tamdar - wikipedia, the free encyclopedia
DESCRIPTION
TAMDARTRANSCRIPT
12/21/2015 TAMDAR Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/TAMDAR 1/4
TAMDARFrom Wikipedia, the free encyclopedia
TAMDAR (Tropospheric Airborne Meteorological Data Reporting) is a weather monitoring system thatconsists of an insitu atmospheric sensor mounted on commercial aircraft for data gathering. It collectsinformation similar to that collected by radiosondes carried aloft by weather balloons. It was developedby AirDat LLC, which was acquired by Panasonic Avionics in April 2013 and now operates asPanasonic Weather Solutions based in the United States.
Contents
1 History
2 System capabilities
2.1 Icing Observations
2.2 Turbulence Observations
3 Forecast Models And Validation
3.1 SkewT Profiles
4 External links
History
In response to a government aviation safety initiative in the early 2000s, NASA, in partnership with theFAA, NOAA, and private industry, sponsored the early development and evaluation of a proprietarymultifunction insitu 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 DataReporting (TAMDAR) system based on requirements provided by the Global Systems Division (GSD)of NOAA's Earth System Research Laboratory, the FAA, and the World Meteorological Organization(WMO).
The TAMDAR sensor was originally deployed in December 2004 on a fleet of 63 Saab SF340 aircraftoperated by Mesaba Airlines in the Great Lakes region of the United States as a part of the NASAsponsored Great Lakes Fleet Experiment (GLFE). Over the last twelve years, equipage of the sensorshas expanded beyond the continental US (CONUS) to include Alaska, Caribbean, Mexico, CentralAmerica, 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 BAe146 FAAM aircraft. Recently, an installation agreement has been reached with a large Southeast Asianairline as well. The TAMDAR system has been in continuous operation since initial deployment inDecember 2004.
12/21/2015 TAMDAR Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/TAMDAR 2/4
System capabilities
TAMDAR observations include temperature, pressure, winds aloft, relative humidity (RH), icing andturbulence that are critical to both aviation safety and the operational efficiency of the U.S. NationalAirspace System (NAS) and other world airspace management systems as well as other weatherdependent operational environments such as maritime, defense and energy. Additionally, eachobservation includes GPSderived horizontal and vertical (altitude) coordinates, as well as a time stampto the nearest second. With a continuous stream of observations, TAMDAR provides higher spatial andtemporal resolution compared to the Radiosonde (RAOB) network, as well as better geographiccoverage, and a more complete data set than sent over Aircraft Communication Addressing andReporting System (ACARS), which lacks RH, icing, and turbulence.
Upperair observing systems are normally subject to latency based on the communication networks usedand quality assurance protocol. TAMDAR observations are typically received, processed, qualitycontrolled, and available for distribution or model assimilation in less than one minute from thesampling time. The sensor requires no flight crew involvement; it operates automatically and samplingrates and calibration constants can be adjusted by remote command from a USbased operations center.TAMDAR sensors continuously transmit atmospheric observations via a global satellite network in realtime as the aircraft climbs, cruises, and descends.
The system is normally installed on fixedwing airframes ranging from small, unmanned aerial systems(UAS) to longrange widebodies such as the Boeing 777 or Airbus A380. Upon completion of theinstallations scheduled for 2015, more than 6,000 daily soundings will be produced in North America,Europe, and Asia at more than 400 locations. Emphasis has been placed on equipping regional carriersas these flights tend to (i) fly into more remote and diverse locations and (ii) be of shorter durationthereby producing more daily vertical profiles while remaining in the boundary layer for longerdurations.
Icing Observations
TAMDAR icing data provides the first high volume, objective icing data available to the airlineindustry. Ice reporting is normally available via pilot reports (PIREPs); while helpful, these subjectivereports do not provide objective accuracy and density. Highdensity, realtime TAMDAR icing reportsprovide accurate spatial and temporal distribution of icing hazards, as well as realtime observationswhere icing is not occurring. The icing data can be made available in raw observation form, or it can beused to improve icing potential model forecasts.
Turbulence Observations
The TAMDAR sensor provides objective, highresolution eddy dissipation rate (EDR) turbulenceobservations. These data are collected for both median and peak turbulence measurements and arecapable of being sorted on a finer (7point) scale than current subjective pilot reports (PIREPs), whichare reported as light, moderate, or severe. The EDR turbulence algorithm is aircraftconfiguration andflightcondition independent, thus it does not depend on the type of plane, nor does it depend on loadand flight capacity.
This highdensity realtime insitu turbulence data can be used to alter flight arrival and departure routes.It also can be assimilated into models to improve predictions of threatening turbulence conditions, aswell as being used as a verification tool for longerrange numerical weather prediction (NWP)basedturbulence forecasts. As with the icing observations, potential utility of this data in air traffic controldecision making for avoidance and mitigation of severe turbulence encounters can be significant.
12/21/2015 TAMDAR Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/TAMDAR 3/4
Forecast Models And Validation
Thirdparty studies have been conducted by NOAAGSD, the National Center for AtmosphericResearch (NCAR), and various universities and government agencies to verify the accuracy ofTAMDAR data against that of weather balloons and aircraft test instrumentation, as well as quantifyingthe TAMDARrelated impacts on NWP. Ongoing data denial experiments show that the inclusion ofTAMDAR data can significantly improve forecast model accuracy with the greatest gains realizedduring more dynamic and severe weather events.
Upperair observations are the single most important data set driving a forecast model. Finescaleregional forecast accuracy is dependent on a representation of the mid and upperlevel atmospheric flow,moisture, and wave patterns. If these features are properly analyzed during the model initializationperiod, then an accurate forecast will ensue. TAMDAR data has been shown to increase forecastaccuracy over the U.S. on the order of 30 to 50percent for a monthly average, even for 3DVar (GSI)models.
The FAA funded a fouryear TAMDAR impact study that was concluded in January 2009. The studywas conducted by the Global Systems Division (GSD) of NOAA under an FAA contract to ascertain thepotential benefits of including TAMDAR data to the 3DVar Rapid Update Cycle (RUC) model, whichwas the current operational aviationcentric model run by National Centers for Environmental Prediction(NCEP). Two parallel versions of the model were run with the control withholding the TAMDAR data.The results of this study concluded that significant gains in forecast skill were achieved with theinclusion of the data despite using 3DVar assimilation methods. The reduction in 30day running meanRMS error averaged throughout the CONUS domain within the boundary layer for model state variableswere:
• Up to 50% reduction in RH error
• 35% reduction in temperature error
• 15% reduction in wind error
This study was conducted using a 3DVar model on a 13 km horizontal grid. Likewise, the nature of the30day mean statistics dilutes the actual impact provided by TAMDAR's higher resolution data duringcritical weather events. The forecast skill gain during dynamic events is typically much greater thanwhat is expressed in a CONUSwide monthly average. In other words, the increase in model accuracy isgreatest during dynamic weather events where air traffic and other operational impacts are greatest.
The Panasonic Weather Solutions RTFDDAWRF forecast runs on a North America domain with 4 kmgrid spacing and can include multiple nested 1 km domains. A fouryear collaborative study with NCARusing the same data as in the studies referenced above has shown that the FDDA/4DVar assimilationmethodology can nearly double the improvement in forecast skill over an identical model running a 3DVar configuration. Results from this study are summarized below using the same 30day running meanverification statistics as employed by NOAA. TAMDAR impact using FDDA/4DVar resulted in:
• Reduction in humidity forecast error of 74%
• Reduction in temperature forecast error of 58%
• Reduction in wind forecast error of 63%
12/21/2015 TAMDAR Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/TAMDAR 4/4
Forecast skill, like the example presented above, is made possible by having (i) an asynoptic insituobserving system like TAMDAR that streams continuous realtime observations to (ii) a forecast model(deterministic or probabilistic) that has the ability to assimilate asynoptic data in four dimensions.
SkewT Profiles
TAMDAR sensors are currently set to sample at 300ft intervals on ascent and descent. This resolutioncan be adjusted in real time to whatever interval is desired for the receiving forecasting model. Thesatellite connection to the sensor is a twoway connection so sampling rates, calibration constants,variables and parameters, and reporting frequencies can all be changed remotely from a groundbasedlocation. The sampling rate in cruise is timebased. The soundings, or vertical profiles, are built as eachobservation is received. All of the profilebased variable calculations (e.g., CAPE, CIN, etc.) arecalculated when an aircraft enters cruise or touches down. When an airport is selected, successivesoundings can be displayed within a certain time window. This enables the user to view the evolution ofthe profile.
External links
Panasonic Weather Solutions (http://www.panasonicweather.com/)Panasonic Avionics Corporation
Retrieved from "https://en.wikipedia.org/w/index.php?title=TAMDAR&oldid=684586973"
Categories: Meteorological data and networks
This page was last modified on 7 October 2015, at 15:42.Text is available under the Creative Commons AttributionShareAlike License; additional termsmay apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is aregistered trademark of the Wikimedia Foundation, Inc., a nonprofit organization.