Detection of Cloud-Base Height Using Jenoptik CHM15K and Vaisala CL31 Ceilometers

Giovanni Martucci School of Physics, and Centre for Climate and Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Galway, Ireland

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Conor Milroy School of Physics, and Centre for Climate and Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Galway, Ireland

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Colin D. O’Dowd School of Physics, and Centre for Climate and Air Pollution Studies, Environmental Change Institute, National University of Ireland, Galway, Galway, Ireland

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Abstract

Twelve case studies of multilayer cloud-base height (CBH) retrievals from two collocated ceilometers (Vaisala CL31 and Jenoptik CHM15K) have been analyzed. The studies were performed during the period from September to December 2008 at the Mace Head Atmospheric Research Station in Ireland. During the period of measurement, the two instruments provided vertical profiles of backscattered laser signal as well as the manufacturer’s operational cloud-base product. The cases selected covered a diverse range of cloud-cover conditions, ranging from single to multiple cloud layers and from cloud-base heights varying from only a few hundreds meters per day up to 3–5 km in a few hours. The results show significant offsets between the two manufacturer-derived CBHs along with a considerable degree of scatter. Using a newly developed temporal height-tracking (THT) algorithm applied to both ceilometers, significant improvement in the correlation between CBH derived from both instruments results in a correlation coefficient increasing to R2 = 0.997 (with a slope of 0.998) from R2 = 0.788 (with an associated slope of 0.925). Also, the regression intercept (offset) is reduced from 160 m to effectively zero (−3 m). For the worst individual case study, using the THT algorithm resulted in the correlation coefficient improving from R2 = 0.52, using the manufacturer’s output, to R2 = 0.97 with a reduction in the offset reducing from 569 to 32 m. Applying the THT algorithm to the backscatter profiles of both instruments led to retrieved cloud bases that are statistically consistent with each other and ensured reliable detection of CBH, particularly when inhomogeneous cloud fields were present and changing rapidly in time. The THT algorithm also overcomes multiple false cloud-base detections associated with the manufacturer’s output of the two instruments.

Corresponding author address: Giovanni Martucci, School of Physics, National University of Ireland, Galway, University Road, Galway ROI, Ireland. Email: giovanni.martucci@nuigalway.ie

Abstract

Twelve case studies of multilayer cloud-base height (CBH) retrievals from two collocated ceilometers (Vaisala CL31 and Jenoptik CHM15K) have been analyzed. The studies were performed during the period from September to December 2008 at the Mace Head Atmospheric Research Station in Ireland. During the period of measurement, the two instruments provided vertical profiles of backscattered laser signal as well as the manufacturer’s operational cloud-base product. The cases selected covered a diverse range of cloud-cover conditions, ranging from single to multiple cloud layers and from cloud-base heights varying from only a few hundreds meters per day up to 3–5 km in a few hours. The results show significant offsets between the two manufacturer-derived CBHs along with a considerable degree of scatter. Using a newly developed temporal height-tracking (THT) algorithm applied to both ceilometers, significant improvement in the correlation between CBH derived from both instruments results in a correlation coefficient increasing to R2 = 0.997 (with a slope of 0.998) from R2 = 0.788 (with an associated slope of 0.925). Also, the regression intercept (offset) is reduced from 160 m to effectively zero (−3 m). For the worst individual case study, using the THT algorithm resulted in the correlation coefficient improving from R2 = 0.52, using the manufacturer’s output, to R2 = 0.97 with a reduction in the offset reducing from 569 to 32 m. Applying the THT algorithm to the backscatter profiles of both instruments led to retrieved cloud bases that are statistically consistent with each other and ensured reliable detection of CBH, particularly when inhomogeneous cloud fields were present and changing rapidly in time. The THT algorithm also overcomes multiple false cloud-base detections associated with the manufacturer’s output of the two instruments.

Corresponding author address: Giovanni Martucci, School of Physics, National University of Ireland, Galway, University Road, Galway ROI, Ireland. Email: giovanni.martucci@nuigalway.ie

1. Introduction

Clouds are critical to the global radiation budget and the hydrological cycle. They typically contribute to 40%–50% of the global earth albedo with cloud albedo being partly determined by atmospheric dynamics and partly by the availability of cloud condensation nuclei (CCN). Twomey (1977) highlighted that an increase in pollution-derived CCN could lead to increased cloud droplet number concentration (CDNC) and reduced effective radius, leading to brighter clouds that consequently contribute to a global cooling effect, which could partly negate global warming driven by greenhouse gases. Charlson et al. (1987) also highlighted this so-called indirect aerosol effect, whereby marine biota could increase their productivity as a result of global warming and participate in a negative feedback loop involving dimethylsulphide emissions subsequently being converted to aerosol sulfate and ultimately CCN, leading to brighter clouds. Work by O’Dowd et al. (1999) highlighted that, even in marine environment, an increase in CCN does not necessarily lead to an increase in CDNC and that CDNC depends on a complex nonlinear competition between dynamics, different nuclei sources, and chemical composition. Recent studies have shown also that aerosol or CCN availability can increase or decrease precipitation rates, leading to flooding or droughts, depending on a complex interactions between aerosols, CCN, and dynamics (Rosenfeld et al. 2008). For the above climate effects, it is critical to understand cloud distributions, reflectance, lifetime, and precipitation. In addition, for weather forecasting purposes, cloud physical properties are also a necessity.

In situ measurements of cloud properties are essential but are quite costly and typically limited in time and spatial location. Satellite remote sensing of cloud properties, although also expensive, provides extensive temporal and spatial information once operational; however, the output products perhaps are not yet the most accurate. For instance, satellite cloud products are (i) quasi-continuous cloud scenes from geostationary orbit and rapid repeat cloud scenes from polar orbiters such as the Moderate Resolution Imaging Spectroradiometer (MODIS; Garrett et al. 2009), which provide limited vertical information, and (ii) detailed vertical profiles from radar profilers such as Cloudsat and lidars such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and the Ice, Cloud and Land Elevation Satellite (ICESat), which have high spatial resolution along track; however, because the repeat time for any particular location is very large, the diurnal cycle is not captured (CEOS 2006). In addition, although satellite measurements of cloud top are routinely provided, they are unlikely to provide accurate cloud-base measurements. To enhance cloud observing capabilities, two large-scale, ground-based remote sensing cloud property programs have been initiated in recent years. The Atmospheric Radiation Monitoring (ARM) Program in the United States (Stokes and Schwartz 1994) and the Cloudnet program in Europe (Illingworth et al. 2007) have aimed to provide near-continuous and near-real-time cloud properties for both forecasting objectives and for advancing of cloud–climate interactions. In terms of Cloudnet, synergetic cloud properties can be derived from a combination of three instruments, namely, a ceilometer (lidar), a microwave profiler, and a millimeter cloud radar. These two products can be used along with the other synergetic instruments to determine CDNC. Retrieving CDNC and droplet size distribution from cloud-measured properties is made possible by the determination of vertical profiles of cloud optical properties (e.g., extinction profiles) and liquid water content. The vertical profiles can be obtained using the aforementioned synergetic remote sensing instrumentation through iterated measurements of the backscatter coefficients, liquid water content, and liquid water path. Two values are particularly needed to improve the accuracy of cloud profiling, especially in case of a single layer of clouds: cloud-base height (CBH) and cloud-top height. Synergetic remote sensing instrumentations allow the continuous monitoring of the atmosphere and of most of the above-mentioned variables, including the actual cloud thickness. Half of the work to obtain the vertical cloud liquid water content and the extinction profile is in determining the height of the cloud base with sufficiently high resolution (≤15 m). Intercomparison studies involving the concurrent use of multiple remote sensors have been performed (Boers et al. 2000; Clothiaux et al. 2000) and based on sophisticated algorithms able to detect the cloud base with relatively high accuracy. These studies highlighted the convenience of using lidars and ceilometers instead of millimeter-wave radars to improve the efficiency of the cloud-base detection procedure. Ceilometers are robust instruments providing continuous and accurate cloud-base determinations as a standard output. Additionally, depending on the instrument capabilities, a ceilometer can provide full backscatter profiles of the lower troposphere, making the ceilometer a cost-effective alternative (though not as quantitative) to a full-blown lidar. Research-mode lidars have been extensively used for both boundary layer structure profiling and cloud-base detection in many studies (e.g., Kunz et al. 2002; Martucci et al. 2007; Morille et al. 2007).

Accurate determination of cloud base is crucial (i) to provide operational and real-time cloud-base information to the aviation industry; (ii) to initialize meteorological or numerical weather prediction models and for use in data assimilation, especially when ceilometer networks are available and provide continuous cloud-base time series over a large areas; and (iii) as a key variable in the inversion equations to retrieve the CDNC. The above applications can be enabled in a much more cost-effective manner using ceilometers rather than lidars; however, there is a scarcity of data available on the evaluation of commercial or operational ceilometers in terms of their performance and intercomparability. This study compares the operational CBH products from the 1064-nm-wavelength Jenoptik (JEN) CHM15K and the 910-nm-wavelength Vaisala (VAIS) CL31 ceilometers. The comparison of these two instruments’ outputs is also extended in that their raw data are reprocessed using an in-house temporal height-tracking (THT) algorithm in an attempt to improve the accuracy and comparability of these instruments’ products.

2. Site overview and airmass classifications

a. The site

Located on the west coast of Ireland, the Atmospheric Research Station at Mace Head, Carna, County Galway, is unique in Europe (O’Connor et al. 2008). Its position offers westerly exposure to the North Atlantic Ocean through the “clean sector” (Fig. 1, dashed lines edging the 180°–300°N sector) and the opportunity to study atmospheric composition under Northern Hemispheric background conditions as well as European continental emissions when the winds favor transport from that region. The site location, at 53.20°N, 9.54°W, is in the path of the midlatitude cyclones that frequently traverse the North Atlantic. The instruments are located 300 m from the shoreline on a gently sloping hill (4° incline).

b. Airmass and cloud classifications

The Mace Head meteorological records show that, on average, over 60% of the air masses arrive at the station via the clean sector. These air masses are ideal for carrying out background aerosol and trace gas measurements. Significant pollution events also occur at the site when European continental air masses, generally originating from an easterly direction, reach Mace Head. The Mace Head Atmospheric Research Station is uniquely positioned for resolving these different air masses and for comparative studies of their constituents and characteristics. For the presented study, 12 cases of single to multilayer clouds have been selected based on parallel information about (i) the origin of the air masses [obtained from 7-day backward trajectories calculated by the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model]; (ii) the actual cloud pattern [as observed by the thermal infrared (IR) channel (10.3–11.3 μm) of the Advanced Very High Resolution Radiometer (AVHRR) satellite and manually from synoptic stations located at Shannon (52.71°N, 8.87°W) and Belmullet (54.22°N, 9.99°W)]; and (iii) the meteorological conditions, with no selected cases showing precipitation events longer than 30 min. Table 1 shows the origin of the air masses determined by the 7-day backward trajectories.

The airmass arrival directions are within the clean sector in most of the cases, except for three exceptions for southwest-stagnant air masses associated with a high pressure system centered over the United Kingdom. These three cases show a single developed layer of stratus (St) cloud persisting throughout several days. In contrast, the other nine case studies represent a more complex pattern, with multilayer structures of different cloud types. The attenuated backscatter profile time series measured by the two ceilometers and the aforementioned satellite and synoptic information are used for cloud-type classification (see Table 2).

The cases can be sorted in four groups: (i) a single-layer stratus cloud deck with relatively constant base height; (ii) a single-layer cloud type resulting from lowering of altostratus (As) into precipitating nimbostratus (Ns); (iii) a two-layer structure comprising a higher layer of altostratus clouds and a lower layer of stratus or nimbostratus; (iv) a three-layer structure comprising a higher layer of altostratus clouds, an intermediate layer of convective (open or closed) cumuliform cells, and a lower layer of boundary layer cumuli. In Fig. 2, four examples of different cloud patterns, as observed by the infrared channel of the AVHRR satellite, are shown. The relatively wide range of case study conditions was chosen to test the robustness of the retrieval algorithms under differing cloud fields. Figure 2a shows a case, 22 October 2008, of three cloud layers with a complex middle layer of convective open cells (north and south of Ireland at the moment of satellite pass). Such a situation, such as the case in Fig. 2c, is ideal for testing an algorithm’s efficiency, especially when two consecutive cloud-base detections are separated by several kilometers and associated with different cloud layers.

3. The instruments

Both instruments deployed for this study are lidar-based ceilometers with an optoelectronic laser sensor using relatively standard lidar methods. The Jenoptik CHM15K and Vaisala CL31 ceilometers are able to detect three cloud layers simultaneously, providing cloud thickness where the layers do not totally attenuate the laser beam. Real-time algorithms analyze the pulse flight time and the intensity of backscattered light to calculate a height-resolved aerosol profile along with other related measurement values. Figure 3 shows the vertical profiles of the attenuated backscatter coefficients from both instruments. The CHM15K return shows a higher sensitivity to the aerosol detection (i.e., more features are detected below the cloud base).

a. Vaisala CL31

The enhanced single lens technology applied in the CL31 ensures realistic data recording over the nominal range 0–7.5 km with full overlap at about 70 m (see Fig. 3). The good quality of the received signal is made possible by the strong and stable signal over the whole measurement range. The single lens technology is meant to provide reliability during precipitation, although the receiving system becomes very quickly saturated during rain events. The laser is an InGasAs diode emitting at the 910-nm wavelength with a manufacturing estimated accuracy of ±5 m (against hard target) equal to the highest vertical resolution Δz = 5 m (see Table 3 for technical data).

b. Jenoptik CHM15K

The CHM15k ceilometer measures atmospheric target backscatter profiles over the nominal range 0.03–15 km with full overlap height at about 100 m. The operating range is 15 km where it can reliably detect lower cloud layers as well as cirrus clouds, although the latter could be significantly hidden in the noisy component of the signal at these high ranges. The highest vertical resolution at which the instrument can work is 15 m with measured full vertical profiles of aerosol backscatter and detected cloud height, boundary layer height, and visibility values. The measuring principle is lidar based with a photon counting detection system and a solid-state Nd:YAG laser source emitting at the 1064-nm wavelength with undeclared manufacturing accuracy (see Table 3 for technical data).

4. Data analysis

The power of the lidar signal P(h) backscattered by an atmospheric layer of thickness Δh (range gate) centered at altitude h can be expressed in the form (Weitkamp 2005)
i1520-0426-27-2-305-e1
where PL is the emitted optical power, K is the overall optical efficiency of the instrument, O(h) is the overlap function, A is the receiver area, and is the round-trip transmission factor. Variables α and β are the extinction (m−1) and the volume backscattering (sr−1 m−1) coefficients, respectively. The last term, B, takes into account the sum of the electronic and optical background noise. The coefficients α and β can be written as the combination of their aerosol and molecular components: that is, α = αaer + αmol and β = βaer + βmol. For the utilized wavelengths (910 and 1064 nm) and the probed lower troposphere, the relation αaer, βaerαmol, βmol can be applied, especially at ranges where fog and cloud layers are present. This assumption applies also to the gradient of the received power (which will be used in the data analysis to retrieve the CBH), because the vertical changes in aerosol/hydrometeor concentration dominate the received signal at both long (λ ≈ 1 μm) and short (λ ≈ 0.5 μm) wavelengths. The extinction and the backscatter coefficients can then be written as ααaer and ββaer, respectively. In this study, only altitudes with the receiver’s field of view completely overlapping the laser beam [i.e., O(h) ≡ 1] will be taken into account.

a. The THT algorithm

In the signal processing, the attenuated atmospheric volume backscatter coefficient βatt is computed as
i1520-0426-27-2-305-e2
The THT scheme is based on the information about the mutual positions of the local maxima in the βatt vertical profile and its vertical gradient (GS, where the gradient applies to the natural logarithm of βatt). Each βatt profile has temporal and vertical resolutions of 15 s and 5 m for the Vaisala CL31 and of 30 s and 15 m for the Jenoptik CHM15K. In the postprocessing procedure, the Vaisala CL31 resolutions have been reduced to 30 s and 15 m to make it fully comparable with the CHM15K. The choice of the altitude range and temporal resolution is determined by the range and time as a trade-off between noise and feature discrimination.
For each case study with duration of measurements within the range of time (0000–2359 UTC), the index i in Eq. (3) indicates the single successive measurements in the selected period with time resolution 30 s. The single GSi profiles provide single evaluations of the CBH and, combined in a sequence, give its temporal evolution,
i1520-0426-27-2-305-e3

The algorithm computes the mean GS and values from single GSi and profiles averaged over 10 min. The choice to set the time of averaging equal to 10 min was taken to ensure relatively nonvariable cloud base at the start of each algorithm block. On its first step, the algorithm detects, starting from ground level, the heights of the largest GS and maxima, respectively. The mean value between the heights of the two maxima is the reference height href. The related uncertainty is σref = σ(2/GS) + σ(2/βatt), where the standard deviations σGS and answer for the variability of the single GS and βatt calculated heights during 10 min, on average, and are assumed to be statistically independent. The σref accounts then for the overall temporal variability of the single detected heights and represents the href related uncertainty. The assumption of statistical independence of σGS and is made possible by the fact that, despite the analytic relation between and its gradient GSi, the heights of the two maxima are determined using different thresholds on the βatt and GS profiles [threshold on the signal-to-noise ratio (SNR) set to 1.5 for βatt and 2 for GS], making the selection of the β and GS maxima two independent processes. Once determined href (i = 1), a range of altitudes hj ∈ [0.85(hrefσref) − 1.15(href + σref)] limits the vertical region in which to look for the principal maxima of GS1 and . The entire vertical length of each profile is scanned for maxima, and these are iteratively rejected until they enter the interval [0.85(hrefσref) − 1.15(href + σref)]. Every time the algorithm selects a GS maximum GSmax along the profile, this is checked in value against the GS maximum corresponding to the reference height . The ratio between the two values is used as a threshold (to be exceeded) to accept the current GSmax selected by the algorithm as a valid CBH, despite its position along the profile. This procedure ensures the correct determination of real cloud bases, even when they are located far (outside the current vertical interval) from the previous CBH determination. Once the GS1 and maxima are determined, the mean value between the two heights is the first cloud-base height CBH1. The CBH-related uncertainty is the standard deviation of all the rejected heights after each algorithm’s step. For 2 ≤ i ≤ 20, the algorithm determines, in the new range of altitudes [0.85(CBHi−1σCBHi−1) − 1.15(CBHi−1 + σCBHi−1)], a new value of CBH at each i step. For i = 21, the algorithm computes two new GS and profiles averaged over the time interval specified by i ∈ [11–30]. The algorithm performs the same process as for the previous 20 i steps starting by determining, for i = 21, a new href value to be used for the calculation of CBH21. The algorithm is built in blocks of 20 steps each until the end of the dataset is reached. An outline of the algorithm’s logic is shown in Fig. 4. In the left side of the figure, the βatt time–height cross section is shown with a highlighted time interval showing three cloud layers. In the right side of the figure, arrows indicate the rejected maxima lying outside the interval [0.85(hrefσref) − 1.15(href + σref)] and the selected one within it. The algorithm can distinguish between different cloud layers, even when they are very close, simply by selecting different href values on the GS and profiles. Once all the CBH determinations are obtained for each instrument, the values are compared for same temporal interval and vertical range (the vertical range goes from 0 to 7 km because of the Vaisala CL31’s limited range). From the detailed description of the algorithm rationale, it emerges that the tracking system of the single cloud-base detections along with the threshold value are two outstanding features allowing smooth CBH temporal evolution and realistic jumps between contiguous (in time) layers at different heights, respectively.

5. Ceilometer CBH intercomparison

a. Methodology

The intercomparison was carried out through three steps: first, the VAIS and JEN manufacturer’s operational CBH outputs are compared and tested for the linearity between the two outputs (correlation coefficient for linear fit on scatterplot of the two outputs), the relative gain between the two outputs (slope of the scatterplot), and any bias or offset between the two (from the linear fit intercept on the y axis). The value of the slope also provides information about the SNR of the backscatter profiles. Second, the THT algorithm is applied to both instruments’ backscatter data and gives qualitative interpretation about the laser alignment of the two instruments (intercept of the linear fit) and the level of the SNR (slope of the linear fit). A low SNR means a higher number of spurious peaks in the βatt profiles, generating larger dispersion of the CBH determinations and affecting the value of the angular coefficient of the linear fit. Third, a graphic comparison between THT and JEN and THT and VAIS outputs allows extraction of additional information about under-/overestimations of the CBH determinations using the two algorithms.

b. VAIS versus JEN

The values of the correlation coefficient R2, the slope a, and y-axis intercept b are shown in Fig. 5 for each case study. Cases in the figure are sorted columnwise by increasing cloud-base height from left to right. The average value of the slope was a = 0.91 and, within the range of the statistical variance (σa = ±0.15), it is comparable to the unity slope; nevertheless, the statistical variance is quite large, on the order of 16%. The average intercept was b = 192.57 m and illustrates a large offset between the two manufacturer’s proprietary algorithms. The statistical variance (σb = ±177.52 m) suggests that the VAIS retrievals provide a systematic higher cloud-base estimate than that from the JEN.

Figures 5c,e show the best and the worst cases (based on the R2 values) for the VAIS versus JEN intercomparison. The worst case exhibits R2 = 0.52, slope of 0.62, and intercept of 570 m, whereas the best case exhibits R2 = 0.99, slope of 1.01, and intercept of 75 m. The cases presented in the other panels show, beyond the value of R2, how cloud-base heights can be widely scattered over the whole range of altitudes between 0 and 7 km. Figure 5c clearly shows the existence of a higher CBH VAIS retrieval than that from JEN. The cases covering the period 23–25 December 2008 are examples of sustained and stable stratus clouds throughout an extended period. For these cases, the values of the intercepts vary very little, being approximately 80 m through the three cases. The case in Fig. 5n reports cloud-base data from a single layer of altostratus lowering and thickening to become nimbostrati during the late evening (Table 2, 18 September 2008 case). Data are widely scattered in both domains of altitudes, in the CHM15K’s and CL31’s, showing in particular the existence of three false layers in the CL31’s retrievals situated between the ground level and the actual cloud base (this artifact is discussed later).

All of the VAIS and JEN individual case study data are grouped in a single scatterplot and shown in Fig. 6, where R2 = 0.788, slope = 0.925, and an intercept corresponding to an average instrument CBH offset of 160 m is seen. The outcome of the comparison can be summarized as follows: (i) the two algorithms provide in several cases very different estimates of the same cloud base; (ii) the distribution of the retrieved CBH values become more scattered as the number of cloud layers increases; and (iii) spurious cloud-base echoes exist especially in the CL31 βatt profiles. The results of the comparison suggested investigating further the relation between the βatt profiles from the two ceilometers and derivation of CBH using an independent algorithm.

c. Application of THT algorithm

CBHs resulting from the application of the THT algorithm to the βatt profiles are shown in Fig. 7 for the 12 cases, along with the associated linear regression data. As with the previous comparison, cases are sorted columnwise by increasing cloud-base height from left to right in Fig. 7. Data points have been linearly fitted to extract information about the slope a and the intercept b. The average slope value was a = 0.99; within the statistical variance (σa = ±0.02), it is effectively unity slope. The average value of the intercept was b = 13.56 m; within the statistical variance (σb = ±37 m), it is comparable to an effective zero intercept.

Figures 7n,i are shown the best and the worst cases, respectively, based on the values of the coefficient of determination R2 ranging from 0.95 to 0.996. Figures 7h,c show two other examples of linear correlation with values of R2 between the two extremes. The case in Fig. 7h is an example of the robustness of the algorithm that determines very close values of cloud base for the two instruments, even in conditions of complex cloud pattern (see Table 2, 22 October 2008 case). Error bars in the graphs show the uncertainty of each CBH estimate based on the standard deviation of the CBH values rejected during each algorithm step. All the THTVAIS and THTCHM15K outputs are grouped in a single scatterplot shown in Fig. 8. For the full dataset, R2 has increased to 0.997, whereas the slope is 0.998 and the intercept is −3 m (effectively zero).

d. THT versus VAIS and THT versus JEN

The third step of the intercomparison is a visual comparison between THT and JEN outputs and, separately, between THT and VAIS outputs. Figure 9 shows the time–height βatt cross section with superimposed CBH values as determined by the THT and JEN algorithms (Fig. 9a) and THT and VAIS algorithms (Fig. 9b). The case on 24 December 2008 is an example of well-developed stratus layer establishing in a vast area of high pressure centered over the United Kingdom. These two examples clearly show the differences between the three algorithms. The THT detects the cloud base slightly above the JEN retrievals and definitely below the VAIS. The percentage difference between THT and JEN retrievals and between THT and VAIS retrievals is 6% of the average THT retrievals for both cases.

The previously mentioned false layer detection is evident in both panels of Fig. 10, where several spurious layers of cloud base are seen as “detected” by JEN and VAIS algorithms below the actual cloud layer of altostrati. The effect is likely because neither JEN nor VAIS algorithms seem to track the cloud layer, allowing the unrealistic jumps between contiguous CBH detections.

Figures 11a,b show again the scattered CBH values determined by the two built-in algorithms above and below the real cloud targets. On the contrary, the THT algorithm performs an efficient detection of the cloud base, even in such a complicated cloud pattern as in the 22 October 2008 case.

It is the authors’ belief that the determination of the cloud base should occur at the level where the βatt value starts to increase firmly (Eberhard 1986): namely, at that level where the βatt maximum rate of growth occurs (Pal et al. 1992; Campbell et al. 1998; Gaumet et al. 1995). Other algorithms have been created, and they purely rely on thresholds of the backscatter signal used to discriminate clear- from cloudy-sky profiles (Clothiaux et al. 2000), but they fail in detecting low scattering targets like high clouds. On the contrary, the THT algorithm is based on the analysis of changes in the slope of the backscattered powers with height and can then detect all types of clouds, including high clouds such as cirrus.

6. Conclusions

The intercomparison between the Jenoptik CHM15K and the Vaisala CL31 manufacturer’s CBH outputs revealed significant differences between the two instruments. For the worst-case scenario, the correlation coefficient R2 between the two CBH products was 0.52, with slope of 0.62, and intercept (offset) of 570 m. For the best case, R2 was 0.99, with slope of 1.01 but still significant intercept (offset) of 75 m. For the 12 combined cases, the overall dataset illustrated R2 = 0.788, slope = 0.925, and intercept of 160 m. Using the new THT algorithm on the backscatter profiles to derive CBH from both instruments, R2 ranged from 0.95 to 0.99 with an average slope and intercept effectively unity and zero, respectively. For the combined THT-processed dataset, regression parameters were R2 = 0.997, slope = 0.998, and intercept of −3 m (i.e., statistically zero). The accuracy of the two proprietary algorithms decreases as soon as the number of cloud layers increases. Further, the proprietary operational outputs for both instruments show, under certain conditions, single or multiple spurious cloud-base heights. The VAIS output presents the higher number of spurious CBH determinations.

In contrast, the THT algorithm takes advantage of an efficient filtering procedure of the spurious cloud bases as well as of the tracking system of each cloud layer to avoid unrealistic jumps between two consecutive CBH determinations. Concurrently, there is a constant monitoring of the threshold value to allow, once it is exceeded, realistic differences between time-contiguous points belonging to different cloud layers. The results of the comparison can be summarized as follows: (i) the statistical variances on the slope a and intercept b are significantly smaller than these obtained by comparing the VAIS and JEN algorithms. In terms of overall comparability of CBH derived from both instruments, there is effectively perfect agreement using the THT algorithm. However, we cannot say whether either one of the manufacturer algorithms applied to both instruments would produce similar agreement. (ii) The Jenoptik CHM15K and the Vaisala CL31 do not show significant differences in the laser alignment; that is, cloud targets are detected at the same range within the interval of accuracy of the algorithm. (iii) There is more scatter in CBH determinations below the cloud layer in CL31 βatt profiles than in CHM15K’s.

A visual comparison between THT and JEN and between THT and VAIS algorithms allowed direct observation of the existing biases between THT and JEN (JEN provides lower CBH estimates) and between THT and VAIS (VAIS provides higher CBH estimates). An overall conclusion drawn from the intercomparison outcome is that the backscatter profiles generated by the CL31 have a lower signal-to-noise ratio compared to those generated by the CHM15K. The data collected and the results of the intercomparison suggest that the new THT algorithm can provide more accurate estimates of the CBH in both simple and complex cloud patterns and that caution must be exercised when using VAIS and JEN operational CBH outputs. The THT procedure, currently performed during the postprocessing phase, can readily be optimized into a real-time processing system with very little modification.

Acknowledgments

This study was supported by the 4th Higher Education Authority Programme for Research in Third Level Institutions (HEA PRTLI4). This work was also conducted as part of COST Action ES0702 (EG-CLIMET).

REFERENCES

  • Boers, R., Russchenberg H. , Erkelens J. , and Venema V. , 2000: Ground-based remote sensing of stratocumulus properties during CLARA, 1996. J. Appl. Meteor., 39 , 169181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, J. R., Hlavka D. L. , Spinhirne J. D. , Turner D. D. , and Flynn C. J. , 1998: Operational cloud boundary detection and analysis from micro pulse lidar data. Proc. Eighth Atmospheric Radiation Measurement (ARM) Science Team Meeting, Washington, D.C., U.S. Department of Energy, DOE/ER-0738, 119–122.

    • Search Google Scholar
    • Export Citation
  • CEOS, 2006: Satellite observation of the climate system: The Committee on Earth Observation Satellites (CEOS) response to the global climate observing system (GCOS), implementation plan. CEOS Response to the GCOS Implementation Plan, 54 pp.

    • Search Google Scholar
    • Export Citation
  • Charlson, R. J., Lovelock J. E. , Andreae M. O. , and Warren S. G. , 1987: Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature, 326 , 655661.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., Ackerman T. P. , Mace G. C. , Moran K. P. , Marchand R. T. , Miller M. A. , and Martner B. E. , 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39 , 645665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eberhard, W. L., 1986: Cloud signals from lidar and rotating beam ceilometer compared with pilot ceiling. J. Atmos. Oceanic Technol., 3 , 499512.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garrett, K. J., Yang P. , Nasiri S. L. , Yost C. R. , and Baum B. A. , 2009: Influence of cloud-top height and geometric thickness on a MODIS infrared-based ice cloud retrieval. J. Appl. Meteor. Climatol., 48 , 818832.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaumet, J. L., Heinrich J. C. , Peyrat O. , Cluzeau M. , Pierrard P. , and Prieur J. , 1995: Cloud base height measurements with a single pulse Erbium glass laser ceilometer. Preprints, Ninth Symp. on Meteorological Observation and Instrumentation, Charlotte, NC, Amer. Meteor. Soc., 101–105.

    • Search Google Scholar
    • Export Citation
  • Illingworth, A. J., and Coauthors, 2007: Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Amer. Meteor. Soc., 88 , 883898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunz, G. J., De Leeue G. , Becker E. , and O’Dowd C. D. , 2002: Lidar observations of atmospheric boundary layer structure and sea spray aerosol plumes generation and transport at Mace Head, Ireland (PARFORCE experiment). J. Geophys. Res., 107 , 8106. doi:10.1029/2001JD001240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martucci, G., Matthey R. , Mitev V. , and Richner H. , 2007: Comparison between backscatter lidar and radiosonde measurements of the diurnal and nocturnal stratification in the lower troposphere. J. Atmos. Oceanic Technol., 24 , 12311244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morille, Y., Haeffelin M. , Drobinski P. , and Pelon J. , 2007: STRAT: An automated algorithm to retrieve the vertical structure of the atmosphere from single-channel lidar data. J. Atmos. Oceanic Technol., 24 , 761775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Connor, T. C., Jennings S. G. , and O’Dowd C. D. , 2008: Highlights of the fifty years of atmospheric aerosol research at Mace Head. Atmos. Res., 90 , 338355. doi:10.1016/j.atmosres.2008.08.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Dowd, C. D., Lowe J. , Smith M. H. , and Kaye A. D. , 1999: The relative importance of sea-salt and nss-sulphate aerosol to the marine CCN population An improved multi-component aerosol-droplet parameterisation. Quart. J. Roy. Meteor. Soc., 125 , 12951313.

    • Search Google Scholar
    • Export Citation
  • Pal, S. R., Steinbrecht W. , and Carswell A. , 1992: Automated method for lidar determination of cloudbase height and vertical extent. Appl. Opt., 31 , 14881494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., Lohmann U. , Raga G. B. , O’Dowd C. D. , Kulmala M. , Fuzzi S. , Reissell A. , and Andreae M. O. , 2008: Flood or drought: How do aerosols affect precipitation? Science, 321 , 13091313. doi:10.1126/science.1160606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stokes, G. M., and Schwartz S. E. , 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75 , 12011221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Twomey, S. A., 1977: The influence of pollution on the short wave albedo of clouds. J. Atmos. Sci., 34 , 11491152.

  • Weitkamp, C., 2005: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere. Optical Sciences Series, Vol. 102, Springer, 455 pp.

    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Mace Head Atmospheric Research Station is located on the western Irish coast with exposure to the North Atlantic Ocean through the clean sector (180°–300°N).

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 2.
Fig. 2.

AVHRR satellite images from thermal IR channel, 10.3–11.3 μm. Indications of developing fronts in the area over Mace Head are given by using the cloud classification nomenclature.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 3.
Fig. 3.

Vertical profiles of the logarithmic attenuated backscatter coefficient log10βatt from both ceilometers: (left) Jenoptik CHM15K and (right) Vaisala CL31.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 4.
Fig. 4.

Outline of the THT algorithm’s logic. (left) The time–height cross section of the βatt profiles is shown with superimposed (white crosses) artistic representation of the CBH determinations. (right) The idealized GS and βatt profiles from a time–height interval are shown (dashed rectangle) with alongside-detected maxima. The horizontal dashed line indicates the reference height href as obtained by averaging over the previous 10 min of CBH detections. The vertical range interval defining the temporal tracking properties of the algorithm is centered in href. Uncertainty of the selected CBH value comes from the standard deviation of the rejected detections.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 5.
Fig. 5.

Linear correlation between CBH retrievals obtained applying the VAIS and JEN algorithms to CL31 and CHM15K βatt profiles, respectively. (left)–(right) The cases are sorted columnwise by increasing CBH. (c) The worst and (e) the best correlations based on the R2 values are shown. Coefficients a and b are slope and intercept in the linear fit equation y = ax + b, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 6.
Fig. 6.

Total linear correlation between CBH retrievals obtained applying the VAIS and JEN algorithms to CL31 and CHM15K βatt profiles, respectively, for all the selected cases.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 7.
Fig. 7.

Linear correlation between CBH retrievals obtained applying the THT algorithm to CL31 and CHM15K βatt profiles. (left)–(right) The cases are sorted columnwise by increasing CBH. (n) The worst and (i) the best correlation based on the R2 values are shown. Coefficients a and b are slope and intercept in the linear fit equation y = ax + b, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 8.
Fig. 8.

Total linear correlation between CBH retrievals obtained applying the THT algorithm to CL31 and CHM15K βatt profiles for all the selected cases.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 9.
Fig. 9.

The 24 Dec 2008 case: time–height βatt cross section with superimposed CBH values as determined by (a) the THT and JEN algorithms and (b) the THT and VAIS algorithms.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 10.
Fig. 10.

The 18 Sep 2008 case: time–height βatt cross section with superimposed CBH values as determined by (a) the THT and JEN algorithms and (b) the THT and VAIS algorithms.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Fig. 11.
Fig. 11.

The 22 Oct 2008 case: time–height βatt cross section with superimposed CBH values as determined by (a) the THT and JEN algorithms and (b) the THT and VAIS algorithms.

Citation: Journal of Atmospheric and Oceanic Technology 27, 2; 10.1175/2009JTECHA1326.1

Table 1.

Airmass origins: 7-day backward trajectories [NOAA HYSPLIT model and Global Data Assimilation System (GDAS) data].

Table 1.
Table 2.

Cloud classification.

Table 2.
Table 3.

Vaisala CL31 and Jenoptik CHM15K technical data.

Table 3.
Save
  • Boers, R., Russchenberg H. , Erkelens J. , and Venema V. , 2000: Ground-based remote sensing of stratocumulus properties during CLARA, 1996. J. Appl. Meteor., 39 , 169181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, J. R., Hlavka D. L. , Spinhirne J. D. , Turner D. D. , and Flynn C. J. , 1998: Operational cloud boundary detection and analysis from micro pulse lidar data. Proc. Eighth Atmospheric Radiation Measurement (ARM) Science Team Meeting, Washington, D.C., U.S. Department of Energy, DOE/ER-0738, 119–122.

    • Search Google Scholar
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  • CEOS, 2006: Satellite observation of the climate system: The Committee on Earth Observation Satellites (CEOS) response to the global climate observing system (GCOS), implementation plan. CEOS Response to the GCOS Implementation Plan, 54 pp.

    • Search Google Scholar
    • Export Citation
  • Charlson, R. J., Lovelock J. E. , Andreae M. O. , and Warren S. G. , 1987: Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature, 326 , 655661.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., Ackerman T. P. , Mace G. C. , Moran K. P. , Marchand R. T. , Miller M. A. , and Martner B. E. , 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39 , 645665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eberhard, W. L., 1986: Cloud signals from lidar and rotating beam ceilometer compared with pilot ceiling. J. Atmos. Oceanic Technol., 3 , 499512.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garrett, K. J., Yang P. , Nasiri S. L. , Yost C. R. , and Baum B. A. , 2009: Influence of cloud-top height and geometric thickness on a MODIS infrared-based ice cloud retrieval. J. Appl. Meteor. Climatol., 48 , 818832.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaumet, J. L., Heinrich J. C. , Peyrat O. , Cluzeau M. , Pierrard P. , and Prieur J. , 1995: Cloud base height measurements with a single pulse Erbium glass laser ceilometer. Preprints, Ninth Symp. on Meteorological Observation and Instrumentation, Charlotte, NC, Amer. Meteor. Soc., 101–105.

    • Search Google Scholar
    • Export Citation
  • Illingworth, A. J., and Coauthors, 2007: Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Amer. Meteor. Soc., 88 , 883898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunz, G. J., De Leeue G. , Becker E. , and O’Dowd C. D. , 2002: Lidar observations of atmospheric boundary layer structure and sea spray aerosol plumes generation and transport at Mace Head, Ireland (PARFORCE experiment). J. Geophys. Res., 107 , 8106. doi:10.1029/2001JD001240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martucci, G., Matthey R. , Mitev V. , and Richner H. , 2007: Comparison between backscatter lidar and radiosonde measurements of the diurnal and nocturnal stratification in the lower troposphere. J. Atmos. Oceanic Technol., 24 , 12311244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morille, Y., Haeffelin M. , Drobinski P. , and Pelon J. , 2007: STRAT: An automated algorithm to retrieve the vertical structure of the atmosphere from single-channel lidar data. J. Atmos. Oceanic Technol., 24 , 761775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Connor, T. C., Jennings S. G. , and O’Dowd C. D. , 2008: Highlights of the fifty years of atmospheric aerosol research at Mace Head. Atmos. Res., 90 , 338355. doi:10.1016/j.atmosres.2008.08.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Dowd, C. D., Lowe J. , Smith M. H. , and Kaye A. D. , 1999: The relative importance of sea-salt and nss-sulphate aerosol to the marine CCN population An improved multi-component aerosol-droplet parameterisation. Quart. J. Roy. Meteor. Soc., 125 , 12951313.

    • Search Google Scholar
    • Export Citation
  • Pal, S. R., Steinbrecht W. , and Carswell A. , 1992: Automated method for lidar determination of cloudbase height and vertical extent. Appl. Opt., 31 , 14881494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., Lohmann U. , Raga G. B. , O’Dowd C. D. , Kulmala M. , Fuzzi S. , Reissell A. , and Andreae M. O. , 2008: Flood or drought: How do aerosols affect precipitation? Science, 321 , 13091313. doi:10.1126/science.1160606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stokes, G. M., and Schwartz S. E. , 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75 , 12011221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Twomey, S. A., 1977: The influence of pollution on the short wave albedo of clouds. J. Atmos. Sci., 34 , 11491152.

  • Weitkamp, C., 2005: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere. Optical Sciences Series, Vol. 102, Springer, 455 pp.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Mace Head Atmospheric Research Station is located on the western Irish coast with exposure to the North Atlantic Ocean through the clean sector (180°–300°N).

  • Fig. 2.

    AVHRR satellite images from thermal IR channel, 10.3–11.3 μm. Indications of developing fronts in the area over Mace Head are given by using the cloud classification nomenclature.

  • Fig. 3.

    Vertical profiles of the logarithmic attenuated backscatter coefficient log10βatt from both ceilometers: (left) Jenoptik CHM15K and (right) Vaisala CL31.

  • Fig. 4.

    Outline of the THT algorithm’s logic. (left) The time–height cross section of the βatt profiles is shown with superimposed (white crosses) artistic representation of the CBH determinations. (right) The idealized GS and βatt profiles from a time–height interval are shown (dashed rectangle) with alongside-detected maxima. The horizontal dashed line indicates the reference height href as obtained by averaging over the previous 10 min of CBH detections. The vertical range interval defining the temporal tracking properties of the algorithm is centered in href. Uncertainty of the selected CBH value comes from the standard deviation of the rejected detections.

  • Fig. 5.

    Linear correlation between CBH retrievals obtained applying the VAIS and JEN algorithms to CL31 and CHM15K βatt profiles, respectively. (left)–(right) The cases are sorted columnwise by increasing CBH. (c) The worst and (e) the best correlations based on the R2 values are shown. Coefficients a and b are slope and intercept in the linear fit equation y = ax + b, respectively.

  • Fig. 6.

    Total linear correlation between CBH retrievals obtained applying the VAIS and JEN algorithms to CL31 and CHM15K βatt profiles, respectively, for all the selected cases.

  • Fig. 7.

    Linear correlation between CBH retrievals obtained applying the THT algorithm to CL31 and CHM15K βatt profiles. (left)–(right) The cases are sorted columnwise by increasing CBH. (n) The worst and (i) the best correlation based on the R2 values are shown. Coefficients a and b are slope and intercept in the linear fit equation y = ax + b, respectively.

  • Fig. 8.

    Total linear correlation between CBH retrievals obtained applying the THT algorithm to CL31 and CHM15K βatt profiles for all the selected cases.

  • Fig. 9.

    The 24 Dec 2008 case: time–height βatt cross section with superimposed CBH values as determined by (a) the THT and JEN algorithms and (b) the THT and VAIS algorithms.

  • Fig. 10.

    The 18 Sep 2008 case: time–height βatt cross section with superimposed CBH values as determined by (a) the THT and JEN algorithms and (b) the THT and VAIS algorithms.

  • Fig. 11.

    The 22 Oct 2008 case: time–height βatt cross section with superimposed CBH values as determined by (a) the THT and JEN algorithms and (b) the THT and VAIS algorithms.

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