A43D-0138
Towards a New AVHRR High Cloud Climatology from PATMOS-x
Andrew K Heidinger, Michael J Pavolonis, Aleksandar Jelenak* and William Straka III
Contact Information
Email: heidinger@ssec.wisc.edu
Web: http://cimss.ssec.wisc.edu/clavr
NOAA/NESDIS/Center for Satellite Applications and Research
Advanced Satellite Product Branch, Madison Wisconsin
*UCAR, Washington D.C.
Cooperative Institute for Meteorological Studies, Madison, Wisconsin
Comparisons with Other Climatologies
Background
The AVHRR Pathfinder Atmospheres Extended (PATMOS-x) is a new reprocessing
effort with a goal of improving the AVHRR data quality and deriving some improved
cloud,aerosol and surface parameters. This poster reports on our progress on the
PATMOS-x climatology of high cloud properties (amount, temperature and
emissivity). PATMOS-x processes the 4km GAC data into a series of products at 55
km resolution for climate studies. All 5-channel AVHRR data is used including data
from both orbits (morning and afternoon). Some results from PATMOS-x are in a
recent Journal of Climate paper (Heidinger and Pavolonis, 2005)
Retrieval Approach
We adopt a 1d-variation retrieval approach where we use the 11 mm
brightness temperature and 11-12 mm brightness temperature difference
(BTD) to estimate the cloud temperature (Tc) and cloud emissivity (ec). The
images below show the retrieval performance for one scene and some of the
diagnostic measures from the 1d-var approach. The higher the reliance of
the retrieved parameter on the observations, the less the reliance on the first
guess. The results show successful retrieval is achieved with under 5
iterations and with minimal reliance on the first guess except for very thin
clouds as seen near the edges of this storm,
Performance Relative to MODIS
A comparison was done for one month of level 3 MODIS/AQUA data and PATMOS-x data for July
2004. The data shown here are for gridcells filled by ice clouds with near simultaneous data from both
sensors. The results are shown for all ice clouds and but are done for all CLAVR-x cloud types
(Pavolonis et al, 2005). The results show a strong correlation with a <2 K bias in cloud temperature and
very small bias in cloud emissivity. We plan to improve this analysis by going to pixel level comparisons
and we think some of this bias is due to limitations in the comparisons. Again, results show a strong
correlation in the cloud emissivities even for optically thin cloud.
Cloud Emissivity Results
We are concerned with lack of consensus of many cloud climatologies
and pursuing methods to reduce these discrepancies. As predicted by
our rough agreement with MODIS in terms of cloud temperature and
emissivity, the MODIS/AQUA and PATMOS-x high cloud amount
time-series agree more closely than others.
Cloud Temperature Results
Why Attempt this?
• The AVHRR data spans from 1981 to 2012(est) and therefore relevant to decadal climate studies.
• The large day/night differences in the ISCCP high cloud properties limit its use for diurnal studies.
•High Cloud Properties are critical important in modulating radiative fluxes are best observed from
satellites on a global scale.
•Our goal is use is to derive properties that are physically consistent with MODIS where the limited
spectral information of AVHRR allows.
Long Term Time Series
Why Use this Approach (the Split-Window)?
• Results indicate its performance is comparable to VIS/IR approaches (ISCCP day) in the
Regional Trends in High Cloud Amount
Our initial goal was to make time-series that were free of discontinuities
from the transition from one satellite to another. It appears from timeseries such as those shown that these effects are minimal.
estimation of Tc and ec.
We are beginning to analyze the climate variability signals in PATMOS-x. While we may not see
large scale in cloudiness, certain regions do possess significant sustained changes over the past three
decades as shown below where the mean July High Cloud Amount as an example. We are
developing and collaborating with others to conduct more extensive analyses.
• Because it uses only infrared channels, its performance is similar for day/night and
terminator
• Because it offers comparable performance, all AVHRR data (4 views/day) can be used to
study diurnal effects and improve the daily average.
Having met this goal, we are beginning to explore the information
content of the 25 year time series from PATMOS-x
Linear Trend 1982-2005
Mean for all Julys
•While ISCCP provides 8 views/day, it’s use of very different algorithms for cloud height
estimation during the day and night limits its ability for diurnal studies.
•This method has been studied previously, but has never been applied globally
Data from the Tropics (20S – 20N), monthly and daily averaged
Accuracy of Split-Window to VIS/IR and IR-window Method
The split-window measurements are fundamentally sensitive to Tc, ec and b. We have chosen to
fix b and estimate Tc and ec. To explore the errors in b, we have used the measurements of b
from Giraud (1997) who measured the mean of b to be 1.1 with a standard deviation of 0.04.
•Plots below show simulated performance of split-window method compared to VIS/IR and IR
• ISCCP uses a VIS/IR approach during day and an IR-window approach at night
• Split-Window simulations assume errors in b of one standard deviation (1s)– values based on
Giraud (1997)
• VIS/IR simulations assume 30% error in optical depth – conservative given uncertainty in ice
scattering properties
• Split-window Tc estimates appears to always outperform IR-window and VIS/IR for ec > 0.2
• Split-Window estimates of ec are better than VIS/IR with 30% error in optical depth
Explained Variance of Linear Fit
Example Time Series with High Exp. Var.
Visual Comparison with MODIS
The two images show a comparison of the cloud temperature for Hurricane Ivan from
MODIS/AQUA (MOD06) and the split-window approach applied to NOAA-16
AVHRR. The two data sets are separated in time by 20 minutes. This image qualitatively
indicates that the split-window method is successfully placing thin cirrus (edges of outer
bands) at high levels.
circled region
Conclusions
• The Split-Window approach provides a day/night consistent approach for
determining a cloud temperature and emissivity.
References
Giraud, V., Buriez, J. C., Fouquart, Y., Parol, F., Seze, G., Jun. 1997. Large-Scale Analysis of Cirrus Clouds
from AVHRR Data: Assessment of Both a Microphysical Index and the Cloud-Top Temperature. Journal of
Applied Meteorology 36, 664–675.
Heidinger, Andrew and M. J. Pavolonis, 2005: Global Daytime Distribution of Overlapping Cirrus Cloud from
NOAA's Advanced Very High Resolution Radiometer. Journal of Climate, Vol. 18, No. 22, pages 4772–4784
Michael J. Pavolonis, Andrew K. Heidinger and Taneil Uttal. 2005: Daytime Global Cloud Typing from AVHRR and
VIIRS: Algorithm Description, Validation, and Comparisons. Journal of Applied Meteorology: Vol. 44, No. 6, pp. 804–
826.
•Based on published variations of b (Giraud, 1997) , a fixed-b approach appears to
outperform IR and VIR/IR approaches such as used by ISCCP.
•The PATMOS-x method appears to be consistent with MODIS
• The Split-window appears to offer excellent estimates of ice cloud emissivity
(optical depth) for semitransparent ice clouds.
•The PATMOS-x time series are stable and we are beginning to analyze them.