CALIPSO Quality Statements:
Lidar Level 2 Cloud and Aerosol Layer Products
Version Release: 1.10

In the very best case, lidar surface elevations are as reliable as the DEM. GTOPO30 tends to be very reliable over oceans, and considerably less so in rugged terrain such as in the Andes mountains of Peru. However, due to aberrations in the signal caused by a non-ideal transient response in the 532 nm detectors, the geometric thickness associated with the lidar surface elevation (i.e., surface top - surface base) can be extremely misleading. IMPORTANT: At present, users should treat ALL signal beneath the reported lidar surface elevation top as being pure instrument artifact introduced by the non-ideal transient response of the detectors. No geophysical significance should be attributed to the (apparent!) subsurface portion of the lidar return.

The uncertainties associated with detection of cloud and aerosol layers in backscatter lidar data are examined in detail in Section 5 of the CALIPSO Feature Detection ATBD (PDF). The ATBD contains quantitative assessments of feature finder performance derived using simulated data sets, for which all layer boundaries were known exactly. In the real world of layer detection, we do not have access to this underlying truth. Therefore in this document we provide the following set of "rules of thumb" that users can apply to the data products to obtain a qualitative understanding of the layer boundaries reported, and of the optical properties associated with these layers.

1. Strongly scattering features are easier to detect than weakly scattering features. The scattering intensity of each layer is reported in the 532 nm and 1064 nm attenuated backscatter statistics and by the integrated attenuated backscatter at 532 nm and 1064 nm.

2. Detection of layers during the nighttime portion of the orbits is more reliable than during the daytime portion of the orbits. Due to solar background signals, the noise levels in the daytime measurements are much larger than those at night, and this additional noise can obscure faint features, and can lead to boundary detection errors even in more strongly scattering layers.

3. Features become increasingly difficult to detect with increasing optical depth above feature top. Put another way, detection of the lower layers in a multi-layer scene is made more difficult by the signal losses that occur as the laser light passes through the upper layers. (In a sense, this is a restatement of (a), since the backscatter intensity of secondary features is reduced from what it otherwise might be by the signal attenuation caused by the overlying features.)

4. In general, our confidence in the location of the top of a layer is somewhat greater than our confidence in the location of the base of the same layer. For transmissive features, one reason for this is that the backscatter signal is attenuated by traversing the feature, thus degrading the potential contrast between feature and "non-feature" at the base. Additionally, in strongly scattering layers, multiple scattering effects and signal perturbations introduced by the non-ideal transient response of the 532 nm detectors can also make base determination less certain.

5. For opaque features that completely attenuate the backscatter signal, the base altitude reported must be considered as an "apparent" base rather than a true base.

6. Stratospheric features reported during daylight -- especially those reported between 60N and 60S -- should be treated with extreme suspicion.

For the uppermost layer in any column, the quality of the estimate for γ′₅₃₂ is determined by the accuracy of the top and base identification, the reliability of the 532 nm channel calibrations, and by the signal-to-noise ratio (SNR) of the backscatter data within the layer. For layers beneath the uppermost, the quality of our estimate for γ′₅₃₂ also depends on either obtaining an independent estimate of the two-way transmittance, T², for all overlying layers, or by estimating this quantity directly from the lidar backscatter data. Estimating T² directly from the data is something of a black art. In tractable situations (i.e., where there exists an extended region of "clear air" between successive layers, and where the uppermost layer has no more than a moderate optical depth of -- say -- 1.0 or less), the calculation can be fairly reliable. In especially awkward situations (e.g., vertically adjacent layers, such as clouds embedded in aerosols), the only way to estimate T² is to compute a full extinction retrieval for the profile being examined. Furthermore, the effects of errors caused by misestimating T² can increase sharply as the optical thickness above a layer increases. We note that the CALIOP processing scheme always attempts to correct estimates of γ′₅₃₂ for the attenuation imparted by previously identified overlying features. As a consequence, we will occasionally report unrealistically large values for γ′₅₃₂.

There are occasions (e.g., in regions of especially low SNR) where the uncertainty calculation can fail. In these cases, the value recorded in the data product will be set to -1. In all other cases, uncertainty values will be positive.

As is the case for γ′₅₃₂, in the uppermost layer within any column, the quality of the estimate for γ′₁₀₆₄ is determined by the accuracy of the top and base identification, the reliability of the 1064 nm calibration constant, and by the signal-to-noise ratio (SNR) of the backscatter data within the layer. In layers beneath the uppermost, γ′₁₀₆₄ will be underestimated by a factor equal to the total particulate two-way transmittance, T², above the layer. In contrast to the techniques applied at 532 nm, reliable estimates of T² cannot be derived from an analysis of the 1064 nm backscatter signal in the (assumed to be) clear air regions.

There are occasions (e.g., in regions of especially low SNR) where the uncertainty calculation can fail. In these cases, the value recorded in the data product will be set to -1. In all other cases, uncertainty values will be positive.

The quality of the estimate for δ_layer is determined by the accuracy of the top and base identification, the reliability of the polarization gain ratio calibration, and by the signal-to-noise ratio (SNR) of the backscatter data within the layer. In general, the CALIOP δ_layer estimates are highly reliable. Histograms of δ_layer compiled for midlatitude cirrus in the northern hemisphere compare very well with previously reported distributions, e.g., Sassen & Benson, 2001 (PDF).

There are occasions (e.g., in regions of especially low SNR) where the uncertainty calculation can fail. In these cases, the value recorded in the data product will be set to -1. In all other cases, uncertainty values will be positive.

In regions with acceptable SNR, the accuracy with which the range resolved depolarization ratios can be determined will depend almost entirely on the accuracy of the polarization gain ratio calibration.

The quality of the estimate for χ′_layer is determined by the accuracy of the top and base identification, the reliability of the 532 nm calibration constant and the 1064 nm calibration constant, and by the signal-to-noise ratio (SNR) of the backscatter data within the layer.

There are occasions (e.g., in regions of especially low SNR) where the uncertainty calculation can fail. In these cases, the value recorded in the data product will be set to -1. In all other cases, uncertainty values will be positive.

Users can have high confidence in the calculation of all of the values in the attenuated total color ratio statistics fields. However, as with the 532 nm depolarization ratio statistics, the meaning of the various numbers can be somewhat misleading. Like the depolarization ratios, the attenuated total color ratios are produced by dividing one noisy number (the 1064 nm attenuated backscatter coefficient) by a second noisy number (the 532 nm attenuated backscatter coefficient). Depending on the noise in any pair of samples, the resulting values can range from large negative values to extremely large positive values. When computing layer means, standard deviations, and centroids, these outliers can dominate the calculation, and thus return entirely unrealistic estimates.

The CAD scores reported in the CALIPSO layer products range between -100 and 100. The sign of the CAD score indicates the feature type: positive values signify clouds, whereas negative values signify aerosols. The absolute value of the CAD score provides a confidence level for the classification. The larger the magnitude of the CAD score, the higher our confidence that the classification is correct. An absolute value of 100 therefore indicates complete confidence. Absolute values less that 100 indicate some ambiguity in the classification; that is, the scattering properties of the feature are represented to some degree in both the cloud PDF and in the aerosol PDF. In this case, a definitive classification cannot be made; that is, although we can provide a "best guess" classification, this guess could be wrong, with a probability of error related to the absolute value of the CAD score. A value of 0 indicates that a feature has an equal likelihood of being a cloud and an aerosol.

Users are encouraged to refer to the CAD score when the cloud and aerosol classification results are used and interpreted.