• DOI:10.1002/qj.2109
• Corpus ID: 56428416

Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations

@article{Qin2013ConditionsUW,
  title={Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations},
  author={Xiaohao Qin and Wansuo Duan and Mu Mu},
  journal={Quarterly Journal of the Royal Meteorological Society},
  year={2013},
  volume={139},
  url={https://api.semanticscholar.org/CorpusID:56428416}
}

• Xiaohao Qin, W. Duan, M. Mu
• Published 1 April 2013
• Environmental Science
• Quarterly Journal of the Royal Meteorological Society

To determine whether profound improvements in tropical cyclone (TC) forecasting are achievable by deploying dropwindsondes according to conditional nonlinear optimal perturbation (CNOP) sensitivity, observing system simulated experiments (OSSEs) were conducted on 20 TCs that developed over the western North Pacific during 2010 using Mesoscale Model 5 and its 3DVar assimilation system. Of the 20 cases, 13 showed neutral or improved track forecasts of between 0% and 51.2%. Eliminating initial… 

To determine whether profound improvements in tropical cyclone (TC) forecasting are achievable by deploying dropwindsondes according to conditional nonlinear optimal perturbation (CNOP) sensitivity, observing system simulated experiments (OSSEs) were conducted on 20 TCs that developed over the western North Pacific during 2010 using Mesoscale Model 5 and its 3DVar assimilation system. Of the 20 cases, 13 showed neutral or improved track forecasts of between 0% and 51.2%. Eliminating initial errors within the CNOP pattern, which are related to either the storm directly or the surrounding regimes indirectly, reduced the subsequent track forecast errors. The remaining 7 TCs showed deterioration in the accuracy of the track forecasts over the 48 h forecast period. Accurate forecasts made without adaptive observations, a low sensitivity of forecast errors to initial errors, or major forecast errors associated with regimes other than the TC, can lead to a decline in the accuracy of TC track forecasts.