1. Introduction

Upper-tropospheric cirrus clouds continue to be the focus of many efforts estimating the role of clouds in the earth’s radiation budget. Global studies have been made with satellites (Gruber and Chen 1988; Stowe et al. 1988) and surface weather observations (Warren et al. 1986, 1988). A larger and more comprehensive study is under way with the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer 1991). These studies have relied primarily on data from the 0.7- and 11-μm window channel sensors, which have difficulty identifying optically thin upper-tropospheric clouds. Recently more sensitive methods for detecting upper-tropospheric clouds have been demonstrated using several infrared channels on atmospheric sounding instruments (Chahine 1974; Smith et al. 1974; McCleese and Wilson 1976; Smith and Platt 1978; Wielicki and Coakley 1981; Wylie and Menzel 1989; Menzel et al. 1992; Wylie et al. 1994; Susskind et al. 1997). The infrared channels from 13 to 15 μm, which are on all of the weather satellites operated by the United States, provide a means of detecting optically thin clouds and estimating their density that is more sensitive than techniques using only the visible and infrared window channels. This paper reports on an ongoing investigation of upper-tropospheric clouds using the High resolution Infrared Radiation Sounder (HIRS), which is flown on the polar-orbiting National Oceanic and Atmospheric Administration (NOAA) satellites. Eight years of HIRS observations of clouds have been collected. Wylie et al. (1994) describe the frequency, geographical distribution, and seasonal changes of observations of upper-tropospheric clouds over 4 years (1989–93). That work has been continued using succeeding satellites in the NOAA operational series. This paper presents the statistics of the cloud observations of NOAA-10, -11, -12, and -14 from 1989 through 1997. The frequency of HIRS detection of cloud is referred to as the probability of cloud occurrence; the percentage of cloud cover is inferred from the cloud density (with adjustments for the size of the HIRS field of view).

Comparisons of the University of Wisconsin (UW) HIRS cloud data to other large cloud datasets have been made. Each of the other cloud studies report different frequencies of clouds because of the differences in their ability to detect clouds. In comparing the UW HIRS and ISCCP cloud data, Jin et al. (1996) note that the UW HIRS dataset reports about 12% more cloud cover due to its higher sensitivity to thin cirrus than ISCCP. Wylie and Wang (1997) describe a comparison between the HIRS and SAGE-II cloud data, finding that SAGE-II reports even more cloud cover than the UW HIRS dataset because of an even higher sensitivity to thin cirrus and a limb scanning perspective. In addition, a comparison between the ISCCP and SAGE-II cloud data can be found in Liao et al. (1995a,b). The ISCCP and UW HIRS datasets are being expanded and more comparisons are planned for future publications.

This paper builds on Wylie et al. (1994) adding more information on upper- (above 6 km) tropospheric clouds. The following sections include discussions on the geographic distributions of these clouds, as well as the densities of these clouds and geographic variances in density. Now that the dataset comprises 8 yr, a search for trends is pursued in more detail than previously undertaken. This reexamination reveals a sensitivity to individual satellite characteristics not previously noted; important lessons emerge for the development of cloud climatologies with the Earth Observing System instruments.

2. Technique

The technique for extracting cloud height and density information, along with frequency of occurrence (assumed to be synonymous with detection in this paper), from the HIRS data has been described in detail in Wylie et al. (1994). This method uses infrared radiation emanating from the earth and atmosphere in partially absorbing CO₂ channels from 13 to 15 μm. There is no appreciable solar reflection in these channels. They are spectrally positioned so that each channel detects radiation from different levels in the troposphere, hence the association of the term CO₂ slicing with this technique. The technique is based on a simplification of the radiative transfer equation that assumes the attenuation of upwelling radiation by a cloud occurs at one level in the troposphere. A more realistic depiction requires knowledge of the vertical profile of cloud density; however, since clouds come in a wide variety of vertical distributions, the single-layer assumption is the only method for obtaining a unique solution. This approach is consistent with other algorithms proposed for detecting clouds using multispectral infrared sounders (Chahine 1974; Smith et al. 1974; McCleese and Wilson 1976; Smith and Platt 1978; Wielicki and Coakley 1981;Susskind et al. 1987; Menzel et al. 1992).

Using the multispectral CO₂ channel data, the cloud level can be solved directly without knowing the infrared emissivity of the cloud or the fractional cloud cover in the field of view (Wylie et al. 1994). Assuming that the emission and absorption are identical in two spectrally close CO₂ channels, a direct calculation of the cloud level is possible in a radiative transfer formulation. Other techniques use visible channels to estimate the cloud density and emissivity with a radiative scattering model; adjustments are then made to the measured infrared window radiance (at 11 μm) for transmission through the cloud (Minnis et al. 1993a,b). CO₂ slicing does not require visible data and provides an alternate means of detecting upper-tropospheric clouds that complements the ISCCP.

In this study, HIRS CO₂ channels are used to calculate cloud levels when the cloud radiance signal, defined as the expected clear minus measured radiance for a field of view (R_clr − R_m), is greater than five times the instrument noise level (R_clr − R_m > 5σ); otherwise the infrared window temperature is used to determine an opaque cloud-top pressure. This assures that the measured cloud signal is safely outside of the instrument noise and indicates the presence of cloud in the CO₂ channels with a high degree of confidence. A field of view (FOV) is determined to be clear if the moisture corrected 11-μm brightness temperature is within 2.5°C of a surface temperature estimate. Over land this is inferred from the Global Data Assimilation System (Kanamitsu 1989; Parrish and Derber 1992; Parrish et al. 1997) at the National Centers for Environmental Prediction (NCEP); over the oceans this is the sea surface temperature analysis from the National Environmental Satellite Data and Information Service (NESDIS). Temperature errors (defined by the root-mean-square differences between in situ data and the existing analysis values) are between 1 and 3 K. No surface temperature changes nor trends have been found during the course of this 8-yr cloud analysis; it can be concluded that surface temperature did not cause any trends in UW HIRS cloud study.

HIRS data from NOAA-10, -11, -12, and -14 are sampled to include only data from every third FOV on every third line with a zenith angle less than 10°. With two satellites, about one-half of the earth is sampled each day. Morning orbits (0000–1200 LT) over land are rejected because a good estimate of the morning land surface temperature is unavailable, which makes discerning cloudy from clear FOVs difficult. In multiple cloud layers, the height and emissivity of a single cloud layer best representing the radiative mean of the cloud scene is estimated.

After the cloud level has been determined, the cloud effective emissivity in the 11-μm window is estimated from the ratio of the radiance signal of the measured cloud with the radiance signal of an opaque cloud at the calculated cloud level. The cloud effective emissivity is the product of cloud emissivity, ε, and the fraction of cloud coverage in the FOV, N. Since it is calculated with a radiance measured from a 20-km diameter FOV, when the cloud effective emissivity, Nε, is less than unity, HIRS may be observing broken cloud (N < 1, ε = 1), overcast transmissive cloud (N = 1, ε < 1), or broken transmissive cloud (N < 1, ε < 1). All of these possibilities imply an observation where the HIRS radiometer detects radiation from below a cloud layer as well as radiation from the cloud-layer top. All observations where Nε < 0.95 are labeled as transmissive or“cirrus” in this paper; those where Nε > 0.95 are considered to be opaque clouds.

To evaluate which component of Nε, cloud fraction or emissivity, is dominant, Wylie et al. (1994) presented a comparison of Nε calculated with CO₂ slicing of the 20-km HIRS data to N inferred from 1-km Advanced Very High Resolution Radiometer (AVHRR) data within the HIRS FOV. When Nε > 0.50, all of the HIRS FOVs were entirely cloud covered (N_AVHRR = 1.0). For thinner clouds, Nε < 0.5, N_AVHRR was always greater than Nε. A larger study has been completed using 4-km AVHRR GAC data. N_AVHRR was evaluated for all NOAA-12 HIRS FOVs in July 1994 within 20°–40° lat over ocean. Figure 1 shows N_AVHRR plotted against Nε for clouds above 700 hPa, where N_AVHRR was determined using the visible and infrared screening described in Frey et al. (1996). For Nε > 0.5 (thick clouds), all HIRS FOVs were found to be completely cloud covered (N_AVHRR = 1.0), similar to the results in Wylie et al. (1994). For thinner clouds (Nε < 0.5), there was a variety of cloud coverage that had an average N_AVHRR of 0.72 within HIRS FOVs. To summarize, this work indicates that Nε represents ε entirely when Nε ≥ 0.5 (optically thicker clouds); otherwise, the contributions of N and ε are approximately equal (optically thinner clouds with Nε < 0.5). Estimates of cloud cover assume total FOV cloud obscuration when Nε ≥ 0.5, and 72% cloud obscuration of the FOV when Nε < 0.5.

The calculation of effective emissivity does not account for IR scattering into the FOV. According to the model of Platt and Stephens (1980), scattered radiation could raise Nε by 0.1 over the value determined for absorption only. Scattering, and hence an increase in Nε, is most likely for moderately transmissive clouds with Nε ≃ 0.5; very thin (Nε ≃ 0.0) and very dense clouds (Nε ≃ 1.0) will have little scattered radiation in the HIRS FOV.

3. Geographical cloud distributions

The statistical summary of over 60 million cloud observations from HIRS between June 1989 through February 1997 is shown in Table 1. The statistics are separated by cloud type into clear sky (τ_vis < 0.1), thin (τ_vis < 1.4), thick (τ_vis > 1.4), and opaque (τ_vis > 6) clouds and separated by level in the atmosphere above 6 km, between 3 and 6 km, and below 3 km. The frequency of cloud observations in a given level of the atmosphere in Table 1 are the percentages of only observations to that level; this is different from the percentage of all observations (as presented in Wylie et al. 1994). On the average for summer and winter, HIRS finds thin clouds in 22% of all observations, thick clouds in 21%, and opaque clouds in 32%. From section 2, thin clouds cover 72% of the HIRS FOV on average and thick and opaque clouds cover 100%. Thus these HIRS observations imply that clouds cover about 69% of the earth from 65°S to 65°N lat. This estimate of cloud cover is believed to be valid within 3% (Menzel et al. 1992).

Little variation is found in the boreal summer versus winter cloud statistics; clear skies are found in 27% of all observations in the boreal summer and only in 24% in the boreal winter. High clouds above 6 km are evident in 34% of the observations; 6 km roughly corresponds to 490 hPa in the Tropics and 450 hPa at 50° lat. Summer to winter, 26%–32% of the observations at midlevels between 3 and 6 km found clouds; 3 km roughly corresponds to 700 hPa in the Tropics and 685 hPa at 50° lat. Observations at low levels below 3 km found clouds 45%–47% of the time. Cirrus and transmissive clouds (with effective emissivities less than 0.95) were found in 42%–44% of all observations. All clouds below 3 km are designated as opaque because the CO₂ algorithm is not capable of resolving cloud fraction and emissivity low in the troposphere. Many of the midlevel transmissive observations are most likely broken clouds.

Figure 2 shows the geographical distribution of all clouds in the summer and winter seasons (lighter regions indicate more frequent cloud detection). The months of December–February represent the boreal winter (austral summer) and the months of June–August represent the boreal summer (austral winter). The seasonal summaries are compiled using a uniformly spaced grid of 2° lat × 3° long. Each grid box for each season has at least 1000 observations. The major features of the 8-yr summary have not changed appreciably from those reported in the 4-yr summary (Wylie et al. 1994). The intertropical convergence zone (ITCZ) is readily discernible as the region of more frequent clouds (white band in the Tropics); the midlatitude storm belts are also evident. The ITCZ is seen to move north with the sun from boreal winter to summer. The subtropical high pressure systems are seen in the regions of less frequent cloud cover. Over the Indonesian region the ITCZ expands in latitudinal coverage from boreal winter to summer. In the central Pacific Ocean, the ITCZ shows both a southern and northern extension during the boreal winter months. In the Southern Hemisphere, the eastern Pacific Ocean off South America and the eastern Atlantic Ocean off Africa remain relatively free of clouds throughout the year. The Southern Hemispheric storm belt is evident throughout the year. In the Northern Hemisphere’s midlatitude storm belts, the frequency of clouds increases during the winter with the strengthening of the Aleutian low in the North Pacific Ocean and the Icelandic low in the North Atlantic Ocean. The North American cloud cover shows little seasonal change. Large convective development occurs during the austral summer (boreal winter) in South America and Africa, which is readily apparent in the increased detection of clouds.

Some regions exhibit cloudy conditions for a complete season. In two grid boxes in the African ITCZ (8°N, 10.5°W and 2°N, 19.5°E), all HIRS observations found cloud during all boreal summers of this study. Eight grid boxes were cloudy for all boreal winters of this study; four were located in the central Amazon (centered at 8°S, 58°W), two in the African Congo (6°S, 26°E), and two in the Indonesian area (2°S, 113°E and 4°S, 146°E). With each grid box covering approximately 75 000 km², in the boreal winter there are about 300 000 km² in the Amazon that are always cloud covered. There were no grid boxes where all HIRS observations found cloud in all seasons.

Observations of clouds above 6 km (Fig. 3) exhibit the same general geographical patterns found for total clouds in Fig. 2. They are most predominant in the tropical ITCZ and move with the seasons. During the boreal winter in a few areas such as southern Brazil, tropical South Africa, and Indonesia, high clouds are found in more than 90% of the observations. Less frequent occurrence is found in the midlatitude storm belts. Differences in observations of high clouds and observations of all clouds are evident in the subtropical highs over the extratropical oceans where marine stratus clouds are prevalent and higher clouds are far less frequent. This is evident along the west coasts of North and South America and Africa.

Comparison with ISCCP data reveals that the UW HIRS multispectral analysis is finding roughly twice as many transmissive clouds than the ISCCP visible and infrared window analysis. Jin et al. (1996) studied collocated ISCCP and HIRS data for four months (July 1989, October 1989, January 1990, and April 1990). HIRS finds cloud in 76% of its observations and ISCCP finds cloud in 63%; the HIRS detection of thin clouds (17% compared to 7% for ISCCP) accounts for most of this difference. Jin et al. (1996) note a lower number of UW HIRS reports of cloud in mountainous areas due to a cold bias in the surface temperatures used in the UW algorithm. In one year (March 1985–February 1986) of ISCCP data, Hartmann et al. (1992) find semitransparent clouds (visible optical depth less than 9.4) 21% of the time; HIRS finds 44% in the 8 yr of this study (June 1989–May 1993). These comparisons point to the ISCCP difficulty in detecting thin transmissive clouds; if one were to exclude clouds with τ_vis < 1.4 from the HIRS data (roughly 20% of the observations) and increase the frequency of low opaque clouds in the HIRS data (adding roughly 5%), all adjusted HIRS cloud categories would agree with Jin et al. (1996) and Hartmann et al. (1992) to within a few percent.

Comparison with SAGE II data from the summers of 1989 and 1990 and the winters of 1990 and 1991 (Wylie and Wang, 1997) reveals good correlation (greater than 0.8) in the geographical distribution of clouds above 5 km. This must be considered in the context of the differing observing characteristics of the two systems (SAGE limb scanning over 2.5 × 200 km FOVs versus HIRS nadir 20-km FOVs); in addition SAGE can detect clouds with τ_vis ≥ 0.0002 whereas HIRS is sensitive to clouds when τ_vis ≥ 0.1. There is good comparison of the percentage of observations that find clouds in the winter hemispheres and the Tropics (within 5%), but in the summer hemispheres SAGE finds more clouds (up to 15%). On the average, 87% of the SAGE observations found cloud in 1989 and 1990, whereas 76% of the HIRS observations found cloud; SAGE is expected to find more clouds given its limb scanning and larger FOV. Overall, the similarities in these two cloud datasets are encouraging.

4. Cloud densities

The effective emissivities derived from the CO₂ slicing analysis are shown in Fig. 4. They show the same basic geographical distributions as the frequencies of high clouds (Fig. 3), but the spatial gradients are less distinct. The ITCZ and midlatitude storm belts have the highest effective emissivities, averaging about 0.6. The subtropical high and subtropical desert regions have smaller values, averaging between 0.2 and 0.3. The effective emissivities are partially correlated with frequency of high cloud; a correlation of 0.30 is found in the boreal winter and 0.35 in the boreal summer (see scatterplots in Fig. 5). The average effective emissivity is 0.51 (in both winter and summer), which can be equated with τ_vis = 1.4 using Eqs. (1) and (2). Most of the clouds above 6 km have visible optical depths between 0.5 to 1.8 (effective emissivities between 0.2 and 0.6).

5. Time trends

Wylie et al. (1994) reported a possible increase in the observation of upper-tropospheric clouds. As the HIRS record has grown to eight years and evolved to four satellites, the changes over time in the HIRS data have been reexamined in greater detail. The monthly averages of infrared window brightness temperature and cloud frequency (total and high) have been investigated for each satellite. Oceanic data were distinguished from the land data. These time series are shown in Figs. 8 and 9.

As discussed in section 2, the UW HIRS cloud study uses surface temperature data to distinguish clear HIRS FOVs. Since the CO₂ algorithm requires a clear radiance value for each channel at each location (Wylie et al. 1994), clear radiances must be inferred from nearby clear FOV measurements or calculated where the temperature structure of the atmosphere is well known. Given the likelihood of biases in the radiances calculated from temperature data in the global model analysis, we measure radiances in known clear FOVs and interpolate these values into the cloudy areas. The cloud mask compares the moisture-corrected HIRS 11-μm radiance blackbody temperature to surface temperature measurements or estimates; the cloud mask then decides if the FOV is clear or possibly cloudy. Corrections for water vapor attenuation are made using the moisture sensitive HIRS channel at 8.3 or 12.7 μm. Tests of the cloud mask show that lowering the surface temperature by 2 K causes 15% of the HIRS FOVs, which are mostly thin cirrus, to be labeled as clear. Realizing the sensitivity of the UW HIRS cloud data to surface temperatures, we examined our temperature data for possible trends and found none.

The surface temperature data used in the UW HIRS cloud study do not show any trend over land or water. The HIRS infrared window channel measurements are shown in Fig. 8. The warmer temperature plot is the monthly average of the clear only HIRS FOVs, whereas the colder plot is the average of all clear and cloudy HIRS FOVs. There are two satellites and three latitude belts shown in each of Figs. 8a, 8b, 8c, and 8d; Figs. 8a and 8b show data over land and 8c and 8d show data over oceans. NOAA-10 and -12 are the sunrise and sunset operational satellites, and NOAA-11 and -14 are the midday and midnight satellites. Each NOAA satellite is in a sun-synchronous orbit, which attempts to sample an area twice per day at the same local solar times. NOAA-10 and -12 are intended to have overpass times of 0800 and 2000 LT and NOAA-11 and -14 have target times of 0200 and 1400 LT. Seasonal cycles are obvious over land, especially in the Northern Hemisphere. Winter to summer changes of 20–30 K are apparent. Over oceans, a weaker seasonal cycle is found. The seasonal cycle is strongest in the Northern Hemisphere and barely visible in the Southern Hemisphere. The Tropics do not exhibit any seasonal cycle over oceans and only a very weak cycle over land. These characteristics were expected.

Some problems were encountered as the NOAA-11 orbit changed. NOAA-11 regressed 2.5 h in local solar time from 1989 to 1995. As land cools in the afternoon, this caused an unexpected decrease of 7–12 K in the NOAA-11 HIRS brightness temperatures over land. The drift in the NOAA-11 orbit to later in the afternoon also increased the reported frequency of clouds over land. All cloud forms increased by 16%–21% (percentage of all observations both clear and cloudy) whereas high clouds increased by 7%–15%. These were probably low broken clouds and thin cirrus with Nε ≪ 1.0. Over oceans, NOAA-11’s orbit change did not affect the monthly average HIRS brightness temperatures or frequency of cloud reports.

Another anomaly appeared April–June 1993 when NOAA-11 occasionally reported unrealistically large window channel brightness temperatures. These temperatures were often 20 K larger than the SST in the Tropics and the Southern Hemisphere (Fig. 8d). These anomalous window channel temperatures are now screened out; any HIRS temperatures 5 K greater than the SST or 20 K greater than the land temperature are discarded.

A final difficulty was found in the determination of clear-sky radiances. As indicated in section 2, the UW HIRS cloud study restricted investigation of clouds to data with HIRS scan angles less than 10° from nadir in an attempt to study clouds from a top down view (rather than a side view) and to mitigate the effect of increased pathlength through the atmosphere on transmittance calculations (as seen in limb darkening). However for June 1989 to May 1991, data from scan angles up to 25° were used to infer the clear-sky radiances in the cloudy FOVs while cloud properties were calculated only for data from scan angles less than 10°. Thus the clear-sky radiances included some limb darkening effects (less radiance detected), whereas the cloudy sky radiances did not. The effect was to decrease the frequency of cloud reports by 1% when clear-sky radiances included some limb darkened (out to 25°) radiances. High clouds were decreased by 3%. In May 1991, scan angles were restricted to less than 10° for all processing (both clear and cloudy FOVs). Adjustments to the UW HIRS cloud trend statistics have been made so that all cloud reports (before and after May 1991) refer to data within 10° of nadir. The effects of sensor scan angle on cloud frequency statistics are discussed in further detail in the appendix.

To search for trends in cloud cover, monthly averages of the cloud frequencies were calculated for each satellite and plotted as one continuous record (see Fig. 9). NOAA-12 replaced NOAA-10 in September 1991, and NOAA-14 replaced NOAA-11 in April 1995. Cloud frequency trends over land are credible only for NOAA-10 and -14 (Fig. 9a), as the changes in the NOAA-11 orbit confuse the trends over land for the midday and midnight times (Fig. 9b). When NOAA-14 replaces NOAA-11 and HIRS observations resume at the nominal slots in the diurnal cycle (0200 and 1400LT), there is a noticeable decrease in the frequency of HIRS detection of cloud over land. The trends over ocean (Fig. 9c and 9d) reveal that an annual cycle of all clouds over oceans becomes noticeable in 1993 in the southern midlatitudes; in the Tropics and the northern midlatitudes there is little discernible change in cloud cover with the seasons. High clouds show little seasonal fluctuation anywhere. Subtle trends can be seen in the annual increase of cloud cover for all three regions.

Table 2 summarizes the changes in cloud cover during the 8 yr of this study period inferred from a linear fit to the trends in Fig. 9. Over oceans, larger changes are evident in the NOAA-10 and -12 sunrise and sunset observations than in the NOAA-11 and -14 midday and midnight observations. NOAA-10 and -12 find increases in all clouds ranging from 1.7% in the southern midlatitudes to 6.6% in the Tropics. NOAA-11 and -14 find smaller increases from 1.5% to 2.1%. The largest increase in total cloud cover is in the Tropics. High clouds (pressure height < 440 hPa) exhibit the largest changes in the northern midlatitudes where increases of 4.9 and 3.3% are found. The Tropics show minimal changes in high cloud. Over land, the NOAA-10 and -12 trends are very small (increases of 2% or less are found). Southern midlatitude land coverage is small and thus the apparent increase in high clouds is not considered significant. NOAA-11 and -14 trends over land are not included in the table; NOAA-11 trends over land are problematic because of the orbit drift and NOAA-14 trends are difficult as they cover only 1.7 yr to date.

6. Summary and conclusions

Clouds with optical depths τ_vis > 0.1 cover 69% of the earth between 65°N and 65°S. High clouds are found more often in the Tropics than in the northern and southern midlatitudes, indicating the ITCZ. Large latitudinal movement in cloud cover with the changing seasons is apparent on geographical plots.

Clouds that are semitransparent in the infrared (0.1 < τ_vis < 6) are found in 43% of the HIRS observations. These clouds have a mean effective emissivity between 0.5 and 0.6 (the larger value evident in areas of frequent cloud cover).

Annual trends are small and hard to distinguish from larger seasonal differences. There is evidence of a gradual increase in the occurrence of high clouds in the northern midlatitudes (0.5% yr⁻¹) and all clouds in the Tropics (0.8% yr⁻¹); the southern midlatitudes show very little change in cloud cover of any type during this study.

The UW HIRS cloud datasets are available for further study at http://www.ssec.wisc.edu/∼donw.