Operational Meteorology Forecasters

Meteorological observations are made for different reasons. They are used for continuous monitoring of climate conditions, extreme weather warnings, investigating the atmosphere, local weather-dependent activities, hydrology, farming meteorology, meteorology research, and climatology. Meteorologists analyze aspects of the Earth’s climate and their effect on the ecosystem. Climate forecasters apply their research to air pollution, horticulture, air and sea transportation, and atmospheric changes. Meteorologists who gauge the climate are called operational meteorologists.

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They constitute the biggest class of experts under the category of a meteorologist. Operational meteorologists assemble information about different climate conditions, for example, gaseous pressure, temperature, humidity, and wind speed. This data is gathered from satellite equipment, weather radar, weather balloons, and other remote detecting devices. Operational meteorologists frequently feed this data into powerful computer workstations to assist with the weather forecast. These measures are not just intended to inform members of society; different ventures utilize them for safety reasons. This paper will discuss different techniques and analytic tools used by operational meteorologists. The paper will also consider the characteristics and concepts of weather forecasting tools.

The general requirements of tools used in weather forecasting include simplicity, reliability, security, convenience, adjustability, and support. Other attributes include durability and cost. Concerning the primary prerequisites, it is vital for an instrument to have the capacity to adapt to different terrain and conditions. Calibration and alignment of the instruments will uncover deviations from the ideal, requiring corrections when gathering data. Simplicity, quality of development, and maintenance are essential, as most meteorological instruments are in persistent use and need repairs. Adherence to these qualities will lessen the general expense of obtaining reliable information and not exceed the underlying cost.

Recording Instruments

For many instruments used in meteorology, levers that move a pen on a chart amplify the motion of the detecting component. Such instruments should be free from resistance between the pen and paper. Thus recording instruments should be sensitive, stable, and adaptable to slight changes, and have a high resolution. In addition, recording instruments should have a quick response time. Instruments with high resolution are able to recognize close estimations of the quantity being investigated and express these close estimations quantitatively. (Cunha, Smith, Krajewski, Baeck, & Seo, 2015).

Weather Forecasting Tools and Techniques

Tools used by operational meteorologists include the advanced weather interactive processing system (AWIPS), weather observation devices, weather analytic tools, and numerical climate prediction. The AWIPS tool assists meteorologists with gathering data correspondences and with storing, handling, and representing the results. AWIPS uses the D2D embedded application (Didal, Todawat, & Choudhary, 2017). The program is a simple architecture that allows users to gather information on weather conditions globally. Consequently, whether observatory tools can be used for surface information, soundings, satellite information, radar information, and wind profiles. Weather analytic tools collect data from observatory positions to determine climate trends. Forecasters depend on extrapolation from current climate patterns. Such measures are called “nowcasts.” Nowcasting methods depend on extrapolation, measurement, and experience-based instinct instead of complex climate models (Didal et al., 2017). Consequently, they are precise on short timescales.

Operational meteorologists assemble information on different climate conditions, for example, gaseous pressure, temperature, humidity, and wind speed. This data is gathered from satellite equipment, weather radar, weather balloons, and other remote detecting hardware. Operational meteorologists frequently feed this data into powerful computer workstations to assist them with weather forecasting (Rautenhaus et al., 2017).

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Forecast Funnel

This technique is connected to a time pyramid that shows how much time is spent during each stage. By implication, the best measure of time conversely corresponds to the size of the forecast field. The forecast funnel has been relegated mainly to learning purposes because some activities that were tedious and repetitive are now computerized. Consequently, some aspects can now be conducted with the aid of supercomputers and workstations. Enhanced models enable the forecaster to utilize computer-aided investigation where formerly manual examination would have been required. Likewise, with enhanced communication and networking devices, it is more sensible for the forecast funnel procedure to be distributed among separate forecasters situated in various workplaces.

Advanced Weather Interactive Processing System

AWIPS is a new framework used by operational meteorologists. Forecasters used the AWIPS design to provide computational and functional capacities at operational NWS locations. The display design provides open access through NOAAPORT to broad NOAA information groups that are produced centrally. AWIPS is used to acquire and process information from a variety of meteorological sensors and local sources. Through it, forecasters provide an intelligent communications framework to interconnect NWS activity destinations and communicate information to these locales. The display tool can be used to disseminate warnings and forecasts quickly and consistently.

Doppler Radar

Doppler radar is the meteorologist’s tool for monitoring strong storms. Doppler radar identifies a wide range of precipitation, the axis of hurricane clouds, airborne tornado debris, wind speed, and direction (Libertino, Allamano, Claps, Cremonini, & Laio, 2015).

Satellite Information

Climate satellites screen the Earth from space, gathering weather information for operational meteorologists. Polar satellites in near-Earth orbit take point-by-point pictures daily. Geostationary satellites remain over a similar area high over the surface taking pictures as often as possible and at regular intervals. Satellites use the Earth to screen themselves against intense solar storms (Libertino et al., 2015).


Meteorologists use radiosondes to collect information from the upper atmosphere. At least twice every day, radiosondes are attached to weather balloons that are launched in different locations to collect weather information. During their journey, the radiosonde ascends to the upper stratosphere where it gathers and transmits information about air pressure, temperature, relative humidity, wind speed, and direction. During extreme weather, radiosondes are dispatched to gather extra information about storm conditions.

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Robotized Surface-Observing Frameworks

This tool screens climate conditions on the Earth’s surface. More than 900 stations in the US provide information about atmospheric situations, surface visibility, precipitation, and temperature.

Visualization in Meteorology

Visualization techniques in operational meteorology include visual mapping of observations and simulations, analysis of flow, detection/tracking of climate features, comparison/fusion of heterogeneous data, analysis of uncertainty in simulations, use of interactivity in workflows, and technical aspects (Alder & Hostetler, 2015). Operational forecasting deals with atmosphere conditions, including short-term daily forecasts and medium-term storm forecasting. The computational chain at climate observation sites includes routine observations and numerical climate forecasting models. Despite increasingly computerized systems, the forecaster and observations interpreted by the forecaster play a role in weather observations. Advanced meteorology utilizes information from environmental observations and computational modeling. As forecasters collect information through various modalities, coordinate frameworks vary. In meteorological research, visualization systems and instruments are different from those used in operational settings, mirroring the variety of questions being researched. Like operational estimation, 2D visualization predominates in meteorological research, although 3D systems are increasing (Bibi, Kazmi, Javed, Shamim, & Rauf, 2017).

Information Correlation and Fusion

The correlation of environmental information is a continuous challenge in both operational meteorology and research. The task includes correlating structures in various fields. Consequently, forecasters are able to combine information from heterogeneous sources to get a comprehensible view of weather conditions. The process of combining data is called fusion.

Windy Website

For observation, the website “windy.com” is a useful and open source for climate forecasting. It enables forecasters to analyze atmospheric and oceanographic information. The site’s convenience and user experience could make possible new computerized work processes, which would expand or supplant the forecast funnel system. When utilizing windy.com, the choice of an area is necessary, as is selecting the range of values required. Different climate models are integrated into windy.com, for example, ECM, GFS, and NAM, which give momentum, barometrical and oceanographic conditions. Another valuable option is the capacity to load Meteograms to help distinguish critical climate conditions where different models may concur or disagree. Weather parameters used for data analysis include radar, lightning, wind, rain, thunder, temperature, clouds, and waves. The requirements for tools used in weather forecasts include simplicity, reliability, convenience for the task, adaptability, and support. Other attributes include durability and cost. Concerning the primary two prerequisites, instruments should have the capacity to adapt to different terrain and conditions. The website provides a global overview of weather conditions using different forecasting models.

Data Analytics

It is crucial for operational forecasters to understand the results they gather from weather tools. The ability to recognize, interpret and evaluate the data enhances comprehension of weather conditions (Lokers, Knapen, Janssen, Randen, & Jacques, 2016). Thus, forecasters must be able to understand pattern abstraction, trend recognition, change detection time budgeting, and forecast heuristics

Pattern Abstraction

An accomplished forecaster requires few values to understand the climate conditions. This ability is obtained from the forecaster’s information about data science and the behavior of climate conditions. Pattern abstraction permits the forecaster to process and forecast complex climate systems at global and mesoscale without having to remember all information related to the pattern. This data structure is called memory through clarification or memory for significant relationships. Memory through clarification is the condition where a climate trend is stored in the brain with the use of an illustrative instrument. Memory for significant relationships is a condition where information is obtained from important connections using information that exists in the forecaster’s memory. The amount of information required relies on the experience level of the forecaster and the unpredictability of climate conditions (Mbogo, Rakitin, & Visheratin, 2017).

Trend Recognition

Perceiving patterns permits a forecaster to reason in reverse to distinguish causes or work forward to distinguish indirect impacts. Pattern recognition requires the forecaster to consider the rate of change as opposed to the individual information components. Pattern recognition is achieved spontaneously; however, some pattern prediction includes finding patterns in the midst of extensive “information turbulence.” Computerization in the middle of critical turbulence requires human perception or digital methods. Trend recognition has been automated and sometimes includes radar windstorm trackers and proof of source air areas. Notwithstanding, patterns can be seen in a wide assortment of informational collections, for example, how well a specific model handles a particular climate condition, the progressive change in the computerized model forecast, and change in temperature or mass (Mbogo et al., 2017).

Change Detection

Change detection is the acknowledgment of a change in the estimation of information sets. Change detection in a few circumstances can be simple if the change is large, predicted, or produces a concrete outcome. Nevertheless, change detection can be exceptionally troublesome in circumstances where the change is small, the level of “turbulent information” is high, false warnings are frequently combined with real changes, or where one does not expect the change. People often reject a proof of change when they have a personal stake in the continuation of the current pattern. This is often the situation with an estimate; proof in opposition to one’s forecast would be proof of a mistake in the analysis and require much work to change the forecast.

Four-Dimensional Data

Weather parameters exist in four dimensions, which include latitude, longitude, time, and elevation. A forecaster considers the climate in n>4 measurements, each as an alternative measurement. However, modern display devices are constrained to two dimensions. The challenge for inventors is to allocate data that exist in 4-D onto a 2-D display. Systems for this mapping are animations and shading-coded overlays. Some meteorological parameters aim to compress an entire measurement into a single information component (Mbogo et al., 2017). For instance, height and depth information can combine height and temperature information into one value that can be shown in 3-D information or a 2-D presentation.

Time Budgeting

Forecasters allocate time with the goal of conveying forecast expectations on schedule. The time required to create a weather forecast can fluctuate depending on the level of uncertainty. Additionally, the amount of time available to finish a task can differ significantly due to administrative work and other responsibilities. Thus, experienced operational forecasters use precision to allocate timelines for subtasks.

Weather Heuristics

Forecasters have large collections of heuristics that are utilized to disentangle programming procedures. Heuristics assume a vital part in analyzing and forecasting weather conditions. As a forecaster becomes more experienced, new heuristics are created, and existing heuristics are refined. Some heuristics are summarized below.

  1. Teleconnections or anomaly patterns are provided in heuristics.
  2. Modifications to forecast measurements are connected because of heuristics acquired from the forecast information. The utilization of model direction is sifted through an arrangement of heuristics dependent on previous execution.
  3. Changes to the forecast because of the terrain or season are heuristics.
  4. Findings concerning the impact of different structures dependent on form, position, and quality represent heuristics.

Parameter Calculation and Data Transformation

Forecasters memorize many equations used for data analysis. However, equations that are not considered often are quickly forgotten. Many of these calculations are automated in current frameworks. With the advance of technology, it is easy to compute and calculate weather values using supercomputers and workstations. Conversions such as temperature measurements, density, and relative humidity can be done without errors.

Forecast Verification and Feedback

Data verification and feedback require a massive amount of information that incorporates different parameters over an extensive forecast region for days. A forecaster will remember a mistaken estimate for a long time; however, simple patterns can remain unclear. Notwithstanding the sheer size of this dataset, different elements make feedback information hazardous including the challenge of acknowledging personal mistakes and fear of being punished for such errors. However, feedback and forecast verification play a role in validating weather conditions.


Alder, J., & Hostetler, S. (2015). Web-based visualization of large climate data sets. Environmental Modelling & Software, 68, 175-180.

Bibi, N., Kazmi, Z., Javed, B., Shamim, A., & Rauf, S. (2017). Web based weather visualization and forecasting system for Pakistan. Journal of Science, 30(4), 152-161.

Cunha, L., Smith, J., Krajewski, W., Baeck, L., & Seo, B. (2015). NEXRAD NWS polarimetric precipitation product evaluation for IFloodS. Journal of Hydrometeorology, 16(4), 1676-1699. Web.

Didal, V., Todawat, B., & Choudhary, K. (2017). Weather forecasting in India: A review. International Journal of Current Microbiology and Applied Sciences, 6(11), 577-590. Web.

Libertino, A., Allamano, P., Claps, P., Cremonini, R., & Laio, F. (2015). Radar estimation of intense rainfall rates through adaptive calibration of the Z-R relation. Atmosphere, 6(10), 1559-1577.

Lokers, R., Knapen, R., Janssen, S., Randen, Y., & Jacques, J. (2016). Analysis of big data technologies for use in agro-environmental science. Environmental Modelling & Software, 84, 494-504.

Mbogo, G., Rakitin, S., & Visheratin, A. (2017). High-performance meteorological data processing framework for real-time analysis and visualization. Procedia Computer Science, 119, 334-340.

Rautenhaus, M., Bottinger, M., Siemen, S., Hoffman, R., Kirby, R., Mirzargar, M.,… Westermann, R. (2017). Visualization in meteorology: A survey of techniques and tools for data analysis tasks. Transactions on Visualization and Computer Graphics, 24(12), 3268-3296. Web.

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