CALIPSO Quality Statements:
Lidar Level 2 Vertical Feature Mask
Version Releases: 2.01, 2.02
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CALIPSO Quality Statements: |
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This document provides a high-level quality assessment of the Level 2 lidar vertical feature mask product, as described in Section 2.7 of the CALIPSO Data Products Catalog (Version 2.4) (PDF). As such, it represents the minimum information needed by scientists and researchers for appropriate and successful use of these data products. We strongly suggest that all authors, researchers, and reviewers of research papers review this document for the latest status before publishing any scientific papers using these data products.
The purpose of these data quality summaries is to inform users of the accuracy of CALIOP data products as determined by the CALIPSO Science Team and Lidar Science Working Group (LSWG). This document is intended to briefly summarize key validation results; provide cautions in those areas where users might easily misinterpret the data; supply links to further information about the data products and the algorithms used to generate them; and offer information about planned algorithm revisions and data improvements.
The primary new parameters included in the version 2.0 release of the Vertical Feature Mask (VFM) product are aerosol type and cloud ice/water phase).
This data product describes the vertical and horizontal distribution of cloud and aerosol layers observed by the CALIPSO lidar. Cloud and aerosol discrimination for detected features is reported as a single value, the CAD_Score, which can be found in the Lidar Level 2 Cloud and Aerosol Layer data products. In this data product clouds and aerosols are distinguished by the "feature type" bits, and the CAD_Score is interpreted in the following fashion:
If CAD_Score > 0, feature is a cloud.
If CAD_Score < 0, feature is a aerosol.
Use of the CAD_Score to produce the feature typing QA bits, can be found below.
| Bits | Field Description | Bit Interpretation |
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| 1-3 | Feature Type |
0 = invalid (bad or missing data) 1 = "clear air" 2 = cloud 3 = aerosol 4 = stratospheric feature 5 = surface 6 = subsurface 7 = no signal (totally attenuated) |
| 4-5 | Feature Type QA |
0 = none 1 = low 2 = medium 3 = high |
| 6-7 | Ice/Water Phase |
0 = unknown / not determined 1 = ice 2 = water 3 = mixed phase |
| 8-9 | Ice/Water Phase QA |
0 = none 1 = low 2 = medium 3 = high |
| 10-12 | Feature Sub-type | |
| If feature type = aerosol, bits 10-12 will specify the aerosol type |
0 = not determined 1 = clean marine 2 = dust 3 = polluted continental 4 = clean continental 5 = polluted dust 6 = smoke 7 = other |
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| If feature type = cloud, bits 10-12 will specify the cloud type. |
0 = low overcast, transparent 1 = low overcast, opaque 2 = transition stratocumulus 3 = low, broken cumulus 4 = altocumulus (transparent) 5 = altostratus (opaque) 6 = cirrus (transparent) 7 = deep convective (opaque) |
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| If feature type = Polar Stratospheric Cloud, bits 10-12 will specify PSC classification. |
0 = not determined 1 = non-depolarizing PSC 2 = depolarizing PSC 3 = non-depolarizing aerosol 4 = depolarizing aerosol 5 = spare 6 = spare 7 = other |
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| 13 | Cloud / Aerosol /PSC Type QA | 0 = not confident 1 = confident |
| 14-16 | Horizontal averaging required for detection (provides a course measure of feature backscatter intensity) |
0 = not applicable 1 = 1/3 km 2 = 1 km 3 = 5 km 4 = 20 km 5 = 80 km |
List of the data quality summaries and user notes for the feature classification flags.
The cloud aerosol discrimination (CAD) algorithm uses the feature integrated color ratio, χ′, and the feature mean attenuated backscatter coefficient, <β′532>, to compute the CAD_Score. These parameters depend on the quality of the 532 nm and 1064 nm channel calibrations. Significant errors in the calibration of either channel may result in the misclassification of a particular feature.
The current probability distribution functions of χ′ vs. <β′532> for clouds and aerosols that are used by the CAD algorithm were developed based on expert manual classification of all layers detected during one full day of data acquired by CALIOP during August 2006. From these results, a single set of cloud and aerosol PDFs was constructed. This set of PDFs is applied globally for all seasons and at all latitudes. Despite the use of these updated PDFs, which significantly enhance overall performance, the current algorithm (v 2.01) (PDF) continues to have some difficulty correctly classifying optically dense biomass burning layers as aerosol. Users should also be aware that clouds embedded within optically dense aerosols will likely be identified by the feature finder algorithm as one feature and, consequently, these features will likely be classified as clouds.
In the Antarctic region where polar stratospheric clouds (PSCs) have been observed, there may be times when a vertical strip of the PSC may be classified as cloud. In many situations this happens because the base of the PSC drops below the GMAO- reported tropopause or because the PSC is vertically adjacent to a cloud system in the troposphere. The current version of the feature finding algorithm reports only a single feature even if its vertical extent spans the tropopause.
In summary, the algorithm classifies aerosol layers that have volume depolarization ratio (δv) greater than 0.2 as desert dust and 0.075 < δv < 0.2 as polluted dust. Note that polluted dust could be a component of urban pollution, i.e., it is not confined to desert regions but is any type of aerosol composed of some dust-like particles. Of the non-depolarizing aerosols, layers lofted above 1 km are assumed to be smoke, and layers less than 1 km above the surface are either clean continental if the layer IAB is small or polluted continental if the layer IAB is large.
Specifies the amount of horizontal averaging required for a feature to be detected. For all data versions up to and including 2.01 release, the values decoded from the bits in this field will be either 1/3 km, 1 km, 5 km, 20 km, or 80 km.
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| The Feature_Classification_Flag values are stored as an 5515 element array (as rows in the HDF file) for a 5 km "chunk" of data. The numbers in this image indicate the column indices for the array. Only start and end indices are shown. |
| Altitude Region | Vertical Resolution (meters) |
Horizontal Resolution (meters) |
Profiles per 5 km |
Samples per Profile |
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| Base (km) | Top (km) | ||||
| -0.5 | 8.2 | 30 | 333 | 15 | 290 |
| 8.2 | 20.2 | 60 | 1000 | 5 | 200 |
| 20.2 | 30.1 | 180 | 1667 | 3 | 55 |
| Total | 545 | ||||
| Lidar Level 2 Vertical Feature Mask (VFM) Information Half orbit (Day) geolocated data radiances |
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| Release Date | Version | Data Date Range | Maturity Level |
| October 2008 | 2.02 | September 14, 2008 to present |
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| January 25, 2008 | 2.01 | June 13, 2006 to September 13, 2008 |
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Version 2.02 of the Level 2 data products is a maintenance release that implements the following changes.
The impacts of these changes on the Level 2 data products are as follows:
Layer detection: As a result of the first two changes, the 532 nm and 1064 nm calibration constants are larger, on average, by ~1%, resulting in corresponding decreases in the magnitudes of the attenuated backscatter coefficients at both wavelengths. These changes in the level 1 data result only small changes to the layer detection statistics. For example, the difference in the total number of layers detected by the two different versions on August 12, 2006 was 4: 9680 layers were detected by the version 2.01 code, versus 9676 layers by the version 2.02 code.
Cloud-aerosol discrimination: with one exception, there were only minimal changes in cloud-aerosol discrimination results. The exception occurs in the polar regions when PSCs are present. For the August 12, 1006 test case, corrections to the interpolation algorithms applied to the GMAO data result in a slight upward shift in the tropopause heights, and as a consequence, more clouds and fewer stratospheric layers are identified in the version 2.02 results.
Ice-water phase determination: because this classification is based on depolarization ratio and temperature no substantial changes, there were no substantial changes in the assessments of cloud thermodynamic state.
Aerosol subtype identification: Correcting the level 2 runtime script error will reduce the number of layers identified as smoke, and increase the number of layers identified as sea salt.
Cloud and aerosol extinction profiles and optical properties: changes in backscatter and extinction coefficients at the tops of layers are small, and proportional to the changes in the calibration coefficients ... however, due to the cumulative nature of error propagation in the extinction retrieval, differences increase with increasing penetration depths, and can grow large when the optical depths of the clouds are large (i.e., > 3).
The CALIPSO vertical feature mask (VFM) data product reports a single 16-bit integer for each lidar altitude resolution element in the data stream downlinked from the satellite. Upon decoding each of these bit-mapped integers, users will obtain information describing layer location (both vertically and horizontally), layer type, and the amount of horizontal averaging required for the layer to be detected.
The primary new parameters included in the version 2.01 release of the vertical feature mask product are aerosol type classification and cloud ice/water phase discrimination. These are not independently derived results, but instead are the end products derived from a fully automated scene classification process. As such, the quality and correctness of the individual classifications depends not only on the specific input data and its associated pattern recognition algorithm, but also on the accuracy of several prior decisions made in other parts of the processing stream.
Dense aerosol layers (primarily very dense dust and smoke over and close to the source regions) are sometimes labeled as cloud. Because the CAD algorithm operates on individual layers, without a contextual awareness of any surrounding features, it can happen that small but strongly scattering regions within an extended aerosol layer can occasionally be labeled as cloud. This occurs because the optical properties (backscatter and color ratio) within the region are similar to what would be expected for the relatively faint clouds that fall within the PDF overlap region. These misclassifications are often apparent from studying the Level 1 browse images. Based on the initial analysis of the CALIOP measurements, the cloud and aerosol distributions show variabilities that depend on season and on geophysical location. The globally averaged PDFs used in the current release will have a larger overlap between the cloud and aerosol than would occur for more regionally specific statistics. For future versions of the CAD algorithm, we expect to develop and deploy PDFs that will correctly reflect both seasonal and latitudinal variations.
Many optically thin clouds, both ice and water, are encountered in the polar regions. The current CAD PDFs do not work as well in the polar regions as at lower latitudes and misclassifications of clouds as aerosol are more common. In particular, thin ice clouds which can extend from the surface to several kilometers in altitude, are sometimes misclassified as aerosol.
Correct classification of heterogeneous layers is always difficult, and the process can easily go awry. An example of a heterogeneous layer would be an aerosol layer that is vertically adjacent to a cloud or contains an embedded cloud, but which is nonetheless detected by the feature finder as a single entity. By convention, heterogeneous layers should be classified as clouds. However, depending on the relative strengths of the components, these layers are sometimes erroneously identified as aerosol.
Some so-called features identified by the layer detection scheme are not legitimate layers, but instead are artifacts due to the noise in the signal, multiple scattering effects, or to artifical signal enhancements caused by non-ideal detector transient response or an over estimate of the attenuation due to overlying layers. These erroneous "pseudo-features" are neither cloud nor aerosol; however, because they are not properly interdicted in the processing stream, the CAD algorithm nonetheless attempts to assign them to one class or the other. Very frequently these layers can be identified by their very low CAD scores (typically less than 20).
The algorithm assumes that in the ocean aerosol layers whose base height is greater than 1 km, (i.e., above the mean marine boundary layer height) are not marine aerosols. The algorithm assigns a biomass burning smoke type to such layers if the volume depolarization ratio is less than 0.075.
In Arctic and Antarctic regions, the airmasses can either be clean Arctic and Antarctic or Arctic haze. The aerosol types in the first two cases are modeled as clean continental types. Arctic haze results mainly from emissions of industrial pollution throughout Europe and Russia and is therefore modeled as polluted continental. The subtyping scheme does not allow the identification of dust in polar regions because the presence of low altitude ice particles (e.g., diamond dust) does not allow unambiguous attribution of high depolarization to dust.
Over the ocean, the algorithm classifies all aerosol elevated above 1 km and with volume depolarization ratios less than 0.075 as smoke. Since not all these are smoke layers, the frequency of smoke layers over the ocean will be somewhat biased high.
The aerosol subtype product is generated downstream of the cloud-aerosol discrimination (CAD) scheme and, therefore, depends on the cloud-aerosol classification scheme in a very fundamental way. If a cloud feature is misclassified as aerosol, the aerosol subtype algorithm will identify this 'aerosol' as one of the aerosol subtypes. The user must exercise caution where the aerosol subtype looks suspicious or unreasonable. Such situations can occur with some frequency in the southern oceans and the polar regions.
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