Attainment of National Ambient Air Quality Standard-NAAQS for exposure limits to air pollutants is of great concern to State and Local agencies and communities in the United State because of potential health impacts. This is particularly important and challenging in urban areas because of high population densities and complex terrain. Exceedances of NAAQS requires states to develop implementation plans to address them and as such, studying the horizontal and vertical distribution and mixing of pollutants is key to understanding their transport and evolution. In this study, vertical and scanning horizontal lidar measurements together with in situ observations from particulate matter and trace gas analyzers from state air quality networks are used to shed light on mechanisms that impact movement of aerosol, including emissions from power generating stations at periods of high electricity demand.
In this study, multiple remote sensing and in-situ measurements are combined in order to obtain a comprehensive
understanding of the aerosol distribution in New York City. Measurement of the horizontal distribution of aerosols is
performed using a scanning eye-safe elastic-backscatter micro-pulse lidar. Vertical distribution of aerosols is measured
with a co-located ceilometer. Furthermore, our analysis also includes in-situ measurements of particulate matter and
wind speed and direction. These observations combined show boundary layer dynamics as well as transport and
inhomogeneous spatial distribution of aerosols, which are of importance for air quality monitoring.
Using UV Raman Lidar for aerosol extinction (αext), and combining microwave radiometer-derived liquid water path (LWP) with multifilter rotating shadowband radiometer-derived cloud optical depth (τcod) to retrieve cloud droplet effective radius (Reff), we observe clear signatures of the Twomey aerosol indirect effect (IE) under certain specialized conditions. The aerosol–cloud index (ACI) or IE slope relating cloud droplet radius to aerosol loading is calculated and shown to be quantitatively consistent with theoretical constraints. To demonstrate consistency, we use both a neural network multiband (default) approach and a dual-channel (DC) approach for the LWP and observe that the DC approach is generally more robust with more successful retrievals leading to a reduction of error in our regression analysis. We also perform an uncertainty analysis of the IE regression slope taking into account the major sources of error in cloud property retrieval and demonstrate that only sufficiently high values of the IE slope should be observable. Finally, based on the results of multiple cases, we observe the importance of vertical wind uptake on the IE signature.
The vertical stratification and optical characteristics of aloft aerosol plumes are critical to evaluate their influences on climate radiation and air quality. In this study, we demonstrate the synergistic measurements of aloft aerosol plumes by a ground-based NOAA-CREST lidar network (CLN) along the US East Coast, the AERONET-sun/sky radiometer network at lidar sites, and satellite observations. During the plume intrusion period on March 6, 2012, the CLN and AERONET measurements were consistent in illustrating the onset of dust aerosol plumes. We observed two-layers of aerosol located at 1.0 ~ 8.0 km altitude. The column-average volume size distributions show increasing concentration of both fine- and coarse-modes aerosols, but are dominated by the coarse-mode. Direct lidar inversions illustrate that the aerosol plume layers contributed up to 70% of the total AOD. NOAA-HYSPLIT back-trajectories and CALIPSO observations indicate the trans-Pacific transport of Asian-dust at 3 - 8 km altitude to the US East Coast. Meanwhile, the NOAA-HMS fire and smoke products illustrate the transport and possible mixture of dust with fine-mode smoke particles from the middle and southwestern US. The small Angstrom exponents of MODIS/Aqua in the US East Coast imply the dominance of coarse-mode particles. Accordingly, the upper layer of coarse mode aerosols is most likely transported from the East Asia, while the lower layer at 1-3 km altitude probably consists of continental dust particles from the western US mixed with fine-mode smoke particles. In addition, the transport and vertical structure of aerosol are investigated with the NAAPS global aerosol transport model.
In this work, we focus on estimations of fine particulate matter using MODIS AOD as part of a neural network scheme
and compare this to both simple linear regressions and GEOS-CHEM products. In making this comparison, it is well
known the seasonal and geographical dependences observed in the PM2.5-AOD relationship; thus, to enhance our
predictions, we apply WRF PBL information to our neural network method and assess its performance. As part of our
analysis, we first explore the baseline effectiveness of AOD and PBL as strong factors in estimating PM2.5 in a local
experiment using data collected at one site in New York City. Then, we expand our analysis to a regional domain
where daily estimations are obtained based on site location and season. In our local test, we find the high efficiency of
the neural network estimations when AOD, PBL and seasonality are primarily assessed (R~0.94 in summer). Later, we
test our regional network and compare it with the GEOS-CHEM PM2.5 product. From this, we see better estimations
from our experiment using urban/non-urban stations and applying different spatial schemes for training the neural
network (RNN~0.80, RGEOS-CHEM~0.57 in an urban station with a distance radius of 0.1 degree; RNN~0.74, RGEOSCHEM~0.69 in a non-urban station with a distance radius of 0.3 degree). Finally, we create regional daily PM2.5 maps and compare them to GEOS-CHEM outputs, evaluating the corresponding estimations using ground readings.
Fine particulate matter measurements (PM2.5) are essential for air quality monitoring and related public health; however, the shortage of reliable measurmennts constrains researchers to use other means for obtaining reliable estimates over large scales. In particular, model forecasters and satellite community use their respective products to develop ground particulate matter estimations but few experiments have explored how the remote sensing approaches compare to the high resolution models. . In this paper we focus on studying the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellites (GOES) regression based estimates in comparison to more direct bias corrected outputs from the Community Multiscale Air Quality (CMAQ) model, We use a two-year dataset (2005-2006) and apply urban, season and hour filters to illustrate the agreement between estimated and in-situ measured fine particulate matter from the New York State Department of Environmental Conservation (NYSDEC). We first begin by analyzing the correspondence between ground aerosol optical depth (AOD) measurements from an AERONET (AErosol RObotic NETwork) Cimel sun/sky radiometer with both satellite and model products in one urban location; we show that satellite readings perform better than model outputs, especially during the summer (RMODIS>=0.65, RCMAQ>=0.37). This is a clear symptom of the difficulty in the models to properly model realistic optical properties. We then turn to a direct assessment of PM2.5 presenting individual comparisons between ground PM2.5 measurements with satellite/model predictions and demonstrate the higher accuracy from model estimations (RurbanMODIS ≥ 0.74, RurbanCMAQ ≥ 0.77; Rnon-urbanMODIS ≥ 0.48, Rnon-urbanCMAQ ≥ 0.78). In general, we find that the bias corrected CMAQ estimates are superior to satellite based estimators except at very high resolution. Finally, we show that when using both model and satellite approximations as separate estimators merged optimally, our product (PM2.5 average) becomes closer to real measurements with improved correlations (RAVE ~ 0.86) in urban areas during the summer.
This paper explores the performance of current remote sensing methods for estimation of fine particulate matter
(PM2.5, diameter < 2.5μm) in the New York City area (40.821°N, 73.949°W) during 2010. We analyze the
relationship between surface PM2.5 mass concentration and column aerosol optical depth (AOD) at 500-nm by using
the synergy measurements of surface in-situ, AERONET-sunphotometer, lidar and NOAA-GOES satellite. The
regression slopes and correlation coefficients between PM2.5 and AOD show the good performance in summer and
indicate dramatic monthly variation which are associated with the seasonal differences of PBL-heights, fine-mode
contribution to the total AOD and aerosol volume-to-extinction ratio. Additionally, the relationship of PM2.5 and
fine-mode AOD shows higher correlations than the PM2.5 and total AOD (R2total = 0.5011, R2fine = 0.6132, R2coarse = -0.0235). Also, when considering the lidar-derived PBL-heights in the different months and removing aloft layer and
cloudy cases, the PM2.5 estimations using AOD show improvements during the cold months; furthermore, the
correction on aerosol volume-to-extinction ratio results in better estimations of fine particulate matter concentrations
and therefore confirms the importance of including these parameters into air quality models. Moreover, the AOD
data from NOAA-Geostationary Operational Environmental Satellites (GOES) are initially evaluated by comparing
with AERONET-AOD, and further illustrate the good correlation with PM2.5 concentration.
The aerosol-cloud interaction is a complex and critical process in assessing the climate radiative effects of aerosol and
cloud. Lidar can simultaneously measure the range-resolved distribution of aerosol-cloud with the high spatial-temporal
resolution, and hence provides the opportunity to explore the cloud-aerosol optical properties and their interaction. Their
interactions have been indicated by the significant variation of optical properties and droplet size of aerosol and cloud at
the cloud vicinity or edges. But due to dramatic non-linear or irregular variation of lidar returns by the cloud, the
evaluation of lidar algorithm deriving cloud extinction coefficient becomes quite important especially at the edges
because the common algorithms may result in the artificial influence on the retrievals of cloud extinction and extinctionto-
backscatter ratio (e.g. lidar ratio or S-ratio). In particular, the relationships of water cloud optical properties with the
droplet size are simulated which include lidar ratio, color ratio and extinction ratio are used and general trends with
measurements are demonstrated. To obtain color ratios (355/1064), a good calibration procedure for the 1064nm
channel is required and we show that calibration errors using low water drop clouds allow absolute calibration < 10%.
Preliminary results seem to indicate that small pre-nucleated droplets form at the aerosol - cloud boundary which is
consistent with aerosol uptake into clouds. In addition, we also explore the increase in aerosol lidar-ratio below cloud
indicative of hygroscopic growth.
In this paper, the simulations of the Weather Research and Forecast (WRF) and Community Multiscale Air Quality
(CMAQ) Models applied to the New York City (NYC) area are assessed with the aid of vertical profiling and column
integrated remote sensing measurements. First, we find that when turbulent mixing processes are dominant, the WRFderived
planetary boundary layer (PBL) height exhibits a strong linear correlation (R>0.85) with lidar-derived PBL
height. In these comparisons, we estimate the PBL height from the lidar measurements using a Wavelet Covariance
Transform (WCT) approach that is modified to better isolate the convective layer from the residual layer (RL).
Furthermore, the WRF-Lidar PBL height comparisons are made using different PBL parameterization schemes,
including the Asymmetric Convective Model-version2 (ACM2) and the Modified Blackadar (BLK) scheme (which are
both runs using hindcast data), as well as the Mellor-Yamada-Janjic (MYJ) scheme run in forecast mode. Our findings
show that the correlations for these runs are high (>0.8), but the hindcast runs exhibit smaller overall dispersion (≈0.1)
than the forecast runs. We also apply continuous 24-hour/7-day vertical ceilometer measurements to assess WRFCMAQ
model forecasts of surface PM2.5 (particulate matter has aerodynamic diameter <2.5μm). Strong overestimations
in the surface PM2.5 mass that are observed in the summer prior to sunrise are particularly shown to be strongly
connected to underestimations of the PBL height and less to enhanced emissions. This interpretation is consistent with
observations that TEOM (Tapered Element Oscillating MicroBalance) PM2.5 measurements are better correlated to pathintegrated
CMAQ PM2.5 than the near-surface measurements during these periods.
Measurements of low-altitude cloud and its interaction with aerosol are analyzed with a multiple-wavelength elastic-
Raman scattering lidar. Using the numerical experiment approach, we first evaluate the retrieval accuracy of cloud
extinction from the Raman-lidar algorithms, in particular at the cloud edges. For the low-level water-phase cloud,
the simulation also shows the dramatic variation of lidar-ratio, color-ratio and extinction-ratio with the small
droplets and their correlation. Then, the measurement examples by CCNY elastic-Raman lidar illustrate that the
small droplets probably appear at the cloud edges, which might imply the new particle formation or the cloudaerosol
interaction.
The planetary boundary layer (PBL) heights are derived from the CALIOP/CALIPSO level-1B attenuated backscatter
profile using the wavelet transform technique. The results are compared to those by the radiosonde and ground-based
lidar coincident measurements. The comparison generally indicates the good agreement and the correlation coefficient is
greater than 0.7. In addition, we found the good consistence between the CALIOP-derived PBL height and the selected
aerosol-layer-top of CALIPSO level-2 aerosol-layer products (5-km average). Finally, the spatial distribution of PBL
heights and their seasonal differences are initially illustrated over the US continent.
With the dramatically climate changing we are facing today atmospheric monitoring is of major importance.
Several atmospheric monitoring instruments are used for measuring atmospheric composition, optical
coefficients, PM2.5, aerosol optical depth, size distribution, PBL height and many other parameters. However
an inexpensive method of determining these parameters is by use of models and one model that depicts the
aerosol dynamics in the atmosphere is the Community Multi-scale Air Quality (CMAQ) model. Our paper is
comparing the lidar, sunphotometer and TEOM measurements performed at City College of the City University
of New York against CMAQ model.
The need to characterize in a robust way Planetary Boundary Layer (PBL) heights is crucial as in air quality forecast and
transport models. In particular, incorrect determination of PBL heights can severely distort the surface air quality
predictions such as PM2.5. Local properties and morphological features can influence PBL dynamics through local
circulation phenomena such as the sea-breeze development as well as influences from the Urban Heat Island Canopy
resulting in multiple layers that need to be resolved. In this paper, based on a combination of wavelet and image
processing methods, we develop methods to quantify multilayer PBL's and assess their dynamics with meteorological
measurements including temperature, wind and humidity profiles. In particular, meteorologically based PBL heights
based on both the Potential Temperature Gradient and Richardson Number are compared against both lidar and
ceilometer measurements. It is shown that in general, the Potential Temperature Gradient method is better correlated to
the PBL dynamics. Meanwhile, the Hysplit model provides sounding data which can be used for comparison between
actual sounding and lidar measurements. On the other hand, when strong atmospheric instability is present or layering
develops, the comparison between different methods can provide information about the PBL internal structure. Further
comparisons with air quality models such as MM5 are also made and illustrate the difficulty in these models properly
predicting the PBL dynamics seen in urban areas.
Indentifying and quantifying ambient aerosols are important for air-quality applications. Unlike trace gases where
chemical spectral signatures are sharp and well defined, aerosol spectral signatures are broader and highly overlapping.
Therefore separation of aerosols into different size classes requires very broad spectral coverage from the visible (VIS)
to mid-infrared (MIR). In this paper, we investigate the feasibility in using a VIS (0.65μm) diode laser combined with a
suitable pulsed high power Quantum Cascade Laser (4.6μm) to obtain backscatter measurements that can be used to
isolate fine and coarse mode aerosol fractions. Based on realistic source characteristics, we study the information content
in the spectral extinction using different combinations of extinction measurements using Least Squares Minimization
applied to a wide range of aerosol multimode mixtures obtained using realistic models obtained from the Optical
Properties of Aerosol and Clouds (OPAC) model. This model is especially convenient since the optical spectral
extinction and backscatter spectra are evaluated over a wide wavelength range from 250nm to 40μm. In particular, we
find that with the latest QCL systems, it is possible to achieve signal to noise ratio (SNR) values ~10 with suitable
temporal and spatial averaging for aerosol layers ~1.5km making it suitable for PBL layer studies.
Smoke and dust aerosol plumes are observed by the ground-based multi-wavelength elastic-Raman lidar, sunphotometer
and space-borne lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization). Lidar-derived multi-wavelength
aerosol extinction profiles and column lidar ratios are constrained by the independently measured optical
depths. The aloft smoke plume layers from Idaho/Montana forest fires were measured at 2~8 km altitude by the ground
lidar on Aug. 14~15, 2007. High aerosol optical depths (AOD) are shown with the value of 0.6~0.8 at wavelength 500
nm and Angstrom exponent of 1.8. The CALIOP observations generally show consistent plume height distribution with
the ground lidar, but partly misclassify these smoke plumes as clouds. The forest fire sources and intra-continental
smoke transport are clearly illustrated by CALIOP and MODIS satellite imageries. For the moderate dust-like plumes on
April 18, 2008, they were observed at the altitude of 2~6 km. Aerosol optical depths vary from 0.2 to 0.4 at wavelength
500 nm with Angstrom exponent <1.0 in the plume-layer. Ground-lidar and CALIOP retrievals show the good
agreement in dust-like layer heights, extinction profiles and aerosol species classification.
Lidar calibration at the 1064-nm channel is explored by using the low-level water-phase cloud and high cirrus cloud.
Based on a known constant of lidar ratio in the optically thick water cloud, the lidar calibration constant is estimated by
integrating lidar returns in the cloud. By using wavelength independence of cirrus cloud backscatter, the lidar constant is
analyzed with the two-wavelength signals ratio at 532-nm and 1064-nm after correcting aerosol transmittance from
sunphotometer measurement. Calibration constants by these two separate methods are compared on the same day and
show consistency with the relative difference of less than 30% in general. We further verify the calibration constant by
regressing lidar signals with calibrated ceilometer data in the low planetary boundary layer (PBL). Moreover, the
calibration result is tested by applying it to estimate aerosol backscatter at 1064-nm and Angstrom exponent. In the end,
normalized daily averages of lidar constants over two-month period are presented.
To provide reasonable forcasts of near surface PM2.5
levels, it necessary that satellite measurments provide a
reasonable estimator of PM2.5 which can be coupled to
a transport model. Unfortunately this requires that the
aerosol be homogeneously mixed and that the extent of
the PBL be sufficiently accurate. For example, the
IDEA product (Infusing satellite Data into
Environmental Applications) used by the EPA relies on
a static relationship connecting PM2.5 to MODIS
aerosol optical depth (AOD) which relies on a static
model of the PBL aerosol height. In this paper, we show
that the PBL height is far from static and by taking the
variable PBL into account, a better prediction of PM2.5
from the MODIS (AOD) measurements is obtained. In
addition, seasonal variations in the microphysical
properties are also demonstrated and accounting for the
additional variability further improves the PM25/AOD
slope predictor.
KEYWORDS: Aerosols, LIDAR, Clouds, Backscatter, Ocean optics, Signal attenuation, Mass attenuation coefficient, Signal detection, Calibration, Refractive index
The distributions of aerosol and planetary boundary layer (PBL) heights are presented from CALIOP/CALIPSO (Cloud-
Aerosol Lidar with Orthogonal Polarization) and ground-based lidar measurements. We initially assess the approach of
using the MODIS-retrieved aerosol optical depth over ocean to constrain lidar-ratio (extinction-to-backscatter ratio) and
extinction profile at wavelength 532-nm. Statistical comparisons of cloud and aerosol layers are performed with the 20-
day cases. We find in general excellent correlations exist between both cloud-base and top even for multiple deck cases
that are not to close. In the clear skies, the CALIPSO-derived aerosol-layer-tops are consistent with ground-lidar derived
PBL heights although the accuracy degrades if capped by cloud layers. In addition, PBL heights are derived from the
CALIPSO level-1B profiles and mapped over the continental US. Finally, we perform a detailed comparison for a smoke
plume event including aerosol classification, derivation of extinction profile and lidar ratio.
KEYWORDS: Clouds, LIDAR, Aerosols, Raman spectroscopy, Backscatter, Scattering, Atmospheric modeling, Signal attenuation, Atmospheric particles, Signal to noise ratio
In this paper, the properties of Low-level clouds are explored with a Raman-elastic lidar. In particular, we examine two
complementary methods to measure thin cloud optical depth (COD). The first is direct integration of Raman Derived
extinction while the second method utilizes a regression technique. We show that if we correct for aerosol influences the
regression method for low cloud optical depth can be dramatically improved. Furthermore, estimates of extinction to
backscatter ratio can be made within the cloud. We find that when the lidar ratio in cloud is averaged over the vertical
extent, an S ratio on the order of 20 sr is found which is consistent with conventional water phase cloud droplet models.
Key words: Low cloud, optical depth, Raman lidar. Finally, correlations between aerosol loading below clouds and small
droplets in cloud interior are studied illustrating possible connections between aerosols and small droplet seeding.
In this paper, the properties of Low-level clouds are explored with a Raman-elastic lidar. In particular, we examine two
complementary methods to measure thin cloud optical depth (COD). The first is direct integration of Raman Derived
extinction while the second method utilizes a regression technique. We show that if we correct for aerosol influences the
regression method for low cloud optical depth can be dramatically improved. Furthermore, estimates of extinction to
backscatter ratio can be made within the cloud. We find that when the lidar ratio in cloud is averaged over the vertical
extent, an S ratio on the order of 20 sr is found which is consistent with conventional water phase cloud droplet models.
In this paper, we explore the possibility of determining thenature and variability of urban aerosol hygroscopic properties
using multi-wavelength Raman lidar measurements at 355nm, as well as backscatter measurements at 532nm and
1064nm.. The addition of these longer wavelength channels allow us to more accurately validate the homogeneity of the
aerosol layer as well as provide additional multiwavelength information that can be used to validate and modify the
aerosol models underlying the hygroscopic trends observed in the Raman channel. In support of our hygroscopic
measurements, we also discuss our calibration procedures for both the aerosol and water vapor profiles. The calibration
algorithm we ultimately use for the water vapor measurements are twilight measurements where water vapor radiosonde
data from the OKX station in NYS, are combined with total water vapor obtained from a GPS MET station. These
sondes are then time correlated with independent near surface RH measurements to address any bias issues that may
occur due to imperfect calibration due to lidar overlap issues and SNR limitations in seeing the water vapor at high
altitudes.. In particular, we investigate the possibility of using ratio optical scatter measurments which eliminate the
inherent problem of variable particle number and illustrate the sensitivity of different hygroscopic aerosols to these
measurements. We find that the use of combine backscatter color ratios between 355 and 1064 together with the
conventional extinction to backscatter ratio at 355nm should be able to improve retrieval of hygroscopic properties.
In this paper, we present results showing the usefulness of multi-wavelength lidar measurements to study the interaction
of aerosols in the PBL with long range advected aerosol plumes. In particular, our measurements are used to determine
the plume angstrom exponent, which allows us to differentiate smoke events from dust events, as well as partitioning the
total aerosol optical depth obtained from a CIMEL sky radiometer between the PBL and the high altitude plumes.
Furthermore, we show that only if the optical depth from the upper level plumes is taken into account, the correlation
between the lidar derived PBL aerosol optical depth and surface PM2.5 is high. In addition, we also observe the
dynamic interaction of high altitude plumes interacting with the PBL, resulting in a dramatic rise in surface PM10
concentrations without a corresponding dramatic rise in PM2.5 concentrations. These observations strongly suggest the
deposition of large particulates into the PBL which is consistent with both lidar angstrom coefficient measurements and
back-trajectory analysis. Finally, we investigate the correspondence between surface PM2.5 concentrations and optical
backscatter coefficients as a function of altitude. To perform this study, our lidar system is replaced by a ceilometer
(Vaisala CL-31) which can determine backscatter to near surface level. In particular, we confirm that near surface
backscatter within the first 100 meters is a good proxy for PM2.5 but as altitude increases beyond 500 meters, the
correlations degrades dramatically. These studies are useful in identifying the vertical length scales in which spaced
based lidars such as Calipso can be used to probe surface PM2.5.
In this paper, we implement and compare two complementary methods for the measurement of low cloud optical depth
with a Raman-Mie lidar over the metropolitan area of New York City. The first approach, based on the method of S.
Young, determines the cloud optical depth by regressing the elastic signal against a molecular reference signal above
and below the cloud. Due to high aerosol loading below and above the low cloud, correction for aerosol influence was
necessary and achieved with the combined Raman-elastic returns. The second approach uses N2-Raman signal to derive
cloud extinction profiles and then integrate them to determine optical depth. We find excellent agreements between
these two retrievals for cloud optical depths as large as 1.5. Extinction-to-backscatter ratio within the low cloud is
obtained and is shown to be consistent to values calculated from liquid water cloud model. The varied lidar ratios at
cloud edge may imply the changes of cloud droplet size providing clues to the CCN seeding process. Furthermore,
multiple-scattering effects on retrieving cloud optical depths are estimated by using an empirical model and specific
lidar parameters.
In this paper, we explore the possibility of determining the nature and variability of urban aerosol hygroscopic
properties using multi-wavelength Raman lidar measurements at 355nm, as well as backscatter measurements at 532nm
and 1064nm. The addition of these longer wavelength channels allow us to more accurately validate the homogeneity of
the aerosol layer as well as provide additional multiwavelength information that can be used to validate and modify the
aerosol models underlying the hygroscopic trends observed in the Raman channel. In support of our hygroscopic
measurements, we also discuss our calibration procedures for both the aerosol and water vapor profiles. The calibration
algorithm we ultimately use for the water vapor measurements are twilight measurements where water vapor radiosonde
data from the OKX station in NYS, are combined with total water vapor obtained from a GPS MET station. These
sondes are then time correlated with independent near surface RH measurements to address any bias issues that may
occur due to imperfect calibration due to lidar overlap issues and SNR limitations in seeing the water vapor at high
altitudes. In particular, we investigate the possibility of using ratio optical scatter measurements which eliminate the
inherent problem of variable particle number and illustrate the sensitivity of different hygroscopic aerosols to these
measurements.
KEYWORDS: Clouds, Calibration, LIDAR, Backscatter, Aerosols, Multiple scattering, Signal attenuation, Atmospheric modeling, Near infrared, Signal to noise ratio
In conventional lidar, calibration of the backscatter signal is performed by comparing the returns to aerosol clean layers
where the backscatter signal can be calculated directly. Unfortunately, in the IR, this approach is not practical since the
aerosol contribution to the signal is always a significant fraction even for high altitudes. Two approaches which have
been suggested include the calibration from high altitude cirrus clouds and the calibration from low altitude
stratocumulous (water phase) clouds. In this paper, we perform an inter-comparison of these methods over long time
periods both as a means to assess the accuracy of the calibration methods as well as determining the stability of the lidar
calibration coefficient. To improve upon the standard calibration using the cirrus cloud approach, a forward iterative
integration scheme is employed from below cloud base to determine the backscatter at 532nm. It is found that the cirrus
cloud approach in reasonable agreement with the low altitude water cloud method. Furthermore, a small 15%
discrepancy between the methods is explained based on reasonable multiple scattering corrections which must be
introduced into the low altitude water cloud technique.
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