IEEE Geoscience and Remote Sensing Magazine - June 2014 - 88
Upward Flux (W m-2)
340
320
300
280
260
240
220
200
ARM/SGP/CIRC Longwave Upward Flux Validation (Top of Atmosphere)
340
Observation
LBLRTM
RRTM
RRTMG
320
300
280
260
240
220
200
Cloudy Cases
Clear Cases
(a)
EUMETSAT for the operational processing of the future MeteoSat Third
Generation (MTG) Infrared Sounder
(IRS) Level 2 processing.
Downward Flux (W m-2)
Numerical model
data assimilatioN
aNd operatioNs
With on-site staff at the Air Force
Weather Agency (AFWA), AER
assisted in the development of a
unified, multi-dimensional (3DARM/SGP/CIRC Longwave Downward Flux Validation
(Surface)
500
500
Var and 4D-Var) numerical weather
450
450
prediction architecture. We were
400
400
also involved in the transition and
350
350
optimization of WRF for opera300
300
tional uses [11]. We are currently
250
250
collaborating with the National
200
200
Center for Atmospheric Research
150
150
(NCAR) on the development of
Clear Cases
Cloudy Cases
the components of a four-dimen(b)
sional variational analysis (4D-Var)
data assimilation system, and the
Figure 1. Comparison of top of atmosphere upward longwave flux (a) and surface downward
inclusion of techniques to correct
longwave flux (b) between observations (black) and calculations with the line-by-line model
systematic displacement errors of
LBLRTM (red), and the broadband models RRTM (green) and RRTMG (blue) for several varied
meteorological features [12].
clear sky cases and two cloudy cases.
In addition to our AFWA support, AER has experience in deploying NWP models in operational settings, including realwith improved computational efficiency. In practice, the
time applications supporting research and operational
best accuracy versus computational speed trade-off results
forecast applications for the New England region, the
in an accuracy threshold set to a 0.05K brightness tempera2004 Athens Olympics [13]-[14], and the Meteorological
ture difference between the OSS model and LBLRTM. This
Department of Thailand.
threshold accuracy is small compared to the impact of uncerWithin the area of atmospheric transport and dispertainties in spectroscopic parameters. Nonetheless, with this
sion (ATD) applications, AER has developed a novel "typiaccuracy the OSS method provides an order of magnitude
cal day" approach as a means of intelligently using climacomputational speed gain compared to the traditional
tological data [15]. The typical day approach, developed
approaches used for the modeling of radiances from hyperfor the Defense Threat Reduction Agency (DTRA), uses
spectral infrared sounders such as the NASA Atmospheric
a multivariate frequency analysis of climatological data
Infrared Sounder (AIRS) instrument, the Infrared Atmoto define the most frequently occurring meteorological
spheric Sounding Interferometer (IASI) on board EUMETconditions and combines this with a unique sampling
SAT's MetOp-A and B, or the Suomi National Polar-orbiting
approach to generate physically consistent meteorologiPartnership (S-NPP) Cross-track Infrared Sounder (CrIS).
cal input datasets for ATD models. These datasets are now
Other key advantages of OSS are its robustness and its flexdistributed by DTRA to users of its Hazard Prediction and
ibility in terms of the handling of minor atmospheric constitAssessment Capability (HPAC) model.
uents with variable concentrations (current models handle
up to 20 species in the infrared), and its direct applicability to
3. Major PrograMs
the modeling of non-positive instrument functions (such as
unapodized interferometric instrument line shapes, e.g., sinc
cdFs-ii
functions) and to spectra compressed using principal comStarting in the early 1990's, AER began development of
ponent representation.
what became the Cloud Depiction and Forecast System-
OSS models were first deployed for the CrIS sensor [10]
Version II (CDFS-II). This system is now running operaand are running operationally on the Interface Data Protionally at the Air Force Weather Agency (AFWA). In near
cessing Segment (IDPS) that does operational processing of
real-time, the CDFS-II combines visible and infrared radioS-NPP data. OSS has also been integrated within the Joint
metric data to construct three-dimensional depictions of
Center for Satellite Data Assimilation (JCSDA) Community
cloud amount and type from surface and space-based data
Radiative Transfer Model (CRTM) and has been selected by
88
ieee Geoscience and remote sensing magazine
june 2014
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