PIs: Kwang-Yul Kim and T. N. Krishnamurti
Department of Meteorology
Florida State University
404 Love Bldg., Rm 404
Tallahassee, FL 32306-4520
Phone: (850) 644-1268
Fax:
Email: kwang@cyclo.met.fsu.edu
The seasonal forecasting of the Asian-Australian monsoon is of interest both for scientific and economical reasons. In order to forecast the monsoon accurately, it is important to understand the monsoon variability with respect to physical processes assocaited with it. We have to rely heavily on numerical models for monsoon forecasting, and it is pertinent to ask if the present generation of numerical models reproduces the monsoon variability accurately. A critical comparison between observational data and models will show where the discrepancy is and how numerical models can be improved further. Despite its importance, the seasonal and longterm forecasting of the monsoon, at present, is in a crawling stage. In this study, the potential of the so-called super-ensemble forecasting method (Krishnamurti et al. 2000a, b) will be explored as it applies to the seasonal and longterm forecasting of the monsoon. It is essential then, to assess the accuracy of individual models, which is an integral component of the super-ensemble strategy.
One of primary objectives in the proposed study is the analysis of model statistics (primarily second-moment statistics) and their comparisons with observational data. The model statistics we intend to calculate are the variance, spatial correlation, temporal correlation, EOFs and cyclostationary EOFs (CSEOFs; Kim and North 1997). Such an analysis will show how accurate is the present generation of numerical models in reproducing physical processes in the observational data. More importantly, however, the analysis results will show how individual numerical models can be improved further. The second primary objective is the development of EOF- or CSEOF-based super-ensemble forecasting method using model outputs.
The second-moment statistics (including EOFs and cyclostationary EOFs) of the model outputs will be examined on several different time scales including diurnal, seasonal, and annual scales (Kim et al. 1996). A comparison of the model statistics with those of the observational data will reveal whether the present generation of numerical models is sufficiently accurate for the development of a super-ensemble forecasting method. More importantly, however, such a comparison will critically show whether a certain physical process is accurately handled in numerical models and its prediction limit set by numerical models. We intend to use CSEOF analysis extensively, which is more apt for extracting time-dependent physical processes (Kim and Chung 2000). The observational data sets we will use include, but are not limited to, da Silva et al. (1994) surface data, NCEP daily and monthly reanalysis (Kalnay et al. 1994), ocean assimilation data (Giese and Carton 1999; Carton et al. 2000a, b) and Xie-Arkin precipitation data (Xie and Arkin 1997).
A super-ensemble forecasting method will be developed in EOF or CSEOF space
rather than in physical space in order to take advantage of spatial and
temporal correlations of physical processes. A preliminary test strongly
indicates that the performance of a super-ensemble method can significantly
be improved by resolving the spatio-temporal correlation structures of
physical processes appropriately. The super-ensemble method in CSEOF
space will also show the predictability of each physical process. Such
information is critical in addressing the inherent predictability of the
monsoon, the source of prediction uncertainty, and specific directions of
model improvement for better forecasting.
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