Comparison of the prediction of Indian monsoon low-pressure systems by Subseasonal-to-Seasonal prediction models

View More View Less
  • 1 Department of Meteorology, University of Reading, Reading, United Kingdom
  • 2 National Centre for Atmospheric Science, and Department of Meteorology, University of Reading, Reading, United Kingdom
© Get Permissions
Restricted access

Abstract

This study analyses the prediction of Indian monsoon low-pressure systems (LPSs) on an extended time scale of 15 days by models of the Subseasonal-to-Seasonal (S2S) prediction project. Using a feature-tracking algorithm, LPSs are identified in the eleven S2S models during a common re-forecast period of June September 1999-2010, and then compared with 290 and 281 LPSs tracked in ERA-Interim and MERRA-2 reanalysis datasets. The results show that all S2S models under-simulate the frequency of LPSs. They are able to represent transits, genesis and lysis of LPSs; however, large biases are observed in the Australian

Bureau of Meteorology, China Meteorological Administration (CMA) and Hydrometeorological Centre of Russia (HMCR) models. The CMA model exhibits large LPS track position error and the intensity of LPSs is overestimated (underestimated) by most models when verified against ERA-Interim (MERRA-2). The European Centre for Medium-Range Weather Forecasts and UK Met Office models have the best ensemble spread-error relationship for the track position and intensity, whereas the HMCR model has the worst. Most S2S models are underdispersive - more so for the intensity than the position. We find the influence of errors in the LPS simulation on the pattern of total precipitation biases in all S2S models. In most models, precipitation biases increase with forecast lead time over most of the monsoon core zone. These results demonstrate the potential for S2S models at simulating LPSs, thereby giving the possibility of improved disaster preparedness and water resources planning.

Corresponding author: Akshay Deoras, Department of Meteorology, University of Reading, Whiteknights, Reading, RG6 6BB, United Kingdom. E-mail: a.s.deoras@pgr.reading.ac.uk

Abstract

This study analyses the prediction of Indian monsoon low-pressure systems (LPSs) on an extended time scale of 15 days by models of the Subseasonal-to-Seasonal (S2S) prediction project. Using a feature-tracking algorithm, LPSs are identified in the eleven S2S models during a common re-forecast period of June September 1999-2010, and then compared with 290 and 281 LPSs tracked in ERA-Interim and MERRA-2 reanalysis datasets. The results show that all S2S models under-simulate the frequency of LPSs. They are able to represent transits, genesis and lysis of LPSs; however, large biases are observed in the Australian

Bureau of Meteorology, China Meteorological Administration (CMA) and Hydrometeorological Centre of Russia (HMCR) models. The CMA model exhibits large LPS track position error and the intensity of LPSs is overestimated (underestimated) by most models when verified against ERA-Interim (MERRA-2). The European Centre for Medium-Range Weather Forecasts and UK Met Office models have the best ensemble spread-error relationship for the track position and intensity, whereas the HMCR model has the worst. Most S2S models are underdispersive - more so for the intensity than the position. We find the influence of errors in the LPS simulation on the pattern of total precipitation biases in all S2S models. In most models, precipitation biases increase with forecast lead time over most of the monsoon core zone. These results demonstrate the potential for S2S models at simulating LPSs, thereby giving the possibility of improved disaster preparedness and water resources planning.

Corresponding author: Akshay Deoras, Department of Meteorology, University of Reading, Whiteknights, Reading, RG6 6BB, United Kingdom. E-mail: a.s.deoras@pgr.reading.ac.uk
Save