Comparison of NOAA's Operational AVHRR Derived Cloud Amount to other Satellite Derived Cloud Climatologies.

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1 Comparison of NOAA's Operational AVHRR Derived Cloud Amount to other Satellite Derived Cloud Climatologies. Sarah M. Thomas University of Wisconsin, Cooperative Institute for Meteorological Satellite Studies (CIMSS) Andrew K. Heidinger NOAA/NESDIS Office of Research and Applications Michael J. Pavolonis University of Wisconsin, Cooperative Institute for Meteorological Satellite Studies (CIMSS) Submitted to: Journal of Climate Date Submitted: 11/05/2003 Date Revised: 05/04/2004 Date Accepted:

2 Abstract 1 A comparison is made between a new operational NOAA AVHRR global cloud amount product to those from established satellite-derived cloud climatologies. The new operational NOAA AVHRR cloud amount is derived using the cloud detection scheme in the extended Clouds from AVHRR (CLAVR-x) system. The cloud mask within CLAVR-x is a replacement for the CLAVR-1 cloud mask. Previous analysis of the CLAVR-1 cloud climatologies reveals that its utility for climate studies is reduced by poor high latitude performance and inability to include data from the morning orbiting satellites. This study demonstrates, through comparison with established satellite-derived cloud climatologies, the ability of CLAVR-x to overcome the two main shortcomings of the CLAVR-1 derived cloud climatologies. While systematic differences remain in the cloud amounts from CLAVR-x and other climatologies, no evidence is seen that these differences represent a failure of the CLAVRx cloud detection scheme. Comparisons for July 1995 and January 1996 indicate that for most latitude zones, CLAVR-x produces less cloud than ISCCP and UW/HIRS. Comparisons to MODIS for April 1-8, 2003 also reveal that CLAVR-x tends to produce less cloud. Comparison of the seasonal cycle (July-January) of cloud difference with ISCCP, however, indicates close agreement. It is argued that these differences may be due to the methodology used to construct a cloud amount from the individual pixel level cloud detection results. Overall, the global cloud amounts from CLAVR-x appear to be an improvement over those from CLAVR-1 and compare well to those from established satellite cloud climatologies. The CLAVR-x cloud detection results have been operational since late 2003, and are available in real-time from NOAA.

3 2 1. Introduction Cloud radiative effects play a central role in the Earth's climate system (Liou 1986; Ramanathan et al. 1989; Rossow and Lacis 1990; Stephens and Greenwald 1991). Cloud cover is a key factor in determining the magnitude of the exchange of incoming solar energy and outgoing terrestrial energy (Pavolonis and Key, 2003); an exchange that is central to understanding the natural fluctuations in the Earth's climate system. Hence, an accurate determination of the global extent of cloud cover is imperative for studies of the Earth's climate. As satellite imagers continue to become more advanced, there are an increasing number of opportunities to study the Earth's atmospheric, biological, and geophysical processes, as well as land and ocean surface properties in great detail. However, many of these studies, such as retrievals of aerosol optical properties and surface temperature, or studies of snow and sea ice extent, rely on clear sky radiances in the data reduction process. Even small amounts of cloud contamination in a scene can dramatically change the radiative properties derived using satellite measurements. Although cloud amount is a fundamental quantity, satellite derived estimates of it vary significantly. Trends in cloud amount from a variety of studies have even shown regional trends of differing sign. For example, the decrease in tropical cloud amount evident in the ISCCP (International Satellite Cloud Climatology Project) products during the 1990s (Wielicki et al., 2002) is not present in the UW-HIRS (University of Wisconsin High resolution Infrared Radiation Sounder) cloud climatology (Wylie and Menzel, 1999). For these reasons, a great deal of effort has been focused on developing algorithms that use satellite radiometric data in a

4 3 temporally consistent manner to detect clouds accurately on a global scale. One instrument that provides data useful for these types of studies is the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). The purpose of this study is to examine the performance of the extended Clouds from AVHRR (CLAVR-x) cloud detection algorithm over a range of seasonal conditions and satellite equator crossing times. This will be accomplished through comparisons with cloud amounts from ISCCP, Clouds from AVHRR phase 1 (CLAVR-1), Moderate resolution Imaging Spectroradiometer (MODIS), AVHRR Polar Pathfinder (APP), and UW-HIRS. Characteristics of the global cloud distribution from CLAVR-x relative to other cloud products will be presented. Of primary importance is the question of whether or not CLAVR-x provides results that are consistent with other estimates of the global cloud distribution, while offering improvements over previous cloud detection algorithms that use AVHRR data. A detailed description of each of the cloud detection algorithms used for comparison is given in section 2. In section 3, global cloud amounts from CLAVR-x are compared to the results from the other aforementioned cloud climatologies. Several examples are given that highlight the key similarities and differences between CLAVR-x and each of the other products, for a variety of satellite equator crossing times and seasonal conditions. Potential strengths and/or shortcomings of CLAVR-x are discussed in this section. Section 4 summarizes the results from this study.

5 4 2. Overview of Cloud Amount Algorithms Satellite imagers have the ability to assess global cloud properties on much finer spatial and temporal scales than any other type of instrument currently available. Hence, many efforts have been made over the past 25 years to develop accurate methods by which data from satellite imagers may be used not only to detect clouds, but also to document the global extent of cloud occurrence and properties. a. ISCCP Cloud Amount Algorithm One of the first large scale, organized attempts to use satellite data to create a global cloud climatology was ISCCP, which was established in An overview of the ISCCP program is given by Schiffer and Rossow (1983). Radiance data from geostationary satellites such as GOES (Geostationary Operational Environmental Satellite), METEOSAT (geostationary Meteorological Satellite), and GMS (Geostationary Meteorological Satellite) are averaged over 3 hr intervals to provide complete coverage of the tropics and mid-latitudes at a high temporal resolution. Global coverage is attained by using AVHRR data from a suite of NOAA polarorbiting satellites to provide measurements poleward of 60 N/S. ISCCP pixels are mapped to a 250 km equal area grid, and as described by Schiffer and Rossow (1983), variations over this area are small compared to time variations. The ISCCP data record spans from , and is the longest and most complete satellite derived cloud climatology currently available. A complete description of the ISCCP cloud amount algorithm is given by Rossow and Garder (1993). ISCCP clear/cloudy classification is based largely on the analysis of spatial and temporal variability in reflectance and/or brightness temperature (BT) over a geographically

6 5 small region. These uniformity tests are based on the assertion that clear pixels tend to exhibit less variability in both space and time than do cloudy pixels (McClain et al. 1985). For a pixel to be classified as cloudy, it must have a BT much colder than the warmest pixel in a small spatial domain. In addition, the pixel must exhibit significant variability in BT over a period of 3 consecutive days. A pixel is classified as cloudy only if it meets both the spatial and temporal variability requirements. In addition, clear sky statistics are compiled once every 5 days using both IR and VIS data. These statistics are used to enhance the cloud mask by providing additional threshold values by which pixels may be classified as clear or cloudy (e.g. A pixel is cloudy if it is colder than the 5-day statistical mean clear-sky BT, or has a higher VIS reflectance than the statistical mean clear-sky reflectance.) After all pixels have been classified, total cloud fraction is calculated for each 250 km grid cell by taking the ratio of the number of cloudy pixels to the total number of pixels. This calculation carries the assumption that there are no partially cloud filled pixels, and every cloudy pixel is 100% cloud covered. b. CLAVR-1 Cloud Amount Algorithm The ISCCP cloud amount algorithm uses IR and VIS data from AVHRR in order to attain measurements over polar regions and achieve global coverage. However, AVHRR has cloud detection capabilities beyond the two channel methods used by ISCCP. Utilization of all the spectral information provided by AVHRR was one motivating factor for the development of an AVHRR-only cloud mask by NOAA, and resulted in the creation of the CLAVR-1 cloud mask (Stowe et al., 1999). AVHRR data from satellites prior to NOAA-15 provide radiances over 5 wavelength bands with central wavelengths of 0.63, 0.86, 3.75, 10.8, and 12 µm.

7 6 AVHRR data from after the launch of NOAA-15 provide observations over an additional band with a central wavelength of 1.6 µm, however cannot simultaneously provide observations from the 3.75 µm channel. AVHRR has a spatial resolution of 1 km, but these data are available over limited areas. The Global Area Coverage (GAC) AVHRR data are used for this study, and have a spatial resolution of 4 km at nadir. The traditional 5 channel AVHRR data record encompasses over 23 years of data ( ). The AVHRR data record is scheduled to continue until 2018, through the European organization for the exploitation of meteorological satellites (EUMETSAT) polar-orbiting operational meteorological satellites (MetOp) program. This gives AVHRR the potential to be very valuable for use in climate studies, including studies of global cloud distribution and physical properties. The CLAVR-1 cloud detection algorithm is a pixel level cloud mask that uses all of the spectral information that AVHRR provides. It was developed for use in multiple NESDIS (National Environmental Satellite Data and Information Service) products, and its heritage lies in the NESDIS operational sea surface temperature algorithm (McClain, 1989). A detailed description of the CLAVR-1 cloud mask is provided by Stowe et al. (1999). CLAVR-1 implements three primary types of tests in order to determine whether a pixel is clear or cloudy: contrast signature tests, spectral signature tests, and spatial signature tests. The contrast signature tests require that for each pixel, the reflectance or BT for a single AVHRR band be compared against a fixed threshold value that separates clear and cloudy conditions. The threshold used for each test is adjusted based on the surface type (e.g. vegetated land, ocean, desert... etc.) of the scene. The spectral signature tests involve the combination of multiple

8 7 AVHRR bands. These tests compare either the ratio or difference of two bands against a clear/cloudy threshold value. Finally, a spatial uniformity test is implemented. Similar to ISCCP, this test operates on the assumption that over small spatial areas (in the case of CLAVR- 1, a 2x2 pixel array), cloud free scenes are relatively uniform in their reflectance and BT. Based on the results of the aforementioned tests, a pixel is classified into one of three categories: clear, mixed, or cloudy. Cloud fraction, f(c), is calculated based on the assumption that cloudy scenes are 100% cloudy, mixed scenes are 50% cloudy, and clear scenes are 0% cloudy, using the following expression: (1) f(c) = N cloudy + 0.5*N mixed N total Where N cloudy is the number of cloudy pixels, N mixed is the number of mixed pixels, and N total is the total number of pixels in the scene. c. CLAVR-x Cloud Amount Algorithm Further development of CLAVR-1 has led to the extended CLAVR algorithm (CLAVR-x). CLAVR-x became an operational product in late 2003, and the CLAVR-x cloud detection results are currently available in the space alloted in the NOAA AVHRR 1b format. CLAVR-x is based on the same physical principles as CLAVR-1. However, numerous updates have been made in order to improve upon some of the documented shortcomings of CLAVR-1. An algorithm theoretical basis document (ATBD) is available on-line for CLAVR-x, which fully describes the cloud amount algorithm (Heidinger, 2004). In addition, parts of the CLAVR-x cloud mask are described by Heidinger et al. 2002, and Heidinger et al One of the major

9 8 improvements of CLAVR-x over CLAVR-1 is the breakdown of the mixed category into two new categories: mixed-cloudy and mixed-clear. Mixed cloudy pixels are those pixels that are determined to be cloudy by one or more of the contrast or spectral signature tests, but are spatially non-uniform as determined by uniformity tests. Likewise, mixed-clear pixels are those pixels that are determined to be clear but are spatially non-uniform. The mixed category is divided into two sub-categories to improve the accuracy of the total cloud fraction calculation by allowing more realistic percent cloud cover values be assigned to mixed pixels. CLAVR-x cloud fraction is calculated based on the assumption that cloudy scenes are 100% cloudy, mixedcloudy scenes are 88% cloudy, mixed-clear scenes are 13% cloudy, and clear scenes are 0% cloudy. These percentages are derived by analyzing the mean radiances from grid-cells that report both clear and cloudy pixels. A radiometric balance approach similar to the one described in Molnar and Coakley (1985) is used to estimate the cloud fraction of the partly clear and the partly cloudy pixels. This approach calculates the radiance for a partly clear/cloudy pixel assuming that it is a linear function of the fully cloudy and fully clear radiances. Cloud fraction is then derived using the following expression: (2) f(c) = I mixed-cloudy -I clear I cloudy -I clear Where I mixed-cloudy is the mixed-cloudy radiance, I cloudy is the fully cloudy radiance, and I clear is the clear sky radiance. This radiometric balance approach is applied to the mean 11 µm radiances derived from the clear, partly clear, partly cloudy, and cloudy pixels. Only pixels with valid clear and cloudy radiances are used. Figure 1 shows the distribution of these cloud fractions for the partly clear and partly cloudy pixels from one day of data from June In this figure, the

10 9 partly cloudy results are shown separately for ice and water clouds and the mean cloud amounts for each distribution are given in the figure legend. Occasionally, the radiance for a partly cloudy pixel will exceed the radiance for a fully cloudy pixel, which leads to the cloud fraction weight being greater than 1. Similarly, sometimes the radiance of a partly clear pixel will be lower than that of a fully clear pixel, which results in the cloud fraction weight being less than 0. These occurrences, however, are rare and do not affect the final cloud fractions assigned. The semi-transparent nature of partly cloudy ice pixels is the likely cause of the decrease in the partly cloudy ice fraction relative to the partly cloudy water cloud fraction. Because the radiometric balance approximation assumes clouds are opaque and this assumption is most valid for water clouds, the partly cloudy fraction computed for water clouds will be used for all clouds. For the rest of the study, the cloud fraction weights of the mixed-clear and mixed-cloudy pixels are 13% and 88%, respectively. Using these fixed values, CLAVR-x cloud fraction, f(c), is calculated using the following expression: (3) f(c) = N cloudy *N mixed-cloudy *N mixed-clear N total where N cloudy is the number of cloudy pixels, N mixed-cloudy is the number of mixed-cloudy pixels, N mixed-clear is the number of mixed-clear pixels,and N total is the total number of pixels in the scene. Another key development in CLAVR-x is the more accurate detection of clouds in polar regions and regions of snow or sea-ice cover. This involves not only the modification of cloud mask thresholds in regions with snow or ice, but also the improvement of the snow and ice detection algorithm itself. The threshold modifications are based largely on the previous methods of the AVHRR Polar Pathfinder (APP) cloud mask (Key and Barry, 1989), which will

11 10 be described later. This cloud mask contains developments specifically designed for use poleward of 45 o N or S. The CLAVR-x cloud mask procedures in the high latitudes are based on the APP methods when possible. However, APP also employs temporal tests in its cloud mask determination that are absent in CLAVR-x. The difference between CLAVR-x and APP will in some part be an indication of the relative impact of the APP temporal tests. Similar to APP, CLAVR-x includes a tighter range of values for the µm test (TMFT), and a lower threshold value for the µm test (FMFT) than did CLAVR-1. These threshold modifications lead to an overall lower cloud fraction in polar regions than produced by CLAVR- 1. This lower cloud fraction is in better agreement with other cloud climatologies. In addition to modified thresholds for polar regions, CLAVR-x includes a revised algorithm for the detection of snow and ice. This algorithm employs the use of the Normalized Difference Snow Index (NDSI), developed for use with MODIS Snowmap (Hall and Salomonson, 2001), for scenes where AVHRR channel 3a (1.64 µm) is available. In all other cases, a grouped threshold approach similar to that described by Baum et al. (1999) is used. This approach makes use of the characteristic low reflectance of snow at 3.75 µm, and the low BTD ( µm) as compared to clouds. Since clouds and snow typically share similar spectral properties such as high reflectance at 0.65 µm and a low 11 µm BT, improved methods of distinguishing between clouds and snow leads to a more accurate calculation of cloud fraction in polar regions. As noted by Stowe et al. (1999), the CLAVR-1 cloud detection algorithm should be applied only to data from satellites with afternoon equator crossing times. This is due to the fact

12 11 that CLAVR-1 is not seasoned at detecting clouds in regions where the solar zenith angle is high. Therefore, since satellites that cross the equator in the morning (such as NOAA-12) tend to view much of the globe around dawn (when the solar zenith angle is high), CLAVR-1 can not be reliably applied to data from these satellites. Where CLAVR-1 uses single value thresholds for the reflectance tests, CLAVR-x adopts reflectance tests that have a dependence on the solar and satellite viewing geometry. These modifications to CLAVR-x appear to have extended the applicability of the CLAVR-x cloud detection to all orbits. As will be discussed later, the CLAVR-x total cloud amounts behave similarly in the morning and the afternoon orbits, allowing for a more true estimate of the diurnal average. d. MODIS Cloud Amount Algorithm Advancement in satellite imager technology has made more rigorous cloud detection algorithms involving multi-spectral techniques possible. The Moderate Resolution Imaging Spectroradiometer (MODIS) was launched aboard the NASA Earth Observing System (EOS) Terra platform in 1998 and Aqua platform in A complete description of the MODIS instrument is given by Salomonson et al. (1989). This focus of this study is on cloud amounts derived from MODIS-Terra data rather than MODIS-Aqua data, because the orbit of the Terra platform is closer to that of NOAA-16. The Terra platform has a polar-orbiting, sunsynchronous orbit. MODIS has a total of 36 spectral bands between and µm. Spatial resolutions for this instrument are 250 m (1 VIS, 1 NIR band), 500 m (2 VIS, 3 NIR bands), and 1000 m (29 total VIS, NIR and IR bands). The high spatial resolution and large number of spectral bands available with this instrument make it an excellent tool for studying the

13 12 intricacies of the Earth's land, ocean, atmosphere, and biological and geophysical processes. However, the MODIS data record covers only 2000-present, so it has limited use for climate applications. A complete description of the MODIS cloud mask is given by Ackerman et al. (1998). Most cloud detection with the MODIS cloud mask occurs using pixel level spectral tests. Similar to the CLAVR cloud masks, the MODIS cloud mask classification of a pixel as clear or cloudy depends on the results from a series of fixed threshold tests. The main difference between MODIS and CLAVR-x is the availability of specific channels on MODIS that greatly improve cloud detection in certain regions. For example, channels in the 1.38 and 7.7 µm water vapor absorption bands greatly improve the detection of thin cirrus (during the day) and clouds in the polar regions. In addition, CLAVR-x uses spatial uniformity and background fields of climatological sea surface temperature and vegetation condition to a much greater extent than MODIS. Similar to CLAVR-x the MODIS cloud mask classifies each pixel into four categories: confident clear, probably clear, probably cloudy, and confident cloudy. However, unlike CLAVR-x, the occurrence of two intermediate classes is relatively rare (<10%). In addition, when a cloud amount is computed from the MODIS cloud mask, the four level mask is converted to a binary mask, with the clear and probably clear pixels having an assumed zero cloud amount, and the probably cloudy and confident cloudy having an assumed 100% cloud amount. e. UW/HIRS Cloud Amount Algorithm In addition to those studies that use satellite imager data, some studies make use of the cloud detection capabilities of high spectral resolution, low spatial resolution satellite

14 13 sounding instruments. UW-HIRS is one such sounder-derived cloud climatology, and spans from mid-1989 to the present. The High Resolution Infrared Sounder (HIRS) has flown aboard the NOAA polar-orbiting satellite platforms since its inception in It senses infrared radiation in 18 spectral bands between 3.9 and 15 µm, with a spatial resolution of 18.9 km at nadir. Fields of view are determined to be clear or cloudy by an examination of the 11.2 µm BT. If the 11.2 µm BT (corrected for moisture absorption) is within 2K of the surface temperature (taken from hourly surface observation data where available, or if surface data is unavailable, the surface temperature is assumed to be the warmest temperature across a small geographic area), then the scene is classified as clear. In addition, the UW-HIRS cloud climatology implements a CO 2 slicing technique aimed at the detection of high clouds (Wylie and Menzel, 1989). This approach exploits the differences in weighting function for three different CO 2 absorption bands (14.2 µm, 14.0 µm, and 13.3 µm), and is especially skillful at detecting high, thin clouds often missed by other tests. Similar to other cloud masks discussed, the UW-HIRS cloud mask does not estimate fractional cloud cover in a single field of view, and thus cloud amount is calculated by dividing the number of cloudy pixels by the total number of pixels. f. APP Cloud Amount Algorithm The AVHRR Polar Pathfinder (APP) cloud mask (Key and Barry, 1989; Key 2002) uses tests similar to those in CLAVR-x to detect cloud using AVHRR data. However, unlike CLAVR-x, the tests implemented by APP have been specifically tuned for application to high latitudes and are available poleward of 45 o N and 45 o S only. The APP data is AVHRR GAC data mapped to a 5 km resolution polar stereographic grid and is produced twice daily. A

15 14 combination of spectral and temporal uniformity tests are used to make a final clear or cloudy determination. No sub-pixel cloud fraction is estimated. Therefore, cloud amount is derived by dividing the number of cloudy pixels by the total number of pixels in a scene. Numerous studies have been conducted to validate APP products in the polar regions (Wang and Key, 2004; Pavolonis and Key, 2003; Wang and Key, 2003; Key et al. 2001; Maslanik et al. 2001). These studies have compared results from APP against ISCCP cloud properties as well as surface observations from the First ISCCP Regional Experiment Arctic Cloud Experiment (FIRE-ACE) and Surface Heat Budget of the Arctic Ocean (SHEBA), and observations from meteorological stations throughout the Arctic and Antarctic. These studies have shown APP products to be consistent, in many cases, with ground based observations, although some discrepancies still exist in cases with high, optically thin clouds (Maslanik, et al., 2001). Wang and Key (2004) show that in the Arctic, APP cloud fraction is more consistent with ground based observations than is ISCCP, especially during the summer months. Studies showing a direct comparison between APP and ISCCP cloud fraction over the Antarctic have yet to be published. However, Pavolonis and Key (2003) indicate that APP derived cloud forcing in the Antarctic shows better agreement with ground based observations than ISCCP derived cloud forcing, which can be directly linked to ISCCP under-detection of clouds over snow during the summer months. Based on the results from these previous studies, this study will assume the APP cloud amount to be the closest representation to the actual cloud amount in the high latitudes.

16 15 3. Data and Methods This study provides cloud amount comparisons for the months of July 1995, January 1996, and part of April A summary of the different types of comparisons made, as well as the cloud masks used and spatial resolution of each comparison is provided in Table 1. A range of months is included to provide comparisons from several different seasonal conditions. The CLAVR-x cloud mask is applied to AVHRR level 1b radiance data from NOAA-12, NOAA-14, and NOAA-16. The approximate daytime equator crossing time for each of these satellites is listed in Table 2. Since each of these satellites is in a sun-synchronous orbit, the nighttime equator crossing time for each satellite is 12 hours after the daytime equator crossing time. This variety of satellites is used to illustrate the utility of the CLAVR-x cloud mask as applied to data from both morning and afternoon satellites. CLAVR-x products have 1 degree spatial resolution when used in comparison with CLAVR-1 and MODIS, and 2.5 degree spatial resolution when used in comparison with ISCCP. ISCCP D2 monthly mean cloud product data are used for both July 1995 and January Prior to 2000, CLAVR-1 cloud mask results were processed as a part of the NOAA PATMOS (AVHRR Pathfinder Atmosphere) project. Because PATMOS processing stopped in 2000, this study uses CLAVR-1 data from July 1995 and January 1996 only. Total cloud amount data from APP are used for July 1995 and January Zonal mean cloud amount is calculated from the level 2 monthly mean cloud amount, poleward of 45 o N or S. Monthly mean cloud amount from UW-HIRS level 2 data are also used for July 1995 and January TERRA MODIS cloud mask data from April 1 to April are also used for this

17 16 study. Data from the CIMSS MODIS real-time processing system is used, and cloud amounts are recomputed from the pixel level cloud mask. This allows the MODIS results to be mapped to the same projection as the CLAVR-x results. Because the MODIS cloud mask is most validated for daytime applications, our comparison to MODIS is restricted to daytime data. While data from NOAA-17 more closely matches the observation time of TERRA, the reflectance calibration of the AVHRR on NOAA-17 has not yet been validated. Therefore, NOAA-16 data, calibrated using the methods given by Heidinger et al. (2002), are used. This comparison implicitly assumes the algorithmic differences will not be masked by the diurnal effects in the four hour time difference between the two satellites. Individual grid cell comparisons are made for CLAVR-x vs. CLAVR-1,,ISCCP, and MODIS. These comparisons follow the statistical methods set forth in Hou et al. (1993). Statistical scores are assigned for each comparison, based on how similar or dissimilar the data sets are. A detailed description of each score and the method by which it is derived is provided by Hou et al. (1993); a brief description of each of the statistical scores is as follows. The S 20 score represents the percentage of grid cells where the two cloud amounts (CLAVR-x and either CLAVR-1, ISCCP, or MODIS) differ by 20% or less. For example, a S 20 score of 0.9 indicates that for 90% of the grid cells in a given scene, the two instruments report an absolute cloud fraction difference of 20% or less. Note that this does not necessarily indicate that 90% of the grid cells in a given scene differ by 20% or less of the mean value of cloud fraction for the scene. This score varies from 0-1, and provides an indication of how well the two datasets agree. Higher scores indicate better agreement. The S -60 score is a natural counterpart to the S 20 score.

18 17 It represents the percentage of grid cells where the two cloud amounts differ by more than 60%. This score also varies from 0-1, and indicates whether or not the two datasets disagree. Higher scores indicate lesser agreement. This score can be used as an indicator of how often the geographic location of clouds are different between two cloud masks, because differences in cloud amount greater than or equal to 60 will most likely occur when one product indicates a fully clear grid cell and another product indicates fully cloudy. The Heidike score, S h, ranges from 0 to 1, and measures how closely the two cloud datasets are statistically related to each other. Higher Heidike scores indicate an increased likelihood that the two datasets are not statistically independent of each other. The root mean square error, S rms, is a commonly used indicator of the difference between two datasets. High values of S rms indicate a greater difference between the two datasets. The bias score, S bias, ranges from -1 to 1, and is used to compare overall difference in cloud amount. A large positive (negative) bias score indicates that the comparison data set (ISCCP, CLAVR-1 or MODIS) has a much higher (lower) overall cloud amount than CLAVR-x. Finally, the absolute difference score, S abs, shows the mean magnitude of the absolute difference between the two datasets. Lower scores indicate that the two datasets are in better agreement. An individual grid cell comparison is not performed for APP or UW-HIRS due to differences in the gridding of each of these products, and the complexities involved in regridding each them to be similar to CLAVR-x. Instead, a 2.5 degree zonal mean comparison is performed, so that a general regional comparison could be made without the complexity of attempting an individual grid cell comparison.

19 18 4. Results In this section, results from a series of cloud mask comparisons are presented. These comparisons provide an analysis of global cloud amount and distribution of CLAVR-x and the five additional cloud masks previously described. Zonal mean comparisons are performed for each of the cloud masks. In addition, individual grid cell comparisons are performed for CLAVR-x vs. CLAVR-1, ISCCP and MODIS. The CLAVR-1 vs. CLAVR-x comparison includes analysis of both the ascending and descending orbits of the satellite. For all other comparisons, the CLAVR-x cloud amount is averaged over a diurnal cycle using the ascending and descending passes of the both the morning and afternoon satellites (NOAA-12 and NOAA-14). The comparisons are shown separately for July 1995 and January Because MODIS data was not available for these times, a separate section comparing MODIS to CLAVR-x for April 2003 is presented at the end. a. July 1995 The cloud masks used for comparison for July 1995 are ISCCP, CLAVR-1, CLAVR-x, APP, and UW-HIRS. As described previously, CLAVR-1 cloud mask may only be applied to data from afternoon orbiting satellites (e.g. NOAA-14). The resulting cloud amounts are compared to CLAVR-x results from the same orbits. ISCCP cloud amount is produced every three hours and is therefore capable of producing a truer diurnal average. The diurnal average of the APP cloud amount is produced by averaging two daily fields produced at 0200 and 1400 LST. To estimate a diurnal average using CLAVR-x, the algorithm is applied to AVHRR data from the ascending and descending passes of both the morning and afternoon satellites. Figure 2

20 19 shows the July 1995 diurnally averaged zonal mean cloud amount from CLAVR-x derived using this methodology, as well as the zonal mean cloud amount from each of the individual satellite passes. This figure illustrates that cloud amounts from each of the satellite orbits follow a consistent trend, regardless of the time from which they were derived. In addition, the diurnal average falls within approximately10% of each of the individual orbits. These diurnally averaged results from CLAVR-x will be compared to each of the previously mentioned products. Figure 3 shows the difference between CLAVR-x and CLAVR-1 cloud amount for the ascending (daytime) and descending (nighttime) passes of NOAA-14, at 1 degree resolution. In these figures, lighter areas indicate regions where CLAVR-x cloud amount exceeds CLAVR- 1, and darker areas indicate the opposite. The greatest differences between the two datasets occur in the polar regions. This is to be expected, given that the CLAVR-x algorithm has been modified significantly from CLAVR-1, to more accurately detect clouds in these areas. CLAVR-x consistently observes less cloud in both the Antarctic and Arctic, including Greenland. In the non-polar regions, cloud amounts from the ascending pass show more variability than the descending pass. Figure 3 (top) shows that for the ascending pass, CLAVR-x exceeds CLAVR-1 by 20% or more in many areas, especially mid to high latitude oceanic regions. These differences may result from the fact that some of the cloud mask tests employed by CLAVR-x refer to a Reynolds SST climatology to help determine thermal threshold values, while CLAVR-1 relies on a set of single-value thermal thresholds. Cloud amounts from the descending pass show closer agreement between CLAVR-x and CLAVR-1, with differences between the two averaging less than 20%, excluding the polar regions. On average, excluding

21 20 the polar regions, the two datasets tend to agree to within about 20%, with CLAVR-x observing slightly more cloud than CLAVR-1. Table 3 shows the CLAVR-x vs. CLAVR-1 statistical scores. These scores quantify the agreement between CLAVR-x and CLAVR-1 cloud amount derived from the average over the ascending and descending passes of NOAA-14. The S 20 scores indicate that the CLAVR-1 and CLAVR-x cloud amounts agree to within 20% approximately 69% of the time globally, and about 87% of the time between 60 o S and 60 o N. Differences greater than 20% (but less than 60%) may be caused in part by the fact that some pixels that are classified as mixed clear or mixed cloudy by CLAVR-x (and thus assigned a cloud amount of 0.13 or 0.88) will be classified as partly cloudy by CLAVR-1 (and assigned a cloud amount of 0.5). The S -60 score indicates that between 60 o S and 60 o N, the two datasets do not differ by more than 60% in any location, which suggests that they are in excellent agreement on the geographic location of clouds. This further supports that in this region, the differences in cloud amount implied by the S 20 score are not due to the failure of either cloud mask to detect cloud consistent with the other, but rather due to the two cloud masks assigning different cloud amounts to pixels in which at least some cloud is detected. The bias scores indicate that CLAVR-1 observes only slightly less cloud than CLAVR-x between 60 o S and 60 o N but in excess of 30% more cloud in the polar regions. The region between 60 o S and 60 o N is characterized by a moderately high Heidike score, and low root mean square and absolute errors, all of which suggest very good agreement between the two datasets. The agreement indicated by these scores decreases globally, however, the global values still indicate moderate agreement between the two datasets. As should be expected

22 21 considering the changes made to the CLAVR-x algorithm in the polar regions, all scores suggest poor agreement between the two datasets poleward of 60 o S or 60 o N. Table 4 shows the July 1995 global cloud amount from each of the cloud mask products. Because CLAVR-x observes more cloud outside of the polar regions and less cloud within the polar regions than CLAVR-1, its global cloud amount is only slightly larger. Both ISCCP and UW-HIRS observe significantly more cloud globally than either of the CLAVR products. One possible cause for this may be a function of how cloud amount is computed for ISCCP and UW-HIRS vs. CLAVR. There are no partly cloudy pixels in either ISCCP or UW- HIRS, and every cloudy pixel is considered to be 100% cloud covered. This philosophical difference with CLAVR may account for systematic cloud amount differences in regions of broken cloudiness. To quantify this effect, the CLAVR-x cloud classifications from 7 ascending orbits of NOAA-14 were analyzed. Table 5 shows the mean and standard deviation of the percentage of the cloud mask for all categories (mixed-clear, mixed-cloudy, and cloudy categories) calculated over these seven orbits. When all clouds are considered, nearly half of the pixels in these orbits fall into one of the mixed categories. When only water clouds are considered, this increases to about 55%. When calculated using the same methodology used by CLAVR-x, the values listed in Table 5 lead to an overall cloud fraction of 56.39% (all clouds). If we exclude the mixed categories and assign a cloud fraction of 1 to all mixed-cloudy and cloudy pixels, and a cloud fraction of 0 to all mixed-clear and clear pixels, the cloud fraction increases to 57.63%. However, if all mixed-clear, mixed-cloudy, and cloudy pixels are assigned a cloud fraction of 1, then this number increases to 77.64%. These values are enhanced when

23 22 only water clouds are included, with the percentages being 62.57%, 63.57%, and 87.05%, respectively. Although it is unclear exactly how the mixed pixels would be classified by ISCCP, it is likely that most of the CLAVR-x mixed-cloudy pixels and some of the mixed-clear pixels will be classified as cloudy by ISCCP. It follows that the difference in the way cloud amount is assigned by CLAVR-x vs. ISCCP is capable of producing the differences in global cloud amount observed between these two products. There are several factors that may contribute to the UW-HIRS cloud mask observing the highest cloud amount. One is that the CO 2 slicing technique it implements has skill at detecting optically thin cirrus clouds. These clouds may be missed with the threshold tests and temporal sampling methods used by CLAVR and ISCCP. However, the zonal distributions (shown in Figures 5 and 8) indicate that the largest differences between UW-HIRS and the other products occur over a wide region including the subtropics and tropics. The fact that these large differences occur over regions including those not dominated by cirrus may indicate that factors other than the UW-HIRS CO 2 slicing cirrus detection capability may be the cause of this difference. For example, the UW-HIRS instrument has a much larger field of view at nadir than does AVHRR (nearly 19 km, compared to 4 km for AVHRR). For fields of view classified as cloudy, this results in a much larger geographic area being assigned a cloud amount of 100%, and will lead to an overestimation of cloud amount, especially in areas of broken cloudiness. Finally, the 2K 11 µm BT threshold used in the UW-HIRS cloud detection algorithm is significantly lower than the threshold implemented by any of the other cloud masks described, which leads to a higher likelihood that fields of view will be determined as cloudy.

24 23 Figure 4 shows the comparison between ISCCP and CLAVR-x both globally and zonally. The top two plots show the actual cloud amounts for CLAVR-x (left) and ISCCP (right). The bottom left plot shows the difference between ISCCP and CLAVR-x cloud amount derived from NOAA-14 and NOAA-12. Lighter areas in this plot indicate regions where the CLAVR-x cloud amount exceeds the ISCCP cloud amount. Darker areas indicate the opposite. As was the case with CLAVR-1, the largest differences occur in the polar regions. Unlike CLAVR-1, however, the differences at the north and south poles are of opposite sign. CLAVR-x shows very few clouds over the Antarctic, and ISCCP cloud amount exceeds CLAVR-x significantly in this region. Over most of the Arctic, the CLAVR-x cloud amount exceeds ISCCP by more than 20%. CLAVR-x and ISCCP use different tests and thresholds, and thus these differing results are expected. It may be noted that the ISCCP-D2 dataset has a tendency to underestimate cloud fraction during polar summer (Wang and Key, 2004; Pavolonis and Key, 2003). During polar winter, the spatial and temporal uniformity tests employed by ISCCP could aid in the detection of cloud, when many of the threshold tests used by CLAVR-x are not implemented due to the high solar zenith angle. In addition to the polar regions, ISCCP cloud amount also exceeds CLAVR-x over the Tibetan Plateau, the Andes Mountains of South America, and the North American Rockies. This is possibly due to the fact that CLAVR-x employs the use of a detailed terrain map which could aid in discriminating between snow and cloud in mountainous regions. Additionally, there is a significant swath that stretches from Madagascar northward over the Indian Ocean, where ISCCP cloud amount exceeds CLAVR-x by more than 20%. This is a feature of the ISCCP data set, and is due to the gaps in

25 24 geostationary satellite data coverage over the Indian Ocean. On average, the global trend in cloud amount is similar between ISCCP and CLAVR-x, as is shown by the zonal mean plot in the bottom right of the figure. The magnitude of cloud amount differs slightly between the two, with ISCCP observing more cloud globally (excluding the Arctic) than CLAVR-x. Table 6 shows the July 1995 CLAVR-x vs. ISCCP statistical scores for both the NOAA-12 and NOAA-14 orbits. For both orbits, all scores indicate good agreement between CLAVR-x and ISCCP, with 20% or better agreement occurring for over 72% of the pixels globally. For either orbit, the two datasets disagree by 60% or more less than 1% of the time globally, all of which occurs in the polar regions. As was the case with CLAVR-1, the scores show better agreement when polar regions are excluded. In addition, both the NOAA-12 and the NOAA-14 scores indicate that globally, CLAVR-x agreement with ISCCP is similar to or better than its agreement with CLAVR-1, despite the fact that the total global cloud amount would indicate the opposite. This suggests that globally, the trend in CLAVR-x cloud amount more closely follows ISCCP than CLAVR-1. Figure 5 shows the zonal mean comparison of cloud amount from CLAVR-x (averaged over NOAA-14 and NOAA-12) with ISCCP, CLAVR-1, UW-HIRS, and APP. Excluding the polar regions, CLAVR-x shows a similar trend in zonal mean cloud amount with all of the products. CLAVR-x cloud amount tends to be lower than both ISCCP and UW-HIRS for reasons discussed previously. The zonal mean cloud amount from CLAVR-x exhibits substantial differences with ISCCP, UW-HIRS, and CLAVR-1 poleward of about 70 o N. However, CLAVR-x cloud amount in this region very closely mirrors that given by APP.

26 25 Poleward of 60 o S, CLAVR-x exhibits a trend similar to APP and ISCCP, but shows a lower cloud amount than either of these products. However, it should be noted that in this region, CLAVR-x shows significantly better agreement with other products than does CLAVR-1. CLAVR-x differs from ISCCP by about 20% at most, while CLAVR-1 differs from ISCCP by as much as 60%. The maximum differences from APP are similar for both CLAVR-1 and CLAVR-x, but CLAVR-x follows the trend of APP much more closely than does CLAVR-1. b. January 1996 The cloud masks used for comparison for January 1996 are ISCCP, CLAVR-1, CLAVR-x, APP, and UW-HIRS. Similar to the previous case, cloud amounts from CLAVR-x and CLAVR-1 are compared for similar orbits of NOAA-14. The diurnally averaged cloud amounts from ISCCP and APP are compared to diurnally averaged CLAVR-x cloud amounts, derived by averaging over both the ascending and descending passes of NOAA-12 and NOAA- 14. The biggest difference between the July and January results are in the performance of each algorithm in detecting cloud in the snow-covered regions of the Northern Hemisphere. Figure 6 shows the difference between CLAVR-x and CLAVR-1 cloud amount for the ascending (daytime) and descending (nighttime) passes of NOAA-14, as well as the diurnal average. For both orbits, CLAVR-1 cloud amount exceeds CLAVR-x in the polar regions. Such is also the case in the continental Northern Hemisphere, north of approximately 50 o N, where snow cover is prevalent in the winter. This pattern is particularly apparent in the descending orbit. The snow and ice detection algorithm implemented by CLAVR-x is much more rigorous than CLAVR-1, which supports the conclusion that in this case, CLAVR-1 is most likely falsely

27 26 detecting cloud in regions of snow or ice cover. Elsewhere, trends in cloud amount are similar for CLAVR-1 and CLAVR-x, especially over oceanic regions. Globally, CLAVR-x observes slightly less cloud than CLAVR-1. Table 7 shows the CLAVR-x vs. CLAVR-1 statistical scores. The S 20 scores indicate that agreement between these two datasets is slightly lower than the July 1995 case across all of the regions examined. According to these scores, CLAVR-x and CLAVR-1 agree to within 20% for approximately 63% of the pixels globally, and for over 82% of the pixels between 60 o S and 60 o N. In the polar regions, these two datasets agree to within 20% for only slightly more than 25% of the pixels. The S -60 scores indicate that CLAVR-x and CLAVR-1 differ by more than 60% for about 3% of the pixels globally, less than 1% of which occur outside of the polar regions. This suggests that between 60 o S and 60 o N, CLAVR-x and CLAVR-1 are generally in good agreement as to the geographic location of clouds. Poleward of 60 o S and 60 o N, the S -60 score indicates that CLAVR-x and CLAVR-1 disagree on the geographic location of clouds 9% of the time, which is significantly better than the July 1995 case. The bias scores indicate that in the polar regions, CLAVR-1 observes significantly more cloud than CLAVR-x, and between 60 o S and 60 o N, CLAVR-x observes only marginally less. As was the case with the July 1995 case, the region between 60 o S and 60 o N has a moderately high Heidike score, and low root mean square and absolute errors, all of which suggest very good agreement between the two datasets. The agreement indicated by these scores decreases globally, due to the substantial disagreement between the to datasets at high latitudes. Table 4 shows the January 1996 global cloud amount for each of the cloud mask

28 27 products. CLAVR-x has the lowest global cloud amount of the four products; only slightly lower than CLAVR-1. UW-HIRS observes the highest global cloud amount, followed by ISCCP. These results are consistent with those from July 1995, and the reasoning described previously applies here as well. Figure 7 shows the comparison between January 1996 cloud amounts from ISCCP and CLAVR-x. The top two plots show the global distribution of cloud amount from CLAVR-x (left) and ISCCP (right). The plot in the bottom left of this figure shows the difference between ISCCP and CLAVR-x cloud amount for January This plot indicates that the two datasets agree to within 20% over most of the oceanic regions, excluding over the Indian Ocean (for reasons described in section 3a.) The two datasets differ significantly over the poles, with CLAVR-x observing more cloud across the Antarctic, and ISCCP observing more cloud in the arctic. ISCCP observes significantly more cloud over the Northern Hemisphere land covered surfaces that are typically snow covered in January, such as Canada and Siberia. This is likely due to the fact that snow is difficult to distinguish from clouds using the temporal and spatial tests implemented by ISCCP. However, despite these differences, the overall trend of cloud amount, as shown by the zonal averages in the bottom right plot, is similar between CLAVR-x and ISCCP. On average, excluding the antarctic, ISCCP observes slightly more cloud than CLAVR-x globally. Table 8 shows the ISCCP vs. CLAVR-x comparative statistics from January, 1996, for both NOAA-12 and NOAA-14. These scores indicate good agreement between CLAVR-x and ISCCP, with 20% or better agreement occurring for over 73% of the pixels globally. The

29 28 S -60 score indicates that the two datasets differ by more than 60% less than 1% of the time globally, none of which occurs outside of the polar regions. The bias score indicates that ISCCP observes slightly more cloud globally than CLAVR-x. Spatial and temporal trends in cloud amount can be difficult to detect over snow covered surfaces. This may lead to an overestimation of cloud by ISCCP for regions poleward of 60 o N, and some land covered surfaces as far south as 40 o N. When polar regions are excluded, all scores indicate that there is excellent agreement between the two datasets, with cloud amounts agreeing to within 20% close to 82% of the time. Overall, the agreement between CLAVR-x and ISCCP for this month is only slightly less than for the summertime case (July 1995). Figure 8 shows the January 1996 zonal mean cloud amounts from CLAVR-x, CLAVR-1, ISCCP, UW-HIRS, and APP. The largest differences between the different products occur in the polar regions, especially in the Northern Hemisphere. Poleward of 45 o N and S, CLAVR-x exhibits a trend similar to APP, while the other products all differ significantly not only from APP but also from one another. The CLAVR-x threshold tests have been modified in the polar regions based on the APP algorithm, and thus some agreement is expected. However, this agreement also lends credibility to CLAVR-x given that there are still significant differences between the two algorithms, and since APP has been established as the most reliable cloud mask for use in the polar regions. Between approximately 60 o S and 40 o N, CLAVR-x is in excellent agreement with both CLAVR-1 and ISCCP. However, CLAVR-x shows much less cloud over regions of snow and ice cover than either CLAVR-1 or ISCCP. UW-HIRS shows a similar trend, but with a significantly higher cloud amount in the tropics than any other product. As was

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