Download Charaterisation of Cirrus Clouds using Photometric and Lidar

Charaterisation of Cirrus Clouds using
Photometric and Lidar Measurements
PC4199 Honours Project in Physics, Project Report
Department of Physics
Academic Year 15/16
Submitted by:
Wong Meng Cheng Joel (A0098698X)
Supervised by:
Dr. Santo V. Salinas Cortijo
Dr. Liew Soo Chin
Acknowledgements
I would like to express my gratitude to Dr. Santo V. Salinas Cortijo for
initiating and supervising this project and my progress over the last year. I
would also like to thank Dr. Liew Soo Chin for the occasional and helpful
input in this project. This project has allowed me to develop my interest in
atmospheric radiation, and my supervisors have been a large part of it. Both
Dr. Santo and Dr. Liew are the principal investigators of the AERONET site
located in Singapore, from which the data for this project is taken. I would
also like to acknowledge Dr. Ellsworth J. Welton, the principal investigator
of the MPLNET site in Singapore.
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Abstract
Cirrus clouds play an important role in the radiative heating and cooling of
the Earth and its atmosphere. Some important properties of cirrus clouds
used in climate research include the single-scattering albedo, asymmetry parameter, and the optical depth. In this project, we developed a cirrus cloud
detection algorithm to be applied to Aerosol Robotic Network (AERONET)
data, in order to indirectly detect cirrus clouds physical and optical properties from photometric measurements. In addition, data from the Micro-Pulse
Lidar Network (MPLNET) was also used to determine cloud heights. However, after successfully analyzing several months of data, we found that the
main limitation was due to the inherent geometry of the instruments used
for cloud detection. i.e. photometer and lidar. Another limitation was due
to the fact that AERONET routinely removes any form of cloud presence in
its data set. This severely reduces the amount of data available for analysis.
Nevertheless, we found a reduced set of successful retrievals that allowed us
to evaluate, at least as a first approximation, the radiative properties of such
a clouds by using an appropriate radiative transfer model.
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Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
General Properties of Cirrus Clouds . . . . . . . . . . . . .
Past Research and Project Motivation . . . . . . . . . . .
Using AERONET for Clouds . . . . . . . . . . . . . . . . .
Project Objectives . . . . . . . . . . . . . . . . . . . . . .
Theory and Instrumentation . . . . . . . . . . . . . . . . . . . .
The Sun Photometer and AERONET . . . . . . . . . . . .
AERONET Inversions . . . . . . . . . . . . . . . . . . . .
The Micro Pulse Lidar and MPLNET . . . . . . . . . . . .
Summary of Relevant MPLNET and AERONET Products
Algorithm Characteristics and Criteria . . . . . . . . . . . . . .
Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . .
Results and Preliminary Observations . . . . . . . . . . . . . . .
Without Additional Optical Criteria . . . . . . . . . . . .
Imposing Additional Optical Criteria . . . . . . . . . . . .
Optical Properties of the Screened Datasets . . . . . . . .
Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . .
Radiative Forcing . . . . . . . . . . . . . . . . . . . . . . . . . .
Brief Theory and Results . . . . . . . . . . . . . . . . . . .
Discussion on Radiative Forcing . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction
General Properties of Cirrus Clouds
Cirrus clouds are high altitude, geometrically thin clouds consisting predominantly of ice crystals, the average temperature being −20◦ C to −30◦ C
(McGraw-Hill Editorial Staff, 2005). They are high clouds located 6 km and
above, and may range in thickness from 100 m to 8000 m (Wylie et al, 1994).
An example of thin, wispy cirrus clouds may be seen in Figure 1.
Figure 1: Image from the Space Shuttle Endeavour showing cirrus clouds
Image Credits: NASA Earth Observatory,
http://earthobservatory.nasa.gov/Features/Clouds/clouds3.php
Most cirrus occurrences form as cirrus, cirrocumulus, or cirrostratus clouds
(Friess & Oliver, 2015). Cirrus clouds are wispy, and have a fibrous appearance, as shown in Figure 1. Cirrocumulus contain small amounts of liquid
water and exists as multiple individual ”cloudlets” spanning across the sky.
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They do not cast shadows on the earth. Cirrostratus clouds are a uniform,
thin sheet of cloud spanning a large area, and have a white veil-like appearance. It does not obstruct the sun, and halos may be seen in the day with this
type of cloud. Cirrocumulus and cirrostratus clouds may be seen in Figure
2. In this report, we will use ”cirrus” to refer to any type of cirrus clouds,
which are defined as being located above 6 km in altitude. Individual types
of cirrus clouds will be properly named when the specificity is required.
Figure 2: Cirrocumulus (bottom half) blending into cirrostratus (top left)
Image Credits: NASA Langley Research Center S’Cool,
http://scool.larc.nasa.gov/cirrocumulus.html
One defining characteristic of cirrus clouds is its composition of highly nonspherical ice crystals, which makes its optical properties differ greatly from
other atmospheric particles. These ice crystals can vary in size ranging from
10 µm to several thousand microns (Heymsfield & Miloschevich, 2003). The
geometry of cirrus ice crystals may vary from single pristine shapes such as
hexagonal ice columns and plates, single bullets, to complex aggregates of
roughened columns or bullets (Baran, 2004). The geometry of the particles
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depends on the conditions of its formation, such as the temperature and specific humidity, which are in turn dependent on the altitude and geographical
location. This makes cirrus clouds a more complex topic of study, as the
scattering by such particles are not easy to model.
In this project, we are interested in investigating high-altitude cirrus clouds
located above 13 km in altitude, as these are clouds which play a role in the
radiative forcing of the Earth and its atmosphere.
Past Research and Project Motivation
Due to the impact of cirrus clouds on the radiative forcing of the Earth,
there has been much research in this area, attempting to solve the transfer of
solar radiation through the cloud layer. Of the different factors involved in
the Earth’s radiation budget, these clouds are a large source of uncertainty
(Liou, 1986), since their properties differ depending on the physical conditions of their formation. Cirrus clouds tend to absorb solar radiation and
re-emit in the long wavelength range, acting as a shield against heating from
the Sun (Solar Albedo Effect). However, there is also a similar effect on the
upward ground radiance, trapping heat within the atmosphere (Greenhouse
Effect). It is generally agreed that optically thin cirrus clouds at high altitudes tend to have a net radiative forcing and greenhouse effect on the Earth,
while thick cirrus clouds may cause cooling (Stephens & Webster, 1981; Fu
& Liou, 1993). One recent and interesting study was done to examine the
radiative forcing by geo-engineered cirrus clouds, in hopes of finding a solution to global warming (Cirisan et al, 2013). Hence, having a large database
of cirrus cloud physical and optical data will be an advantage to such studies.
Due to the high altitudes at which cirrus clouds are usually located, fewer
studies have been done on them compared to other components in the atmosphere (Liuo, 1986). Also, they contain almost exclusively non-spherical
ice crystals on various shapes. As such, cirrus clouds are considered to be
a major unsolved component in weather and climate research (Fu, 1996).
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The optical properties of cirrus clouds have traditionally been measured by
in situ flight-based instruments, or by satellite imaging in remote sensing
data. The main problem with flight-based measurements is the lack of large
databases of consistent measurements, since it is not economical to regularly
send flights to make measurements. On the other hand, while remote sensing
data might be abundant and cover vast geographical locations, the quality
of data is variably dependent on factors like cloud cover, ground emission,
and ground reflectance. Also, optically thin cirrus clouds (τc < 0.3) are not
sufficiently detectable by instruments such as MODIS (Moderate Resolution
Imaging Spectroradiometer) (Lee et al, 2009) on board NASA’s Aqua and
Terra satellites. While remote sensing studies of cirrus cloud are feasible,
this project aims to provide an additional method of collecting cirrus cloud
data, and possibly make up for the limitations in using satellite images.
The average cirrus cloud coverage over the Earth is 20%-25%, but about 70%
in tropical regions (McGraw-Hill Editorial Staff, 2005). A statistical study
was done on cirrus clouds (Wylie et al, 1994), confirming the higher occurrence of high cirrus clouds at low latitudes. Figure 3 gives a geographical
visualisation of cirrus cloud occurrence. This provides a strong motivation
for cirrus clouds above Singapore to be investigated. If a usable algorithm is
successfully developed through this project, it may also be applied to other
tropical regions where co-located AERONET and MPLNET sites exist.
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Figure 3: Four season average geographical probability of cirrus clouds
Image Credits: Wylie et al, ”Four Years of Global Cirrus Cloud Statistics
Using HIRS,” (1994).
This project proposes to obtain the optical properties of cirrus clouds by
ground-based Sun Photometer measurements. The radiance from the Sun
may be assumed to be constant across all wavelengths. This is an advantage
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over remote sensing images, since the input radiance from the Sun at the top
of atmosphere (TOA) is known to a relatively precise value across spectral
bands. Also, ground-based photometer measurement data are in abundance
and freely available, especially from NASA’s AERONET, which takes multiple daily measurements at various stations around the world. However,
the AERONET system was designed for the investigation of aerosols in the
atmosphere, and there may be limitations in using it to obtain cloud properties.
Using AERONET for Clouds
In using AERONET to obtain cloud properties, we encounter a large obstacle
in available data for use. Since the it was designed for the investigation of
aerosols, it is required for the available data to be free from cloud contamination. Thus, cloud-contaminated data are eliminated in AERONET after
they are found. Inversions to obtain optical properties are then applied to
datasets which are free from cloud contamination. In this subsection, we will
briefly state how cloud filtering is done in AERONET, and consider ways we
might still obtain usable data from the database.
The AERONET data are classified by ”Levels” according to the quality of
data based on the the removal of cloud-contaminated measurements. Raw
AERONET Data (Level 1.0) are not considered useful to AERONET, since
no cloud screening has been done at all. The inversions are thus not applied to Level 1.0 data. While it may be possible to differentiate clouds and
aerosols using Level 1.0 data based on direct sun parameters, we will not be
able to obtain climate-related optical properties since the inversions are not
available, nullifying the intention of the project. A cloud screening algorithm
is thus applied to AERONET data (Smirnov et al, 2000), resulting in Level
1.5 data. Finally, Level 2.0 data is obtained after manual cloud screening.
Inversions are available for both Level 1.5 and Level 2.0 data.
It is extremely unlikely that we will detect cirrus clouds in Level 2.0 AERONET
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data, since the manual inspection would have removed almost all possible
data containing cloud contamination. Even if there are some remaining, it
will be difficult to find using just an algorithm, since it evaded both the
screening algorithm and manual inspection. The number of data sets will
also be small, and we will not be able to conduct any statistical distributions
for analysis. Thus, the only option is to use the Level 1.5 data. Specifically,
we will be looking for cloud data which has passed undetected by the cloudscreening algorithm. Since inversions are available for Level 1.5 data, which
makes this choice ideal for the project. However, the AERONET photometer
is not able to locate the vertical position of particles in the atmosphere as
they can only see the total column of particles. To solve this problem, we
use the capabilities of a collocated MPLNET Lidar. Such an instrument is
capable of profiling the vertical location of aerosol and cloud particles.
Project Objectives
The objectives of this project are therefore as follows:
1. To explore the feasibility of using photometric (AERONET Level 1.5)
and Lidar (MPLNET) data in obtaining climate-related physical and optical
properties of cirrus clouds.
2. To develop a cirrus cloud detection algorithm which imposes appropriate criteria on AERONET and MPLNET data, in order to obtain Level 1.5
datasets containing cirrus clouds and low aerosol contamination.
3. To determine the net heating and cooling effect on the Earth of selected
cirrus cloud measurements in the presence of a standard aerosol atmosphere,
using a suitable radiative transfer code.
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Theory and Instrumentation
The Sun Photometer and AERONET
A Sun Photometer (SP) is a type of photometer which takes radiance measurements by direct Sun radiation. The instrument used in AERONET is
a CIMEL Electronique CE-318 automatic Sun-tracking photometer. It is
mounted about 30 m in elevation and located in Singapore (1.29◦ N, 103.78◦ E).
This particular instrument is located in National University of Singapore, and
has been taking measurements since November 2006.
Figure 4: CIMEL Electronique CE-318 Sun-Photometer
Image Credits: Cimel Sunphotometer Handbook, ARM Climate Research
Facility.
Since the SP is a ground-based instrument, it does not measure exactly the
TOA direct solar radiance, but an attenuated value due to the absorption
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and scattering by aerosol and cloud particles in the atmosphere. This makes
it useful for investigating the components of the atmosphere. A SP may
obtain two key optical quantities of aerosols through direct measurements of
the Sun- the aerosol optical depth, and the Ångström exponent. The direct
solar measurements are taken at the spectral bands of 340, 380, 440, 500,
670, 870, 940, and 1020 nm. A simple schematic of a SP taking direct sun
measurements is shown in Figure 5. We have excluded the molecular optical
depth in the schematic.
Figure 5: Simple schematic of a Sun-photometer
For attenuation of a direct electromagnetic beam from the Sun through the
atmosphere, the Beer-Lambert-Bourger (BLB) Law (Salinas et al, 2009):
F (λ) = F0 (λ)e−Ke (λ)m(θ)
(1)
where F (λ) represents the solar irradiance detected at the SP for a specific
wavelength λ; F0 is the TOA irradiance from the Sun; Ke is the total extinction coefficient, which includes the scattering and absorption; and m is the
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optical air mass, as a function of the solar zenith angle θ.
Since the TOA irradiance and optical air mass are known quantities, we
may solve for the total extinction using the SP measurement of the direct
solar irradiance. Taking the natural logarithm of the BLB Law, and plotting
detected irradiance on air mass, we will obtain the total atmospheric optical
depth. The total extinction atmospheric optical depth may be seen as a
composition of optical depths due to various elements in the atmosphere
(Salinas et al, 2009):
gas
mol
total
(λ) + τabs
(λ)
(λ) = τ tot,aer (λ) + τscat
τext
(2)
mol
is the
where τ tot,aer is the optical depth due to atmospheric aerosols, τscat
gas
optical depth due to molecular scattering, and τabs is the optical depth due
gas
mol
to molecular absorption. Known values of τscat
and τabs
for a standard atmosphere may easily be found, leaving us with the aerosol and cloud optical
depths in the equation. Figure 6 is an example plot of the AOD recorded on
29 November 2012 over various spectral bands and times.
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Figure 6: Example plot of AOD from Level 1.5 AERONET Data, Singapore
The aerosol optical thickness may also be seen as a composite of absorption
and scattering terms:
aer
aer
τ tot,aer (λ) = τabs
(λ) + τscat
(λ)
(3)
where the subscripts ’abs’ and ’scat’ represent absorption and scattering respectively.
A SP typically takes direct Sun measurements at multiple spectral bands,
each with a center wavelength. Using a known value of the aerosol optical
depth from measurements, we are able to determine the aerosol optical depth
at any other wavelength using the Ångström formula (Salinas et al, 2009):
τ aer (λ) = τ0aer λ−α
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(4)
where τ0aer is the known aerosol optical depth at 1 nm wavelength, and α is
the Ångström exponent.
Since a SP typically obtains aerosol optical depths for at least two spectral
bands, we are able to determine the Ångström exponent (Salinas et al, 2009):
ln[τ aer (λ2 ) − τ aer (λ1 )]
α=
ln(λ1 ) − ln(λ2 )
(5)
And example plot of the Ångström exponent obtained from AERONET measurements over a single day is given in Figure 7
Figure 7: Example plot of Ångström exponent from Level 1.5 AERONET
data, Singapore.
The Ångström is more than a scaling exponent, but also gives a measure of
particle size. A higher value for the exponent usually indicates fine particles,
which are common in aerosols. A smaller value like those seen near the 00
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Hour in Figure 7 indicates coarse particles which are common in clouds. This
property of the exponent will be useful in this project, as will be seen in the
later part of this report.
AERONET Inversions
In addition to the direct solar measurements, the CIMEL Sun Photometer
also measures the downwelling sky radiance at the spectral bands of 400, 670,
870, and 1020 nm. These wavelengths are selected to avoid strong gaseous
absorption (Dubovik & King, 2000) from a typical atmospheric layer. The
measurement of the diffuse downward radiation allows AERONET to provide
inversion products. These are quantities such as the volume-size distribution, scattering phase functions, asymmetry parameter, and single-scattering
albedo. The AERONET inversions are based on (Dubovik & King, 2000) and
(Nakajima et al, 1983).
Briefly, two types of modelling are used in the AERONET inversions- Radiative Transfer Modelling and Microphysics Modelling of Aerosol Optical
Properties.
Radiative Transfer Modelling
Radiative transfer modelling is used in AERONET to completely characterise
an atmospheric layer, by modelling the total optical depth, total single scattering albedo (SSA), and total phase function. Firstly, the diffuse downward
radiance (Dubovik & King, 2000):
If θ 6= θ0 ,
I(Θ, λ) =F0 (λ)m(θ0 )
[exp(−m(θ0 )τext (λ)) − exp(−m(θ)τext (λ))]
m(θ0 ) − m(θ)
· (ω0 (λ)P (Θ, λ) + G(...)),
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(6)
and if θ = θ0 ,
I(Θ, λ) =F0 (λ)m(θ0 ) exp(−m(θ0 )τext (λ))
· (ω0 (λ)P (Θ, λ) + G(...))
(7)
where I(Θ, λ) is the spectral sky radiance at wavelength λ and scattering
angle Θ, F0 is the exoatmospheric flux, θ0 is the solar zenith angle, θ is the
observation angle of the instrument, m is the air mass, τext is the extinction
optical depth, ω0 is the single scattering albedo, and P is the scattering phase
function. G(...) represents the effects of multiple scattering.
The three following optical properties may then be modelled by (Dubovik &
King, 2000):
gas
total
aer
aer
mol
τext
(λ) = τscat
(λ) + τabs
(λ) + τscat
(λ) + τabs
(λ)
P total (Θ, λ)
total
aer
mol
τscat
(λ)
τscat
(λ) + τscat
(λ)
total
= total
ω0 (λ) =
total
τext (λ)
τext (λ)
mol
aer
τ (λ) mol
τscat (λ) aer
P (Θ, λ) + scat
P (Θ, λ)
= total
total
τscat (λ)
τscat
(8)
(9)
(10)
Here, (8) is a result of (2) and (3). The molecular scattering term may be
determined from the surface pressure, and the gaseous absorption is calculated using the Global Atmospheric ModEl (GAME) (Dubuisson et al, 1996).
Examples of AERONET inversions of the single scattering albedo and scattering phase function from measurements in a single day, are shown in the
figures below:
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Figure 8: Example plot of single scattering albedo from AERONET inversions, Singapore
The SSA values in this example are fairly high, and indicate that a large
amount of radiation are scattered instead of absorbed. A low SSA in contrast,
indicates high absorption over scattering.
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Figure 9: Example plot of scattering phase functions from AERONET inversions, Singapore
In this example, the phase function increases to a peak value near the forward scattering direction. This is a case of strong forward scattering, where
most of the radiation is scattered in the forward direction (<90◦ ).
Now the diffuse radiance may be modelled in terms of the Aerosol Optical Depth, the Single-Scattering Albedo, and the Scattering Phase Function
(Dubovik & King, 2000):
aer
I(Θ, λ) = I(τext
(λ), ω0aer (λ), P aer (Θ, λ))
(11)
The three optical properties vary across spectral bands and observation angles, and the set of equations (6), (7), (8), (9), and (10) are solved for each
Θ and λ. We also note that the equations are not integrated over individual layers in the atmosphere. Instead, the modelling assumes a vertically
homogeneous atmosphere, and treats the entire atmospheric column as one
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single, total layer. This is because AERONET is only interested in radiance
received at the ground, which is not strongly dependent on the variation in
the vertical dimension. However, this is not completely ignored, and the
vertical variability of the atmosphere is treated by other techniques in the
algorithm.
Microphysics Modelling of Aerosol Optical Properties
Microphysics Modelling in the inversion procedure is required to determine
the complex refractive index of and particle size distribution of the atmospheric layer. This can be done independent of the radiative transfer modelling described in the previous sub-subsection. First, the aerosol optical
depths for extinction, scattering, and absorption are modelled in terms of
microphysics parameters (Dubovik & King, 2000):
Z
rmax
Kscat (Θ, λ, m̃, r)n(r)dr
τscat (λ)P (Θ, λ) =
(12)
rmin
rmax
Z
τ (λ) =
Kτ (λ, m̃, r)n(r)dr
(13)
rmin
where r is the particle radius, n(r) is the particle number distribution in the
vertical column, Kscat is the scattering cross section, Kτ is the extinction
cross section, and n(r) is the particle size distribution given by (Dubovik &
King, 2000):
n(r) =
dN (r)
dr
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(14)
Figure 10: Example plot of the particle-size distribution for two measurements on the same day.
Figure 10 gives an example of the particle-size distribution for two measurements on the same day. It is plotted on a logarithmic scale. In one of the
measurements (red line), the fine mode (smaller particle size) is lower compared with the coarse mode (larger particle size), which generally indicates
a relatively lower aerosol content and a higher cloud particle content.
The complex refractive index (Dubovik & King, 2000):
m̃(λ) = n(λ) − ik(λ)
(15)
The atmospheric diffuse radiance can then be modelled as a function of the
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size distribution and complex refractive index (Dubovik & King, 2000):
I(Θ, λ) = I(dN (r)/dr, m̃(λ))
(16)
The Micro Pulse Lidar and MPLNET
The Micro Pulse Lidar (MPL) is a compact, eye-safe lidar system which
operates by transmitting a short pulse of laser energy (527nm) in the vertical
direction from the ground.
Figure 11: Micro Pulse Lidar
Image Credits: ARM Climate Research Facility,
https://www.arm.gov/instruments/mpl
The time taken for the pulse to return is measured together with the signal
strength. An immediately determinable property is thus the height of cloud
or aerosol particles in the atmosphere (Eberhard, 1986):
z=
22
ct
2
(17)
where c is the speed of light, and t is the time lapse between transmission
and detection.
The MPL takes measurements continuously, except for a period of time when
the Sun is directly overhead, in order to protect the instrument. A simple
schematic in Figure 12 illustrates how the MPL works. Backscattered signals are measured from multiple altitudes as long as particles are present to
backscatter the laser. This way, the Lidar is able to obtain the whole vertical
profile of backscatter from one measurement.
Figure 12: Micro Pulse Lidar Schematic
Further applications of Lidar measurements may be derived from the processed return signal. The Lidar equation (vertical viewing) is given by
(Campbell et al, 2008):
({n(z)D [n(z)]} − nap (z, E) − nb ) z 2
= Cβ(z)T (z)2
Oc (z)E
(18)
where n is the photoelectron counts per second at height z, D as a function of
n(r) is the ”dead time” factor for photon-coincidence, nap is the contribution
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from the afterpulse, nb is the background contribution from ambient light,
Oc is the optical overlap correction, E is transmitted laser pulse energy, C is
a calibration constant, β(r) is the backscatter coefficient, and T the atmospheric transmission.
Solving the Lidar equation allows us to determine the normalised relative
backscatter (photoelectrons km−2 µs−1 µJ) signal due to particulate matter,
which is essential for determining the cloud base height. An example of the
normalised relative backscatter (NRB) signal from MPLNET is shown below:
Figure 13: Exmaple Plot of MPLNET Normalised Relative Backscatter
The cloud base height (CBH) is generally found to be the altitude where
is the signal reaches its highest value (Eberhard, 1986). However, this is a
rough estimate and a definite or sudden peak in the signal may not always
be found in a cloud. Many new signal interpretation techniques have been
developed, and MPLNET employs an algorithm which determines the CBH.
In this project, we will make use of the CBH values to determine the height
of the cirrus clouds of interest, provided by MPLNET under Data Product
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Level 1.5b.
Summary of Relevant MPLNET and AERONET Products
The following tables summarise the physical and optical properties obtainable
through MPLNET and AERONET. There are many more than those listed,
but we will only mention those relevant to this project.
MPLNET
Level 1.0
Level 1.5
Normalised Relative
Cloud Base Height
Backscatter
Table 1: Summary of relevant MPLNET Products
AERONET
Direct Sun
Inversions
Aerosol Optical Depth
Aerosol Optical Depth
Ångström Parameter
Single-Scattering Albedo
Scattering Phase Function
Asymmetry Parameter
Particle Size Distribution
Table 2: Summary of relevant AERONET Products
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Algorithm Characteristics and Criteria
With that, we are now ready to develop a AERONET filtering algorithm
to find datasets containing predominantly clouds with little or no aerosols.
Based on the previous section, we have chosen certain defining criteria which
an aerosol-screening criteria should possess. Our algorithm should contain
the following key characteristics and criteria:
1) Detects spatially uniform, temporally persistent, high cirrus
clouds (Spatial Criterion).
This follows from the AERONET cloud-screening algorithm, where the clouds
likely to pass undetected do not vary temporally over a time period. Considering the types of cirrus clouds, this is most likely to happen with cirrostratus
clouds, which spatially fills up the sky with a veil of optically thin cloud. It
may also be possible that wispy cirrus and cirrocumulus clouds pass unscreened if there is a large distribution of similar cloudlets across the sky.
However, this is more unlikely to happen than the previous case. There are
also few studies done on cloud occurrence and frequency in Singapore, which
makes it hard to base this algorithm on any of such cloud research. Thus, we
expect the algorithm to mostly find cirrostratus clouds. Also, we remember
that it is our objective to investigate high cirrus cloud, so the criterion of
z > 13 km will be imposed We will refer to this as the Spatial Criterion.
2) The Sun-photometer and Micro Pulse Lidar measures the same
cloud or series of clouds (Temporal Criterion).
The SP takes measurements by tracking the sun as it rises above the horizon,
until it goes beyond at the end of the day. The MPL on the other hand,
remains vertically viewing throughout the course of the day, while taking
measurements. Thus, it is important for us to consider how we can be sure
that the colocated AERONET and MPLNET data correspond to the same
cloud. This is an obvious requirement for the algorithm, and should be done
without mention. However, ensuring this can be difficult and imprecise. For
this, we borrow a method used in a previous cirrus cloud study (Chew et al,
26
2011).
Figure 14: Geometry of a collocated AERONET and MPLNET setup.
Image Credits: Chew et al, 2011, ”Tropical Cirrus Cloud Contamination in
Sun Photometer Data,” (2011).
The method used in this previous study assumed a 20 m/s horizontal wind
speed at about 10 km in altitude. This gives an allowance window of about
20-30 minutes between the SP’s observation angle and the vertical-viewing
MPL. For example, we expect that a cloud passing the SP’s line of sight will
arrive above the MPL’s line of sight within 20-30 minutes. Similarly, a cloud
detected first by the MPL will later be detected by the SP as it moves with
the wind speed. Since the SP obtains the AOD and optical properties using
the sky-scanning mode, all directions around the MPL are scanned, and the
wind direction does not have to be taken into account. Again, it may be
seen that this will work especially well for large cirrostratus clouds, and vast
cumulations of wispy cirrus or cirrocumulus clouds, but less so for individual
cloudlets. For this assumption, we find that the allowable solar zenith angle
27
(SZA) is about |45◦ |. Thus, we should impose the restriction:
(SZA) ≤ |45◦ |
(19)
This corresponds to a 6 hour range centred on the time in which the Sun is
directly overhead. This can be taken to be roughly 0500h UTC, or 1300h local
time. Thus, the AERONET measurement time (MT) should be restricted
to:
0200h U T C ≥ M T ≤ 0800h U T C
(20)
While this approximation may be a rough one, it should do well enough to
obtain the collocated measurements. It would be good to consider local radiosonde data for wind speed estimations at the higher altitudes. However, it
was not done in this project due to the high variability of such data and the
lack of assurance of its precision. We will rely on the approximation made
by Chew et al (2011) for this project. In this project, we will refer to this
criterion as the Temporal Criterion.
3) The data does not include measurements containing predominantly aerosols over clouds, or mixed aerosol-cloud compositions
(Optical Criterion).
We currently are not able to deconvolve the optical depth, SSA, or phase
functions precisely in the event that a measurement is taken in high aerosol
and high cloud conditions. This is the reason why AERONET completely
eliminates datasets suspected of being cloud-contaminated. For the same
reason, we must eliminate any datasets in which the presence of aerosols
would cause the measured optical properties to be inaccurately assumed to
be solely from a cirrus cloud. This is by far the strictest criterion we have
to impose, since there is no other way to avoid aerosol contamination. Since
aerosols and clouds differ significantly on some of the optical properties, we
can impose such criteria to differentiate between the two. For this project,
we will explore and analyse the use of a few of the optical properties for
28
screening, in order to find the best way to screen aerosol-contaminated data.
The properties to be considered are explained below:
a) Particle-size Distribution (PSD)
It is widely agreed that aerosols are commonly fine particles while cirrus cloud
particles are coarse, or larger in size. A study done on the common aerosols
in Singapore found sea-salt, dust and urban pollution and dust-like aerosol
particles (Salinas et al, 2009), which were mostly in the fine mode. Fine
mode particles can be considered to have a radius smaller than 1 µm, while
coarse mode particles have radii greater than 1 µm. Thus, to screen aerosols
using the PSD, we will have to eliminate measurements in which the fine
mode is strong compared with the coarse mode, and only accept data where
the coarse mode is dominantly stronger than the fine mode. Figure 15 is
an empirical modelling of cirrus ice crystal effective radii on temperature. It
may be seen that the bulk of the measurements lie above re > 1.0 µm, even at
extremely low temperatures. It is thus safe to screen out any measurements
where the particle size is smaller than required of a cirrus cloud.
29
Figure 15: In situ flight-based measurements of cirrus ice crystal effective
radii
Image Credits: Garett et al, ”Small, Highly Reflective Ice Crystals in
Low-latitude Crystals,” (2003).
b) Sphericity of Particles
Cirrus clouds are composed of ice crystals which are highly non-spherical,
in contrast to the generally spherical aerosol particles (Baran, 2004; Salinas
et al, 2009). Figure 16 visualises some models of cirrus ice crystals. The
non-sphericity and shape variability is obvious.
30
Figure 16: Mathematical idealisations of cirrus ice crystal geometries.
Image Credits: Baran, ”On the Scattering and Absorption Properties of
Cirrus Cloud,” (2004).
Since the particle sphericity is a product of the AERONET inversions, it is
possible to differentiate between aerosols and cirrus cloud particles using the
sphericity. Measurements with high sphericity are very likely aerosols and
the dataset may be eliminated. Measurements of cirrus clouds may then be
found if we impose a threshold on the sphericity.
c) Ångström Exponent
As introduced earlier, the Ångström exponent is a derivation from the AOD,
which is a direct sun product. Specifically, it gives a measure of the variability
of optical depth with wavelength. Low variability leads to a low Ångström
exponent value, which is associated with clouds. Conversely, the optical
31
depth of aerosols are more variable spectrally, and yield a higher Ångström
exponent value. Salinas et al, (2009) characterised various aerosol particles
in Singapore by the Ångström exponent and the AOD. The findings are summarised in Table 3 below:
Aerosol Type
Dust
Maritime
Urban
Aerosols in Singapore
Ångström Exponent Aerosol Optical Depth
<1.0
>0.2
>1.0
<0.2
>1.0
0.2 - 0.4
Table 3: Aerosol properties in Singapore, (Salinas et al, 2009)
In the study, particles are classified into fine or coarse mode depending on
the whether the Ångström exponent is greater than (fine) or smaller than
(coarse) 1.0. Cirrus cloud ice crystals can be considered to be always in
the coarse mode as we have seen in the section on particle-size distribution.
Thus, we can settle on the criterion that the Ångström exponent for cirrus
clouds will be smaller than 1.0. While this may be in the same range as dust
particles, the previous criterion of the CBH being greater than 13 km will
ensure that we detect cirrus clouds.
d) Asymmetry Parameter
One final property to consider in aerosol-screening is the asymmetry parameter, which is derived from the scattering properties of the particles. Cirrus
cloud ice crystals are known to exhibit strong forward scattering and thus, a
high asymmetry parameter. Various studies have been done to measure the
asymmetry of cirrus cloud particles using both remote sensing and in situ
measurements. We consider a summary (Garrett, 2008) of in situ measurements done by different groups in Table 4:
32
g
0.76
0.74
0.75
0.77
0.74
Location
mid-latitude NH
Arctic
Florida anvil
mid-latitude NH and SH
Antarctic
Reference
Auriol et al. (2001)
Gerber et al. (2000); Garrett et al. (2001)
Garrett et al. (2003)
Gayet et al. (2004)
Baran et al. (2005)
Table 4: Experimental values of cirrus cloud asymmetry parameter measured
in situ, (Garrett, 2008).
It is clear that the asymmetry parameter of cirrus clouds are expected to
be consistently above 0.70. Meanwhile, the asymmetry parameter of aerosol
particles have been found to be consistently in the range of 0.40-0.70, but
rarely exceeding 0.70 (Kassianov et al, 2007; Andrews et al, 2006; Fiebig
& Ogren, 2006). Kassianov et al (2007) showed that in the AERONET
inversions the asymmetry was overestimated compared with other sensing
instruments in a few specific cases. However, their sample size was small and
we cannot make a sure conclusion on this. It is thus a good criterion to set
the screening value to be at 0.70.
Proposed Algorithm
The algorithm to screen aerosol-contaminated data from the AERONET
Level 1.5 data is written using the Interactive Data Language (IDL). A free
variant of IDL, called the GNU Data Language (GDL), may be used on Linux
systems. Our code is completely compatible with GDL.
Using the key characteristics and the three earlier defined criteria, spatial,
temporal, and optical, we have successfully written a cirrus cloud detection
algorithm for the objective of this project. Figure 17 is a flowchart demonstrating the steps in our algorithm:
33
Figure 17: Cirrus cloud detection algorithm flowchart
34
All the Singapore AERONET data from October 2009 to February 2013
are first downloaded using the AERONET Download Tool1 . MPLNET currently lacks a similar download tool, so each data file for every day in which
an AERONET dataset exists has to be downloaded manually. The Synergy
Tool2 may be used to speed up this process, as available AERONET and
MPLNET data may be simultaneously viewed.
The IDL program begins with the temporal criterion by searching for AERONET
datasets taken within the time period of 0200h-0800h UTC. A loop is then
begun for each of the datasets which passes the temporal criterion. This loop
defines a time range of 30 minutes before and after the AERONET dataset
measurement time. The program then searches for the correct MPLNET file,
and executes the spatial criterion by searching for the CBH within the defined
time range, and above 13 000 m in altitude. If no CBH points are found in the
range, the AERONET dataset is eliminated. If the CBH point is found, then
the program returns to the AERONET data and extracts the Ångström exponent, sphericity, and asymmetry Parameter. The optical criterion is then
imposed- only datasets in which the Ångström exponent is below 1 (α < 1)
passes. At this point, we are confident that the AERONET dataset is one
which contains cirrus clouds and negligible aerosol-contamination. Further
optical criteria may be imposed using the other properties if one wishes to
be more strict in the aerosol-screening process. After the loop for each of
the AERONET datasets is completed, the program counts the number of
usable datasets, and writes the relevant optical properties and fluxes from
the AERONET file into a comma-separated value (CSV) file.
1
2
http://aeronet.gsfc.nasa.gov/cgi-bin/webtool opera v2 inv
http://aeronet.gsfc.nasa.gov/cgi-bin/bamgomas interactive
35
Results and Preliminary Observations
Since we have a variable algorithm such that further criteria may be imposed
at the end, we will first present the results without any additional criteria.
This will give us an initial idea of the feasibility of using MPLNET and
AERONET in obtaining cirrus cloud properties. In the second subsection,
we will then experiment with a combination of additional criteria.
Without Additional Optical Criteria
At each critical step of the algorithm, we included a counter to see how the
number of useful datasets decreases after each criterion. For this first case,
we did not impose any additional optical criteria after screening by Ångström
exponent. Table 5 summarises the results for this case:
Algorithm Step
Total downloaded from AERONET
Impose 0200h-0800h UTC time range
(Temporal Criterion)
Find MPL data point above 13 km
(Spatial Criterion)
Impose Ångström exponent <1.0
(Optical Criterion)
No. of datasets
available
723
Step Reduction
Percentage (%)
-
65
91.0
18
72.3
6
66.7
Table 5: Reduction of useful datasets after each algorithm step.
We can immediately see that the result of the screening is an extremely small
number of useful cirrus cloud datasets. This number is too small to conduct
any statistical distributions. The largest decrease in the number of datasets
is from imposing the Temporal and Optical Criteria, while little reduction is
observed in the Spatial Criterion.
The reduction from the Temporal Criterion is expected, since clouds tend
to form in the day as the Earth is heated by Solar irradiation. Due to the
AERONET cloud-screening algorithm, many datasets in the near noon time
36
will be eliminated due to various types of clouds, including cirrus. As an
exploration of potential research, we now relax this criterion to include an
hour before and after the original time range. We see the results in Table 6:
Algorithm Step
Total downloaded from AERONET
Impose 0100h-0900h UTC time range
(Temporal Criterion)
Find MPL data point above 13 km
(Spatial Criterion)
Impose Ångström exponent <1.0
(Optical Criterion)
No. of datasets
available
723
Step Reduction
Percentage (%)
-
293
59.4
92
68.6
37
59.7
Table 6: Reduction of useful datasets after altering the Temporal Criterion.
The reduction due to the Temporal Criterion is now much less compared
to the previous case. This shows that about 30% of the data lies between
0100h-0200h UTC and 0800h-0900h UTC. Even with this improvement, the
number of usable datasets at the end of the algorithm is still small, and we
will not be able to obtain a good normal distribution.
Using the datasets from this result, we plot the dependence of the Ångström
exponent on Measurement Time in Figure 18:
37
Figure 18: Plot of Ångström exponent on Measurement time (0100h-0900h
UTC)
Firstly, this plot allows us to indirectly deduce that many AERONET datasets
from our original time range of 0200h-0800h UTC have been screened out
by the AERONET cloud-screening algorithm. Widening the time range to
0100h-0900h UTC shows that the two additional hours contain many datasets
that would have otherwise passed. Secondly, it may also be seen that the
bulk of measurements with (α < 1.0) lie before 0200h UTC (10 am local
time). This is an interesting observation which we will discuss in a later
section.
Imposing Additional Optical Criteria
For the objective of this project, the criterion, Ångström exponent < 1.0,
should always be imposed because the dataset may contain large amounts
of both aerosol and cloud particles. For this subsection, we will explore
screening the data by sphericity and asymmetry for both the time ranges
of 0200h-0800h UTC, and 0100h-0900h UTC. This is because the number of
remaining datasets after the full algorithm is applied is too small, and we will
not be able to see the effect of the screening by the other optical properties.
38
First, applying additional optical criteria on result using 0200h-0800h UTC
(from 6 usable datasets):
Optical Criterion
No. of datasets
available
Step Reduction
Percentage (%)
5
16.6
1
83.3
Asymmetry Parameter
(g>0.65)
Sphericity
(sph<70%)
Table 7: Applying additional optical criteria (0200h-0800h UTC).
Next, we apply the same criteria to the result using 0100h-0900h UTC (from
37 usable datasets):
Optical Criterion
No. of datasets
available
Step Reduction
Percentage (%)
35
5.4
11
70.2
Asymmetry Parameter
(g>0.65)
Sphericity
(sph<70%)
Table 8: Applying additional optical criteria (0100h-0900h UTC).
Most of the datasets already filtered by the full algorithm seem to exhibit a
high asymmetry parameter, indicative of cirrus clouds. However, filtering by
sphericity seems to eliminate a considerable percentage of the data.
Optical Properties of the Screened Datasets
We present the key optical properties of the six datasets obtained from the
aerosol screening process in Table 9. We only display the results for 440 nm,
which we will use in calculating the radiative forcing. The results for the
other wavelengths are in the Appendix.
39
Set
1
2
3
4
5
6
Date
(DD/MM/YYYY)
02/02/2010
27/05/2010
21/09/2010
05/01/2011
05/02/2011
01/04/2012
Time
(UTC)
0420
0204
0200
0213
0222
0210
COD
SSA
Asymmetry
0.41429
0.38067
0.52102
0.15238
0.33507
0.14463
0.7917
0.8708
0.8759
0.9074
0.9490
0.8018
0.73463
0.67012
0.69713
0.70188
0.71303
0.64678
Table 9: Optical properties of the six datasets which passed the algorithm.
40
Analysis and Discussion
It is apparent from the results that we are unable to obtain enough AERONET
data after screening from aerosols. From Table 5, only 6 useful datasets were
obtained. We are thus unable to conduct any statistical distribution of cirrus
cloud optical properties. It will also not be feasible to base climate research
on cirrus clouds on AERONET Level 1.5 data due to the small amount of
data. The main reason behind this is the use of Level 1.5 data, which have
already been cloud-screened before the inversions were applied. This makes
our process of obtaining cirrus cloud properties very data-inefficient, since
the data is screened twice- once for clouds, and once for aerosols. It is inevitable that some cirrus cloud data are also lost in the aerosol-screening
process depending on how strictly the criteria is enforced.
The data we have obtained from applying our algorithm, though limited, can
be used to obtain a first hand approximation of tropical cirrus cloud radiative
forcing. A brief analysis of the values in Table 9 shows that the asymmetry
parameter reflects well the presence of cirrus cloud particles in the atmosphere, since they lie in the range of values previously discussed. However,
the SSA values are lower than expected for cirrus clouds, since we would expect almost total scattering from the ice crystals. This indicates that there
is still a slight presence of aerosols in the six datasets, which is unavoidable.
A plot of the asymmetry parameter on SSA in Figure 19 suggests a proportional dependence between the two parameters, with the exception of Set 1.
If we consider the SSA and asymmetry values to be a convolution of cirrus
cloud and slight aerosol-contamination, we can deduce that the presence of
aerosols lowers both the SSA and asymmetry. Higher aerosol-contamination
results in higher absorption, and less forward scattering.
41
Figure 19: Plot of asymmetry parameter on SSA from the screened results.
A possible reason for the anomalous point (Set 1) may be the presence of
other types of cloud in the measurement. We note that of the 6 datasets,
Set 1 is taken at 0420h UTC, while the other 5 sets are taken close to 0200h
UTC. This corresponds in local time to 1020 am and 0800 am respectively.
It is likely that at a later measurement time like 0420h UTC, the heating
from the Sun would have caused the formation of low clouds like cumulus,
which are abundant in Singapore, affecting the cirrus cloud measurement.
However, a larger amount of data should be considered in order to make
such a conclusion.
We are fairly confident that Sets 1-5 contain cirrus cloud data, and do not
contain significant aerosol-contamination. Thus, we may use the optical
properties from these measurements to determine the radiative forcing in
different types of standard aerosol conditions. We will also continue to use
Set 1 in the radiative forcing calculations, keeping in mind its anomalous
nature.
42
Evaluation of the Temporal Criterion
One observation from the results is the large number of measurements between 0100h-0200h UTC and 0800h-0900h UTC, compared the adjacent
hours 0200h-0300h UTC and 0700h-0800h UTC. Since we know that the
AERONET SP makes measurements consistently throughout the day, the
difference in the number of measurements in the Level 1.5 data is likely due
to the cloud-screening process in AERONET. This is consistent with what
we know about cloud formation, as they tend to form later in the day due
to the effect of Solar heating of the Earth. As hypothesised earlier, the formation of optically thick and temporally variable cumulus clouds, which are
abundant in Singapore, might have caused many AERONET measurements
to be eliminated. Optically thin cirrus clouds however, would be able to pass
under AERONET’s cloud screening. Such clouds may be the main composition of the measurement sets from 0100h-0200h UTC. It is unfortunate that
our Temporal Criterion is such that a large number of measurements in the
early morning are missed. This also means that there may potentially be
many more cirrus-containing datasets which have already been eliminated
from cloud-screening.
Another observation we made was that there seems to be a concentration of
datasets containing low Ångström exponent values, within the time period
of 0100h-0200h UTC, corresponding to 9-10 am local time. These values are
indicative of optically thin clouds, which could have remained undetected by
the cloud-screening process due to their low optical depth and spatial uniformity. We do not see the same effect in the time period of 0800h-0900h UTC,
even though there are more available measurements as mentioned in the previous paragraph. From Table 3, the only common aerosol with low Ångström
exponent is dust, which is unlikely to exhibit such temporal behavior. Thus,
it is probable that those datasets contain cirrus cloud particles, though more
research should be done to confirm this.
From these observations, we can see that the main limiting factor in our
43
aerosol-screening algorithm is the Temporal Criterion that we adopted from
Chew et al, (2011), which is illustrated in Figure 14. In this project, we did
not ourselves deduce the geometry behind this assumption, though now it
may seem like a good idea to do so. The geometry is based on the knowledge
of the wind speed at high altitudes, as well as the assumption that the wind
speed increases linearly with altitude. On hindsight, the wind speed of 20-30
m/s at such high altitudes seem like an underestimate. A brief check on the
radiosonde data in Singapore3 by NOAA/ESRL shows wind speed values at
13 km varying from 20 m/s to above 200 m/s. The high variability of the
wind speed at such high altitudes casts some uncertainty on our original wind
speed assumption. Since there is a large database of radiosonde data, it would
be good to determine the accuracy of such radiosonde data, and consider
its usage in forming the geometries of collocated SP and MPL projects in
general. This however, would be a study to be done on its own.
Screening by Particle-Size Distribution
We originally intended to screen out aerosol-contaminated data using the
particle-size distribution instead of the Ångström exponent. It was a more
direct way screening since the particle radius was explicitly known. However, one of the problems we encountered was the lack of any quantifiable
standard to decide if the coarse mode was ”dominant” enough over the fine
mode. In Figure 20, we see a strong coarse mode and a relatively weaker
fine mode. However, we are unable to decide if this should be considered
aerosol-contaminated, and be eliminated, because we do not know how much
the fine mode would affect the optical properties like SSA and asymmetry.
There is also no deconvolution available for these parameters into fine and
coarse mode.
3
http://esrl.noaa.gov/raobs/
44
Figure 20: PSD of one AERONET measurement
Another problem we encountered is that not every measurement of particlesize distribution exhibits a clear distinction between the fine and coarse mode
particles at r = 1 µm, and there may be peaks belonging to neither mode,
like in Figure 21. This makes it difficult to classify the particles in that
measurement, as we do not find clear criteria in past literature. Thus, we
decided to use the Ångström exponent over the particle-size distribution,
since it is commonly used to classify particle size.
45
Figure 21: PSD with a peak not clearly classified as fine or coarse
Screening by Asymmetry Parameter and Sphericity
In the previous section, we used (g > 0.65) as the screening criterion for
the asymmetry Parameter. Asymmetry values of 0.60-0.70 have previously
been found (Spinhirne et al, 1996) for cirrus clouds. Thus, a small allowance
was given from the value of 0.7 as previously chosen, so as to allow cirrus cloud particles on the lower end of the spectrum to also pass though
the algorithm. We do not expect this to contribute significantly to aerosolcontamination, since the bulk of aerosol asymmetry values are found to be
lower than 0.6. From Tables 7 and 8, we see that most measurements filtered
by the Ångström exponent already posses a high asymmetry value. This
gives us a confirmation that the screening done by the Ångström exponent
is effective in finding cirrus clouds in the data. Since there is currently a
significant amount of research agreeing on the asymmetry values of cirrus
46
clouds, we can be confident that our algorithm without any additional optical criteria works well in screening aerosols.
The criterion for sphericity, however, is more arbitrary. While it is generally
agreed on that cirrus cloud particles are highly non-spherical, the sphericity
parameter derived in AERONET seems to be a fairly new quantity in this
area of research. We were unable to find any agreeing literature on sphericity
values of cirrus ice crystals, so we imposed a value of 70% to filter out aerosols
which are mostly spherical. Figure 16 earlier showed that the shape of cirrus
particles can be highly variable. The same imaging study (Baran, 2004)
showed that the shape of the crystals could vary with altitude, and are highly
random. Figure 22 is an extract of some of his results:
47
Figure 22: 2D and Cloud Particle Imager (CPI) images of cirrus ice crystals
Image Credits: Baran, ”On the Scattering and Absorption Properties of
Cirrus Cloud,” (2004).
The large reduction in the number of usable datasets after screening by
sphericity may be due to a large group of cirrus particles having high sphericity. Since we are unable to define quantifiable criterion for this parameter,
and due to the high variability of cirrus particle shapes, we decided not to
use it in the detection of cirrus particles in AERONET data.
48
Radiative Forcing
Brief Theory and Results
We now use the 6 datasets from the results as input in a radiative transfer code. The Discrete Ordinates Radiative Transfer Program for a MultiLayered Plane-Parallel Medium (DISORT), is a radiative transfer algorithm
developed for vertically inhomogeneous layered media (Stamnes et al, 1988;
Lin et al, 2015), using Fortran95. It will allow us to simulate a standard
atmosphere and aerosol situation together with the cloud data we have obtained from AERONET. The program treats each layer as spatially separate
from one another, and computes the radiative transfer after radiation has
passed through each layer. Since we are only interested in the the net flux at
the bottom of all atmospheric layers, this radiative transfer code will satisfy
what we require.
The radiative transfer equation for a diffuse monochromatic beam in a planeparallel atmospheric layer is (Lin et al, 2015):
±µ
dI(τ, ±µ, φ)
= I(τ, ±µ, φ) − S(τ, ±µ, φ),
dτ
(21)
where I(τ, ±µ, φ) is the radiance, τ is the optical depth of the layer, µ =
|cos θ|, with θ as the polar angle, and φ is the azimuth angle. The source
term, S(τ, ±µ, φ) is given by (Lin et al, 2015):
ω̄F0
p(±µ, φ; −µ0 , φ0 )e−τ /µ0 + (1 − ω̄)B(T )
4πZ Z
2π
1
ω̄
+
p(±µ, φ; µ0 , φ0 )I(τ, µ0 , φ0 )dµ0 dφ0 ,
4π 0
−1
S(τ, ±µ, φ) =
(22)
where ω̄ is the single-scattering albedo, p(±µ, φ; µ0 , φ0 ) is the scattering phase
function, and B(T ) is the Planck function. These are dependent on the optical depth τ but omitted in the equation for brevity. The first term describes
the single-scattering incident collimated beam pseudo-source resulting from
the diffuse-direct splitting, with (−µ0 , φ0 ) being the direction of the incident
49
beam and µ0 F0 the normal irradiance. The second term represents thermal
emission, and the third term represents multiple scattering. Most radiative
transfer codes ignore either the pseudo-source term or the thermal emission
term, but not both. In our simulations using DISORT, we will neglect the
thermal emission term.
With the optical depth, SSA, and asymmetry parameter known for each layer,
we can choose a phase function and allow DISORT to solve the radiative
transfer equation for the net radiative forcing at the ground. We choose to
use the spectral optical properties at 440 nm, since it is the closest to 500
nm, where such calculations are commonly made. We cannot do it at 500
nm since we do not have the AERONET inversions at that wavelength. In
DISORT, we use the Test Problem 15: Multi-Layers and BRDFs to simulate
a single Rayleigh layer and one aerosol layer. An additional layer is added
as the cirrus cloud layer. The code we used is available for reference in the
Appendix. The input parameters used are:
Input Parameter
(440 nm)
Parameter Value
AOD
0.2422 (lower)
0.4845 (upper)
1.0330 (haze)
Spectral Irradiance
(W m−2 nm−1 )
Rayleigh Optical Depth
COT
SSA
Asymmetry
1.8507
0.2426
From AERONET
From AERONET
From AERONET
Table 10: Input Parameters used in the DISORT program
The three AOD values represent standard AOD values in Singapore. The
first two values are the lower and upper limits to the AOD of urban aerosols
(Salinas et al, 2009). The third value simulates the event of a haze. All the
optical depth values at 440 nm are obtained using the Equations 4 and 5, with
the known values at other wavelengths. The spectral irradiance is obtained
50
from LISIRD4 at a single chosen day. Since we are only interested in the
effects in a simulation, we do not need to use the specific daily irradiances.
DISORT requires the choice of scattering phase function for each layer. We
choose the following:
Layer
Cloud
Aerosol
Molecular
Phase Function
Henyey-Greenstein
Aerosol as specified by
Kokhanovsky et al, 2010
Rayleigh
Table 11: Phase functions used for each layer in DISORT
The radiative forcing for the three types of aerosol layers, using the six sets
of AERONET data at 440nm is generated using the DISORT program. A
control set is also displayed in the results below, in which the cloud optical
depth is zero. The results below are presented by aerosol layer type (low
aerosol, high aerosol, haze), and are the values given at the TOA. The downward direct flux, downward diffuse flux, and upward diffuse flux are the direct
results from DISORT. the net downward flux is obtained from:
dif f use
net
direct
dif f use
Fdown
= Fdown
+ Fdown
− Fup
(23)
A comparison is made with the control set ’C’, and the percentage difference
between the dataset and the control set is determined from:
P ercentage Dif f erence =
net
net
Fdown
− F0,down
× 100%
net
F0,down
(24)
net
where F0,down
is the net downward flux for the control set with no clouds. A
positive value indicates more heating compared to the case with no clouds,
while a negative value indicates more cooling. All flux values are given in
W m−2 nm−1 .
4
http://lasp.colorado.edu/lisird/sorce/sorce ssi/
51
Set
C
1
2
3
4
5
6
Downward
Direct
1.8507
1.8507
1.8507
1.8507
1.8507
1.8507
1.8507
Downward
Diffuse
-8.82E-006
3.22E-006
4.29E-006
1.05E-004
-1.19E-005
1.39E-005
6.32E-006
Upward
Diffuse
3.61E-001
3.02E-001
3.47E-001
3.40E-001
3.59E-001
3.74E-001
3.44E-001
Net
Downward
1.489
1.548
1.503
1.510
1.491
1.476
1.506
Percentage
Diff (%)
3.99
0.98
1.42
0.14
-0.84
1.15
Table 12: Radiative Forcing in a low urban aerosol atmosphere.
Set
C
1
2
3
4
5
6
Downward
Direct
1.8507
1.8507
1.8507
1.8507
1.8507
1.8507
1.8507
Downward
Diffuse
4.45E-005
1.28E-005
1.44E-005
9.30E-005
2.38E-005
2.48E-005
1.11E-005
Upward
Diffuse
3.83E-001
3.19E-001
3.66E-001
3.59E-001
3.81E-001
3.96E-001
3.65E-001
Net
Downward
1.467
1.531
1.484
1.491
1.470
1.454
1.486
Percentage
Diff (%)
4.32
1.12
1.61
0.15
-0.89
1.25
Table 13: Radiative forcing in a high urban aerosol atmosphere.
Set
C
1
2
3
4
5
6
Downward
Direct
1.8507
1.8507
1.8507
1.8507
1.8507
1.8507
1.8507
Downward
Diffuse
1.12E-004
-3.58E-006
5.60E-006
1.49E-005
-2.62E-006
2.41E-005
1.34E-005
Upward
Diffuse
4.32E-001
3.59E-001
4.10E-001
4.01E-001
4.29E-001
4.45E-001
4.11E-001
Net
Downward
1.418
1.491
1.440
1.449
1.421
1.406
1.440
Table 14: Radiative forcing in a hazy atmosphere
52
Percentage
Diff (%)
5.18
1.54
2.19
0.22
-0.87
1.52
Discussion on Radiative Forcing
In 5 out of 6 of the cases, the layer of cirrus cloud results in a net heating
effect above the control set, while only in set 5 do we see a cooling effect. We
note that Set 5 contains the highest SSA value, indicative of cirrus clouds
with low aerosol contamination. Also, we remember that Set 1 is anomalous
and we see that it has the highest net heating above the control set. For the
other cases, it is possible that the slight aerosol contamination negated the
cooling effect of the clouds and caused an net heating effect. The percentage
differences are small, and a slight change in the column’s composition can
cause a change between heating or cooling. If this is the case, it also means
that the presence of optically thin cirrus clouds may cause cooling, but only
to a small degree.
Figure 23: Percentage difference of flux on dataset
In the cases of net heating, it was also found that the contribution of the
cloud layer to heating increased as the AOD of the aerosol layer increased,
which may be seen in Figure 23. This is possibly due to the trapping of
53
heat in the atmosphere by the cloud layer, which resulted in an increased
downward flux into the aerosol layer and thus increased total absorption in
the atmosphere.
Summary
In this project, we have developed an aerosol screening algorithm to be applied to Level 1.5 AERONET data. This screening algorithm used physical
and optical properties from MPLNET and AERONET, which use the Lidar
and Sun-photometer respectively. After applying our algorithm, we obtained
too few datasets for a statistical study, and concluded that such a technique
is not feasible for obtaining large amounts of cirrus cloud data. After some
evaluation, we found that the main limitation of the algorithm was the time
window which we imposed on the data based on the geometry of the SP and
Lidar instruments.
From applying our algorithm, we obtained 6 datasets which had a high possibility of containing cirrus clouds. This can be clearly seen on table 9 where
values of asymmetry parameters and SSA are shown. Values of the asymmetry parameter are within the expected ranges for cirrus clouds, unfortunately corresponding values of SSA are not. This highlights the difficulty of
extracting cloud optical properties from photometric measurements specially
in tropical regions like Singapore.
Finally, the optical properties of these datasets were used in the DISORT
radiative transfer code, creating a cloud layer and simulating an aerosol and
a molecular layer. A net heating effect was found in most cases, and a slight
net cooling effect in one. It is possible however, that the net heating may be
caused by a slight aerosol contamination in the data, which is unavoidable
in the usage of ground-based instruments.
54
Future Work
We identified earlier that the geometry of the set-up we used in this project
was the main limitation to the outcome. As of today, NASA has developed
a ”Cloud Mode” for the AERONET programme, which is able to measure
the cloud optical depth using the SP. However, optical properties are not
yet available, and the cloud mode is also not implemented in the Singapore
instrument. Eventually, we hope that a suitable instrument and inversions
will be made available for cloud studies. One other way to overcome this
limitation is to use a Lidar instrument which scans the sky together with the
SP. This negates the need for a time window to be imposed, and frees up a
large amount of data that can be used.
55
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59
Appendix
AERONET Data of screened datasets
1) Date, Time, and Ångström exponent.
Set
1
2
3
4
5
6
Date
DD/MM/YYYY
02/02/2010
27/05/2010
21/09/2010
05/01/2011
05/02/2011
01/04/2012
Time
(UTC)
0420
0204
0200
0213
0222
0210
Angstrom
Exponent
0.78338
0.89712
0.41520
0.79966
0.44520
0.84854
Table 15: Date, time, and Ångström exponent.
2) AERONET Optical Properties at 440 nm.
Set
1
2
3
4
5
6
AOT(440)
0.41429
0.38067
0.52102
0.15238
0.33507
0.14463
SSA(440)
0.7917
0.8708
0.8759
0.9074
0.9490
0.8018
Asym(441)
0.73463
0.67012
0.69713
0.70188
0.71303
0.64678
Table 16: AERONET Optical Properties at 440 nm.
3) AERONET Optical Properties at 675 nm.
Set
1
2
3
4
5
6
AOT(675)
0.27462
0.24170
0.40642
0.09872
0.26221
0.08391
SSA(675)
0.7156
0.8446
0.8858
0.8853
0.9186
0.7564
Asym(674)
0.69893
0.63887
0.68453
0.68089
0.69873
0.62838
Table 17: AERONET Optical Properties at 675 nm.
4) AERONET Optical Properties at 840 nm.
60
Set
1
2
3
4
5
6
AOT(870)
0.24896
0.20885
0.39415
0.08903
0.24741
0.08534
SSA(870)
0.6977
0.8298
0.8965
0.8730
0.9019
0.7282
Asym(871)
0.68789
0.63248
0.69092
0.69455
0.70490
0.64796
Table 18: AERONET Optical Properties at 840 nm.
5) AERONET Optical Properties at 1020 nm.
Set
1
2
3
4
5
6
AOT(1020)
0.1880
0.18125
0.38002
0.07441
0.23284
0.07118
SSA(1020)
0.6982
0.8285
0.9038
0.8727
0.8994
0.7267
Asym(1020)
0.69450
0.63396
0.69629
0.70462
0.71004
0.66248
Table 19: AERONET Optical Properties at 1020 nm.
Aerosol-Screening Algorithm (IDL-Exelis)
The program we developed to screen AERONET Level 1.5 data from aerosols
(all filter.pro) is listed below. Lines beginning with ”;” are comments, while
the symbol ”$” tells IDL to continue on the next line.
pro all_filter
;
;
;
;
;
;
;
;
;
This program does the following in sequence:
1) Opens AERONET dubovik file
2) Finds the dataset within AERNONET which falls between
UTC 0200-0800H
3) Define time range within 30 mins of AERONET data
3) Opens the relevant MPLNET15b files using ncdfread.pro
4) Searches the MPL file for cloud base heights >13km within
the time range.
5) Writes relevant AERONET data into a csv file
; The following programs are required for this program:
61
; read_csv.pro (IDL)
; ncdfread.pro (In-house)
; write_csv.pro (IDL)
; The following function
; READ_CSV.pro
; has been edited for customised use in this program
;
;
;
;
;
;
;
Choose country
Singapore - sg
Jambi - jambi
Abacos Hill - abracos
Kuching - kuching
Palangkaraya - palang
Bac Lieu - baclieu
country = ’sg’
mplname = country + ’_mpl’
; Set solar noon time (HH, GMT)
sz_time = 5
; Set AERONET time range (HH, GMT)
start_lim = 2
end_lim = 8
; Set time window from data time (mins)
time_win = 30
; Defining directories
root_directory = ’/home/joelwong17/Desktop/joelwong/IDLdirectory’
data_directory = root_directory + ’/all_data’
aero_file = data_directory + ’/sg_aero_filtered.csv’
mpl_directory= data_directory + ’/’ + mplname
; Book-keeping & Define Counters
close, /ALL
Tcount = 0
Timecount = 0
62
Ccount = 0
Ycount = 0
output = []
; Load AERONET data
data = read_csv(aero_file, record_start=4)
filesize = size(data.field01)
nsets = filesize(1)
Tcount = Tcount + nsets
; Read individual AERONET data sets
for iset = 0, (nsets-1) do begin
date = STRING(data.field01(iset))
time = STRING(data.field02(iset))
timehr = strmid(time, 0, 2)
timemin = strmid(time, 3, 2)
if timehr GE start_lim then begin
if timehr LT end_lim || (timehr EQ end_lim && timemin EQ 0) $
then begin
Timecount = Timecount + 1
; Set time range for MPLNET
mtime = timehr*60 + timemin
blimit = mtime - time_win
flimit = mtime + time_win
; Create MPLNET file name
dateyr = strmid(date, 6, 4)
datemth = strmid(date, 3, 2)
dateday = strmid(date, 0, 2)
mpldate = dateyr + datemth + dateday
mplname = mpl_directory + ’/mplnet-15b_sing_40402_’ $
+ mpldate + ’_v2.cdf’
fulldate = dateyr + datemth + dateday
fulltime = timehr + timemin
; Check if the MPL data exists
mpl_test = file_test(mplname)
if mpl_test EQ 1 then begin
; Load MPLNET data and assign cloud base ht to clb
ncdfread, mplname, ’cloud_base’, clb, dim31
; Filter clb data to within AERONET time range
nclb = clb[*,blimit:flimit]
63
; Find data in nclb above 13km
fclb = where(nclb gt 13)
; Output if data above 13km is found
if fclb[0] EQ -1 then begin
endif else begin
Ccount = Ccount+1
; Read data from AERONET file
; Angstrom Parameter (440-870 gradient)
ang = STRTRIM(STRING(data.field30(iset)), 1)
; Sphericity
sph = STRTRIM(STRING(data.field32(iset)), 1)
; Single-scattering Albedo
ssa440 = STRTRIM(STRING(data.field33(iset)),1)
ssa675 = STRTRIM(STRING(data.field34(iset)), 1)
ssa870 = STRTRIM(STRING(data.field35(iset)), 1)
ssa1020 = STRTRIM(STRING(data.field36(iset)), 1)
; Vol-size Distribution
vs = FLTARR(22, 1)
; Loop for vol-size distribution
for ivs = 0, 21 do begin
fn = ivs + 7
vs(ivs) = STRTRIM(STRING(data.(fn)(iset)), 1)
; Asymmetry
asym441 = STRTRIM(STRING(data.field04(iset)),1)
asym674 = STRTRIM(STRING(data.field05(iset)),1)
asym871 = STRTRIM(STRING(data.field06(iset)),1)
asym1020 = STRTRIM(STRING(data.field07(iset)),1)
; Aerosol Optical Thickness
AOT440 = STRTRIM(STRING(data.field37(iset)),1)
AOT500 = STRTRIM(STRING(data.field38(iset)),1)
AOT675 = STRTRIM(STRING(data.field39(iset)),1)
AOT870 = STRTRIM(STRING(data.field40(iset)),1)
AOT1020 = STRTRIM(STRING(data.field41(iset)),1)
; Spectral Fluxes
DownF441 = STRTRIM(STRING(data.field42(iset)),1)
DownF674 = STRTRIM(STRING(data.field43(iset)),1)
DownF871 = STRTRIM(STRING(data.field44(iset)),1)
DownF1020 = STRTRIM(STRING(data.field45(iset)),1)
UpF441 = STRTRIM(STRING(data.field46(iset)),1)
UpF674 = STRTRIM(STRING(data.field47(iset)),1)
UpF871 = STRTRIM(STRING(data.field48(iset)),1)
64
UpF1020 = STRTRIM(STRING(data.field49(iset)),1)
DiffF441 = STRTRIM(STRING(data.field50(iset)),1)
DiffF674 = STRTRIM(STRING(data.field51(iset)),1)
DiffF871 = STRTRIM(STRING(data.field52(iset)),1)
DiffF1020 = STRTRIM(STRING(data.field53(iset)),1)
end
; Count no of valid sets
if ang lt 1 then begin
crit = ’T’
Ycount = Ycount + 1
; Write 1D Array of output values
outsin = [dateyr, datemth, dateday, fulltime, $
AOT440, AOT500, AOT675, AOT870, AOT1020, ang, $
ssa440, ssa675, ssa870, ssa1020, asym441, asym674, $
asym871, asym1020, sph, vs, DownF441, DownF674, $
DownF871, DownF1020, UpF441, UpF674, UpF871, UpF1020, $
DiffF441, DiffF674, DiffF871, DiffF1020]
output = [[output], [outsin]]
endif else begin
crit = ’F’
endelse
print_data = date + ’_’ + time + ’_’ + ang + ’_’ + crit $
+ ’_’ + asym441 + ’_’ + sph
print, print_data
endelse
endif
endif
endif
end
print, ’Total AERONET sets =’, Tcount
print, ’AERONET Sets within time range =’, Timecount
print, ’Total coincidence =’, Ccount
print, ’Valid Sets =’, Ycount
; Write output to file
outname = country + ’_output’
65
outfile = root_directory + ’/’ + outname
header = [’year’, ’month’, ’day’, ’time(GMT)’, ’AOT(440)’, $
’AOT(500)’, ’AOT(675)’, ’AOT(870)’, ’AOT(1020)’, ’ang’, $
’ssa440’, ’ssa675’, ’ssa870’, ’ssa1020’, ’asym441’, ’asym674’, $
’asym871’, ’asym1020’, ’sph’, ’0.05’, ’0.065604’, ’0.086077’, $
’0.112939’, ’0.148184’, ’0.194429’, ’0.255105’, ’0.334716’, $
’0.439173’, ’0.576227’, ’0.756052’, ’0.991996’, ’1.301571’, $
’1.707757’, ’2.240702’, ’2.939966’, ’3.857452’, ’5.06126’, $
’6.640745’, ’8.713145’, ’11.432287’, ’15’, ’DownF441’, $
’DownF674’, ’DownF871’, ’DownF1020’, ’UpF441’, ’UpF674’, $
’UpF871’, ’UpF1020’, ’DiffF441’, ’DiffF674’, ’DiffF871’, $
’DiffF1020’]
write_csv, outfile, output, $
HEADER=header
end
66