Weather and Forecasting
Internal Waves
Even with the advanced computing simulation systems, weather forecasting technologies, modeling simulation (microscale, mesoscale or global scale) techniques and weather observatories, it is still not enough to establish a sufficient weather prediction analysis. One of the most critical sources of prognosis uncertainty are the atmospheric and oceanic internal waves which can significantly influence weather patterns and thus, weather predictability accuracy. Internal waves are propagating disturbances of gravitationally-stable density stratifications or in more simplistic terms, internal waves are waves that propagate between layers of low-density and high-density air. Because of their great influence to the weather patterns, the natural phenomena cannot be always accurately predicted.
Both oceanic and atmospheric internal waves, could carry a significant amount of of momentum and energy that may influence the thermodynamics characteristics over a coastal zone or the shear-layer instability evolution. Internal waves provide an important route of energy fluctuations from shear instabilities to the ocean interior that may also alter the climate performance of the area. Because small or large scale internal waves cause a substantial mixing, not only affect the local circulation patterns but also energitically control the global-scale meridional overturning circulation. Hence, to understand and analyze the structure and interactions of these phenomena to the climate plays an important role in ensuring prognosis accuracy.
A promising technique for modelling the effect of internal waves on the accuracy of weather estimation is the implementation of Ray simulation models. Ray modelingn methods and tools can be used to assess and evaluate the internal-wave spectra and internal-wave dissipation rates in parts of the atmosphere and ocean and last but not least, to parameterize the internal-wave drag in atmospheric circulation models. The main advantage of the Ray models is that can be formulated in spatial coordinates, in wave-number coordinates, or in a mix of the both of them.
Mesoscsale Models
In an attempt to approach the weather predictability issues, several computational mesoscale models have also been used. According to a research paper named, Recipes for Correcting the Impact of Effective Mesoscale Resolution on the Estimation of Extreme Winds from the Wind Energy devision at the Natiolal Laboratory of Denmark, the spectrum domain analysis of these mesoscale models demonstrates a critical energy deficit in the mesoscale range and thus an underestimation of the extreme wind speeds is revealed. Hence, a significant difference between the measured and simulated spectrum which is likely to influence the climate prognosis, comes to light.
In spite of the significant advances that have been made over the past decades in the improvement of weather modeling systems, a number of outstanding challenges remain. The limited mesoscale models deterministic predictability and internal waves influence, illustrate the need for new meaningful forecasting verification and validation approaches and procedures. It has been well known that the influence of several factors such as, the nonlinear interaction between the convection and synoptic scales, initial and boundary conditions, physical processes parameterization and the temporal and spatial scales spectrum have a major impact on forecasting accuracy. However, understanding the synergistic interactions between the oceanic and atmospheric internal waves seems to be one of the most important steps for an accurate and consistent forecasting process.
