Satellite observation collocation algorithms are generally used to spatially match observations or products from different
satellite systems. The spatially matched and integrated satellite datasets are commonly used in integrated retrievals,
satellite instrument inter-calibration and satellite observation validation. Instrument physical based collocation
algorithms are developed at NOAA/NESDIS/STAR to support the development of the satellite observation integration
system. The algorithms are applied within the Geostationary satellite & Polar satellite (GEO-LEO) integration system
for IASI/SEVRI and will applied in the future CrIS/GOES-R observation integration system. In this paper, the details of
the algorithms for IASI/SEVERI and AIRS/SEVIRI collocation are described and some results for both are presented.
As a part of the Joint Polar Satellite System (JPSS, formerly the NPOESS afternoon orbit), the instruments Cross-track
Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) make up the Cross-track Infrared and
Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure
profiles and calibrated radiances [1]. In preparation for the NPP launch in 2011, we have ported and tested the
operational CrIMSS Environmental Data Record (EDR) algorithms using both synthetic and proxy data generated from
the IASI, AMSU, MHS data from Metop-A satellite.
With the availability of very accurate six hour forecasts, the metric of accuracy alone for the evaluation of
the performance of a retrieval system can produce misleading results: the retrievals may be statistically
accurate, but be of little value compared to the accurate forecast. A useful characterization of the quality of
a retrieval system and its potential to contribute to an improved weather forecast is its skill, which we
define as the ability to make retrievals of geophysical parameters which are closer to the truth than the six
hour forecast. We illustrate retrieval skill using one day of AMSU-A and AIRS data with three different
retrieval algorithms. In the spirit of achieving global retrievals under clear and cloudy conditions, we
evaluated retrieval accuracy and skill for 90% of the covered area. Two of the three algorithms meet the 1
K/1 km "RAOB quality" accuracy requirement and have skill between 900 and 150 hPa, but none have
skill between the surface and 900 hPa.
AIRS was launched on the EOS Aqua spacecraft in May 2002 into a 705 km polar sun-synchronous orbit
with accurately maintained 1:30 PM ascending node. Essentially un-interrupted data are freely available
since September 2002.
The U.S. National Polar-orbiting Operational Environmental Satellite System (NPOESS) is a satellite system being
developed to monitor global environmental conditions and collect and disseminate data related to weather, atmosphere,
oceans, land and near-space environment. The NPOESS Preparatory Project (NPP) mission is a joint effort involving the
National Aeronautics and Space Administration (NASA) and the NPOESS Integrated Program Office (IPO). The NPP
mission is currently scheduled to launch in 2010. NPP has two objectives: to extend the measurement trends begun by
the NASA EOS missions and to validate four of the primary NPOESS sensors. The CrIMSS will provide the
atmospheric vertical temperature and moisture profiles, two of the NPOESS key Environmental Data Records (EDRs).
Two sensors, the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS)
provide the input data to the CrIMSS retrieval algorithm. This talk will detail the calibration and validation programs
being executed for these two sensors, and the retrieval algorithm. The discussion will include prelaunch testing, with a
performance summary, validation planning activities and exercises, and post launch validation plan status. The launch
ready calibration/validation plan is scheduled to be ready for release November, 2008 and the status of the plan will also
be briefed.
An evaluation of the temperature, water vapor, and ozone profile retrievals from the AIRS data is performed with more
than three years of collocated radiosondes (RAOBs) and ozonesonde (O3SND) measurements. The Aqua-AIRS version
4.0 retrievals, global RAOB and O3SND measurements, forecast data from the NCEP_GFS, ECMWF, and the NOAA-
16 ATOVS retrievals are used in this validation and relative performance assessment. The results of the inter-comparison
of AIRS temperature, water vapor and ozone retrievals reveal very good agreement with the measurements
from RAOBs and O3SND s. The temperature RMS difference is close to the expected product goal accuracies, viz. 1oK
in 1 km layers for the temperature and close to 15% in 2-km layers for the water vapor in the troposphere. The AIRS
temperature retrieval bias is a little larger than the biases shown by the ATOVS, NCEP_GFS, and ECMWF forecasts.
With respect to the ozone profile retrieval, the retrieval bias and RMS difference with O3SNDs is less than 5% and 20%
respectively for the stratosphere. The total ozone from the AIRS retrievals matches very well with the Dobson/Brewer
station measurements with a bias less than 2%. Overall, the analysis performed in this paper show a remarkable degree
of confidence in the AIRS retrievals.
The AIRS instrument was launched in May 2002 into a polar sun-synchronous orbit onboard the EOS Aqua Spacecraft. Since then we have released three versions of the AIRS data product to the scientific community. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts. The first version of software, Version 2.0 was available to scientists shortly after launch with Version 3.0 released to the public in June 2003. Like all AIRS product releases, all products are accessible to the public in order to have the best user feedback on issues that appear in the data. Fortunately the products have had exceptional accuracy and stability. This paper presents the improvement between AIRS Version 4.0 and Version 5.0 products and shows examples of the new products available in Version 5.0.
The Atmospheric Infrared Sounder (AIRS) sounding suite, launched in 2002, is the most advanced atmospheric
sounding system to date, with measurement accuracies far surpassing those of current operational weather satellites.
From its sun-synchronous polar orbit, the AIRS system provides more than 300,000 all-weather soundings covering
more than 90% of the globe every 24 hours. Usage of AIRS data products, available to all through the archive system
operated by NASA, is spreading throughout the atmospheric and climate research community. An ongoing validation
effort has confirmed that the system is very accurate and stable and is close to meeting the goal of providing global
temperature soundings with an accuracy of 1 K per 1-km layer and water vapor soundings with an accuracy of 20%
throughout the troposphere, surpassing the accuracy of radiosondes. This unprecedented data set is currently used for
operational weather prediction in a number of countries, yielding significant positive impact on forecast accuracy and
range. It is also enabling more detailed investigations of current issues in atmospheric and climate research. In addition
to the basic soundings related to the hydrologic cycle, AIRS also measures a number of trace gases, the latest such
product being the global distribution of carbon dioxide. We discuss some examples of recent research with AIRS data.
Atmospheric and surface thermodynamic parameters retrieved with advanced hyperspectral remote sensors of Earth observing satellites are critical for weather prediction and scientific research. The retrieval algorithms and retrieved parameters from satellite sounders must be validated to demonstrate the capability and accuracy of both observation and data processing systems. The European AQUA Thermodynamic Experiment (EAQUATE) was conducted mainly for validation of the Atmospheric InfraRed Sounder (AIRS) on the AQUA satellite, but also for assessment of validation systems of both ground-based and aircraft-based instruments which will be used for other satellite systems such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) from the NPOESS Preparatory Project and the following NPOESS series of satellites. Detailed inter-comparisons were conducted and presented using different retrieval methodologies: measurements from airborne ultraspectral Fourier transform spectrometers, aircraft in-situ instruments, dedicated dropsondes and radiosondes, and ground based Raman Lidar, as well as from the European Center for Medium range Weather Forecasting (ECMWF) modeled thermal structures. The results of this study not only illustrate the quality of the measurements and retrieval products but also demonstrate the capability of these validation systems which are put in place to validate current and future hyperspectral sounding instruments and their scientific products.
Because gravity wave effects control middle atmosphere circulation
patterns, and because those effects depend sensitively on the
properties of the waves, reseachers have been trying for decades to
ascertain the global properties of gravity waves to better constrain global circulation models. Space-based observations hold promise for providing the needed information, but the small scales of gravity waves have posed observational challenges. Traditional analyses of averaged temperature variance do not provide the needed information. We will present statistics from alternative analyses of Atmospheric Infrared Sounder (AIRS) images of gravity waves in the stratosphere.
The high spatial resolution of the AIRS observations permit resolution of gravity waves with horizontal wavelengths as small as 50 km. We present the results of wavelet analyses of AIRS images at 667cm-1 (in the CO2 15 μm band) that spatially resolve gravity wave amplitudes, horizontal wavelengths, and propagation directions. These analyses reveal both local maximum amplitudes as well as frequencies of wave occurrence, while in contrast these quantities are inseparably blended within traditional wave temperature variance or Fourier analysis methods. The AIRS observations are known to detect only long vertical wavelength waves, whose occurrences are in turn known to be highly dependent on the strength of background wind speeds. The AIRS data permit for the first time detailed studies of the relationships between the occurrence of these waves and their propagation directions relative to the background winds.
Much progress has been made toward modeling the spectral infrared
(IR) emissivity of wind-roughened water surfaces. Existing
emissivity models explicitly calculate the ensemble mean
emissivity of the wavy surface for a given observer zenith angle
and local wind speed. However, field observations of emissivity
spectra obtained by the Marine Atmospheric Emitted Radiance
Interferometer (M-AERI) suggest that emissivity models are
deficient at larger view angles and wind speeds. In this
preliminary work, we attempt to identify and explain the sources
of error in these models using M-AERI data acquired at sea (e.g.,
during AEROSE 2004). Our results demonstrate that proper
accounting for non-unity surface emissivity must ultimately
include appropriate specification of the reflected IR radiation
field, especially in window channels. Atmospheric IR surface
reflectance becomes important for high accuracy applications
(e.g., sea surface skin temperature), that rely on window channel
observations at zenith angles ≳45 deg. Lookup tables of
ensemble mean effective incidence angle, rather than mean
emissivity, are generated using different published mean square
slope PDF models. These results roughly agree with recent
findings. Lookup tables of ensemble mean local zenith incidence
angle are also generated. This new approach to
emissivity/reflection modeling will be refined and validated
against M-AERI field data from several previous oceanographic
cruises, and will be the subject of a forthcoming paper.
Understanding upper troposphere humidity is important in the context of radiative forcing and climate. We present a detailed statistic comparison of upper troposphere water vapor retrieval profiles derived from the Atmospheric Infrared Sounder (AIRS) and in-situ measurements. The in-situ measurements are based on a recently compiled database of "best estimate" atmospheric state profiles, obtained from a careful selection of RS-90 radiosondes at Department of Energy Atmospheric Radiation Measurement (ARM) sites, during AIRS overpasses. The aim of this research is to improve the skill and accuracy of the retrieval algorithms in order to understand and quantify the biases between AIRS and RS-90 radiosondes.
Traditional cloud clearing methods utilize a clear estimate of the atmosphere inferred from a microwave sounder to extrapolate cloud cleared radiances (CCR's) from a spatial interpolation of multiple cloudy infrared footprints. Unfortunately, sounders have low information content in the lower atmosphere due to broad weighting functions, interference from surface radiance and the microwave radiances can also suffer from uncorrected side-lobe contamination. Therefore, scenes with low altitude clouds can produce errant CCR's that, in-turn, produce errant sounding products. Radiances computed from the corrupted products can agree with the measurements within the error budget making detection and removal of the errant scenes impractical; typically, a large volume of high quality retrievals are rejected in order to remove a few errant scenes. In this paper we compare and contrast the yield and accuracy of the traditional approach with alternative methods of obtaining CCR's. The goal of this research is three-fold: (1) to have a viable approach if the microwave instruments fail on the EOS-AQUA platform; (2) to improve the accuracy and reliability of infrared products derived from CCR's; and (3) to investigate infrared approaches for geosynchronous platforms where microwave sounding is difficult. The methods discussed are (a) use of assimilation products, (b) use of a statistical regression trained on cloudy radiances, (c) an infrared multi-spectral approach exploiting the non-linearity of the Planck function, and (d) use of clear MODIS measurements in the AIRS sub-pixel space. These approaches can be used independently of the microwave measurements; however, they also enhance the traditional approach in the context of quality control, increased spatial resolution, and increased information content.
Today, most Numerical Weather Prediction (NWP) centers are assimilating cloud-free radiances. Radiances from the Atmospheric Infrared Sounder have been directly assimilated in NWP models with modest positive impacts. However, since only 5% percentage of AIRS fields of view (fovs) are cloud-free, only very small amounts of the data in the lower troposphere are assimilated. (Note that channels in the mid-upper stratosphere are always assimilated since they are never contaminated by clouds.) The highest vertical resolving power of AIRS is in the lower troposphere. To further improve forecast skill we must increase the use of channels in the lower troposphere. This can be accomplished by assimilating cloud-cleared radiances, which has a yield of about 50%. Since cloud-cleared radiance may have residual cloud contamination and forecast accuracy is very sensitive to the accuracy of the input observations, a technique has been developed to use the 1 km infrared channels on the Moderate Resolution Imaging Spectroradiometer (MODIS) to quality control the cloud-cleared radiances derived from an array of 3 x 3 high spectral infrared sounder AIRS 14 km fovs. This is accomplished by finding MODIS clear radiances values within the AIRS field of view. The MODIS clear radiances are compared to cloud-cleared AIRS radiances that have been convolved to the MODIS spectral resolution. Our studies have found that the cloud-cleared radiances error statistics are very similar to cloud-free (clear) when MODIS data are used to remove potential outliers in the population of AIRS cloud-cleared radiances.
Observations from the high spectral resolution Atmospheric InfraRed Sounder (AIRS) on the NASA EOS AQUA platform are providing improved information on the temporal and spatial distribution of key atmospheric parameters, such as temperature, moisture and clouds. These parameters are important for improving real-time weather forecasting, climate monitoring, and climate prediction. Trace gas products such as ozone, carbon dioxide, carbon monoxide, and methane are also derived. High spectral resolution infrared radiances from AIRS are assimilated into numerical weather prediction models. The soundings and radiances are provided in near real-time by NOAA/NESDIS to the NWP community.
A significant component of the NOAA/NESDIS AIRS processing is to apply Principal Component Analysis (PCA) to the original AIRS 2000+ channel radiances. PCA is used for monitoring of the AIRS detectors, dynamic noise estimation and filtering, errant channel recovery, radiance reconstruction, and deriving an initial guess for profiles of temperature, moisture, ozone and other geophysical parameters. Since PCA has the ability to reduce the dimensionality of a dataset while retaining the significant information content, investigations are being done on its applications to AIRS data compression and archiving. Data compression is one of the key issues for the new generation of high spectral resolution satellite sensors.
Our current AIRS research will allow us to provide valuable information and real-time experience to the generation of products for future sensors, such as the EUMETSAT IASI and NPOESS CrIS advanced infrared sounders. Examples of each application, along with details on the generation and application of eigenvectors are presented in this paper.
This paper discusses activities related to mesoscale product development in preparation for the GOES-R satellite to be launched in 2012. These new image products will feature improved spatial, temporal, spectral, and radiometric resolution compared to current GOES imagery. Emphasis in this paper is on simulations of GOES-R data using observations from existing operational and experimental satellites.
KEYWORDS: Data centers, Computer simulations, Data conversion, Data processing, Data modeling, Satellites, Ozone, Data acquisition, Infrared radiation, Spectral resolution
A near real-time AIRS processing and distribution system is fully operational at NOAA/NESDIS/ORA. The AIRS system went though three separate production phases: design and development, implementation, and operations. The design and development phase consisted of two years of preparation for the near real-time AIRS data. The approach was to fully emulate the AIRS measurement stream. This was accomplished by using a forecast model to represent the geophysical state and computation of simulated AIRS measurements using the characteristics of the AIRS channels. The preparation included file format development and the creation of a program to subset the radiance and product data. The implementation phase lasted over a year and involved utilizing AIRS/AMSU/HSB simulated data quasi-operationally. This simulated data was placed into deliverable files and distributed to the customers for their pre-launch preparations. The operational phase consisted of switching the simulation system to real data and is the current system status. Details of what went right and wrong at each production phase will be presented. This methodology eased the transition to operations and will be applied to other advanced sounders such as IASI and CrIS.
KEYWORDS: Binary data, Clouds, Data acquisition, Data processing, Infrared radiation, Data centers, Data modeling, Algorithm development, Satellites, Atmospheric modeling
Development and testing of the IASI processing and distribution system is currently ongoing at NOAA/NESDIS/ORA. Level 1C data for 8461 channels will be available to NESDIS/NOAA from EUMETSAT shortly after MetOp 1 launch (currently scheduled for October 2005). Prior to launch, a simulation system will provide pseudo near-real time data for system testing and refinement. This will allow for a smooth and immediate system transition to the actual data processing when it becomes available. The ingested EUMETSAT level1C data will be subset both spectrally and spatially and then placed into BUFR format for a number of products including: (1) Level 1C (calibrated, apodized, and navigated) brightness temperatures, (2) cloud-cleared radiances, and (3) PCA reconstructed radiances. The subset level 1C data will be delivered within three hours of observation. System validation will consist of comparing the products to collocated radiosonde observations and model forecasts.
The AIRS instrument was launched on the Aqua satellite in May of
2002. In addition to the core level 2 products, that include cloud
cleared radiances; temperature, moisture, and ozone profiles; surface
skin temperature; NDVI (from AIRS visible channels); surface spectral
emissivity and reflectivity; and cloud products, the AIRS science team is also developing research algorithms for the retrieval of carbon monoxide (CO), methane (CH4), and carbon dioxide (CO2). These algorithms are being tested by the National Oceanographic and Atmosphere Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) in simulation and applied to real AIRS radiances. The trace gas retrievals require cloud free infrared radiances. In practice, we observe that AIRS measurements without cloud contamination occur less than 5% of the time. A key feature of the AIRS algorithm is the utilization of cloud cleared radiances that removes the effects of clouds and increases the yield of trace gas products to 50-60%. The increased yield should allow a better assessment of sources and sinks of these gases. Determination of sources and sinks of these trace gas requires an unprecedented precision for a remote sounding measurement. In addition, both the variability and errors in the trace gas products tend to be correlated with variability and errors in other products (e.g., clouds, temperature, moisture, and ozone profile). Multi-spectral, high-resolution measurements can minimize the effects of this correlation. Currently, for the AIRS products, we estimate a precision of 15% for CO, 0.5% for CO2 and 1% for CH4. The remote sounding methodology for these trace gases is discussed in detail. The METOP IASI and NPOESS CrIS instruments can extend the continuity of these trace gas products over the next two decades. Simulation experiments are used to assess the relative performance of the trace gas retrievals from these sounders.
Since October, 2002, NESDIS has provided specially tailored radiance and retrieval products derived from Aqua AIRS and AMSU-A observations operationally (24 hours x 7 days) to a number of Numerical Weather Prediction (NWP) centers, including NCEP, ECMWF and the UK Met. Office. Two types of products are available -- thinned radiance data and full resolution retrieval products consisting of atmospheric temperature, moisture and ozone as well as surface parameters of temperature and emissivity. The radiances are thinned because of current limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: a) spatial and spectral thinning, and b) data compression using principal component analysis (PCA). PCA is used for a) reconstructing radiances with the properties of reduced noise, b) independent instrument noise estimation, c) quality control, and d)
deriving the retrieval first guess used in the AIRS processing software. The radiance products also include cloud cleared radiances. The cloud clearing procedure remove the effect of cloud contamination in partial overcast conditions and have been demonstrated to increase the amount of data that can be treated as clear from 5% to 50%. The AIRS temperature and moisture retrieval are significantly more accurate than AMSU-only retrievals in clear, cloud contaminated and cloud-cleared conditions. Most NWP centers are currently assimilating clear radiances, which we believe severely limits the impact of AIRS data. Fortunately, results presented in this paper have stimulated new upcoming experiments to test the impact of cloud-cleared radiances.
AIRS was launched on EOS Aqua on May 5 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU/HSB are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an rms error of 1K, and layer precipitable water with an rms error of 20%, in cases with up to 80% effective cloud cover. Pre-launch simulation studies indicated that these results should be achievable. Minor modifications have been made to the pre-launch retrieval algorithm as described in this paper. Sample fields of parameters retrieved from AIRS/AMSU/HSB data are presented and validated as a function of retrieved fractional cloud cover. As in simulation, the degradation of retrieval accuracy with increasing cloud cover is small. Select fields are also compared to those contained in the ECMWF analysis, done without the benefit of AIRS data, to demonstrate information that AIRS can add to that already contained in the ECMWF analysis.
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