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

• Autorzy: S. Taraphdar , P. Mukhopadhyay , L. Ruby Leung , Kiranmayi Landu
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Prediction skill; Synoptic scale transients; Random error

• Wydawca: De Gruyter Open
• Czasopismo: Mathematics of Climate and Weather Forecasting
• Rocznik: 2016
• Tom: 2
• Numer: 1

Daty

otrzymano

2016-07-29

zaakceptowano

2016-11-09

online

2016-12-05

Twórcy

autor

S. Taraphdar

• Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, 16802, USA
• Pacific Northwest National Laboratory (PNNL), Richland, WA, USA,

autor

P. Mukhopadhyay

• Indian Institute of Tropical Meteorology (IITM), Pune 411008, India

autor

L. Ruby Leung

• Pacific Northwest National Laboratory (PNNL), Richland, WA, USA,

autor

Kiranmayi Landu

• Pacific Northwest National Laboratory (PNNL), Richland, WA, USA,
• Indian Institute of Tropical Meteorology (IITM), Pune 411008, India

Identyfikatory

DOI

10.1515/mcwf-2016-0002