Improving Tropical Precipitation Forecasts from a Multianalysis Superensemble

Krishnamurti, T. N.; Kishtawal, C. M.; Shin, Dong Jik; Williford, C. Eric

doi:10.1175/1520-0442(2000)013<4217:itpffa>2.0.co;2

Cited by 115 publications

(74 citation statements)

References 20 publications

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“…Figure 4 schematically outlines the superensemble strategy for constructing the multimodel superensemble. This method has been applied most recently to improve large-scale NWP forecasts of the monsoon (Shin and Krishnamurti 2003;Krishnamurti et al 1999Krishnamurti et al , 2000Krishnamurti et al , 2001Mishra and Krishnamurti 2007), hurricane track and intensity forecasts (Krishnamurti et al 1999(Krishnamurti et al , 2000aWilliford et al 2003;Vijay Kumar et al 2003), and seasonal climate forecasts (Krishnamurti et al 2000b;Krishnamurti et al 2006a;Chakraborty and Krishnamurti 2006). In these studies, a common result has been that the multimodel superensemble consistently provides superior forecasts in terms of forecast accuracy, compared to the participating member models.…”

Section: Florida State University Multimodel Superensemblementioning

confidence: 99%

Use of a Dense Rain-gauge Network over India for Improving Blended TRMM Products and Downscaled Weather Models

Krishnamurti

Mishra

Simon

et al. 2009

Journal of the Meteorological Society of Japan

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This study makes use of a network of nearly 2100 rain gauges over India in order to statistically modify the Tropical Rainfall Measuring Mission (TRMM) algorithm 3B42. The validation and usefulness of the modified product is determined against rain gauge datasets and from the training and forecast phase of the Florida State University (FSU) multimodel superensemble. We use downscaled member model forecasts to construct superensemble forecasts. The member model forecasts are scaled down using the modified high-resolution TRMM rains during the training phase of the multimodel superensemble. We demonstrate that a mesoscale superensemble thus constructed has forecast efficiencies superior to those of all of the member models and to the ensemble mean (ENSM) and bias-corrected ensemble mean (BcorENSM). The forecast procedure includes a high-resolution downscaling of the model forecast rain and the construction of a multimodel superensemble. This procedure clearly provides a much-improved forecast of rain using metrics such as equitable threat scores (ETSs), anomaly correlations, root mean square (RMS) errors, and their bias scores. We also demonstrate that the performance of this modified TRMM algorithm provides results very clearly comparable to those obtained from the direct use of rain gauges throughout India and the TRMM 3B42 covering a domain of 50°S to 50°N.

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Section: Florida State University Multimodel Superensemblementioning

confidence: 99%

Use of a Dense Rain-gauge Network over India for Improving Blended TRMM Products and Downscaled Weather Models

Krishnamurti

Mishra

Simon

et al. 2009

Journal of the Meteorological Society of Japan

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“…The MME even shows better results in seasonal forecasting in the tropics than a single-model ensemble with the same ensemble size due to a mixture of additional information from the different models . Finally, techniques for constructing optimal MME forecasting have been developed (Krishnamurti et al, 2000;Doblas-Reyes et al, 2005). These approaches of the MME have shown some prospect in improving the seasonal forecast quality of precipitation beyond the individual models (Yun et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

Rodó

Doblas‐Reyes

2011

Intl Journal of Climatology

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On the basis of the observed all-India rainfall (AIR), Climate Prediction Center (CPC) merged analysis of precipitation (CMAP), and the multimodel rainfall simulated in the Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER) project, the model performance in terms of forecast quality of the seasonal mean Indian summer monsoon rainfall (ISMR) was evaluated. The approach used included the individual model (single-model ensemble) comparison as well as its comparison with the multimodel ensemble (MME). The results obtained reveal that systematic biases are the main source of the low skill in predicting the ISMR, while improved reproduction of interannual variability may increase the overall forecast quality for the ISMR. The MME shows superior performance in reproducing AIR features than the single-model ensembles. This improved forecast quality achieved by the MME with respect to the ISMR variations primarily depends on model performance and secondly on ensemble size. The MME has improved capability of simulating the ISMR variations during 1972-1990 decades, whereas it exhibits a lower performance in the 1990s. The reason of this decadal varying forecast quality clearly deserves more investigation. In summary, these results suggest that the MME's increase in skill absolutely depends on enhancing the individual model quality.

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“…So, a great effort has been devoted to the development of post-processing techniques of the numerical forecasts aimed at their improvement. In this context, Krishnamurti et al (2000aKrishnamurti et al ( ,b, 2001) demonstrated the potential of the ensemble techniques to improve seasonal climate and global weather forecasts. This method statistically combines forecasts obtained from different model integrations and objectively extracts the best information from each member of the ensemble forecast to contribute to the final simulation or forecast.…”

Section: Introductionmentioning

confidence: 99%

“…This method statistically combines forecasts obtained from different model integrations and objectively extracts the best information from each member of the ensemble forecast to contribute to the final simulation or forecast. It is becoming popular and is known as the multi-model super-ensemble (MMS) method (Krishnamurti et al, 2000a(Krishnamurti et al, ,b, 2001). These studies have shown that the MMS method has a skill superior to the traditional forecast scheme in global weather and hurricane intensity and track forecasts, and in seasonal climate simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Overall, the forecasts generated by the MMS technique (Krishnamurti et al, 2000a(Krishnamurti et al, ,b, 2001Chaves et al, 2005a,b) or Bayesian approach (Coelho et al, 2004(Coelho et al, , 2006 to combine and correct forecast have shown better skills than the forecast generated either by individual models or the arithmetic average of the ensembles (each with weight 1). This latter approach has been largely used to produce seasonal forecasts in many operational centres, including the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC).…”

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An analysis of the seasonal precipitation forecasts in South America using wavelets

Pezzi

Kayano

2008

Intl Journal of Climatology

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A post-processing technique was applied to statistically correct the seasonal rainfall forecasts over South America (SA). The aim of this work was to reduce errors in the seasonal climate simulations obtained from the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) atmospheric general circulation model (AGCM) which was run with different deep cumulus convection parameterizations. One of the main contributions of this study is the discussion of the super-ensemble approach to reduce errors in the seasonal rainfall prediction for SA. A novel aspect here is the use of the wavelet technique to compare forecast and observed time series by investigating their time-frequency structures. This methodology has not yet been applied to super-ensemble model validations. The statistical algorithm used in the superensemble technique was based on the linear multiple regression method. The time series of the super-ensemble forecast (FCT), arithmetic averaged forecast (MEM) and individual model forecasts and the observed (OBS) ones for selected areas of SA were compared by calculating the root mean square errors (RMSEs) and by applying the wavelet technique on these time series. In general, for the analysed areas we obtained a super-ensemble skill superior to that for the MEM. The wavelet analysis proved to be very useful to compare forecast and observed time series. In fact, differences and similarities among the time series such as the dominant scale of variability and the time location of the largest variances in the time series were detected with the wavelet analyses.

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Improving Tropical Precipitation Forecasts from a Multianalysis Superensemble

Cited by 115 publications

References 20 publications

Use of a Dense Rain-gauge Network over India for Improving Blended TRMM Products and Downscaled Weather Models

Use of a Dense Rain-gauge Network over India for Improving Blended TRMM Products and Downscaled Weather Models

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

An analysis of the seasonal precipitation forecasts in South America using wavelets

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