DETECTION OF NEARLY SUBVISUAL CIRRUS CLOUDS

Transcription

1 DETECTION OF NEARLY SUBVISUAL CIRRUS CLOUDS Melissa Yesalusky Advisor: William L. Smith Hampton University Abstract This research aims to identify subvisual cirrus (SVC) clouds using remote sensing techniques. These ice clouds tend to transmit and forward scatter the incoming solar radiation. They also act to absorb and therefore reduce, the infrared radiation, which would otherwise exit our atmosphere. These processes play an important role in our weather and radiative climate as their variations modify the radiation balance of our planet. The objective of this study is to validate a technique for detecting SVC and their infrared radiative properties from Earth orbiting satellites. This study utilizes simultaneous and geographically coincident lidar, multi-spectral visible and infrared, and hyperspectral infrared measurements from the CALIPSO, MODIS, AIRS and CloudSat satellite instruments. An assessment of the radiative heating impact of SVC was determined by comparing clear and cloudy fields of view through radiative transfer calculations for typical atmospheric conditions to help identify and quantify the cloud optical properties. Initial findings indicate that the frequency of occurrence of SVC clouds globally is between 2.11 and 4.32%. They are observed more frequently ( %) in the tropics in all case studies. The subtropics and polar regions reveal frequency of occurrences between % and % respectively. Introduction The effect of clouds is considered one of the largest uncertainties in global climate models. The question is not just how many clouds are in the sky, but what is their composition and location that determine the impact clouds have on climate. It has been reported that predicted climate change can vary by a factor of three, depending on how clouds are represented in the forecast model. It has been estimated that the planet would be on average 20 o F warmer if clouds were not present on Earth. 19 Clearly clouds are an important factor to consider. In particular, two influences must be taken into account: (1) the impact of clouds on the incoming shortwave radiation from the sun, and (2) the impact of clouds on the longwave outgoing radiation emitted to space by the surface and atmosphere. In general clouds block incoming solar radiation, reflecting it back out to space, causing a net cooling of the Earth. On the other hand, clouds also absorb the outgoing longwave radiation, which results in the Earth s inability to release absorbed energy to space, causing a net warming effect. The net result of these two events determines the warming or cooling impact that clouds will have on the planet. The optical thickness, altitude, phase, frequency, and the size, shape, orientation of the cloud particles, as well as the surface properties and aerosol composition of the cloud all play a crucial role in determining its effectiveness at reflecting shortwave or trapping longwave radiation. 20 While there are many factors that contribute to a cloud s influence on the global energy budget, this thesis aims to look primarily at optical depth to assess the impact that high altitude, optically thin subvisual cirrus clouds have on upwelling infrared (IR) radiation using a new multi-satellite collocation of cloud radiation observations. Before further examining subvisual cirrus (SVC) clouds, a definition must be applied to clarify the ambiguous term subvisual. SVC are defined as having an optical depth of 0.03 or smaller in the visual portion of the spectrum. 22 Since the scattering efficiency remains about 2 in the visible portion of the spectrum, the optical depth is wavelength independent; as a result, there is not a definitive wavelength for this criterion to be applicable. 17 Despite the low optical depth, SVC are not necessarily geometrically thin, Sassen and Cho (1992) observed subvisual and threshold-visible cirrus as thick as 4 kilometers, 22 and a spatial scale of hundreds of kilometers. 4 SVC are composed of ice crystals ranging from 2-20 µm. 20 They can form naturally either by outflow from a cumulonimbus cloud or through nucleation. 22 Convection brings warm moist air into the upper troposphere where light winds allow dry and moist air to mix and liquid droplets freeze into ice crystals resulting in the formation of clouds. 22, 25 Wind shear can cause a top layer of the cloud to travel outward from the center of a cumulonimbus anvil. The farther away the cloud ventures from the center, the thinner the cloud becomes, eventually leading to a SVC. During this process, ice crystals with a radius greater than µm, will Yesalusky 1

2 precipitate out of the cloud within a few hours. 22 Sometimes they are not associated with large convective anvils, these clouds can develop from nucleation, in which a humid layer rises up through slow, synoptic-scale uplift or shear-driven turbulent mixing into the cold upper troposphere region. This process causes supersaturation resulting in the freezing of sulfuric acid and nitric acid gases. Uplifting can be caused by continental scale bulges, larger scale convective systems, frontal systems, tropical waves, or through elevating above stratiform regions of mesoscale convective systems. 3,20,22 Part of the subvisual family, is aircraft contrails. Contrails are formed from aircrafts that expel exhaust fumes with high relative humidity values into the upper tropopause. 16 They form in two ways: (1) directly, as humid fumes cause the ambient air to reach or exceed liquid saturation levels, 16 and (2) indirectly, as water droplets form on the black carbon soot and liquid volatile aerosol like sulfuric 6, 16 acid particles found within the exhaust. If the air is dry, the contrail will evaporate quickly; however, if the air is supersaturated with ice, the contrail will gradually grow. 23 In the second case, these droplets 6, 16 freeze almost immediately into ice particles, forming a cirrus cloud that would not have formed if the aircraft had not passed through the region to allow for nucleation. 23 As a result, contrails are often called seeds for the formation of SVC. 6 SVC are usually observed in the top 2 km of the troposphere, spreading over an altitude range of 8-20 km, with the highest frequency occurring between km. 19,26 The coldest tropospheric temperatures lie in this region, reaching temperatures between 60 and 70 o C, making an ideal nucleation and condensation site for the development of cirrus clouds. 6,15,19 Some studies have documented SVC above the local tropopause in the midlatitudes region. 4,7,13 They may enter the stratosphere through tropopause folding or occluded air masses. 25 They form above the tropopause more frequently at higher latitudes due to the increased distance separating the tropopause and the hygropause. In the tropics this separation is about 1 km however in the midlatitudes region this distance can increase to 4 km, allowing for a large area for midlatitude cloud formation. 13 The greatest frequency of cirrus clouds (~70%) is found in the top 2 km of the tropical tropopause, around 15.5 km, in the Western Pacific (Micronesia) at about 130 o E during the winter months when the upper troposphere temperatures reach their lowest point. 11,18,25 Two other areas of maximum cirrus frequency are Africa and South America. 5 These areas are witnessed to have minimal outgoing longwave radiation (OLR) values, which indicate high rates of convective activity. The Western Pacific is site of high ocean convection while Africa and South America show significant continental convection. As OLR values decrease there is a general increasing trend for tropospheric level thin cirrus at a constant temperature. 5 The minimum frequency is found over the eastern Pacific Ocean. At 20 o N and 20 o S, and the midlatitudes (50 o N and 50 o S) the frequency is about 20-30%. Frequencies also change with seasonal altitude; thought to be due to the presence of convection, 25 temperature and water vapor levels. High altitude ice clouds are associated with many complicating factors, which allow these clouds to either cool or warm the planet depending on a variety of issues. Uncertainty in this area comes from questions concerning the scattering properties of ice clouds, due to the variety of shapes and sizes of the ice crystals that make up cirrus clouds. 14 Large ice crystals have a tendency to scatter light in the forward direction while smaller ice crystals scatter light more equally in all directions. These clouds do not cause substantial disruption to incoming shortwave radiation. 18 However, cirrus clouds have a strong greenhouse effect since the small ice particles have been shown to exhibit strong absorption features in the 8-12 µm window, which is characteristic to the longwave radiation leaving the earth. 19 One study revealed that they could absorb up to fifty percent of the upwelling longwave radiation. High altitude clouds are much colder than the Earth s surface and as a result; the energy re-radiated from the cloud is much smaller due to a much lower temperature compared to that of the Earth s surface or low cloud. 24 Longwave absorption, in many cases can outweigh the albedo effect, causing a warming effect on the planetary temperatures. 14 Kumar (2003) states that there is a nonlinear relationship between the OLR flux with temperature and optical depth. For SVC, the OLR flux range is Wm -2 while thin and opaque cirrus range from Wm -2 and less than 370 Wm -2 respectively. 15 Wang (1996) estimated that SVC would cause OLR flux to decrease by about 1 W m -2. This would cause the longwave flux to drop from 295 W m -2 in clear sky to W m -2. The effect on incoming shortwave radiation is an increase in the Earth s albedo from to The maximum effect for a dark clear ocean would be about 0.5 W m -2 for a 1 km thick cloud. If a thicker cloud were present, more warming would occur; however, if another surface type were present below the cloud, the albedo would decrease causing less warming. The end result is a net warming effect ( cloud forcing ) of W m -2 in the tropics. 25 Yesalusky 2

3 The optically thinner a cloud, the less impact it has on the radiative energy. 9 This could be due to the differences in cloud particle size. Räisänen (2006) showed that a larger particle size, greater than 4µm, results in a net warming effect while sizes less than 2µm, results in a net cooling effect, as the negative shortwave effect outweighs the longwave. This is thought to be due to the fact that the extinction efficiency of small particles relative to wavelength is much less than For SVC, a cooling effect of -0.1 K day -1 can be seen when a thick cloud is present below the SVC while a warming effect of 1.3 K day -1 is present when it is alone in the atmosphere. For an optically thicker cloud these values reach -0.7 and 5.4 K day -1 respectively. 8 Jensen (1996) also saw similar results, estimating that SVC could cause heating rates of a few K day Iwasaki (2004) found that the radiative impact of these clouds have been estimated at about 1 Wm McFarquhar (2000), results reveal radiative impact values as high as 4-5 Wm -2 for SVC. 18 Research Background For this study a hyper-spectral sounding instrument, AIRS (Atmospheric Infrared Sounder), is used to detect the impact of the infrared spectrally varying absorption. AIRS contain 2378 spectral channels that cover the infrared spectral range from µm, µm, and µm at a nominal spectral resolution of λ/ λ = 1200, each sensitive to different aspects of the atmosphere. 2 The high quantities of measurements allow for more accuracy and more detailed atmospheric profiles when compared to other instruments with limited channels. 12 CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) is used to aid in the identification and altitude of SVC. CALIPSO consists of three co-aligned instruments; the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR) and the Wide Field Camera (WFC). All three nadir-viewing instruments work in parallel to create illustrations of aerosols and clouds vertically within the atmosphere at m vertical resolution, and a horizontal resolution of 333 m. CALIOP is an active polarization three-channel lidar that measures at two wavelengths, 1064 nm and 532 nm parallel and perpendicular. The IIR is a passive infrared non-scanning imager; it is a single microbolometer detector array, with a rotating filter wheel. Measurements are taken at three infrared channels located within the atmospheric window region at 8.65 µm, 10.5 µm, and µm. Finally the WFC is a passive visible imager with a wavelength of 645 nm, which only acquires data in daylight hours. 26 Since CALIPSO only observes along the suborbital track, there is uncertainty about the conditions surrounding the CALIPSO track. MODIS (Moderate Resolution Imaging Spectroradiometer), cloud mask product is acquired to verify the state of the scene surrounding the CALIPSO track. It has 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm. 28 The MODIS cloud mask product is generated by placing the level 1B-radiance data through a series of test to determine if clouds or optically thick aerosols obstruct the underlying surface. 1 Since cirrus clouds appear nearly indistinguishable in visible wavelengths, CloudSat was also employed. By comparing the cloud layers identified by CloudSat with the cloud layers seen by CALIPSO, the possible presence of cirrus clouds can be determined. CloudSat is a millimeter-wave radar. It is 1000 times more sensitive than the current land based weather radars. CloudSat can sense smaller particles of liquid and ice, and view the internal structure of the cloud. It has a minimum detectable cloud reflectivity of -28 dbz which allows for vertical profiling of clouds at 500-meter resolution. 9 These four instruments are part of the Afternoon Constellation or A-train and will allow information to be collected on the frequency of SVC as a function of geographical location. An analysis of the radiative heating impact of invisible cirrus clouds will be determined through comparing clear and cloudy fields of view (FOV) through radiative transfer calculations for typical atmospheric conditions. Spectral distributions can be made for emissivity, transmissivity, and optical depth in order to assess the radiative impact of SVC. Methodology For this study, data was collected and analyzed for four days in 2007, August 7, November 12, February 3 and May 5, covering each of the four seasons. Cases were chosen due to their close proximity to the middle of the season and for days where AIRS, CALIPSO, MODIS and CloudSat showed at or near 100% data availability. The first task involved in identifying SVC is to gather collocated data between the three satellites, AIRS, CALIPSO, and MODIS. To accomplish this crucial task, two FORTRAN programs written by Fred Nagle and Bob Holz from the University of Wisconsin-Madison are utilized to identify CALIPSO and MODIS points located within each AIRS FOV. Both programs use AIRS as the core footprint, since it has the lowest resolution (IR nadir spatial resolution of 13.5 km). This study uses Version 5 Level 1B Infrared Radiance (AIRIBRAD) Product. Each granule contains 90 FOV across the flight track (along Yesalusky 3

4 longitudinal lines) and 135 along-track FOV in the latitudinal direction. The result is a total of 12,150 FOV within each granule, each containing calibrated and geolocated radiance data for all 2378 IR channels 27 in milliwatts/m 2 cm -1 steradian. The first program is the CALIPSO-AIRS collocation program, which identifies the CALIPSO shots numbers located within each AIRS FOV and the weight of each shot number to determine its proximity to the center of the AIRS FOV. In this study CALIPSO Lidar Level 1B, Version 2, profile data was utilized. Since CALIPSO measures along a sub-orbital track every along track is utilized; however, the across track only varies between about six and seven of the 90 across FOV. The second program is the MODIS- AIRS collocation program, which identifies the collocated MODIS shot numbers within the AIRS footprint. This program uses Level 1, Version 5 (MYD03) Aqua MODIS 1-km geolocation data. This program only identifies those MODIS shots which lies 100% within the AIRS FOV. The MODIS and CALIPSO shots are now identified within each AIRS FOV; these points now correspond to data, which is representative of the other instruments. See figure 1 for a summary of how each data set relates to each other. Cloudy FOV are identified using the CALIPSO cloud height data obtained by the CALIPSO-AIRS collocation program for each AIRS FOV. In this study cloud height refers to the CALIPSO cloud top height only. Since the CALIPSO cloud height data will later be compared to the cloud heights of CloudSat, the CALIPSO cloud heights are placed into the same 250 m thick altitude bins as CloudSat. After the CALIPSO cloud heights are placed into the CloudSat bins for each FOV, they are Figure 1: A summary of data placement for each instrument with an AIRS footprint. compared across each cloud bin. Due to scene variation a cloud maybe present in one CALIPSO shot but not the next. For this study a cloud only needs to be seen in one CALIPSO shot to be identified as a cloud layer. Each cloud bin is examined across each FOV, if a cloud is present anywhere in the FOV at the bin in question then that bin receives a 1, meaning a cloud is present, if not it receives a zero. This allows each FOV to contain one set of 125 bins with either a 1 or 0, to identify all clouds seen by CALIPSO in the AIRS FOV. Limitations are imposed to exclude multilevel cloud layers, due to complicating factors of where the radiative contribution is originating. FOV with three or more cloud layers are excluded and omitted from the analysis. The number of layers is determined by looking at the number of cloud bins containing clouds for each FOV. If concurrent cloud bins contain clouds then they are classified as one layer. Cloud layers below 3 km are not included as a cloud layer; this region shows the presence of low level aerosols and not clouds. As a result, cases are also rejected if the highest cloud layer is less than 3 km. All remaining FOV with one or two cloud layers are processed and will be examined at a later time. Now that preliminary cloudy cases have been identified using CALIPSO, the results from the MODIS- AIRS collocation program are utilized to examine the remaining AIRS FOV surrounding the CALIPSO track to determine the cloud fraction. This process eliminates the false identification of a FOV as cloudy based on the small fraction of the FOV under the CALIPSO track. Instead the entire FOV must be considered before being labeled as a cloudy scene. The MODIS shot numbers identified from the MODIS-AIRS collocation programs are used to obtain cloud mask data from the Level 2, Version 5, Aqua MODIS (MYD35) data, for each AIRS FOV. MODIS cloud mask data will reveal confidence levels of 0.99/0.95/0.66/0 confidence of clear for each MODIS shot within the AIRS FOV. These values are averaged to obtain the percent cloudy and percent clear for each FOV. Due to the low optical depth and identification difficulty of SVC, this study only rejects a FOV if the probability of cloudy is less than 10%. The remaining FOV are classified as cloudy FOV and the AIRS spectral radiance data from those FOV are referred to as R cld. In order to compare the radiative effects of a cloud, the cloudy FOV must be compared to a clear FOV in the same region, which have similar atmospheric conditions. To locate a clear FOV, an examination is conducted on a 13x13 block of FOV surrounding the cloudy FOV in question. The MODIS cloud mask is utilized again, being applied to each FOV in the 13x13 Yesalusky 4

5 block. The surrounding FOV is labeled as clear if the probability of cloudy is less than 10%. After identifying all of the clear FOVs within the 13x13 box, the AIRS radiance values are obtained for those clear FOV, then weighted and averaged to obtain a single representative clear sky radiance spectrum for the region possessing the cloudy FOV. This value is referred to as R clr. If a clear FOV is not found, then the cloudy FOV is discarded from the cloud optical property determination. Due to the fact that atmospheric absorption above the cloud is not accounted for in the cloud property determination process, only radiance values void of above cloud atmospheric contributions are considered here. These values include observations made within the atmospheric windows between 800 and 1000 cm -1 and between 1100 and 1130 cm -1. Measurement noise also exists within the AIRS spectrum; as a result, a three point filtering system is employed. The filter involves comparing the differences from three consecutive spectral radiance points (A B, B C, A C). The minimum of these three values is identified as CritØ. It was decided that acceptable values would be within five times the minimum value, defined as Crit. If two spectral differences are greater than the Crit value then the common point between them is considered a bad point and eliminated from the data. The equations below describe the filtering process used: X 1 = α(i)- α(i-1) X 2 = α(i)- α(i+1) X 3 = α(i+1)- α(i-1) CritØ = minimum (X 1, X 2, X 3 ) Crit=3(CritØ) IF(X 1 >Crit and X 2 >Crit) Then ε(i)=bad IF(X 2 >Crit and X 3 >Crit) Then ε(i+1)=bad IF(X 3 >Crit and X 1 >Crit) Then ε(i-1)=bad Where α is one of 2378 spectral radiance values. This process is conducted on all 2378 radiance points and eliminates the majority of outliers within the radiance measurement data set. The last piece of information needed to determine spectral emissivity is the blackbody cloud radiance value defined using Planck s law. The Planck equation describes the spectral radiance emitted by a blackbody at a given temperature and frequency and can be written: B(ν,T cld ) = [c 1 ν3]/[e c2ν/t 1] c 1 = x 10-5 (mw/m 2 /ster/cm -4 ) c 2 = (cm deg K) Where υ is the frequency (i.e. wavenumber) of the AIRS radiance measurements and T cld is the temperature of the cloud. Atmospheric temperature-pressure profile data is acquired using constant pressure level analyses preformed by the National Centers for Earth Prediction (NCEP), which performs these analyses every six hours. A series of interpolations are needed to acquire the temperatures for each given case. Bi-linear interpolation is used with respect to latitude and longitude, followed by linear interpolation with respect to time and altitude. The hypsometric equation is used to define the geopotential height of the pressure levels used for the atmospheric temperature analyses. From the altitude versus pressure relationship, a linear interpolation of atmospheric temperature can be performed to find the temperature of the atmosphere at the height of the cloud defined from the CALIPSO observations. Since atmospheric absorption above the cloud is not accounted for in the atmospheric window, the AIRS radiance values in this region receive information for each FOV from two sources: (1) emitted energy from the cloud which is proportional to the effective emissivity of the cloud, and (2) atmospheric energy from the surrounding clear sky. Due to the study location in the atmospheric window, the radiance from the cloud can be replaced by Planck s Law. These equations will allow for the development of an equation for effective spectral emissivity: R cld = ε * cldr BOVC + (1- ε * cld)r clr R BOVC = B λ (T cld ) ε * (υ) = R cld (υ) - R clr (υ) B λ (T cld ) - R clr (υ) where R cld and R clr are the spectral radiance values that correspond to the cloudy and clear FOV respectively, R BOVC is the radiance from above the cloud, B λ (T cld ) is the Planck radiance corresponding to the cloud temperature, and ε * is the effective spectral emissivity of the cloud. Effective cloud emissivity reflect the total Yesalusky 5

6 radiance change, it does not take into account how much of the FOV scene is covered by cloud. It is the product of the true emissivity of the cloud and the fractional cloud cover, N (i.e., ε* = N ε). To calculate spectral emissivity (ε), the effective emissivity (ε*) is divided by the cloud fraction (N). This value is calculated from the MODIS cloud mask product, it is the fractional probability that the FOV is cloudy. This equation now fulfills the definition for emissivity, the ratio of radiance emitted by a cloud at a temperature to the radiance emitted by a blackbody obeying Planck s law. These values will now describe the clouds ability to absorb and radiate energy Next the CALIPSO IIR level 1B, version 1 radiance data was acquired. By comparing emissivity values calculated from: (1) the CALIPSO IIR radiance data from under the CALIPSO track, with (2) the CALIPSO IIR radiance data from the entire AIRS FOV, it can then be determined if the AIRS emissivities are valid (i.e., whether or not the clouds along the CALIPSO measurement strip are representative of the clouds covering the AIRS FOV). The CALIPSO IIR data was obtained for each AIRS FOV, using the MODIS data points obtained from the CALIPSO-AIRS collocation program as the boundaries for each AIRS FOV. The CALIPSO IIR data was also obtained for the CALIPSO lidar path, CALIOP, which is located at the center of the CALIPSO IIR swath. Its latitudinal position for each FOV was also determined using the MODIS data points. IIR measurements are taken at three infrared channels located within the atmospheric window region at 8.7 µm, 10.5 µm, and 12.0 µm; therefore, the AIRS radiance values must be spectrally averaged over these same three channels. This is accomplished by examining the spectral response for the CALIPSO IIR. The spectral response function is used as a spectral weighting function, which defines how the IIR instrument weights the incoming radiance to produce the observed radiance signal. The spectral weighting process is performed for all R clr (υ), and blackbody cloud radiance value (B λ (T cld )). Spectral effective emissivity can be calculated for the three IIR channels by replacing the R meas (υ) from the effective emissivity equation with the new IIR radiance data. The new R IIR (υ) data includes data from the CALIPSO IIR data from below the CALIPSO path and the CALIPSO IIR data from each AIRS FOV at all 3 IIR channels. The B λ (T cld ), R clr (υ), and cloud fraction values remain constant for all IIR calculations; therefore the only difference emerge as a result of the FOV radiance values. After the new emissivity values have been calculated a scene filter was applied to the spectral emissivity data by first averaging the emissivity values over the 8, 10 and 12 µm channels. Then the difference was taken between the averaged emissivity using the CALIPSO IIR data from the CALIPSO path and the averaged emissivity using the CALIPSO IIR data from the entire AIRS FOV. If the difference was greater than 0.15 then the corresponding FOV was rejected from the data set because the clouds along the CALIPSO track are not representative of the clouds covering the AIRS FOV. After the data has passed through the scene filter using the CALIPSO IIR data, the remaining data is examined to see how it is distributed globally and vertically throughout the atmosphere. Figure 2 shows how emissivity is distributed across the four seasons and latitudinal lines. In this figure, emissivity is broken up into 10 bins, from 0 to 1 with an increment of 0.1. Within each bin, data is divided into the four seasonal cases, February, May, August and November respectively. These seasonal cases are then separated into latitudinal bins, which include two polar regions (- 90 o -60 o or 60 o 90 o ), two midlatitudes (-60 o -30 o or 30 o 60 o ), and one tropical region (-30 o 30 o ). Figure 2 shows a large tendency for the resultant clouds to be located in the tropics within the first and second emissivity bins (0 0.1 and.1 0.2). This region is the main region of interest in the identification of SVC. Since SVC are difficult to detect in the visible portion of the spectrum. An examination was conducted using CloudSat level 2B-CLDCLASS data. By identifying cases in which CALIPSO identifies a cloud layer yet CloudSat fails to detect the layer will help to classify a portion of the data as high altitude cirrus clouds as opposed to water clouds which are easily observed in the visible portion of the spectrum. For the purposes of this study, the type of cloud is irrelevant, only the altitude of the cloud is important in comparison with CALIPSO cloud heights. The CloudSat cloud layers were combined across the same path length as the CALIPSO cloud heights for each AIRS FOV. Figure 3 illustrates that only in a few cases does CALIPSO fail to identify a higher altitude cloud seen by CloudSat. On the other hand, CloudSat frequently neglects high altitude clouds that are perceived by CALIPSO. It is within this region of the data where the SVC will be found. Yesalusky 6

7 Figure 2: The frequency distribution of emissivity over latitude and season. The dashes between emissivity values correspond to the frequency between the upper and lower emissivity value in each season, February, May, August and November respectively. Results and Conclusions Next the feature classification flags within the CALIPSO Lidar L2 1 km cloud layer data were examined. This feature allows each layer identified by CALIPSO to be classified as cloud, aerosol, polar stratospheric cloud, polar stratospheric aerosol, surface, subsurface, clear air, or unknown. This feature also separates layers into ice, water, mixed or of unknown phase. The highest cloud layer within each FOV is verified as being a cloud and then its phase is determined. Only points with a high or medium confidence level within the quality flags are used. Once the data sets are separated into ice and water clouds the cloud heights provided by CALIPSO and Cloudsat were examined. SVC are found in the region where CALIPSO sees high ice clouds and CloudSat sees either the cloud layer below the identified CALIPSO layer or the surface. Ice clouds with a CALIPSO cloud height greater than six kilometers above the CloudSat cloud height were examined for potential SVC, and represented as a blue point in Figure 3. The emissivities and optical depths were obtained to identify SVC located within the four data sets. SVC are related to the mode of its optical depth. The visible optical depth for SVC has been documented at However, for this study the emissivity and optical depth measurement are examined in the IR portion of the spectrum. Figure 4, illustrates how optical depth for the potential SVC are distributed for low optical depths. This figure reveals a frequency peak near an optical depth value of This value will become the optical depth threshold for SVC. Initial findings indicate that SVC clouds occur globally between %. They are observed more frequently ( %) in the tropics in all case studies. The subtropics and Polar Regions reveal occurrences at % and % respectively see Table 1. Figure 5, shows the global positions of ice clouds and the locations of identified SVC. The data is divide by latitudinal region, the Tropics (-30-30), Midlatitudes ( and 30-60) and Polar ( and 60-90). The results of this study show the highest frequency in the tropics which is consistent with other reports; however, the frequency of observed SVC during these case studies is low globally than other studies of SVC. This possibly due to the limitation put on the number of cloud layers. Yesalusky 7

8 Figure 3: The highest cloud layer seen by CALIPSO and CloudSat. Blue points represent the ice cloud region of interest where SVC will be found, green points represent the data not used to locate SVC. Figure 4: The distribution of cloud heights over latitude for ice, water and mixed phased clouds. Yesalusky 8

9 Table 1: Frequency of SVC Case February May August November Tropics Subtropics Polar Global Acknowledgements I cannot fully express my gratitude to my advisor, Dr. William L. Smith for his guidance and support throughout my research. For their generous assistance, I would like to thank Bob Holz and Fred Nagle from the University of Wisconsin- Madison for the development of the collocation program. I would also like to thank NOAA CREST, the Virginia Space Grant Consortium and National Security Technology for the financial assistance to purchase improved equipment and gain indispensable experience and knowledge through travel. References [1] Ackerman S., et al., (2006) Discriminating Clearsky from cloud with MODIS algorithm theoretical bases document (MOD35). Version 5.0. [2] Aumann, H.H., et al., (2003), AIRS/AMSU/HSB on the Aqua Mission: Design, Science Objectives, Data Products, and Processing Systems. IEEE Transactions on Geioscience and Remote Sensing, VOL. 41, NO. 2, PAGES [3] Bregman, B., P.-H. Wang, and J. Lelieveld (2002), Chemical ozone loss in the tropopause region on subvisible ice clouds, calculated with a chemistrytransport model. Journal of Geophysical Research, 107. [4] Corti, T., et al., (2006), The impact of cirrus clouds on tropical troposphere-to-stratosphere transport. Atmospheric Chemistry and Physics, VOL. 6, PAGES [5] Dessler, A. E., et al., (2006), Tropopause-level thin cirrus coverage revealed by ICESat/Geoscience Laser Altimeter System. Journal of Geophysical Research, 111, D08203, doi: /2005jd [6] Flores, J.M., et al., (2006), Tropical Subvisual Cirrus and Contrails at -80 C. aper_ htm. [7] Goldfarb,L. et al., (2001), Cirrus climatological results from lidar measurements at OHP (44 o N,6 o E). Journal of Geophysical Research, 28, [8] Hartmann, D. L., J. R. Holton, and Q. Fu (2001), The Figure 5: Global distribution of ice cloud and subvisual cirrus clouds due the case study Yesalusky 9

10 heat balance of the tropical tropopause, cirrus, and stratospheric dehydration. Geophysical Research Letters, VOL. 28, NO. 10, PAGES [9] Im, E., C. Wu, and S. L. Durden. Cloud Profiling Radar for the CloudSat Mission. Jet Propulsion Laboratory. bitstream/2014/37690/1/ pdf [10] Iwasaki, et al., (2004), Subvisual cirrus cloud observations using a 1064-nm lidar, a 95 GHz cloud radar, and radiosondes in the warm pool region. Geophysical Research Letters, 31, L09103, doi: /2003/gl [11] Jensen, E.J., et al., (1996), On the formation and persistence of subvisual cirrus clouds near the tropical tropopause. Journal of Geophysical Research, 101, 95JD [12] Jet Propulsions Laboratory, California Institute of Technology, How AIRS Works, Detail Description. how_airs_works_detail. [13] Kärcher, B (2002), Properties of subvisible cirrus clouds formed by homogenous freezing. Atmospheric Chemistry and Physics, 2, [14] Kinne, S (1998), Cirrus Clouds and Climate. NASA Ames Research Center. cirrusclimate_kinne/cirrusclimate_kinne.html. [15] Kumar, S.V.S, K. Parameswaran, B.V.K. Murthy (2003), Lidar observations of cirrus cloud near the tropical tropopause: general features. Atmospheric Research, VOL. 66, PAGES [16] Liou, K.K (2005), Cirrus clouds and climate. Reprinted from the McGraw-Hill Yearbook of Science & Technology ou_yearbook_2005.pdf. [17] Macke, A., et al., (1998), The Role of Ice Particle Shapes and Size Distributions in the Single Scattering Properties of Cirrus Clouds. Journal of the Atmospheric Science, VOL. 55, PAGES [19] National Aeronautics and Space Administration (1999), NASA Facts: Earth s Energy Balance. rces/energy_balance.pdf. [20] Räisänen, P., et al., (2006), Impact of H 2 SO 4 /H 2 O coating and ice crystal size on radiative properties of sub-visible cirrus. Atmospheric Chemistry and Physics, 6, [21] Sassen, K., M.K. Griffin, and G.C. Dodd (1989), Optical scattering and microphysical properties of subvisual cirrus clouds, and climate implications. Journal of Applied Meteorology, 28. [22] Sassen, K., B.S. Cho (1992), Subvisual-thin cirrus lidar dataset for satellite verification and climatological research. Journal of Applied Meteorology, 31. [23] Schröder, F., et al., (1992), On the transition of contrails into cirrus clouds. Journal of Atmospheric Science, 57, [24] Smith, W.L, S. Ackerman, H. Revercomb, H. Huang, D.H. DeSolver, W. Feltz, and L. Gumley (1998), Infrared spectral absorption of nearly invisible cirrus. Geophysical Research Letters, 25, 97GL [25] Wang, P.H, et al., (1996), A 6-year climatology of cloud occurrence frequency from Stratospheric Aerosol and Gas Experiment II observations ( ). Journal of Geophysical research, 101, D23. [26] Winker, D.M., J. Pelon, and M.P. McCormick (2003) The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds. Proceedings of SPIE VOL [27] Won, Y.I. (2008), README Document for AIRS Level-1B Version 5 IR Calibrated Radiance Products: AIRIBRAD, AIRIBRAD_NRT, AIRIBQAP, AIRIBQAP_NRT. National Aeronautics and Space Administration, Goddard Earth Sciences Data and Information Services Center. [28] Xiong, X., et al.,modis Reflective Solar Bands Calibration Algorithm and On-orbit Performance. E_CHINA [18] McFarquhar, et al., (2000), Thin and Subvisual Tropopause Tropical Cirrus: Observations and Radiative Impacts. Journal of the Atmospheric Sciences, 25. Yesalusky 10