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Weather Forecasting with Digital Signals

Keywords: dsp technology, dsp applications

INTRODUCTION:

Digital signal processing (DSP) is concerned with the representation of the alerts by a sequence of numbers or symbols and the handling of these signals. Digital signal control and analog transmission handling are subfields of signal processing.

The analog waveform is sliced into equivalent segments and the waveform amplitude is measured in the center of each portion. The assortment of measurements accocunts for the digital representation of the waveform. Converting a consistently changing waveform (analog) into some discrete levels (digital)

Applications of DSP

DSP technology is nowadays commonplace in such devices as cell phones, multimedia personal computers, video recorders, Compact disc players, hard disk drive controllers and modems, and can soon replace analog circuitry in Tv set sets and telephones. An important software of DSP is in indication compression and decompression. Transmission compression can be used in digital cell phones to allow a greater number of telephone calls to be dealt with simultaneously within each local "cell". DSP indication compression technology allows people not and then talk to one another but also to see each other on their computer screens, using small camcorders mounted on the computer screens, with only a typical telephone line linking them collectively. In audio Disc systems, DSP technology is employed to perform complex error detection and correction on the raw data as it is read from the Compact disc.

some of the mathematical theory root DSP techniques, such as Fourier and Hilbert Transforms, digital filtration system design and signal compression, can be pretty complex, the numerical businesses required actually to execute these techniques are extremely simple, consisting mainly of procedures that could be done on an inexpensive four-function calculator. The structures of your DSP chip was created to perform such operations incredibly fast, processing vast sums of samples every second, to provide real-time performance: that is, the capability to process a signal "live" as it is sampled and then output the processed transmission, for example to a loudspeaker or video display. Every one of the practical types of DSP applications described prior, such as hard disc drives and cell phones, demand real-time operation.

Weather forecasting- is the science of earning predictions about standard and specific weather phenomenon for confirmed area predicated on observations of such weather related factors as atmospheric pressure, breeze speed and path, precipitation, cloud cover, temperature, humidity, frontal moves, etc.

Meteorologists use several tools to help them forecast the elements for a location. These are categorized as two categories: tools for collecting data and tools for coordinating and interpreting data.

Weather forecasting- is the research of making predictions about general and specific weather phenomenon for confirmed area based on observations of such weather related factors as atmospheric pressure, blowing wind speed and course, precipitation, cloud cover, temperature, humidity, frontal activities, etc.

Meteorologists use several tools to help them forecast the weather for a location. These fall under two categories: tools for collecting data and tools for coordinating and interpreting data.

In a typical weather-forecasting system, recently collected data are given into your personal computer model in a process called assimilation. This ensures that the computer model holds the current weather conditions as accurately as is feasible before deploying it to predict the way the weather may change over the next couple of days.

Weather forecasting is an exact knowledge of data collecting, but interpretation of the data accumulated can be difficult because of the chaotic nature of the factors that have an impact on the weather. These factors can follow generally accepted developments, but meteorologists recognize that many things make a difference these trends. With the arrival of computer models and satellite imagery, weather forecasting has improved upon greatly.

Weather forecasting- is the knowledge of making predictions about basic and specific weather phenomenon for confirmed area based on observations of such weather related factors as atmospheric pressure, wind flow speed and route, precipitation, cloud cover, temperature, humidity, frontal activities, etc.

Meteorologists use several tools to help them forecast the weather for a location. These fall under two categories: tools for collecting data and tools for coordinating and interpreting data.

* Tools for collecting data include musical instruments such as thermometers, barometers, hygrometers, rain gauges, anemometers, wind socks and vanes, Doppler radar and satellite imagery (like the Moves weather satellite).

* Tools for coordinating and interpreting data include weather maps and computer models.

In a typical weather-forecasting system, recently gathered data are given into a pc model in an activity called assimilation. This means that the computer model holds the current weather conditions as accurately as is possible before using it to predict the way the weather may change over the next few days.

Weather forecasting is an exact technology of data collecting, but interpretation of the data gathered can be difficult because of the chaotic nature of the factors that affect the elements. These factors can follow generally acknowledged developments, but meteorologists understand that many things make a difference these trends. With all the advent of computer models and satellite imagery, weather forecasting has increased greatly. Since lives and livelihoods depend on exact weather forecasting, these advancements have helped not only the understanding of weather, but how it impacts living and no living things on the planet.

Weather forecasting is the knowledge of earning predictions about basic and specific weather phenomena for confirmed area based on observations of such weather related factors as atmospheric pressure, wind speed and path, precipitation, cloud cover, temperature, humidity, frontal moves, etc.

Meteorologists use several tools to help them forecast the weather for a location. These are categorized as two categories: tools for collecting data and tools for coordinating and interpreting data.

Tools for collecting data include tools such as thermometers, barometers, hygrometers, rain gauges, anemometers, wind socks and vanes, Doppler radar and satellite imagery (like the Proceeds weather satellite).

Tools for coordinating and interpreting data include weather maps and computer models.

In a typical weather-forecasting system, recently accumulated data are given into your personal computer model in a process called assimilation. This ensures that the computer model holds the weather conditions as accurately as is possible before using it to predict the way the weather may change over the next few days.

Weather forecasting can be an exact knowledge of data collecting, but interpretation of the data accumulated can be difficult due to chaotic characteristics of the factors that have an impact on the elements. These factors can follow generally acknowledged styles, but meteorologists recognize that many things can affect these trends. Along with the introduction of computer models and satellite imagery, weather forecasting has increased greatly. Since lives and livelihoods rely upon correct weather forecasting, these advancements have helped not only the understanding of weather, but how it influences living and nonliving things on Earth.

Weather forecasting is the application of knowledge and technology to forecast the point out of the atmosphere for a future time and confirmed location. Humans have attemptedto predict the elements informally for millennia, and formally since at least the nineteenth century. Weather forecasts are created by collecting quantitative data about the current express of the atmosphere and using scientific knowledge of atmospheric functions to project the way the atmosphere will evolve.

Once an all-human endeavor based mainly after changes in barometric pressure, current weather conditions, and sky condition, forecast models are actually used to determine future conditions. Individuals input is still required to select the greatest forecast model to foundation the forecast upon, that involves pattern identification skills, teleconnections, knowledge of model performance, and understanding of model biases. The chaotic characteristics of the atmosphere, the large computational power required to solve the equations that illustrate the atmosphere, error involved with measuring the original conditions, and an imperfect knowledge of atmospheric processes imply that forecasts become less appropriate as the difference in current time and enough time that the forecast has been made (the range of the forecast) raises. The use of ensembles and model consensus help slim the error and select the most likely final result.

There are a variety of end uses to weather forecasts. Weather warnings are important forecasts because they are used to protect life and property. Forecasts based on temperature and precipitation are important to agriculture, and therefore to stock traders within commodity market segments. Temperature forecasts are being used by power companies to estimate demand over returning days. On an everyday basis, people use weather forecasts to determine what to wear on confirmed day. Since outdoor activities are severely curtailed by heavy rain, snow and the wind chill, forecasts may be used to plan activities around these incidents, also to plan in advance and endure them.

History of weather control

If we dispense with legends, at least Native American Indians got methods that they believed to stimulate rain. The Finnish people, on the other side, were thought by others to have the ability to control all weather. Thus Vikings refused to have Finns on the raids by sea. Remnants of this opinion lasted well in to the modern age, with many ship crews being unwilling to simply accept Finnish sailors.

The early modern era found people discover that during battles the firing of cannons and other firearms often precipitated precipitation. The first example of weather control which continues to be considered workable is just about the lightning conductor.

For millennia people have attempted to forecast the elements. In 650 BC, the Babylonians expected the elements from cloud habits as well as astrology. In about 340 BC, Aristotle explained weather habits in Meteorologica. Later, Theophrastus compiled a e book on weather forecasting, called the Publication of Indicators. Chinese weather prediction lore extends at least as far back as 300 BC. In 904 AD, Ibn Wahshiyya's Nabatean Agriculture discussed the weather forecasting of atmospheric changes and signals from the planetary astral alterations; indications of rain predicated on observation of the lunar phases; and weather forecasts predicated on the activity of winds.

Ancient weather forecasting methods usually relied on experienced patterns of happenings, also termed pattern acceptance. For example, it might be witnessed that if the sunset was specifically red, the next day often helped bring good weather. This experience accumulated within the generations to create weather lore. However, not absolutely all of the predictions show reliable, and many of them have since been found never to stand up to rigorous statistical screening. It was not before invention of the electric telegraph in 1835 that the present day get older of weather forecasting began. Before this time, it was not possible to move information about the existing state of the elements any faster when compared to a steam teach. The telegraph allowed records of climate from a wide area to be received almost instantaneously by the late 1840s. This allowed forecasts to be produced by knowing what the weather conditions were like further upwind. The two men most credited with the delivery of forecasting as a research were Francis Beaufort (kept in mind chiefly for the Beaufort size) and his protg Robert FitzRoy (creator of the Fitzroy barometer). Both were influential men in British isles naval and governmental circles, and even though ridiculed in the press at the time, their work gained clinical credence, was accepted by the Royal Navy, and formed the foundation for most of today's weather forecasting knowledge. To mention information accurately, it became necessary to have a typical vocabulary explaining clouds; this was achieved by means of a series of classifications and, in the 1890s, by pictorial cloud atlases.

Great improvement was made in the research of meteorology during the 20th century. The possibility of numerical weather prediction was proposed by Lewis Fry Richardson in 1922, though pcs did not are present to complete the vast number of calculations necessary to create a forecast before the event had occurred. Sensible use of numerical weather prediction started in 1955, spurred by the development of programmable electronic pcs.

* Modern aspirations

There are two factors which will make weather control extremely difficult if not fundamentally intractable. The first one is the enormous level of energy contained in the atmosphere. The second is its turbulence.

Effective cloud seeding to create rain is definitely some 50 years away. People do utilize even the priciest and experimental types of it, but more in hope than self-confidence.

Another even more speculative and expensive strategy that is semiseriously mentioned is the dissipation of hurricanes by exploding a nuclear bomb in the eye of the storm. It is doubtful that it'll ever before even be tried out, because if it failed, the result will be a hurricane bearing radioactive fallout combined with the destructive power of its winds and rainfall.

* Present day weather forecasting system

Components of a modern weather forecasting system include:

* Data collection

Observations of atmospheric pressure, temperature, breeze speed, wind route, humidity, precipitation are created nearby the earth's surface by trained observers, automatic weather stations or buoys. THE ENTIRE WORLD Meteorological Organization operates to standardize the instrumentation, observing methods and timing of these observations worldwide. Stations either record hourly in METAR reports, or every six time in SYNOP reviews. Diurnal (daily) rhythm of air pressure in northern Germany (dark-colored curve is air pressure) Atmospheric pressure is the pressure at any point in the Earths atmosphere. . . . For other uses, see Temperature (disambiguation). . . . An AWS in Antarctica An computerized weather train station (AWS) is an automated version of the original weather station, either to save lots of human labour or to allow measurements from remote areas. . . . Weather buoys are musical instruments which acquire weather and ocean data within the worlds oceans. . . . WMO flag THE EARTH Meteorological Group (WMO, French:, OMM) is an intergovernmental organization with a account of 188 Member State governments and Territories. . . . METAR (for METeorological Aerodrome Survey) is a format for reporting weather information. . . . SYNOP (surface synoptic observations) is a numerical code (called FM-12 by WMO) used for reporting marine weather observations made by manned and robotic weather stations. . . .

Measurements of temperature, humidity and wind flow above the top are found by releasing radiosondes (weather balloon). Data are usually from nearby the surface to the center of the stratosphere, about 30, 000 m (100, 000 ft). In recent years, data sent from commercial airplanes through the AMDAR system has also been contained into upper air observation, generally in numerical models. radiosonde with measuring tools A radiosonde (Sonde is German for probe) is a product for use in weather balloons that actions various atmospheric guidelines and transmits them to a fixed receiver. . . . Rawinsonde weather balloon just after release. . . . Atmosphere diagram displaying stratosphere. . . . Aircraft Meteorological Data Relay (AMDAR) is a program initiated by the World Meteorological Company. . . .

Increasingly, data from weather satellites are being used due to their (almost) global coverage. Although their visible light images are very helpful for forecasters to see development of clouds, little of this information can be used by numerical weather prediction models. The infra-red (IR) data however can be used as it gives information on the temperature at the top and cloud tops. Specific clouds can be tracked in one time to the next to provide information on wind direction and strength at the clouds steering level. Polar orbiting satellites provide soundings of temperature and moisture throughout the depth of the atmosphere. Weighed against similar data from radiosondes, the satellite data gets the gain that coverage is global, however the accuracy and resolution is much less good. A weather satellite is a kind of manufactured satellite that is generally used to monitor the weather and/or local climate of the Earth. . . . Sounding - The historical nautical term for measuring depth. . . .

Meteorological radar provide information on precipitation location and depth. . Additionally, when a Pulse Doppler weather radar is employed then wind rate and way can be motivated. .

* Data assimilation

Data assimilation (DA) is a method used in the elements forecasting process in which observations of the current (and possibly, past) weather are coupled with a earlier forecast for that point to produce the meteorological `evaluation'; the best estimate of the existing condition of the atmosphere. Weatherman redirects here. . . .

Modern weather predictions assist in timely evacuations and possibly save lives and property destruction.

More generally, Data assimilation is a method to use observations in the forecasting process.

In weather forecasting there are 2 main types of data assimilation: 3 dimensional (3DDA) and 4 dimensional (4DDA). In 3DDA only those observations are being used available at the time of analyses. In 4DDA the past observations are included (thus, time dimensions added).

The first data assimilation methods were called the "objective analyses" (e. g. , Cressman algorithm). This is in contrast to the "subjective analyses", when (before practice) numerical weather predictions (NWP) forecasts were arbitrarily corrected by meteorologists. The target methods used simple interpolation methods, and so were the kind of 3DDA methods. The similar 4DDA methods, called "nudging" also are present (e. g. in MM5 NWP model). They derive from the simple notion of Newtonian relaxation. The idea is to add in the right part of dynamical equations of the model the term, proportional to the difference of the determined meteorological variable and the observation value. This term, that has a negative sign "keeps" the calculated state vector closer to the observations.

The first break down in the field of data assimilation was presenting by L. Gandin (1963) with the "statistical interpolation" (or "optimal interpolation" ) method. It developed the previous ideas of Kolmogorov. That method is the 3DDA method and is the kind of regression analyses, which utilizes the information about the spatial distributions of covariance functions of the errors of the "first guess" field (previous forecast) and "true field". These functions should never be known. However, the different approximations were assumed.

In fact best interpolation algorithm is the reduced version of the Kalman filtering (KF) algorithm, when the covariance matrices aren't calculated from the dynamical equations, but are pre-determined in advance. The Kalman filter (named after its inventor, Rudolf Kalman) is an effective recursive computational solution for tracking a time-dependent state vector with noisy equations of movement instantly by the least-squares method. . . .

When this is recognised the attempts to bring in the KF algorithms as a 4DDA tool for NWP models were done. However, this was (and remains) a very difficult task, since the full version of KF algorithm requires solution of the enormous large number of additional equations. Regarding the that the special kind of KF algorithms (suboptimal) for NWP models were developed.

Another significant move forward in the development of the 4DDA methods was using the optimum control theory (variational methodology) in the works of Le Dimet and Talagrand, 1986, based on the previous works of G. Marchuk. The significant benefit of the variational strategies is usually that the meteorological fields gratify the dynamical equations of the NWP model and at the same time they minimize the useful, characterizing their difference from observations. Thus, the condition of constrained minimization is resolved. The 3DDA variational methods also can be found (e. g. , Sasaki, 1958). Maximum control theory is a mathematical field that can be involved with control regulations that can be deduced using search engine optimization algorithms. . . .

As it was shown by Lorenc, 1986, the all abovementioned sorts of 4DDA methods are in some limit equivalent. I. e. , under some assumptions they reduce the same cost functional. However, these assumptions never accomplish.

The swift development of the many data assimilation methods for NWP is linked to the two main points in the field of numerical weather prediction: 1. Utilizing the observations currently seems to be the most promicing challange to improve the quality of the forecasts at different scales (from the planetary range to the local city, or even street range) 2. The number of different kinds of observations (sodars, radars, sattelite) is swiftly growing.

The DA methods are used not also in weather forecasting, but in various environmental forecasting problems, e. g. in hydrological forecasting. Simply the same types of DA methods, as those, detailed above are in use there. Data assimilation is the challange for the every forecasting problem.

Numerical weather prediction

Numerical weather prediction is the science of predicting the weather using mathematical types of the atmosphere. Manipulating the huge datasets and carrying out the complex computations necessary to do that on an answer fine enough to help make the results useful can require a few of the most powerful supercomputers on earth. Image File background File links NAM_500_MB. PNG File links The next pages on the English Wikipedia link to this record (pages on other tasks are not outlined): Numerical weather prediction Block (meteorology). . . Image File history File links NAM_500_MB. PNG Record links The following webpages on the English Wikipedia connect to this document (webpages on other assignments are not outlined): Numerical weather prediction Block (meteorology). . . A millibar (mbar, also mb) is 1/1000th of the bar, a unit for dimension of pressure. . . . Geopotential level is a vertical coordinate referenced to Earths mean sea level - an adjustment to geometric level (elevation above mean sea level) using the deviation of gravity with latitude and elevation. . . . Weather is a term that includes phenomena in the atmosphere of the planet. . . . A numerical model is an abstract model that uses numerical language to describe the behavior of something. . . . A supercomputer is your computer that leads the globe in conditions of handling capacity, particularly rate of calculation, at the time of its intro. . . .

An exemplory case of 500 mbar geopotential elevation prediction from a numerical weather prediction model

Model output post processing

The raw output is often customized before being presented as the forecast. This is in the form of statistical techniques to remove known biases in the model, or of adjustment to take into consideration consensus among other numerical weather forecasts. For other senses of this term, see bias (disambiguation). . . .

In the past, the individual forecaster used to be accountable for generating the entire weather forecast from the observations. However today, for forecasts beyond 24hrs individuals input is generally limited to post-processing of model data to include value to the forecast. Humans must interpret the model data into weather forecasts that are understandable to the end user. Additionally, humans can use knowledge of local effects which may be too small in size to be fixed by the model to add information to the forecast. However, the increasing precision of forecast models proceeds to decrease the necessity for post-processing and real human input. Examples of weather model data are available on Vigilant Weather's Model Pulse.

Presentation of weather forecasts

The final stage in the forecasting process is perhaps the most important. Knowledge of what the finish customer needs from a weather forecast must be taken into account to present the information in a good and understandable way.

* Public information

One of the main end users of the forecast is everyone. Thunderstorms can cause strong winds, dangerous lightning strikes leading to ability outages, and wide-spread hail harm. Heavy snow or rainfall may bring transportation and commerce to a stand-still, as well as cause flooding in low-lying areas. Unnecessary heat or frigid waves can kill or sicken those without sufficient utilities. The Country wide Weather Service provides forecasts and watches/warnings/advisories for every area of america to protect life and property and keep maintaining commercial interests. Traditionally, tv set and radio weather presenters have been the key method of informing the public, however more and more the internet has been used because of the vast amount of information that can be found.

* Air traffic

The aviation industry is especially sensitive to the elements. Fog and/or exceptionally low ceilings can prevent many aircraft getting and removing. Likewise, turbulence and icing can be dangers whilst in flight. Thunderstorms are problems for any aircraft, scheduled to severe turbulence and icing, as well as large hail, strong winds, and lightning, all of which can cause fatal harm to an aircraft in flight. Over a day to day basis airliners are routed to have good thing about the jet stream tailwind to boost fuel efficiency. Air crews are briefed prior to remove on the conditions to expect en route with their vacation spot.

* Power companies

Electricity companies count on weather forecasts to assume demand that can be strongly influenced by the weather. In winter, severe cold weather can cause a surge in demand as people arrive their heating. Likewise, in summer time a surge popular can be linked with the increased use of air conditioning systems in hot weather.

* Private sector

Increasingly, private companies pay for weather forecasts personalized to their needs so that they can increase their earnings. For example, supermarket chains may change the stocks on their shelves in expectation of different consumer spending habits in different weather conditions.

a) =Ensemble forecasting=

Although a forecast model will anticipate reasonable looking weather features evolving realistically into the faraway future, the errors in a forecast will inevitably grow as time passes due to the chaotic nature of the atmosphere. The aspect that can be given in a forecast therefore diminishes with time as these errors increase. There becomes a point when the errors are so large that the forecast is totally wrong and the forecasted atmospheric express has no correlation with the real condition of the atmosphere.

However, looking at an individual forecast provides no indication of how likely that forecast is usually to be correct. Ensemble forecasting uses a lot of forecasts produced to mirror the uncertainty in the initial status of the atmosphere (scheduled to errors in the observations and inadequate sampling). The doubt in the forecast can then be assessed by the range of different forecasts produced. They have got been proven to be better at detecting the likelihood of extreme events at long range.

Ensemble forecasts are progressively more being used for functional weather forecasting (for example at ECMWF, NCEP, and the Canadian forecasting center).

b) =Nowcasting=

The forecasting of the elements in the 0-6 hour timeframe is also known as nowcasting. It is in this range that the human forecaster still has an gain over computer NWP models. In this time range you'll be able to forecast smaller features such as specific bathtub clouds with reasonable accuracy, however these are often too small to be settled by a computer model. A individuals given the latest radar, satellite and observational data can make a much better analysis of the small range features present and so will be able to make a far more accurate forecast for the following few hours.

Signal Processing

Generating imagery for forecasting terror threats

Intelligence analysts and military planners need predictions about likely terrorist targets in order to better plan the deployment of security forces and sensing equipment. We have tackled this need using Gaussian-based forecasting and doubt modeling. Our procedure excels at indicating the best threats expected for every single point along a travel journey and then for a 'global warfare on terrorism' objective. In addition, it excels at identifying the greatest-likelihood collection areas that might be used to see a concentrate on.

1 on geospatial evaluation and asymmetric-threat forecasting in the urban environment. He demonstrated how to extract distinct signatures from associations made between historical event information and contextual information resources such as geospatial and temporal politics databases. We have augmented this to include uncertainty quotes associated with historical incidents and geospatial information layers. 2

Event Forecasting Spatial Preferences

The idea of spatial preferences has been used to find potential crime1 and threat3 'hot locations. ' The premise is a terrorist or felony is aimed toward a certain location by a set of qualities, such as geospatial features, demographic and financial information, and recent political events. Concentrating on geospatial information, we believe the intended focus on is associated with features a small distance from the event location. We assign the best likelihoods to the distances between each key feature and the function, and taper them from these distances. This patterns is modeled utilizing a kernel function centered at each of these distances. To get a Gaussian kernel applied to a discretized map, the possibility density function for a given grid cell g and doubt estimates u is given by

Dig is the distance from feature i to the grid cell, Din is the length from the feature to event location n, c is a constant, E and F are the position uncertainty for event and features respectively, I is the full total quantity of features, and N is the full total number of incidents. Number 1(a) shows a sample forecast image predicated on this process, denoting threat level with colors which range from blue for least expensive menace, through red for highest risk. For the same group of features and incidents, Physique 1(b) shows a more manageable forecast-in conditions of allocating security resources-determined by aggregating feature layers preceding to generating the likelihood values.

Modeling Doubt

One of the main aspects of forecasting is having an estimation of the self-assurance in the supporting numerical principles. In numerical weather prediction, there is always a value of self-assurance designated with each forecast. For example, predicting an 80% chance of rain means that numerical weather models given suggestions parameter variations, forecasted eight out of ten will try that it would rain.

Similarly, for our event forecasts, we've discovered three key resources of uncertainty. They are: first, positional doubt associated with geospatial locations for geographic, demographic, monetary, political-event, and historical-event data; second, problem associated with feature lowering; and lastly, methodological error associated with the event forecasting algorithms. Here, we will target only on the positional mistake of historical event locations.

The historical event record of the data we used included the night out, location, type of attack, business claiming responsibility, a description of what took place, and assurance of the documented data. The confidence prices for the locations are rated from 1 to 5, with error beliefs starting at ±10m and increasing by a electricity of 10 for every single list. The ratings stand for analyst assurance in the precise event location. Problem values in the event locations, uE, are incorporated in to the distance measurements by establishing the feature-to-event distance, Din, to Din ± uE. We account for this variation by discretizing the distance range, and sampling by Monte Carlo simulation. Characters 1(c) and 1(d) show the impact of accounting for the doubt. These forecast images were changed into Google Keyhole Markup Terms and are shown in the Google Globe program, 4 overlaying the correct georegistered surfaces.

Satellites to Profile Weather, Improve Forecasts through GPS

BOULDER-A revolutionary, globe-spanning satellite network will furnish round-the-clock weather data, screen local climate change, and improve space weather forecasts by using impulses from the Global Positioning System (Gps unit). Through atmosphere-induced changes in the air indicators, scientists will infer the express of the atmosphere above some 3, 000 locations every a day, including vast stretches of ocean inadequately profiled by current satellites and other tools. Almost 100 scientists from over twelve countries are get together in Boulder on August 21-23 to help plan the use of data from this $100 million objective, which will get started procedures in 2005.

Called COSMIC, the satellite network is now being developed by way of a U. S. -Taiwan relationship based on something design provided by the College or university Firm for Atmospheric Research, where in fact the COSMIC Project Office is based. Taiwan's Country wide Science Council and Country wide Space Program Office (NSPO) and the U. S. Country wide Science Groundwork are providing major support for COSMIC.

"The increased coverage will improve weather forecasts by providing data where there previously was nothing or insufficient, " says Ying-Hwa Kuo, job director for the Constellation Observing System for Meteorology, Ionosphere and Weather (COSMIC), also called ROCSAT-3 in Taiwan. With six satellite receivers, COSMIC will accumulate a global, 3-D data establish likely to improve analyses of both weather and weather change. By traffic monitoring temperature in the upper atmosphere up to 30 miles high, COSMIC could help clarify whether these locations are cooling scheduled to heat-trapping greenhouse gases closer to the surface. Also, by tracking moisture in underneath 12 miles of the atmosphere, COSMIC provides much-needed information on the three-dimensional distribution of atmospheric normal water vapor, which is essential for exact prediction of precipitating weather systems. COSMIC will also evaluate high-altitude electron density, probably improving forecasts of ionospheric activity and "space weather. "

COSMIC's satellites will probe the atmosphere using radio occultation, a technique developed in the 1960s to study other planets but more recently applied to Earth's atmosphere. Each satellite will intercept a Gps navigation indication after it passes through (is occulted by) the atmosphere close to the horizon. Such a way brings the sign through a profound cross-section of the atmosphere. Versions in electron density, air density, temp, and moisture bend the transmission and change its swiftness. By measuring these shifts in the sign, scientists can determine the atmospheric conditions that produced them. The effect: profiles along thousands of angled, pencil-like sections of atmosphere, each about 200 miles long and a few hundred feet extensive.

Rather than replacing other observing systems, COSMIC will blend with them, filling in major gaps and boosting computer forecast models. Many satellite-based products are like topographic maps that track the contours of atmospheric elements in confirmed height range with high horizontal accuracy. COSMIC is more akin to a couple of probes that drill through the depth of atmosphere with high vertical detail. Thus, says Kuo, "COSMIC will go with the existing and organized U. S. meteorological satellites. "

Radiosondes (weather sensors launched by balloon) have developed vertical profiles because the 1930s. However, they are really launched only twice a day in most places, and few are deployed in the ocean. In contrast, the COSMIC data will be gathered continuously across the globe. The Gps navigation radio signs can be picked up by the low-orbiting COSMIC receivers even through clouds, that are an obstacle for satellite-borne devices that sense infrared rays of the spectrum.

UCAR and acquaintances began exploring the utilization of GPS-based observing systems in 1995 with the successful kick off of an test satellite. Other systems have been launched by researchers in the U. S. , Germany, and Argentina. All of these are research-based systems, with the data made available within days or weeks. COSMIC's data will be accessible within three time of the observations, making them a potential boon to day-to-day forecast functions. The COSMIC Project Office will provide as a clearinghouse for research use of the data from COSMIC and other GPS-based systems by experts in the United States, Taiwan, and anywhere else.

UCAR is overseeing ground-based facilities, satellite payloads, kick off services, and data handling set ups for COSMIC. Orbital Sciences Company is accountable for spacecraft design. The first spacecraft will be built at Orbital's facilities in Dulles, Virginia. The rest of the constellation will be built and tested in Taiwan, where the system's objective control will be based. NSPO and Taiwan professional partners will join in satellite system development. Other collaborators include NASA, the Country wide Oceanic and Atmospheric Administration, the Air Power, Jet Propulsion Laboratory, and Naval Research Laboratory.

REFERENCES:

http://www. ucar. edu/communications/newsreleases/2002/cosmic. html

http://spie. org/x8500. xml?spotlight=x2410&ArticleID=x8500

http://www. indopedia. org/Weather_forecasting. html

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