Optical properties of clouds and heavy aerosol retrieved from satellite measurements are the most important elements for calculating surface solar radiation (SSR). The Himawari-8/Advanced Himawari ...Imager (AHI) satellite measurements receive high spatial, temporal and spectral signals, which provides an opportunity to estimate cloud, aerosol and SSR accurately.
In this study, we developed the AHI official cloud property product (version 1.0) for JAXA P-Tree system. A look-up table (LUT) method was used to calculate high-temporal (10 min) and high-spatial (5 km) SSR from AHI cloud properties. First, the LUT of the SSR estimation was optimized through a radiative transfer model to account for solar zenith angle, cloud optical thickness (COT), effective particle radius (CER), aerosol optical thickness and surface albedo. Following this, COT and CER were retrieved from the AHI data, with ice cloud parameters being retrieved from an extended Voronoi ice crystal scattering database and water cloud parameters being retrieved from the Mie–Lorenz scattering model. The retrieved COT and CER for water clouds were compared well with MODIS collection 6 cloud property products, with correlation coefficients of 0.77 and 0.82, respectively. The COT of ice cloud also shows good consistency, with a correlation coefficient of 0.85. Finally, the SSR was calculated based on the SSR LUT and the retrieved cloud optical parameters. The estimated SSR was validated at 122 radiation stations from several observing networks covering the disk region of Himawari-8. The root-mean-square error (RMSE) at CMA (China Meteorological Administration) stations was 101.86 Wm−2 for hourly SSR and 31.42 Wm−2 for daily SSR; RMSE at non-CMA stations was 119.07 Wm−2 for instantaneous SSR, 81.10 Wm−2 for hourly SSR and 26.58 Wm−2 for daily SSR. Compared with the SSR estimated from conventional geostationary satellites, the accuracy of the SSR obtained in this study was significantly improved.
•The AHI official cloud algorithm (version 1.0) is developed for the JAXA P-Tree system.•The Voronoi ice crystal scattering model is used to develop the ice cloud product.•High-accuracy SSR is estimated using the AHI cloud parameters.
With a global aerosol transport‐radiation model coupled to a general circulation model, changes in the meteorological parameters of clouds, precipitation, and temperature caused by the direct and ...indirect effects of aerosols are simulated, and its radiative forcing are calculated. A microphysical parameterization diagnosing the cloud droplet number concentration based on the Köhler theory is introduced into the model, which depends not only on the aerosol particle number concentration but also on the updraft velocity, size distributions, and chemical properties of each aerosol species and saturation condition of the water vapor. The simulated cloud droplet effective radius, cloud radiative forcing, and precipitation rate, which relate to the aerosol indirect effect, are in reasonable agreement with satellite observations. The model results indicate that a decrease in the cloud droplet effective radius by anthropogenic aerosols occurs globally, while changes in the cloud water and precipitation are strongly affected by a variation of the dynamical hydrological cycle with a temperature change by the aerosol direct and first indirect effects rather than the second indirect effect itself. However, the cloud water can increase and the precipitation can simultaneously decrease in regions where a large amount of anthropogenic aerosols and cloud water exist, which is a strong signal of the second indirect effect. The global mean radiative forcings of the direct and indirect effects at the tropopause by anthropogenic aerosols are calculated to be −0.1 and −0.9 W m−2, respectively. It is suggested that aerosol particles approximately reduce 40% of the increase in the surface air temperature by anthropogenic greenhouse gases on the global mean.
Common requirements for cloud detection methods including the adjustability with respect to incorrect results are clarified, and a method is proposed that satisfies the requirements by applying the ...support vector machine (SVM). Because the conditions of clouds and Earth's surfaces vary widely, incorrect results in actual cloud detection operations are unavoidable. Cloud detection methods therefore should be adjustable to easily reduce the frequency of incorrect results under certain conditions, without causing new incorrect results under other conditions. Cloud detection methods are also required to resolve a characteristic issue: the boundary between clear-sky and cloudy-sky areas in nature is vague, because the density of the cloud particles continuously varies. This vagueness makes the cloud definition subjective. Furthermore, the training dataset preparation for machine learning should avoid circular arguments. The SVM learning is generally less likely to result in overfitting: this study suggests that only typical data are sufficient for the SVM training dataset. By incorporating the discriminant analysis (DA), it is possible to subjectively determine the definition of typical cloudy and clear sky and to obtain typical cloud data without direct cloud detection. In an approach to adjust the classifier, data typical of certain conditions that lead to incorrect results are added to the training dataset. In this study, an adjustment procedure is proposed, which quantitatively judges, whether an addition is actually effective for reduction of the frequency of incorrect results. Another approach for the adjustment is improving feature space used for cloud detection. Indices as quantitative guidance to estimate whether an addition or elimination of a feature actually reduces the frequency of incorrect results can be obtained from the analysis of the support vectors. The cloud detection method incorporating the SVM is therefore able to integrate practical adjustment procedures. Applications of this method to Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrate that the concept of the method satisfies the requirements and the adjustability to various conditions can be realized.
•Requirements for cloud detection methods including adjustability are clarified.•Support vector machine is incorporated to satisfy the requirements.•Training dataset preparation without the circular argument is enabled.•Practical adjustment procedures with quantitative guidance are provided.
Observations from the new Japanese
geostationary satellite Himawari-8 permit quasi-real-time estimation of
global shortwave radiation at an unprecedented temporal resolution. However,
accurate ...comparisons with ground-truthing observations are essential to
assess their uncertainty. In this study, we evaluated the Himawari-8 global
radiation product AMATERASS using observations recorded at four SKYNET
stations in Japan and, for certain analyses, from the surface network of the
Japanese Meteorological Agency in 2016. We found that the spatiotemporal
variability of the satellite estimates was smaller than that of the ground
observations; variability decreased with increases in the time step and
spatial domain. Cloud variability was the main source of uncertainty in the
satellite radiation estimates, followed by direct effects caused by aerosols
and bright albedo. Under all-sky conditions, good agreement was found between
satellite and ground-based data, with a mean bias in the range of
20–30 W m−2 (i.e., AMATERASS overestimated ground observations) and a
root mean square error (RMSE) of approximately 70–80 W m−2. However,
results depended on the time step used in the validation exercise, on the
spatial domain, and on the different climatological regions. In particular,
the validation performed at 2.5 min showed largest deviations and RMSE
values ranging from about 110 W m−2 for the mainland to a maximum of
150 W m−2 in the subtropical region. We also detected a limited
overestimation in the number of clear-sky episodes, particularly at the pixel
level. Overall, satellite-based estimates were higher under overcast
conditions, whereas frequent episodes of cloud-induced enhanced surface
radiation (i.e., measured radiation was greater than expected clear-sky
radiation) tended to reduce this difference. Finally, the total mean bias was
approximately 10–15 W m−2 under clear-sky conditions, mainly because
of overall instantaneous direct aerosol forcing efficiency in the range of
120–150 W m−2 per unit of aerosol optical depth (AOD). A seasonal
anticorrelation between AOD and global radiation differences was evident at
all stations and was also observed within the diurnal cycle.
A new concept for cloud detection from observations by multispectral spaceborne imagers is proposed, and an algorithm comprising many pixel‐by‐pixel threshold tests is developed. Since in nature the ...thickness of clouds tends to vary continuously and the border between cloud and clear sky is thus vague, it is unrealistic to label pixels as either cloudy or clear sky. Instead, the extraction of ambiguous areas is considered to be useful and informative. We refer to the multiple threshold method employed in the MOD35 algorithm that is used for Moderate Resolution Imaging Spectroradiometer (MODIS) standard data analysis, but drastically reconstruct the structure of the algorithm to meet our aim of sustaining the neutral position. The concept of a clear confidence level, which represents certainty of the clear or cloud condition, is applied to design a neutral cloud detection algorithm that is not biased to either clear or cloudy. The use of the clear confidence level with neutral position also makes our algorithm structure very simple. Several examples of cloud detection from satellite data are tested using our algorithm and are validated by visual inspection and comparison to previous cloud mask data. The results indicate that our algorithm is capable of reasonable discrimination between cloudy and clear‐sky areas over ocean with and without Sun glint, forest, and desert, and is able to extract areas with ambiguous cloudiness condition.
Both weather–climate models and ice cloud remote sensing applications need to obtain effective ice crystal scattering (ICS) properties and the
parameterization scheme. An irregularly shaped Voronoi ...ICS model has been
suggested to be effective in remote sensing applications for several
satellite programs, e.g., Himawari-8, GCOM-C (Global Change Observation
Mission–Climate) and EarthCARE (Earth Cloud Aerosol and Radiation Explorer).
As continuation work of Letu et al. (2016), an ice cloud optical property
parameterization scheme (Voronoi scheme) of the Voronoi ICS model is
employed in the Community Integrated Earth System Model (CIESM) to simulate
the optical and radiative properties of ice clouds. We utilized the
single-scattering properties (extinction efficiency, single-scattering
albedo and asymmetry factor) of the Voronoi model from the ultraviolet to
the infrared, combined with 14 408 particle size distributions obtained from
aircraft measurements to complete the Voronoi scheme. The Voronoi scheme and
existing schemes (Fu, Mitchell, Yi and Baum-yang05) are applied to the CIESM
to simulate 10-year global cloud radiative effects during 2001–2010. Simulated
globally averaged cloud radiative forcings at the top of the atmosphere (TOA)
for Voronoi and the other four existing schemes are compared to the Clouds and
the Earth's Radiant Energy System Energy Balanced and Filled (EBAF) product.
The results show that the differences in shortwave and longwave
globally averaged cloud radiative forcing at the TOA between the Voronoi
scheme simulations and EBAF products are 1.1 % and 1.4 %, which are
lower than those of the other four schemes. Particularly for regions (from
30∘ S to 30∘ N) where ice clouds occur frequently, the
Voronoi scheme provides the closest match with EBAF products compared with the
other four existing schemes. The results in this study fully demonstrated
the effectiveness of the Voronoi ICS model in the simulation of the
radiative properties of ice clouds in the climate model.
This study developed an algorithm for estimating solar radiation from space using a neural network (NN) with an improved learning algorithm to approximate radiative transfer code. The NN solver for ...the solar radiation budget is based on radiative transfer calculations. All data sets for testing and training the NN were generated from radiative transfer code. Thus the NN traces the radiative transfer calculation that is approximated by a learning algorithm. To demonstrate the effectiveness of the NN approach for high‐speed estimation and multiparameter problems, the NN was applied to data from a geostationary satellite and a Sun‐synchronous subrecurrent orbit satellite. The developed algorithm was applied to data from the Multi‐functional Transport Satellite‐1 Replacement (MTSAT‐1R) geostationary satellite, and estimations were validated against in situ observations for March 2006 at four SKYNET sites. Byproducts of the algorithm include UVA, UVB, and photosynthetically active radiation (PAR) fluxes as well as direct and diffuse components. The NN approach enables semi‐real‐time analysis of these products by high‐speed calculation. In addition, the NN allows for consideration of detailed particle optical parameters in the solar radiation budget without the need for a massive database. The method was also applied to observations from the Advanced Earth Observing Satellite‐II/Global Imager (ADEOS‐II/GLI) for May 2003. The results showed trends in the direct and diffuse components of downward solar radiation over the North Pacific Ocean. This report outlines the construction of the NN for radiation budget estimation and demonstrates the effectiveness of the NN approach.
Key Points
Development of estimation algorithm for SW radiation budget
Neural network algorithm based on radiative transfer calculation
Feature trend of direct and diffuse component in East Asia to North Pacific
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 forest (RF) 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 (<inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula>) 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, in which the CTT <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula> value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula> 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 here capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.
Abstract
This study describes an approach for combining CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations to investigate the microphysical processes of warm ...clouds on the global scale. MODIS column optical thickness is vertically distributed between the cloud top and cloud bottom according to adiabatic and condensational growth assumptions and used as a vertical coordinate system to analyze profiles of CloudSat-observed radar reflectivity. The reflectivity profiles thus rescaled as a function of the in-cloud optical depth clearly depict how the cloud-to-rain particle growth processes take place within the cloud layer and how these processes vary systematically with variations in MODIS-derived effective particle radius. It is also found that the effective radii retrieved using two different wavelengths of MODIS tend to trace the microphysical change of reflectivity profiles in a different way because of the difference in the layer depth that characterizes these two effective radii.
The reflectivity profiles as a function of optical depth are also interpreted in terms of drop collection processes based on the continuous collection model. The slope of the reflectivity change with optical depth provides a gross measure of the collection efficiency factor. The systematic changes of reflectivity profiles with MODIS-derived particle sizes are then interpreted as demonstrating a strong dependency of the collection efficiency on particle size. These results provide a quantitative insight into the drop collection process of warm clouds in the real atmosphere.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Hydrometeor droplet growth processes are inferred from a combination of Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) cloud particle size observations and CloudSat/Cloud ...Profiling Radar (CPR) observations of warm water clouds. This study supports the inferences of a related paper (Part I) (i) that MODIS-retrieved cloud droplet radii (CDR) from the 3.7-μm channel (R37) are influenced by the existence of small droplets at cloud top and (ii) that the CDR obtained from 1.6- (R16) and 2.1-μm (R21) channels contain information about drizzle droplets deeper into the cloud as well as cloud droplets. This interpretation is shown to be consistent with radar reflectivities when matched to CDR that were retrieved from MODIS data. This study demonstrates that the droplet growth process from cloud to rain via drizzle proceeds monotonically with the evolution of R16 or R21 from small cloud drops (on the order of 10–12 μm) to drizzle (CDR greater than 14 μm) to rain (CDR greater than 20 μm). Thus, R16 or R21 is an indicator of hydrometeor droplet growth processes whereas R37 does not contain information about coalescence. A new composite analysis, the contoured frequency diagram, is introduced to combine CloudSat/CPR reflectivity profiles and reveals a distinct trimodal population of reflectivities corresponding to cloud, drizzle, and rain modes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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