A NOVEL TOPOGRAPHY BASED LIMITED AREA MODEL FOR MAURITIUS

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A NOVEL TOPOGRAPHY BASED LIMITED AREA MODEL FOR MAURITIUS Mr. R. VirasamiPr. S.D.D.V. RughooputhDr. B. PathackResearch ObjectivesThe main aim behind this study is to…
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A NOVEL TOPOGRAPHY BASED LIMITED AREA MODEL FOR MAURITIUS
  • Mr. R. Virasami
  • Pr. S.D.D.V. Rughooputh
  • Dr. B. Pathack
  • Research Objectives
  • The main aim behind this study is to adapt a high resolution limited area model for Mauritius. The model should have sufficient number of grid points so as to compare the different meteorological parameters with the actual observations over the island.
  • This work is targeted towards developing a regional model for the island taking into the consideration the elevation and the size.
  • To apply novel information technology techniques and, at the same time, assuring the soundness of the physics and mathematics for running such a model..
  • To make use of model output statistics so as to increase the precision of the dynamic outputs of the regional model.
  • To study the climate of the island with respect to those small scale events especially which are topography related
  • Background
  • Weather plays a very important role in everyday life and knowledge about its future state is crucial for our economy and daily life.
  • Numerical weather prediction is a model, (in this context a computer) program, that produces meteorological information (the weather) for future times at given positions and altitudes.
  • For the model, nonlinear mathematical equations for the physics and dynamics of the atmosphere are solved but since no exact solution can be derived numerical methods are used to obtain approximate solutions, the forecast.
  • The aim behind Regional Numerical Weather Prediction (NWP) models is to produce more detailed forecasts of the weather than those available from global models. A finer computational grid on a specific area, more detailed specification of terrain, and more sophisticated prescription of physical processes are the other crucial elements which make up a regional model
  • Introduction to NWP (A brief history)In 1904, the Norwegian hydrodynamist V. Bjerknes suggested that the weather could be quantitatively predicted by applying the complete set of hydrodynamic and thermodynamic equations to carefully analysed initial atmospheric states. A British mathematician named Lewis Fry Richardson spent three years developing Bjerkness techniques and procedures to solve these equations but lack the computational facilities . He envisaged that some time in future there would a forecast factory with 26,000 accountants doing the calculation to determine the weather patterns around the world In 1948, a young meteorological theoretician, JuleCharney, succeeded to derive simplified mathematical models of the atmospheric motions, based on the quasi-geostrophic approximations. These equations would be able to forecast the large scale flow in spite of minor inaccuracies in the initial analysesAfter several decades, meteorological observation, research, and technology struggled to reach the level necessary to make the computations envisioned by Richardson .. In April 1950, the first one-day, nonlinear weather prediction was made but required the round-the-clock services of the modelers and, because of several ENIAC breakdowns, more than 24 hours to execute. However, this first forecast was successful in proving to the meteorological community that numerical weather prediction was feasible. Since then development of improved and new NWP followed rapidly as computer technology improvedThe atmosphere
  • The object of numerical weather prediction models is to assist in the prediction of the weather.
  • However, the atmosphere is unstable and small perturbations in the flow are able to grow exponentially in time by means of the mechanisms of baroclinic and barotropic instability
  • The property of the atmosphere, instability, is related mathematically to the non linearity of the primitive equations.
  • Chaos theory is also applicable to the atmosphere and thus the future state of the atmosphere is extremely sensitive to its initial state.
  • Components of atmospheric modelsScheme of NWPPrimitive equations
  • The foundation of any model is a set of conservation principles, and for atmospheric models are:
  • - (i) conservation of mass -the continuity equation
  • (ii) conservation of heat -first law of thermodynamic
  • (iii) conservation of motion -Newton‘s second law
  • (iv) conservation of water
  • (v) conservation of other gaseous and aerosol materials -equation of state for ideal gas
  • These principles are coupled into a set of relations which must be solved simultaneously.
  • Global models
  • A model is run by applying the basic equations to a 3-dimensional grid of the earth and evaluating the results.
  • The aim of atmospheric models is to predict the future state of the atmosphere especially parameters like winds, heat transfer, radiation, relative humidity, and surface hydrology at each grid point and to evaluate interactions with neighboring points.
  • Limited Area Models
  • Despite the use of global models, the need for more detailed outputs of meteorological parameters has lead to the development of Limited Area Models also known as Regional models.
  • Another factor which contributed to this development was the computing power available nowadays as compared to some years back.
  • Limited Area Models used a finer computational grid on a specific area which is more representative of the actual topography and more detailed computation for dynamic processes.
  • General structure of a regional NWP systemprovided for HRM by DWDTopographical dataGraphicsVisualizationMOSKalmanRegionalNWPModelInitial data (analysis)Direct modeloutput (DMO)Applications Wave model,TrajectoriesLateral boundary dataVerificationDiagnosticsShort Description of the High-Resolution Regional Model (HRM)Hydrostatic limited-area meso- and meso- scale numerical weather prediction modelPrognostic variablesSurface pressure psTemperature TWater vapour qvCloud water qcCloud ice qiHorizontal wind u, vSeveral surface/soil parametersDiagnostic variablesVertical velocity Geopotential Cloud cover clcDiffusion coefficients tkvm/hNumerics of the HRM
  • Regular or rotated latitude/longitude grid
  • Mesh sizes between 0.25° and 0.05° (~ 28 to 6 km)
  • Arakawa C-grid, second order centered differencing
  • Hybrid vertical coordinate, 25 to 50 layers
  • Split semi-implicit time stepping; t = 150s at  = 0.25°
  • Lateral boundary formulation due to Davies
  • Radiative upper boundary condition as an option
  • Fourth-order horizontal diffusion, slope correction
  • Adiabatic implicit nonlinear normal mode initialization
  • or diabatic digital filter initialization (Lynch, 1997)Hardware and software specificationHardware : High Performance Server, FUJITSU SIEMENS - TX 300Operating system: Scientific Linux ( based on RedHat Enterprise Linux)Compiler : Intel Fortran SuiteParalell processing : MPICHVisualization : GradsCase study: Tropical Cyclone Daniella (December 1996)Gusts and rainfall associated with passage of DaniellaHighest gusts recordedSource :MMS cyclone reportSource : Martin Seul (1999)HRM : Computational aspectsHRM: Track of T.C. DaniellaHRM: Accumulated rainfallHRM: Surface windsHRM: GustsUpper levelsResults & Discussions
  • It has been found that the model performed well on the synoptic scale and for the case study of tropical cyclone Daniella, its track as well as its intensity was quite realistic when compared to the actual scenario.
  • Over the island, the HRM output of precipitation and wind captured the microscale signals but, however, lacked the precision in magnitude. It is must be noted that it is the first time that a study of the effect of a tropical cyclone at this resolution over the island of Mauritius is being carried out using a hydrostatic model.
  • Moreover, a lot of effort was also made for setting up the server with the appropriate software and running this model as the server at the University was provided in the beginning of year 2009 without any operating system or software. All work were carried out initially on laptop and then started from scratch again using the server.
  • The concept of parallel processing using MPICH is a first at the University and was successfully implemented on the server, thus optimizing the time for the running the model at 5-7 km.
  • Future works (i)
  • Mauritius is affected by different weather systems which is at times small scale. These weather events will be studied separately using input data from DWD global systems in an attempt to find the model strengths and weaknesses when dealing with small islands.
  • Additionally, the large scale interaction between the planetary systems and thesmall scale phenomena over Mauritius will also be studied.
  • The same weather events will be studied using a non-hydrostatic model and the results between these two models will be compared.
  • Additionally, model output statistics will be applied to enable the study of the localized effects of meteorological parameters like ,excessive rainfall, associated to these events.
  • Future works (ii)
  • The topography plays an important role in atmospheric variables such as pressure, temperature, humidity and wind speed and directions, precipitation distribution over the island. The high resolution dynamic modelling approach along with model output statistics can help to study these different parameters with respect to small to microscale phenomena. The precipitation distribution over the island will be extracted from a local statistical precipitation model which is related to the topography and a correlation will be carried between the dynamic and statistical results.
  • The correlation will be used to incorporate elevation and also other atmospheric variables and to develop models which will be implemented which will be using artificial neural networks.
  • Given the importance of the effects of tropical cyclones in SouthWest Indian Ocean, the studies will put more emphasis on the predictability (development and tracking) of such systems. Data and information from ECMWF, Era-Interim, and HRM will also be utilized in this endeavour.
  • Thank you
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