Tropospheric Airborne Meteorological Data Reporting

81 Pages·2012·6.81 MB·English

by  James Ladd

Tropospheric Airborne Meteorological Data Reporting
Neil Jacobs
Jeff Rex
September 18, 2012
Background
• TAMDAR was developed to fill the “data gap” identified by the 1997
Clinton/Gore Aviation Safety and Security Program (AVSSP)
• AVSSP Goal: Reduce aviation fatalities by 80% over 10 years
(1997-2007)
• AVSSP funded collaboration between FAA, NASA, NOAA, to
encourage private sector participation in tackling critical aviation
requirements
The TAMDAR goal
FAA, NASA, NOAA analyses concluded:
– Better aviation forecasts are essential to ATM safety and
efficiency
– Better forecasts are achievable with better, richer RAOB-type
data
– Need to fill the spatial/temporal data gap of the RAOB program
The TAMDAR goal:
– Deliver all the metrics of RAOBs from commercial aircraft, which
assures space-time data coverage for airports
– GPS stamp all data
– Add additional e-PIREPs not currently available (icing,
turbulence)
– Focus TAMDAR deployment on regional aircraft to provide better
geographic coverage
TAMDAR coverage
Coverage: 13 airlines covering most of North America, including:
– CONUS
– Alaska
– Hawaii
– Bahamas/Caribbean
– Mexico
– Europe
Current daily observations: 65,000
Current daily soundings: 2,600
FAA/NOAA TAMDAR impact study
FAA participated in developing TAMDAR goals and standards
Once commercial deployment initiated by AirDat (2004), FAA contracted
NOAA GSD to begin RUC/RR 4 year continuous data-denial study of
“TAMDAR/No TAMDAR” impact
Study design (determined by FAA/NOAA):
- Run 2 parallel forecast models identical to the operational RUC
- Add TAMDAR to input feed on one model; the other is a “control”
- Compare both model forecasts to RAOB observations
- Forecast error = difference between model forecast and RAOB
- Improvement from TAMDAR = reduction in error between models
AirDat & NCAR independent studies
• Both AirDat and NCAR ran parallel studies, replicating FAA/NOAA
methodology (previous slide), to enable result comparisons
• Model configurations:
core grid vert. levels assimilation type
NOAA/FAA RUC 13km 57 3-D (x,y,z)
NCAR 3D-Var WRF 12km 45 3-D (x,y,z)
AirDat 3D-Var WRF 12km 57 3-D (x,y,z)
AirDat 4D-Var WRF 36km 57 4-D (x,y,z,t)
AirDat FDDA WRF 4km 91 4-D (x,y,z,t)
FDDA = Four dimensional data assimilation
3D-Var = Three dimensional variational data assimilation
4D-Var = Four dimensional variational data assimilation
Study Results
Chart of improvements in forecast skill
of the experimental (with TAMDAR)
over the control (without TAMDAR).
All forecasts were verified using
RAOBs as "truth".
NOAA's most optimized model for
aircraft data is RUC
AirDat 3D-Var WRF is essentially the
same code as NCAR 3D-Var WRF
AirDat RT-FDDA and 4D-Var WRF
are best suited to utilize asynoptic
observations
7
Forecast model RH error showing Chautauqua Airlines contribution
to error reduction using a 30-day average (3DVAR RUC CONUS)
Dev (No TAMDAR)
Dev2 (TAMDAR)
300 mb above Mesaba FL
Forecast skill improvement from 40
Chautauqua ERJs added to NOAA feed
April '08
Moninger, W. R., S. G. Benjamin, B. D. Jamison, T. W. Schlatter, T. L. Smith, and E. J. Szoke, 2009: TAMDAR jet fleets and their impact on Rapid Update Cycle (RUC) forecasts, (IOAS-AOLS), AMS, Phoenix, AZ.
Study Results
Forecast Sensitivity to Observation 2 month study (Jan and Jun 2010)
North America domain / 24 h forecast / all cycles averaged / T,RH,U,V
FAA / NOAA Conclusions
• TAMDAR produced significant forecast error reductions for all three key
input variables (RH, temp, and winds)
• Both the with- and without-TAMDAR models (i.e., all models) contained
the full MDCRS and AMDAR data feeds, so the improvements reported
in this study were on top of (i.e., in addition to) any improvements from
MDCRS and AMDAR
• Since TAMDAR is an asynoptic observing system (sampling around the
clock) opposed to synoptic (sampling at the top of synoptic hours), using
an assimilation scheme that accounts for time (4-D versus 3-D) doubles
(on average) the impact value.