1. Introduction
The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission is an international Earth observing platform constructed and operated jointly by the United States and France (Winker et al. 2007). Since its launch on 28 April 2006, CALIPSO has been making nearly continuous measurements of clouds and aerosols in the earth’s atmosphere. Uncertainties in the roles played by clouds and aerosols in the earth’s radiation budget limit our understanding of the climate system and the potential for global climate change (Bernstein et al. 2007). The CALIPSO mission was designed specifically to address these uncertainties (Winker et al. 2009). Clouds and aerosols are highly variable in space and time, and thus long-term and continuous satellite observations are essential for understanding their spatial distributions and climatic impacts on regional and global scales.
The CALIPSO payload consists of three coaligned near-nadir viewing instruments: a 2-wavelength polarization-sensitive lidar [the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)], a three-channel imaging infrared radiometer (IIR), and a single-channel high-resolution wide field-of-view camera (WFC). Although all three instruments provide high-quality observations of clouds and aerosols, CALIOP alone provides the height-resolved measurements that provide a long-term global mapping of the vertical structure of the earth’s atmosphere. CALIOP measures elastic backscatter at 532 and 1064 nm and linear depolarization ratios at 532 nm. A solid-state Nd:YAG laser produces simultaneous coaligned pulses at 1064 and 532 nm. The backscatter from these pulses is measured using three receiver channels. A dichroic beam splitter separates the 532- and 1064-nm signals. The 1064-nm total backscatter is measured using an avalanche photodiode (APD). A polarizing beam splitter further separates the 532-nm backscatter into polarization components oriented parallel and perpendicular to the polarization plane of the outgoing beam. Each component is measured separately using photomultiplier tubes (PMTs). Additional information about the CALIOP transmitter and receiver design and operation can be found in Hunt et al. (2009).
Accurate calibration of all three lidar signals is essential for layer detection and the subsequent retrieval of layer optical properties. Complete details of all CALIOP calibration algorithms are presented in Hostetler et al. (2006). Essential aspects of the CALIOP calibration procedures are presented in a series of three papers. Here, we describe the nighttime calibration of the 532-nm parallel channel and the calibration of the 532-nm perpendicular channel. During nighttime operations, the 532-nm parallel channel can be calibrated using the traditional high-altitude molecular normalization technique (Russell et al. 1979). The nighttime calibration coefficients computed for the 532-nm parallel channel function as the main system calibration; all other channels are calibrated relative to this measurement. This work reviews the use of the lidar equation and the theoretical basis of the molecular normalization technique as applied to CALIOP data, and it describes a procedure for detecting and removing spurious high-energy events that otherwise would introduce large errors into the calculations. It also briefly describes the mechanics and computational methods necessary to calibrate the 532-nm perpendicular channel relative to the 532-nm parallel channel. Complete error analyses are derived for both calibration procedures and comparisons are made to validation data acquired by the National Aeronautics and Space Administration (NASA) airborne high–spectral resolution lidar (HSRL; Hair et al. 2008). Part II of the series will present the suite of algorithms developed to estimate the 532-nm calibration coefficients applied to the daytime measurements. CALIOP daytime measurements are strongly affected by the ambient solar background, which causes a substantial deterioration of the signal-to-noise ratio (SNR) for molecular backscatter measurements, thus precluding the use of the high-altitude molecular normalization technique. Different calibration strategies are therefore employed for nighttime and daytime operations. Different calibration strategies must also be employed for the 1064-nm channel, because the magnitude of the molecular scattering signal at 1064 nm is insufficient for calibration using the normalization technique for both night and day. Part III of the series will describe the method used to calibrate the 1064-nm channel.
Figure 1 contains a block diagram representing the basic flow of the CALIOP three-channel calibration process. When calibrating the daytime 532-nm parallel, 532-nm perpendicular, and 1064-nm channels, input data specific to each channel are required.
The CALIPSO level 0 data and ancillary data products are delivered daily to the NASA Langley Research Center Atmospheric Science Data Center (ASDC). The level 0 data contain the telemetry data with communication artifacts removed and include both the payload science data and the payload health and status data. The ancillary data products contain the gridded meteorological and ozone fields from the NASA Global Modeling and Assimilation Office (GMAO), satellite attitude and postprocessed ephemeris data from the Centre National d’Études Spatiales (CNES), and maps and ice from the National Snow and Ice Data Center (NSIDC). Several static datasets are also used: the International Geosphere-Biosphere Programme (IGBP) surface vegetation cover map and the U.S. Geological Survey (USGS) Global 30 Arc-Second Elevation Data (GTOPO30) digital elevation model (DEM). The CALIPSO automated processing system (CAPS) monitors the delivery of data; when all necessary data products have been received, CAPS automatically initiates the level 1 processing (Winker et al. 2009).
An initial processing step creates an intermediate level 1A data product by converting the level 0 signal profiles into altitude-registered and georeferenced data. The next processing step calculates the calibration coefficients and applies the calibration to the signal profiles to produce the level 1B data product. The primary level 1B data products include profiles of 532-nm total attenuated backscatter (parallel plus perpendicular), 532-nm perpendicular attenuated backscatter, and 1064-nm attenuated backscatter; they also include the associated calibration coefficients and calibration uncertainties (level 1B data files are available online at http://eosweb.larc.nasa.gov).
Two internal files provide input to the level 1B process. The CALIPSO calibration-coefficient history data file contains daily nighttime 532-nm parallel-channel calibration-coefficient estimates that are subsequently applied when calibration coefficients cannot be calculated. The CALIPSO polarization gain ratio (PGR) data file stores the PGR, which is used to quantify the differences in the responsivity and gain of the two 532-nm detection channels. The PGR operation calculates the ratio of the nearly equal optical fluxes incident on the parallel- and perpendicular-channel detectors as a spatial pseudodepolarizer is inserted into the 532-nm receiver optical path (Hunt et al. 2009).
2. Basic equations and nomenclature
a. Lidar equation and calibration coefficient
The lidar signals are acquired as profile elements separated by approximately 15 m in range along the path of the laser pulse. An illustration of the CALIPSO nominal spacecraft geometry and the lidar signal altitude registration is provided in Fig. 2. The profile data are averaged vertically and horizontally, as described in Winker et al. (2009), and then downlinked and sent to the NASA Langley Atmospheric Sciences Data Center as CALIPSO level 0 data (King et al. 2004).
The instrument payload controller automatically adjusts the data acquisition start time for each shot so that altitudes of the signal elements, after onboard averaging, correspond to the altitude elements in a predefined fixed-altitude array. This altitude registration and the level 0 geolocation are based on spacecraft position and attitude data from the onboard GPS system, processed through orbital propagation software. The first step in the conversion of the CALIOP level 0 data to altitude-registered geolocated level 1A signal profiles is to recalculate the altitude registration and geolocation using postprocessed ephemeris data, which provides a higher accuracy in spacecraft position and attitude.
The level 0 signal profiles received at the ground are averaged vertically and horizontally (averaging scale dependent upon altitude) prior to downlink and exhibit some jitter in altitude registration because of inexact onboard ephemeris data. To correct the altitude registration and geolocation, the profiles are first expanded to pseudo-single-shot profiles with 30-m vertical sample spacing, replicating data values as necessary where the downlink resolution is greater than 30 m vertically and one shot horizontally. Each reconstituted profile is then geolocated and the altitude of each sample is computed. Based on the computed altitudes, the samples are mapped onto a fixed-altitude array and averaged back to the downlink resolutions.
The lidar footprint location, or geolocation point, is defined by a geodetic latitude and longitude and determined as the point where the lidar pulse intersects a geoid model of the earth. Computing the geolocation requires an accurate geoid model and the instantaneous spacecraft position and attitude. The position of the spacecraft relative to a location on Earth is defined in a coordinate reference system. This system consists of a set of parameters based on a geoid model, which is defined by adding geoid undulations to a reference ellipsoid, and can then serve to approximate mean sea level.
The calculated altitudes in z vary slightly for each signal profile. To provide consistency in altitude values for all signal profiles, each profile is mapped onto the fixed-altitude array, starting at the bottoms of the profiles. The first signal element whose altitude is a good match to an element of the fixed-altitude array is assigned to the element number of the matching altitude element. The remaining signal data elements are assigned one by one in ascending order to the element locations of the fixed-altitude profile. If there is a shift between the original signal element numbers and the revised element numbers, then there will be an element at the top or bottom of the altitude profile that has fill data because no signal data is mapped to it.
Although the atmospheric parameters β and σ are considered to be functions of altitude z, the two-way signal attenuation is a function of optical pathlength through the atmosphere above the scattering volume and hence can be considered a function of either altitude or range.
b. Output data products
3. Nighttime 532-nm parallel-channel calibration
The CALIOP calibration procedure begins with the calibration of the 532-nm parallel channel for nighttime conditions. The 532-nm parallel-channel calibration coefficient is determined from the ratio of the range-scaled energy- and gain-normalized signal X ‖ to estimates of attenuated backscatter computed from a modeled atmospheric density profile in the 30–34-km altitude region, where the backscatter source is primarily molecules (Russell et al. 1979; Hostetler et al. 2006; Reagan et al. 2002). Details on the mathematical basis for the algorithms, calibration procedure, uncertainty analysis, and artifact removal are described in the following subsections.
a. Mathematical basis
The parallel component of molecular backscatter is calculated from an estimate of the total molecular backscatter β̂m and the expected depolarization ratio for molecular backscatter δm. The total molecular backscatter is the product of the molecular number density and the total Rayleigh scattering cross section for air (Reagan et al. 2002; Cairo et al. 1999).
A global model of R̂‖ that provides an estimate of the relative contribution of aerosol backscatter present at altitudes within the calibration range can be derived from other measurements such as the 20-yr Stratospheric Aerosol and Gas Experiment (SAGE) II aerosol record (Thomason and Peter 2006). The current calibration procedure does not include such a model and globally defines R̂‖ to unity. Plans are in progress to modify the calibration to include the contribution of aerosol backscatter in future versions of the CALIOP data products.
Figure 3 contains an example that compares the measured signal X ‖ and signals derived from the model β̂m,‖T̂2, as defined by Eqs. (18) and (20). Both the measured signals and model estimates were averaged over one week for 4–10 February (Fig. 3a) and 4–10 August 2007 (Fig. 3c). The averaging was performed in three steps. First, the profile elements contained within the calibration altitude range (30–34 km) were averaged horizontally over 165 km along the lidar measurement ground track. Second, the mean value of each averaged profile was calculated and stored as a function of the midlatitude value of each ground track segment. Third, the calculated means were averaged over 1°-latitude segments to produce the averages displayed in Fig. 3. In an additional step, the model estimates were scaled by the mean of the calibration coefficients C ‖ for the same time period [refer to Eq. (6)]. The latitudinal range of the CALIPSO nighttime measurements shifts with season between maximum bounds of 82°N and 82°S, as demonstrated by the differences in latitude coverage of the calibration data 7 February (Fig. 3b) and 7 August 2007 (Fig. 3d). The data in Fig. 3 and later figures are plotted as a function of “extended latitude.” Extended latitude is a plotting technique to maintain visual continuity of the curves by artificially extending the latitude beyond the turnaround point at the northernmost or southernmost excursion of the orbit. This plotting technique is used to display a complete nighttime orbit segment without discontinuities. To calculate extended latitude, the difference between the local maximum latitude and the actual latitude is added to the local maximum latitude. The orbit track locations near the south polar region for August 2007 (Fig. 3d) show how the latitude coverage for each track repeats on either side of the node transition.
The signals in X ‖ are closely matched to the model estimates, with two exceptions. The first exception occurs near the tropical latitudes, where the measured signals are higher than the model estimates. This is consistent with the SAGE II stratospheric aerosol measurements acquired during 2001–02 over the tropical latitudes, which report estimates of backscatter ratios ranging from 1.03 to 1.10 (Hostetler et al. 2006; Thomason et al. 2007). Because the current CALIOP calibration algorithm does not correct for the presence of aerosol in the stratosphere, a small error of approximately 3% is incurred for calibrations near the tropical region (see section 4a and Fig. 9). The second exception is near the Southern Hemisphere night-to-day terminator, where the signal decreases as the satellite enters sunlight and thermal changes begin to effect the alignment of the lidar transmitter and receiver (Hunt et al. 2009).
b. Calibration procedure
The vertical extent of the standard CALIOP calibration region extends down from 34 to 30 km. This region is high enough in the stratosphere to be relatively (albeit not completely) free of aerosols, and it is low enough to ensure that the mean molecular number density will generate an acceptably large backscatter signal. As explained in Hunt et al. (2009), before being transmitted back to Earth, the backscatter data in this high-altitude region are averaged on board the satellite to a spatial resolution of 300 m vertically and 5 km horizontally. However, the SNR in the calibration region still remains quite low and significant amounts of additional averaging are required to produce accurate estimates of the calibration coefficients. The calibration procedure therefore performs increased averaging in three steps: 1) horizontal averaging, 2) vertical averaging, and 3) the application of a running average.
The data in the calibration region are first averaged to a horizontal resolution of 55 km. These profiles of averaged data are then converted into composite profiles of intermediate calibration coefficients by application of Eq. (18); that is, a profile of range-resolved calibration estimates is formed by computing the ratio of the horizontally averaged signal profiles of X ‖ and the model estimates that correspond to the same altitude region and horizontal-averaging resolution. Prior to the signal-averaging procedure, the individual X ‖ values are filtered for transient effects arising from high-energy protons and/or cosmic rays. These high-energy events introduce extreme noise excursions into the backscatter signal, which then cause significant error in the calculation of the calibration coefficients. A complete description of the filtering technique is presented in section 3d.
The subsequent vertical-averaging procedure produces a single calibration-coefficient estimate for each calibration region. These improved calibration-coefficient estimates are calculated by vertically averaging the intermediate calibration coefficients within each of the composite profiles.
The final step further averages the calibration-coefficient estimates by smoothing the sequence of values via a 27-point running average filter, resulting in an effective 1485-km average between independent samples. The smoothed calibration coefficients are used to interpolate/extrapolate calibration coefficients continuously throughout the orbit. These, in turn, are applied to the X ‖ profiles to produce attenuated backscatter coefficient profiles.
c. Error analysis
Assuming uncertainties that should reasonably apply in the stratosphere for 532 nm in the calibration altitude between 30 and 34 km, the current best estimates of the components of the systematic error are listed in Table 1. Estimates of the contributing error terms will be improved as more knowledge is gained on the accuracy of the products used to compute them. At present, the various components combine to give an overall relative systematic error ΔC ‖/C ‖ of ∼5%.
Random error in the calibration coefficients is dominated by shot noise in the lidar measurements. We estimate the random component of the calibration uncertainty by using continuously updated values of the noise scale factor (NSF; Liu et al. 2006; Hostetler et al. 2006). Use of the NSF technique enables accurate estimates of the random noise in a signal without requiring a large number of samples. Values of the NSF are computed as part of the level 1B process and are included in the output data product. Although the NSF for a specific detector can be derived theoretically, in practice it is typically computed by dividing the standard deviation of the background signal by the square root of the mean. Techniques for the derivation and application of the NSF to various measurement scenarios are described in detail in Liu et al. (2006).
d. Data filtering high-energy transient effects
Prior to performing the horizontal-averaging step in the calibration process [i.e., step 1 of Eq. (21)]; the sampled data are filtered for signal spikes resulting from high-energy proton or cosmic ray events. The high-energy events that cause the signal spikes can occur randomly throughout the orbit, but they mostly occur in the South Atlantic anomaly (SAA), where the inner Van Allen radiation belts make their closest approach to Earth’s surface (Hunt et al. 2009). If left unchecked, the high-energy transients generate significant error in the calculation of the calibration coefficients.
The high-energy removal filter technique uses a combination of an adaptive filter and a noise-to-signal ratio (NSR) test to remove out-of-range signals prior to calibration (Lee et al. 2008). The filter technique is applied to each calibration region as a three-step process to successively filter the signals deemed as valid by the previous step.
As an example of the adaptive filter application, Fig. 5a contains the X ‖ signals at altitude z = 32.66 km as a function of extended latitude for 1 February 2007, beginning at 0438:06 UTC. The corresponding orbit track is plotted (left track) in the bottom panel of Fig. 4. The large variation in signal can be seen between the equator and 60°S as the orbit passes through the SAA. Positive signal spikes result when high-energy transients occur at the measurement altitude. Negative signal spikes result when high-energy transients occur in the dc offset calculation (Hunt et al. 2009). This produces larger than expected offset values and negative signals may result when large dc offsets are subtracted from the measured signals. The dotted lines in the figure mark the minimum and maximum threshold values defined by the adaptive filter. The signals that remain after application of the filter are shown in Fig. 5b.
The second step of the filter technique applies an NSR test to the signals in the calibration region that were identified as valid by the adaptive filter. The NSR test is applied to each 4-km vertical by 55-km horizontal calibration region and used to determine if large variations in signal magnitude exist within that region. The NSR is calculated as the ratio of the standard deviation and the mean of all the valid signals [i.e., NSR(Xvalid) = σstd(Xvalid)/μ(Xvalid)] and the resulting value is compared to an empirically defined NSR threshold value. The NSR threshold level is set to selectively identify only those variations in signal resulting from high-energy events. Calibration regions that fail the NSR test are excluded from the calibration process and are instead assigned a calibration coefficient computed from historical trending of valid calibration coefficients. For accepted calibration regions, all valid signal elements in the individual profiles are averaged into a single calibration-ready profile. The calibration-ready profile is the same as described in step 1 of Eq. (21). If any of the elements in this profile are empty (which can happen if the adaptive filter rejects all samples at a particular altitude), the entire calibration region is rejected and assigned a daily calibration-coefficient estimate. The daily calibration-coefficient estimates are calculated by taking the average of all valid calibration coefficients over an entire day. The time-referenced daily calibration-coefficient estimates are then posted to a calibration-coefficient history data file. The daily calibration-coefficient estimates are obtained from the calibration-coefficient history data file by searching for the latest date prior to the date of data acquisition.
Figure 6a contains the NSR values plotted as a function of extended latitude for the same orbit described in Fig. 5. For this example, the NSR threshold is set at 2.2 and is marked as a solid line. Figures 6b,c illustrate how the adaptive filter and the NSR test are applied for single calibration regions. In each figure, the 11 X ‖ signal profiles from a calibration region are displayed as functions of altitude. The calibration region in Fig. 6b is located near the center of the SAA, and the calibration region in Fig. 6c is located near the edge of the SAA. In comparing the signal levels for the two regions, the number and intensity of the high-energy events are higher for the region near the center of the SAA (Fig. 6b). The shaded regions mark the valid signals identified by the application of the adaptive filter. The signal elements outside of the shaded regions are not included in the NSR test. In applying the NSR test to the valid signals, the NSR value yields ∼15 for Fig. 6b and ∼1.6 for Fig. 6c. The NSR value for Fig. 6b exceeds the NSR threshold and the calibration region is assigned a daily calibration-coefficient estimate. The NSR value for Fig. 6c is within the NSR threshold limit and the calibration-ready profile is calculated by horizontally averaging the valid signal elements in the individual profiles.
In the third step, the adaptive filter is applied to the mean of the calibration-ready profile. The minimum and maximum threshold values in XM are defined using Eqs. (28) and (29) with parameters specific to each calibration region. Here, the latitude l is the midpoint latitude of the calibration region, and X̂‖(l), which approximates the mean of the calibration-ready profile, is computed using the molecular number density from the vertical midpoint of the calibration region. The parameters ΔPBG,‖,
This final filter test determines if the mean of the values in the calibration-ready signal profile is within a specified range of the approximate mean stored in the parameter X̂‖(l). If the calibration-ready profile passes this filter test, then it is used by the calibration procedure as described in step 1 of Eq. (21). If the calibration-ready profile does not pass the final filter test, then the calibration region is assigned a daily calibration-coefficient estimate.
Figure 6d contains the calibration-ready profile produced by horizontally averaging the valid profile elements displayed in Fig. 6c. The mean of the calibration-ready profile is drawn as a vertical dotted line, and the minimum and maximum threshold values defined by the second application of the adaptive filter are drawn as vertical solid lines. The mean of the calibration-ready profile shown is within the limits of the dual threshold and is therefore used in the calibration procedure.
Figure 5c shows calibration coefficients computed using both filtered and unfiltered signals in the calibration process. The filtered and unfiltered calibration coefficients are plotted as solid and dotted lines, respectively. These results demonstrate that, by applying this filter technique, the CALIOP calibration procedure can correctly calculate the 532-nm calibration coefficients in a wide variety of different noise environments.
4. 532-nm nighttime parallel-channel calibration assessment and validation
Assessments and validations of the nighttime 532-nm parallel-channel calibration are performed continuously throughout the mission. The performance of the calibration procedure is assessed by using the CALIOP attenuated backscatter data to calculate clear-air attenuated scattering ratios and comparing the CALIOP results to the theoretical clear-air scattering ratio of 1. We refer to cloud-free regions of very low aerosol loading as clear air. We limit the altitude range over which the scattering ratios are calculated to between 8 and 12 km. Experience has shown that the altitude region between 8 and 12 km often exhibits very low aerosol loading such that the observed total scattering ratio is very near 1. Validation is performed by comparing CALIOP level 1B attenuated backscatter profiles to those derived from the NASA airborne HSRL. This comparison determines the biases that exist within the CALIOP calibration. The following sections provide a detailed description and results of both the assessment and validation procedures.
a. Clear-air attenuated scattering ratio assessment
CALIOP nighttime 532 nm calibration is internally assessed by compositing attenuated scattering ratio estimates from regions identified as clear air and comparing them to values appropriate for a purely molecular atmosphere (i.e., R′ = 1). If the calibration is correct and the aerosol loading is sufficiently low, then the clear-air scattering ratios calculated from the level 1B data should not differ from the estimates for molecular backscatter by more than the relative calibration uncertainty (i.e., ±5%).
In applying this benchmark to the calculated clear-air attenuated ratios, several factors that may bias the calibration assessment must be considered. These factors include the accuracy of the scattering model, the presence of noise in the measured data, and the degree of aerosol contamination found within so-called clear-air regions. This last factor is directly related to the minimum detectable backscatter coefficient for CALIOP, which in turn is a function of instrument design and the layer detection scheme applied to the data (Vaughan et al. 2005; McGill et al. 2007).
The scattering model is the same as the one used for the 532-nm parallel calibration process described in section 3a, in which the molecular and ozone number densities are provided by NASA GMAO. Noise in the data can be reduced by averaging; however, small biases resulting from aerosol loading below the CALIOP detection threshold will remain. Any aerosols that are in the selected clear-air regions will cause the scattering ratios to have larger values than those for completely clear air.
The clear-air regions are determined using the CALIOP lidar level 2 cloud and aerosol layer data product (Vaughan et al. 2004, 2009). Regions are selected if they contain clear air above 8 km and extend continuously for at least 200 km along the orbit track. Figure 7 contains an illustration of the clear-air selection process using lidar level 1B 532-nm total attenuated backscatter coefficients from 1 February 2007, beginning at 0438:06 UTC. The red boxes between 8 and 12 km in altitude and 200 km in length illustrate clear-air regions.
For each 200-km segment, profiles of clear-air attenuated scattering ratios are calculated as the ratio of 532-nm total attenuated backscatter signals to the scattering model derived from molecular and ozone number densities provided by GMAO. Total attenuated scattering ratios are used because the contributions to the clear-air scattering ratios from the perpendicular channel are negligible.
Figure 8 illustrates the noise in four consecutive clear-air attenuated scattering ratio profiles at single-profile resolution
In Fig. 9, the means of each clear-air attenuated scattering ratio profile at single-profile resolution
Averaged mean clear-air attenuated scattering ratios
The results obtained from the assessment show that the majority of the medians of
b. Validation with HSRL
A validation study of CALIOP nighttime 532-nm parallel-channel calibration was performed by comparing spatially coincident data acquired with the NASA airborne HSRL to the CALIOP nighttime 532-nm total attenuated backscatter coefficients to determine biases in CALIOP calibration. The airborne HSRL is internally calibrated to a high accuracy (∼1%–2%; Hair et al. 2008) and does not rely on normalization of estimated backscatter from assumed clear-air regions for calibration. As such, it provides an ideal dataset for assessment of CALIOP calibration errors. To date, 68 flights of the airborne HSRL were flown along the CALIPSO ground track and timed to the CALIPSO overpass.
The NASA HSRL has been making coincident measurements with CALIPSO since the start of the CALIPSO mission. Figure 11 shows the mean calibration error estimate for each day (temporally averaged) as a function of latitude. The data are color coded by month. The variability in the daily estimates is determined as the standard deviation of the temporally averaged error profiles for each day. The results show that the mean nighttime 532-nm parallel channel calibration is within 4.9% of HSRL.
The positive bias (∼3%–7%) seen in the error estimates indicates that the HRSL 532-nm total attenuated backscatter coefficients are systematically larger than those of CALIPSO. The source of this bias is the presence of stratospheric aerosols within the CALIPSO calibration region. Recent estimates put the magnitude of the scattering ratio of the stratospheric aerosols in the range of 1.03 to 1.10, with variations depending on latitude and season (Hostetler et al. 2006; Thomason et al. 2007). Accounting for the presence of stratospheric aerosols in the calibration equation [i.e., as in Eqs. (18) and (19)] would effectively eliminate the bias. As noted earlier (see section 3a), such modifications are planned for future versions of the CALIPSO data products.
5. 532-nm perpendicular-channel calibration
Calibration of the 532-nm perpendicular channel is transferred from the 532-nm parallel channel using data acquired during the polarization calibration operation (Hunt et al. 2009). During the calibration, a spatial pseudodepolarizer is inserted into the 532 nm receiver optical path, thereby randomizing the polarization of the return so that nearly equal optical fluxes are incident on the parallel- and perpendicular-channel detectors. The ratio of the two measured signals is the PGR and is used to quantify the differences in the responsivity and gain of the two detection channels. The 532-nm perpendicular-channel calibration coefficient is then defined as the product of PGR and the 532-nm parallel-channel calibration coefficient.
a. Mathematical basis
b. Error analysis
Because the pseudodepolarizer directs essentially equal optical intensities on both detectors and because the long durations of the PGR calibration operations permit extensive signal averaging, the SNR for both the parallel and perpendicular components of the PGR calculation is quite high. Consequently, the random error in
6. Discussion
Planned algorithm enhancements (or upgrades or improvements) include the addition of a stratospheric aerosol model into the calibration process. This model will likely be based on analysis of data from CALIOP and from Global Ozone Monitoring by Occultation of Stars (GOMOS), one of the three instruments dedicated to atmospheric composition sounding on board the European Space Agency (ESA) satellite Envisat, which launched on 1 March 2002 (Kyrölä et al. 2004). Once the stratospheric aerosol model is implemented within the calibration procedure, all of the CALIOP level 1B data will be reprocessed and archived at the NASA Langley Research Center Atmospheric Science Data Center. There are also plans to continuously monitor the performance of the calibration procedure using the clear-air attenuated scattering ratio assessment presented in section 3a and to periodically schedule CALIOP calibration validation flights with the HSRL.
7. Summary
The traditional molecular normalization calibration scheme applied to the CALIOP nighttime 532-nm parallel channel has a long and well-documented history of success in the lidar community. A new capability of removing spurious high-energy events that would otherwise introduce large errors into the calibration was added to the CALIOP calibration process. The HSRL validation data shows that the CALIOP calibration is performing well within the expected error budget (∼5%). The 532-nm perpendicular channel is calibrated relative to the 532-parallel channel using data acquired during the PGR operation.
The calculated 532-nm parallel and perpendicular calibration coefficients are applied to corresponding signal data to produce profiles of attenuated backscatter coefficients with units of km−1 sr−1. The data products archived at the NASA Langley Research Center Atmospheric Sciences Data Center (available online at http://eosweb.larc.nasa.gov/) include both profile and calibration products. The attenuated backscatter profiles are reported as 532-nm total attenuated backscatter (parallel plus perpendicular) and 532-nm perpendicular attenuated backscatter. The calibration products are composed of the calibration coefficients and calibration uncertainties for the 532-nm parallel channel and the PGR and PGR uncertainties for the 532-nm perpendicular channel.
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Components of systematic error in the calculation of the 532-nm parallel calibration coefficient.