A44A-01 16:00h
Large-scale Carbon Monoxide Transport as Revealed by AIRS During the NASA INTEX-A Field Campaign
During NASA's Intercontinental Chemical Transport Experiment (INTEX-A) the AIRS retrieval algorithm was run semi-operationally to provide next-day maps of Carbon Monoxide (CO) to assist with flight-planning. With AIRS L1b radiances supplied by NOAA/NESDIS, the research version of the AIRS retrieval algorithm was run at the University of Maryland Baltimore County (UMBC) in a mostly autonomous mode with separate AM and PM orbital track maps automatically posted to the web. The time-period covered ran from June 1 to August 16, 2004, with INTEX-A itself running from July 1 to August 15, 2004. Retrievals were performed on all AIRS granules falling within longitudes 180W to 30E and latitudes 15N to 70N. The online maps present AIRS retrievals binned on a 1 degree scale. Large forest fires burning in the Alaskan and Canadian Yukon from June 29-July 23 represent the major CO sources observed during this time. The AIRS maps clearly show large CO plumes from these fires transiting across Canada and as far south as the Louisiana coast on the Gulf of Mexico and as far east as Western Europe. These plumes were sampled numerous times by the DC-8 and various NOAA and international aircraft participating in the International Consortium for Atmospheric Research Transport and Transformation (ICARTT) field campaign. AIRS observations also reveal significant periodic sources of CO from the industrial and Southeastern United States and perhaps Asia. Ongoing analysis and interpretation seeks to quantify all these sources and determine the source regions (Asia, Siberia, Alaska?) for the enhanced CO observed over the Pacific Ocean north and west of Hawaii. We will summarize AIRS CO measurements during INTEX-A and present preliminary comparisons with in situ measurements from the NASA DC-8.
http://asl.umbc.edu/pub/mcmillan/www/index.html
A44A-02 16:12h
Determination of Atmospheric Minor Gases from the Residuals of the Solution of the Radiative Transfer Equation
We present an analytical method for the determination of minor gases from observations made with a spectral resolution of 1/1200, that does not necessarily resolve the spectral lines of those gases. We require that the measured radiance spectra be sensitive to variations of the concentration of active minor gases, and that this sensitivity exceeds the measurements noise. We use a radiative transfer algorithm and proceed to separate the contributions of each individual minor gas by a generalization of a basic property of the partial derivatives of the residuals (the square of difference between measured and computed radiances). We illustrate the method through application to real measurements from the Infrared Atmospheric Sounder (AIRS) currently flying on the NASA Aqua Mission.
A44A-03 16:24h
Two year trend analysis of AIRS and AMSU data over cloud-free tropical oceans for climate applications
We present the analysis of two year of trends in AIRS and AMSU data. AIRS is a hyper-spectral infrared sounder and AMSU is a microwave sounder on the EOS Aqua satellite, which was launched into 705 km altitude sun-synchronous orbit in May 2002. Both instruments are cross-track scanners, with synchronized scan coverage. Instrumentation on polar orbiting satellites is ideally suited to the global monitoring of environmental variables. Since the predicted effects of global changes require measurement accuracy and stability at the faction of a degree Kelvin per year level, validation and trend analysis of the data are critical to characterize their applicability to climate research. The AIRS radiances are based on a NIST traceable onboard calibration blackbody. These radiances are tied via the 2616cm-1 window channel to the Real Time Global SST, RTGSST. The RTGSST, generated daily by NCEP for the GCM in support of weather forecasting, provides the tie of the AIRS calibration to the global network of drifting buoys, which serve as quasi tertiary standards. Analysis of the AIRS 2616 cm-1 based sea surface temperature measurements relative to the RTGSST shows better than 7mK per year radiometric stability, with a residual cold bias of 200mK. The radiometric stability for all AIRS channels is established through the common onboard blackbody and space view. The radiometric stability of AMSU was tested by comparing the brightness temperatures measured by AMSU channel 5 at 53 GHz with co-located measurements by the AIRS 2388cm-1 channel. The two channels sound at 5 km altitude in the mid-troposphere with closely matching weighting functions, but the microwave opacity is due to oxygen, while the infrared opacity is due to co2. The comparison was limited to cloud-free tropical oceans with less than 35 degree slant path. Analysis of two years of data shows that the brightness temperatures measured by AIRS 2388cm-1 channel are slowing getting colder relative to the AMSU channel 5 temperatures at the rate of about 100mK/year. The interpretation of this relative cooling as co2 increase at the rate of about 2.2 ppmv/year is supported by the presence of the expected seasonal co2 abundance maxima in May 2003 and May 2004. This result, which is the first measurement of the global increase in co2 using the AIRS and AMSU on EOS Aqua, is an important step in confirming the potential value of the data to climate research.
A44A-04 16:36h
Validation and Covariation of Aqua measurements of Trace gases and Clouds in the Upper Troposphere and Lower Stratosphere
AIRS and MODIS data the the Upper Troposphere and Lower Stratosphere (UT/LS) are validated and used to understand the covariation of clouds and humidity in this critical and sparsely observed region of the atmosphere. AIRS and MODIS data are validated against in-situ aircraft data of trace gases and clouds from the NASA WB57 aircraft in January and February 2004. Satellite observations compare well to aircraft data; temperature is generally within $\pm$1.5K and water vapor is within $\pm$25% of aircraft observations for pressures above 150 hPa and mixing ratios above 10ppmv. Thicker cirrus clouds are well resolved from the satellite. 2 years of data from AIRS and MODIS are used to illustrate the climatology of Temperature, Ozone & Water Vapor in the UT/LS. As an example of the potential of this data, the data is used to demonstrate the covariation of clouds from MOIDS and humidity from AIRS.
A44A-05 16:48h
Retrieval of cloud properties from MODIS and AIRS
The Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from NASA's Earth Observing System's (EOS) Aqua satellite enable global monitoring of the distribution of clouds during day and night. The MODIS is able to provide a high spatial resolution (1 ~ 5km) cloud mask, cloud classification mask (CCM), cloud phase mask (CPM), cloud-top pressure (CTP), effective cloud amount (ECA) during the daytime and the nighttime, as well as cloud particle size (CPS) and cloud optical thickness (COT) at 0.55 mm during the daytime. The AIRS high spectral resolution measurements reveal cloud properties with coarser spatial resolution (13.5 km at nadir). The combined MODIS/AIRS systems offer the opportunity for improved cloud products over those possible from either system alone; the improvement on CTP and ECA retrievals has been demonstrated with both simulated and real radiances with wavenumbers between 650 - 790 cm-1 (12.66 - 15.38 mm). A fast cloudy radiative transfer model for AIRS accounting for cloud scattering and absorption is described in this paper. The difference between the fast cloud model and the Discrete Ordinates Radiative Transfer (DISORT) calculation is less than 0.5 K for most AIRS spectral channels. One-dimensional variational (1DVAR) and minimum residual (MR) methodologies are used to retrieve the CPS and COT from AIRS longwave window region (790 - 970 cm-1 or 10.31 - 12.66 mm, and 1050 - 1130 cm-1 or 8.85 - 9.52 mm) cloudy radiance measurements. Operational CPS product from the high spatial resolution MODIS serves as background and first guess information in the AIRS 1DVAR cloud retrieval (here and after, refers as MODIS+AIRS 1DVAR), while the cloud microphysical property retrievals are also derived from AIRS radiances with the MR algorithm (here and after, refers as AIRS MR). In both 1DVAR and MR procedures, the CTP is derived from the AIRS radiances of CO2 channels while the cloud phase information is derived from the collocated MODIS 1km phase mask for AIRS CPS and COT retrievals. In addition, the collocated 1km MODIS cloud mask refines the AIRS cloud detection in both the MODIS+AIRS 1DVAR and the AIRS MR procedures. The atmospheric temperature profile, moisture profile and surface skin temperature used in the AIRS cloud retrieval processing are from the European Center for Medium-range Weather Forecasting (ECMWF) forecast analysis. The results from the MODIS+AIRS 1DVAR are compared with the operational MODIS products and the AIRS MR cloud microphysical property retrieval. A Hurricane Isabel case study shows that the MODIS+AIRS 1DVAR retrievals have high correlation with either the operational MODIS cloud products or the AIRS MR cloud property retrievals. The MODIS+AIRS 1DVAR provides an efficient way for cloud microphysical property retrieval during the daytime, while the AIRS MR provides the cloud microphysical property retrievals during both the daytime and nighttime.
A44A-06 17:00h
Validation of Inter-annual Monthly Mean Difference of AIRS Products
Geophysical parameters derived from AIRS/AMSU-A observations are important for both improving numerical weather prediction and studying climate variability and trends. For the latter purpose, interannual differences of AIRS monthly mean products should be consistent with those of analogous geophysical parameters derived from other sources, where they are accurate, and add new information where they are less accurate. AIRS/AMSU-A data has been analyzed for the months of January 2003 and January 2004. Monthly mean temperature and water vapor fields of retrieved parameters have been generated and compared to comparable fields obtained from collocated ECMFW 6-hour forecasts to determine the degree that AIRS inter-annual difference fields are consistent with expectations at locations and pressure levels where ECMWF forecasts should be accurate, and add information where ECMWF forecasts are questionable. AIRS inter-annual differences are also compared with inter-annual differences of TOVS Pathfinder Path-A products, CERES OLR, and Spencer and Christy MSU2R and MSU4 products to assess compatibility with long term satellite data sets.
A44A-07 17:12h
The Impact of AIRS Data on Numerical Weather Predicition at NASA/GSFC
A series of data assimilation and forecast experiments are being performed as a component of the geophysical validation of AIRS data at NASA GSFC. These experiments are aimed at evaluating the impact of AIRS data in several forms: clear vs. partially cloudy data; AIRS Team physical retrievals; and AIRS radiances. Two different data assimilation systems are used for this purpose: FVDAS (or GEOS 4) which is the operational data assimilation system of the Global Modeling and Assimilation Office, and FVSSI which is a combination of the NASA global atmospheric model with the NOAA NCEP SSI analysis scheme. The results from these experiments show the overall utility of the different AIRS data sets and the ability of AIRS to represent atmospheric fields. The initial experiments show a positive impact of AIRS data on Southern Hemisphere analyses and forecasts with both data assimilation systems. In the Northern Hemisphere the impact is smaller but on occasion significant positive impacts occur.
A44A-08 17:24h
Analysis of the Structure and Evolution of the Madden-Julian Oscillation Using AIRS Data
Since its discovery, the Madden and Julian Oscillation [MJO; a.k.a. Intraseasonal Oscillation (ISO)] has continued to be a topic of significant interest due to its complex nature and the wide range of phenomena it interacts with. For example, the onset and break activity of the Asian-Australian monsoon system are strongly influenced by the propagation and evolution of MJO events. Apart from this significant local influence, there are also important downstream influences that arise from the MJO. This includes the development of persistent North Pacific circulation anomalies, including extreme rainfall events along the western United States, during Northern Hemisphere winter that have been linked to the evolution and eastward progression of convective anomalies associated with MJO events. In addition, MJO convective activity has been linked to Northern Hemisphere summer time precipitation variability over Mexico and South America as well as to wintertime circulation anomalies over the Pacific - South American Sector. Studies have also shown that particular phases of the MJO are more favorable than others in regards to the development of tropical storms/hurricanes in both the Atlantic and Pacific sectors. Finally, the passage of MJO events over the western Pacific Ocean has been found to significantly modify the thermocline structure in the equatorial eastern Pacific Ocean via their connection to westerly wind bursts. This latter interaction has even been suggested to play an important role in triggering variations in El Nino - Southern Oscillation (ENSO). As influential as the MJO is on our weather and climate, we still struggle to properly represent the MJO in our general circulation models (GCMs) used for weather prediction and climate simulation. The greatest uncertainty in this representation is associated with the hydrological components, namely water vapor, cloud and the condesation/evaporation processes. Most work to date on diagnosing this problem and trying to improve model representations of the convective and other cloud processes has been restricted to analysis of 2-dimensional data in the horizontal plane (e.g., OLR, upper-level winds, surface characteristics). In this study, we seek to exploit the three-dimensional spatial structure afforded by AIRS data along with its high spatial and temporal resolution to better understand the spatial-temporal evolution of the MJO, particularly in regards to moist processes. Our goal is to develop an observed depiction of the MJO evolution from AIRS and supporting data sets (e.g., TRMM) and compare this to GCMs to learn what aspects of the physics may not be properly represented in the models.
A44A-09 17:36h
Propagation of Kelvin Waves in the Troposphere as Seen in AIRS Data
While Kelvin waves are known to propagate upward in the tropical stratosphere from satellite data and in situ measurements reveal signs of upward propagation in the stratosphere, no observations linking Kelvin wave activity and the distribution of tropospheric water vapor have been made. Retrievals of tropospheric temperature, water vapor, and outgoing longwave radiation from AIRS data offer a rich new opportunity to probe the interaction of clouds, convection, water vapor and dynamics associated with tropical waves. We focus on Kelvin waves. We have extracted all successful retrievals of temperature and water vapor using AIRS infrared data between 2S and 2N latitude for the month of January, 2003. Because the data is irregularly sampled in longitude and time, we use Bayesian interpolation with zonal propagation as the basis functions to find what Kelvin waves are present. We have found a class of Kelvin waves which originate in the troposphere and propagate into the stratosphere. These waves are defined by monotonically decreasing phase of temperature anomalies starting within the troposphere. An animation of the temperature anomalies reveals nearly barotropic structure between 300 hPa and 500 hPa with specific humidity anomalies strong correlated with temperature anomalies. Below this layer is evidence of a checkerboard pattern in the longitude-height temperature anomaly field, an indication of the presence of both upward and downward propagation as might be induced by reflection at the Earth's surface. That specific humidity and temperature are in phase in the 300- to 500-hPa layer poses problems for theories of Kelvin wave generation. If diabatic effects were responsible for wave generation, then the phasing between convection/humidity and temperature would have convective anomalies leading temperature anomalies by 90. Furthermore, kinematic effects could not be responsible for wave generation because its efficiency approaches zero at increasingly large horizontal scales. We will speculate on other potential Kelvin wave generation mechanisms which may be at work.
A44A-10 17:48h
Validation of AIRS-based dust property retrievals using data from the portable infrared aerosol transmission experiment (PIRATE) - Caribbean and AERONET
The retrieval of mineral aerosol (dust) properties near Puerto Rico in June 2004 using a physically-based inversion algorithm with AIRS fluxes is presented. The retrievals are validated using results from the Portable Infrared Aerosol Transmission Experiment (PIRATE) - Caribbean and an AERONET sensor. PIRATE - Caribbean was a ground-based experiment that measured the infrared transmission through dust transported across the Atlantic Ocean from the Sahara Desert. A Fourier Transform Infrared (FTIR) spectrometer was used in Boqueron, Puerto Rico from June 23 through June 30, 2004 as a high-resolution infrared sun photometer. The visible aerosol optical depth (AOD) at the time of each FTIR measurement was taken from a nearby AERONET sensor at La Parguera, Puerto Rico. The FTIR recorded the direct solar radiance from 3 to 14 microns at 4 cm-1 spectral resolution. The measured infrared extinction efficiency of the dust is incorporated into a new AIRS-based dust property retrieval algorithm which, determines the dust AOD at 550 nm, the dust effective radius, the dust effective temperature, and the surface temperature. The retrieved AOD is compared with the AERONET results, and the retrieved surface temperatures are compared with the 11.1 micron AIRS brightness temperatures. During high AOD episodes, the 11.1 micron AIRS brightness temperature is over 2 C cooler than the actual surface temperature, indicating the presence of dust can lead to erroneous surface temperature retrievals unless the dust properties are included in the retrievals.