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TAMDAR

TAMDAR is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand TAMDAR rather than just read about it. In short: TAMDAR (Tropospheric Airborne Meteorological Data Reporting) is a weather monitoring system that consists of an in situ atmospheric sensor mounted on commercial aircraft for data gathering. It collects information similar to that collected by radiosondes carried aloft by weather balloons.

Key takeaways

  • TAMDAR belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect TAMDAR to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of TAMDAR from memory before moving on to harder problems.

Reference excerpt

TAMDAR (Tropospheric Airborne Meteorological Data Reporting) is a weather monitoring system that consists of an in situ atmospheric sensor mounted on commercial aircraft for data gathering. It collects information similar to that collected by radiosondes carried aloft by weather balloons. It was developed by AirDat LLC, which was acquired by Panasonic Avionics Corporation in April 2013 and was operated until October 2018 under the name Panasonic Weather Solutions. It is now owned by FLYHT Aerospace Solutions Ltd.

History In response to a governmental aviation safety initiative in the early 2000s, NASA (Daniels, Tsoucalas), in partnership with the FAA, NOAA, and private industry, sponsored the early development and evaluation of a proprietary multifunction in situ atmospheric sensor for aircraft. The predecessor to Panasonic Weather Solutions, AirDat (formerly ODS of Rapid City, SD), located in Morrisville, North Carolina and Lakewood, Colorado, was formed in 2003 to develop and deploy the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system based on requirements provided by the Global Systems Division (GSD) of NOAA's Earth System Research Laboratories (ESRL), the FAA, and the World Meteorological Organization (WMO). The TAMDAR sensor was originally deployed in December 2004 on a fleet of 63 Saab SF340 aircraft operated by Mesaba Airlines in the Great Lakes region of the United States as a part of the NASA-sponsored Great Lakes Fleet Experiment (GLFE). Over the last twelve years, equipage of the sensors has expanded beyond the continental US to include Alaska, the Caribbean, Mexico, Central America, Europe, and Asia. Airlines flying the system include Icelandair, Horizon (Alaska Air Group), Chautauqua (Republic Airways), Piedmont (American Airlines), AeroMéxico, Ravn Alaska, Hageland, PenAir, Silver Airways, and Flybe, as well as a few research aircraft including the UK Met Office BAe-146 FAAM aircraft. Recently, an installation agreement has been reached with a large Southeast Asian airline as well. The TAMDAR system has been in continuous operation since its initial deployment in December 2004. In 2014, TAMDAR data began being implemented in the national mesonet program consisting of NOAA and its partners. In October 2018, Panasonic Weather Solutions was acquired by FLYHT Aerospace Solutions, which has integrated TAMDAR with its AFIRs hardware package for airplanes (providing real-time data transmission via satellite connection).

System capabilities TAMDAR observations include temperature, pressure, winds aloft, relative humidity, icing, and turbulence information which is critical for both aviation safety, the operational efficiency of the U.S. National Airspace System (NAS), and other world airspace management systems as well as other weather-dependent operational environments such as maritime, defense, and energy. Additionally, each observation includes GPS-derived horizontal and vertical (altitude) coordinates, as well as a time stamp to the nearest second. With a continuous stream of observations, TAMDAR provides spatial, temporal resolution, and geographic coverage. Upper air observing systems are normally subject to latency based on the communication networks used and quality assurance protocol. TAMDAR observations are typically received, processed, quality controlled, and available for distribution or model assimilation in less than one minute from the sampling time. The sensor requires no flight crew involvement; it operates automatically and sampling rates and calibration constants can be adjusted by remote command from a US-based operations center. TAMDAR sensors continuously transmit atmospheric observations via a global satellite network in real-time as the aircraft climbs, cruises, and descends. The system is normally installed on fixed-wing airframes ranging from small, unmanned aerial systems (UAS) to long-range wide-bodies such as the Boeing 777 or Airbus A380. Upon completion of the installations scheduled for 2015, more than 6,000 daily soundings were produced in North America, Europe, and Asia at more than 400 locations.

Icing observations TAMDAR icing data provides high-volume objective icing data available to the airline industry. TAMDAR icing reports provide accurate spatial and temporal distribution of where icing is present. The icing data can be made available in raw observation form, or can be used to improve icing potential model forecasts.

Turbulence observations The TAMDAR sensor provides objective, high-resolution eddy dissipation rate (EDR) turbulence observations. This data is collected for both median and peak turbulence measurements and are capable of being sorted on a 7-point scale which are reported as light, moderate, or severe. The EDR data collection process does not depend on aircraft type or configuration, flight conditions, or load. This turbulence data can be used to alter flight arrival and departure routes. It can be added into models to improve predictions of turbulence conditions, as well as being used as a verification tool for longer-range numerical weather prediction (NWP) based turbulence forecasts. As with the icing observations, the potential utility of this data in air traffic control decision-making for avoidance of turbulence encounters can be significant for cost and flight time.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with TAMDAR

Start with the simplest possible case. Write down what TAMDAR claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to TAMDAR before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about TAMDAR ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of TAMDAR

In research
TAMDAR appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses TAMDAR in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
TAMDAR is common in secondary-school and first-year university syllabi. It links to neighbouring topics Meteorological data and networks, so understanding it makes those chapters shorter.
In everyday life
Look for TAMDAR outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study TAMDAR in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what TAMDAR means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain TAMDAR out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is TAMDAR in simple terms?

TAMDAR (Tropospheric Airborne Meteorological Data Reporting) is a weather monitoring system that consists of an in situ atmospheric sensor mounted on commercial aircraft for data gathering. It collects information similar to that collected by radiosondes carried aloft by weather balloons.

Why does TAMDAR matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study TAMDAR?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on TAMDAR.

Tags

  • Meteorological data and networks

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