1. Cal/Val-Related Activities at CICS
Andy Harris, Ralph Ferraro
Hai-Tien Lee

2. CICS Is the NOAA Cooperative Institute for Climate Studies
Is based within the Earth System Science Interdisciplinary Center at U. Maryland
CICS is generally tilted towards
Climate
Geophysical applications
Calibration is important, but not “primary function”
But it does matter for what we do – sometimes folks have to get their hands dirty!

3. Inter-satellite Calibration for HIRS OLR
Satellites
Bias (Wm-2) TN
0.15
N06
1.80
N07
2.13
N08
2.03
N09
Reference
N10
0.53
N11
-5.36
N12
-2.42
N14
-5.14
N15
-3.65
N16
-3.25
Collocation:
• 1°x1° lat/lon
• ±30 minutes
• n > 1
Homogeneity filter:
• Std error of mean OLR < 1 Wm-2
HIRS OLR retrievals were collocated for each of the overlapping pairs of POES over a target of 1°x1° within plus/minus 30 minutes. Minimum number of retrievals is 2.
The black curve histogram shows the distribution of the OLR differences. This example is for TIROS-N and NOAA6. (we label TIROS-N as N05)
A homogeneity filter was applied to improve the data quality - only mean OLR with a standard error of less than 1 Wm-2 is used. The red dashed curve histogram shows the distribution of OLR differences from homogeneous scenes, with corresponding scatter plot shown at the bottom.
There are two approaches for the calibration. Method 1 determines mean biases referenced to NOAA9. Method 2 determines linear adjustments also referenced to NOAA9. Table on the right shows method 1 results.

4. Improvement with inter-satellite calibration
The blended HIRS monthly mean OLR data agrees better with CERES with the adjustments determined by inter-satellite calibration.
This is the time series of monthly mean OLR averaged over the tropics (20S-20N).
The brown curves are the HIRS OLR monthly mean derived from individual satellites, including NOAA11, 12, 14, 15 and 16, for various periods. The differences in sampling time and the systematic biases caused the deviation within these time series.
The black solid (dotted) line is the blended product of the HIRS OLR that the individual satellite retrievals were first adjusted according to the inter-satellite calibration method 1 (2) results and then temporally integrated.
CERES OLR is from broadband measurements that is considered here as the “ground truth” reference. Apparently, the blended HIRS OLR with the inter-satellite adjustment agrees much better with the CERES than any of the individual ones.
Tropical Mean
Magenta - CERES (TRMM, Terra, Aqua)
Black solid/dotted - HIRS, blended with calibration method 1/2
Brown - HIRS from individual satellites: NOAA11, 12, 14, 15, 16

5. AMSU-A Asymmetry
Preliminary coefficients derived using 18 days of AVHRR, GDAS and AMSU-A data
Clear sky
RT calculations
Asc/Dsc (example is Asc)
Expand to 30 days of matchups
Testing impact in MSPPS, in particular, TPW and CLW
The AMSU-A sensor, flown on N-15, -16, -17 and now -18 (also on Aqua as part of AIRS package) has a known “asymmetry” (i.e., left to right bias in TB across scan due to a number of factors related to instrument performance). Largest asymmetry has been found with 31.4 and 50.3 GHz channels.
A methodology was developed by Fuzhong Weng to characterize this asymmetry by matching AVHRR IR and GDAS model data with AMSU-A and then performing forward RT calculations on AMSU-A channels. This work is done to improve hydrological product retrievals (MSPPS – Microwave Surface and Precipitation Products System) and we are most concerned with “window” channels at 23.8, 31.4, 50.3 and 89 GHz.
Shown is an example for the recently (May 2005) launched N-18 satellite. The plot shows the difference between the RT calculations (“The Truth”) and the actual measurements as a function of LZA. It is for clear sky (as determined from AVHRR IR), ocean only (where the impact is the greatest). This is for 18 days of data, we will go to at least 30 days.

6. Product Impacts – Cloud Liquid Water (CLW)
After asymmetry coeff’s implemented
The impact on developing a proper asymmetry correction is illustrated in the cloud liquid water (CLW) product from AMSU, probably the one most sensitive to the asymmetry at 31.4 GHz. The image shows the impact on the day a preliminary version of the correction was implemented (denoted by the 4 orbits with the arrows).
Note how without the corrections, there is an apparent increase in CLW on the left side of each orbit; with the correction implemented, this bias is removed.

7. RTM improvements: GOES-9 Case Study
Unusually large scatter and warm bias at low atmospheric corrections may be due to diurnal warming
Nighttime retrievals also show small trend vs atmospheric correction
Updated RT model removes most of the trend
Perhaps note that the GOES-9 Imager is an old sensor so these results are pretty good for something launched a decade ago. Note that nighttime 2-channel has quite good S.D. (~0.6 K) and bias around -0.2 K (same as triple-window algorithm), so increased daytime scatter & bias are due to diurnal warming rather than actual errors in retrieved SST. **NOTE**, there isn’t a text bullet to accompany the last plot on this slide! Round up this slide by saying that the diurnal warming issue must be tackled, for a number of reasons. This leads into the next slide.
Application of daytime coefficients to nighttime data gives small –ve bias (expected)

8. Summary
Climate-focused work in particular requires very stringent calibration – at least of the end-product
Study of products can often highlight areas which feed back into more fundamental aspects, such as calibration and forward modeling
Other product areas include snow mapping & aerosols
Some applications have more complex responses to calibration errors – often have to be “tuned” (e.g. ocean color → biological model)