Pathfinder Atmospheres Extended Research Articles

Comparison of Proxy-Shortwave Cloud Albedo from SBUV Observations with CMIP6 Models

Abstract We construct a long-term record of top of atmosphere (TOA) shortwave (SW) albedo of clouds and aerosols from 340-nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an equilibrium climate sensitivity (ECS) of ∼3.5°C, with an uncertainty from 2.7° to 5.1°C. Our ECS estimates are sensitive to the instrument calibration, which drives the wide range in ECS uncertainty. We use instrument calibrations that assume a neutral change in reflectivity over the Antarctic ice sheet. Our observations show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California. To produce our SW cloud+aerosol albedo, we first retrieve a black-sky cloud albedo (BCA) and empirically correct the sampling bias from diurnal variations. Then, we estimate the broadband proxy albedo using multiple nonlinear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Pathfinder Atmospheres–Extended (PATMOS-x) observational dataset. Significance Statement Equilibrium climate sensitivity is a measure of the rise in global temperature over hundreds of years after a doubling of atmospheric CO2 concentration. Current state-of-the-art climate models forecast a wide range of equilibrium climate sensitivities (1.5°–6°C), due mainly to how clouds, aerosols, and sea surface temperatures are simulated within these models. Using data from NASA and NOAA satellite instruments from 1980 to 2013, we first construct a dataset that describes how much sunlight has been reflected by clouds over the 34 years and then we compare this data record to output from 47 climate models. Based on these comparisons, we conclude the best estimate of equilibrium climate sensitivity is about 3.5°C, with an uncertainty range of 2.7°–5.1°C.

Effects of Forbush decreases on clouds determined from PATMOS-x

This study examines the relationship between cosmic rays and clouds during Forbush decreases (FDs) to understand the cause-effect relationships between cloud microphysics, cloud condensation nuclei (CCN), and ionisation in the atmosphere. The results of a Monte Carlo analysis of cloud parameters during FDs, which were obtained using newly calibrated satellite data (Pathfinder Atmospheres Extended (PATMOS-x)) from 1978 to 2018, show the connections between some cloud parameters and FDs. For context, FD is the event where the number of cosmic rays arriving in the atmosphere decreases and recovers over several days. Other studies have shown that FDs impacted the cloud fraction, aerosol optical depth, CCN, water content, and cloud effective radius (reff) in the atmosphere. Using the Monte Carlo analysis, nine atmospheric parameters from the dataset were evaluated and exhibited a significant response to FDs. Each added FD event (after the first event) reduces the noise, but only the strongest events add a significant signal (exceptionally when the 2nd and 5th rank FD data are added, the signal/noise ration dropped due to a change in the satellite version). We found that cloud fraction shows statistically significant signals following FDs at an achieved significance level of 0.33%. Cloud emissivity also showed highly significant signals from the analysis; however, these cannot be classified as physical causes of FDs since the response starts a week before the FDs. In contrast, the cloud optical depth, integrated total cloud water over the entire column, and reff did not show any significant signals in the frameworks of the applied methods. The top of the atmosphere brightness temperatures (TABTs) at nominal wavelengths of 3.75, 11.0, and 12.0 μm were analysed again along with surface BTs and showed significant signals. The estimated changes in the BT were determined using a radiative transfer model (Fu-Liou model) and showed consistent results with the observed changes in cloud parameters during FD events. Among the analysed several atmospheric/cloud/aerosol parameters, cloud fraction and TABT at nominal wavelengths of 3.75, 11.0, and 12.0 μm are the only parameters depicting a statistically significant and correct-phase response to FDs.

Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method

Aerosols play an important role in global climate change, which requires long-term data records. Advanced very high-resolution radiometer (AVHRR) provides continuous observations for up to 40 years since 1979, which makes it worthwhile to retrieve aerosol optical depth (AOD) from AVHRR over land. A novel algorithm for retrieving AOD from AVHRR is developed based on the machine learning (ML) method. The AVHRR observations from pathfinder atmospheres–extended (PATMOS-x) Level-2 dataset and corresponding AOD products ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.55~\mu \mathrm {m}$ </tex-math></inline-formula> ) from moderate resolution imaging spectroradiometer (MODIS) in 2014 are used as training data. And AOD products in three years (2015, 2006, and 1998) named AVHRR XGB-AOD were generated for evaluation. Comparisons show that the AVHRR XGB-AOD is consistent with the MODIS AOD with correlation coefficients greater than 0.80 and RMSE less than 0.18 for most months in 2015 and 2006. The temporal and spatial characteristics from AVHRR XGB-AOD are similar to those from the MODIS AOD, but those from the previous AVHRR AOD with deep blue (DB) algorithm are significantly different. Validation with AERONET indicates that more than 68% of the matchups fall within expected error [EE, ±( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.05\,\,\pm \,\,0.25\times {\mathrm {AOD}}_{\mathrm {AERONET}}\mathrm {)] }$ </tex-math></inline-formula> in 2015 and 2006, while the fraction is 66% in 1998. Compared to the DB algorithm, the ML-based algorithm performs better in high-AOD conditions over vegetated regions, such as in Southeast Asia, where the DB algorithm significantly underestimates. In low-AOD conditions, the ML-based algorithm performs better over western North America and Australia, where the aerosol composition varies greatly.

A 40-year global data set of visible-channel remote-sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

Abstract. A consistently calibrated 40-year-long data set of visible-channel remote-sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time series. The data set uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record for top-of-atmosphere (TOA) visible-channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote-sensing reflectance. The resulting data set is produced at daily, and archived at monthly, resolution, on a 0.1∘×0.1∘ grid at https://doi.org/10.1594/PANGAEA.892175. The primary aim of deriving this data set is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania huxleyi (where lith concentration &gt;2–5×104 mL−1) over the past 40 years.

Cloud glaciation temperature estimation from passive remote sensing data with evolutionary computing

AbstractThe phase partitioning between supercooled liquid water and ice in clouds in the temperature range between 0 and −37°C influences their optical properties and the efficiency of precipitation formation. Passive remote sensing observations provide long‐term records of the cloud top phase at a high spatial resolution. Based on the assumption of a cumulative Gaussian distribution of the ice cloud fraction as a function of temperature, we quantify the cloud glaciation temperature (CGT) as the 50th percentile of the fitted distribution function and its variance for different cloud top pressure intervals, obtained by applying an evolutionary algorithm (EA). EAs are metaheuristics approaches for optimization, used in difficult problems where standard approaches are either not applicable or show poor performance. In this case, the proposed EA is applied to 4 years of Pathfinder Atmospheres‐Extended (PATMOS‐x) data, aggregated into boxes of 1° × 1° and vertical layers of 5.5 hPa. The resulting vertical profile of CGT shows a characteristic sickle shape, indicating low CGTs close to homogeneous freezing in the upper troposphere and significantly higher values in the midtroposphere. In winter, a pronounced land‐sea contrast is found at midlatitudes, with lower CGTs over land. Among this and previous studies, there is disagreement on the sign of the land‐sea difference in CGT, suggesting that it is strongly sensitive to the detected and analyzed cloud types, the time of the day, and the phase retrieval method.

Climatology and changes in cloud cover in the area of the Black, Caspian, and Aral seas (1991–2010): a comparison of surface observations with satellite and reanalysis products

ABSTRACTThis article presents a climatology of total cloud cover (TCC) in the area of the three inland Eurasian seas (Black, Caspian, and Aral Sea). Analyses are performed on the basis of 20 years of data (1991–2010), collected from almost 200 ground stations. Average TCC is 49%, with broad spatial and seasonal variability: minimum TCC values are found in summer and to the southeast, whereas maximum values correspond to winter and to the northwest. For the whole area, linear trend analyses show that TCC did not vary during the study period. We only detected a statistically significant positive trend (+1.2% decade−1) in autumn. We obtained different results for the regions delimited by means of a principal component analysis: a clear decrease, both for the annual, spring, and summer series, was detected for the south of Black Sea, while increasing TCC was found for the annual, autumn, and winter series in the north Caucasus and the west and north of Black Sea. We also analysed the TCC data from global gridded products, including satellite projects [International Satellite Cloud Climatology Project (ISCCP), Pathfinder Atmospheres Extended (PATMOS‐x), cLoud, Albedo &amp; Radiation (CLARA)], reanalyses [ERA‐interim, National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), Modern‐Era Retrospective Analysis for Research and Applications (MERRA)], and surface observations [Climatic Research Unit (CRU)]. Although all these products capture the seasonal evolution over the study area, they differ substantially both among them and in relation to the ground observations: reanalyses produce much lower values of TCC, while ISCCP and CLARA provide a summer minimum that is too high. Trend analyses applied to these data generally showed a decrease in TCC; only CRU and NCEP/DOE tally with the ground data as regards the absence of overall trends. These results are discussed in relation to previous studies presenting trends of other variables such as sunshine duration, diurnal temperature range, or precipitation; we also discuss the connections with changes in synoptic patterns and environmental changes, in particular in the Aral Sea region.

Intercomparison of Independent Calibration Techniques Applied to the Visible Channel of the ISCCP B1 Data

AbstractThe International Satellite Cloud Climatology Project (ISCCP) B1 data, which were recently rescued at the National Oceanic and Atmospheric Administration’s National Climatic Data Center (NOAA/NCDC), are a resource for the study of the earth’s climate. The ISCCP B1 data represent geostationary satellite imagery for all channels, including the infrared (IR), visible, and IR water vapor sensors. These are global 3-hourly snapshots from satellites around the world, covering the time period from 1979 to present at approximately 10-km spatial resolution. ISCCP B1 data will be used in the reprocessing of the cloud products, resulting in a higher-resolution ISCCP cloud climatology, surface radiation budget (SRB), etc. To realize the promise of a higher-resolution cloud climatology from the B1 data, an independent assessment of the calibration of the visible band was performed. The present study aims to accomplish this by cross-calibrating with the intercalibrated Advanced Very High Resolution Radiometer (AVHRR) reflectance data from the AVHRR Pathfinder Atmospheres–Extended (PATMOS-x) dataset. Since the reflectance calibration approach followed in the PATMOS-x dataset is radiometrically tied to the absolute calibration of the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imager instrument, the present intercalibration scheme yields calibration coefficients consistent with MODIS. Results from this study show that the two independent sets (this study and the ISCCP) of results agree to within their mutual uncertainties. An independent approach to calibration based on multiyear observations over spatially and temporally invariant desert sites has also been used for validation. Results reveal that for most of the geostationary satellites, the mean difference with ISCCP calibration is less than 3% with the random errors under 2%. Another result is that this extends the intercalibrated record to beyond what ISCCP provides (prior to 1983 and beyond 2009).

Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations

Abstract Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1 for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.

Empirical Removal of Artifacts from the ISCCP and PATMOS-x Satellite Cloud Records

AbstractThe International Satellite Cloud Climatology Project (ISCCP) dataset and the Pathfinder Atmospheres–Extended (PATMOS-x) dataset are two commonly used multidecadal satellite cloud records. Because they are constructed from weather satellite measurements lacking long-term stability, ISCCP and PATMOS-x suffer from artifacts that inhibit their use for investigating cloud changes over recent decades. The present study describes and applies a post hoc method to empirically remove spurious variability from anomalies in total cloud fraction at each grid box. Spurious variability removed includes that associated with systematic changes in satellite zenith angle, drifts in satellite equatorial crossing time, and unrealistic large-scale spatially coherent anomalies associated with known and unidentified problems in instrument calibration and ancillary data. The basic method is to calculate for each grid box the least squares best-fit line between cloud anomalies and artifact factor anomalies, and to let the residuals from the best-fit line be the newly corrected data. After the correction procedure, the patterns of regional trends in ISCCP and PATMOS-x total cloud fraction appear much more natural. The corrected data cannot be used for studies of globally averaged cloud change, however, because the methods employed remove any real cloud variability occurring on global scales together with spurious variability. An examination of Moderate Resolution Imaging Spectroradiometer (MODIS) total cloud fraction data indicates that removing global-scale variability has little impact on regional patterns of cloud change. Corrected ISCCP and PATMOS-x data are available from the Research Data Archive at NCAR.

Entering the Era of +30-Year Satellite Cloud Climatologies: A North American Case Study

Abstract The emergence of satellite-based cloud records of climate length and quality hold tremendous potential for climate model development, climate monitoring, and studies on global water cycling and its subsequent energetics. This article examines the more than 30-yr Pathfinder Atmospheres–Extended (PATMOS-x) Advanced Very High Resolution Radiometer (AVHRR) cloudiness record over North America and assesses its suitability as a climate-quality data record. A loss of ~4.2% total cloudiness is observed between 1982 and 2012 over a North American domain centered over the contiguous United States. While ENSO can explain some of the observed change, a weather state clustering analysis identifies shifts in weather patterns that result in loss of water cloud over the Great Lakes and cirrus over southern portions of the United States. The radiative properties of the shifting weather states are characterized, and the results suggest that extended cloud satellite records may prove useful tools for increasing knowledge of cloud feedbacks, a long-standing issue in the climate change community.

The Pathfinder Atmospheres–Extended AVHRR Climate Dataset

The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoid...

Spatiotemporal variations of cloud amount over the Yangtze River Delta, China

Based on the NOAA’s Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres Extended (PATMOS-x) monthly mean cloud amount data, variations of annual and seasonal mean cloud amount over the Yangtze River Delta (YRD), China were examined for the period 1982–2006 by using a linear regression analysis. Both total and high-level cloud amounts peak in June and reach minimum in December, mid-level clouds have a peak during winter months and reach a minimum in summer, and low-level clouds vary weakly throughout the year with a weak maximum from August to October. For the annual mean cloud amount, a slightly decreasing tendency (−0.6% sky cover per decade) of total cloud amount is observed during the studying period, which is mainly due to the reduction of annual mean high-level cloud amount (−2.2% sky cover per decade). Mid-level clouds occur least (approximately 15% sky cover) and remain invariant, while the low-level cloud amount shows a significant increase during spring (1.5% sky cover per decade) and summer (3.0% sky cover per decade). Further analysis has revealed that the increased low-level clouds during the summer season are mainly impacted by the local environment. For example, compared to the low-level cloud amounts over the adjacent rural areas (e.g., cropland, large water body, and mountain areas covered by forest), those over and around urban agglomerations rise more dramatically.

Using SURFRAD to Verify the NOAA Single-Channel Land Surface Temperature Algorithm

AbstractBecause of spectral shifts from instrument to instrument in the operational NOAA satellite imager longwave infrared channels, the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) has developed a single-channel land surface temperature (LST) algorithm based on the observed 11-μm radiances, numerical weather prediction data, and radiative transfer modeling that allows for consistent results from the Geostationary Operational Environmental Satellite-I/L (GOES-I/L), GOES-M–P, and Advanced Very High Resolution Radiometer (AVHRR)/1 through 3 sensor versions. This approach is implemented in the real-time NESDIS processing systems [GOES Surface and Insolation Products (GSIP) and Clouds from AVHRR Extended (CLAVR-x)], and in the Pathfinder Atmospheres–Extended (PATMOS-x) climate dataset. An analysis of the PATMOS-x LST against that derived from the upwelling broadband longwave flux at each Surface Radiation Network (SURFRAD) site showed that biases in PATMOS-x were approximately 1 K or less. The standard deviations of the PATMOS-x minus SURFRAD LST biases are generally 2.5 K or less at all sites for all sensors. Using the PATMOS-x minus SURFRAD LST distributions to validate the PATMOS-x cloud detection, the PATMOS-x cloud probability of correct detection values were shown to meet the GOES-R specifications for all sites.

Systematic and random errors between collocated satellite ice water path observations

AbstractThere remains large disagreement between ice‐water path (IWP) in observational data sets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics (±30° latitude) in 2007 was made using collocated measurements. The radio detection and ranging(radar)/light detection and ranging (lidar) (DARDAR) IWP data set, based on combined radar/lidar measurements, is used as a reference because it provides arguably the best estimate of the total column IWP. For each data set, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, in the moderate resolution imaging spectroradiometer (MODIS), advanced very high resolution radiometer–based Climate Monitoring Satellite Applications Facility (CMSAF), and Pathfinder Atmospheres‐Extended (PATMOS‐x) datasets, were found to be correlated with DARDAR over a large IWP range (~20–7000 g m‐2). The random errors of the collocated data sets have a close to lognormal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way, the upper limit for the random error of all considered data sets is determined. Data sets based on passive microwave measurements, microwave surface and precipitation products system (MSPPS), microwave integrated retrieval system (MiRS), and collocated microwave only (CMO), are largely correlated with DARDAR for IWP values larger than approximately 700 g m‐2. The combined uncertainty between these data sets and DARDAR in this range is slightly less MODIS‐DARDAR, but the systematic bias is nearly an order of magnitude.

PATMOS-x: Results from a Diurnally Corrected 30-yr Satellite Cloud Climatology

Abstract Satellite drift is a historical issue affecting the consistency of those few satellite records capable of being used for studies on climate time scales. Here, the authors address this issue for the Pathfinder Atmospheres Extended (PATMOS-x)/Advanced Very High Resolution Radiometer (AVHRR) cloudiness record, which spans three decades and 11 disparate sensors. A two-harmonic sinusoidal function is fit to a mean diurnal cycle of cloudiness derived over the course of the entire AVHRR record. The authors validate this function against measurements from Geostationary Operational Environmental Satellite (GOES) sensors, finding good agreement, and then test the stability of the diurnal cycle over the course of the AVHRR record. It is found that the diurnal cycle is subject to some interannual variability over land but that the differences are somewhat offset when averaged over an entire day. The fit function is used to generate daily averaged time series of ice, water, and total cloudiness over the tropics, where it is found that the diurnal correction affects the magnitude and even the sign of long-term cloudiness trends. A statistical method is applied to determine the minimum length of time required to detect significant trends, and the authors find that only recently have they begun generating satellite records of sufficient length to detect trends in cloudiness.

Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x

AbstractThis paper describes the daytime cloud optical and microphysical properties (DCOMP) retrieval for the Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset. Within PATMOS-x, DCOMP is applied to observations from the Advanced Very High Resolution Radiometer and employs the standard bispectral approach to estimate cloud optical depth and particle size. The retrievals are performed within the optimal estimation framework. Atmospheric-correction and forward-model parameters, such as surface albedo and gaseous absorber amounts, are obtained from numerical weather prediction reanalysis data and other climate datasets. DCOMP is set up to run on sensors with similar channel settings and has been successfully exercised on most current meteorological imagers. This quality makes DCOMP particularly valuable for climate research. Comparisons with the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 dataset are used to estimate the performance of DCOMP.

A Naive Bayesian Cloud-Detection Scheme Derived from CALIPSO and Applied within PATMOS-x

AbstractThe naive Bayesian methodology has been applied to the challenging problem of cloud detection with NOAA’s Advanced Very High Resolution Radiometer (AVHRR). An analysis of collocated NOAA-18/AVHRR and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations was used to automatically and globally derive the Bayesian classifiers. The resulting algorithm used six Bayesian classifiers computed separately for seven surface types. Relative to CALIPSO, the final results show a probability of correct detection of roughly 90% over water, deserts, and snow-free land; 82% over the Arctic; and below 80% over the Antarctic. This technique is applied within the NOAA Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset and the Clouds from AVHRR Extended (CLAVR-x) real-time product generation system. Comparisons of the PATMOS-x results with those from International Satellite Cloud Climatology Project (ISCCP) and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate close agreement with zonal mean differences in cloud amount being less than 5% over most zones. Most areas of difference coincided with regions where the Bayesian cloud mask reported elevated uncertainties. The ability to report uncertainties is a critical component of this approach.

Regional assessment of microphysical properties of marine boundary layer cloud using the PATMOS‐x dataset

Cloud droplet number concentration and geometrical thickness of marine boundary layer clouds are inferred from 25 years of National Oceanic and Atmospheric Administration's AVHRR Pathfinder Atmospheres‐Extended (PATMOS‐x) Level 2b retrievals of optical thickness and cloud droplet effective radius over the period 1982 through 2009. A novel approach to addressing nonphysical values of cloud droplet number concentration N owing to satellite orbital drift is applied by normalizing estimated droplet number concentrations with respect to local observation time. Cloud geometrical thickness H is also normalized to a common reference time by scaling H against diurnal values from a passive microwave liquid water path climatology. The effectiveness of the methods applied to correct N and H are evaluated. Both quantities are spatially and temporally characterized in several subtropical subsidence regions for likely drizzle‐free observations. Estimated liquid water path from PATMOS‐x is further validated against 20 years of liquid water path values from the Special Sensor Microwave/Imager (SSM/I). Good agreement between SSM/I and PATMOS‐x is found in coastal regions. Cloud droplet number concentrations in excess of 300 cm−3 are found along the western boundaries of the American and African continents, with greatly lower values found further out to sea with no observed long‐term trends in cloud properties.