Coupled model biases and extended-range prediction skill during the onset phase of the Indian summer monsoon with different initializations related to land surface and number of observations

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY(2023)

引用 0|浏览5
暂无评分
摘要
In this study, we have analyzed the role of the initialization in the forecast of the monsoon onset phase beyond 10 days lead time (i.e., in the extended range) to understand the impact of displacement or shift in initial conditions in the extended-range forecast. Two displacement errors are considered: (a) the initial error arising due to a change in land surface initial conditions and (b) the initial error arising due to a change in the number of observations. For the first part (a), we have analyzed and compared the difference in prediction skills in the United Kingdom Met Office (UKMO) GloSea5 forecasts run with two different land surface initial conditions (IC) configurations. In one configuration, the IC is prepared using a monthly land surface climatology; in the other configuration, it is based on daily land surface reanalysis. In the other part (b), we used the Indian Institute of Tropical Meteorology's Climate Forecast System (IITM_CFS) model with two different ICs (NCEP and NCMRWF differing in the number of observations over the Indian land region). Both runs indicate a shift in the initial condition, which manifests as displacement error. UKMO and IITM_CFS runs have the same land surface model when the corresponding twin experiments are compared in (a) and (b). Analysis of the initial displacement errors from these runs indicates that improving the realism in the land surface initial conditions can effectively modulate or change the surface meteorological fields in the prediction model during the onset phase. The result shows that the rotational and divergence components of the surface winds differ in the two sets of runs. Results also indicate that the difference in surface initialization manifests as differences in rotational and divergent kinetic energy. This could lead to a difference in the forecast of monsoon onset rain. Further analysis also suggests that the local land surface initial condition error, in addition to an error in large-scale teleconnections, affects the monsoon onset forecast and its prediction skill in the extended-range time-scale.
更多
查看译文
关键词
coupling strength, land surface initialization, monsoon onset over Kerala, prediction skill, surface bias
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要