The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) generates products derived
from AIRS/AMSU-A observations, starting from September 2002 when the AIRS instrument became stable, using the
AIRS Science Team Version-5 retrieval algorithm. This paper shows results of some of our research using Version-5
products from the points of view of improving forecast skill as well as aiding in the understanding of climate processes.
This paper compares recent spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLRCLR (Clear Sky
OLR) as determined using CERES and AIRS observations over the time period September 2002 through June 2010. We
find excellent agreement in OLR anomaly time series of both data sets in almost every detail, down to the 1° x 1° spatial
grid point level. This extremely close agreement of OLR anomaly time series derived from observations by two different
instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the
anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper then
examines anomaly time series of AIRS derived products over the extended time period September 2002 through April
2011. We show that OLR anomalies during this period are closely in phase with those of an El Niño index, and that
recent global and tropical mean decreases in OLR and OLRCLR are a result of a transition from an El Niño condition at
the beginning of the data record to La Niña conditions toward the end of the data period. This relationship can be
explained by temporal changes of the distribution of mid-tropospheric water vapor and cloud cover in two spatial regions
that are in direct response to El Niño/La Niña activity which occurs outside these spatial regions.
The AIRS instrument is currently the best space-based tool to simultaneously monitor the vertical distribution of
key climatically important atmospheric parameters as well as surface properties, and has provided high
quality data for more than 5 years. AIRS analysis results produced at the GODDARD/DAAC, based on
Versions 4 & 5 of the AIRS retrieval algorithm, are currently available for public use. Here, first we present
an assessment of interrelationships of anomalies (proxies of climate variability based on 5 full years, since Sept.
2002) of various climate parameters at different spatial scales. We also present AIRS-retrievals-based global,
regional and 1x1 degree grid-scale "trend"-analyses of important atmospheric parameters for this 5-year period.
Note that here "trend" simply means the linear fit to the anomaly (relative the mean seasonal cycle) time series
of various parameters at the above-mentioned spatial scales, and we present these to illustrate the usefulness of
continuing AIRS-based climate observations. Preliminary validation efforts, in terms of intercomparisons of
interannual variabilities with other available satellite data analysis results, will also be addressed. For example,
we show that the outgoing longwave radiation (OLR) interannual spatial variabilities from the available state-of-the-art CERES measurements and from the AIRS computations are in remarkably good agreement. Version
6 of the AIRS retrieval scheme (currently under development) promises to further improve bias agreements for
the absolute values by implementing a more accurate radiative transfer model for the OLR computations and by
improving surface emissivity retrievals.
Satellites provide an ideal platform to study the Earth-atmosphere system on practically all spatial and temporal
scales. Thus, one may expect that their rapidly growing datasets could provide crucial insights not only for
short-term weather processes/predictions but into ongoing and future climate change processes as well. Though
Earth-observing satellites have been around for decades, extracting climatically reliable information from their
widely varying datasets faces rather formidable challenges. AIRS/AMSU is a state of the art
infrared/microwave sounding system that was launched on the EOS Aqua platform on May 4, 2002, and has
been providing operational quality measurements since September 2002. In addition to temperature and
atmospheric constituent profiles, outgoing longwave radiation [OLR] and basic cloud parameters are also
derived from the AIRS/AMSU observations. However, so far the AIRS products have not been rigorously
evaluated/validated on a large scale. Here we present preliminary assessments of climatically important
"Level3" (monthly and 8-day means, 1° x 1° gridded) AIRS "Version 4.0" retrieved products (available to the
public through the DAAC at NASA/GSFC) to assess their utility for climate studies. Though the current AIRS
climatology covers only ~4.5 years, it will hopefully extend much further into the future. First we present
"consistency checks" by evaluating the ~4.5-yr long time series of global and tropical means, as well as grid-scale
variability and "anomalies" (relative to the first full years worth of AIRS "climate statistics" of several
climatically important retrieved parameters). Finally, we also present preliminary results regarding
interrelationships of some of these geophysical variables, to assess to what extent they are consistent with the
known physics of climate variability/change. In particular, we find at least one observed relationship which
contradicts current general circulation climate (GCM) model results: the global water vapor climate feedback
which is expected to be strongly positive is deduced to be slightly negative (shades of the "Lindzen effect"?).
Global energy balance of the Earth-atmosphere system may change due to natural and man-made climate
variations. For example, changes in the outgoing longwave radiation (OLR) can be regarded as a crucial
indicator of climate variations. Clouds play an important role -still insufficiently assessed- in the global energy
balance on all spatial and temporal scales, and satellites provide an ideal platform to measure cloud and largescale
atmospheric variables simultaneously. The TOVS series of satellites were the first to provide this type of
information since 1979. OLR [Mehta and Susskind1], cloud cover and cloud top pressure [Susskind et al.2] are
among the key climatic parameters computed by the TOVS Pathfinder Path-A algorithm using mainly the
retrieved temperature and moisture profiles. AIRS, regarded as the 'new and improved TOVS', has a much
higher spectral resolution and greater S/N ratio, retrieving climatic parameters with higher accuracy.
First we present encouraging agreements between MODIS and AIRS cloud top pressure (Ctp) and
'effective' (Aeff, a product of infrared emissivity at 11 μm and physical cloud cover or Ac) cloud fraction
seasonal and interannual variabilities for selected months. Next we present validation efforts and preliminary
trend analyses of TOVS-retrieved Ctp and Aeff. For example, decadal global trends of the TOVS Path-A and
ISCCP-D2 Pc and Aeff/Ac values are similar. Furthermore, the TOVS Path-A and ISCCP-AVHRR [available
since 1983] cloud fractions correlate even more strongly, including regional trends.
We also present TOVS and AIRS OLR validation effort results and (for the longer-term TOVS Pathfinder
Path-A dataset) trend analyses. OLR interannual spatial variabilities from the available state-of-the-art CERES
measurements and both from the AIRS [Susskind et al.3,4] and TOVS OLR computations are in remarkably
good agreement. Global monthly mean CERES and TOVS OLR time series show very good agreement in
absolute values also. Finally, we will assess correlations among long-term trends of selected parameters,
derived simultaneously from the TOVS Pathfinder Path-A dataset.
The AIRS/AMSU (flying on the EOS-AQUA satellite) sounding retrieval methodology allows for the retrieval of key atmospheric/surface parameters under partially cloudy conditions (Susskind et al., 2003). In addition, cloud parameters are also derived from the AIRS/AMSU observations. Within each AIRS footprint, cloud parameters at up to 2 cloud layers are determined with differing cloud top pressures and "effective" (product of infrared emissivity at 11 microns and physical cloud fraction) cloud fractions. However, so far the AIRS cloud product has not been rigorously evaluated/validated. Fortunately, collocated/coincident radiances measured by MODIS/AQUA (at a much lower spectral resolution but roughly an order of-magnitude higher spatial resolution than that of AIRS) are used to determine analogous cloud products from MODIS. This allows us for a rather rare and interesting possibility: the intercomparisons and mutual validation of imager vs. sounder-based cloud products obtained from the same satellite positions. First, we present results of small-scale (granules) instantaneous intercomparisons. Next, we will evaluate differences of temporally averaged (monthly) means as well as the representation of inter-annual variability of cloud parameters as presented by the two cloud data sets. In particular, we present statistical differences in the retrieved parameters of cloud fraction and cloud top pressure. We will investigate what type of cloud systems are retrieved most consistently (if any) with both retrieval schemes, and attempt to assess reasons behind statistically significant differences.
Marine statiform clouds (MSC) cover large areas of the globe that are visible to GOES. The operational satellite cloud retrieval algorithms are prone to biases when analyzing MSC, due to the often sub-pixel size cloud elements and radiative temperatures close to that of the underlying ocean. For example, the relatively large pixel size and calibration drifts in GOES-7 imagery have made it difficult to extract unbiased MSC properties using thermal threshold techniques. Here, we apply a novel retrieval approach to the two important MSC regimes which can be monitored well from the GOES-8 satellite: the Pacific Ocean just west of California/Baja and Peru/Chile. MSC cloud parameters for these areas are retrieved together with surface temperature and column water vapor in a temporally and spatially consistent manner that is insensitive to sensor resolution and calibration errors. Semi-operational analysis of GOES-8 imagery began in December 1995. So, the main focus is on assessing the diurnal variability of MSC. Following a brief description of the retrieval technique, we present initial results describing the full diurnal cycle of MSC fractional cloud cover and cloud top temperature, monitored using the single-channel version of the algorithm. In addition, we address the daytime variability of other important cloud parameters using a bispectral extension of the retrieval scheme. The results are also compared with other pertinent MSC analyses.
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