Search

A cloud identification and profiling algorithm is being developed for the multi-spectral imager (MSI), which is one of the four instruments that the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) spacecraft will feature. During recent work, we noticed that the MSI response function could shift substantially among some wavelengths (0.67 and 1.65 µm bands) owing to the spectral misalignment (SMILE), in which a shift in the center wavelength appears as a distortion in the spectral image. We evaluated how SMILE affects the cloud retrieval product qualitatively and quantitatively. We chose four detector pixels from bands 1 and 3 with the nadir pixel as the reference to elucidate how the SMILE error affects the cloud optical thickness (τ) and effective cloud droplet radius (re) by simulating the MSI forward radiation with Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM). We also evaluated the error in simulated scenes from a global cloud system-resolving model and a satellite simulator to measure the effect on actual observation scenes. For typical shallow warm clouds (τ = 8, re = 8 µm), the SMILE error on the cloud retrieval was not significant in most cases (up to 6 % error). For typical deep convective clouds (τ = 8, re = 40 µm), the SMILE error on the cloud retrieval was even less significant in most cases (up to 4 % error). Moreover, our results from two oceanic scenes using the synthetic MSI data agreed well with the forward radiation simulation, indicating that the SMILE error was generally within 10 %. Generally, this negligible impact of the SMILE is true for water surfaces, but it still needs to be investigated further for land surfaces in future works.

Currently, terahertz remote sensing technology is one of the best ways to detect the microphysical properties of ice clouds. Influenced by the representativeness of the ice crystal scattering (ICS) model, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. In this study, based on the Voronoi ICS model, we developed a terahertz remote sensing inversion algorithm of the ice water path (IWP) and median mass diameter (Dme) of ice clouds. This study utilized the single-scattering properties (extinction efficiency, single-scattering albedo, and asymmetry factor) of the Voronoi, sphere, and hexagonal column ICS models in the terahertz region. Combined with 14 408 groups of particle size distributions obtained from aircraft-based measurements, we developed the Voronoi, sphere, and column ICS schemes based on the Voronoi, sphere, and column ICS models. The three schemes were applied to the radiative transfer model to carry out the sensitivity analysis of the top-of-cloud (TOC) terahertz brightness temperature differences between cloudy and clear skies (BTDs) on the IWP and Dme. The sensitivity results showed that the TOC BTDs between 640 and 874 GHz are functions of the IWP, and the TOC BTDs of 380, 640, and 874 GHz are functions of the Dme. The Voronoi ICS scheme possesses stronger sensitivity to the Dme than the sphere and column ICS schemes. Based on the sensitivity results, we built a multi-channel look-up table for BTDs. The IWP and Dme were searched from the look-up table using an optimal estimation algorithm. We used 2000 BTD test data randomly generated by the RSTAR model to assess the algorithm's accuracy. Test results showed that the correlation coefficients of the retrieved IWP and Dme reached 0.99 and 0.98, respectively. As an application, we used the inversion algorithm to retrieve the ice cloud IWP and Dme based on the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) airborne terahertz radiation measurements. Validation against the retrievals of the Bayesian algorithm reveals that the Voronoi ICS model performs better than the sphere and hexagonal column ICS models, with enhancement of the mean absolute errors of 5.0 % and 12.8 % for IWP and Dme, respectively. In summary, the results of this study confirmed the practicality and effectiveness of the Voronoi ICS model in the terahertz remote sensing inversion of ice cloud microphysical properties.

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.

The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite launched on December 23, 2017, observes various geophysical parameters with the aim of a better understanding of the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, a very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth’s surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties—cloud optical depth (COD) and cloud-particle effective radius (CER)—of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with the SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases on the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as the better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia–Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016 to 2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy, and high-resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher, but the product accuracy is also higher than that of those products. In particular, hourly and daily root-mean-square errors of the new SWDR are 104.9 and 31.5 W m−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 W m−2, respectively), ERA5 (at 176.6 and 39.5 W m−2, respectively), and GLASS (daily of 36.5 W m−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 W m−2, respectively, which are comparable to that of CERES and ERA5, and even better over high-altitude regions.

Phenological responses of plants to temperature dynamics are receiving attention due to the ongoing challenge with climate change. Most previous phenological studies have focused on flowering phenology rather than fruiting phenology. However, at mid-latitudes, the air temperature periodically changes during the growing period of plants. Hence, cool temperatures early and late in the growing period may affect reproductive success through pollination and fruit-development processes. To evaluate the linkages between reproductive phenology and success, we compared the phenologies, photosynthetic capacities, and fruit-set rates among 25 species within a cool-temperate community in northern Japan. The ambient temperature during the flowering periods of individual species increased with delays in flowering-onset time, whereas the ambient temperature during fruit-development periods was highest for species which bloomed in July. Fruit-set success among species was positively correlated with the ambient temperatures during the reproductive periods and with the photosynthetic capacity. Fruit production of early blooming species was limited by cool temperatures during the flowering period, presumably because of low pollinator activity, whereas that of late-blooming species was limited by cool temperatures during the fruit-development period because of slow fruit-development (i.e., sink limitation). Thus, limiting factors of fruit production varied depending on the flowering phenology of individual species within a community.

A cloud identification and profiling algorithm is being developed for the multi-spectral imager (MSI), which is one of the four instruments that the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) spacecraft will feature. During recent work, we noticed that the MSI response function could shift substantially among some wavelengths (0.67 and 1.65 µm bands) owing to the spectral misalignment (SMILE), in which a shift in the center wavelength appears as a distortion in the spectral image. We evaluated how SMILE affects the cloud retrieval product qualitatively and quantitatively. We chose four detector pixels from bands 1 and 3 with the nadir pixel as the reference to elucidate how the SMILE error affects the cloud optical thickness (τ) and effective cloud droplet radius (re) by simulating the MSI forward radiation with Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM). We also evaluated the error in simulated scenes from a global cloud system-resolving model and a satellite simulator to measure the effect on actual observation scenes. For typical shallow warm clouds (τ = 8, re = 8 µm), the SMILE error on the cloud retrieval was not significant in most cases (up to 6 % error). For typical deep convective clouds (τ = 8, re = 40 µm), the SMILE error on the cloud retrieval was even less significant in most cases (up to 4 % error). Moreover, our results from two oceanic scenes using the synthetic MSI data agreed well with the forward radiation simulation, indicating that the SMILE error was generally within 10 %. Generally, this negligible impact of the SMILE is true for water surfaces, but it still needs to be investigated further for land surfaces in future works.