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\"This key new textbook provides a state-of-the-art view of the physics of cloud and precipitation formation, covering the most important topics in the field: the microphysics, thermodynamics, and cloud-scale dynamics. Highlights include: the condensation process explained with new insights from chemical physics studies; the impact of the particle curvature (the Kelvin equation) and solute effect \"-- Provided by publisher.

The geographical regions of northwestern Mexico consisting of the Pacific Ocean, the Baja California Peninsula with its mountain range along it, the Gulf of California, and the coastal zone with its Western Sierra Madre Mountain range, configure an alternation of water, land, water, land, all interacting with the atmosphere. It suggests investigating the cloud patterns, what clouds are most relevant in controlling radiation and the climate, and what type of cloud is associated with observed precipitation patterns in the region. The principal aim was to carry out a climatology of five types of clouds: cumulonimbus, cumulus, altostratus, stratocumulus and nimbostratus. The data set was obtained from the MODIS sensor, placed on the Aqua and Terra satellites, covering the period from 2001 to 2020. The results revealed that the precipitation distribution predominantly relates to the deep convective cloud pattern. A minor fraction of rain is associated with the nimbostratus cloud pattern. The pattern of the fraction of total coverage of the five types of clouds coincides very well with lower radiation values, demonstrating the regulatory role of clouds in the area’s climatology.

ISCCP continues to quantify the global distribution and diurnal-to-interannual variations of cloud properties in a revised version. This paper summarizes assessments of the previous version, describes refinements of the analysis and enhanced features of the product design, discusses the few notable changes in the results, and illustrates the long-term variations of global mean cloud properties and differing high cloud changes associated with ENSO. The new product design includes a global, pixel-level product on a 0.1° grid, all other gridded products at 1.0°-equivalent equal area, separate satellite products with ancillary data for regional studies, more detailed, embedded quality information, and all gridded products in netCDF format. All the data products including all input data, expanded documentation, the processing code, and an operations guide are available online. Notable changes are 1) a lowered ice–liquid temperature threshold, 2) a treatment of the radiative effects of aerosols and surface temperature inversions, 3) refined specification of the assumed cloud microphysics, and 4) interpolation of the main daytime cloud information overnight. The changes very slightly increase the global monthly mean cloud amount with a little more high cloud and a little less middle and low cloud. Over the whole period, total cloud amount slowly decreases caused by decreases in cumulus/altocumulus; consequently, average cloud-top temperature and optical thickness have increased. The diurnal and seasonal cloud variations are very similar to earlier versions. Analysis of the whole record shows that high cloud variations, but not low clouds, exhibit different patterns in different ENSO events.

The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds. Plain Language Summary The accurate identification of low clouds over the Tibetan Plateau (TP) is crucial for climate regulation, ecosystems, aviation safety, research, and modeling. However, satellite‐based methods often miss these clouds, misclassifying them as high‐level clouds. To remedy this, we developed a new algorithm using cloud‐top temperature and optical thickness, applied to Advanced Himawari Imager data. This significantly improves low‐cloud detection, better aligning with actual cloud phases. Simultaneously, we analyzed diurnal cloud variations over the TP with the new algorithm. Cloud types at different altitudes in the TP exhibit strong seasonality. The dominant cloud types in winter and summer are high and mid‐level, respectively. In spring, low clouds dominate during the day (2:00–10:00 UTC), transitioning to high clouds at night (10:00–18:00 UTC), with mid‐level clouds prevailing at other times. In autumn, low clouds dominate during the day, transitioning to mid‐level clouds at other times, with fewer occurrences of high clouds. Key Points Employing cloud‐top temperature instead of pressure resolves classification‐phase inconsistencies for clouds in the Tibetan Plateau (TP) Lidar validation shows new algorithm's low cloud detection outperforms the conventional International Satellite Cloud Climatology Project algorithm for both TP and plains The study reveals significant diurnal and seasonal variations in low clouds over the TP

The characteristics of ice clouds with a wide range of optical depths are studied based on satellite retrievals and radiative transfer modeling. Results show that the global-mean ice cloud optical depth, ice water path, and effective radius are approximately 2, 109 g m−2, and 48 , respectively. Ice cloud occurrence frequency varies depending not only on regions and seasons, but also on the types of ice clouds as defined by optical depth values. Ice clouds with different values show differently preferential locations on the planet; optically thinner ones ( < 3) are most frequently observed in the tropics around 15 km and in midlatitudes below 5 km, while thicker ones ( > 3) occur frequently in tropical convective areas and along midlatitude storm tracks. It is also found that ice water content and effective radius show different temperature dependence among the tropics, midlatitudes, and high latitudes. Based on analyzed ice cloud frequencies and microphysical properties, cloud radiative forcing is evaluated using a radiative transfer model. The results show that globally radiative forcing due to ice clouds introduces a net warming of the earth–atmosphere system. Those with < 4.0 all have a positive (warming) net forcing with the largest contribution by ice clouds with ~ 1.2. Regionally, ice clouds in high latitudes show a warming effect throughout the year, while they cause cooling during warm seasons but warming during cold seasons in midlatitudes. Ice cloud properties revealed in this study enhance the understanding of ice cloud climatology and can be used for validating climate models.

This study examines the progress made by reanalyses and satellite products in the estimation of cloud cover over China: the ECMWF reanalyses ERA5 and ERA-Interim, geostationary satellite observation Himawari-8 (H8) and the International Satellite Cloud Climatology Project H-series (ISCCP) product. There is great similarity in spatial patterns of cloud cover in reanalyses and satellite observations, especially between ERA5 and H8. Distinct characteristics of the seasonal evolution of cloud cover are shown over the Tibetan Plateau (TP), the southeast (SE) and northeast (NE) of China. Differences in magnitudes of cloud cover exist. Overestimations are about 10% for reanalyses and about 20% for ISCCP in compared with certain cloud cover in H8. When probable cloud (about 10%) in H8 is included in the estimation, biases reduce the most in ERA5. The cloud hit rate (CHR) and false alarm rate (FAR) in against H8 and ISCCP reveal that simulated clouds in ERA5 have been improved especially over eastern China, but with limited improvement over TP in compared with ERA-Interim. Diurnal variations of cloud cover are characterized by increases during daytime over those three regions. Amplifications of diurnal variation vary over different regions and months. Satellite observations and ERA5 indicate distinguished diurnal cycle of cloud cover over TP, while further investigation based on ERA5 reveals coherent diurnal cycle in meteorological environment. Long-term changes of cloud cover highlight decreasing trends over TP and particular during March in past decades based on ISCCP and ERA5, which require further investigation in future.

This article presents the GCM‐Oriented Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud Product (GOCCP) designed to evaluate the cloudiness simulated by general circulation models (GCMs). For this purpose, Cloud‐Aerosol Lidar with Orthogonal Polarization L1 data are processed following the same steps as in a lidar simulator used to diagnose the model cloud cover that CALIPSO would observe from space if the satellite was flying above an atmosphere similar to that predicted by the GCM. Instantaneous profiles of the lidar scattering ratio (SR) are first computed at the highest horizontal resolution of the data but at the vertical resolution typical of current GCMs, and then cloud diagnostics are inferred from these profiles: vertical distribution of cloud fraction, horizontal distribution of low, middle, high, and total cloud fractions, instantaneous SR profiles, and SR histograms as a function of height. Results are presented for different seasons (January–March 2007–2008 and June–August 2006–2008), and their sensitivity to parameters of the lidar simulator is investigated. It is shown that the choice of the vertical resolution and of the SR threshold value used for cloud detection can modify the cloud fraction by up to 0.20, particularly in the shallow cumulus regions. The tropical marine low‐level cloud fraction is larger during nighttime (by up to 0.15) than during daytime. The histograms of SR characterize the cloud types encountered in different regions. The GOCCP high‐level cloud amount is similar to that from the TIROS Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). The low‐level and middle‐level cloud fractions are larger than those derived from passive remote sensing (International Satellite Cloud Climatology Project, Moderate‐Resolution Imaging Spectroradiometer–Cloud and Earth Radiant Energy System Polarization and Directionality of Earth Reflectances, TOVS Path B, AIRS–Laboratoire de Météorologie Dynamique) because the latter only provide information on the uppermost cloud layer.

The liquid water path (LWP) adjustment due to aerosol–cloud interactions in marine stratocumulus remains a considerable source of uncertainty for climate sensitivity estimates. An unequivocal attribution of LWP adjustments to changes in aerosol concentration from climatology remains difficult due to the considerable covariance between meteorological conditions alongside changes in aerosol concentrations. We utilise the susceptibility framework to quantify the potential change in LWP adjustment with boundary layer (BL) depth in subtropical marine stratocumulus. We show that the LWP susceptibility, i.e. the relative change in LWP scaled by the relative change in cloud droplet number concentration, in marine BLs triples in magnitude from −0.1 to −0.31 as the BL deepens from 300 to 1200 m and deeper. We further find deep BLs to be underrepresented in pollution tracks, process modelling, and in situ studies of aerosol–cloud interactions in marine stratocumulus. Susceptibility estimates based on these approaches are skewed towards shallow BLs of moderate LWP susceptibility. Therefore, extrapolating LWP susceptibility estimates from shallow BLs to the entire cloud climatology may underestimate the true LWP adjustment within subtropical stratocumulus and thus overestimate the effective aerosol radiative forcing in this region. Meanwhile, LWP susceptibility estimates in deep BLs remain poorly constrained. While susceptibility estimates in shallow BLs are found to be consistent with process modelling studies, they overestimate pollution track estimates.

This paper describes the new global long-term International Satellite Cloud Climatology Project (ISCCP) H-series climate data record (CDR). The H-series data contain a suite of level 2 and 3 products for monitoring the distribution and variation of cloud and surface properties to better understand the effects of clouds on climate, the radiation budget, and the global hydrologic cycle. This product is currently available for public use and is derived from both geostationary and polar-orbiting satellite imaging radiometers with common visible and infrared (IR) channels. The H-series data currently span July 1983 to December 2009 with plans for continued production to extend the record to the present with regular updates. The H-series data are the longest combined geostationary and polar orbiter satellite-based CDR of cloud properties. Access to the data is provided in network common data form (netCDF) and archived by NOAA's National Centers for Environmental Information (NCEI) under the satellite Climate Data Record Program (https://doi.org/10.7289/V5QZ281S). The basic characteristics, history, and evolution of the dataset are presented herein with particular emphasis on and discussion of product changes between the H-series and the widely used predecessor D-series product which also spans from July 1983 through December 2009. Key refinements included in the ISCCP H-series CDR are based on improved quality control measures, modified ancillary inputs, higher spatial resolution input and output products, calibration refinements, and updated documentation and metadata to bring the H-series product into compliance with existing standards for climate data records.

This study investigates the impact of the adjustment times of the atmospheric boundary layer (ABL) on the control of low-cloud coverage (LCC) climatology by large-scale atmospheric conditions in the subtropics. Using monthly data, we calculate back-trajectories and use machine learning statistical models with feature selection capabilities to determine the influence of local and upstream large-scale conditions on LCC for four physical cloud regimes: the stratocumulus (Sc) deck, the along-flow transition into the Sc deck (“Inflow”), the Sc-to-cumulus transition, and trade-cumulus clouds. All four regimes have unique local and upstream relationships with the large-scale meteorological variables within our parameter space, with upstream controls of LCC being the dominant processes in Sc deck and Sc-to-cumulus transition regimes. The time scales associated with these upstream controls across all regimes are consistent with known adjustment time scales of the ABL, determined in both modeling and observational studies. We find that low-level thermodynamic stratification (estimated inversion strength) is not the most important large-scale variable for LCC prediction in transition and trade-cumulus regimes despite its ubiquitous use as a proxy for LCC throughout the subtropics. Including upstream control provides significant improvements to the skill of statistical models predicting monthly LCC, increasing explained variance on the order of 15% in the Inflow, Sc deck, and transition regimes, but provides no improvement in the trade-cumulus regime.