Mixed-phase clouds (MPCs) involve complex microphysical and dynamical processes of cloud formation and dissipation, which are crucial for numerical weather prediction and cloud-climate feedback. However, satellite remote sensing of MPC properties is still challenging, and there is seldom MPC result inferred from passive spectral observations. This study examines the spectral characteristics of MPCs in the shortwave-infrared (SWIR) channels over the wavelength of 0.4-2.5 mu m, and evaluates the potential of current operational satellite spectroradiometer channels for MPC retrievals. With optical properties of MPCs based on the assumption of uniform mixing of both ice and liquid water particles, the effects of MPC ice optical thickness fraction (IOTF) and effective radius on associated optical properties are analyzed. As expected, results indicate that the MPC optical properties show features for ice and liquid water clouds, and their spectral variations show noticeable differences from those for homogeneous cases. A radiative transfer method is employed to examine the sensitivity of SWIR channels to given MPC cloud water path (CWP) and IOTF. MPCs have unique signal characteristics in the SWIR spectrum. The 0.87-mu m channel is most sensitive to CWP. Meanwhile, the 1.61-and 2.13-mu m channels are more sensitive to water-dominated MPCs (IOTF approaching 0), and the 2.25-mu m channel is sensitive to both water-dominated and ice-dominated MPCs (IOTF approaching 1). Such spectral differences are potentially possible to be used to infer MPC properties based on radiometer observations, which will be investigated in future studies.

Abstract. Satellite visible reflectance observations in cloud- and precipitation-affected regions contain substantial information on weather systems, while data assimilation (DA) of visible data is still challenging due to the complexity of forward operators and the non-Gaussian distribution of cloud variables. This study developed an interface within the framework of the popular Gridpoint Statistical Interpolation (GSI) system to assimilate synthetic visible reflectance simulated by Community Radiative Transfer Model (CRTM). The interface employed a spatial interpolation to ensure accurate alignment between model grids and satellite data, and also facilitating a bidirectional mapping between the state variable space and the observation space. The key implementations within the newly developed GSI-EnKF-CRTM-Vis DA technique include integrating a new observation type from geostationary visible imager, incorporating the module for simulating visible reflectance in CRTM, and extending cloud-related control variables. We employed an ensemble-based DA framework in which ensemble members were initialized with multiple physical parameterization schemes, thereby better representing the ensemble spread arising from cloud parameterization differences. The performance of the GSI-EnKF-CRTM-Vis, configured with the Ensemble Square Root Filter (ENSRF) algorithm, was evaluated by assimilating the Himawari-8 Advanced Himawari Imager (AHI) 0.64 μm visible reflectance for a heavy rainfall event over East Asia on 21 September 2024 under the framework of Observing System Simulation Experiment (OSSE). The experimental results demonstrated that DA of visible reflectance effectively corrected the overestimated cloud water path (CWP), reducing the mean absolute error by 1.5 % on average with forecast improvements lasting 6 hours. Probability density function analysis confirmed significant correction of thin clouds (with reflectance less than 0.2 and CWP less than 0.1 kg·m⁻²). DA of visible reflectance improved the spatial extent of light precipitation, as is evidenced by the improved Equitable Threat Score (ETS) across thresholds (except the 0.1 mm threshold) and the reduced False Alarm Rate (FAR). For the U- and V-component winds, temperature, water vapor mixing ratio, DA of visible reflectance generated negligible adjustments as visible reflectance data are insensitive to these non-cloud variables. The newly developed GSI-EnKF-CRTM-Vis DA technique facilitates the ensemble-based DA of satellite visible reflectance with ensemble members initialized with multiple physical parameterization schemes.

Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals.

A large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi-layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice-over-water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice-over-water clouds over the Asia-Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum similar to 27%) and lower (minimum 6%-10%) in winter. Regarding daily variations, ice-over-water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling. ML clouds with complex vertical structures introduce significant challenges in global climate models owing to a lack of accurate observations and universally applicable theories. While active satellite instruments provide valuable information about ML clouds, they are limited by temporal resolution and spatial discontinuity. This study improves on a passive geostationary satellite spectral imager-based method for detecting ice-over-water clouds. Based on the advantages of geostationary satellites, the annual and monthly distributions, as well as daily variations of ice-over-water clouds, are revealed. Our results provide a valuable observational foundation for investigating ML cloud properties. New thresholds of multi-layer cloud detection algorithms for Advanced Himawari Imager and Advanced Geostationary Radiation Imager are introduced and validated Daytime spatial and temporal variations of multi-layer clouds over the Asia-Pacific region are presented There are substantial daily variations (up to over 15%) on the multi-layer cloud occurrence frequencies

Radiative calibration of satellite spectral radiometers is essential for their downstream applications. The Medium Resolution Spectral Imager (MERSI-II) is a key instrument of the Chinese polar orbit Fengyun-3D (FY-3D) satellite. However, its calibration performance has not been sufficiently studied, which limits its broad application. This study revealed the feasibility of a cloud-target method for assessing the MERSI-II calibration performance in solar bands. The top-of-atmosphere (TOA) reflectances for six MERSI-II reflective solar bands (RSBs) were numerically simulated using a rigorous forward radiative transfer method and cloud properties from well-collocated and well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) operational cloud products with strict constraints. Only ice cloud targets were examined in the collocation due to their better homogeneity. The excellent agreement between our simulated reflectance and the MODIS reflectance (relative differences (RDs) of over 90% are within a 5% uncertainty range in six bands) validates our models. The simulated results in MERSI-II bands 1–4 showed reasonable agreements with the MERSI-II operational reflectance, i.e., mean RDs < 3%, while the RDs in bands 6 and 7 reaches 12% and 6%, respectively. Our systematic cloud-target-calibration results over three years (2019–2021) indicated clear seasonal calibration biases and signal degradation of the MERSI-II solar bands, and those in the two cloud-absorbing bands, which reached $\sim $ 15% and $\sim $ 12% (in the three years), respectively. More importantly, we removed these seasonal and degradation biases to improve the current calibration accuracy to a stable value within 3%. Due to its robust performance, our cloud-target-based calibration method can be applied to future MERSI-II sensors to monitor solar band stability.

本文从聂绀弩文学创作的内在矛盾和愿景追求两方面来看待聂绀弩的文学人生,并提出"理想国"之概念,作为认识和理解聂绀弩为人、为文的基石.聂绀弩热衷于政治活动却表现出独树一帜的个性特征,在遭受牢狱之灾时却展现出一介书生本色,这清晰地展现了他对于"理想国"的苦苦追寻.这种矛盾不但成为聂绀弩个性特点和心理信念,他还以曲折的方式表现为他对正义和民族意识的发扬和坚守.

Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations in cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is introduced and performed to evaluate the quality of cloud properties from reanalysis datasets. The China Meteorological Administration reanalysis (CRA); the ECMWF fifth-generation reanalysis (ERA5); and the Modern-Era Retrospective analysis for Applications, Version 2 (MERRA-2), i.e., those reanalyses providing sufficient cloud information, are considered. To avoid the influence of assumptions and uncertainties on satellite retrieval algorithms, forward radiative transfer simulations are used as a bridge to translate the reanalyses to corresponding radiances that are expected to be observed by satellites. The simulated reflectances and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager onboard the Himawari-8 satellite in the East Asia region. We find that the simulated reflectances and BTs based on CRA and ERA5 are close to each other. CRA represents the total and midlayer cloud cover better than the other two datasets, and ERA5 depicts deep-convection structures more closely than CRA does. Comparisons of the simulated and observed BT differences suggest that water clouds are generally overestimated in ERA5 and MERRA-2, and MERRA-2 also overestimates the ice clouds over cyclone centers. Overall, clouds from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis has the best capability to represent the cloudy atmospheres over East Asia, and the CRA representations are close to those in ERA5.

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach "compresses" the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

Atmospheric reanalysis normally provides a comprehensive record of Earth’s weather and climate changes over time, and is extensively used in climate and energy researches. Among various atmospheric variables, clouds play a unique and important role in the atmosphere. However, there are still significant uncertainties on our understanding of cloud properties, and it is still unclear how those newly developed atmospheric reanalyses represent cloud spatiotemporal characteristics. This study evaluates the cloud properties in two atmospheric reanalyses, i.e., the China Meteorological Administration Reanalysis data (CRA) and the ECMWF's Fifth-generation Reanalysis (ERA5), and monthly mean cloud cover from satellite radiometers are considered as the reference. Both CRA and ERA5 generally represent cloud temporal and spatial distributions. To be more specific, ERA5 gives monthly mean cloud cover closely to the MODIS observation over land, but its value over ocean is ~10% smaller than MODIS. CRA underestimates global cloud cover for ~20%, especially over subtropical zones. The differences between the MODIS and reanalysis cloud cover are mostly significant over the polar regions, whereas they can be contributed by the uncertainties of both. Furthermore, the interannual climate variability of cloud cover influenced by ENSO can also be reasonably captured by the two reanalyses, especially ERA5, whereas the CRA and ERA5 cloud cover anomalies are both spreading over a slightly larger region than those from MODIS. Such evaluations are suggested to be extended to even more detailed cloud properties and variations in the reanalysis for further improvements.

Mineral dust, as one of the most important aerosols, plays a crucial role in the atmosphere by directly interacting with radiation, while there are significant uncertainties in determining dust optical properties to quantify radiative effects and to retrieve their properties. Laboratory and in situ measurements of the refractive indices (RIs) of dust differ, and different RIs have been applied in numerical studies used for model developments, aerosol retrievals, and radiative forcing simulations. This study reveals the importance of the dust RI for the development of a model of dust optical properties. The Koch-fractal polyhedron is used as the modeled geometry, and the pseudospectral time domain method and improved geometric-optics method are combined for optical property simulations over the complete size range. We find that the scattering matrix elements of different kinds of dust particles are reasonably reproduced by choosing appropriate RIs, even when using a fixed particle geometry. The uncertainty of the RI would greatly affect the determination of the geometric model, as a change in the RI, even in the widely accepted RI range, strongly affects the shape parameters used to reproduce the measured dust scattering matrix elements. A further comparison shows that the RI influences the scattering matrix elements in a different way than geometric factors, and, more specifically, the P-11, P-12, and P-22 elements seem more sensitive to the RI of dust. In summary, more efforts should be devoted to account for the uncertainties on the dust RI in modeling its optical properties, and the development of corresponding optical models can potentially be simplified by considering only variations over different RIs. Considerably more research, especially from direct measurements, should be carried out to better constrain the uncertainties related to the dust aerosol RIs.

Ice crystal submicron structures have a large impact on the optical properties of cirrus clouds and consequently on their radiative effect. Although there is growing evidence that atmospheric ice crystals are rarely pristine, direct in situ observations of the degree of ice crystal complexity are largely missing. Here we show a comprehensive in situ data set of ice crystal complexity coupled with measurements of the cloud angular scattering functions collected during a number of observational airborne campaigns at diverse geographical locations. Our results demonstrate that an overwhelming fraction (between 61 % and 81 %) of atmospheric ice crystals sampled in the different regions contain mesoscopic deformations and, as a consequence, a similar flat and featureless angular scattering function is observed. A comparison between the measurements and a database of optical particle properties showed that severely roughened hexagonal aggregates optimally represent the measurements in the observed angular range. Based on this optical model, a new parameterization of the cloud bulk asymmetry factor was introduced and its effects were tested in a global climate model. The modelling results suggest that, due to ice crystal complexity, ice-containing clouds can induce an additional short-wave cooling effect of −1.12 W m2 on the top-of-the-atmosphere radiative budget that has not yet been considered.

Radiance-based comparison between model-simulated and satellite-observed atmosphere shows its unique advantage for model evaluation. This study couples a fast radiative transfer model, namely, the community radiative transfer model, and the Weather Research and Forecasting (WRF) to generate synthetic satellite observed infrared (IR) brightness temperatures (BTs) over East Asia. Simulated IR BTs are used to evaluate WRF cloud properties by direct comparison with observations from the Visible and Infrared Spin-Scan Radiometer (onboard the FengYun-2E geostationary satellite). A deep cyclone over North China is investigated as our case study. The simulated and observed BTs in the 6.8-m channel demonstrate agreement with an averaged bias of <3K, whereas the performances in two IR window channels are relatively poor with averaged BT differences almost 10K. For typical precipitation regions, BT differences between the 6.8- and 10.8-m channels indicate that the WRF simulation underestimates low- to midlevel clouds but reproduces high-level clouds better. Overall, the WRF has solid capabilities to capture the cloud characteristics broadly, whereas some limitations are shown in the cloud temporal and variation cloud cover, especially 3days after the WRF initialization.

There are three connotative meanings in NieGannu′s novels: the vivid folk-custom,the concern for individual life and the insight of national psychological traits.The feature of character-creating is the description of the lives of small potatoes.The feature of expression is dialect which is used a lot in novels.And the feature of scenario is the simple plot and the concentrated scenes.Novel was endued by NieGannu as weapon which could enlighten the public.

本文通过对聂绀弩杂文的归纳分类阅读,从本真的自由、自由主义的体现和自由主义文学思潮的传承以及独立人格的自由三方面阐释、分析其作品中的"自由"精神。