The METOP-A satellite Infrared Atmospheric Sounding Interferometer (IASI) Level 2 products comprise
retrievals of vertical profiles of temperature and water vapor. The L2 data were validated through
assessment of their error covariances and biases using radiosonde data for the reference. The radiosonde
data set includes dedicated launches as well as the ones performed at regular synoptic times at Lindenberg
station (Germany). For optimal error estimate the linear statistical Validation Assessment Model (VAM)
was used. The model establishes relation between the compared satellite and reference measurements based
on their relations to the true atmospheric state. The VAM utilizes IASI averaging kernels and statistical
characteristics of the ensembles of the reference data to allow for finite vertical resolution of the retrievals
and spatial and temporal non-coincidence. For temperature retrievals expected and assessed errors are in
good agreement; error variances/rms of a single FOV retrieval are 1K between 800 - 300 mb with an
increase to ~1K in tropopause and ~2K at the surface, possibly due to wrong surface parameters and
undetected clouds/haze. Bias against radiosondes oscillates within ±0 5K . between 950 - 100 mb. As for
water vapor, its highly variable complex spatial structure does not allow assessment of retrieval errors with
the same degree of accuracy as for temperature. Error variances/rms of a single FOV relative humidity
retrieval are between 10 - 13% RH in the 800 - 300 mb range.
A mathematical model for statistical estimate of the bias and noise in satellite retrievals of atmospheric profiles and a
case study are presented. The model allows accurate validation of actual performance of the remote sensing system while
in orbit by comparing its measurements to correlative data sets, e. g. radiosonde network. The model accounts for the
following factors: (i) The satellite and validating systems sample volumes of the atmosphere at times and locations that
are not exactly co-located. (ii) The validated and validating systems have different characteristics, e. g. different vertical
resolution and noise level. All the above factors cause apparent difference between the data to be compared. The
presented model makes the comparison accurate by allowing for the differences. To demonstrate its practicability we
present the case study that involves the radiosonde data from three stations: ARM Tropical Western Pacific (0.5° S, 167°
E), ARM Southern Great Planes (37° N, 98° W), and Lindenberg (52° N, 14° E). For each station we considered
temperature profile validation scenario and estimated associated errors. The model can be used for interpretation of the
validation results when the above mentioned sources of discrepancies are significant, as well as for evaluation of
validation data sources, e.g. GRUAN (GCOS Reference Upper-Air Network).
The state-of-the-art electro-optical sensors being designed for today's space-based environmental applications require a complete characterization and thorough calibration. This is especially true for sensors designed to assess global climate change, which require very small uncertainties. This paper describes a system-level approach that addresses each phase of calibration, from planning to on-orbit operations. This approach encourages early planning and continuity of effort throughout the lifetime of the project (pre- and post-flight) to promote an optimum calibration approach that will minimize uncertainty for the intended application. This paper also discusses considerations for component level characterization, ground calibration and standards, in-flight calibration sources and trending, and in-flight validation assessment.
A linear mathematical error model for the assessment of validation activity of atmospheric retrievals is presented. The purpose of the validation activity is to assess the actual performance of the remote sensing validated system while in orbit by comparing its measurements to some relevant-validating-data sets. The validating system samples volumes of the atmosphere at times and locations that are different from the ones when and where the validated system makes its own observations. The location of the validating system can be either stationary, e.g. a ground ARM site, or movable, e.g. an aircraft or some other satellites. The true states may be correlated or not. The sampled volumes differ from each other by their location, timing, and size. The validated and validating systems have different vertical resolution and grid, absolute accuracy, and noise level. All the above factors cause apparent differences between the data to be compared. The validation assessment model makes the comparison accurate by allowing for the differences. The model can be used for assessment and interpretation of the validation results when the above mentioned sources of discrepancies are significant, as well as for evaluation of a particular validating data source.
Infrared radiance spectra from near nadir observations have provided information about tropospheric carbon monoxide (CO). The NPOESS Airborne Sounder Testbed-Interferometer (NAST-I) aboard a high altitude aircraft with a spectral coverage of 650-2700 cm<sup>-1</sup> and a spectral resolution of 0.25 cm<sup>-1</sup> has been successfully collecting the data during many field campaigns. The spectral sensitivity of CO retrievals to uncertainties in atmospheric temperature, water vapor, and surface properties is assessed in order to understand the correlation between the IR emission and the atmospheric and surface state. The profiles are determined using a three-stage approach that combines three algorithms: (1) statistical eigenvector regression, (2) simultaneous non-linear matrix inversion, and (3) CO-physical iteration retrieval. Retrieved CO abundances are obtained in addition to temperature, moisture, ozone profiles, and surface properties. Preliminary results from several NAST-I field campaigns are presented including those from observations over the western Pacific Ocean made in conjunction with airborne truth atmospheric chemistry profiles associated with the TRACE-P campaign.