To appear in the Journal of Geophysical Research, probably 1/27/99
Detecting tropical convection using AVHRR satellite data Richard M. van Hees and Jos Lelieveld Institute for Marine and Atmospheric Research Utrecht, Utrecht, Netherlands
William D. Collins National Center for Atmospheric Research, Boulder, Colorado
Abstract. A new method is introduced to identify active convective cloud regions in high-resolution AVHRR satellite data. This method is developed to overcome several limitations of existing visible/infrared techniques. Steep temperature gradients between the convective overshoots of cumulonimbus clouds and the surrounding cirrus are used to identify the edges of active convective regions. The limitations of previous methods are shown using up to 1.1 km resolution AVHRR data of the NOAA-11 and NOAA-12 satellites collected during the Central Equatorial Pacific Experiment (CEPEX), a measurement campaign that took place over the central Pacific Ocean between March 7 and April 6, 1993. Differences in the identification of convection between the new algorithm and the existing algorithms are clarified by a case study and further illustrated by three collocations between NOAA-11 and NOAA-12 overpasses and observations made with the Massachusetts Institute of Technology (MIT) 5 cm Doppler radar onboard the research vessel R/V Vickers. 1. Introduction
quently used to estimate convective rainfall or occurrences of (mesoscale) convective complexes [?]. This approach has the advantage that its implementation is straightforward and it can be applied to data from geostationary satellites which cover large areas with a high temporal resolution. However, it is not obvious how various cold cloud properties, such as size, altitude, lifetime, and microphysical properties, are related to convection. Therefore our aim is to identify active convective regions. The resolution of infrared data from geostationary satellites is not sufficient for this purpose because the active convective regions are comparable to the nadir resolution of these satellites. This leaves two options: to use higher-resolution data from the advanced very high resolution radiometer (AVHRR), or to use a completely different wavelength band. Microwave data from the special sensor microwave/imager (SSM/I) can be used to separate active convective regions from the cold stratiform cloud shield [???], despite its low resolution ( 10 km), because a very low 85 GHz brightness temperature is an indication for large amounts of ice. ? have measured ice particle concentrations of the order of hundreds per liter in the convective regions using a particle measurement system (PMS) cloud probe. A disadvantage of both AVHRR and SSM/I data sets is the poor temporal resolution. A given geographic region is observed only twice a day, which makes it impossible to monitor the evolution of a convective region. The derived daily convec-
Deep convection is important in the vertical distribution of momentum, heat, and moisture, in particular in the Earth’s tropical and subtropical atmosphere. Furthermore, deep convection leads to the exchange of relatively short-lived chemical compounds between the boundary layer and the free troposphere, and possibly also between the troposphere and the lower stratosphere [????]. Thus accurate quantification of deep convective activity is important for the understanding of regional and global atmospheric circulations and the chemical composition of the atmosphere. In this paper we present a new method to identify active deep convection by the presence of convective overshoots in high-resolution infrared and visible satellite data. Deep convective activity is indicated by the presence of thick cumulonimbus clouds that reach to high (cold) altitudes. The actual deep convection takes place in small regions, where rising air parcels with diameters of about 1-10 km penetrate the entire troposphere in under an hour [??]. The cirrus anvils associated with well-developed convection can be much larger ( 105 km2 ) and survive long after the actual convection has ceased [?]. A common approach to derive indices for deep convective activity in infrared satellite imagery is to determine cold cloud cover. The cold cloud cover, defined by the area of the pixels with a brightness temperature lower than an arbitrary infrared threshold, is fre1
2 tive activity may also be biased by the diurnal cycle of cold cloudiness [??]. AVHRR has the advantage that it can resolve a convective core, while the infrared data are accurate enough to detect the temperature difference between the cirrus anvil and the cloud top lifted by convective overshoot. However, collocated radar and infrared observations by ? indicate that the coldest cloud tops are associated with the overshooting of convective cells only during a short period of less than 30 min. Furthermore, the area of coldest cloud tops are observed to be progressively displaced from the areas of convective activity during the evolution of a deep convective cloud system, and the larger contiguous regions of cold cloudiness ( 100 km2 ) are more likely to be a result of adiabatic cooling above a stratiform region [?]. In this paper we introduce an infrared technique to identify convection in high-resolution satellite data obtained within the Central Equatorial Pacific Experiment (CEPEX). It is designed to overcome limitations of existing visible/infrared algorithms (section 2). A statistical study of the CEPEX AVHRR data set shows that both visible and infrared data are affected by the viewing geometry, especially the satellite zenith angle. In section ?? we derive thresholds as a function of satellite zenith angle specific to the AVHRR. The differences between our Edge technique, introduced in section ??, and existing threshold methods are illustrated in section ??. Our aim was to validate the Edge method using observations made during CEPEX with a MIT 5 cm Doppler radar onboard the research vessel R/V Vickers (section ??). However, direct validation of the Edge method using these radar observations was limited. Since only 15 collocations with radar data occurred, of which only a small number contain high clouds. Therefore we will present a validation of the Edge method using ship radar data collected during the Tropical Ocean Global Atmosphere Coupled Ocean/Atmosphere Response Experiment (TOGA COARE) in a separate paper. In section ?? we illustrate the difficulties that occur in comparing such data sets by showing three collocations between NOAA-11 and NOAA-12 overpasses and CEPEX R/V Vickers radar data.
2. Brief Survey of Previously Applied Threshold Techniques A generally accepted method to derive indices of convection from satellite data is to apply thresholds to one or more spectral radiances. Data pixels that pass these tests, for example, pixels with colder brightness temperatures or larger reflectances, are assumed to represent convective cloudiness. Convective activity is derived from the size and average temperature of connected cold cloud regions as functions of location, year, and time of day. The thresholds presented in the literature are often fairly simplistic since the variation of the radiances with the viewing geometry is usually ignored. In addition, cloud optical depths inferred from visible radiances are not only a function of viewing geometry. The optical depths are functions of the cloud microphysical parame-
Hees et al. ters, in particular the cloud ice-particle or water-droplet size distribution. ? have shown that the optical depth derived from visible radiances observed by satellite can vary substantially, depending on cloud microphysics. Using waterdroplet phase functions for an optically thin ice cloud may result in an underestimation of cloud height by 2 km or more. The infrared radiance is also a complicated function of cloud top height, cloud optical depth, and cloud microphysics [?]. The following threshold methods will be considered: single infrared threshold (SIT), visible and infrared threshold (VIT), and multiple infrared threshold (MIT). SIT is commonly used to identify convective clouds using a fixed threshold in infrared brightness temperature. A single-channel infrared measurement expressed as an equivalent brightness temperature (BT) is a good approximation for the cloud top temperature only if the cloud fills the field of view completely and is optically thick. The accuracy of the cloud top determination depends mainly on the following assumptions: (1) distribution of ice/liquid water content within the cloud as function of altitude, (2) microphysical properties, for example, ice/water particle size distribution, and (3) assumed temperature/water/ozone profiles. ? have presented a list of infrared thresholds obtained from the literature for tropical deep convection using geostationary satellite data. These infrared thresholds, expressed as brightness temperature BT, range from 198 to 235 K. The “warm” threshold provides the best linear regression with the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) rainfall, while the “cold” threshold approximates the Equatorial Mesoscale Experiment (EMEX) radar echo area. Note that this “cold” threshold is only 10 above the average tropopause temperature as derived from 85 radiosondes launched from the R/V Vickers in the CEPEX region. Thus a “cold” threshold will only identify optically thick clouds that reach up to near-tropopause altitudes. A warmer threshold will include optically thinner clouds and lower-level optically thick clouds, and the SIT method leaves no possibility to distinguish between them. MIT is based upon a fixed threshold in infrared brightness temperature combined with a brightness temperature difference threshold at two infrared wavelength bands. The method is derived from the work by ?, who proposed a simple objective cloud-type classification method based on the brightness temperature of AVHRR channel 4 (BT4) and the brightness temperature difference between AVHRR channels 4 and 5 (BTD45). The multiple-channel infrared measurements, as measured with AVHRR, can be used to derive cloud optical depth (0 25 τ 6) and cloud ice-/waterparticle size distribution [?, Figures 6 and 7]. This technique allows for a better separation of optically thin clouds from thick clouds in day and nighttime data, and it is mainly used to detect cirrus clouds. A fixed threshold in infrared brightness temperature combined with a fixed threshold in reflectance has the advantage over the SIT method that the reflectance adds valuable information about the column-condensed water content.
Detecting Tropical Convection However, this method requires several assumptions about the cloud microphysics and the cloud ice-/water-particle size distribution, and the results are sensitive to the satellite viewing geometry. One of the first attempts to derive optical depth from visible satellite data was made by ? for water clouds. The retrieval method has been further developed by ? for several ice-crystal size distributions. The use of visible data is restricted to daytime, whereas convective cloud formation over the tropical oceans has a maximum during night [?].
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3. Analysis of CEPEX AVHRR Data Local area coverage AVHRR data with 1.1 km nadir resolution, collected during CEPEX, are used to compare the results of our newly developed technique to identify convection with existing methods. The scientific objectives and experimental design of CEPEX are described in detail by ?. The principal goal of CEPEX was to test the cirrus thermostat hypothesis [?] and other proposed mechanisms for regulation of tropical sea surface temperatures (SSTs) [?]. The SST in the western tropical Pacific regularly exceeds 300 K, favorable for the occurrence of deep convection [?]. The experiment began March 7 and ended April 6, 1993. Several adjustments have been applied to the existing methods in order to apply them to the high-resolution AVHRR data. For example, most methods to identify convection are developed using the visible and/or infrared data from geostationary satellites. The visible channel of geostationary satellites (0.5–0.75 µm) has a similar spectral band pass to channel 1 of AVHRR (0.55–0.68 µm), although the latter has a narrower spectral response function. The infrared channel of geostationary satellites (10.5–12.5 µm) has a much broader spectral response function than the AVHRR channels 4 and 5 (10.5–11.5 µm and 11.5–12.5 µm, respectively). Both AVHRR infrared channels are significantly affected by water vapor, but the absorption by water vapor continuum is stronger in channel 5 than in channel 4. Therefore we have used channel 4 as the primary infrared channel, expressed as brightness temperature (BT4). In this section the specific viewing geometry of the AVHRR data and its impact on the infrared and visible data are characterized. To understand the statistics of the CEPEX AVHRR data set, we can use approximations for the average ice/water cloud microphysics and visible/infrared radiative transfer models. The radiative transfer package Streamer is used to calculate infrared radiances [?] and includes effects of the absorbing gases of water vapor and ozone. The discrete ordinates radiative transfer (DISORT) model is used to compute radiances [?]. The ice cloud radiative properties are approximated by spherical particles [?]. The extinction efficiency, asymmetry factor, and single-scattering albedo are calculated on the basis of Mie theory [?]. The radiative transfer in the visible is based on the cirrostratus model proposed by ?. Cirrostratus clouds are modeled as homogeneous ice clouds with randomly oriented hexagonal ice crystals having
Figure 2. Viewing geometry of a cloud at height h as observed by AVHRR; β is the angle between a given position at the surface and the cloud top for a satellite zenith angle α. Clouds on the left are seen with a large solar zenith angle, so most light observed is reflected from cloud sides (especially by Cb towers). Clouds on the right are seen with relatively small solar zenith angles, so most light is reflected from the cloud tops (see section ??). infrared observations from both clouds can include parts of the cloud sides within the field of view. Effects of cloud geometry will increase BT4 because cloud sides are generally warmer than cloud top and will increase BDT45 (sections ?? and ??) due to nonlinear behavior of the Planck function.
length-to-width ratios (L/D) of 85µm/40µm.
3.1. AVHRR Viewing Geometry The data from the AVHRR instrument on NOAA 11 and NOAA 12 were collected at Fiji, located at 187 E and 17 S. Figure ?? shows the area coverage of the CEPEX AVHRR data set and the number of satellite overpasses. The area covered includes not only the Pacific warm pool but also a few islands, the northern part of New Zealand, and the east coast of Australia. The overpass times of the NOAA-11 satellite were at 0115 LT and 1315 LT, and the overpass times of the NOAA-12 satellite were at 0725 LT and 1925 LT, respectively (Fiji local time). The Sun zenith angle (θ0 ) of the nadir pixels of the daytime overpasses was 55 for NOAA 11 and 70 for NOAA 12 over the duration of CEPEX. The Sun zenith angle changes nearly 15 at the end of a cross-scan when the satellite zenith angle (θ) is nearly 70 . The ground resolution of the high-resolution AVHRR data is 1.1 km at nadir and 2 3 6 4 km at the end of a crossscan. The relative azimuth angles (ψ) correspond to both forward and backward scattering directions because of the Sun-synchronous orbit and its cross-track scan pattern. Figure ?? illustrates the AVHRR viewing geometry.
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Figure ?? shows the cumulative distribution of the BT4 pixels as a fraction of the total number of data pixels. Approximately 10% of the data collected during CEPEX has a BT4 290 K 10 and may be considered cloud free [?]. Only 3% has a BT4 5 208 K a value, which is often assumed to approximate the boundary between the core and the cirrus deck of a tropical convective system [see ?]. Increasing the BT4 threshold 1 from 208 to 220 K will only increase the number of data 0.5 pixels classified as cirrus decks by a few percent. Figure ?? shows the normalized frequency distribution of BT4 as a function of the satellite zenith angle. The frequency 0.1 distribution shows several distinct clusters of observations.
180 200 220 240 260 280 300 Most of the very cold BT4 pixels less than 200 K are detected BT4
(K) with satellite zenith angles less than 40 . The emittance of these clouds must be nearly 1 because BT4 is close to the Figure 3. Cumulative frequency distribution of BT4, shown tropopause temperature so that the detection should not be for different BTD45 thresholds (see section ??): all data a function of θ. However, there is a geometrical effect that (solid), 2.5 (dotted), 2.0 (dashed), 1.5 (dashed-dotted), 1.0 increases BT4 by a few degrees. The resolution decreases as (dashed-dotted-dotted-dotted), and 0.5 K (long dashed). θ increases, and inhomogeneities in the cloud layer are more
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Figure 5. BT4 as a function of satellite zenith angle for a homogeneous ice cloud with the cloud top at 15 km. Calculations are performed assuming a particle effective radius of 40 µm and ice water concentration of 0.05 g/m3 . Shown are DISORT results for τ = 1.5, 2, 5, and 12. 108
3.3. AVHRR Split-Channel Data The presence of small water droplets or ice crystals can produce significant brightness temperature differences between two infrared wavelengths due to the nonlinearity of the Planck radiance with temperature and to the nonlinear relationship between emissivity and transmissivity at infrared window wavelengths [???]. Theoretical calculations have shown that brightness temperature differences are approximately zero for opaque clouds, which effectively behave like blackbodies, and for clouds where the extinction and absorption cross sections of the hydrometeors at the different wavelengths are almost equal. The brightness temperature differ-
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likely to occur within the satellite’s field of view. And there is a larger chance of detecting the (relatively) warmer cloud side at very large θ. In addition, most of the cold cloud cover occurred in a region that was most frequently covered by the NOAA satellite with small satellite zenith angles [?]. The very warm pixels with BT4 exceeding 300 K are daytime clear-sky observations from eastern Australia made by NOAA 11. The band of pixels with BT4 decreasing as function of θ from about 300 K to 285 K represents clear-sky areas over the Pacific Ocean. This decrease of BT4 with θ is due to water vapor absorption and atmospheric refraction. Boundary layer clouds can be identified as a band with BT4 between 270 and 285 K. Another prominent feature is the band with BT4 between 200 and 270 K. Most likely, optically thin cirrus clouds are responsible for this feature due to their large horizontal extent and lifetime (see Figure ??). The effective emittance of optically thin clouds increases with satellite zenith angle so that BT4 decreases [?].
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Figure 6. Frequency histogram of the NOAA-11 BT3 and BT4 distribution (nighttime data only) represented by a solid and dotted curve, respectively.
ence (BTD) between AVHRR channels 4 and 5 (BTD45) is less sensitive than BTD for the AVHRR channels 3 and 5 (BTD35). However, channel 3 is not sensitive enough for the detection of (tropical) deep convective clouds during nighttime. AVHRR channel 3 includes a spectral region centered at 3.7 µm which is sensitive to both emitted and reflected radiation. However, the radiation emitted from clouds with a cloud top temperature below 205 K is very small close to the detection limit of the AVHRR instrument (Figure ??). Our split-channel technique uses the brightness temperature difference between the AVHRR channels 4 and 5. The BTD45 signal is sensitive to different cloud properties: ice-
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Figure 7. BTD45 as a function of BT4 for (a) nadir pixels, and BTD45 as a function of satellite zenith angle for (b) BT4 = 190 0 5 K, (c) BT4 = 235 0 5 K, and (d) BT4 = 275 0 5 K for the CEPEX NOAA-11 and NOAA-12 data set. Figures ??a-??c (from bottom to top) show the 10, 25, 50, 75, and 90 percentile levels, and Figure ??d shows grey scales (from dark to almost white, respectively) at 10, 25, 50, and 75 percentile levels. /water-particle size distribution, cloud optical depths, and inhomogeneities in the cloud layer. The fact that BTD45 is not very sensitive to large ice and water particles might be a limitation for the distinction between deep convective cores and cirrus clouds. Both are known to include large ice particles such as graupel in active convective cores [e.g., ?] and large crystals at higher ice contents and higher temperatures which might dominate the cloud radiative properties [e.g., ??]. BTD45 in the CEPEX AVHRR data set ranges between 2 and 6 K, as shown in Figure ??a. The upper bound on BTD45 puts the lower size limit on the particle size distribution at roughly Reff 30 µm, since optically thin clouds containing smaller particles would result in larger BDT45 values (Figure ??). The median of BTD45 increases almost linearly with BT4, from 1 K for BT4 200 K to 2 K for BT4 270 K, most likely because of a decrease in optical depth of the (cirrus) clouds within the field of view. At BT4, higher than 270 K, the median value of BTD45 decreases because of the effect of optically thick stratus. For BT4, between 280 K and 300 K, BTD45 increases with BT4 because
of a decrease in optical depth of the stratus clouds [?, Figure 11]. The negative values of BTD45 are unexpected since BTD45 should approach zero for optically thick clouds in the troposphere with a small root-mean-square (RMS) uncertainty 0 5 K due to calibration and truncation errors. More than 90% of the pixels, with BT4 less than 200 K and θ less than 40 , have a negative BTD45. Our radiative transfer model is able to produce negative values of BTD45 for a cloud with the cloud top more than 1 km above the tropopause. However, the temperature profile used for these calculations is not affected by the convection, which is not realistic. ? have shown that the moist adiabatic lapse rate would be more appropriate. An alternative hypothesis for negative BTD45 is the presence of relatively warm water vapor in the lower stratosphere [see ?]. Then BT5 may be larger than BT4 because BT5 is more affected by the relatively warm water vapor, but this effect is too small to explain the negative BTD45 values found, according to our model calculations. BTD45 increases as a function of θ, especially for pixels
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Figure 8. BTD45 as a function of BT4 for a homogeneous ice cloud with the cloud top at 15 km. Calculations are performed assuming ice water concentration of 0.05 g/m3 and τ ranging from 0.5 to 24. Shown are DISORT results for Reff = 20, 30, 40, and 60 µm, respectively. with BT4 between 190 K and 260 K (Figures ??b-??d) because the optical depth of clouds for a given BT4 decreases as function of θ (Figure ??). The frequency distribution of BTD45 is single peaked for BT4 below 270 K (Figures ??b and ??c), and it is clearly double peaked for BT4 between 275 and 285 K (Figure ??d) due to the detection of optically thin cirrus and boundary layer clouds. Figure ??d clearly shows this double-peaked distribution as function of satellite zenith angle. The boundary layer clouds are detected with BTD45 less than 1 K, while most of the high optically thin clouds are detected with BTD45 around 4 K. The shift of both peeks toward BTD45 2 K with increasing BT4 is consistent with the calculations presented by ?. 3.4. AVHRR Visible Data Visible data can be used as an independent means to infer cloud optical depth. The International Satellite Cloud Climatology Project (ISCCP) uses a threshold of τ ! 23 to identify deep convection [?]. Note that infrared data are nearly insensitive to differences in optical depth above τ 6. However, the observed reflectance is a strong function of cloud optical depth, cloud microphysics, cloud geometry, and viewing and illumination angles. The viewing geometry is the only known parameter. The satellite viewing angle and the Sun zenith angle change rapidly in the cross-scan direction. While the angles change gradually in the satellite track direction. The relative azimuth angle changes from backward to forward scattering directions (or vice versa) at nadir. The feature that is most difficult to quantify is the cloud geometry. ? have shown the impact of cloud inhomogeneities on nadir reflectance using Monte Carlo simulations involving three-dimensional cloud fields. From ? it is clear that cloud geometries cannot be ignored for broken
clouds and structured cloud tops (such as cumuli and the overshoots of convective clouds). Both enhance the amount of reflected radiation in the nadir direction with solar zenith angle. ? primarily consider the enhancement of reflectance from geometrical effects, but it is also possible that other parts of optically thick clouds may be obscured from direct sunlight and appear to be darker. Fluctuations in the reflectances, including shadows of overshoots on the surrounding cirrus anvils, can easily be identified by visual inspection of AVHRR images. The variability related to geometry can explain the large difference between the 10 and the 90 percentile reflectance-contour for pixels with BT4 190 K (Figures ??a and ??a). Cloud geometry only seems to affect the coldest pixels, but it probably also affects a relatively small number of warmer pixels (not shown in our statistics). ? have derived bidirectional reflectance patterns for water and various ice-containing cirrus-like clouds, which are expected in the CEPEX cloud population. Figure ?? shows the theoretical bidirectional reflectances as a function of satellite zenith angle according to cirrostratus ice-crystal size distribution and a water-droplet size distribution (equivalent to the ISCCP water-droplet size distribution). The Sun zenith angle and relative azimuth used in these calculations are the average solar zenith and relative azimuth angles from the CEPEX AVHRR data, as a function of the satellite zenith angle. The observed reflectances of NOAA 11 with BT4 190 0 5 K (Figure ??b) are in good agreement with the theoretical cirrostratus calculations for optical depths between 6 and 23. The observed reflectances are slightly lower than the model calculations at large satellite zenith angles. This is probably due to a relatively larger contribution of optically thin clouds. Boundary layer clouds can also be distinguished from cirrus clouds in the visible as shown in Figure ??d. The distribution of reflectances of pixels with BT4 275 0 5 K agree well with the theoretical bidirectional reflectances of cirrostratus clouds with τ 1 and water clouds with τ " 6. The average satellite zenith angle of the NOAA-12 data is large compared to the NOAA-11 data, and as expected, the reflectance of the NOAA-11 data is on average lower, especially for relatively large satellite zenith angles (Figure ??). The cirrostratus model seems to underestimate the satellite zenith angle dependence for θ 50 . The characteristic dip in the water cloud reflectance distribution is also visible in NOAA-12 data (Figure ??d). It is important to realize that the use of average Sun zenith angles and average relative azimuth angles in the model calculations may lead to errors due to the nonlinear dependence of the reflectance on these variables and that the theoretical reflectances are sensitive to the shape and size distribution of the ice crystals. Using the same size distribution for the regions with active convection and cirrus anvils is a convenient but ad hoc approximation. 3.5. Threshold Definitions In the previous sections we have investigated properties of AVHRR infrared and visible data and used radiative trans-
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Figure 9. Reflectance as function of BT4 for (a) nadir pixels, and reflectance as a function of satellite zenith angle for (b) BT4 = 190 0 5 K, (c) BT4 = 235 0 5 K, and (d) BT4 = 275 0 5 K for the CEPEX NOAA-11 dataset. Contours and grey levels are chosen as in Figure ??. Note that θ0 decreases from 70 to 45 with increasing θ, and the radiation is backscattered for θ 0 and forward scattered for θ 0 . fer model results. In this section, objective thresholds for the SIT, MIT, and VIT methods are defined for comparison against our new method to identify convection (section ??). Theoretical calculations have shown that we have to decrease the BT4 threshold as a function of θ (BT4thres $ θ % ) to select clouds with a given cloud top temperature and a minimum optical depth (Figure ??). However, this threshold will not be sufficient to reject clouds with lower optical depth and lower cloud top temperature because such a combination may result in a BT4 below BT4thres $ θ % . Additional methods that can be used to determine cloud optical depth include a BTD45 threshold or a visible threshold. Both thresholds have several disadvantages due to their sensitivity to viewing geometry, cloud geometry, cloud inhomogeneity and water droplet size distribution, and ice particle shape and size distribution. BTD45 has the advantage that it can be used for daytime as well as nighttime data. Theoretical computations have shown that BTD45 has a maximum for clouds with τ 1 [?]. This maximum is larger for cold clouds composed of small
particles and diminishes for large particles (Figure ??). The difference between BT4 and BT5 diminishes when the difference between cloud top temperature and surface temperature decreases, or when θ increases (for clouds with τ ! 1). Thus a BTD45 threshold (BTD45thres ) is, like BT4thres , a function of cloud top temperature and optical depth and, in addition, a function of cloud microphysics (neglecting the cloud geometry). Theoretical model results as well as our statistical analysis of the visible data have shown that visible data can be used to identify optically thick clouds, but this threshold is a complicated function of viewing geometry and cloud microphysics. Therefore we use a parameterization for clouds with a cirrostratus ice-crystal size distribution to obtain reflectances as function of cloud optical depth and viewing geometry [?]. The infrared threshold as a function of satellite zenith angle are derived using Streamer. Atmospheric profiles are derived from a number of pressure, temperature, humidity, and ozonesondes launched from the R/V Vickers [?]. BT4thres $ θ %
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Figure 10. As in Figure ?? except for the NOAA-12 data set. Note that θ0 decreases from 85 to 60 with increasing θ. and BTD45thres $ θ % are calculated for a cloud with its cloud top at 15 km and Reff 40 µm. An infrared threshold at BT4thres $ 0 %& 235 K for high cloudiness is used by the SIT, MIT, and VIT methods. The SIT method uses an additional BT4thres $ 0 %' 208 K to identify convective regions. The MIT method rejects optically thin clouds using an additional BTD45thres $ θ % . The VIT method identifies convective regions using an additional reflectance threshold τVIS ! 23.
4. New Approach: BT4 Edge Detection Technique We have developed a new convective cloud classification scheme, based only upon infrared measurements, to overcome several limitations in previously applied methods. The objective is to identify the convective regions within high, optically thick clouds. The convective-stratiform technique developed by ? assumed local minima in BT4 to be active convective regions embedded within a cirrus anvil; this approach significantly improved previous visible/infrared techniques to estimate precipitation. Our new convective cloud classification scheme identifies convective regions through their temperature gradient relative to the cirrus anvil. The
contrast is determined with the Sobel nonlinear edge-enhancement operator [?]. The Sobel operator returns a large value if the contrast is large (see the Appendix). Figure ?? shows the Sobel values of BT4 of the CEPEX NOAA-11 data set as a function of BT4 for nadir pixels. The Sobel values are large for intermediate BT4 values due to the large BT4 difference between the high clouds and the warm ocean. The clear-sky pixels are characterized by very low Sobel values, caused by detections of large clear-sky regions with homogeneous BT4. The presence of boundary layer clouds causes the local minimum in the Sobel values at 275 K, indicating that these clouds are, on average, larger than a few AVHRR data pixels. The Sobel values for high clouds are increasing with BT4, which can only be explained if most of the very cold pixels are surrounded by slightly warmer pixels of the cirrus anvils rather than clear-sky pixels, which would result in a very large Sobel value. The Sobel values are dependent of θ for pixels with BT4 between 220 K and 270 K, because at large θ the optically thin clouds (τ 1) appear to have larger emissivities and thus lower BT4s than the same clouds viewed with θ 0. The resulting limb darkening increases the contrast in BT4 between the cloud and the warm ocean surface.
Hees et al.
10 b) NOAA-12 (Cs)
Reflectance
a) NOAA-11 (Cs)
(
1.2
1.2
1
1
0.8
23
0.6
6
0.4
0.8
23
0.6
6
0.4 1
0.2
1
0.2
0
0 -60 -40 -20
0
20
40
60
-60 -40 -20
40
60
Reflectance
1.2
1.2
1
1
0.8
-60 -40 -20 0 20 40 Satellite zenith angle
60
0.8
23
0.6
0.6 6
0.4
0.4 1
0.2
20
d) NOAA-12 (W10)
c) NOAA-11 (W10)
(
0
0.2
0
23 6 1
0 -60 -40 -20 0 20 40 Satellite zenith angle
60
Figure 11. Bidirectional reflectances as a function of satellite zenith angle for τ 1, 6, and 23, calculated for cirrostratus ice-crystal size distribution (top panels) and water drops with an effective radius of 10 µm and an effective variance of 0.05 [see ?]. The Sun zenith angle and relative azimuth used are averages from the CEPEX AVHRR data as a function of the satellite zenith angle. The Edge method derives local BT4 thresholds, BT4edge , to identify optical thick cirrus and active deep convective regions. BT4edge is defined by the coldest pixel around an Edge pixel. An Edge pixel is identified as a pixel with a Sobel value larger than an empirical derived threshold. The Sobel threshold, Sthres , for optical thick cloud is a function of θ to correct for limb darkening of optically thin cloud:
a) Nadir (θ = 0o) 120
Sobel Values (K)
100
)
80
Sthres $ θ %
60 40 20 0 180
200
220
240 260 BT4 (K)
280
300
Figure 12. Sobel values of BT4 of the CEPEX NOAA-11 data set as a function of the central BT4 value for nadir pixels. Contours are drawn at the 10, 25, 50, 75, 90, and 95 percentile level.
150 * 45 $ 1 cos$ θ %+% K
BT4edge is not used when the pixel with BT4edge has a Sobel value larger than Sthres . Only the maximum BT4edge of connected BT4edge pixels is used as a local BT4 threshold. All connected pixels with BT4 BT4edge , starting from the BT4edge pixel, are designed as cirrus anvil. The above described technique is applied twice to identify the convective regions within the cirrus anvils. The Sobel threshold for the convective regions is much smaller, and all nine pixels used by the Sobel operator have to be designed as cirrus anvil. For convective cores, Sthres " 35 K Note that Sthres might be a function of θ because the resolution of the data decreases and the contrast in BT4 between
Detecting Tropical Convection the overshoot and the cirrus anvil decreases. Additional conditions for the cirrus-anvil/convective region designation are as follows: convective regions larger than 100 km2 are rejected, these are more likely stratiform precipitating regions, and cirrus-anvils without convective regions smaller than 100 km2 designed as convective region. The main advantage of this method is that the threshold is not very sensitive to the temperature profile of the atmosphere nor to the tropopause temperature. The method identifies local BT4 minima in the optically thick parts of cirrus anvils. Most likely these have been caused by lifting of the cloud top due to convection, because BT4 is nearly insensitive to variation in the very high optical depths typical for deep convective clouds.
5. Comparison of Retrieval Methods We illustrate the differences between the threshold techniques using a small region of (256 pixels)2 centered at 18.5 S, 173 W (Plate ??), where AVHRR detected a convective cloud system on April 6, 1993, at 0324 UT. Most of the convective clouds have a BT4 below 220 K, with a reflectance exceeding 0.6. The northeast side of the system does not show much structure, presumably because the active convective phase has ceased. The southern end of the cloud system shows a few very bright spots with reflectances exceeding 1.0, which mark the convective overshoots. One can even identify their shadows on the cirrus anvil with reflectances below 0.4. Plates ??c–??f show the results of the SIT, MIT, VIT, and Edge detection methods, respectively. The SIT (BT4thres $ 0 % 208 K) is more selective than the MIT (BT4thres $ 0 %, 235 K and τIR 5). The latter still includes most of the anvil, which cannot be improved because the required BTD45 is already very small. Using τIR - 6 would imply a BTD45 less than 0.5 K, which is not feasible with an uncertainty of several tenths of a degree in BT4 and BT5. The results of the SIT and MIT are equal when both are applied using a BT4thres $ 0 %' 208 K, independent of the optical depth requirement. The results of the VIT are very sensitive to the cloud top structure and the viewing geometry, and the VIS algorithm also identifies cloud sides. The sides of the convective overshoots in shadow are rejected, and this results in very elongated structures. This erroneous effect cannot be improved using more advanced radiation schemes because it is not possible to quantify the effects of cloud geometry without active remote sensing or multiple views of the same scene. The edge technique clearly performs best in identifying the convective regions, although some of the very cold regions in the center of the cirrus anvil are reduced to only a few pixels.
6. Collocations With R/V Vickers Radar The convective regions identified in the CEPEX AVHRR data have been compared against data from the MIT 5 cm
11 Doppler radar onboard the research vessel R/V Vickers. The radar was operated in volume-scan mode, performing a series of constant elevation angle sweeps from 0.8 to 55 . These measurements are distributed as reflectivity constant altitude plan-position indicators (CAPPIs) on a 2 2 km Cartesian grid at 2 and 6 km altitude for each volume scan. The volume scans were performed approximately every 20 min. We note, however, that the 5 cm Doppler radar provides only limited information to validate a convective cloud retrieval algorithm because it has not been designed to detect clouds or measure vertical air motions. ? has shown that convection is unambiguously present when the radar reflectivity exceeds 40 dBZ. Furthermore, they state that weaker reflectivities may also indicate convection if the pixel stands out from the surrounding pixels. Unfortunately, the number of collocations is very small. Only 15 collocations were found (see Table ??) because the R/V Vickers sailed on a transect of the central Pacific at 2 S, which is at the border of the area covered by the CEPEX NOAA-11 and NOAA-12 data set (Figure ??). Table ?? shows that most collocations occurred for large satellite zenith angles ( 30 ), and thus projection effects and the increase of effective emissivity with θ will be notable. The horizontal resolution of the AVHRR data is less than or comparable to the radar data due to these large zenith angles. Therefore we have projected the AVHRR data onto the radar coordinates. The average BT4 within the radar field of view is larger than 235 K except for the first collocation. Only the first collocation has more than 30% high cloud cover and more than 1% convective cloud cover, according to the SIT and Edge methods (columns 7 and 9). In columns 11 and 12 (Table ??) we present the number of stratiform and convective precipitating pixels based on the algorithm developed by ?. The number of stratiform and convective precipitating pixels, according to this algorithm, is much larger than the number of high-cloud pixels. This cannot be explained by precipitating boundary layer clouds because almost all pixels that contain precipitation are also detected in the CAPPIs at 6 km altitude (not shown). This discrepancy is due to high reflectivities measured by the radar below clouds with BT4 between 260 and 230 K and BTD45 1 K. The collocations indicated with a “plus” in Table ??, colum 1 are shown in the Plates ?? to ??, of which two contain some deep convective clouds, according to the threshold techniques. Significant displacements appear to occur between the precipitating clouds derived from the radar reflectivity and the optically thick clouds derived from the BTD45 signal. These displacements, which has also been observed in several other studies [e.g., ??], are likely due to the time evolution of the convective region. Vertical and horizontal advection of anvil ice relative to the convective region takes place in storm-related drafts. Note that the data are not corrected for projection effects. The longitudes and latitudes of the AVHRR data are calculated for clear-sky observations and not adjusted for clouds at high altitudes detected with large satellite zenith angles, which introduces errors in the
Hees et al.
12
. a) Reflectance 17.5 S
17.5 S
1 0.73
18.0oS
2 253
18.5oS
2 212
19.0oS
191
1 0.91
18.5oS
1 0.56 1 0.39
19.0oS
1 0.22
19.5oS
0 oW 170
1600 oW
1 0.04
232
170
19.5oS
/ o 180
2 295
3 c) SIT
2 274
o
17.5 S
0 oW 170
1600 oW
d) MIT 17.5 S
18.0 S 18.5oS
2 212
18.5oS
19.0oS
191
19.0oS
1.79 1.07
o
18.0 S
232
170
19.5oS
/ o 180
0 oW 170
1600 oW
150
2 274
17.5 S
253
18.0oS
2 232
18.5oS
212
o
191
19.0 S
170
19.5oS
/ o 180
0 oW 170
0.36 -0.36 -1.07 -1.79
19.5oS
/ o 180
2 295
e) 4 VIT o
1600 oW
150
150
2.50
o
2 253
o
295
2 274
o
1.08
18.0oS
/ o 180
b) BT4 (K)
1.25
o
0 oW 170
1600 oW
-2.50
2 295
f) 5 Edge
2 274
o
17.5 S
253
18.0oS
2 232
18.5oS
212
o
191
19.0 S
170
19.5oS
/ o 180
0 oW 170
1600 oW
150
Plate 1. (a) Visible, and (b) BT4 images for a (256 pixels)2 region centered at 18.5 S, 173 W on April 6, 1993, at 0324 UT. The results of the various threshold techniques are show in Plates ??c–??f: SIT (BT4 208 K), MIT (BT4 235 K and τIR 5), VIT (BT4 235 K and τVIS 23), and Edge detection, respectively. The color scale for the SIT, VIS, and Edge method are BT4. The color scale for the MIT method is BTD45.
Detecting Tropical Convection
13
Table 1. Collocations Between CEPEX AVHRR Satellite Data and R/V Vickers Radar Data Time, UT
+ + + -
θ,
BT4,
SIT
Edge
Radar
Date§
AVHRR
Radar
deg
K
Anvil
Turret
Anvil
Turret
Strat
Conv
93/3/09 93/3/10 93/3/10 93/3/10 93/3/10 93/3/11 93/3/11 93/3/11 93/3/11 93/3/12 93/3/13 93/3/13 93/3/13 93/3/14 93/3/15
1656 0348 0736 1644 2025 0337 0715 1630 2003 0325 0313 1606 1920 1553 0249
1700 0400 0732 1640 2020 0340 0720 1640 2000 0320 0311 1600 1920 1600 0240
50.8 38.7 41.2 56.4 51.5 30.2 47.0 60.1 45.7 25.9 21.8 62.2 20.7 63.5 15.4
233.9 265.0 268.6 257.4 267.2 266.1 274.9 274.3 290.4 295.3 294.1 289.2 294.0 286.6 280.5
0.290 0.030 0.004 0.024 0 0.075 0 0.062 0.003 0 0 0 0 0 0.020
0.037 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.526 0.027 0 0.086 0.001 0.052 0 0.134 0.005 0 0 0 0 0 0.015
0.022 0 0 0 0 0 0 0.001 0 0 0 0 0 0 0
0.364 0.272 0.249 0.266 0.094 0.073 0.029 0.147 0.064 0.041 0.045 0.033 0.013 0 0.002
0.094 0.098 0.147 0.122 0.056 0.028 0.005 0.065 0.042 0.005 0.007 0 0 0 0
§ Read
93/3/09 as March 9, 1993
Collocation indicated with a “plus” in column 1 are shown in Plates ??–??. Given are time of the AVHRR image, time of the radar data set, average satellite zenith angle (AVHRR) and average BT4 (AVHRR), results of the SIT method as the fractional area by anvil and convective pixels (turret), results of the Edge method, and the fractional area with stratiform and convective precipitating pixels (radar). Only AVHRR pixels within the radar field of view are considered.
scan direction up to 10–30 km (indicated by beta in Figure ??). The uncertainty of the geographical position of the research vessel R/V Vickers is small ( 1 km) compared to the projection-effect errors. Plate ?? depicts the only collocation containing mainly high clouds. The heaviest precipitation is detected below the cold cloud deck northeast of the Vickers. Note that the coldest regions do not exactly coincide with the heaviest precipitation. This explains why the Edge method identifies only regions close to high radar reflectivity northeast and southwest of the Vickers. The cold clouds in the northwest are nonprecipitating, and the relatively large BTD45 signal indicates that these clouds are also optically thin. Plate ?? shows that clouds with BT4 below 235 K can produce significant precipitation. Clouds northwest and southwest of the Vickers are producing stratiform precipitation and are optically thick, based upon the small BTD45 values. The Edge method fails to identify the aligned convective cores between 30 km east, 80 km south and 10 km west, 30 km north, because these young clouds are not deep and cold enough. The Edge method does identify the large cold stratiform precipitating cloud south of the Vickers. Plate ?? illustrates that the Edge algorithm correctly identifies a few pixels of the large cloud located southwest of the Vickers, which is the only part of this cold cloud deck
with heavy precipitation. The small convective cells located south and east of the Vickers at less than 50 km distance are missed.
7. Conclusions We have presented a new method to identify active convective cores in high-resolution AVHRR satellite data. This method has been developed to overcome several weaknesses of existing visible/infrared techniques. The limitations of these existing methods are shown using AVHRR data of the NOAA-11 and NOAA-12 satellites collected during CEPEX. Using this data set we have shown the impact of the viewing geometry on the visible as well as the infrared data. Optically thick very cold clouds, optically thin cirrus, and boundary layer clouds are the dominant cloud types found. We have not obtained indications for significant cloud presence at altitudes between the boundary layer and 12 km altitude in the CEPEX area. Cirrus with optical depths between 1.5 and 2 contribute significantly to the number of clouds detected with BT4 between 220 K and 270 K. These results are supported by the split-channel data analysis. The number of pixels with small BTD45 (thus moderate optical depth) and with BT4 between 220 K and 270 K is very small. On the basis of our simulations of the CEPEX cloud systems the particle size distribution of these high and optically thin clouds must
Hees et al.
14
a) BT4 6 (K)
:274 253
50
:232
0
:212
-50
191
2.50
100 Distance S/N (km)
100 Distance S/N (km)
b) BT45 < (K)
295
1.79 1.07
50
0.36
0
-0.36 -1.07
-50
170
-100 -100 7
-50 07 50 8 Distance E/W 9 (km)
100 7
; Color) c) Edge (False
150
-100 7
10 8
0
6 4
-50
100 7
40 30
50
20
0
10 0
-50
2
-100 -100 7
-50 07 50 8 Distance E/W (km)
100 7
0
-2.50
50
100
12
50
-50 07 50 8 Distance E/W 9 (km)
; (dBZ) d) Radar
14
Distance S/N (km)
Distance S/N (km)
100
-1.79
-100
-10
-100 -100 7
-50 07 50 8 Distance E/W (km)
100 7
-20
Plate 2. Collocation between the 5 cm Doppler radar onboard the research vessel R/V Vickers and AVHRR on March 9, 1993 at 1700 UT; shown are (a) BT4, (b) BTD45, (c) results of the Edge method (anvil cirrus and convective core are indicated by orange and blue, respectively), and (d) the radar reflectivity. The AVHRR data are projected in the same Cartesian coordinate system as the radar data. be dominated by small crystals, Reff between 30 and 60 µm. The occurrence of pixels with BT4 below 200 K and negative BTD45 needs further investigation because they provide potential independent signs of deep convection. The parameterization of the bidirectional reflectance as a function of cloud optical depth and the viewing and illumination angles for the cirrostratus ice-crystal size distribution has been shown to represent the detected reflectances quite well. Further analyses have shown that a single infrared threshold is useful to obtain cold cloud cover but not to identify active convection. The split-channel technique has shown to be useful to discriminate optically thin clouds, but its use is limited to relatively low optical depths (τ 6), while most of the cirrus anvils have much larger optical depths. The visible data are limited to daytime and are very sensitive to cloud top geometry. It might be possible to exploit the geometry effects to identify regions of active convection from the high reflectances on the Sun-lit side of convective overshoots and/or the related shadows of convective overshoots on the cirrus anvil.
Our newly developed Edge detection method uses temperature gradients observed in cold cirrus anvils of deep convective clouds to identify regions where active convection takes place. These regions with dimensions less than 100 km are likely to be due to deep convection. Unfortunately, the number of collocations between the CEPEX AVHRR data set and the CEPEX R/V Vickers radar data set turned out to be too small for validation of the Edge method. Since only 15 collocations with radar data were available, of which only a small number contain high clouds. However, the few available collocations have shown that a pixel-to-pixel validation is very difficult mainly due to displacement between the coldest BT and the location of radar-derived convective cells. An extensive comparison between the Edge method and the radar data will be presented in a separate paper using TOGA COARE data. This paper will also include a statistical comparison of the Edge method and the SIT, MIT, and VIT methods.
Detecting Tropical Convection
15
a) BT4 6 (K)
:274 253
50
:232
0
:212
-50
191
2.50
100 Distance S/N (km)
100 Distance S/N (km)
b) BT45 < (K)
295
1.79 1.07
50
0.36
0
-0.36 -1.07
-50
170
-100 -100 7
-50 07 50 8 Distance E/W 9 (km)
100 7
; Color) c) Edge (False
150
-100 7
10 8
0
6 4
-50
-2.50
100 7
50
100
12
50
-50 07 50 8 Distance E/W 9 (km)
; (dBZ) d) Radar
14
Distance S/N (km)
Distance S/N (km)
100
-1.79
-100
40 30
50
20
0
10 0
-50
2
-100 -100 7
-50 07 50 8 Distance E/W (km)
100 7
0
-10
-100 -100 7
-50 07 50 8 Distance E/W (km)
-20
100 7
Plate 3. As in Plate ?? except for the collocation on March 10, 1993 at 0400 UT.
Appendix: Sobel Operator
The direction of the gradient is defined by
The Sobel operator represents an estimate of the gradient at point F $ i = j % . Consider the 3 3 image region
>> >> >>
A0 A7 A6
A1 F $ i= j% A5
A2 A3 A4
>>
x
>> >>
where we define Gx as the difference between the first and the third rows, and Gy as the difference between the first and the third columns, with the elements closer to F $ i = j % weighted twice compared to the corner values. Gx
?$ A2 *
2A3 * A4 %
$ A0 *
2A7 * A6 %
Gy
?$ A0 *
2A1 * A2 %
$ A6 *
2A5 * A4 %
Then Gx and Gy represent estimates of the derivatives in the x and y direction, respectively. The Sobel operator is defined as @ G $ i= j%
G2x * G2y
or alternatively (a faster computation approximation) G $ i= j%
BA Gx A * A Gy A
E F arctan G GGy xH φG $ i = j % DC arctan G GGy H
if Gy
"
0
otherwise
Edge pixels used to trace the boundaries of objects in an image are selected using cumulative histograms of G $ i = j % . The 5 to 10% of the pixels with the largest gradients are declared as edges [see also ?]. Acknowledgments. This work was supported by the NSF Science and Technology Center for Clouds, Chemistry and Climate (C4 ) and the Space Research Organization Netherlands (SRON). WDC was supported in part by NSF ATM95-25800. The authors gratefully acknowledge J. A. Coakley Jr. for his thoughtful comments and reviewing this article. We thank P. Minnis for providing the parameterization of reflectance of cirrostratus ice-crystal and water-droplet size distributions and B. Hobson for providing the MIT radar data. The paper was improved considerably by reviews from two anonymous referees. This paper is report I 222 from C4 .
References Ackerman, T. P., K.-N. Liou, F. P. J. Valero, and L. Pfister, Heating rates in tropical anvils, J. Atmos. Sci., 45, 1606–1623, 1988.
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a) BT4 6 (K)
:274 253
50
:232
0
:212
-50
191
2.50
100 Distance S/N (km)
100 Distance S/N (km)
b) BT45 < (K)
295
1.79 1.07
50
0.36
0
-0.36 -1.07
-50
170
-100 -100 7
-50 07 50 8 Distance E/W 9 (km)
100 7
; Color) c) Edge (False
150
-100 7
10 8
0
6 4
-50
100 7
40 30
50
20
0
10 0
-50
2
-100 -100 7
-50 07 50 8 Distance E/W (km)
100 7
0
-2.50
50
100
12
50
-50 07 50 8 Distance E/W 9 (km)
; (dBZ) d) Radar
14
Distance S/N (km)
Distance S/N (km)
100
-1.79
-100
-10
-100 -100 7
-50 07 50 8 Distance E/W (km)
100 7
-20
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W. D. Collins, National Center for Atmospheric Research, Boulder, Co 80307. R. M. van Hees and J. Lelieveld, Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, Princetonplein 5, 3584 CC Utrecht, Netherlands. (e-mail:
[email protected]) Received November 25, 1997; revised July 16, 1998; accepted November 2, 1998.
This preprint was prepared with AGU’s LATEX macros v5.01, with the extension package ‘AGU ’ by P. W. Daly, version 1.6b from 1999/08/19.
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