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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. 1 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. 2 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4 6 9 10 10 11 16 22 25 26 33 36 36 38 39 40 48 49 52 54 54 56 60 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. 4 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 5 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). 6 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. 7 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 8 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 9 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. 10 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 11 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 12 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. 13 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 14 (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 15 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(...)), 16 (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: 17 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. 18 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 19 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 20 (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 21 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 23 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 24 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 25 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. 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Jayaweera, ”Numerically Stable Algorithm for Discrete-Ordinate-Method Radiative Transfer in Multiple 58 Scattering and Emitting Layered Media,” Appl. Op., 27(12), 2502-2509 (1988). Stephens G. L., P. J. Webster, ”Clouds and Climate: Sensitivity of Simple Systems,” J. Atmos. Sci., 38, 235-247 (1981). Welton E. J., J. R. Campbell, J. D. Spinhirne, V. S. Scott, ”Global Monitoring of Clouds and Aerosols using a Network of Micro-pulse Lidar Systems,” Proc. Int. Soc. Opt. Eng. 4153, 151-158 (2001). Wylie D. P., W. P. Menzel, H. M. Woolf, K. I. Strabala, ”Four Years of Glocal Cirrus Clouds Statistics Using HIRS,” J Clim., 7, 1972-1986 (1994). 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