The new distributions of the statistics of wave groups based on the maximum entropy principle are presented. The maximum entropy distributions appear to be superior to conventional distributions when applied to a limited amount of information. Its applications to the wave group properties show the effectiveness of the maximum entropy distribution. FFF filtering method is employed to obtain the wave envelope fast and efficiently. Comparisons of both the maximum entropy distribution and the distribution of Longuet-Higgins （1984） with the laboratory wind-wave data show that the former gives a better fit.
The model for whitecap coverage and wave breaking probability are parameterized by the dimensionless wind fetch X^-. This paper aims at replacing X^- with other parameters such as the average wave period T^-, wind speed U10 or wave age ξ in order to improve the suitability and convenience of the model for application. First, W and B are expressed in terms of T^- and U10, which are relatively easy to measure in the field. Further, U10 is replaced with the friction velocity U. by use of the empirical relationship. As wave age has been widely used to parameterize spectral models of ocean waves and air-sea fluxes, W and B are then expressed as a simple function of wave age, respectively. The new forms of the model obtained are W= 1 - Ф（3.02ξ0＂76） and B = exp（ - 4.54ξ^1.52） . The two forms are mere applicable in pracrice, since ξ is relatively easy to measure or determine from wave and wind records. Comparisons between these expressions and data collected from published literature are made and agreement is fairly good.
Based on the maximunl-entropy （ME） principle, a new power spectral estimator for random waves is derived in the form of S（ω）=a/8H^2^-（2π）^（d＋2）exp[-b（2π/ω）^n],1）y solving a variational problem subject to some quite general constraints. This robust method is comprehensive enough to describe the wave spectra even in extreme wave conditions and is superior to periodogranl method that is not suit＇able to process comparatively short or intensively unsteady signals for its tremendous boundary effect and some inherent defects of FKF. Fortunately, the newly derived method for spectral estimation works fairly well, even though the sample data sets are very short and unsteady, and the reliability and efficiency of this spectral estimator have been preliminarily proved.
