Cloud Top Pressure Research Articles

The Variations of Outgoing Longwave Radiation in East Asia and Its Influencing Factors

Outgoing longwave radiation (OLR) data are one of the key factors in studying the radiation balance of the earth–atmosphere system in East Asia. It is of great significance to explore the influence factors on OLR. This paper processes the data of nearly 19 years, from September 2002 to February 2022, and conducts in-depth research using the exponential smoothing method, empirical orthogonal decomposition (EOF), correlation analysis, and other methods. We found that the spatial distribution of OLR is zonal symmetry and gradually decreases with the increase of latitude. Using EOF analysis, it is found that the total variance contribution of the first four decomposed spatial features exceeds 70%, and the overall change trend of the four-time coefficients in the past 19 years all show a downward trend. OLR is positively correlated with total column water vapor (TCWV), air temperature (AT), and cloud top temperature (CTT), but negatively correlated with cloud top pressure (CTP). OLR has a similar spatial correlation distribution with TCWV and AT, while the spatial correlation between OLR and CTP is opposite to the first two parameters. In most parts of East Asia, the spatial correlation with CTT exceeds 0.8. The change in OLR value is affected by various meteorological parameters. In East Asia, the positive correlation between 30° N and 60° N is significantly affected by TCWV, AT, and CTT; and the negative correlation is more significantly affected by CTP. At 0–25° N, the positive correlation is significantly affected by CTP and CTT, while the negative correlation is significantly affected by TCWV and AT.

Using Model Cloud Information to Reassign Low-Level Atmospheric Motion Vectors in the ECMWF Assimilation System

Abstract This paper investigates the use of model cloud information in the assimilation of low-level atmospheric motion vectors (AMVs) in the ECMWF global data assimilation system, with the aim to characterize and address issues encountered in the assimilation of these observations. An analysis of background departure statistics (comparison of observations with the model background) shows that AMVs placed above the model cloud show larger deviations from the model background relative to those placed unrealistically close to the surface. Reassigning the pressure of AMVs diagnosed above the model cloud layer to either the model cloud top, cloud base, or average pressure leads to improvements in root-mean-square vector difference (RMSVD) and speed bias against the background wind fields. In assimilation experiments, reassigning AMVs placed above the model cloud to the model cloud top, cloud base, or average pressure results overall in a positive impact on subsequent forecasts. The reassignment to an average model cloud pressure performs best in this respect, and this approach has been implemented in the operational ECMWF system in October 2021.

The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission

Abstract. This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.

Retrieving Vertical Cloud Radar Reflectivity from MODIS Cloud Products with CGAN: An Evaluation for Different Cloud Types and Latitudes

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity > −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms.

TROPOMI/S5P Total Column Water Vapor validation against AERONET ground-based measurements

Abstract. Water vapor plays an important role in the greenhouse effect, rendering it an atmospheric constituent that requires continuous and global monitoring by different types of remote sensing instruments. The TROPOspheric Monitoring Instrument Sentinel-5 Precursor (TROPOMI/S5P) Total Column Water Vapor (TCWV) is a new product retrieved from the visible blue spectral range (435–455 nm), using an algorithm that was originally developed for the GOME-2/MetOp sensors. For the purposes of this work, 2.5 years of continuous satellite observations at high spatial resolution are validated against co-located (in space and in time) precipitable water Level 2.0 (quality-assured) ground-based measurements from the NASA AERONET (AErosol RObotic NETwork). The network uses Cimel Sun photometers located at approximately 1300 stations globally to monitor precipitable water among other products. Based on data availability, 369 of the stations were used in this study. The two datasets, satellite- and ground-based, were co-located, and the relative differences of the comparisons were calculated and statistically analyzed. The Pearson correlation coefficient of the two products is found to be 0.91, and the mean bias of the overall relative percentage differences is of the order of −2.7 %. For the Northern Hemisphere midlatitudes (30–60∘ N), where the density of the ground-based stations is high, the mean relative bias was found to be −1.8 %, while in the tropics (±15∘) the TROPOMI TCWV product has a relative dry bias of up to −10 %. The effect of various algorithm and geophysical parameters, such as air mass factor, solar zenith angle, clouds and albedo, is also presented and discussed. It was found that the cloud properties affect the validation results, leading the TCWV to a dry bias of −20 % for low cloud heights (cloud top pressure (CTP) >800 hPa). Moreover, cloud albedo introduces a wet bias of 15 % when it is below 0.3 and a dry bias up to −25 % when the clouds are more reflective. Overall, the TROPOMI/S5P TCWV product, on a global scale and for moderate albedo and cloudiness, agrees well at -2.7±4.9 % with the AERONET observations but probably within about −8 % to −13 % with respect to the “truth”.

An Assessment of the Influences of Clouds on the Solar Photovoltaic Potential over China

Clouds are important modulators of the solar radiation reaching the earth’s surface. However, the impacts of cloud properties other than cloud cover are seldom mentioned. By combining the satellite-retrieved cloud properties, the latest radiative transfer model, and an advanced PVLIB-python software for solar photovoltaic (PV) estimation, the impacts of different types of clouds on the maximum available solar PV potential (measured with the plane-of-array-irradiance, POAI) are quantified. The impacts of ice and liquid water clouds are found to be the highest on Tibetan Plateau over western China in spring, and central and southern China in winter, respectively. The reduction of POAI by liquid water clouds is almost twice of that by ice clouds except for spring. It is found that the POAI can be reduced by 27–34% by all clouds (ice + liquid water clouds) in different seasons. The sensitivities of the solar PV potential to the changes in cloud properties including the cloud fraction, cloud top pressure, cloud effective radius, and cloud water path are also analyzed. Three kinds of settings of PV panel tilting, namely fixed tilt, one-axis tracking, and two-axis tracking, are considered. It is found that the cloud properties are essential to estimate the solar PV potentials, especially for the cloud fraction. The attenuation of solar radiation by clouds are growingly larger as the solar plane tilting settings get more complicated. The outlook of solar PV potential is quite variable as the changes in cloud properties are highly uncertain in the future climate scenarios.

Clouds form on the hot Saturn JWST ERO target WASP-96b

Context. WASP-96b is a hot Saturn exoplanet, with an equilibrium temperature of ≈1300 K. This is well within the regime of thermo-dynamically expected extensive cloud formation. Prior observations with Hubble/WFC3, Spitzer/IRAC, and VLT/FORS2 have been combined into a single spectrum for which retrievals suggest a cold but cloud-free atmosphere. Recently, the planet was observed with the James Webb Space Telescope (JWST) as part of the Early Release Observations (ERO). Aims. The formation of clouds in the atmosphere of exoplanet WASP-96b is explored. Methods. One-dimensional profiles were extracted from the 3D GCM expeRT/MITgcm results and used as input for a kinetic, non-equilibrium model to study the formation of mineral cloud particles of mixed composition. The ARCiS retrieval framework was applied to the pre-JWST WASP-96b transit spectrum to investigate the apparent contradiction between cloudy models and assumed cloud-free transit spectrum. Results. Clouds are predicted to be ubiquitous throughout the atmosphere of WASP-96b. Silicate materials contribute between 40% and 90% cloud particle volume, which means that metal oxides also contribute with up to 40% cloud particle volume in the low-pressure regimes that affect spectra. We explore how these cloudy models match currently available transit spectra. Reduced vertical mixing acts to settle clouds to deeper in the atmosphere, and an increased cloud particle porosity reduces the opacity of clouds in the near-IR and optical region. These two effects allow for clearer molecular features to be observed while still allowing clouds to be in the atmosphere. Conclusions. The atmosphere of WASP-96b is unlikely to be cloud free. Retrievals of HST, Spitzer, and VLT spectra also show that multiple cloudy solutions reproduce the data. JWST observations will be affected by clouds, where the cloud top pressure varies by an order of magnitude within even the NIRISS wavelength range. The long-wavelength end of NIRSpec and the short-wavelength end of MIRI may probe atmospheric asymmetries between the limbs of the terminator on WASP-96b.

Variations in aerosols and aerosols–cloud interactions in Bangkok using MODIS satellite data during high PM2.5 concentrations

The relationships between particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5), aerosol optical depth (AOD), and cloud parameters were investigated during high PM2.5 concentrations from 1 January 2016 to 31 December 2021 in Bangkok using satelliteremote sensingdatafrom Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud parameters included: Cloud effective radius (CER), Cloud fraction (CF), Cloud optical depth (COD), Cloud liquid water (CLW), Cloud top pressure (CTP), and Cloud top temperature (CTT). The analysis showed a strong negative correlation between PM2.5 and CF. There was a negative correlation between CF and AOD. In addition, PM2.5 showed a negative relationship with CER. In contrast, PM2.5 showed a positive correlation with CTP and CTT. A negative correlation was detected between COD and PM2.5. During high PM2.5 concentrations, cloud parameters are significantly influenced by PM2.5 concentrations in the atmosphere. The three-month of CER, CF, CLW, COD, CTP, and CTT ranges from 16.34 μm to 37.05 μm, 0.22 to 0.86, 15.67 g/m2 to 169.80 g/m2, 1.14 to 19.06, 261.67 hPa to 991.67 hPa, and −66.26C to –22.17C, respectively. Consequently, Altostratus clouds most appear during high PM2.5 concentrations in Bangkok. The findings may help better understand Bangkok's cloud properties and atmospheric aerosols, providing more information for rainmaking during high PM2.5 concentrations.

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).

Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers

Abstract Satellite low-Earth-orbiting (LEO) and geostationary (GEO) imager estimates of cloud-top pressure (CTP) have many applications in both operations and in studying long-term variations in cloud properties. Recently, machine learning (ML) approaches have shown improvement upon physically based algorithms. However, ML approaches, and especially neural networks, can suffer from a lack of interpretability, making it difficult to understand what information is most useful for accurate predictions of cloud properties. We trained several neural networks to estimate CTP from the infrared channels of the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Baseline Imager (ABI). The main focus of this work is assessing the relative importance of each instrument’s infrared channels in neural networks trained to estimate CTP. We use several ML explainability methods to offer different perspectives on feature importance. These methods show many differences in the relative feature importance depending on the exact method used, but most agree on a few points. Overall, the 8.4- and 8.6-μm channels appear to be the most useful for CTP estimation on ABI and VIIRS, respectively, with other native infrared window channels and the 13.3-μm channel playing a moderate role. Furthermore, we find that the neural networks learn relationships that may account for properties of clouds such as opacity and cloud-top phase that otherwise complicate the estimation of CTP. Significance Statement Model interpretability is an important consideration for transitioning machine learning models to operations. This work applies several explainability methods in an attempt to understand what information is most important for estimating the pressure level at the top of a cloud from satellite imagers in a neural network model. We observe much disagreement between approaches, which motivates further work in this area but find agreement on the importance of channels in the infrared window region around 8.6 and 10–12 μm, informing future cloud property algorithm development. We also find some evidence suggesting that these neural networks are able to learn physically relevant variability in radiation measurements related to key cloud properties.

Characteristic Differences of CrIS All-Sky Simulations of Brightness Temperature with Different Microphysics Parameterization Schemes

Abstract Influences of cloud liquid water, cloud ice, rain, snow, and graupel on all-sky simulations of the Cross-track Infrared Sounder (CrIS) brightness temperature (TB) are assessed for the 399 data assimilation (DA) channels. The analyses generated by the Gridpoint Statistical Interpolation (GSI) 3D-Var system assimilating conventional and clear-sky satellite radiance observations are used as initial conditions for the Weather Research and Forecasting Model to generate 6-h forecasts with three different microphysics schemes (WSM6, Thompson, and Morrison), which are then used as input to the Community Radiative Transfer Model for all-sky TB simulations. Under all-sky conditions, biases with the WSM6 scheme are negative for all channels and greater in magnitude than −3.5 K. The biases with the Thompson and Morrison schemes vary between −1 and 1 K for all channels. Bias differences among three MP schemes are small in stratus, altocumulus, and cumulus clouds, but large in cirrus and cirrocumulus clouds. The TB simulations in stratus, altocumulus, and cumulus clouds are mostly influenced by the cloud top pressure, while that in cirrus and cirrocumulus clouds depends strongly on cloud optical depth. All-sky TB simulations in cirrus conditions are more positively biased than those under cirrocumulus conditions, probably due to the microphysics schemes producing too thick cirrus clouds. Sensitivity experiments suggest that the TB discrepancies among DA experiments with three MP schemes are mostly caused by the ice or snow type rather than the effective radius of hydrometeor in the upper troposphere. Finally, we propose to combine a cloud-effect parameter with cloud types for modeling observation error characteristics in all-sky DA.

Investigating temporal changes in Jupiter’s aerosol structure with rotationally-averaged 2015–2020 HST WFC3 images

Annual imaging observations of Jupiter from 2015 onward by the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) as part of the Outer Planet Atmospheres Legacy (OPAL) program (Simon et al., 2015) have provided a valuable data set from which Jupiter’s temporal variability can be characterized and modeled. Spectral samples at discrete filter bands from the UV to short near-IR wavelengths also provide an opportunity to connect temporal changes in reflectivity with temporal changes in cloud structure. To improve constraints on that cloud structure we used center-to-limb variations that were noise-reduced by rotational averaging during each annual OPAL observation sequence. Fitting observations at multiple and distinctly different observer zenith angles helps to make up for the lack of multiple methane-band filters in discerning the vertical distribution of aerosols. The short-wavelength limit of the OPAL observations also provides better constraints than ground-based observation on the properties of Jupiter’s chromophore, i.e., the compound that creates Jupiter’s variety of red colorations to clouds and hazes composed mainly of colorless condensates. We chose four latitudes to investigate with radiative transfer models based on a simple 3-layer aerosol structure consisting of a deep putative NH3 cloud layer, with a physically thin chromophore layer that has a fitted location at or near the top of the deep cloud, and an overlying stratospheric haze layer. Our chosen planetocentric latitudes were 5.5°S (Equatorial Zone South,EZS), 0° (Equatorial Zone, EZ), 10°N (North Equatorial Belt, NEB), and 21.75°N (North Temperate Belt, NTB). Two major color transformations occurred during the 2015–2020 period we analyzed. One is a dramatic dimming of the center of the Equatorial Zone at short wavelengths, especially after 2017, and a significant reddening of that region. The second is a significant reddening of part of the North Temperate Belt (near 22°N) between 2016 and 2017. Both reddening events can be modeled by doubling of the optical depth of the chromophore layer during the transitions. Comparison with prior 2017 model results for the NEB, EZ, and NTB have revealed agreement with Braude et al. (2020) on the vertical location of the chromophore layer but disagreements on cloud-top pressures and the vertical distribution of stratospheric haze particles. We identified what seems to be common tendency of models based on HST/WFC3 observations to either infer very little stratospheric haze opacity or to put that opacity at too high a pressure.

Quantifying the Long-Term MODIS Cloud Regime Dependent Relationship between Aerosol Optical Depth and Cloud Properties over China

Aerosols modify cloud properties and influence the regional climate. The impacts of aerosols on clouds differ for various cloud types, but their long-term relationships have not been fully characterized on a cloud regime basis. In this study, we quantified the cloud regime-dependent relationship between aerosol optical depth (AOD) and cloud properties over China using Moderate-Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2019. Daily clouds in each 1° by 1° grid were categorized into seven cloud regimes based on the “k-means” clustering algorithm. Overall, the cloud height increased, the cloud thickness and liquid water path increased, and the total cloud cover decreased for all cloud regimes during the study period. Linear correlations between AOD and cloud properties were found within stratocumulus, deep convective, and high cloud regimes, showing consistency with the classic aerosol–cloud interaction paradigms. Using stepwise multivariable linear regression, we found that the meteorological factors dominated the variation of cloud top pressure, while AOD dominated the variation of total cloud cover for most cloud regimes. There are regional differences in the main meteorological factors affecting the cloud properties.

Characterizing atmospheres of cloudy temperate mini-neptunes with JWST

ABSTRACT The upcoming James Webb Space Telescope (JWST) promises a generational shift in the study of temperate mini-Neptune atmospheres using transit spectroscopy. High-altitude clouds, however, threaten to impede their atmospheric characterization by muting spectral features. In this study, we systematically investigate JWST instrument configurations for characterizing cloudy mini-Neptune atmospheres, assessing the importance of instrument choice and wavelength coverage, focusing on NIRISS and NIRSpec. We consider two temperate mini-Neptunes orbiting nearby M dwarfs, K2-18 b, and TOI-732 c, with equilibrium temperatures below 400 K, as case studies and assess observations using different instrument configurations with one transit per instrument. We find that their JWST transmission spectra with modest observing time and adequate wavelength coverage can provide precise abundance constraints of key molecules H2O, CH4, and NH3 even in the presence of clouds at significantly high altitudes. The best constraints are obtained by combining all three high-resolution NIRSpec gratings (G140H+G235H+G395H) that together span the ∼1–5 $\mu$m range. Single-transit observations with this three-instrument configuration allow precise abundance constraints for cloud-top pressures as low as 3 and 0.1 mbar for K2-18 b and TOI-732 c, respectively, assuming a nominal 10× solar metallicity. The constraints vary with instrument combinations. We find that NIRSpec G235H+G395H is the optimal two-instrument configuration, while NIRISS or NIRSpec G235H is optimal for single-instrument observations. Absent high-altitude clouds, even single-instrument observations can provide good abundance constraints for these planets. Our findings underscore the promise of JWST transmission spectroscopy for characterizing temperate mini-Neptunes orbiting nearby M dwarfs.

Assessment of Two Stochastic Cloud Subcolumn Generators Using Observed Fields of Vertically Resolved Cloud Extinction

Abstract We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed phase clouds when blended at ~200 m vertical and ~ 2 km horizontal scales. Upon segmenting the dataset to individual “scenes”, mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud top pressure – cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 W m−2 for the cloud radiative effect in the shortwave part of the spectrum.

Synergistic Use of Far‐ and Mid‐Infrared Spectral Radiances for Satellite‐Based Detection of Polar Ice Clouds Over Ocean

AbstractCloud phase is an important factor affecting cloud contributions to the polar energy budget. Brightness temperature differences (BTDs) between two mid‐infrared (mid‐IR) spectral channels are commonly used in satellite remote sensing to determine the cloud phase, but the mid‐IR has limitations for cloud phase determination in polar regions. This study explores the synergy between the far‐ and mid‐IR for ice cloud phase determinations over polar oceans. A far‐IR BTD test (BTD449‐521) is developed based on the spectral variations of both ice cloud scattering and absorption properties in the far‐IR “dirty window” region (400–600 cm−1 or 16.7–25 μm). Synthetic IR radiances at 1 cm−1 spectral resolution are generated using ERA5 reanalysis data for both polar regions. A subset of these spectra is used to compare the ice phase determination skill of the far‐IR test with an “AIRS‐Like” mid‐IR BTD test (BTD1231‐960) for combinations of effective ice particle diameter (Deff_ice), cloud optical depth (COD), and cloud top pressure (CTP). The far‐IR test is performing better for ice clouds with the smallest Deff_ice that we have studied (20 μm) due to the sensitivity of far‐IR scattering to ice particle size. The mid‐IR test was either comparable to or more successful than the far‐IR test for ice clouds with large particle sizes. For all Deff_ice, increasing COD leads to enhanced far‐IR BTD signals due to stronger multiple scattering in the far‐IR than in the mid‐IR. Overall, the variations in far‐IR and mid‐IR BTD test performance are strongly sensitive to Deff_ice, followed by COD and CTP.

Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)

Abstract. The Aqua, SNPP (Suomi National Polar-orbiting Partnership), and JPSS (Joint Polar Satellite System) satellites carry a combination of hyperspectral infrared sounders (AIRS, Atmospheric Infrared Sounder, and CrIS, Cross-track Infrared Sounder) and high-spatial-resolution narrowband imagers (MODIS, Moderate Resolution Imaging Spectroradiometer, and VIIRS, Visible Infrared Imaging Radiometer Suite). They provide an opportunity to acquire high-quality, long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at the pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS version 7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) version 2 for sounders and the standard Aqua MODIS collection 6.1 and the NASA MODIS–VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at the sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found to be highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at the sounder FOV shows good agreement between the standard and continuity products from different satellites. However, the impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA's progress toward building a continuous cloud data record with sufficient quality to investigate clouds' role in global environmental change.

Impact of aerosols on warm clouds over the Sichuan Basin, China in winter based on the MERRA-2 reanalysis dataset

The impacts of aerosols on low warm clouds over the Sichuan Basin are still not well understood. We used the long-term Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) dataset to examine the relationships between aerosols and warm clouds in winter and explored the influence of meteorological conditions and different types of aerosols. There was a positive relationship between the aerosol optical depth and the cloud effective radius, cloud fraction and cloud optical thickness. However, the cloud top pressure tended to decrease with increasing aerosol optical depth. The influence of aerosols on warm clouds varied with the meteorological conditions. An analysis of the effects of thermal conditions, the vertical velocity and the relative humidity showed that stable atmospheric conditions, strong updrafts and a higher relative humidity increased the cloud fraction. Unstable atmospheric conditions and a high relative humidity also facilitated the formation of higher and thicker clouds. High sulfate aerosols were associated with moist air, a greater cloud fraction, a greater cloud optical thickness and a smaller cloud top pressure. Conversely, there was a smaller cloud fraction and cloud optical thickness and a greater cloud top pressure with drier air when there was a higher load of dust and black carbon aerosols.

Calculating the Climatology and Anomalies of Surface Cloud Radiative Effect Using Cloud Property Histograms and Cloud Radiative Kernels

Cloud radiative kernels (CRK) built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere (TOA) fluxes, and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes, which determines the regional surface temperature change and variability. In this study, CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model (RRTM). Longwave cloud radiative effect (CRE) at the surface is primarily driven by cloud base properties, while TOA CRE is primarily decided by cloud top properties. For this reason, the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month, τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, the surface CRKs on CBP-τ histograms were converted to CTP-τ fields using the statistical relationship between CTP, CBP and τ obtained from collocated CloudSat and MODIS observations. For both climate model outputs and satellites observations, the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes. The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth’s Radiant Energy System (CERES) surface irradiances products.

Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) Version 1.0 Cloud Top Property Product From Himawari-8/AHI: Algorithm Development and Preliminary Validation

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties (cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)). The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forests method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients (R) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, wherein the CTT R value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT R value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed herein capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.