The best weather forecasts result from application of the synoptic method to the latest numerical and statistical information. The forecaster has an ever-increasing number of valuable tools with which to work. Numerical forecast models, owing to faster computers, are capable of explicitly resolving mesoscale weather systems. Geostationary satellites allow for continuous tracking of such dangerous weather systems as hurricanes Fig.
The increased routine use of Doppler radar, automated commercial aircraft observations, and use of data derived from wind and temperature profiler soundings promise to give added capability in tracking and forecasting mesoscale weather disturbances.
Very high-frequency and ultrahigh-frequency Doppler radars may be used to provide detailed wind soundings. These wind profilers, if located sufficiently close to one another, will allow for the hourly tracking of mesoscale disturbances aloft.
Ground- and satellite-based microwave radiometric measurements are being used to construct temperature and moisture soundings of the atmosphere.
The assimilation of such data at varying times in the numerical model forecast cycle offers the promise of improved prediction and improved utilization of data and products by forecasters assisted by increasingly powerful interactive computer systems. Medium-range forecasts, ranging up to two weeks, may be improved from knowledge of forecast skill in relationship to the form of the planetary-scale atmospheric circulation. See also: Doppler radar ; Mesometeorology ; Meteorological radar ; Meteorological rocket ; Meteorological satellites ; Radar meteorology ; Satellite meteorology.
Numerical weather prediction is the prediction of weather phenomena by the numerical solution of the equations governing the motion and changes of condition of the atmosphere. The laws of motion of the atmosphere may be expressed as a set of partial differential equations relating the temporal rates of change of the meteorological variables to their instantaneous distribution in space.
These equations are developed in dynamic meteorology. In principle, a prediction for a finite time interval can be obtained by summing a succession of infinitesimal time changes of the meteorological variables, each of which is determined by their distribution at a given instant of time.
However, the nonlinearity of the equations and the complexity and multiplicity of the data make this process impossible in practice. Instead, it is necessary to resort to numerical approximation techniques in which successive changes in the variables are calculated for small but finite time intervals over a domain spanning part or all of the atmosphere.
Even so, the amount of computation is vast, and numerical weather prediction remained only a dream until the advent of the modern computer. The accuracy of numerical weather prediction depends on 1 an understanding of the physical laws of atmospheric behavior; 2 the ability to define through observations and analysis the state of the atmosphere at the initial time of the forecast; and 3 the accuracy with which the solutions of the continuous equations describing the rate of change of atmospheric variables are approximated by numerical means.
For such space and time scales, the poorly understood energy sources and frictional dissipative forces may be approximated by relatively simple formulations, and rather coarse horizontal resolutions — km or 60— mi may be used. Great progress has been made in improving the accuracy of numerical weather prediction models. Forecasts for three, four, and five days have steadily improved. Because of the increasing accuracy of numerical forecasts, numerical models have become the basis for medium-range 1—10 days forecasts made by the weather services of most countries.
See also: Mesometeorology. If, to the standard dynamic variables, the density of water vapor is added, it becomes possible to predict clouds and precipitation in addition to the air motion. When a parcel of air containing a fixed quantity of water vapor ascends, it expands adiabatically and cools until it becomes saturated. Continued ascent produces clouds and precipitation. The most successful predictions made by this method are obtained in regions of strong rising motion, whether induced by forced orographic ascent or by horizontal convergence in well-developed cyclones.
The physics and mechanics of the convective cloud-formation process make the prediction of convective cloud and showery precipitation more difficult. In , the first operational numerical weather prediction model was introduced at the National Meteorological Center. This simplified barotropic model consisted of only one layer, and therefore it could model only the temporal variation of the mean vertical structure of the atmosphere. By the early s, the speed of computers had increased sufficiently to permit the development of multilevel usually about 10—20 models that could resolve the vertical variation of the wind, temperature, and moisture.
These multilevel models predicted the fundamental meteorological variables for large scales of motion. Global models with horizontal resolutions as fine as km mi are used by weather services in several countries. Global numerical weather prediction models require powerful supercomputers to complete a day forecast in a reasonable amount of time.
For example, a day forecast with a layer global model with horizontal resolution of km 90 mi requires approximately 10 12 calculations. A supercomputer capable of performing 10 8 arithmetic operations per second would then require 10 4 s or 2. See also: Supercomputer. While global models were being implemented for operational weather prediction 1—10 days in advance, similar research models were being developed that could be applied for climate studies by running for much longer time periods.
The extension of numerical predictions to long time intervals many years requires a more accurate numerical representation of the energy transfer and turbulent dissipative processes within the atmosphere and at the air-earth boundary, as well as greatly augmented computing-machine speeds and capacities. With state-of-the-art computers, it is impossible to run global climate models with the same high resolution as numerical weather prediction models; for example, a hundred-year climate simulation with the numerical weather prediction model that requires 2.
Therefore, climate models must be run at lower horizontal resolutions than numerical weather prediction models typically km or mi. Predictions of mean conditions over the large areas resolvable by climate models are feasible because it is possible to incorporate into the prediction equations estimates of the energy sources and sinks—estimates that may be inaccurate in detail but generally correct in the mean.
Thus, long-term simulations of climate models with coarse horizontal resolutions have yielded simulations of mean circulations that strongly resemble those of the atmosphere. These simulations have been useful in explaining the principal features of the Earth's climate, even though it is impossible to predict the daily fluctuations of weather for extended periods.
Climate models have also been used successfully to explain paleoclimatic variations, and are being applied to predict future changes in the climate induced by changes in the atmospheric composition or characteristics of the Earth's surface due to human activities.
See also: Climate history ; Climate modification. Although the relatively coarse grids in global models are necessary for economical reasons, they are sources of two major types of forecast error. First, the truncation errors introduced when the continuous differential equations are replaced with approximations of finite resolution cause erroneous behavior of the scales of motion that are resolved by the models.
Second, the neglect of scales of motion too small to be resolved by the mesh for example, thunderstorms may cause errors in the larger scales of motion. In an effort to simultaneously reduce both of these errors, models with considerably finer meshes have been tested. However, the price of reducing the mesh has been the necessity of covering smaller domains in order to keep the total computational effort within computer capability.
Thus the main operational limited-area model run at the National Meteorological Center has a mesh length of approximately 80 km 50 mi on a side and covers a limited region approximately two times larger than North America. Because the side boundaries of this model lie in meteorologically active regions, the variables on the boundaries must be updated during the forecast.
A typical procedure is to interpolate these required future values on the boundary from a coarse-mesh global model that is run first.
Although this method is simple in concept, there are mathematical problems associated with it, including overspecification of some variables on the fine mesh.
Nevertheless, limited-area models have made significant improvements in the accuracy of short-range numerical forecasts over the United States.
Even the small mesh sizes of the operational limited-area models are far too coarse to resolve the detailed structure of many important atmospheric phenomena, including hurricanes, thunderstorms, sea- and land-breeze circulations, mountain waves, and a variety of air-pollution phenomena.
Considerable effort has gone into developing specialized research models with appropriate mesh sizes to study these and other small-scale systems.
Thus, fully three-dimensional hurricane models with mesh sizes of 8 km 5 mi simulate many of the features of real hurricanes. On even smaller scales, models with horizontal resolutions of a few hundred meters reproduce many of the observed features in the life cycle of thunderstorms and squall lines.
It would be misleading, however, to imply that models of these phenomena differ from the large-scale models only in their resolution. In fact, physical processes that are negligible on large scales become important for some of the phenomena on smaller scales.
For example, the drag of precipitation on the surrounding air is important in simulating thunderstorms, but not for modeling large scales of motion.
Thus the details of precipitation processes, condensation, evaporation, freezing, and melting must be incorporated into realistic cloud models. In another class of special models, chemical reactions between trace gases are considered. For example, in models of urban photochemical smog, predictive equations for the concentration of oxides of nitrogen, oxygen, ozone, and reactive hydrocarbons are solved.
These equations contain transport and diffusion effects by the wind as well as reactions with solar radiation and other gases. Such air-chemistry models become far more complex than atmospheric models as the number of constituent gases and permitted reactions increases.
See also: Computer programming ; Digital computer ; Model theory. Surface meteorological observations are routinely collected from a vast continental data network, with the majority of these observations obtained from the middle latitudes of both hemispheres. Commercial ships of opportunity, military vessels, and moored and drifting buoys provide similar in-place measurements from oceanic regions, although the data density is biased toward the principal global shipping lanes.
Information on winds, pressure, temperature, and moisture throughout the troposphere and into the stratosphere is routinely collected from 1 balloon-borne instrumentation packages radiosonde observations and commercial and military aircraft which sample the free atmosphere directly; 2 ground-based remote-sensing instrumentation such as wind profilers vertically pointing Doppler radars , the National Weather Service Doppler radar network, and lidars; and 3 special sensors deployed on board polar orbiting or geostationary satellites.
The remotely sensed observations obtained from meteorological satellites have been especially helpful in providing crucial measurements of areally and vertically averaged temperature, moisture, and winds in data-sparse mostly oceanic regions of the world. Such measurements are necessary to accommodate modern numerical weather prediction practices and to enable forecasters to continuously monitor global storm such as hurricane activity.
See also: Lidar ; Meteorological instrumentation ; Radar meteorology. At major operational weather prediction centers such as the National Center for Environmental Prediction NCEP, formerly known as the National Meteorological Center or the European Centre for Medium Range Weather Forecasts, ECMWF , the global meteorological observations are routinely collected, quality-checked, and mapped for monitoring purposes by humans responsible for overseeing the forecast process.
At NCEP and ECMWF and other centers the global observational data stream is further machine-processed in order to prepare a full three-dimensional set of global surface and upper air analyses of selected meteorological fields at representative time periods. The typical horizontal and vertical resolution of the globally gridded analyses is about km 60 mi and 25—50 hectapascals millibars , respectively.
Preparation of these analyses requires a first-guess field from the previous numerical model forecast against which the updated observations are quality-checked and objectively analyzed to produce an updated global gridded set of meteorological analyses.
These updated analyses are modified as part of a numerical procedure designed to ensure that the gridded meteorological fields are dynamically consistent and suitable for direct computation in the new forecast cycle. This data assimilation, analysis, and initialization procedure, known as four-dimensional data assimilation, was at one time performed four times daily at the standard synoptic times of , , , and UTC. The four-dimensional data assimilation is performed almost continuously, given that advanced observational technologies such as wind profilers; automated surface observations; automated aircraft-measured temperature, moisture, and wind observations have ensured the availability of observations at other than the standard synoptic times mentioned above.
An example is the rapid update cycle RUC mesoscale analysis and forecast system used at NCEP to produce real-time surface and upper air analyses over the United States and vicinity every 3 h and short-range out to 12 h forecasts.
The rapid update cycle system takes advantage of high-frequency data assimilation techniques to enable forecasters to monitor rapidly evolving mesoscale weather features. The forecast models used at NCEP and ECMWF and the other operational prediction centers are based upon the primitive equations that govern hydrodynamical and thermodynamical processes in the atmosphere. The closed set of equations consists of the three momentum equations east-west, north-south, and the vertical direction , a thermodynamic equation, an equation of state, a continuity equation, and an equation describing the hydrological cycle.
Physical processes such as the seasonal cycle in atmospheric radiation, solar and long-wave radiation, the diurnal heating cycle over land and water, surface heat, moisture and momentum fluxes, mixed-phase effects in clouds, latent heat release associated with stratiform and convective precipitation, and frictional effects are modeled explicitly or computed indirectly by means of parametrization techniques. A commonly used vertical coordinate in operational prediction models is the sigma coordinate, defined as the ratio of the pressure at any point in the atmosphere to the surface station pressure.
The medium-range forecast is run once daily out to 14 days from the UTC analysis and initialization cycle in support of medium-range and extended-range weather prediction activities at NCEP.
The medium-range forecast run commences shortly before UTC in order to allow sufficient time for delayed UTC surface and upper air data to reach NCEP and to be included in the analysis and initialization cycle. The aviation forecast model and medium-range forecast are identical models except that the AVN is run with a data cutoff of about 3 h and only out to 72 h in order to minimize the time necessary to get the model forecast information to the field.
The MRF runs with a vertical resolution of 38 levels, a horizontal resolution of about km 63 mi [triangular truncation in spectral space] for the first eight days, and a degraded horizontal resolution of about km mi for the last six days of the day operational forecast. The medium-range forecast is also the backbone of the ensemble forecasting effort a series of parallel medium-range forecast runs with slightly changed initial conditions and medium-range forecast runs from different time periods that was instituted in the early s to provide forecaster guidance in support of medium-range forecasting 6—day activities at NCEP.
The scientific basis for ensemble prediction is that model forecasts for the medium and extended range should be considered stochastic rather than deterministic in nature in recognition of the very large forecast differences that can occur on these time scales between two model runs initialized with only very slight differences in initial conditions.
Continuing advances in computer power have made it practical to begin operational ensemble numerical weather prediction. Given that each member of the ensemble the ensemble consists of 38 different model runs represents an equally likely model forecast outcome, the spread of the ensemble forecasts is taken as a measure of the potential skill of the forecasts.
Regional forecast models such as the NCEP Eta model are utilized to help predict atmospheric circulation patterns associated with convective processes and terrain forcing. Given the increasing importance of forecasting significant mesoscale weather events such as squall lines, flash flood occurrences, and heavy snow bands that occur on time scales of a few hours and space scales of a few hundred kilometers, the Eta model will continue to be developed at NCEP in support of regional model guidance to local forecasters.
An important challenge to mesoscale models is to make better precipitation forecasts, especially of significant convective weather events. Success in this endeavor will require increased use of satellite- and land-based data sets, especially measurements of water vapor, that are coming online. These are classified as longer term greater than two weeks and shorter term. The scientific basis for this initiative rests upon the understanding of the coupled nature of global atmospheric and oceanic circulations on intraseasonal and interannual time scales.
Customized wind forecasts to help determine areas that might be impacted B. Incident meteorologists who can monitor conditions and provide 'spot' forecasts C. Anticipated health impacts of the hazardous substances D.
Dispersion modeling to determine how the materials will be transported Weegy: c. What is the primary hazard you should be aware of for your community? Damaging winds causing power outages B.
Development of convective storms C. Onset of a drought period D. Increased wildland fire danger Weegy: d. Onset of a drought period User: Condensation is a process that can lead to precipitation, flooding, and storm development. Condensation can occur and begin providing the fuel for severe weather when: A. Factors you should account for when doing a threats analysis for your community are: What weather events are likely and at what time of year, Percentage of population and property likely to be affected, and Expected impacts of the hazard on critical infrastructure.
The answer is all of the above. Weegy: c. Dispersion modeling to determine how the materials will be transported User: Condensation is a process that can lead to precipitation, flooding, and storm development. Weegy: b. The dewpoint temperature is significantly less than the air temperature. User: Severe weather season is around the corner. You would like to begin some public awareness efforts, as well as make sure your spotters are trained and organized.
Who in the National Weather Service should you call to work with you on these activities? No one; both of these tasks are your responsibilities B. Lead Duty Forecaster Weegy: The answer is c. Lead Duty Forecaster User: The components that determine the difference between an inconvenient weather situation and one that is hazardous are: A.
Event type B. Event severity C. Community vulnerability D. All of the above Weegy: D. All of the above User: Today's Hazardous Weather Outlook refers to an inversion that is likely to break after am.
What conditions might you expect while the inversion persists? Strong thunderstorms could result B. Any particulate matter near the surface will quickly disperse C. Skies will be cloud-free increasing visibilities D. Fog could be present in low-lying areas Weegy: The answer is C. But what happens when they don't? Despite public dissatisfaction with the term it's often viewed as a forecast loophole for meteorologists , it's actually meant to express that atmospheric temperatures are such that they're unlikely to support only one precipitation type during the forecast period.
Deciding whether or not inclement weather will occur—and if so, what type—is only half of the battle. Neither of these is much good without an accompanying idea of how much is expected. To determine snow accumulations, both the amount of precipitation and the ground temperature must be taken into account. Precipitation amount can be gathered from looking at how moist air is at a given time, as well as the total amount of liquid precipitation expected over a certain period of time.
However, this leaves one with the amount of liquid precipitation. In order to convert this into the amount of corresponding frozen precipitation , the liquid water equivalent LWE must be applied.
Expressed as a ratio, LWE gives the amount of snow depth in inches it takes to produce 1" of liquid water.
Dry snow, which has little liquid water content due to extremely cold temperatures throughout the troposphere, can have LWE values of up to An LWE of is considered average. Ice accumulations are measured in increments of tenths of an inch. Of course, the above is only relevant if ground temperatures are below freezing. Actively scan device characteristics for identification. Use precise geolocation data. Select personalised content. Create a personalised content profile.
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