ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 40 Issue 7 > 2014,40(7):777-786. DOI:10.7519/j.issn.1000-0526.2014.07.001 Prev Next
Analysis of Spread Skill Relations Using the ECMWF Ensemble Prediction in Medium Term Period over China
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Predictive ability of numerical model for medium term weather forecast is one of the important applications of ensemble forecast. To study the predictive ability of forecast skill of ensemble prediction in medium term period (96-360 h) in China, our analysis applies forecast data covering the period 2007-2011 from ECMWF global ensemble prediction system and chooses 500 hPa geopotential height and 850 hPa temperature as the variables, and then a comparative analysis is carried out by two different measures of spread skill relations. The results show that: (1) Forecast skill (RMSENS) and spread (SPRMSE) represented by root mean square errors show a seasonal cycle, i.e., winter (summer) is high (low), which is an inherent atmospheric property. However, the forecast skill (ACENS) and spread (SPAC) represented by anomaly correlations have no clear inherent seasonal cycle. (2) By comparing the two different measures of spread skill relations, it is known that the spread skill relations represented by anomaly correlations can reflect forecast skill better in medium term period forecast, and this spread skill relations based on T850 is stronger than based on Z500. (3) Quantitative analysis of spread skill relations indicates that good forecast skill can be reflected when spread is small, and this relationship declines about 20% from 96 to 360 h samples, while spread skill relations is weaker in the case of large spread, and this relationship does not decrease significantly with the increase of the valid time of forecast. (4) Statistic data shows that samples of the consistency of SPRMSE and SPAC account for 59% to 66% in medium term period, which does not show high consistency. The above analysis results can provide qualitative and quantitative reference for the forecast skill of ensemble prediction in medium term forecast.
Keywords:
Project Supported:
Clc Number:
Mobile website
