Journal of the Meteorological Society of Japan, Vol. 80, No. 6, pp ,

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1 Journal of the Meteorological Society of Japan, Vol. 80, No. 6, pp , Radiative Effects of Various Cloud Types as Classified by the Split Window Technique over the Eastern Sub-tropical Pacific Derived from Collocated ERBE and AVHRR Data Toshiro INOUE Meteorological Research Institute, Tsukuba, Japan and Steven A. ACKERMAN Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, Wisconsin, USA (Manuscript received 29 March 2002, in revised form 15 August 2002) Abstract The radiative effects of several cloud types as classified by the split window (11 and 12 mm) technique were studied using coincident and collocated Earth Radiation Budget Experiment (ERBE) S-8 data and Advanced Very High Resolution Radiometer (AVHRR) data from NOAA-9. The parameter investigated was cloud radiative forcing (CRF), the difference between clear and cloudy shortwave flux (SW) and longwave flux (OLR) at the top of the atmosphere. In computing the CRF, the accuracy of clear SW and OLR is essential. Clear scene IDs in the ERBE dataset were evaluated using coincident and collocated AVHRR image data. The mean visible reflectance and SW for clear footprints defined by the ERBE are reasonably small and are 3.2% and 89.0 Wm 2, respectively. However, the values computed using our technique are smaller, 2.7% and 83.9 Wm 2, respectively. The use of collocated AVHRR image data improves clear footprint definition and implies that care should be taken when computing CRF from ERBE data alone. The CRF from several cloud types classified by the split window were compared. Cumulonimbus clouds show the largest impact on top of the atmosphere radiation for both SW and OLR. Cirrus and lowlevel cumulus clouds have similar effects on OLR, but large differences between them are seen for SW. The impact of low-level cumulus clouds on SW is much larger than that of cirrus clouds. Some optically thin cirrus clouds show positive cloud radiative forcing (warming effect). The relationships between OLR and cloud types (including cloud-free) as classified by the split window technique were investigated. By using brightness temperature differences between the split window channels, OLR estimation is improved for cloud-free and low-level cumulus clouds when compared with OLR estimated by the National Oceanic and Atmospheric Administration (NOAA) operational algorithm. Corresponding author: Toshiro Inoue, Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba , Japan. ( 2002, Meteorological Society of Japan 1. Introduction Cloud-radiation interactions are considered one of the most critical areas in global climate change research. Clouds impact the energy budget of the earth by reflecting a portion of incoming solar radiation (cooling effect) and

2 1384 Journal of the Meteorological Society of Japan Vol. 80, No. 6 by lessening earth-emitted longwave radiation (warming effect). Although, these cooling and warming effects largely depend on cloud optical properties and cloud height, the effects of cloud type have not been studied extensively because of the lack of observational data. Hartmann et al. (1992) investigated the relationship between the radiative energy balance at the top of the atmosphere measured by the Earth Radiation Budget Experiment ( ERBE) and cloud fields measured by the International Satellite Cloud Climatology Project (ISCCP). They used daily-mean values over 2:5 2:5 latitude/ longitude regions. Multiple linear regression was used to relate the radiation budget data to the cloud data. It was shown that the ISCCP cloud types can be simplified into five cloud types that will explain as much of the variance in the ERBE radiation budget parameters as any other combination of ISCCP cloud types. They found that the five cloud-type descriptions explained about 80 percent of the variance of OLR and albedo in most regions. It was also shown that cloud type information is important to the radiation balance, since regressions on total cloud cover alone predicted a much smaller fraction of the variance of OLR and albedo. A preliminary study of the radiative effects of several cloud types on earth s radiation budget was performed by Ackerman and Inoue (1994) using collocated AVHRR and ERBE data. The cloud types were classified according to a technique developed by Inoue (1987) using mean brightness temperatures ( TBB) at 11 mm (BT11) and mean brightness temperature differences between 11 mm and 12 mm (BTD ¼ BT11 BT12) from AVHRR pixels within ERBE footprints. Cumulonimbus, cirrus, and stratocumulus clouds were identified. They found that cumulonimbus clouds have the largest impact at the top of the atmosphere for both SW and OLR. The difference between cirrus and stratocumulus cloud types is significant in the SW. This paper is an extension of that preliminary investigation. Here, we classified all collocated AVHRR pixels within ERBE footprints by the split window technique, instead of using only mean values of TBB and BTD as described above. Then we selected only the ERBE footprints covered by a single cloud type (including the cloud-free case). Further, we assessed the ERBE clear scene ID that is important when estimating cloud radiative forcing. Also, OLR was analyzed in terms of cloud type as well as in cloud-free regions. 2. Data This study makes use of collocated AVHRR and ERBE observations made from the NOAA- 9 polar orbiting satellite over the eastern Pacific (15 S 35 S, 70 W 110 W) during 1985 and The NOAA-9 platform is in a sunsynchronous orbit with nominal equator crossing times of 0230 and 1430 local solar time (LST). AVHRR Global Area Coverage (GAC) data is used in this study. It has a nominal resolution at nadir of 4 km. The AVHRR has 5 spectral bands: channel 1 ( mm); channel 2 ( mm); channel 3 ( mm); channel 4 ( mm); and channel 5 ( mm). These five channels are located in spectral regions where atmospheric gaseous absorption is weak. The AVHRR data are therefore good for studying surface properties, such as sea surface temperature, and cloud top properties. The calibration procedure of the AVHRR is described in Lauritson et al. (1979). We follow the operational calibration process for the AVHRR. Inoue (1987) developed a cloud type classification technique, referred to as the split window technique, based on BT11 and BTD. This classification scheme is adopted in the present study. ERBE observations are used to specify the broadband energy budget at the top of the atmosphere. At nadir the ERBE footprint is approximately 35 km across. The ERBE instrument package includes a scanner to measure broadband shortwave (0.2 to 5.0 mm) and longwave (5.0 to 50.0 mm) radiances. The instrument and its calibration are described in Kopia (1986). The method of inverting instantaneous scanner observations to the top of the atmosphere fluxes is discussed in Barkstrom et al. (1989), and Smith et al. (1986). Collocation of AVHRR pixels within ERBE footprints is based on a technique developed by Ackerman et al. (1992), which is based in turn on the algorithm by Aoki (1980) for AVHRR and HIRS (High-Resolution Infrared Sounder) data. We first make the template based only on

3 December 2002 T. INOUE and S.A. ACKERMAN 1385 the nominal scan geometry and timing of the AVHRR and ERBE instruments. The template defines which AVHRR pixels fall within the ERBE footprints. We then generate a correlation between mean AVHRR 11 mm brightness temperatures within the ERBE footprints and ERBE OLR based on the initial template. Next, we repeat the collocation and correlation procedure, but move the AVHRR images relative to the ERBE in each direction until we find the highest correlation. The collocation procedure is completed when the highest correlation is found and the template adjusted accordingly. 3. Data analysis 3.1 Clear-sky threshold for cloud type classification Inoue (1987) developed a cloud type classification technique based on the characteristics of split window measurements of cirrus clouds (Inoue 1985). Figure 1 depicts the split window cloud classification scheme used by Inoue (1989). For cloud type classification to proceed, threshold values are needed for clear BT11 and clear BTD. The first step is to determine the clear-sky thresholds BT11 and BTD for each 2:5 2:5 latitude/longitude region within the area of study. This is accomplished through analysis of 3 3 groups of AVHRR pixels that lie within each 2:5 2:5 latitude/longitude region. A first guess threshold of BT11 is determined using the spatial coherence approach of Coakley and Bretherton (1982). The threshold is specified as the foot of the clear-sky arch ( Fig. 2). The 3 3 groups of pixels are then re-analyzed. In the second pass through the data, only those 3 3 groups of pixels that have a standard deviation of less than 0.4 C and a mean BT11 that is greater than the initial threshold selected by the spatial coherence analysis are considered. Each group of AVHRR pixels is assigned an appropriate 2:5 2:5 latitude/ longitude grid box. The mean BT11 and mean BTD for each 2:5 2:5 latitude/longitude region are then computed for each month of the study period and assigned as the clear-sky thresholds values. 3.2 Cloud classification Having selected a clear-sky threshold that is a function of month and geographic region, each individual AVHRR pixel that lies within an ERBE footprint is classified according to the split window technique (Fig. 1). To classify optically thick cloud, the BTD threshold is set to 0.5 C in this study instead of the 1 C shown in Fig. 1. Optically thicker clouds are detected appropriately by the threshold. With a BT11 threshold of 20 C (corresponding to about 400 hpa), the optically thick clouds are classified into two types: cumulonimbus type and low-level cumulus type Fig. 1. Cloud type classification diagram according to the split window technique (Inoue, 1989). Six cloud types are classified using clear/cloudy and 20 C channel-4 brightness temperature thresholds, along with clear and 1 C brightness temperature differences. Fig. 2. Spatial coherence diagram constructed from channel-4 brightness temperature data of April 15, 1985 over the southeast Pacific Ocean.

4 1386 Journal of the Meteorological Society of Japan Vol. 80, No. 6 cloud (B-Type and U-Type in Fig. 1, respectively). Optically thin cirrus clouds can also be classified with the use of the clear-sky BTD. The BTD over cloud-free ocean areas is about 2.5 C in the tropics due to differential absorption by water vapor between 11 mm (channel 4) and 12 mm (channel 5). Pixels containing cirrus clouds have larger BTD than cloud-free ones (Inoue 1987). Again, with the help of the 20 C BT11 threshold, cirrus clouds are classified into two types as cirrus-iii and cirrus-ii (I3-Type and I2-Type in Fig. 1) that are warmer and colder in TBB, respectively. Cirrus-I (I1-Type in Fig. 1) clouds are those with cold cloud tops but with slightly thinner ice cloud layers than cumulonimbus type clouds. N-type clouds are considered here as clouds that include edges of optically thick cloud within the field of view, optically thinner cumulus cloud, or low-level cumulus overlaid by thin cirrus. Inoue (1997) reported the mean optical thickness for each cloud type classified by the split window from a comparison with ISCCP cloud parameter retrievals. Cumulonimbus clouds have the highest optical thickness (33.7). The mean optical thickness of cumulus clouds is The mean optical thickness of cirrus-ii is larger than cirrus-iii, 7.4 and 2.2, respectively. These results suggest that the cloud type classification by the split window technique is reasonable. Recently, Luo et al. (2002) simulated split window brightness temperatures for water and ice clouds. They compared cloud type classifications by the ISCCP algorithm with those from the split window technique. Although there are some differences between the two, the cloud type classification methods agree reasonably well, as seen in Fig. 3. The reader should understand that cloud types in this study are defined by the split window technique. Therefore, the cloud type definition may differ from the ten cloud genera defined by the WMO. 4. Results 4.1 Assessment of clear scene ID by ERBE Cloud radiative forcing (CRF) is widely used to study cloud radiative effects. The CRF is defined as the difference between clear and cloudy conditions. Fig. 3. Simulated split window cloud types on cloud height and optical thickness diagram (after Luo et al. 2002). Thin cirrus, thick cirrus and dense cirrus correspond to I3-Tpye, I2-Type and I1-Type cloud in Fig. 1. Cumulonimbus and cumulus correspond to B-Type and U-Type in Fig. 1. CRF ¼fSW ðclearþ SW ðcloudyþg þfolr ðclearþ OLR ðcloudyþg: Therefore, the accuracy of clear conditions is critical for the computation of the CRF and also for computation of aerosol forcing. The ERBE S-8 data reports a scene ID that indicates clear, partly cloudy or overcast. The ERBE scene identification algorithm (Wielicki and Green 1989) first determines the surface type as ocean, land, snow, desert, or coastal according its latitude and longitude. Then, a bispectral method that utilizes reflected solar radiance and longwave radiance together with a priori data from Nimbus-7 Earth Radiation Budget is used to compute the cloud class. Cloud classes are clear (cloud fraction less than 5%), partly cloudy (cloud fraction between 5% and 50%), mostly cloudy (cloudiness between 50% and 95%), or overcast (cloudiness greater than 95%). The algorithm is based on a maximum likelihood estimation, using measured broadband shortwave and longwave radiance data at 35-km spatial resolution. Here, we compared clear footprints designated by ERBE with clear footprints defined by the split window technique. Figure 4 shows a histogram of cloud amounts determined by the split window technique for ERBE clear footprints. The number of ERBE clear foot-

5 December 2002 T. INOUE and S.A. ACKERMAN 1387 Fig. 4. Histogram of cloud amounts defined by the split window technique within clear-sky ERBE footprints (defined by ERBE scene ID). Many ERBE clear-sky values are contaminated by clouds, according to the split window results. prints that are contaminated by cloud is surprising. Cloud amounts by our definition within ERBE clear footprints range from 0 to 100%. The cloud types that exist in the ERBE clear scenes are mostly cirrus (cirrus-iii) clouds and some N-type clouds. However, the mean visible reflectance and SW for ERBE clear footprints are reasonably small at 3.2% and 89.0 Wm 2, respectively. Footprints defined as clear by the ERBE scene ID, as well as by our clear identification method, are compared using the average visible (channel-1) reflectivity from AVHRR and SW observed by ERBE. We define our clear ERBE footprint as having less than 5% cloud amount. Figures 5a and 5b show histograms of visible reflectivity for clear footprints according to ERBE and the split window technique, respectively. The mean visible reflectivity for the split window technique is 2.7%, smaller by 0.5% than ERBE. Both methods indicate that a majority of clear-sky AVHRR pixels have visible reflectivities less than 4%. However, the percentage of low visible reflectances is 91% for the split window technique and 83% for ERBE clear scene. In like manner, Figs. 6a and 6b show histograms of SW for clear footprints. The mean SW value for the split window is 82.3 Wm 2, 6.7 Wm 2 less than for ERBE. Again, both methods indicate most values are less than 90 Wm 2, however, the percentage of low SW is 75% for the split window and 60% for the ERBE. This suggests that the use of collocated AVHRR cloud information can allow users of ERBE data to choose clear-sky scenes with lesser amounts of cloud contamination. This discrepancy is explained by use of the finer spatial resolution AVHRR data in our method. Loeb and Kato (2002) studied the ERBE-like clear scene identification using the Clouds and Earth s Radiant Energy System (CERES) and imager. They found that the ERBE-like clear scene identification indicated 3 4 Wm 2 differences compared with the use of imager data. We also compared totally cloud-free footprints (0% cloud amount by the split window technique). As expected, the mean values for Fig. 5. Histogram of channel-1 reflectances within ERBE clear-sky footprints defined by a) ERBE scene ID and b) the split window technique.

6 1388 Journal of the Meteorological Society of Japan Vol. 80, No. 6 Fig. 6. Histogram of ERBE shortwave fluxes within ERBE clear-sky footprints defined by a) ERBE scene ID and b) the split window technique. Fig. 7. Scatter plot of ERBE longwave and shortwave fluxes for each cloud type classified by the split window technique. channel-1 reflectivity and SW are smaller for the totally clear footprints. The mean channel-1 reflectivity and the SW for the totally cloud-free footprints are 2.5% and 79.8 Wm 2, respectively. 4.2 Radiative effects of various cloud types Figure 7 depicts the relationship between the ERBE OLR and SW for cloud types categorized by the split window classification method. To construct this figure, we used only ERBE footprints covered with a single cloud type with cloud amount larger than 95%. The split window technique selects either clear, lowlevel cumulus cloud, N-type cloud, cirrus cloud (cirrus-iii) or cumulonimbus cloud. Cumulonimbus clouds have a large SW and very low OLR. The mean value of SW and OLR for these clouds is Wm 2 and Wm 2, respectively. Clear sky exhibits a lower SW and a larger OLR. The mean value of SW and OLR is 82.3 Wm 2 and Wm 2, respectively. Cirrus cloud (cirrus-iii) and low-level cumulus clouds also separate out in the figure. Both have similar OLR, but the cirrus clouds have a much smaller SW. The mean SW for cirrus and low-level cumulus cloud is Wm 2 and Wm 2, respectively. The mean OLR for cirrus and low-level cumulus cloud is Wm 2 and Wm 2, respectively. These results are consistent with the previous study by Ackerman and Inoue (1994). The ERBE flux

7 December 2002 T. INOUE and S.A. ACKERMAN 1389 Fig. 8. Scatter plot of longwave and shortwave cloud radiative forcing for each cloud type. data is somewhat less accurate for instantaneous observations due to angular sampling and anisotropy issues (e.g., Suttles et al. 1992). The uncertainty of SW and OLR instantaneous flux is about 15 Wm 2 and 5 Wm 2, respectively (Barkstrom et al. 1989). However, the difference in the mean SW between cirrus and low-level cumulus clouds is significant and consistent with our expectations. 4.3 Cloud radiative forcing for each cloud type We compute the CRF for each cloud type classified by the split window using cloud-free SW and OLR as determined by our method ( Fig. 8). The data used in the figure are all 100% cloudy within the footprints. We can easily see the CRF dependence on cloud type. The large differences between cirrus cloud (cirrus-iii) and low-level cumulus cloud for SW CRF is as expected. The largest contributions for both SW and OLR CRF are from cumulonimbus clouds, again as expected. This implies that the cloud type classification by the split window is reasonable in terms of radiative effects at the top of the atmosphere. There are 56 footprints that indicate positive CRF (warming effects) in our analysis. The warming effect of these clouds is shown in the histogram (Fig. 9). The CRF is mostly 0 5Wm 2 but ranges to 40 Wm 2. The amount of cirrus cloud in these footprints is shown in Fig. 10. The cloud types within these footprints Fig. 9. Histogram of net cloud radiative forcing (Wm 2 ) for footprints that show the warming effect. Fig. 10. Histogram of cirrus (cirrus-iii) cloud amount within the footprints that show the warming effect.

8 1390 Journal of the Meteorological Society of Japan Vol. 80, No. 6 Fig. 11. Scatter plot of channel-4 brightness temperatures, and ERBE longwave fluxes, for clear-sky, cumulus clouds, and cirrus (cirrus-iii) clouds. are mostly cirrus cloud (cirrus-iii) but some are N-type. It is not appropriate to discuss whether cirrus cloud (cirrus-iii) truly has a warming effect, since the ERBE instantaneous flux has some uncertainty as stated above. However, there is the suggestion in the data presented here that these clouds have a warming effect on the atmosphere. 4.4 OLR and cloud type The OLR of the earth-atmosphere system depends on temperature and humidity profiles, surface temperatures, cloud top pressures and cloud amounts, and on minor atmospheric constituents. NOAA has operationally produced OLR using narrow band window channel data from NOAA polar orbiting satellites from June 1974 to the present. There have been several revisions, but the following basic equation has been used to estimate the OLR using flux equivalent temperatures defined as T f ¼ðaþbBT12ÞBT12; where a and b are coefficients determined from a regression analysis of either observations (Ohring et al. 1984) or theoretical calculations (Ellingson and Ferraro 1983). The OLR flux is related as OLR ¼ s T 4 f ; where s is the Stefan-Boltzmann constant. Figure 11 shows a scatter plot of ERBE OLR and BT11 for clear, low-level cumulus cloud, and cirrus cloud (cirrus-iii). These figures suggest the need for separate regressions when generating coefficients for the above relationship. Different relationships exist between window channel brightness temperatures and broadband fluxes for different cloud types. This is particularly evident in the low-level cumulus and cirrus types. Similar BT11 for these two cloud types yield different OLR. There is a clear systematic difference between the two. Also, the relationship between broadband flux and window channel observations for low-level cumulus cloud is different than for the clear sky case. In constructing this figure, only ERBE footprints that were at least 95% covered with single split window classifications were used. With the use of the split window, water vapor information estimates can be made from the BTD over cloud-free ocean ( Inoue 1990; Ackerman and Inoue 1994). Cloud types and water vapor amounts are important components of OLR. Therefore, the relationship between ERBE broadband OLR and these parameters was investigated to develop an algorithm that could estimate OLR. The role of the split window in the regressions varies with cloud type. In clear-sky cases the split window becomes an estimate, or correction, for water vapor effects. In cloud cases it represents the cloud height

9 December 2002 T. INOUE and S.A. ACKERMAN 1391 Fig. 12. ERBE and predicted longwave fluxes from a) our algorithm and b) the NOAA operational algorithm. Table 1. The rms difference between ERBE longwave flux and two estimation methods (Split Window technique and NOAA Operational algorithm) for each classification. Classification by Split Window Split Window Method NOAA Operational Method Clear 8.2 (Wm 2 ) 13.0 (Wm 2 ) Cumulus-Type N-Type Cirrus-Type Cumulonimbus- Type and combined effect of cloud optical thickness and microphysical properties. Here, data from January, April, July and October are used in a least squares fit as follows, OLR ¼ a þ b BT11 þ c BTD; where a, b and c are coefficients derived from regression analysis. Separate coefficients are derived for each of cloud type classified by the split window technique. The statistics of regression for each scene type are summarized in Table 1. For clear-sky cases the split window improves the regression by lowering the RMS error from 13.0 to 8.2

10 1392 Journal of the Meteorological Society of Japan Vol. 80, No. 6 Wm 2. For low-level cumulus cloud, the RMS error improves from 12.2 to 6.0 Wm 2. For cirrus, N-type, and cumulonimbus clouds, there is little impact on the error when the split window technique is compared with the operational NOAA method. Figure 12a shows a scatter plot of ERBE OLR and OLR predicted by our method. Figure 12b shows a scatter plot of ERBE OLR and OLR predicted by the NOAA method. Our OLR estimation seems to be more systematic than the NOAA OLR. Overall, the fit has a standard error of 6.7 Wm 2 for the split window technique and 10.9 Wm 2 for the NOAA OLR method. Figure 13 shows a histogram of the differences between our predicted OLR and ERBE OLR. Agreement is good between the two, as 60% of the data lie within 5 Wm 2 and 77% within 7.5 Wm 2 of ERBE values. The larger difference between NOAA OLR and ERBE OLR is seen for clear and low-level cumulus cloud. This difference is consistent with the findings by Gruber et al. (1990). They compared the NOAA and ERBE OLR and found they agree reasonably well (rms difference was Wm 2 for daily data), except for desert and trade wind regions. They studied this systematic difference over the trade wind region by computing clear-sky radiances using observed temperature and moisture profiles. They concluded that the main reason for the discrepancy is the temperature inversion and water vapor variations. This suggests that temperature and water vapor profiles affect the OLR over cloud free and low-level cloud regions. Our method shows better estimation for clear areas compared with NOAA OLR because we can take water vapor information into consideration using the BTD. Low-level cloud is generally associated with temperature inversions. The NOAA OLR shows larger differences than ERBE OLR compared with our results because the temperature inversion effect is implicitly included in the regression coefficients. Therefore our OLR estimation over low-level cloud is improved slightly. Ellingson et al. (1994) validated their OLR estimation algorithm from HIRS data with observed ERBE OLR. They found that the rms difference was about 5 Wm 2. Our estimation has 6.7 Wm 2 rms difference, which is not as Fig. 13. Histogram of differences between predicted longwave fluxes by the split window technique and ERBE longwave fluxes. good as the HIRS multi-channel data algorithm. Using a simple radiative equilibrium model, Stephens and Greenwald (1991) demonstrated that the greenhouse parameter (a function of SST and OLR) depends not only on total water vapor amount, but also on the vertical distribution of water vapor and temperature. Ackerman et al. (1992) confirmed that the greenhouse parameter depends on the upper level water vapor amount estimated from the 6.7 mm channel of HIRS. These results indicate the importance of upper level moisture in OLR estimation. Ellingson et al. (1994) used the multichannel HIRS data, including the 6.7 mm water vapor absorption band, which is sensitive to the upper level moisture. Schmetz and Liu (1988) used two infrared bands at 11 mm and 6.7 mm on board METEOSAT to study OLR. They showed that the inclusion of 6.7 mm more than halves the rms error compared with a single IR band approach. The use of 6.7 mm is effective for better OLR estimation. Our OLR estimation algorithm is not as accurate as the HIRS multi-channel method. However, our method is applicable to the recent geostationary satellites that have split window and water vapor channels. Inoue (1997) demonstrated that cloud properties change between day and night. Therefore, the investigation of diurnal variations in OLR is an important issue. High temporal resolution data from geo-

11 December 2002 T. INOUE and S.A. ACKERMAN 1393 stationary satellites is essential when surveying this diurnal variation. 5. Summary The radiative effects of various cloud types as classified by the split window technique were explored using coincident and collocated ERBE S-8 and AVHRR data from NOAA-9. The reliability of cloud radiative forcing (CRF) computations depends on the accuracy of clear-sky data, since CRF is defined as the flux difference between clear and cloudy conditions. In this study, clear ERBE footprints are determined by use of collocated AVHRR infrared data. Flux data from these footprints are then compared with those from ERBE footprints having a clear scene ID as defined in the ERBE S-8 data set. The mean visible reflectance and SW for the latter are reasonably small at 3.2% and 89.0 Wm 2, respectively. However, the mean values for clear footprints as defined by our method are 2.7% and 83.9 Wm 2, respectively. Therefore, we believe our method to be more robust than the use of the ERBE scene ID. Radiative properties from several cloud types classified by the split window are compared with ERBE observations. Cumulonimbus clouds indicate the largest impact on top of the atmosphere radiation for both shortwave and longwave. Cirrus and low-level cumulus clouds show similar effects on OLR, but exhibit large differences on SW. The impact on SW is much larger for low-level cumulus clouds than for cirrus clouds. Results suggest that some optically thin cirrus clouds have a warming effect ( positive cloud radiative forcing). The relationship between cloud types (including cloud-free) and OLR was also investigated. It was found that the relationship between ERBE OLR and BT11 differs depending on cloud type. Therefore, we determined a regression relationship between ERBE OLR, BT11, and BTD for each cloud type. Our OLR estimation is improved for cloud free and lowlevel cumulus clouds in comparison with that from the NOAA operational algorithm that uses flux equivalent temperatures determined only from window channel brightness temperatures. Recently, CERES data has become available for studying cloud radiative effects ( Wielicki, personal communication). This data has taken angular sampling and anisotropy into consideration more extensively, and therefore the accuracy of the flux at the top of the atmosphere is improved by a factor of 2 to 3 (Wielicki et al. 1996). It is our hope that the use of CERES data will further our understanding of cloud radiative effects, including the warming effect of thin cirrus. Acknowledgments The authors wish to express their gratitude to the reviewers for valuable comments and clarifications. References Ackerman, S.A., R.A. Frey, and W.L. Smith, 1992: Radiation budget studies using collocated observations from AVHRR, HIRS/2 and ERBE instruments. J. Geophys. Res., 97, and T. Inoue, 1994: Radiation energy budget studies using collocated AVHRR and ERBE observations. J. Appl. Meteor., 33, Aoki, T., 1980: A method for matching the HIRS/2 and AVHRR pictures of TIROS-N satellite. Tech. Note 2, Meteorological Satellite Center, Japan, Barkstrom, B.R., E.F. Harrison, G. Smith, R. Green, J. Kebler, R. Cess, and the ERBE Science Team, 1989: Earth Radiation Budget Experiment (ERBE) archival and April 1985 results. Bull. Amer. Meteor. Soc., 70, Coakley, J.A. Jr. and F.P. Bretherton, 1982: Cloud cover from high-resolution scanner data: Detecting and allowing for partially filled of fields of view. J. Geophys. Res., 87, Ellingson, R.G. and R.R. Ferraro, 1983: An examination of a technique for estimating the longwave radiation budget from satellite radiance observations. J. Climate Appl. Meteor., 22, , H.-T. Lee, D. Yanuk, and A. Gruber, 1994: Validation of a tequnique for estimating outgoing longwave radiation from HIRS radiance observations. J. Atmos. Oceanic Technol., 11, Gruber, A., P. Ardanuy, M. Weiss, S.K. Yang, and R.G. Ellingson, 1990: A comparison of ERBE and AVHRR logwave flux estimate. NOAA Technical Report NESDIS 50, 88 pp. Hartmann, D.L., M.E. Ockert-Bell, and M.L. Michelsen, 1992: The effect of cloud type on earth s energy balance: Global analysis. J. Climate, 5,

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