Check this dual purpose post (numerical weather forecasting and introductory post)
Numerical weather predictions
Modern Weather forecasting has two major approaches which are the synoptic forecasting and numerical weather forecasting.
Synoptic climatology deals with the study of weather and climate over an area in relation to the pattern of prevailing atmospheric circulation. It employs the use of statistical and cartographical techniques as well as synoptic derived models in the description and explanation of weather and climate, while on the other hand numerical weather predictions uses mathematical models from the atmosphere and oceans to predict weather based on current weather conditions. Though it was first attempted in 1920s, it was not until the advents of computer simulation in the 1950s that numerical weather predictions produced a realistic result.
Mathematical models based on the same physical properties can be used to generate either short term weather forecast or long term climate predictions. Although this improvement made to regional models have allowed for significant improvement in tropical cyclone track and air quality forecast, it has been observed that atmospheric models perform poorly at handling processes that occur in a relatively constricted area, for example wildfire.
The operations of this modern numerical weather predictions and performing the complex calculations requires the use of most powerful supercomputers in the world. Despite the use of this improved computers, the forecast skills of numerical weather models is limited to only six days. Factors like density and quality of observations used as input to the forecast, deficiency in numerical models itself has affected the operations of this models. Model output statistics (MOS)which is a post processing technique has been equally develop to handle errors in numerical predictions. Another problems lies in the chaotic nature of the partial differential equations that governs the atmosphere. It is therefore practically impossible to solve these equations exactly, and small errors grow with time (doubling about every 5 days). It’s however understood that this chaotic behaviour limits accurate forecast to about 14 days even with perfectly accurate inputs and impeccable models. If partial differential equations is to be used, then need be for it be used with parameterization for solar radiation, exchange of heat, soils and vegetations, surface water, moist processes (clouds and precipitations), and the effect of the terrain involved.
In an effort to quantify the large amount of inherent uncertainty remaining in numerical predictions, ensemble forecast has been used since 1990s to help monitor the confidence in this forecast operation and to obtain useful results farther into the future than otherwise possible. This approach analyzes multiple forecasts created with an individual forecast model or multiple models.
Did you know? Numerical weather predictions began in 1920 through the efforts of Lewis fry Richardson, who used procedures originally developed by Vilhem Bjerknes to produce by hand a six(6)-hours forecast for the atmospheric state over two points in central Europe which he did for about six weeks; which consumes a whole lot of time. The predictions process and time was reduced to less than the forecast period itself with the advent of computers and various electronic simulations.
As the computer system become more powerful, the size of the initial data sets has increased and newer atmospheric models have been developed to take advantage of this added available computing power. The new atmospheric models include more physical processes in the simplifications of the equations of motion in numerical simulations of the atmosphere. In 1966, the United Kingdom and West Germany started producing operational forecast based on primitive-equation models, followed by the United Kingdom in 1972, and the Australia in 1977. The development of limited area which is referred to as regional models facilitated advances in predictions of some cyclones and also the quality of air around 1970s and 1980s. By the early 1980s, developed models began to include the interactions of soil and vegetation with the atmosphere which resulted in more realistic forecasts.
In this prediction; we know that the atmosphere is a fluid, as such the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the principles governing thermodynamics and fluid dynamics to estimate the state of the fluid at some time in the future. Entering of observed data into the models to generate initial conditions is called initialization. However the world meteorological organisation acts to standardise the instrumentation, observing practices, and timing of these observations worldwide across the globe, stations either report hourly in METAR or every six hours in SYNOP reports.
WHO AM I?
I haven’t even introduced myself, (I guess I am more interested in what I have to offer than introducing myself). My name is Olatunde samuel a Nigerian. Currently studying agricultural meteorology (a division of meteorology) in the Federal University of Agriculture,Abeokuta (FUNAAB). I am a lover of the Lord and of course a promoter of excellence in whatever one find himself doing. Some people call me a profound business analyst because of my skills in determining the future of a business venture when I am provided with adequate data about the business.
MY VALUES
I am a promoter of excellence, vision and living your life pursuing what you are wired for. Personally I don’t believe there is any empty head walking in our street today. Every man in existence has something to offer, though he might lack adequate knowledge in the field in which he can add impact. But that doesn’t negate the fact that he is actually not meant for greatness.
I AM ON STEEMIT
After gathering so many information about Steemit. I took a bold and pertinent step by creating the future with you wonderful Steemians. I am so much interested in doing my possible best to meeting the content needs in providing adequate content information on current trends and beneficial articles on science,technology (particularly in environmental analysis)discipline.trust mesteemians, it's coming in a new and dynamic way.
MY AIM ON STEEMIT
After observing Steemit for some days now, I have observed that even more than its financial activities is the privilege to have access to information and enlightenment. I have gained several knowledge from 
Now I don’t want to be just a consumer of knowledge but I also want to be a dispenser of that self same knowledge. So in Steemit I aspire to educate my followers and the entire steemit community on the current trends in the Environment, Technology and the weather forecast aspects (Of course while expecting supports through your upvotes and your followership).
WHAT DO I LIKE
I love learning new things about life, a voracious consumer of knowledge, One of my major aims being here is to catch fun, by gaining knowledge on new things and facts. I also love music, playing games but dont enjoy seeing movies sometimes due to emotional instability, personally I love numerical weather predictions because of the application of mathematical models and physics law in predicting weather. I admire the level of knowledge and mindset put into it.
ON A FINAL NOTE
So that I don’t bore the entire community with too much of me let me drop my pen for now. Hope to see more of your comment and your upvotes on my posts. Thanks Steemians!!!
AM SO EXCITED TO BE HERE.
So back to the business.
What is an atmospheric models:
This is a computer program that produces meteorological information for the future times at a given locations and altitudes. A modern model includes set of equations known as primitive equations used to predict the future state of the atmosphere. These equations combined with the ideal gas law are used to evolve the density, pressure, and potential temperature scalar fields and the air velocity (wind) vector field of the atmosphere through time. Also transport equations for pollutants and other aerosols are included in some primitive-equation high-resolution models as well.
Although some equations are too small-scale or too complex to be explicitly included in numerical weather prediction models. Parameterization is the procedures by which these processes are represented by relating the variables on the scales that the model resolves. For instance the gridboxes in weather and climate models have sides that are between 5 kilometers (3 mi) and 300 kilometers (200 mi) in length. A typical cumulus cloud has a scale of less than 1 kilometer (0.6 mi) and would require a grid even finer than this to be represented physically by the equations of fluid motion. Therefore the processes that such cloud represents are parameterized.
The horizontal domain of a model is either global (covering the entire earth) or regional (covering part of the earth). The regional models also known as the limited area models (LAM) allows for the use of finer grid spacing than global models because the available computational resources are focused on a specific area instead of being spread over the globe. This allows regional models to resolve explicitly smaller-scale meteorological phenomena that cannot be represented on the coarser grid of a global model.
Applications of numerical weather predictions.
Air quality modelling: air quality forecasting attempt to predict when the concentration of pollutants will attain levels that are hazardous to public health. The concentrations of pollutants in the atmosphere are determined by their transports or mean velocity of movement through the atmosphere, their diffusion, chemical transformation and ground deposition. In addition to pollutant source and terrain information, these models require data about the state of fluid flow in the atmosphere to determine its transport and diffusion. Meteorological conditions such as thermal inversions can prevent surface air from rising, trapping pollutants near the surface, which makes accurate forecasts of such events crucial for air quality modelling. Urban air modelling however requires very fine computational mesh, requiring the use of high-resolution meso-scale weather models. In spite of this, the quality of numerical weather guidance is the main uncertainty in air quality forecasts.
Climate modelling: a general circulation model CGM is a mathematical model that can be used in computer simulations of the global circulation of a planetary atmosphere or ocean. An atmospheric general circulation model ACGM is essentially the same as a global numerical prediction model. Along with sea ice and land surface components. The ACGM and oceanic CGM are key components of global climate models and are widely applied for understanding the climate and projecting climate change. For aspects of climate change, a range of man-made chemical emission scenarios can be fed into the climate models to see how an enhanced greenhouse effect would modify the earth climate.
Ocean surface modelling:
The transfer of energy between the wind blowing over the surface of an ocean and oceans upper layer is an important element in wave dynamics. The spectral wave transport is used to describe the change in wave spectrum over changing topography. It stimulates wave generation and movement, wave shoaling, refraction, energy transfer between waves, and wave dissipation. Since surface winds are the primary forcing mechanism in the spectral wave transport equation, ocean wave models use information produced by numerical weather prediction models as inputs to determine how much energy is transferred from the atmosphere into the layer at the layer of the ocean. Along with dissipation of energy through whitecaps and resonance between waves, Surface winds from numerical weather models allows for more accurate predictions of the sea surface.
Tropical cyclone forecasting: tropical cyclone forecasting relies also on the data provided by numerical weather models.
There are three classes of tropical cyclone guidance models. Statistical models are based on an analysis of storm behaviour using climatology and correlate a storms position and date to produce a forecast that may not necessarily be based on the physics of the atmosphere at the time. Dynamics models are based on the same principles as the limited-area numerical weather prediction models but may include special computational techniques such as refined spatial domains that move along with the cyclone. Dynamics models solve the equation governing fluid flow in the atmosphere. However. Models that imbibe both approaches are statistical-dynamical models.
References And Further Reading Materials
- numerical weather predictions.RETRIEVED ON AUGUST 19TH,2018
- numerical firecast approaches.RETRIEVED ON AUGUST 19TH,2018
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