A43D-0138 Towards a New AVHRR High Cloud Climatology from PATMOS-x

Andrew K Heidinger, Michael J Pavolonis, Aleksandar Jelenak* and William Straka III

NOAA/NESDIS/Center for Satellite Applications and ResearchAdvanced Satellite Product Branch, Madison Wisconsin

*UCAR, Washington D.C.Cooperative Institute for Meteorological Studies, Madison, Wisconsin

Visual Comparison with MODISThe 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.

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.

Contact InformationEmail: heidinger@ssec.wisc.eduWeb: http://cimss.ssec.wisc.edu/clavr

Retrieval Approach

We adopt a 1d-variation retrieval approach where we use the 11 mm brightness temperature and 11-12 m 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,

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)

Accuracy of Split-Window to VIS/IR and IR-window Method The split-window measurements are fundamentally sensitive to Tc, ec and . We have chosen to fix and estimate Tc and ec. To explore the errors in b, we have used the measurements of from Giraud (1997) who measured the mean of 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 of one standard deviation (1)– 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

Why Use this Approach (the Split-Window)?• Results indicate its performance is comparable to VIS/IR approaches (ISCCP day) in the estimation of Tc and ec.

• 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.

•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

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.

Comparisons with Other ClimatologiesWe 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.

Performance Relative to MODISA 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 Cloud Temperature Results

Regional Trends in High Cloud Amount

Mean for all Julys Linear Trend 1982-2005

Explained Variance of Linear Fit Example Time Series with High Exp. Var.

circled region

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.

Conclusions

• The Split-Window approach provides a day/night consistent approach for determining a cloud temperature and emissivity.

•Based on published variations of (Giraud, 1997) , a fixed- 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.

Long Term Time Series

Our initial goal was to make time-series that were free of discontinuities from the transition from one satellite to another. It appears from time-series such as those shown that these effects are minimal.

Having met this goal, we are beginning to explore the information content of the 25 year time series from PATMOS-x

Data from the Tropics (20S – 20N), monthly and daily averaged