Global Multi-layer Cloud Distribution from AVHRR 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 CH3 3.75 um CH4 – 11 um CH5 – 12 um CH1 – 0.63 um CH2 – 0.86 um 5 channel AVHRR data record started in 1981

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 2 – water cloud 3 – supercooled water cloud 4 – opaque ice 5 - non-overlapped cirrus 6 – overlapped cirrus 11um 4 um ice 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 radar-derived 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 - 30S 30S - 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 - 30S 30S - 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 - 30S 30S - 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 - 30S 30S - 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 - 30S 30S - 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

X 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