W. Paul Menzel
NOAA/NESDIS
Madison, Wisconsin 53706 USA
Donald P. Wylie
Cooperative Institute for Meteorological Satellite Studies
University of Wisconsin-Madison
Madison, Wisconsin 53706 USA
12 year trends
Effects of orbit drift and ancillary Tsfc
Separating diurnal signal
Comparison with CERES

Cloud Properties from CO2 Slicing
RTE for cloudy conditions indicates dependence of cloud forcing
(observed minus clear sky radiance) on cloud amount (


) and cloud top pressure (p c
)
(I

- I
 clr ) =

 p c
 
 p s dB

.
Higher colder cloud or greater cloud amount produces greater cloud forcing; dense low cloud can be confused for high thin cloud. Two unknowns require two equations.
p c can be inferred from radiance measurements in two spectral bands where cloud emissivity is the same.

 is derived from the infrared window, once p c is known.

Different ratios reveal cloud properties at different levels hi - 14.2/13.9
mid - 13.9/13.6
low - 13.6/13.3
Meas Calc
(I

1
-I

1 clr )


1 p c
 

1 dB

1 p s
----------- = ----------------
(I

2
-I

2 clr )


2 p c
 

2 p s dB

2

Determining Cloud Presence and Properties
Use bands where (I

- I
 clr ) > 1 mW/m2/ster/cm-1 in CO2 slicing estimation of p c
Estimate

IRW using IRW radiances
If more than one p c is estimated, use RTE for all bands to select best one
If no bands qualify, try IR window estimate for opaque cld
If too low in atmosphere, declare FOV clear

Generating Clear Sky Radiances in Cloudy FOVs
Use IR Window Moisture Corrected Brightness Temperature Test against a priori surface temperature to identify nearby clear sky FOVs
BT11 + aPW * (BT11 - BT12) - Sfc Temp < 2 C aPW of 0.8 has been used
Sfc Temp estimated from GDAS
Estimate I
 clr by interpolating nearby clear FOVs cld = x x o o o x x x x x x x x x x x x x o x o o o o o o o o o = clr o
Attempt to derive CO2 cloud properties in x (note that CO2 cloud algorithm attempt on x can change FOV to o)

Global high cloud cover stable for TIRW(H2O corr) – Tsfc < 1 to 3

GOES Sounder detecting diurnal change of cloud cover
Diurnal Change of Frequency of Clouds during Winter 1999
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Cloudy (%)
High (%)
1:30 4:30 7:30 10:30 13:30 16:30 19:30 22:30
LST
Diurnal Change of Frequency of Clouds for Summer 1999
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Cloudy (%)
High (%)
1:30 4:30 7:30 10:30 13:30 16:30 19:30 22:30
LST

Deviation from cloud mean 20 N-20S

Wielicki et al (2002) CERES deviation of reflected shortwave flux wrt 1985-89 mean for 20N-20S

NH (36% land)
Tropics (11% land)
SH (very little land)

Conclusions
* 12 yr trends in UW HIRS cloud statistics reveal high clouds increased until 1993 and then gradually decreased below 1989 levels.
* Cloud cover decreases since 1995 occur in all zones (NH, Tropics,
SH); NH shows most. These decreases are mainly observed over land and especially in higher clouds; ocean cloud cover is mostly stable.
* Differences in cloud statistics and their trends are consistent with changes in the local solar times of the orbits for satellites that drifted.
Satellite that did not drift, NOAA 12, shows a very large cloud detection decrease.
•A decrease in tropical cloud cover in the UW HIRS statistics is similar to that reported by Weilicki et al (2002) using CERES data.
Future Work
* Extend CO2 analysis back to 1978 (working with Bates and Jackson)