Numerical Simulation on Climate Effects of Freezing-Thawing Processes Using CCM3

A parameterization of soil freezing-thawing physics for use in the land-surface model of the National Center for Atmospheric Research(NCAR) Community Climate Model(CCM3) is developed and evaluated.The new parameterization scheme has improved the representation of physical processes in the existing land surface model.Numerical simulations using CCM3 with improved land-surface processes and with the original land-surface processes are compared against the NCEP reanalysis.It is found that the CCM3 version using the improved land surface model shows significant improvements in simulating precipitation in China during the summer season,the general circulation over East Asia,and wind fields over the Tibet Plateau.For the summer season,the improved model was able to better simulate the Indian summer monsoon components,including the mean northerly wind in the upper troposphere and mean southerly wind in the lower troposphere.

A Review of Potential Vorticity and Its Possible Application in the Moist Atmospheric Flow

Potential vorticity (PV) has been served as a powerful and useful dynamic tracer for the understanding of the large-scale dynamics and synoptic variations in the atmosphere and oceans.Significant progress has been made on the application of PV.In recent decades there has been a substantial amount of work done on PV in a gen-eral moist atmosphere.In this paper PV and the general-ized moist potential vorticity (GMPV) and their applica-tion in the tropical cyclones and mesoscale meteorologi-cal field are reviewed.The GMPV is derived for a real atmosphere (neither totally dry nor saturated) by intro-ducing a generalized potential temperature instead of the potential temperature or equivalent potential temperature.Such a generalization can depict the moist effect on PV anomaly in the non-uniformly saturated atmosphere.A new convective vorticity vector (CVV) is introduced in connection with GMPV in order to diagnose the devel-opment of tropical deep convections.

Cognitive Computing-Based CDSS in Medical Practice

Importance.The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies.From the diagnosis of diseases till the generation of treatment plans,cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making.This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade.Highlights.(1)A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system.(2)Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework.(3)The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction.Conclusion.Different from medical content providers,cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data.The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories.Given the current status of primary health care like high diagnostic error rate and shortage of medical resources,it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.