CRPSSComposite Replacement Panel Strain Survey (aircraft; Australia)
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Figure 10 shows the CRPSS for basin-averaged, May-July (MJJ) mean precipitation predicted by NMME and
Nevertheless, improvement over the basins in East Asia and Australia is not negligible (CRPSS difference larger than 0.05) in basins such as the Yangtze, Mekong, Ganges, and Murray-Darling.
APPENDIX: HIT RATE, FALSE ALARM RATIO, ETS, AND CRPSS. The nonproba bilistic forecasts for discrete predictands (e.g., a drought event) can be verified by several measures that are based on a 2 x 2 contingency table.
The probabilistic forecasts for continuous predictands (e.g., precipitation) can be verified through the CRPSS. First, the CRPS is defined as
So, a value of CRPSS = 0.2, for example, indicates that the probabilistic forecast error is 20% less than the climatological forecast error.
Developing the Chinese version of the CRPSS. For the purposes of the present study, the CRPSS was translated into Chinese by two native Chinese speakers, who also speak English.
To examine the construct validity of the Chinese version of the CRPSS, we randomly divided the whole sample (N = 677) into two subsamples using SPSS Version 18.
An EFA, using SPSS Version 19, was performed on the first subsample of data from the translated CRPSS. The Bartlett's test of sphericity was statistically significant, thus supporting the factor analysis, x2= 4,264.17, p < .01.
On the basis of tile results of the EFA, the construct validity of the Chinese version of the CRPSS was further validated on the second sub-sample using a CFA.
The CRPSS was translated into Chinese, and the construct validity of the Chinese version was explored using a sample of Chinese students.
Despite some items being redistributed among the four subscales and three items (Items 9, 16, and 21) being removed, the Chinese version of the CRPSS can mirror the original scale.
* the ability to evaluate key attributes of forecast quality, such as reliability, discrimination, and skill, at varying levels of detail, ranging from highly summarized (e.g., skill scores such as CRPSS) to highly detailed (e.g., box plots of conditional errors);