Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a combination of both. From a statistical standpoint, models with combination inputs demonstrated the best performance, followed by models with calibrated radiance inputs, while models relying solely on calibrated radiance had poorer applicability. This work found that cloud top pressure (CTP) and cloud top temperature played a crucial role in CTH retrieval from MODIS data. However, within the same type of models, there were slight differences in the retrieved results, and these differences were not dependent on the quantity of input parameters. Therefore, the model with fewer inputs using cloud parameters and calibrated radiance was recommended and employed for individual case studies. This model produced results closest to the actual cloud top structure of the typhoon and exhibited similar cloud distribution patterns when compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CTHs from a climatic statistical perspective. This suggests that the recommended model has good applicability and credibility in CTH retrieval from MODIS images. This work provides a method to improve accurate CTHs from MODIS data for better utilization.

Assessment and Error Analysis of Terra-MODIS and MISR Cloud-Top Heights Through Comparison With ISS-CATS Lidar

Cloud-top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest-running single-platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud-Aerosol Transport System (CATS) lidar observations between 50ºN and 50ºS. Bias and precision of Terra CTH relative to CATS is shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single-layered, unbroken, optically thick clouds observed over all altitudes, the uncertainties in MODIS and MISR CTH are -540 ± 690 m and -280 ± 370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thin clouds. For multi-layered clouds, errors are summarized herein using both absolute CTH and CATS-layer-altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two-layered system, provided top-layer optical depth <∼0.3, but MISR low-cloud CTH errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter-sensor disagreements, as well as to provide the first estimate of the MISR stereo-opacity bias. For MISR, altitude-dependent wind-retrieval bias (-90 to -110 m) and stereo-opacity bias (-60 to -260 m) and for MODIS, CO2-slicing bias due to geometrically thick cirrus leads to overall negative CTH bias. MISR's precision is largely driven by precision in retrieved wind-speed (3.7 m s-1), whereas MODIS precision is driven by forward-modeling uncertainty.

Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau

The Tibetan Plateau (TP) has profound thermal and dynamic influences on the atmospheric circulation, energy, and water cycles of the climate system, which make the clouds over the TP the forefront of atmospheric and climate science. However, the highest altitude and most complex terrain of the TP make the retrieval of cloud properties challenging. In order to understand the performance and limitations of cloud retrievals over the TP derived from the state-of-the-art Advanced Geosynchronous Radiation Imager (AGRI) onboard the new generation of Chinese Geostationary (GEO) meteorological satellites Fengyun-4 (FY-4), a three-month comparison was conducted between FY-4A/AGRI and the Moderate Resolution Imaging Spectroradiometer (MODIS) for both cloud detection and cloud top height (CTH) pixel-level retrievals. For cloud detection, the AGRI and MODIS cloud mask retrievals showed a fractional agreement of 0.93 for cloudy conditions and 0.73 for clear scenes. AGRI tended to miss lower CTH clouds due to the lack of thermal contrast between the clouds and the surface of the TP. For cloud top height retrievals, the comparison showed that on average, AGRI underestimated the CTH relative to MODIS by 1.366 ± 2.235 km, and their differences presented a trend of increasing with height.

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical Retrievals to the Index of Refraction

A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the cloud effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering albedo, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard Moderate Resolution Imaging Spectroradiometer (MODIS)/Visible Infrared Imaging Radiometer suite (VIIRS) continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR and one in the MWIR. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1–2 µm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature gives more consistent retrievals between the two spectrally distinct 2.2 µm atmospheric window channels on MODIS and VIIRS. As a result, 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.

Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties.

A Decadal Global Climatology of Ice Cloud Fraction with Their Microphysical and Optical Properties Inferred from the CALIPSO and Reanalysis Data

In the present study, the spatiotemporal and vertical distributions of ice cloud properties and their association with meteorological variables are analyzed for the period 2007–2016 using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Modern Era Retrospective-Analysis for Research (MERRA-2) reanalysis observations. The distribution of ice cloud fraction (ICF) with its peak does not overlap with that of the ice water content (IWC) peak during daytime and nighttime due to the sampling bias. Moreover, the vertical distributions of mean IWC exhibited a vaguely “sharp thorn” at an altitude of ~4 km in all seasons at the location of about ±40°, which can be caused by the artifacts. Furthermore, it is noted that different ice cloud optical depth (ICOD) presents significant changes observed in their diurnal variations in the heights of peaks. The maximum diurnal difference of ice cloud properties occurs in the tropical regions of the North Hemisphere (NH) during summer. We also investigated the relation between ICOD and the meteorological variables and found that the ICOD values are dependent on the meteorological parameters.

The Continuity MODIS-VIIRS Cloud Mask

This paper introduces the Continuity Moderate Resolution Imaging Spectroradiometer (MODIS)-Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (MVCM), a cloud detection algorithm designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Soumi National Polar-orbiting Partnership (SNPP) spacecraft. It is based on the MODIS cloud mask that has been operating since 2000 with the launch of the Terra spacecraft (MOD35) and continuing in 2002 with Aqua (MYD35). The MVCM makes use of fourteen spectral bands that are common to both MODIS and VIIRS so as to create consistent cloud detection between the two instruments and across the years 2000–2020 and beyond. Through comparison data sets, including collocated Aqua MODIS and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) from the A-Train, this study was designed to assign statistical consistency benchmarks between the MYD35 and MVCM cloud masks. It is shown that the MVCM produces consistent cloud detection results between Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS and that the quality is comparable to the standard Aqua MODIS cloud mask. Globally, comparisons with collocated CALIOP lidar show combined clear and cloudy sky hit rates of 88.2%, 87.5%, 86.8%, and 86.8% for MYD35, MVCM Aqua MODIS, MVCM SNPP VIIRS, and MVCM NOAA-20 VIIRS, respectively, for June through until August, 2018. For the same months and in the same order for 60S–60N, hit rates are 90.7%, 90.5%, 90.1%, and 90.3%. From the time series constructed from gridded daily means of 60S–60N cloud fractions, we found that the mean day-to-day cloud fraction differences/standard deviations in percent to be 0.68/0.55, 0.94/0.64, −0.20/0.50, and 0.44/0.82 for MVCM Aqua MODIS-MVCM SNPP VIIRS day and night, and MVCM NOAA-20 VIIRS-MVCM SNPP VIIRS day and night, respectively. It is seen that the MODIS and VIIRS 1.38 µm cirrus detection bands perform similarly but with MODIS detecting slightly more clouds in the middle to high levels of the troposphere and the VIIRS detecting more in the upper troposphere above 16 km. In the Arctic, MVCM Aqua MODIS and SNPP VIIRS reported cloud fraction differences of 0–3% during the mid-summer season and −3–4% during the mid-winter.

Measurement of Cloud Top Height: Comparison of MODIS and Ground-Based Millimeter Radar

Cloud top height (CTH) is an essential pareter for the general circulation model in understanding the impact of clouds on the Earth’s radiation budget and global climate change. This paper compares the CTH products, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua and Terra satellites with ground-based Ka band radar data in Beijing from 2014 to 2017. The aim was to investigate the data accuracy and the difference in CTH measurements between passive satellite data and active ground-based radar data. The results show that MODIS, on average, underestimates CTH relative to radar by −1.08 ± 2.48 km, but with a median difference of −0.65 km and about 48% of differences are within 1 km. Statistically, MODIS CTHs which are greater than 6 km show lower discrepancy to radar CTH than those of MODIS CTHs less than 4 km. The CTH difference is independent of cloud fraction and cloud layer. It shows strong dependence on cloud depth, decreasing as cloud depth increases. There is a tendency for MODIS to underestimate high thin clouds but overestimate low thin clouds relative to radar. Total ozone, SO2, CO, NO2, aerosol PM10, total water vapor and temperature inversion show unobvious influences in the CTH discrepancy. It is shown that the MODIS CO2-slicing technique performs much better than IRW (infrared window) technique when cloud layer is higher than 2 km. The average difference calculated from all comparisons by CO2-slicing technique and IRW technique is 0.09 ± 1.58 km, and −2.20 ± 2.73 km, respectively.

The impact of aerosol on MODIS cloud detection and property retrieval in seriously polluted East China

Previous researches proved that aerosols have a significant influence on the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud observation. In East China, this impact is much greater and special compared with other regions because of the frequent haze pollution. This study evaluated the impact of aerosols on cloud detection, cloud top height (CTH) and cloud optical thickness (COT) retrieval in East China primarily using the MODIS and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observation, combined with a cloud detection rectification algorithm. The results showed that, in haze weather, MODIS misjudged large-scale of dense aerosols as "clouds", which increased the observed cloud cover by 0.4 to 0.6 in the most seriously polluted regions. Compared with the clear condition, high aerosol loading with AOD >2 would increase the misjudgment possibility by 35%. Another influence is that MODIS has a 30% higher possibility to obtain an over low CTH of high and thin clouds, and overestimate the COT of thin ice clouds by 2.15 to 3.74 under serious air pollution. Further analyzes found that the cloud detection and COT retrieval was mainly influenced by the dense aerosols, while the CTH retrieval is vulnerable to both thin and dense aerosol. This study made a quantitative measurement of the aerosol influence on MODIS cloud observation, and first made a deep explanation for the effect of air pollution density.

Global Evaluation of the Suitability of MODIS-Terra Detected Cloud Cover as a Proxy for Landsat 7 Cloud Conditions

Clouds limit the quality and availability of optical wavelength surface observations from Earth Observation (EO) satellites. This limitation is particularly relevant for the generation of systematic thematic products from EO medium spatial resolution polar orbiting sensors, such as Landsat, which have reduced temporal resolution compared to coarser resolution polar orbiting sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS on the Terra satellite is in the same orbit as Landsat 7 with an approximately 30 minute overpass difference. In this study, one year of global Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image cloud fractions over land are compared with collocated MODIS cloud fractions, generated by combining the MODIS-Terra global daily cloud mask product (MOD35) with the Landsat 7 ETM+ image footprints and acquisition calendar. The results show high correlation between the MODIS and Landsat 7 ETM+ cloud fractions (R2 = 0.83), negligible bias (median difference: <0.01) and low dispersion around the median (interquartile range: [−0.02, 0.06]). These results indicate that, globally, the cloud cover detected by MODIS-Terra data can be used as a proxy for Landsat 7 ETM+ cloud cover.

Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China

Haze pollution has frequently occurred in winter over Eastern China in recent years. Over Eastern China, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection data were compared with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) for three years (2013–2016) for three kinds of underlying surface types (dark, bright, and water). We found that MODIS and CALIOP agree most of the time (82% on average), but discrepancies occurred at low CALIOP cloud optical thickness (COT < 0.4) and low MODIS cloud top height (CTH < 1.5 km). In spring and summer, the CALIOP cloud fraction was higher by more than 0.1 than MODIS due to MODIS’s incapability of observing clouds with a lower COT. The discrepancy increased significantly with a decrease in MODIS CTH and an increase in aerosol optical depth (AOD, about 2–4 times), and MODIS observed more clouds that were undetected by CALIOP over PM2.5 > 75 μg m−3 regions in autumn and particularly in winter, suggesting that polluted weather over Eastern China may contaminate MODIS cloud detections because MODIS will misclassify a heavy aerosol layer as cloudy under intense haze conditions. Besides aerosols, the high solar zenith angle (SZA) in winter also affects MODIS cloud detection, and the ratio of MODIS cloud pixel numbers to CALIOP cloud-free pixel numbers at a high SZA increased a great deal (about 4–21 times) relative to that at low SZA for the three surfaces. As a result of the effects of aerosol and SZA, MODIS cloud fraction was 0.08 higher than CALIOP, and MODIS CTH was more than 2 km lower than CALIOP CTH in winter. As for the cloud phases and types, the results showed that most of the discrepancies could be attributed to water clouds and low clouds (cumulus and stratocumulus), which is consistent with most of the discrepancies at low MODIS CTH.

Satellites See the World’s Atmosphere

Satellite meteorology is a relatively new branch of the atmospheric sciences. The field emerged in the late 1950s during the Cold War and built on the advances in rocketry after World War II. In less than 70 years, satellite observations have transformed the way scientists observe and study Earth. This paper discusses some of the key advances in our understanding of the energy and water cycles, weather forecasting, and atmospheric composition enabled by satellite observations. While progress truly has been an international achievement, in accord with a monograph observing the centennial of the American Meteorological Society, as well as limited space, the emphasis of this chapter is on the U.S. satellite effort.

Improvement of MODIS cloud mask over severe polluted eastern China

Previous studies have proved that in the regions with severe air pollution, MODIS cloud mask product (MYD35) tends to overestimate the cloud cover largely. An important reason is that the dense aerosols could be misclassified as clouds. Identification of the misdetected "clouds" of passive remote sensing satellites remains challenging. In this study, we built an algorithm combining screening method and adjusted Fisher Discriminant Analysis (AFDA) to rectify the cloud free pixels misclassified as cloudy in the MYD35 product over the eastern China (EC), where heavy haze pollution occurs frequently in fall and winter. The CALIPSO vertical feature mask (VFM) product was used as an accurate reference. The results showed that our algorithm performs well in the discrimination of the true clouds and misdetected clouds, including the ones caused by the misjudgment of near surface aerosols in heavy haze. The average accuracy reached 96.72%. In EC, fogs ought to be classified as clouds often mixed with haze, resulting difficulty to distinguish fogs and haze. Compared with surface observed fogs, our algorithm also has a good effect on identification of the surface fog in EC with an accuracy of 81.53%. Mean values of a series of cloud properties showed great changes after filtering the misclassified MYD35 cloudy pixels. Thereinto, cloud cover decreased by 0.13, other parameters, including cloud top height, cloud optical thickness, cloud effective radius and cloud water path, also changed significantly.

The lifecycle of anvil clouds and the top-of-atmosphere radiation balance over the tropical west Pacific

Observations from a geostationary satellite are used to describe the lifecycle of mesoscale convective systems (MCS), their associated anvil clouds, and their effects on the radiation balance over the warm pool of the tropical west Pacific Ocean. In their developing stages, MCS primarily consist of clouds that are optically thick and have a negative net cloud radiative effect (CRE). As MCS age, ice crystals in the anvil become larger, the cloud top lowers somewhat, and clouds with neutral and positive net CRE become more common. Shading from anvils causes cool anomalies in the underlying sea surface temperature (SST) of up to -0.6 °C. MCS often occur in clusters that are embedded within large westward-propagating disturbances, so shading from anvils can cool SSTs over regions spanning hundreds of kilometers. Triggering of convection is more likely to follow a warm SST anomaly than a cold SST anomaly on timescales of several days. This information is used to test hypotheses on why, over the warm pool, the average shortwave and longwave CRE are individually large but nearly cancel. The results are consistent with the hypothesis that the cancelation in CRE is caused by feedbacks between cloud albedo, large-scale circulation, and SST.

MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds

Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients near the planetary boundary layer (PBL) and tropopause folds. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES) and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or LEO-LEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role.

An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars

Optically very thin ice clouds from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and ground-based Raman lidars (RL) at the atmospheric radiation measurement (ARM) sites of the Southern Great Plains (SGP) and Tropical Western Pacific (TWP) are analyzed. The optically very thin ice clouds, with ice cloud column optical depths below 0.01, are about 23% of the transparent ice-cloudy profiles from the RL, compared to 4–7% from CALIPSO. The majority (66–76%) of optically very thin ice clouds from the RLs are found to be adjacent to ice clouds with ice cloud column optical depths greater than 0.01. The temporal structure of RL-observed optically very thin ice clouds indicates a clear sky–cloud continuum. Global cloudiness estimates from CALIPSO observations leveraged with high-sensitivity RL observations suggest that CALIPSO may underestimate the global cloud fraction when considering optically very thin ice clouds.

Space lidar observations constrain longwave cloud feedback

Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.

Optimizing cirrus optical depth retrievals over the ocean from collocated CALIPSO and AMSR-E observations

Retrievals of particulate optical depths and extinction coefficients from the cloud-aerosol lidar with orthogonal polarization (CALIOP) instrument deployed on the CALIPSO satellite mainly rely on a single global mean extinction-to-backscatter ratio, also known as the lidar ratio. However, the lidar ratio depends on the microphysical properties of particulates. An alternative approach is adopted to infer single-layer semi-transparent cirrus optical depths (CODs) over the open ocean that does not rely on an assumed lidar ratio. Instead, the COD is inferred directly from backscatter measurements obtained from the CALIOP lidar in conjunction with collocated sea surface wind speed data obtained from AMSR-E. This method is based on a Gram-Charlier ocean surface reflectance model relating wind-driven wave slope variances to sea surface wind speeds. To properly apply this method, the impact of multiple scattering between the sea surface and ice clouds should be taken into account. We take advantage of the 532 nm cross-polarization feature of CALIOP and introduce an empirical method based on the depolarization change at the sea surface to correct for potential bias in sea surface backscatter caused by whitecaps, bubbles, foam, and multiple scattering. After the correction, the COD can be derived for individual CALIOP retrievals in a single cloud layer over the ocean with this method. The global mean COD was found to be roughly 14% higher than the current values determined by the Version 4 CALIOP extinction retrieval algorithm. This study is relevant to future improvements of CALIOP operational products and is expected to lead to more accurate COD retrievals.

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors

Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.

Understanding How Low-Level Clouds and Fog Modify the Diurnal Cycle of Orographic Precipitation Using In Situ and Satellite Observations

Satellite orographic precipitation estimates exhibit large errors with space-time structure tied to landform. Observations in the Southern Appalachian Mountains (SAM) suggest that low-level clouds and fog (LLCF) amplify mid-day rainfall via seeder-feeder interactions (SFI) at both high and low elevations. Here, a rainfall microphysics model constrained by fog observations was used first to reveal that fast SFI (2–5 min time-scales) modify the rain drop size distributions by increasing coalescence efficiency among small drops (<0.7 mm diameter), whereas competition between coalescence and filament-only breakup dominates for larger drops (3–5 mm diameter). The net result is a large increase in the number concentrations of intermediate size raindrops in the 0.7–3 mm range and up to a ten-fold increase in rainfall intensity. Next, a 10-year climatology of satellite observations was developed to map LLCF. Combined estimates from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) and CloudSat products reveal persistent shallower cloud base heights at high elevations enveloping the terrain. The regional cloud top height climatology derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) shows high-frequency daytime LLCF over mountain ridges in the warm season shifting to river valleys at nighttime. In fall and winter, LLCF patterns define a cloud-shadow region east of the continental divide, consistent with downwind rain-shadow effects. Optical and microphysical properties from collocated MODIS and ground ceilometers indicate small values of vertically integrated cloud water path (CWP < 100 g/m2), optical thickness (COT < 15), and particle effective radius (CER) < 15 μm near cloud top whereas surface observed CER ~25 μm changes to ~150 μm and higher prior to the mid-day rainfall. The vertical stratification of LLCF microphysics and SFI at low levels pose a significant challenge to satellite-based remote sensing in complex topography.

The relationship between boundary layer stability and cloud cover in the post-cold frontal region

Using NASA-Aqua MODIS and AIRS data, the relationship between low-level cloud cover (cloud top below the 700 hPa level) and boundary layer stability is explored in post-cold frontal conditions. A linear relationship is found between seasonal cloud cover and two separate measures of inversion strength, the lower tropospheric stability (LTS) and the estimated inversion strength (EIS), for two specific regions in the north Atlantic and Pacific in quiescent and weakly subsiding conditions. The relationship barely changes when considering dynamically active and subsiding post-cold frontal conditions for the same regions. To explore the generality of this result and increase sample size, cold front centered composites of cloud cover and stability are constructed. The northern and southern hemisphere seasonal cloud cover and stability distributions in the post-cold frontal regions are then compared. A fairly good correlation between cloud cover and EIS is found in both hemispheres across all seasons, suggesting that a linear relationship between cloud cover and inversion strength proposed for quiescent conditions exists also in more dynamically active subsiding post-cold frontal conditions. However, for a given season and hemisphere, the correlation between cloud cover and EIS degrades in post-cold frontal regions, especially in the northern hemisphere. At these scales, other large scale factors tend to correlate better with cloud cover.

Overview of MPLNET Version 3 Cloud Detection

The National Aeronautics and Space Administration Micropulse Lidar Network Version 3 cloud detection algorithm is described and its differences relative to the previous version highlighted. Clouds are identified from normalized Level 1 signal profiles using two complementary methods. The first considers signal derivatives vertically for resolving low-level clouds. The second, which resolves high-level clouds like cirrus, is based on signal uncertainties given the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multi-temporal averaging scheme is used to improve cloud detection under conditions of weak signal-to-noise. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, MD) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5-km, mean sea level) which increase in occurrence by nearly 6%. There is also an increase in the detection of multi-layered cloud profiles from 9% to 20%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which molecular signal can be reliably retrieved above cirrus clouds occurs between cloud optical depths of 0.5 and 0.8.

Daytime Cirrus Cloud Top-of-Atmosphere Radiative Forcing Properties at a Midlatitude Site and their Global Consequence

One-year of continuous ground-based lidar observations (2012) are analyzed for single-layer cirrus clouds at the NASA Micro Pulse Lidar Network site at the Goddard Space Flight Center to investigate top-of-atmosphere (TOA) annual net daytime radiative forcing properties. A slight positive net daytime forcing is estimated (i.e., warming) : 0.07 - 0.67 W/m² in relative terms, which reduces to 0.03 - 0.27 W/m² in absolute terms after normalizing to unity based on approximated 40% midlatitude occurrence frequency rate estimated from satellite. Results are based on bookend solutions for lidar extinction-to-backscatter (20 and 30 sr) and corresponding retrievals for 532 nm cloud extinction coefficient. Uncertainties due to cloud undersampling, attenuation effects, sample selection and lidar multiple scattering are described. A net daytime cooling effect is found from the very thinnest clouds (cloud optical depth ≤ 0.01) that is attributed to relatively high solar zenith angles. A relationship between positive/negative daytime cloud forcing is demonstrated as a function of solar zenith angle and cloud top temperature. These properties, combined with the influence of varying surface albedos, are used to conceptualize how daytime cloud forcing likely varies with latitude and season, with cirrus clouds exerting less positive forcing and potentially net TOA cooling approaching the summer poles (non-ice and snow covered) versus greater warming at the equator. The existence of such a gradient would lead cirrus to induce varying daytime TOA forcing annually and seasonally, making it a far greater challenge than presently believe to constrain daytime and diurnal cirrus contributions to global radiation budgets.

Cirrus cloud optical and microphysical property retrievals from eMAS during SEAC4RS using bi-spectral reflectance measurements within the 1.88 μm water vapor absorption band

Previous bi-spectral imager retrievals of cloud optical thickness (COT) and effective particle radius (CER) based on the Nakajima and King (1990) approach, such as those of the operational MODIS cloud optical property retrieval product (MOD06), have typically paired a non-absorbing visible or near-infrared wavelength, sensitive to COT, with an absorbing shortwave or midwave infrared wavelength sensitive to CER. However, in practice it is only necessary to select two spectral channels that exhibit a strong contrast in cloud particle absorption. Here it is shown, using eMAS observations obtained during NASA's SEAC⁴RS field campaign, that selecting two absorbing wavelength channels within the broader 1.88 μm water vapor absorption band, namely the 1.83 and 1.93 μm channels that have sufficient differences in ice crystal single scattering albedo, can yield COT and CER retrievals for thin to moderately thick single-layer cirrus that are reasonably consistent with other solar and IR imager-based and lidar-based retrievals. A distinct advantage of this channel selection for cirrus cloud retrievals is that the below-cloud water vapor absorption minimizes the surface contribution to measured cloudy TOA reflectance, in particular compared to the solar window channels used in heritage retrievals such as MOD06. This reduces retrieval uncertainty resulting from errors in the surface reflectance assumption, as well as reduces the frequency of retrieval failures for thin cirrus clouds.

Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans

Moderate Resolution Imaging Spectroradiometer (MODIS) retrieves cloud droplet effective radius (r_e ) and optical thickness (τ) by projecting observed cloud reflectances onto a precomputed look-up table (LUT). When observations fall outside of the LUT, the retrieval is considered "failed" because no combination of τ and r_e within the LUT can explain the observed cloud reflectances. In this study, the frequency and potential causes of failed MODIS retrievals for marine liquid phase (MLP) clouds are analyzed based on 1 year of Aqua MODIS Collection 6 products and collocated CALIOP and CloudSat observations. The retrieval based on the 0.86 µm and 2.1 µm MODIS channel combination has an overall failure rate of about 16% (10% for the 0.86 µm and 3.7 µm combination). The failure rates are lower over stratocumulus regimes and higher over the broken trade wind cumulus regimes. The leading type of failure is the "r_e too large" failure accounting for 60%-85% of all failed retrievals. The rest is mostly due to the "r_e too small" or τ retrieval failures. Enhanced retrieval failure rates are found when MLP cloud pixels are partially cloudy or have high subpixel inhomogeneity, are located at special Sun-satellite viewing geometries such as sunglint, large viewing or solar zenith angles, or cloudbow and glory angles, or are subject to cloud masking, cloud overlapping, and/or cloud phase retrieval issues. The majority (more than 84%) of failed retrievals along the CALIPSO track can be attributed to at least one or more of these potential reasons. The collocated CloudSat radar reflectivity observations reveal that the remaining failed retrievals are often precipitating. It remains an open question whether the extremely large r_e values observed in these clouds are the consequence of true cloud microphysics or still due to artifacts not included in this study.

Method for validating cloud mask obtained from satellite measurements using ground-based sky camera

Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.