AOS 907
Global Multi-layer Cloud Distribution from AVHRR
(AVHRR Reprocessing Activities at NOAA/NESDIS/ORA)
Andrew Heidinger
NOAA/NESDIS/ORA
Michael Pavolonis
UW/CIMSS
March 1, 2004
Outline
• AVHRR Description
• Activities in ORA concerning AVHHR
Reprocessing
• Detection of Multi-layer Cloud (cirrus
overlap) in AVHRR data
• Global Distribution of Cirrus Overlap from
July and January AVHRR data
• Comparison with ISCCP
• Future Work and Opportunities for
collaboration
The AVHRR – Advanced Very High Resolution Radiometer
• Designed in the 1970’s for non-quantitative cloud imagery
•Can be calibrated well enough for other remote sensing apps.
•5 channels with relative broad response functions
CH1 – 0.63 um
CH3 3.75 um
CH2 – 0.86 um
CH4 – 11 um
5 channel AVHRR data record started in 1981
CH5 – 12 um
Why use the AVHRR for Decadal Climate Studies
•The radiometric calibration of the data record is improving should be
good to provide some unambiguous trends (i.e. X Wang & J Key’s APP Artic
Study)
•It is the only imager with a continuous data record for over twenty years
and in the end, it will have a data record of at least 30 years)
•Though it has only five channels, it provides information on
•Surface temperature
•Vegetation health/coverage
•Cloudiness
•Aerosol
•Fires
•Snow/ice extent
•Water turbidity
•The data and products from MODIS/VIIRS (new imagers) for the above
are better and can be used to help quantify the uncertainties in the
AVHRR products (AVHRR is still flying)
AVHRR Reprocessing Activities at NESDIS/ORA
• Roughly 16 Tb of storage being setup to house to entire GAC Record
•Updated thermal calibration procedures
•Simultaneous Nadir Overpasses are being used to characterize and reduce
intersatellite biases (especially in the reflectance channels)
•Errors in spectral responses are being investigated (SST,NDVI)
•Each GAC orbit is being renavigated to fix the time errors (10 km)
•Regeneration of the Pathfinder Atmospheres (PATMOS) data
•Regeneration of SST, NDVI, Polar Winds and other NOAA climate
records
The Global Distribution of Multi-layer cloud
from AVHRR
•One of the first tests of the AVHRR processing system was an initial
run through PATMOS (Pathfinder Atmospheres) for many July and
January months.
•One of few meaningful scientific studies we could conduct with this
limited data-set
•One of the new algorithms was the Clouds from AVHRR extended
(CLAVR-x) suite of cloud mask/type and properties. This algorithm
includes the new multi-layer cloud (cirrus overlap) detection written by
Michael Pavolonis of CIMSS.
Pavolonis, M. and A. K. Heidinger, 2004: Daytime Cloud Overlap
Detection from AVHRR and VIIRS. Accepted by JAM
Why try to derive the occurrence of cirrus overlap from a satellite?
•The assumption of single layer clouds may be inappropriate for many cloud
retrieval products in multi-layer situations.
•Wang and Rossow (1998) used global circulation model (GCM) simulations
to demonstrate that large scale features such as the strength of Hadley
circulation and the distribution of precipitation are sensitive to the
prescription of the cloud vertical structure.
•Morcrette and Jakob (2000) found that the surface and top of the
atmosphere radiative fluxes varied significantly in the European Centre for
Medium-Range Weather Forecasts (ECMWF) general circulation model
when the cloud overlap scheme was varied.
•Gupta et al. (1992) and Wielicki et al. (1995) showed that the earth
radiation budget will be largely influenced by the vertical location and
coverage of clouds.
One obvious example – the effect of multi-layer clouds on the
Infrared Heating Profiles
Below are two infrared heating rates profiles where both situation present
the same Top of Atmosphere visible reflectance and window brightness
temperature (looks equivalent to ISCCP). (total optical depth = 10)
This building is a multi-layer cloud detection from imagers powerhouse
MODIS
•Bryan Baum – developed a method for cirrus overlap detection
using a clustering approach with 1.6 micron reflectance and 11
micron brightness temperature and is leading the effort to apply
this to MODIS data
•Shaima Nasiri –extended Baum’s method and exploring its
application potential to interferometer data and the biases in
MODIS cloud microphysical and optical properties due to overlap.
•Greg McGarragh - applied this approach to MODIS data routinely
and is looking at the biases on the cloud top pressure retrieval
AVHRR/VIIRS
•Michael Pavolonis – developed a single pixel approach for the
operational AVHRR algorithm (used here) and another for VIIRS
(the AVHRR successor). (Baum’s MODIS technique is not applicable
to the AVHRR)
•Andy Heidinger –reprocessing of the AVHRR data (this study)
The Basis of the AVHRR Cirrus Overlap Detection Approach
• For single layer clouds, radiative transfer simulation show that as
optical depth increase beyond 2, the 11 – 12 micron brightness
temperature decreases and approaches some asymptotic value
• Multi-layer clouds exhibit a relationship that can not be modeled
using single layer clouds.
Low Level Water Cloud
High Level Ice Cloud
Cirrus over water cloud (tau=10)
The Basis of the AVHRR Cirrus Overlap Detection Approach
•The actual algorithm uses the visible reflectance as a surrogate for the
optical depth
•The algorithm basically looks for elevated T11-T12 when the visible
reflectance is greater than 30%. (Actual thresholds vary with several
parameters and other screening tests are used)
Low Level Water Cloud
High Level Ice Cloud
Cirrus over water cloud (tau=10)
Threshold function
To determine overlap
Example Application of this Approach
The CLAVR-x Cloud Type Algorithm
The cirrus overlap detection runs with a larger cloud typing algorithm.
The cloud types derived from AVHRR include:
0/1 – clear or not typed
4 um
11um
2 – water cloud
3 – supercooled water cloud
4 – opaque ice
ice
5 - non-overlapped cirrus
6 – overlapped cirrus
water
At the pixel level you can separate: ice from water, opaque from transparent,
single layer from multi-layer layer (this results in the above types)
Example Pixel Level Result of the CLAVR-x Cloud Types with Cirrus Overlap
An example of actual NOAA Cloud Type Products (one node of one day)
Limitations of the Algorithm
•We can only detect cirrus overlap when a semitransparent cloud lies over
a warmer cloud with sufficient thermal contrast.
•Pavolonis and Heidinger(2004) provide sensitivity studies of the
limitations. In general, the algorithm is best able to detect overlap when
the optical thickness of the cirrus is generally between 1 and 2 and the
optical thickness of the lower cloud is at least 5.
•(Wylie and Menzel, 1999: Typical cirrus optical depths 0.5 - 1.8, most low
cloud have optical depths greater than 6)
The AVHRR algorithm will work well over…
• deserts where the visible reflectance exceeds 20% (most deserts)
And may have reduced skill over…
•Snow and ice covered surfaces (3.75 micron used to screen
cirrus/snow)
•The VIIRS algorithm avoids these limitations and is more sensitive to
cirrus overlap.
Validation of this Technique
Pavolonis and Heidinger (2004) present results of the comparison of this
technique to radar derived cloud boundary time series from 3 ARM sites
(not NSA).
The radar data within 15 minutes of the satellite overpass was analyzed to
classify each scene as being single layer cloud, weak overlap (less than
50%) and strong overlap (greater than 50%. ). Clear scenes ignored. (Total
number was 180)
The results showed:
• when single layer cloud present, the
average occurrence of overlap was only
3% (false alarm rate)
•The amount of cirrus overlap detected
from the AVHRR algorithm was larger
in the strong than the weak radarderived overlap scenes.
Jay Mace’s Figure
A First(?) Attempt at global and seasonal look at the
distribution of Cirrus Overlap from Satellites
•The AVHRR GAC data is roughly 23 Terabytes so until the full reprocessing
archive is up and running, we are limited to roughly one equivalent year of data
(400 gigabytes).
•We chose July and January as the months to process first to give a rough
seasonal cycle.
•We will include the morning satellites to study diurnal variability later
•To avoid the complicating effects of orbit drift, we selected months
where the orbits had the same equator crossing time.
•This process resulted in us processing
•July 1982, 1986, 1991 and 1998
•January 1983, 1987, 1992 and 1999
•The July data was roughly at 2:30 and the Janaury data was at 3:00 LST.
The Global Distribution of Cloud Overlap Results
•The months selected were run through the CLAVR-x algorithms and the
occurrence of each cloud type within each 2.5 degree equal area cell were
collected for each month.
•The probabilities of occurrence of cirrus overlap were computed with
respect to
•All pixels
•All cloudy pixels
•All ice cloud pixels
•A spatial resolution of 2.5 degrees was used to facilitate the comparison
with ISCCP. The nominal operational product is 0.5 degrees.
Probability of Any Pixel being Cirrus Overlap - July
60S- 60N 60S - 30S30S - 0S 0N - 30N 30N - 60N
all
10 (6)
13 (14)
4 (14)
13 (5)
11 (10)
land
11 (7)
10 (15)
3 (26)
16 (6)
11 (13)
ocean
10 (7)
14 (15)
4 (14)
12 (8)
11 (8)
Probability of Any Pixel being Cirrus Overlap - January
60S- 60N 60S - 30S30S - 0S 0N - 30N 30N - 60N
all
10 (19)
10 (37)
13 (2)
6 (23)
13 (30)
land
12 (18)
8 (22)
22 (8)
3 (31)
14 (28)
ocean
10 (20)
10 (39)
10 (6)
7 (22)
12 (33)
July – January Distribution in the Cirrus Overlap Probability for All Pixels
Probability of Any Cloudy Pixel being Cirrus Overlap - July
60S- 60N 60S - 30S30S - 0S 0N - 30N 30N - 60N
all
18 (4)
18 (6)
8 (12)
24 (7)
19 (9)
land
26 (5)
28 (14)
10 (20)
33 (9)
26 (12)
ocean
16 (4)
18 (7)
8 (11)
21 (6)
16 (7)
Probability of Any Cloudy Pixel being Cirrus Overlap - January
60S- 60N 60S - 30S30S - 0S 0N - 30N 30N - 60N
all
18 (15)
13 (29)
25 (4)
13 (24)
20 (25)
land
28 (14)
26 (8)
34 (4)
11 (33)
30 (21)
ocean
15 (16)
13 (31)
21 (7)
13 (22)
15 (29)
Comparison with non-satellite data analysis of Wang et al (2000)
•20 years of RAOBS – top row Dec-Jan-Feb, bottom row June-July-Aug
July
January
RAOBS 2-layer
RAOBS Multilayer
all
18
18
28
42
land
26
28
27
37
ocean
16
15
29
44
Probability of Ice Clouds Being Cirrus Overlap - July
60S- 60N 60S - 30S30S - 0S 0N - 30N 30N – 60N
all
41 (3)
40 (12)
35 (3)
40 (12)
51 (5)
land
45 (10)
44 (14)
36 (17)
47 (16)
46 (8)
ocean
40 (3)
40 (13)
35 (3)
37 (11)
58 (4)
Probability of Ice Clouds Being Cirrus Overlap - January
60S- 60N 60S - 30S 30S - 0S 0N - 30N 30N - 60N
all
37 (10)
41 (27)
38 (4)
33 (7)
36 (22)
land
42 (5)
41 (6)
43 (8)
25 (22)
45 (15)
ocean
35 (14)
41 (28)
36 (3)
35 (7)
29 (29)
Comparison of AVHRR and the Wang et (2000) July – January Results
Correlation with the ISCCP Layered Cloud Amounts
•This not a validation, just an attempt to look for consistency of the
cirrus overlap distribution and the ISCCP cloud amounts (a standard)
•Data from same months used here were analyzed, plots below show
relative correlation between the two (color indicates concentration
of points)
•Both ISCCP and CLAVR-x are top-down cloud algorithms
X
Distributions are generally correlated as expected (expect for X)
Conclusions
•A recently published and validated cirrus overlap detection algorithm has
been applied to AVHRR data to produce one of the first satellite derived
estimates of multi-layer cloud occurrence (this study is about to be
submitted).
• Between 60 N – 60 S, the probability of cirrus overlap is:
•Roughly 10% of all pixels
•Roughly 20% of all cloudy pixels
•Roughly 40% of all ice cloud pixels
•Not much variation in 60 N – 60 S July and January statistics but large
variation regionally and zonally
•Higher probabilities of cirrus overlap occur over land than water for most
zones.
Future Work:
•We will continue this study on the full AVHRR data-set when we start our
full ORA reprocessing effort.
•We need to tackle the issue of how to process multi-layer cloudy pixels.
•Looking to partner with others on the utility of this data
Extra Material
Comparison of CLAVR-x Phase Algorithm for single layer mixed phase clouds