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Upper troposphere cloud top heights (CTHs), restricted to cloud top pressures (CTPs) < 500 hPa, inferred using four satellite retrieval methods applied to Twelfth Geostationary Operational Environmental Satellite (GOES-12) data are evaluated using measurements during the July–August 2007 Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The four methods are the single-layer CO_2-absorption technique (SCO2AT), a modified CO_2-absorption technique (MCO2AT) developed for improving both single-layered and multilayered cloud retrievals, a standard version of the Visible Infrared Solar-infrared Split-window Technique (old VISST), and a new version of VISST (new VISST) recently developed to improve cloud property retrievals. They are evaluated by comparing with ER-2 aircraft-based Cloud Physics Lidar (CPL) data taken during 9 days having extensive upper troposphere cirrus, anvil, and convective clouds. Compared to the 89% coverage by upper tropospheric clouds detected by the CPL, the SCO2AT, MCO2AT, old VISST, and new VISST retrieved CTPs < 500 hPa in 76, 76, 69, and 74% of the matched pixels, respectively. Most of the differences are due to subvisible and optically thin cirrus clouds occurring near the tropopause that were detected only by the CPL. The mean upper tropospheric CTHs for the 9 days are 14.2 (+2.1) km from the CPL and 10.7 ( ±2.1), 12.1 ( ±1.6), 9.7 (±2.9), and 11.4 (±2.8) km from the SCO2AT, MCO2AT, old VISST, and new VISST, respectively. Compared to the CPL, the MCO2AT CTHs had the smallest mean biases for semitransparent high clouds in both single-layered and multilayered situations whereas the new VISST CTHs had the smallest mean biases when upper clouds were opaque and optically thick. The biases for all techniques increased with increasing numbers of cloud layers. The transparency of the upper layer clouds tends to increase with the numbers of cloud layers....
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Upper troposphere cloud top heights (CTHs), restricted to cloud top pressures (CTPs) < 500 hPa, inferred using four satellite retrieval methods applied to Twelfth Geostationary Operational Environmental Satellite (GOES-12) data are evaluated using measurements during the July–August 2007 Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The four methods are the single-layer CO_2-absorption technique (SCO2AT), a modified CO_2-absorption technique (MCO2AT) developed for improving both single-layered and multilayered cloud retrievals, a standard version of the Visible Infrared Solar-infrared Split-window Technique (old VISST), and a new version of VISST (new VISST) recently developed to improve cloud property retrievals. They are evaluated by comparing with ER-2 aircraft-based Cloud Physics Lidar (CPL) data taken during 9 days having extensive upper troposphere cirrus, anvil, and convective clouds. Compared to the 89% coverage by upper tropospheric clouds detected by the CPL, the SCO2AT, MCO2AT, old VISST, and new VISST retrieved CTPs < 500 hPa in 76, 76, 69, and 74% of the matched pixels, respectively. Most of the differences are due to subvisible and optically thin cirrus clouds occurring near the tropopause that were detected only by the CPL. The mean upper tropospheric CTHs for the 9 days are 14.2 (+2.1) km from the CPL and 10.7 ( ±2.1), 12.1 ( ±1.6), 9.7 (±2.9), and 11.4 (±2.8) km from the SCO2AT, MCO2AT, old VISST, and new VISST, respectively. Compared to the CPL, the MCO2AT CTHs had the smallest mean biases for semitransparent high clouds in both single-layered and multilayered situations whereas the new VISST CTHs had the smallest mean biases when upper clouds were opaque and optically thick. The biases for all techniques increased with increasing numbers of cloud layers. The transparency of the upper layer clouds tends to increase with the numbers of cloud layers.
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A method for estimating effective ice particle radius R_e at the tops of tropical deep convective clouds (DCC) is developed on the basis of precomputed look-up tables (LUTs) of brightness temperature differences (BTDs) between the...
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A method for estimating effective ice particle radius R_e at the tops of tropical deep convective clouds (DCC) is developed on the basis of precomputed look-up tables (LUTs) of brightness temperature differences (BTDs) between the 3.7 and 11.0 μm bands. A combination of discrete ordinates radiative transfer and correlated k distribution programs, which account for the multiple scattering and monochromatic molecular absorption in the atmosphere, is utilized to compute the LUTs as functions of solar zenith angle, satellite zenith angle, relative azimuth angle, R_e, cloud top temperature (CTT), and cloud visible optical thickness τ. The LUT-estimated DCC R_e agrees well with the cloud retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for the NASA Clouds and Earth's Radiant Energy System with a correlation coefficient of 0.988 and differences of less than 10%. The LUTs are applied to 1 year of measurements taken from MODIS aboard Aqua in 2007 to estimate DCC R_e and are compared to a similar quantity from CloudSat over the region bounded by 140°E, 180°E, 0°N, and 20°N in the Western Pacific Warm Pool. The estimated DCC R_e values are mainly concentrated in the range of 25-45 μm and decrease with CTT. Matching the LUT-estimated R_e with ice cloud R_e retrieved by CloudSat, it is found that the ice cloud τ values from DCC top to the vertical location where LUT-estimated R_e is located at the CloudSat-retrieved R_e profile are mostly less than 2.5 with a mean value of about 1.3. Changes in the DCC τ can result in differences of less than 10% for R_e estimated from LUTs. The LUTs of 0.65 μm bidirectional reflectance distribution function (BRDF) are built as functions of viewing geometry and column amount of ozone above upper troposphere. The 0.65 μm BRDF can eliminate some noncore portions of the DCCs detected using only 11 μm brightness temperature thresholds, which result in a mean difference of only 0.6 μm for DCC R_e estimated from BTD LUTs.
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Two kinds of cloud products obtained from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) are compared and analyzed in this study: Cl...
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Two kinds of cloud products obtained from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) are compared and analyzed in this study: Clouds and the Earth's Radiant Energy System (CERES)-CALIPSOCloudSat- MODIS (CCCM) product and CloudSat radar-lidar products such as GEOPROF-LIDAR and FLXHR-LIDAR. Compared to GEOPROF-LIDAR, low-level (<1 km) cloud occurrences in CCCM are larger over tropical oceans because the CCCM algorithm uses a more relaxed threshold of cloud-aerosol discrimination score for CALIPSO Vertical Feature Mask product. In contrast, midlevel (1-8 km) cloud occurrences in GEOPROF-LIDAR are larger than CCCM at high latitudes (>40°). The difference occurs when hydrometeors are detected by CALIPSO lidar but are undetected by CloudSat radar. In the comparison of cloud radiative effects (CREs), global mean differences between CCCM and FLXHR-LIDAR are mostly smaller than 5 W m~(-2), while noticeable regional differences are found. For example, CCCM shortwave (SW) and longwave (LW) CREs are larger than FXLHR-LIDAR along the west coasts of Africa and America because the GEOPROF-LIDAR algorithm misses shallow marine boundary layer clouds. In addition, FLXHR-LIDAR SW CREs are larger than the CCCM counterpart over tropical oceans away from the west coasts of America. Over midlatitude storm-track regions, CCCM SW and LW CREs are larger than the FLXHR-LIDAR counterpart.
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A cloud frequency of occurrence matrix is generated using merged cloud vertical profiles derived from the satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud profiling radar. The matrix contains ver...
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A cloud frequency of occurrence matrix is generated using merged cloud vertical profiles derived from the satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud profiling radar. The matrix contains vertical profiles of cloud occurrence frequency as a function of the uppermost cloud top. It is shown that the cloud fraction and uppermost cloud top vertical profiles can be related by a cloud overlap matrix when the correlation length of cloud occurrence, which is interpreted as an effective cloud thickness, is introduced. The underlying assumption in establishing the above relation is that cloud overlap approaches random overlap with increasing distance separating cloud layers and that the probability of deviating from random overlap decreases exponentially with distance. One month of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat data (July 2006) support these assumptions, although the correlation length sometimes increases with separation distance when the cloud top height is large. The data also show that the correlation length depends on cloud top hight and the maximum occurs when the cloud top height is 8 to 10 km. The cloud correlation length is equivalent to the decorrelation distance introduced by Hogan and Illingworth (2000) when cloud fractions of both layers in a two-cloud layer system are the same. The simple relationships derived in this study can be used to estimate the top-of-atmosphere irradiance difference caused by cloud fraction, uppermost cloud top, and cloud thickness vertical profile differences.
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Passive satellite retrievals using conventional CO_2absorption techniques tend tosystematically underestimate the upper transmissive cloud top heights (CTHs). Thesetechniques are based on single-layer assumptions that the upper cl...
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Passive satellite retrievals using conventional CO_2absorption techniques tend tosystematically underestimate the upper transmissive cloud top heights (CTHs). Thesetechniques are based on single-layer assumptions that the upper cloud occupies ageometrically thin layer above a cloud-free surface. This study presents a new modifiedCO_2absorption technique (MCO2AT) to improve the inference of transmissive CTHsin the upper troposphere above 600 hPa. The MCO2AT employs an iterative algorithmthat starts with a single-layer CO_2absorption technique (SCO2AT) followed by aniterative procedure to retrieve an enhanced upper CTH based on inferred effectivebackground radiances. Both techniques are applied to the 10.7 and 13.3 μm channeldata of the Twelfth Geostationary Operational Environmental Satellite (GOES 12)imager and their retrievals of upper tropospheric CTHs are compared with two activesensing products: the ground-based Active Remotely Sensed Cloud Location (ARSCL)products from the Atmospheric Radiation Measurement Program (ARM) SouthernGreat Plains (SGP) site and the satellite-based Cloud Aerosol Lidar With OrthogonalPolarization (CALIOP) products. On average, the CTHs from MCO2AT and SCO2ATare lower than those from both of the active sensors by ~1 and 2.4 km, respectively,possibly due to the different sensitivities and spatial resolutions between passive andactive sensors. Preliminary validation of the new modified method is encouraging,especially the improvements for upper transmissive clouds in geometrically thick and/ormultilayered cloud situations. The development of the modified method is particularlyuseful for sensors like the GOES 12, Meteosat-9, and others, which carry only oneCO2 absorption channel at ~13.3 μm.
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Mass-diameter (m-D) and projected area-diameter (A-D) relations are often used to describe the shape of nonspherical ice particles. This study analytically investigates how retrieved effective radius (r_(eff)) and ice water conten...
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Mass-diameter (m-D) and projected area-diameter (A-D) relations are often used to describe the shape of nonspherical ice particles. This study analytically investigates how retrieved effective radius (r_(eff)) and ice water content (IWC) from radar and lidar measurements depend on the assumption of m-D [m(D) =a D~b] and A-D [A(D) = γ Dδ] relationships. We assume that unattenuated reflectivity factor (Z) and visible extinction coefficient (k_(ext)) by cloud particles are available from the radar and lidar measurements, respectively. A sensitivity test shows that r_(eff) increases with increasing a, decreasing b, decreasing γ, and increasing δ. It also shows that a 10% variation of a, b, γ, and δ induces more than a 100% change of r_(eff). In addition, we consider both gamma and lognormal particle size distributions (PSDs) and examine the sensitivity of r_(eff) to the assumption of PSD. It is shown that r_(eff) increases by up to 10% with increasing dispersion (μ) of the gamma PSD by 2, when large ice particles are predominant. Moreover, r_(eff) decreases by up to 20% with increasing the width parameter (ω) of the lognormal PSD by 0.1. We also derive an analytic conversion equation between two effective radii when different particle shapes and PSD assumptions are used. When applying the conversion equation to nine types of m-D and A-D relationships, r_(eff) easily changes up to 30%. The proposed r_(eff) conversion method can be used to eliminate the inconsistency of assumptions that made in a cloud retrieval algorithm and a forward radiative transfer model.
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The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, howe...
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The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth's Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m-2 for downward shortwave and -2.5 (7.1) W m-2 for downward longwave irradiances over ocean and -1.7 (7.8) W m-2 for downward shortwave and -1.0 (7.6) W m-2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
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An improvement was developed and tested for surface longwave flux algorithms used in the Clouds and the Earth's Radiant Energy System processing based on lessons learned during the validation of global results of those algorithms....
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An improvement was developed and tested for surface longwave flux algorithms used in the Clouds and the Earth's Radiant Energy System processing based on lessons learned during the validation of global results of those algorithms. The algorithms involved showed significant overestimation of downward longwave flux for certain regions, especially dry-arid regions during hot times of the day. The primary cause of this over-estimation was identified and the algorithms were modified to (i) detect meteorological conditions that would produce an overestimation, and (ii) apply a correction when the overestimation occurred. The application of this correction largely eliminated the positive bias that was observed in earlier validation studies. Comparisons ofvalidation results before and after the application of correction are presented.
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An extensive dust storm originating on 17 August 2006 in North Africa was observed and tracked by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Over the next several days, the dust layer m...
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An extensive dust storm originating on 17 August 2006 in North Africa was observed and tracked by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Over the next several days, the dust layer moved westward across the Atlantic Ocean and into the Gulf of Mexico. The initial stages of the event were examined using a sequence of CALIPSO measurements. The first of these was acquired very near the source on 18 August. Successive measurements were made over the Atlantic Ocean on 19 and 20 August, at respective locations approximately ～1300 km and ～2400 km from the source region. The later stages of the event were assessed using measurements acquired by the NASA Langley Research Center airborne HSRL over the Gulf of Mexico on 28 August. Within the free troposphere, the intrinsic optical properties of the dust remain relatively unchanged for the first 3 d of transport over the Atlantic Ocean. This is consistent with previous in situ measurements that have shown that there is little change in the size distribution of dust as it crosses the Atlantic. After the 10 d journey to the Gulf of Mexico, some changes are seen in the lidar ratios, the backscatter color ratio, and the optical depth ratio. The linear depolarization ratio appears to remain essentially constant (～0.32) at all four locations mentioned above, demonstrating a notable consistency in the dust particle nonsphericity. The measured 532 nm lidar ratios are 41 ± 3, 41 ± 4, 41 ± 6 and 45.8 ± 0.8 sr, respectively, at locations near the source, over the Atlantic Ocean, and in the Gulf of Mexico. The corresponding 1064 nm lidar ratios are 52 ± 5, 55 ± 5, 54 ± 13 and 44 ± 8.3 sr. The 532 nm lidar ratios are consistent with previous measurements and with CALIPSO's prelaunch models. The lidar ratios retrieved at 1064 nm are somewhat larger than would be expected on the basis of existing modeling studies. The backscatter color ratios are 0.74 ± 0.07, 0.75 ± 0.08, 0.72 ± 0.04 and 0.62 ± 0.01, and the optical depth ratios are 0.97 ± 0.02, 1.01 ± 0.05, 0.93 ± 0.17 and 0.62 ± 0.13, respectively.
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Recent studies utilizing satellite retrievals have shown a strong correlation between aerosol optical depth (AOD) and cloud cover. However, these retrievals from passive sensors are subject to many limitations, including cloud adj...
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Recent studies utilizing satellite retrievals have shown a strong correlation between aerosol optical depth (AOD) and cloud cover. However, these retrievals from passive sensors are subject to many limitations, including cloud adjacency (or three-dimensional) effects, possible cloud contamination, uncertainty in the AOD retrieval. Some of these limitations do not exist in High Spectral Resolution Lidar (HSRL) observations; for instance, HSRL observations are not affected by cloud adjacency effects, are less prone to cloud contamination, and offer accurate aerosol property measurements (backscatter coefficient, extinction coefficient, lidar ratio, backscatter Angstrom exponent, and aerosol optical depth) at a fine spatial resolution (<100 m) in the vicinity of clouds. Hence the HSRL provides an important data set for studying aerosol and cloud interaction. In this study, we statistically analyze aircraft-based HSRL profiles according to their distance from the nearest cloud, assuring that all profile comparisons are subject to the same large-scale meteorological conditions. Our results indicate that AODs from HSRL are about 8–17% higher in the proximity of clouds (～100 m) than far away from clouds (4.5 km), which is much smaller than the reported cloud three-dimensional effect on AOD retrievals. The backscatter and extinction coefficients also systematically increase in the vicinity of clouds, which can be explained by aerosol swelling in the high relative humidity (RH) environment and/or aerosol growth through in-cloud processing (albeit not conclusively). On the other hand, we do not observe a systematic trend in lidar ratio; we hypothesize that this is caused by the opposite effects of aerosol swelling and aerosol in-cloud processing on the lidar ratio. Finally, the observed backscatter Angstrom exponent (BAE) does not show a consistent trend because of the complicated relationship between BAE and RH. We demonstrate that BAE should not be used as a surrogate for Angstrom exponent, especially at high RH.
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