Everyone experiences weather and climate throughout their lives. Over a vast period of time numerous attempts have been made to develop and create an accurate weather and climate predicting model. Such developments have led to various numerical weather predicting models that aids climate predicting models. Our climate changes continuously due to the various interacting components such as the amount of carbon dioxide released into the atmosphere that leads to global warming. It is becoming increasingly important to predict the climate in order to ensure that human life will strive over a vast period of time. Numerical weather prediction uses numerical formulas. The numerical formulas used in the relevant numerical computer prediction is called a model.
Numerical weather prediction is defined by the use of atmospheric and environmental observations of the current weather conditions, and then processing these observations, in data format, by means of a computer model in order to predict the future weather conditions. The present weather conditions and the numerical computer models integrating the numerical data are both equally important factors to predict the future weather and climate conditions. The process known as data assimilation occurs when the present weather observations functions as the input data for the numerical computer models. Through data assimilation we can predict the future weather precipitation, the temperature and numerous other meteorological components. Numerical Weather models are developed through four main processes. These processes are weather observations, data assimilation, integration of the current applicable forecast models, and broadcasting and tweaking the forecast. Weather observations will be the first step to the process of developing an accurate numerical model. The observations that is used at the start of the forecasting development will determine the accuracy of the numerical weather model. The present weather conditions used in computing a model is known as the initial condition. Most of the initial condition data is supplied or determined through the use of radiosondes, satellite measurements, and surface observations.
The second step/process is the assimilation of all the appropriate weather data into a numerical computer forecast model. The numerical forecast model class that divides the atmosphere into grids are know as grid point models. These grids are a set of orderly arranged points on which variables are analysed or predicted. Grid point models calculate weather variables in the centre or middle point of the model in order to make future weather condition predictions. The process when creating an evenly spaced dataset from irregularly spaced observations is known as interpolation. Whereas, the inconsistencies and refine errors in the initial data that will be integrated into a numerical weather forecast model is know as data initialization. Forecast model integration will be the third process in developing an accurate numerical weather model. This process integrates the model formulae and the observed data that was collected. The physical distance between various interpolated points are of utmost importance to ensure a good resolution model. The forecast models’ ability to resolve errors will be increased when smaller spacing between the interpolated distance points in the model is used. The grid points spacing within a model is know as the resolution. The resolution will be fine when the grid points are close to one another, and more coarse when these points are further apart. The time it takes to create coarse resolution models are much quicker than the time used to create fine resolution models.Forecast tweaking and broadcasting can be seen as the last process in developing and creating a numerical weather prediction/forecast model. Weather broadcasting is a skill that varies from the effective communication of the future weather conditions to the developing forecasts that was created through individuals. If one considers the model struggles – such as precipitation – then one can tweak or change the given computing dataset in the model in order to resolve the applicable error.The average weather conditions – usually spanning a period of over thirty years – is defined as climate.
Confining the integer data relating to the variability and the average quantities – such as wind; precipitation and temperature – over a multiple year period. The climate model can be arguable recognized as an extension of the numerical weather prediction models. Developing a climate model requires the use of one out of three general types of unsophisticated climate models. These types are known as the energy balance model; the intermediate complexity model; and lastly the general circulation model. As stated previously, weather models can predict the weather conditions of a short time period spanning over specific area. Whereas climate models analyse longer periods of time and are much broader in comparison to weather prediction models. Climate forecast models predict how the mean conditions will reconstruct over the lapse of a long period of time, such as decades, in a given region. These models are developed from statistical equations that combines a large amount (for example; one thousand) of data points to duplicate the conveying of water and energy that occurs in climatic methodology. Predicting the accurate global limiting weather indicators over a vast period of time in comparison to the time used for typical weather prediction models is attempted by these advanced forecast models. Climate prediction is a copious integrate process. For this reason climate predictions are a good deal more arduous than weather prediction. Numerous crucial climatic aspects are contemplated when predicting the future climate. These aspects involve the following – the chaotic nature of fluid systems states that it is paradoxical to conclusively forecast the future climate conditions. The oceans’ evolution, ice and land interaction and the representative relevant chemistry must be included in the development of climate prediction models.
Numerical models have a restrictive spatial and time related resolution. As a result various activities must be parameterized. Tweaking the small-scale or complex processes resulting in a convenient simplified activity physically represented in the numerical prediction model can be termed as parameterization. Incorporating the knowledge of uncertainty with the resulting spectrum of forecasting results are provided through climate model ensembles. Whereas integrating contractive parameterization with multiple predictions is known as perturbed physics ensembles. The requirements in determining the numerical solutions will be as follows; the division of the atmosphere into a colossal three-dimensional lattice of model grid points where the atmospheric factors was contained in the model that was used to solve the appropriate equations. These equations will be solvable through means of implementing the constrained numerical approximations. The fundamental atmospheric constituents will be included in the global weather model.
The global weather model is expanded in order to for the model to compute the relevant atmospheric constituents and it accounts for the atmospheric part of the model. Factors such as the sea currents, the composition and the heat energy or temperature will contribute to oceanic component of the model. The oceanic component of the model comprises of a correspondingly assembled fluid-dynamic model that is based on the relevant oceanic factors. The fundamental aspects, such as reflectivity of a climate system, will be defined through the ice and land surface properties. For this reason the development of a climate model is a vastly complex endeavour. The atmospheric, oceanic and surface components will be integrated with one another to further develop and evolve a climate model and its ability to predict the upcoming weather and or climate conditions of a given area, region, state or even globally. As stated previously due to the chaotic nature of fluid systems, it can be deducted that the current climate models possess restrictive properties, or weaknesses due to the fact that no system can be one hundred percent accurate. In order to create a more precise forecast influencing features such as the factors observed to govern the climate conditions and the spatial resolution of a relevant contributing model will be integrated by the climate model one set at a time. By means of focusing on aspects such as clouds, carbon dioxide cycles, precipitation and sea ice or glaciers may lead to the improvement of future climatic prediction models. Due to the numerous interactive processes the climate is in a continuous state of change. For this reason the development of a precise climatic prediction model have been attempted by a vast amount of individuals, non of which was successfully triumphant. One might conclude that the exploration to find the root of climate change will be impossible to determine. Meteorologists, climate modellers, physicists, climatologists etc.
All of which are attempting to develop a numerical weather prediction models and or climate prediction models that will be increasingly accurate as time passes. By attempting to include all the relevant processes, even if it has minor effect in the model (hardly detectable observations), and by implementing all new observational data, the different relevant numerical weather prediction models, various other relevant climate prediction models, and any other processes exerting a force on the system into the current climate prediction model. This results in a more precise model, however computing errors will always arise within the statistical data set. Numerical weather prediction models possess an element of uncertainty to which a considerable amount of time have been devoted to understand the mechanism that fuels the uncertainty property. The influence of various business opportunities, the way our cities are planned, and even ones day-to-day routine can be planned in a more effective manner, as a result of a well-designed climate and weather model which is continuously precise when forecasting the upcoming weather and or climate conditions. The effects of climate change can be dangerous to the sustainability of human life. These prediction models will be the best chance to restrict and minimalize these minacious climate changes.
