as an example,the theory of the research was analyzed and applied.This study accessed to the data of cultivated land area per labor can afford when the mechanization level in Suihua area were 0 and 100%respectively through the investigation,and the average cultivated land area data of each labor force in two cases were sorted out and the abnormal data were eliminated at the same time.Finally,using the derived model,the data obtained and the mechanization level and cultivated land area of Suihua in the future,the labor demand amount in Suihua area from 2015 to 2019 were predicted.The model established in this study can be used to calculate the quantity of both current labor demand in planting industry and the labor demand in the various moments in the future through forecasting the future mechanization level and cultivated area which are the two main factors influencing the quantity of labor demand in planting structure.