Determination of forecasts errors arising from different components of model physics and dynamics

This paper addresses a procedure to extract error estimates for the physical and dynamical components of a forecast model. This is a two-step process in which contributions to the forecast tendencies from individual terms of the model equations are first determined using an elaborate bookkeeping of the forecast. The second step regresses these estimates of tendencies from individual terms of the model equations against the observed total tendencies. This process is executed separately for the entire horizontal and vertical transform grid points of a global model. The summary of results based on the corrections to the physics and dynamics provided by the regression coefficients highlights the component errors of the model arising from its formulation. This study provides information on geographical and vertical distribution of forecast errors contributed by features such as nonlinear advective dynamics, the rest of the dynamics, deep cumulus convection, large-scale condensation physics, radiative processes, and the rest of physics. Several future possibilities from this work are also discussed in this paper

On the weakening of Hurricane Lili, October 2002

This paper addresses the weakening of Hurricane Lili of October 2002 just before it made landfall in Louisiana. This hurricane weakened from a category 4 storm on October 3, 2002 at 0000 UTC to a category 1 storm on October 3, 2002 at 1300 UTC. This sudden drop in intensity has been a subject of considerable interest. In this paper we explore a forecast model diagnostic approach that explores the contribution to the hurricane intensity changes arising from a number of dynamical and physical possibilities. Running several versions of a global model at very high resolution, the relative contribution to the intensity drop of Lili arising from cooler sea surface temperatures, dry air advection into the storm, advective non-linear dynamics, non-advective dynamics, and shallow and deep cumulus convection was examined. This line of inquiry led to the conclusion that dry air advection from the north into the storm and the slightly cold sea surface temperatures were not the primary contribution to the observed pressure rise by 22 hPa. The primary contribution to the pressure rise was found to be the 'rest of dynamics' (the non-advective dynamics). The shallow convection contributed slightly to an overall cooling, i.e. a weakening of the warm core of Lili. The effects of deep cumulus convection appeared to be opposite, i.e. towards maintaining a strong storm. A primary term in the 'rest of dynamics', the advection of Earth's angular momentum into the storm, is identified as a major contributor for the intensity change in the analysis. This feature resembles an intrusion of dry air into the core of the storm. This intrusion contributes to a reduction of spin and an overall rapid weakening of the hurricane. The angular momentum partitioning appears quite revealing on the sudden demise of Lili

Seasonal climate forecasts of the South Asian monsoon using multiple coupled models

This study addresses seasonal climate forecasts using coupled atmosphere-ocean multimodels. Using as many as 67 different seasonal-forecast runs per season from a variety of coupled (atmosphere-ocean) models consensus seasonal forecasts have been prepared from about 4500 experiments. These include the European Center's DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction) database and a suite of Florida State University (FSU) models (based on different combinations of physical parametrizations). This is one of the largest databases on coupled models. The monsoon region was selected to examine the predictability issue. The methodology involves construction of seasonal anomalies of all model forecasts for a number of variables including precipitation, 850 hPa winds, 2-m/surface temperatures, and sea surface temperatures. This study explores the skills of the ensemble mean and the FSU multimodel superensemble. The metrics for forecast evaluation include computation of hindcast and verification anomalies from model/ observed climatology, time-series of specific climate indices, and standard deterministic ensemble mean scores such as anomaly correlation coefficient and root mean square error. The results were deliberately prepared to match the metrics used by European DEMETER models. Invariably in all modes of evaluation, the results from the FSU multimodel superensemble demonstrate greater skill for most of the variables tested here than those obtained in earlier studies. The specific inquiry of this study was on this question: is it going to be wetter or drier, warmer or colder than the long-term recent climatology of the monsoon; and where and when during the next season?These results are most encouraging, and they suggest that this vast database and the superensemble methodology are able to provide some useful answers to the seasonal monsoon forecast issue compared to the use of single climate models or from the conventional ensemble averaging

A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts