Nigam and M. Singh : Analysis of vertical dispersion of an elevated plume using video digitization, Atmospheric Environment, 24A, Raman, and P. Zanneti : Numerical investigation of possible role of local meteorology in the Bhopal gas accident, Atmospheric Environment, 29, Singh, P. Agarwaal, S. Nigam, A. Prabhu and S. Ameenullah : Observation of Mean boundary layer structure and turbulence during pre-monsoon and monsoon periods in India, Atmospheric Environment , 24A, Rao : Observations and numerical simulation of the evolution of the tropical planetary boundary layer during total solar eclipses, Atmospheric Environment, 24A, Raman : Role of mesoscale circulations on monsoon rainfall over the west coast of India, Atmospheric Environment, 29, Raman, U.
Mohanty and R. Madala : Sensitivity of monsoon circulations to changes in the sea surface temperatures, Atmospheric Environment, 30, Raman : Simulation of an elevated long range plume transport using a mesoscale meteorological model, Atmospheric Environment, 29, Yadav, A. Gulati, S. Singh, S. Nigam and N. Raman : The role of radiative transfer in the maintenance and destruction of stratocumulus clouds, Atmospheric Environment, 29, Raman : The role of vegetation in the generation of mesoscale circulation, Atmospheric Environment, 29, Raman, A.
Prabhu, R. Prabhu, and R. Madala and S. Raman : Sensitivity of monsoon rainfall predictions to initialization procedures, Atmospheric Research, 30, Raman : A Numerical modeling study of the marine boundary layer over the Gulf Stream during cold air advection, Boundary Layer Meteorology, 45, Raman : An application of the E-e closure model to simulations of mesoscale topographic effects, Boundary Layer Meteorology, 49, SethuRaman, P.
Misra and H. Sahota : Downward non-uniform mixing in shoreline fumigation processes, Boundary Layer Meteorology, 34, Raman, D. Morrison, S. Ramana, and J. Raju : Marine boundary layer variability over Indian ocean during northeast monsoon, Boundary Layer Meteorology, 97, Raman, and D. Raman : The summer time Great Plain low level jet and the effect of its origin on moisture transport, Boundary Layer Meteorology, 88, Neuherz, S. Raman, L. Pietrafesa, K. Keeter, and X. Li : The use of pre-storm boundary layer baroclinicity in determining and operationally implementing the Atlantic surface cyclone intensification, Boundary Layer Meteorology, 8, Part 2.
Heirarchy of interaction -explicit interaction analysis, Boundary Layer Meteorology, 91, Raman and M. Raman : A Case study of the nocturnal boundary layer over a complex terrain, Boundary-Layer Meteorology, 66, SethuRaman : A comparative evaluation of the coastal internal boundary layer height equations, Boundary-Layer Meteorology, 32, Meyers and R. Brown : A comparison of an Eulerian and a Lagrangian time scale for over-water atmospheric flows. Raman : A three-dimensional numerical sensitivity study of convection over Florida peninsula, Boundary-Layer Meteorology, 60, Raman and A.
Prabhu : Boundary layer heights over the monsoon trough region during the active and break phases, Boundary-Layer Meteorology, 57, Brown, G. Raynor and W. Tuthill : Calibration and use of a sail plane variometer to measure vertical velocity fluctuations. SethuRaman and R. Brown : Formation and characteristics of coastal internal boundary layers during onshore flows. BNL Report Raman : Mean and turbulent structure of the marine atmospheric boundary layer during two cold air outbreaks of varying intensities GALE 86, Boundary-Layer Meteorology, 71, Cermak : Mean temperature and mean concentration distributions over a physically modeled three-dimensional heat island for different stability conditions.
Raman : Scales and spectra of turbulence over the Gulf Stream during offshore cyclogenesis, Boundary-Layer Meteorology, 68, Raman and R. Madala : Simulation of monsoon boundary layer processes using a regional scale nested grid model, Boundary-Layer Meteorology, 67, Brown : Validity of the log-linear profile relationship over a rough terrain during stable conditions. Madala : A review of four dimensional data assimilation techniques for numerical weather prediction, Bulletin of American Meteorology Society, 73, Michael, W.
Tuthill and J. McNeil : Instrumentation and data acquisition system for an air-sea interaction buoy. Tuthill : Response characteristics of a new bidirectional vane. SethuRaman and G. Boyles, P. Robinson, S. Raman, G. Fishel : Calculating a daily normal temperature, Bulletin of American Meterological Society, 87, Raman : Dual doppler radar analysis of the marine mixed layer during a cold air outbreak over a strong sea surface temperature gradient, Chapter in Microwave Remote Sensing in the Earth System, Deepak Publishers , Raman : A review of the effect of radiation transfer on the simulation of atmospheric circulations of different scales, Chapter in the Proc.
Raman : A review of coupled ocean-atmosphere models, Chapter in the Proc. Madala : A review of non-hydrostatic numerical models for the atmosphere, Chapter in the Proc. Mohanty, and D. Mohanty, D. Raman, H. In Marine Forecasting, edited by N. Journal of Nichoul, Elsevier, Amsterdam, Raman : Analysis of climate trends in North Carolina , Environment International, 29, Pietrafesa, and S.
The equations used are nonlinear partial differential equations which are impossible to solve exactly through analytical methods,  with the exception of a few idealized cases. Different models use different solution methods: some global models use spectral methods for the horizontal dimensions and finite difference methods for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions.
Weather and climate model gridboxes have sides of between 5 kilometres 3. A typical cumulus cloud has a scale of less than 1 kilometre 0. Therefore, the processes that such clouds represent are parameterized , by processes of various sophistication.
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In the earliest models, if a column of air in a model gridbox was unstable i. More sophisticated schemes add enhancements, recognizing that only some portions of the box might convect and that entrainment and other processes occur. Weather models that have gridboxes with sides between 5 kilometres 3. Still, sub grid scale processes need to be taken into account.
The amount of solar radiation reaching ground level in rugged terrain, or due to variable cloudiness, is parameterized as this process occurs on the molecular scale. Sun angle as well as the impact of multiple cloud layers is taken into account. Thus, they are important to parameterize. The horizontal domain of a model is either global , covering the entire Earth, or regional , covering only part of the Earth.
Regional models also are known as limited-area models, or LAMs. Regional models use finer grid spacing to resolve explicitly smaller-scale meteorological phenomena, since their smaller domain decreases computational demands. Regional models use a compatible global model for initial conditions of the edge of their domain.
Uncertainty and errors within LAMs are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as within the creation of the boundary conditions for the LAMs itself. The vertical coordinate is handled in various ways. High-resolution models—also called mesoscale models —such as the Weather Research and Forecasting model tend to use normalized pressure coordinates referred to as sigma coordinates.
Because forecast models based upon the equations for atmospheric dynamics do not perfectly determine weather conditions near the ground, statistical corrections were developed to attempt to resolve this problem.
Statistical models were created based upon the three-dimensional fields produced by numerical weather models, surface observations, and the climatological conditions for specific locations. These statistical models are collectively referred to as model output statistics MOS ,  and were developed by the National Weather Service for their suite of weather forecasting models.
Model output statistics differ from the perfect prog technique, which assumes that the output of numerical weather prediction guidance is perfect. Forecast parameters within MOS include maximum and minimum temperatures, percentage chance of rain within a several hour period, precipitation amount expected, chance that the precipitation will be frozen in nature, chance for thunderstorms, cloudiness, and surface winds.
In , Norman Phillips developed a mathematical model that realistically depicted monthly and seasonal patterns in the troposphere. This was the first successful climate model. National Oceanic and Atmospheric Administration. The latest update version 3. Air pollution forecasts depend on atmospheric models to provide fluid flow information for tracking the movement of pollutants. In the mid- to lates, the United States Environmental Protection Agency took over the development of the UAM and then used the results from a regional air pollution study to improve it.
The Movable Fine-Mesh model, which began operating in , was the first tropical cyclone forecast model to be based on atmospheric dynamics. And it was not until the s that NWP consistently outperformed statistical or simple dynamical models. As of , dynamical guidance remained less skillful than statistical methods. From Wikipedia, the free encyclopedia. This article duplicates the scope of other articles , specifically, Numerical weather prediction.
Please discuss this issue on the talk page and edit it to conform with Wikipedia's Manual of Style. June Main article: History of numerical weather prediction. Play media. Main article: Parametrization climate. Main article: Model output statistics. Main articles: Climate model and General circulation model.
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Bibcode : JAtS Hobbs Atmospheric Science: An Introductory Survey. Academic Press, Inc. Alan Amsterdam: Elsevier Academic Press. Bibcode : Tell Fundamentals of atmospheric modeling. Cambridge University Press. Journal of Computational Physics. Bibcode : JCoPh.
Archived from the original PDF on The Emergence of Numerical Weather Prediction. Storm Watchers. Bulletin of the American Meteorological Society. Bibcode : BAMS December Australian Meteorological Magazine. Bureau of Meteorology. Phillips April Quarterly Journal of the Royal Meteorological Society.
Cox September This will be improved in future versions of the model. Hess et al. Kaufman et al. Climatological aerosols is a convenient and frequently used way to include aerosols in meteorological and climate models. The goal of this simulation is to test the sensitivity to using a climatological data set with prescribed vertical distribution. These aerosols use the optical properties from the SHADE campaign; that is, they are mostly scattering.
Thus the HESS simulation reflects the uncertainty in the estimates due to optical properties of the aerosols. One flight A flew from to UT on 25 September and data from this flight will be used to validate our model results. Figure 2 shows the modeled hPa geopotential height for midday on 24, 25 and 26 September Values for geopotential height at hPa decrease to below m in this region.
These high winds were responsible for high dust emissions on 24 and 25 September. By 26 September the plume has been transported over the ocean between Mauritania and the Cape Verde Islands with optical depths between 1 and 2. The satellite images show the same plume, and the modeled plume looks very much like the observed one on both 25 and 26 September. The plume is rapidly transported southwestward and diffusing over a larger geographical area.
The simulated optical depths are much larger in the middle of the dust plume, and smaller outside. The climatological optical depths are between 0. The simulated dust optical depth is small outside the dust plume, and most of Mali and Niger have low optical depths once the plume has left. The climatological optical depth is between 0. The flight path is shown in Figure 12 in section 4.
The flight flew at several altitudes between the ground and hPa, that is both over and under the dust plume. The scattering during the flight was measured with a nephelometer [ Haywood et al. We programmed MesoNH so that the dust quantities along the A flight track were included in the output. The figures show that both model and measurements give large scattering until noon, then a clear period until UT.
Between and UT, the model predicts only one dusty period whereas the measurements have two distinct different air masses. Around UT, both model and measurements give a rapid change between a clean air mass followed by a dusty one and then a clean one again. Both model and measurements give clean air around UT, and dusty air at UT. To a large extent the variation in dust extinction is due to the aircraft flying above and below the plume.
The aircraft altitude is shown as a dashed line in Figure 5a right axis. Medium to large extinctions are modeled and measured when the aircraft is around hPa altitude, and low extinction is modeled and measured when the aircraft flies at higher altitude around hPa. When the aircraft flies close to the ground about UT, and around UT the model predicts higher extinction than is measured. This indicates that the vertical gradient is not sharp enough near the ground in the model. Figure 5b shows the gradient of the extinction measured and modeled along the flight path. The gradients are negative around , and UT.
Looking at Figure 5a , these gradients correspond to the aircraft driving in and out of the dust plume. With the exception of the model having more dust close to the ground than the real atmosphere, we are confident the model approximately reproduces what was measured from the aircraft.
The fact that the gradients of extinction are well reproduces encourages us for using the modeled dust plume in studies of atmospheric dynamics. The Dakar station is situated at the west coast of Senegal.
Cape Verde is approximately km west of Dakar. The measurements at Cape Verde Figure 6a shows that there is relatively clean air over the station until the morning hours of 26 September when aerosol optical depths between 1 and 2 arrive. The model predicts a peak of the same order of magnitude lasting most of the day on 26 September.
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The Dakar station measured aerosol optical depths about 1. The model predicts much larger optical depths around 3. However the station is in the middle of large gradients see Figure 3 , and it is difficult to compare point measurements to a model grid average when the concentration gradients are so steep. Figure 3 shows that the aerosol optical depths modeled on 26 September are rather homogeneous over a large geographical area.
Figure 7 shows that right after the emissions on 24 and 25 September, the dust is concentrated in the boundary layer at noon on 25 September with high values of extinction. After surface heating and vertical mixing have taken place during the afternoon on 25 September, the dust plume is mixed vertically to approximately 5 km. Further westward transport takes place during 26 September with the dust in the free troposphere moving faster than the boundary layer dust Figures 7e and 7f.
Figure 7 shows that the vertical structure of the dusty air layer being transported over the Atlantic Ocean corresponds to the altitude of the SAL which is usually around 3—5 km. The modeled extinction is about 0. Figure 8 shows the impact of shortwave and longwave radiation. Over some desert areas, the difference in upward flux is negative. These areas have very large surface albedo [ Masson et al. The flux is smaller when dust is included since some of the energy is reflected by dust before arriving at the surface Figure 8a and because some of the radiation is absorbed by the dust aerosols in the atmosphere.
The absorption gives a heating term in the energy budget of the atmosphere in the aerosol layer.
The net SW flux at the surface is the energy which is available for the surface. Whereas Figure 8b does not take into account surface albedo, Figure 8c does. For very reflective surfaces, little energy is absorbed by the surface, and for these surfaces the effect of dust is smaller. It can be seen that Figure 8c has smaller values than Figure 8b over the very reflective desert areas in western Mauritania. Over the desert, where the surface is hot, there is a significant longwave forcing associated with the dust plume.
Since the desert surface is relatively hot, the greenhouse effect of dust is more evident on it than on a cool surface. This forcing is lower than the one proposed by Haywood et al. Our difference in longwave upward flux, also takes into account that less longwave radiation is emitted from the desert surface since the surface is colder because of less absorbed shortwave radiation Figure 8c. At the surface the difference in shortwave fluxes are negative because of less shortwave radiation reaching the atmosphere in the dust simulation DOWN DUST is small.
On the top of the atmosphere, the difference is still negative, but here, the difference is due to UP DUST being large from the reflecting aerosols. On the surface the difference in shortwave fluxes is negative because of less radiation reaching the ground. The ocean point has large concentrations with a maximum around m and decreasing rapidly above 1 km. Figure 10 shows that the pressure decreases slightly under the dust plume. Only about 0. Larger radiative heating in the HESS run is responsible for the larger decrease in pressure.
That case is different from ours for several reasons. First, our dust plume does not stay in the same place, but is transported rapidly westward, so it cannot influence the weather situation for as long as the plume described by Mohalfi et al. Further, the single scattering albedo of dust aerosols in their case was around 0. This low value gives stronger radiative heating due to dust. The decrease in surface pressure given by Mohalfi et al.
It can be seen that in the desert area under the plume, surface temperature is reduced by 4 K. This cooling, together with warmer atmosphere, significantly decreases turbulent mixing under the dust plume. See section 4. The DUST simulation does not have a large impact on the total precipitation in the two simulations, but the locations on which the precipitation falls are different.
The change in total precipitation would have been larger if the dust plume was transported across the convective land area since a reduced latent heat flux from the land would be the response to the decreased energy flux reaching the surface. Therefore, in the next section we interpret the diabatic sources of sink for energy in the atmosphere in several zones of the model area.
The four zones are shown in Figure The budget is given as in equation 6. Positive values for Adv correspond to arrival of hot air. Conv is convection. Positive values of Conv correspond to release of latent heat associated with convection, Bdl is boundary layer mixing. Positive values of Bdl are associated with mixing of energy from the surface to the atmosphere. Rad is radiation. Positive values of Rad correspond to the atmosphere heating by absorbing more radiation than it emits.
Rad contains both longwave and shortwave radiation. Zone 2 is in the Atlantic Ocean, in the dust plume, i. Zone 3 is in the Sahara, and zone 4 is in the Sahel. However, we think it makes more sense to show the averaged budgets in this first application of the model. Later, more detailed studies can investigate changes over smaller timescales and in smaller zones.
This region is a region where convection is frequently occurring. The same processes dominate at night, but since radiation only included longwave cooling at night, the radiation term is more negative at night. This makes sense since none of the runs have large dust concentrations here. The reason for this can be the vertical profile of the aerosols. The result is similar to the result of Chung and Zhang  who found that lifted absorbing aerosols decreased CAPE, and therefore the probability of convection.
This zone has mostly sinking motion and has less convective activity than zone 1 Figure The same tendencies occur at night, only with radiative cooling being much stronger since there is no shortwave heating. The CLIM run shows 0. In all of the simulations including dust, the extra radiative heating is compensated by advection which can be advection of cold air or less heating by sinking motion.
Less sinking motion is consistent with Miller and Tegen  who found decreased subsidence in nonconvective areas in their climate model when including dust aerosols.