Current Science

Open Access Open Access  Restricted Access Subscription Access

Real-Time Performance of a Multi-Model Ensemble-Based Extended Range Forecast System in Predicting the 2014 Monsoon Season Based on NCEP-CFSv2


Affiliations
1 Indian Institute of Tropical Meteorology, Pune 411 008, India
 

The real-time validation of any strategy to forecast the Indian summer monsoon rainfall requires comprehensive assessment of performance of the model on sub-seasonal scale. The multi-model ensemble (MME) approach based on the NCEP-CFS version 2 models, as developed and reported earlier, has been employed to forecast the 2014 monsoon season on the extended range scale with 3-4 pentad lead time (where a pentad corresponds to five-day average). The present study reports the broad performance of the MME employed on experimental basis to forecast the salient features of the real-time evolution of the 2014 monsoon season during June to September. The MME is successful in predicting both these features well in advance (3-4 pentad or 15-20 days lead time). The assessment of the model performance at pentad scale lead time shows that the weak monsoon conditions that are evident in precipitation and lower level wind anomalies are well captured as a whole up to four pentad advance lead time. The subseasonal propagation during onset and withdrawal is also evident in the forecast. Finally, the region-wise performance shows that the spatial extent of the skillful forecast encompasses central India as well as the monsoon zone for the 2014 monsoon season. Considering the natural variation in the forecast skill of extended range forecast itself as reported in earlier studies, the 2014 monsoon forecast seems to be skillful for operational purposes. For other regions (e.g. North East India), the forecast could be skillful at times, but it still requires further research on how to improve the same.

Keywords

Monsoon Forecast, Multi-Model Ensemble, Pentad, Lead Time.
User
Notifications
Font Size

  • Vitart, F., Monthly forecasting at ECMWF. Mon. Weather Rev., 2004, 132, 2761–2779.

  • Vitart, F., Woolnough, S., Balmaseda, M. A. and Tompkins, A. M., Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon. Weather Rev., 2007, 135, 2700–2715.

  • Seo, K.-H. et al., Evaluation of MJO forecast skill from several statistical and dynamical forecast models. J. Climate, 2009, 22, 2372–2388.

  • Rashid, H. A., Hendon, H. H., Wheeler, M. C. and Alves, O., Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Climate Dyn., 2010, 36, 649–661.

  • Fu, X., Wang, B., Lee, J.-Y., Wang, W. and Gao, L., Sensitivity of dynamical intraseasonal prediction skills to different initial conditions. Mon. Weather Rev., 2011, 139, 2572–2592.

  • Fu, X., Lee, J.-Y., Wang, B., Wang, W. and Vitart, F., Intraseasonal forecasting of the Asian summer monsoon in four operational and research models. J. Climate, 2012, 26, 4186–4203.

  • Sahai, A. K. et al., Simulation and extended range prediction of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Curr. Sci., 2013, 104, 1394–1408.

  • Hudson, D., Marshall, A. G., Yin, Y., Alves, O. and Hendon, H. H., Improving intraseasonal prediction with a new ensemble generation strategy. Mon. Weather Rev., 2013, 141, 4429–4449.

  • Gadgil, S., Srinivasan, J., Nanjundiah, R., Kumar, K. K., Munot, A. and Kumar, K. R., On forecasting the Indian summer monsoon: the intriguing season of 2002. Curr. Sci., 2002, 83, 1307–1309.

  • Gadgil, S., Rajeevan, M. and Nanjundiah, R., Monsoon prediction – why yet another failure? Curr. Sci., 2005, 88, 1389–1400.

  • Gadgil, S. and Srinivasan, J., Understanding and predicting the Indian summer monsoon. Curr. Sci., 2010, 99, 1184–1186.

  • Abhilash, S. et al., Improved spread-error relationship and probabilistic prediction from CFS based grand ensemble prediction system. J. Appl. Meteor. Climatol., 2015, 1569–1578.

  • Abhilash, S. et al., Does bias correction in the forecasted SST improve the extended range prediction skill of active-break spells of Indian summer monsoon rainfall? Atmos. Sci. Lett., 2014, 15, 114– 119.

  • Sahai, A. K. et al., High-resolution operational monsoon forecasts: an objective assessment. Climate Dyn., 2014, 1–12.

  • Abhilash, S., Sahai, A. K., Pattnaik, S., Goswami, B. N. and Kumar, A., Extended range prediction of active–break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP Climate Forecast System. Int. J. Climatol., 2014, 34, 98–113.

  • Borah, N., Sahai, A. K., Chattopadhyay, R., Joseph, S., Abhilash S. and Goswami, B. N., A self-organizing map-based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon. J. Geophys. Res. Atmos., 2013, 118, 9022–9034.

  • Palmer, T. N., Branković, Č., Molteni, F. and Tibaldi, S., Extended-range predictions with ecmwf models: interannual variability in operational model integrations. Q.J.R. Meteorol. Soc., 1990, 116, 799–834.

  • Kalnay, E., et al., The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc., 1996, 77, 437–471.

  • Mitra, A. K., Bohra, A. K., Rajeevan, M. N. and Krishnamurti, T. N., Daily Indian precipitation analysis formed from a merge of rain–gauge data with the TRMM TMPA satellite-derived rainfall estimates. J. Meteorol. Soc. Jpn Ser II, 2009, 87A, 265–279.

  • Saha, S. et al., The NCEP Climate Forecast System Version 2. J. Climate, 2014, 27, 2185–2208.

  • Griffies, S., Harrison, M., Pacanowski, R. and Rosati, A., A technical guide to MOM4. GFDL Ocean Group: NOAA GFDL, 2004; http://www.gfdl.noaa.gov/bibliography/related_files/smg0301.pdf?PHPSESSID=c94ddc382b93e57e39c8f976c83e970d.

  • Saha, S. et al., The NCEP Climate Forecast System Reanalysis. Bull. Am. Meteorol. Soc., 2010, 91, 1015–1057.

  • Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S. and Schlax, M. G., Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 2007, 20, 5473–5496.

  • Roberts, N., Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model. Meteorol. App., 2008, 15, 163–169.

  • Roberts, N. M. and Lean, H. W., Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Weather Rev., 2008, 136, 78–97.

  • Duc, L., Saito, K. and Seko, H., Spatial-temporal fractions verification for high-resolution ensemble forecasts. Tellus A, 2013, 65, doi: 10.3402/tellusa.v65i0.18171.

  • Pattnaik, D., Kumar, A. and Tyagi, A., Development of empirical–dynamical hybrid forecasts for the Indian monsoon rainfall using the NCEP Climate Forecast System. IMD Monograph, 2010, 11/2010.

  • Pattanaik, D. R., Meteorological subdivisional-level extended range forecast over India during southwest monsoon 2012. Meteorol. Atmos. Phys., 2014, 124, 167–182.

  • Pattanaik, D. R., Pai, D. S. and Mukhopadhyay, B., Rapid northward progress of monsoon over India and associated heavy rainfall over Uttarakhand: a diagnostic study and real time extended range forecast. Mausam, 2015, 66, 1–18.

  • Rajeevan, M., Gadgil, S. and Bhate, J., Active and break spells of the Indian summer monsoon. J. Earth Syst. Sci., 2010, 119, 229–247.

  • Xavier, P. K., Marzin, C. and Goswami, B. N., An objective definition of the Indian summer monsoon season and a new perspective on the ENSO–monsoon relationship. Q. J. R. Meteorol. Soc., 2007, 133, 749–764.

  • Krishnamurti, T. N. et al., Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 2000, 13, 4196–4216.


Abstract Views: 269

PDF Views: 116




  • Real-Time Performance of a Multi-Model Ensemble-Based Extended Range Forecast System in Predicting the 2014 Monsoon Season Based on NCEP-CFSv2

Abstract Views: 269  |  PDF Views: 116

Authors

A. K. Sahai
Indian Institute of Tropical Meteorology, Pune 411 008, India
R. Chattopadhyay
Indian Institute of Tropical Meteorology, Pune 411 008, India
S. Joseph
Indian Institute of Tropical Meteorology, Pune 411 008, India
R. Mandal
Indian Institute of Tropical Meteorology, Pune 411 008, India
A. Dey
Indian Institute of Tropical Meteorology, Pune 411 008, India
S. Abhilash
Indian Institute of Tropical Meteorology, Pune 411 008, India
R. P. M. Krishna
Indian Institute of Tropical Meteorology, Pune 411 008, India
N. Borah
Indian Institute of Tropical Meteorology, Pune 411 008, India

Abstract


The real-time validation of any strategy to forecast the Indian summer monsoon rainfall requires comprehensive assessment of performance of the model on sub-seasonal scale. The multi-model ensemble (MME) approach based on the NCEP-CFS version 2 models, as developed and reported earlier, has been employed to forecast the 2014 monsoon season on the extended range scale with 3-4 pentad lead time (where a pentad corresponds to five-day average). The present study reports the broad performance of the MME employed on experimental basis to forecast the salient features of the real-time evolution of the 2014 monsoon season during June to September. The MME is successful in predicting both these features well in advance (3-4 pentad or 15-20 days lead time). The assessment of the model performance at pentad scale lead time shows that the weak monsoon conditions that are evident in precipitation and lower level wind anomalies are well captured as a whole up to four pentad advance lead time. The subseasonal propagation during onset and withdrawal is also evident in the forecast. Finally, the region-wise performance shows that the spatial extent of the skillful forecast encompasses central India as well as the monsoon zone for the 2014 monsoon season. Considering the natural variation in the forecast skill of extended range forecast itself as reported in earlier studies, the 2014 monsoon forecast seems to be skillful for operational purposes. For other regions (e.g. North East India), the forecast could be skillful at times, but it still requires further research on how to improve the same.

Keywords


Monsoon Forecast, Multi-Model Ensemble, Pentad, Lead Time.

References





DOI: https://doi.org/10.18520/cs%2Fv109%2Fi10%2F1802-1813