Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems

S. Taraphdar, et al. "Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems." Mathematics of Climate and Weather Forecasting 2.1 (2016): null. <http://eudml.org/doc/287111>.

@article{S2016,
abstract = {The prediction skill of tropical synoptic scale transients (SSTR) such as monsoon low and depression during the boreal summer of 2007–2009 are assessed using high resolution ECMWF and NCEP TIGGE forecasts data. By analyzing 246 forecasts for lead times up to 10 days, it is found that the models have good skills in forecasting the planetary scale means but the skills of SSTR remain poor, with the latter showing no skill beyond 2 days for the global tropics and Indian region. Consistent forecast skills among precipitation, velocity potential, and vorticity provide evidence that convection is the primary process responsible for precipitation. The poor skills of SSTR can be attributed to the larger random error in the models as they fail to predict the locations and timings of SSTR. Strong correlation between the random error and synoptic precipitation suggests that the former starts to develop from regions of convection. As the NCEP model has larger biases of synoptic scale precipitation, it has a tendency to generate more random error that ultimately reduces the prediction skill of synoptic systems in that model. The larger biases in NCEP may be attributed to the model moist physics and/or coarser horizontal resolution compared to ECMWF.},
author = {S. Taraphdar, P. Mukhopadhyay, L. Ruby Leung, Kiranmayi Landu},
journal = {Mathematics of Climate and Weather Forecasting},
keywords = {Prediction skill; Synoptic scale transients; Random error},
language = {eng},
number = {1},
pages = {null},
title = {Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems},
url = {http://eudml.org/doc/287111},
volume = {2},
year = {2016},
}

TY - JOUR
AU - S. Taraphdar
AU - P. Mukhopadhyay
AU - L. Ruby Leung
AU - Kiranmayi Landu
TI - Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems
JO - Mathematics of Climate and Weather Forecasting
PY - 2016
VL - 2
IS - 1
SP - null
AB - The prediction skill of tropical synoptic scale transients (SSTR) such as monsoon low and depression during the boreal summer of 2007–2009 are assessed using high resolution ECMWF and NCEP TIGGE forecasts data. By analyzing 246 forecasts for lead times up to 10 days, it is found that the models have good skills in forecasting the planetary scale means but the skills of SSTR remain poor, with the latter showing no skill beyond 2 days for the global tropics and Indian region. Consistent forecast skills among precipitation, velocity potential, and vorticity provide evidence that convection is the primary process responsible for precipitation. The poor skills of SSTR can be attributed to the larger random error in the models as they fail to predict the locations and timings of SSTR. Strong correlation between the random error and synoptic precipitation suggests that the former starts to develop from regions of convection. As the NCEP model has larger biases of synoptic scale precipitation, it has a tendency to generate more random error that ultimately reduces the prediction skill of synoptic systems in that model. The larger biases in NCEP may be attributed to the model moist physics and/or coarser horizontal resolution compared to ECMWF.
LA - eng
KW - Prediction skill; Synoptic scale transients; Random error
UR - http://eudml.org/doc/287111
ER -