Comparison Of Ceres-Modis Derived Polar Cloud Properties With Cloudsat/calipso And Ground-Based Measurements

Passive satellites often face difficulty when detecting clouds over snow and ice covered surfaces beneath them. The recent launches of active satellites, which directly measure cloud properties, have allowed scientists to gain a firsthand look at the complex cloud profiles across polar regions. To help quantify the differences between passive and active satellite retrievals, cloud properties derived for the Clouds and Earth\u27s Radiant Energy System (CERES) project using MODerate Resolution Imaging Spectroradiometer (MODIS) data are compared with combined measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat (CC), and Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) observations at the North Slope of Alaska site, from July 2006 to June 2010. The study was then extended to include the entire Arctic and Antarctic. During the 4-year period, monthly mean cloud fractions (CFs) between ARM and CC differ by 5%. While CERES-MODIS CF retrievals agree well with ARM and CC during warm months (May-October), retrievals during the cold season (November-April) significantly underestimate CF. Annual mean cloud-base heights derived from ARM and CC agree within 200 m, while their cloud-top heights (Htop) differ by an average of 1.2 km, due largely to CC detecting more upper-level clouds during the warm months. Effective cloud heights from CERES-MODIS retrievals fall between CC and ARM cloud bases and tops, as expected. Cloud fractions and heights across the span of the Arctic depict similar features as those shown at the ARM NSA site. During summer months, cloud fractions between CERES-MODIS and CC agree well, differing by no more than 10% across most regions of the Arctic. During this same season, however, cloud heights vary by as much as 5.2 km. This is largely due to multi-layer cloud systems, where CC measures the uppermost cloud layer, and CERES-MODIS detects lower cloud layers, resulting in lower CERES-MODIS cloud heights, but equal cloud fractions. Winter shows a contrast to the similarities in cloud fraction detected in summer, with CERES-MODIS underestimating CF by as much as 59%. Cloud heights between the two platforms, however, show better agreement during the cold months, when fewer high clouds occur. The largest differences in CERES-MODIS and CC cloud fractions and heights occur during the cold season (JJA) in the Antarctic. During this time period, CC detects cloud fractions as much as 43% higher than CERES-MODIS, over regions coupled with cloud heights up to 12.3 km higher than CERES-MODIS. These extreme differences are caused by the presence of polar stratospheric clouds, which occur at altitudes between 15 and 25 km, and are nearly impossible for the CERES-MODIS sensor to detect. Finally, single-layered low-level stratus cloud effective radius (re), liquid water path (LWP), and optical depth (τ) retrieved from CERES-MODIS and surface-based retrievals at the ARM NSA site were investigated. When surface snow and sea ice are not present, ARM and CERES-MODIS retrieved cloud droplet re, LWP, and τ agree well. However, when snow and sea ice are introduced, CERES-MODIS retrieved re values are higher than ARM results, while optical depths are lower. These differences suggest that CERES-MODIS cloud fraction retrieval algorithms during polar night and microphysical retrieval algorithms over snow and ice covered surfaces need future improvement

Evaluation of the Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Base Height (CBH) Pixel-level Retrieval Algorithm for Single-layer Water Clouds

Evaluation of the Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Base Height (CBH) product was accomplished. CBH is an important factor for aviation, but a lack of coverage for ground-based retrieval is a significant limitation. Space-based retrieval is essential; therefore, the VIIRS CBH pixel-level retrieval algorithm was assessed for single-layer water clouds. Accurate (truth) measurements were needed not only for the CBH product, but also for VIIRS cloud optical thickness (COT), effective particle size (EPS), and cloud top height (CTH). Data from Atmospheric Radiation Measurement (ARM) sites were used, with VIIRS-ARM matchups created from June 2013 through October 2015 for four locations. After initial CBH results were large and highly variable, the VIIRS CTH product was replaced with the ARM (truth) CTH product. This substantially reduced variability and errors in the VIIRS CBH products, demonstrating that the CBH algorithm is fundamentally sound. Thus, future research is needed to reduce errors in the VIIRS CTH products in order to ensure the CBH products are suitable for aviation support

Validation of the Cloud_CCI cloud products in the Arctic

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions, and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency’s (ESA) Cloud Climate Change Initiative (Cloud_CCI) project, which uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84° N, 24.29° E), North Slope of Alaska (NSA, 71.32° N, 156.61° W), Ny-Alesund (Ny-A, 78.92° N, 11.93° E), and Summit (72.59° N, 38.42° W). The Liquid Water Path (LWP) ground-based data are retrieved from microwave radiometers, while the Cloud Top Height (CTH) has been determined from the integrated lidar-radar measurements. The quality of the satellite products, Cloud Mask and Cloud Optical Depth (COD), have been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-A, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 2.8 Optical Depth (OD) units. When ice clouds are included, the underestimation increases to about 4.6 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by 7 gm−2, over NSA by 16 gm−2, and over Ny-Å by 24 gm−2. Over Summit, CCI LWP is overestimated for values lower than 20 gm−2 and underestimated for values greater than 20 gm−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. For CTH retrievals, the Cloud_CCI product overestimates single-layer clouds. To understand the effects of multi layer clouds on the CTH retrievals, the statistics are compared between the single layer clouds and all types (single + multi layer). When the multi layer clouds are included (i.e., all types), the observed CTH overestimation become underestimations of about 360-420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland-Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements, made at the four high-latitude sites

Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84∘ N, 24.29∘ E), North Slope of Alaska (NSA; 71.32∘ N, 156.61∘ W), Ny-Ålesund (Ny-Å; 78.92∘ N, 11.93∘ E), and Summit (72.59∘ N, 38.42∘ W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 3 optical depth (OD) units. When ice clouds are included, the underestimation increases to about 5 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈7 g m−2, over NSA by ≈16 g m−2, and over Ny-Å by ≈24 g m−2. Over Summit, CCI LWP is overestimated for values ≤20 g m−2 and underestimated for values &gt;20 g m−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. To understand the effects of multi-layer clouds on the CTH retrievals, the statistics are compared between the single-layer clouds and all types (single-layer + multi-layer). For CTH retrievals, the Cloud_CCI product overestimates the CTH for single-layer clouds. When the multi-layer clouds are included (i.e., all types), the observed CTH overestimation becomes an underestimation of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland–Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites.</p

A Global Investigation Of Cloud-Radiative Properties Through An Integrative Analysis Of Observations And Model Simulations

The cloud and radiative properties simulated in an assortment of global climate models (GCMs) and reanalyses are examined to identify and assess systematic biases based upon comparisons with multiple satellites observations and retrievals. The global mean total column cloud fraction (CF) simulated by the 33-member multimodel mean is 7% and 17% lower than the results from passive (Moderate Resolution Infrared Spectroradiometer, MODIS) and active (CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, CALIPSO) satellite remote sensing platforms. The simulated cloud water path (CWP), which is used as a proxy for optical depth, on global average, has a negative bias of ~17 g mâ2. Despite these errors in simulated cloud properties, the simulated top-of-atmosphere (TOA) radiation budgets match relatively well with Clouds and the Earth Radiant Energy System (CERES) measurements. The biases in multimodel mean global TOA reflected shortwave (SW) and outgoing longwave (LW) fluxes and cloud radiative effects (CREs) are less than 2.5 W mâ2. Nevertheless, when assessing models individually, some physically inconsistent results are evident. For example, in the ACCESS1.0 model, the simulated TOA SW and LW fluxes are within 2 W mâ2 of the observed global means, however, the global mean CF and CWP are underpredicted by ~10% and ~25 g mâ2, respectively. These unphysical model biases suggest tuning of the modeled radiation budgets. Two dynamically-driven regimes, based on the atmospheric vertical motion at 500 hPa (Ï500), are identified to provide a more quantitative measure of error in the radiation fields determined separately by biases in CF and CWP. These error types include the regime-averaged biases, biases in the sensitivity of TOA CREs to CF/CWP, and their co-variations. Overall, the biases in simulated CF and CWP are larger in the descent regime (Ï500 \u3e 25 hPa dayâ1) than in the ascent regime (Ï500 \u3c â25 hPa dayâ1), but are better correlated with observations. According to CERES observations, the sensitivity of LW CRE to CF is stronger in the ascent regime than in the decent regime (0.82 vs. 0.23 W mâ2 %â1) and the multimodel mean overestimates this value by ~40%. The difference in sensitivity of SW CRE to CF between the two regimes is less drastic (â1.34 vs. â1.12 W mâ2 %â1). TOA CREs rely independently on CWP in regions of large scale ascent and decent, as their sensitivities are similar between these two regimes (e.g., SW CRE/CWP = â0.28 W gâ1 for both regimes). In general, the total TOA CRE errors are heavily weighted by their biases in simulated sensitivity and biases in the simulated CF. A new observationally-constrained, data product is generated that can be used as a process-oriented diagnostic tool to further identify errors in simulated cloud and radiation fields. Based on the CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE), and through one-dimensional radiative transfer modeling, a global database of radiative heating rate profiles is produced for non-precipitating single-layered ice clouds. Non-precipitating single-layered ice clouds have a global occurrence frequency of ~18% with considerable frequency in the tropical upper troposphere (13â16 km). A variety of ice cloud types exist in the sample of single-layered ice clouds developed here, which is determined by the distribution on cloud-top temperatures (CTT). For example, a peak in the distribution near 190 K (260 K) suggests the existence of cirrus (glaciated ice) clouds. The ice cloud microphysical properties responsible for having the largest impact on radiation (e.g., ice water content [IWC] and effective radius [Re]) are largest in the tropics and mid-latitudes according to 2C-ICE. Accordingly, this is where the strongest TOA SW absorption, and subsequently, the strongest upper tropospheric net radiative heating (\u3e 1.5 K dayâ1) occurs. This newly generated product will provide the data for which new ice cloud parameterizations can be developed in global models

DEVELOPMENT OF IMPROVED TECHNIQUES FOR SATELLITE REMOTE SENSING OF CLOUDS AND RADIATION USING ARM DATA, FINAL REPORT

During the period, March 1997 – February 2006, the Principal Investigator and his research team co-authored 47 peer-reviewed papers and presented, at least, 138 papers at conferences, meetings, and workshops that were supported either in whole or in part by this agreement. We developed a state-of-the-art satellite cloud processing system that generates cloud properties over the Atmospheric Radiation (ARM) surface sites and surrounding domains in near-real time and outputs the results on the world wide web in image and digital formats. When the products are quality controlled, they are sent to the ARM archive for further dissemination. These products and raw satellite images can be accessed at http://cloudsgate2.larc.nasa.gov/cgi-bin/site/showdoc?docid=4&cmd=field-experiment-homepage&exp=ARM and are used by many in the ARM science community. The algorithms used in this system to generate cloud properties were validated and improved by the research conducted under this agreement. The team supported, at least, 11 ARM-related or supported field experiments by providing near-real time satellite imagery, cloud products, model results, and interactive analyses for mission planning, execution, and post-experiment scientific analyses. Comparisons of cloud properties derived from satellite, aircraft, and surface measurements were used to evaluate uncertainties in the cloud properties. Multiple-angle satellite retrievals were used to determine the influence of cloud structural and microphysical properties on the exiting radiation field

cloud property retrieval using synergistic AATSR and MERIS observations

A newly developed daytime cloud property retrieval algorithm FAME-C (Freie Universität Berlin AATSR MERIS Cloud) is presented. Synergistic observations from AATSR and MERIS, both mounted on the polar orbiting satellite ENVISAT, are used for cloud screening. For cloudy pixels two main steps are carried out in a sequential form. First, a micro-physical cloud property retrieval is performed using an AATSR near-infrared and visible channel. Cloud phase, cloud optical thickness, and effective radius are retrieved, and subsequently cloud water path is computed. Second, two independent cloud top height products are retrieved. For cloud top temperature AATSR brightness temperatures are used, while for cloud top pressure the MERIS oxygen-A absorption channel is used. Results from the micro-physical retrieval serve as input for the two cloud top height retrievals. Introduced are the AATSR and MERIS forward models and auxiliary data needed in FAME-C. Also, the optimal estimation method with uncertainty estimates, which also provides for uncertainty estimated of the retrieved property on a pixel-basis, is presented. Within the frame of the ESA Climate Change Initiative project first global cloud property retrievals have been conducted for the years 2007–2009. For this time period verification efforts are presented comparing FAME-C cloud micro-physical properties to MODIS-TERRA derived cloud micro-physical properties for four selected regions on the globe. The results show reasonable accuracies between the cloud micro- physical retrievals. Biases are generally smallest for marine stratocumulus clouds; −0.28, 0.41μm and −0.18 g m−2 for cloud optical thickness, effective radius and cloud water path, respectively. This is also true for the root mean square error. Also, both cloud top height products are compared to cloud top heights derived from ground-based cloud radars located at several ARM sites. FAME-C mostly shows an underestimation of cloud top heights when compared to radar observations, which is partly attributed to the difficulty of accurate cloud property retrievals for optically thin clouds and multi-layer clouds. The bias is smallest, −0.9 km, for AATSR derived cloud top heights for single- layer clouds

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.</p

Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution

This work presents a study on the sensitivity of two satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity is exploited by relating the difference in the retrieved cloud heights to cloud vertical extent. The two cloud height retrievals, performed within the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) algorithm, are based on independent measurements and different retrieval techniques. First, cloud top temperature (CTT) is retrieved from Advanced Along Track Scanning Radiometer (AATSR) measurements in the thermal infrared. Second, cloud top pressure (CTP) is retrieved from Medium Resolution Imaging Spectrometer (MERIS) measurements in the oxygen-A absorption band. Both CTT and CTP are converted to cloud top height (CTH) using atmospheric profiles from a numerical weather prediction model. A sensitivity study using radiative transfer simulations in the near- infrared and thermal infrared were performed to demonstrate the larger impact of the assumed cloud vertical extinction profile on MERIS than on AATSR top- of-atmosphere measurements. The difference in retrieved CTH (ΔCTH) from AATSR and MERIS are related to cloud vertical extent (CVE) as observed by ground- based lidar and radar at three ARM sites. To increase the impact of the cloud vertical extinction profile on the MERIS-CTP retrievals, single-layer and geometrically thin clouds are assumed in the forward model. The results of the comparison to the ground-based observations were separated into single-layer and multi-layer cloud cases. Analogous to previous findings, the MERIS-CTP retrievals appear to be close to pressure levels in the middle of the cloud. Assuming a linear relationship, the ΔCTH multiplied by 2.5 gives an estimate on the CVE for single-layer clouds. The relationship is weaker for multi-layer clouds. Due to large variations of cloud vertical extinction profiles occurring in nature, a quantitative estimate of the cloud vertical extent is accompanied with large uncertainties. Yet, estimates of the CVE can contribute to the characterization of a cloudy scene. To demonstrate the plausibility of the approach, an estimate of the CVE was applied to a case study. In light of the follow-up mission Sentinel-3 with AATSR and MERIS like instruments, Sea and Land Surface Temperature Radiometer (SLSTR) and (Ocean and Land Colour Instrument) OLCI, respectively, for which the FAME-C algorithm can be easily adapted, a more accurate estimate of the CVE can be expected. OLCI will have three channels in the oxygen-A absorption band, thus providing more pieces of information on the cloud vertical extinction profile