Cloud Particle Effective Radius Research Articles

Precipitation and cloud Parameters's characteristics of stratus in summer in Ningxia, eastern of northwest China

Stratus is the main artificial seeding target for weather modification in Ningxia. Meteorological satellites can detect macro and microcosmic physical parameters of large-scale stratus. Researching precipitation and cloud parameter characteristics of stratus can provide scientific and technological support for understanding the development and evolution of stratus to identify seeding conditions and effects. Eight typical stratus precipitation events of the summers of 2016–2021 of Ningxia were selected, using the nine point average method, that matched characteristic cloud parameters retrieved by FY-2G satellite with precipitation classification data of 25 national meteorological observation stations in Ningxia. These stratus precipitation characteristics were correlated with cloud parameters. The results indicate that precipitation decreased rapidly from the southeast to the northwest of Ningxia. In the synoptic background of precipitation, the cloud top height, super-cooled layer thickness, cloud optical thickness, cloud particle effective radius, and cloud liquid water content were all larger than those in the synoptic background of no precipitation. The spatial distribution feature manifests as gradually increased from southeast to northwest. The cloud top temperature and cloud blackbody temperature were smaller than those in the synoptic background of no precipitation. The cloud top temperature was near the cloud “seeding temperature window”, and the depth of the super-cooled layer was thick. This was in line with the “Seeder-Feeder” structure characteristic of cold stratus.

Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research

Abstract. The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950, Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953, Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954, Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158, Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945, Zhuge 2023e).

Analysis of Precipitation Process and Operational Precipitation Enhancement in Panxi Region Based on Cloud Parameters Retrievals from China’s Next−Generation Geostationary Meteorological Satellite FY−4A

Geostationary meteorological satellite data with high spatial and temporal resolution can be used to retrieve cloud physical parameters, which has significant advantages in tracking cloud evolution and development. Based on satellite multispectral RGB composite image and cloud physical analysis methods, we quantitatively analyze the evolution characteristics of cloud parameters in the pre-, mid- and post-artificially influenced weather process, explore the application potential benefits of satellite data in monitoring the operating conditions of the artificially influenced weather in the Panxi region, and verify the feasibility analysis of the evaluation of their effects. In this study, cloud parameters such as cloud particle effective radius (Re), cloud liquid water path (LWP), cloud ice water path (IWP), and cloud top height and temperature (CTH and CTT) are retrieved using FY−4A satellite data. For the Panxi region, the evolution characteristics of typical cloud parameters in the affected area before and after two aircraft artificial operational precipitation enhancements are analyzed. The results show that the satellite retrieval of cloud characteristic parameters in the Panxi region has good application value, which can be used to guide the artificial Operational Precipitation Enhancement. In this precipitation process, there are solid particles in the upper layer cloud and supercooled water in the lower layer cloud. After the cold cloud catalysis, the cloud top height, liquid water and ice water content, particle effective radius and ground precipitation in the operational area increased, and the cloud top temperature decreased. Thus, it effectively alleviated the drought in the Panxi region. The satellite retrieval of cloud characteristic parameters in the Panxi region has a good application value, which can provide a basis and guidance for future weather modification operations in the Panxi region.

Comparison of Cloud Properties between SGLI Aboard GCOM-C Satellite and MODIS Aboard Terra Satellite

This study presents a comprehensive comparison of Level 2.0 cloud properties between a Second-generation Global Imager (SGLI) aboard the GCOM-C satellite and a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, to better understand the qualities of cloud properties obtained from SGLI/GCOM-C launched on 23 December 2017. The cloud pixels identified as water phase by both satellite sensors are highly consistent to each other by more than 90%, although the consistency is only ~60% for ice phase cloud pixels. A comparison of cloud properties—cloud optical thickness (COT) and cloud particle effective radius (CER)—between these two satellite sensors reveals that water and ice cloud properties can have different degrees of agreement depending on underlying surface. The relative difference (RD) values of 22% (18%) and 37% (24%) for water cloud COT (CER) comparison over ocean and land surfaces and respective values of 35% (42%) and 35% (62%) for comparisons of ice cloud properties, and also other comparison metrics, suggest better agreements for water cloud properties than for ice cloud properties, and for ocean surface than for land surface. Though cloud properties differences between MODIS and SGLI can arise from inherent features of cloud retrieval algorithms, such as differences in ancillary data, surface reflectance, cloud droplet size distribution function, model for ice particle habit, etc., this study further identifies the important roles of cloud thickness and Sun and satellite positions for differences in cloud properties between SGLI and MODIS: the differences in cloud properties are found to increase for thinner clouds, higher solar zenith angle, and higher differences in viewing zenith and azimuth angles between these satellite sensors, and such differences are more distinct for water cloud properties than for ice cloud properties.

Combining Cloud Properties from CALIPSO, CloudSat, and MODIS for Top-of-Atmosphere (TOA) Shortwave Broadband Irradiance Computations: Impact of Cloud Vertical Profiles

Abstract Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable. Significance Statement The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.

Tongan Volcanic Eruption Intensifies Tropical Cyclone Cody (2022)

The aerosol−cloud impacts of the Tongan volcanic eruptions on the nearby tropical cyclone (TC) Cody on January 14∼15, 2022 are investigated by the MODIS satellite and ERA5 reanalysis data. Both the precipitation and intensity of Cody were obviously enhanced after the main blast of the Tongan volcanic eruption on January 15, although the sea surface temperature and vertical wind shear of the environmental wind did not change much according to ERA5 data. The vision that a large amount of volcanic aerosol flowed from the Tongan eruption into the inflow of Cody was captured by the MODIS observations on January 15. The cloud top temperature dropped, and the cloud particle effective radius decreased in Cody from then on, which indicated the occurrence of deep convection. The analyzed results of ERA5 show that convection was strengthened in the periphery of Cody at the beginning of the volcanic eruption at 03:00 UTC on January 15 and later in Cody’s inner core after the main blast at 06:00 UTC on January 15. This could be because of the microphysical process of aerosol−cloud interactions, which inhibited stratiform precipitation, increased vertical velocity and enhanced convective precipitation further. Since the deep convection in the inner core was conducive to the development of Cody, both the total precipitation and intensity of Cody increased after the main blast of the volcanic eruption. The result also suggests that major volcanic eruptions could increase the convective intensity and induce heavy precipitation in a nearby organized convective system (e.g., TC or mesoscale convective systems).

Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data

Abstract. The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite, launched on 23 December 2017, observes various geophysical parameters with the aim of better understanding 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, 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 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 in 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 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.

Estimating 250-m Land Surface and Atmospheric Variables From MERSI Top-of-Atmosphere Reflectance

The medium-resolution spectral imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth’s environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear-and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 0.092, 0.084, and 115.305 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations.

Aerosol Loading and Radiation Budget Perturbations in Densely Populated and Highly Polluted Indo-Gangetic Plain by COVID-19: Influences on Cloud Properties and Air Temperature.

Aerosols emitted in densely populated and industrialized Indo‐Gangetic Plain, one of the most polluted regions in the world, modulate regional climate, monsoon, and Himalayan glacier retreat. Thus, this region is important for understanding aerosol perturbations and their resulting impacts on atmospheric changes during COVID‐19 lockdown period, a natural experimental condition created by the pandemic. By analyzing 5 years (2016–2020) data of aerosols and performing a radiative transfer calculation, we found that columnar and near‐surface aerosol loadings decreased, leading to reductions in radiative cooling at the surface and top of the atmosphere and atmospheric warming during lockdown period. Further, satellite data analyses showed increases in cloud optical thickness and cloud‐particle effective radius and decrease in lower tropospheric air temperature during lockdown period. These results indicate critical influences of COVID‐19 lockdown on regional climate and water cycle over Indo‐Gangetic Plain, emphasizing need for further studies from modeling perspectives.

The possible linkage of satellite based cloud microphysical parameters (CMP) to the Indian Summer Monsoon (ISM) active and break phase

The unpredictability of Indian Summer Monsoon (ISM) remains an area of major challenge for operational forecasters. The distribution of active and break days in a monsoon season, though of utmost importance is very difficult to predict. In this regard, the geostationary satellite observations from Indian National Satellite System-3D (INSAT-3D) satellite over the Indian region provide significant insights into the propagation of ISM. In this manuscript, the correlation of satellite derived cloud microphysical parameters (CMP) to the strength and development of the ISM is studied objectively for the years 2017 and 2018. In particular, the association of the CMP to the active and break spell has also been investigated. It is found that the Cloud Particle Effective radius (CER) and the Cloud Optical Thickness (COT) bears a maximum correlation of 0.67 and 0.65 with the daily average rainfall at 1 day lag. The histogram analysis with active/break conditions show that CER exhibit a bimodal distribution (~27 µm and ~36 µm) during an active spell, while in case of break phase, smaller cloud droplets ranging in size between 15 and 25 µm is prevalent. The findings of the study will contribute towards better understanding and prediction of active and break spells of ISM.

Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical Retrievals to the Index of Refraction

A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the cloud effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering albedo, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard Moderate Resolution Imaging Spectroradiometer (MODIS)/Visible Infrared Imaging Radiometer suite (VIIRS) continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR and one in the MWIR. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1–2 µm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature gives more consistent retrievals between the two spectrally distinct 2.2 µm atmospheric window channels on MODIS and VIIRS. As a result, 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.

Comparison of Cloud‐Top Property Retrievals From Advanced Himawari Imager, MODIS, CloudSat/CPR, CALIPSO/CALIOP, and Radiosonde

AbstractThe information on cloud properties and microphysical characteristics is critical for environmental research and application, such as that on weather, climate, hydrology, and green energy and Earth energy budget. The launch of the new‐generation geostationary satellite, for example, the Japanese Himawari‐8, enables retrieval of cloud information through multichannel observation. To confirm the retrievals from the Advanced Himawari Imager (AHI) onboard Himawari‐8 (Himawari‐8/AHI), this study accessed the cloud retrievals from AHI and compare them against those obtained from spaceborne passive and active sensors on the low Earth orbit satellites, such as NASA's CloudSat, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The atmospheric thermodynamic state from in situ radiosonde provides another dimension for evaluating the retrieved cloud‐top temperature and height, which were obtained and analyzed during two research cruises over the South China Sea during monsoon onset season with large cloud variability to ensure the comprehensive intercomparisons. The findings show that the cloud‐top altitude, cloud optical thickness, and cloud effective particle radius retrieved from AHI are consistent with passive Moderate Resolution Imaging Spectroradiometer (MODIS) data. Furthermore, both spaceborne active Cloud Priofiling Radar (CPR) and Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) data confirm the low uncertainty of cloud‐top altitude, particularly when the cloud is optically thick. Large cloud‐top altitude discrepancy among these data in the optically thin cloud might be due to the limited AHI spectral channels or instrumental spatial resolutions. The cloud‐top temperature and altitudes in either liquid or ice phase cloud agree well with collocated sounding profiles, with low difference of 1 K and 170 m, respectively.

An Optimization Approach for Estimating Multiple Land Surface and Atmospheric Variables From the Geostationary Advanced Himawari Imager Top-of-Atmosphere Observations

Since a new generation of geostationary satellite data has incredibly high temporal, spatial, and spectral resolutions, new methodologies are now needed to take advantage of both the temporal and spectral signatures of them for accurate estimation of Earth's environmental variables. This article describes a novel optimization method to estimate a suite of 11 physically consistent land surface and atmospheric variables under all-sky conditions from the geostationary advanced Himawari imager (AHI) top-of-atmosphere (TOA) observations. This method is based on a coupled soil, snow, vegetation, and atmospheric radiative transfer (RT) model from 0.28 to 14 μm. The inversion algorithm consists of three major steps. First, the “clearest” observations at each moment during a temporal window were determined and then the essential variables that characterize surface RT models, such as leaf area index (LAI), leaf chlorophyll concentration, and soil parameters were estimated. Second, the atmospheric variables, including aerosol optical depth (AOD) under clear-sky conditions, and cloud optical thickness (COT) and cloud effective particle radius (CER) under cloudy-sky conditions, were inverted given surface reflectance calculated by the surface RT models. Finally, the inverted atmospheric and land surface variables were fed into the coupled RT model to calculate the remaining set of variables, including spectral directional reflectance, surface broadband albedo, thermal emissivity, incident shortwave radiation (ISR), photosynthetically active radiation (PAR), fraction of absorbed PAR by green vegetation (FAPAR), and TOA shortwave albedo. The retrieved variables were validated using in-situ measurements from Ozflux network sites and compared with the other existing satellite products. Intercomparisons demonstrate that the AHI-retrieved atmospheric variables (AOD, CER, and COT) and surface variables (surface reflectance, LAI, FAPAR, PAR, and surface emissivity) are well correlated with the corresponding JAXA released AHI, NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES), and the Global LAnd Surface Satellite (GLASS) products. Direct validation using in-situ measurements indicates that the retrieved ISR achieves higher accuracy than the CERES ISR product (with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values of 0.95 and 0.89, and root-mean-square error (RMSE) of 20.3 and 29.6 W/ m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the AHI-retrieved and CERES daily ISR, respectively). Validation also shows that the estimated daily surface albedo has an accuracy comparable to the MODIS daily albedo product (RMSE = 0.03). Both the direct validation and product comparisons have demonstrated that this proposed inversion framework works very well for the AHI data. Unlike other algorithms that are usually used for estimating an individual parameter and rely heavily on a separate atmospheric correction, this inversion framework can effectively estimate a group of atmospheric and land surface variables and be easily applied to other similar multispectral geostationary satellite data. A comprehensive sensitivity and validation study is still needed to quantify the uncertainties of the retrieval variables.

Retrieval of cloud properties from spectral zenith radiances observed by sky radiometers

Abstract. An optimal estimation algorithm to retrieve the cloud optical depth (COD) and cloud particle effective radius (CER) from spectral zenith radiances observed by narrow field-of-view (FOV) ground-based sky radiometers was developed. To further address the filter degradation problem while analyzing long-term observation data, an on-site calibration procedure is proposed, which has good accuracy compared with the standard calibration transfer method. An error evaluation study conducted by assuming errors in observed transmittances and ancillary data for water vapor concentration and surface albedo suggests that the errors in input data affect retrieved CER more than COD. Except for some narrow domains that fall within a COD of &lt; 15, the retrieval errors are small for both COD and CER. The retrieved cloud properties reproduce the broadband radiances observed by a narrow FOV radiometer more precisely than broadband irradiances observed by a wide-FOV pyranometer, justifying the quality of the retrieved product (at least of COD) and indicating the important effect of the instrument FOV in cloud remote sensing. Furthermore, CODs (CERs) from sky radiometer and satellite observations show good (poor) agreement.

FY-4A SATELLITE BASED CLOUD MICROPHYSICAL VARIATION ANALYSIS OF AIRBORNE CLOUD SEEDING OPERATIONS IN SICHUAN BASIN

Abstract. Based on a satellite retrieval methodology, the cloud microphysical properties of two airborne cloud seeding operations in Sichuan Basin of Southwest China are analyzed in this paper. The methodology for the retrieval of the cloud particle effective radius (Re), cloud temperature (T) and microphysical structure is based on the data from the AGRI onboard the Chinese FY-4A satellite. A microphysical red–green–blue (RGB) composite visualization has been devised to qualitatively highlight the cloud composition. This RGB scheme is displayed by compositing visible 0.65 micron channel, near infrared 2.2 micron channel and infrared 11.0 micron channel. And the vertical structure and development status of the clouds are demonstrated by the T-Re profiles. The results show that after the cloud seeding operation on June 11, 2018, the cloud particle effective radius increases, and the ground observed PM10 and PM2.5 pollutants decrease as well. As for the cloud seeding operation on November, 17, 2018, the satellite inversion shows that the medium and low level clouds are rich in super-cooled water with small cloud particle effective radius. After the cloud seeding operation, the effective radius of the cloud droplets increases significantly. Both of these two cloud seeding operation in Sichuan Basin demonstrate obvious precipitation enhancement results in the seeding impact area.

A new method to estimate cloud effective radius using Meteosat Second Generation SEVIRI over Middle East

The cloud particle size distribution varies with height and the phase may change from water to mixed phase to ice through the vertical profile of the cloud, giving rise to different radiative characteristics. Researchers suggested an empirical equation for an approximation of the relation between cloud-particle effective radius and reflectance of channel IR3.7 for Advanced Very High Resolution Radiometer (AVHRR) and Multi-Functional Transport Satellite (MTSAT)-1R satellites. However, when we examined this empirical equation for MSG satellites, there was no correlation between the cloud particle effective radius and those from the Terra and Aqua/MODIS (Moderate Resolution Imaging Spectrometers) products. Given the fact that this relationship is empirical and not applicable for Meteosat satellites with 3.9 µm wavelength cloud properties, we need to introduce a new nonlinear equation that is independent from other cloud properties, to retrieve the cloud effective radius from MSG satellite over Middle East. Thus, in this study, a development of the mentioned method, based on a nonlinear regression model, was introduced to estimate the water/ice-cloud particle effective radius from the 3.9 µm wavelength reflectivity of the Meteosat Second Generation Indian Ocean Data Coverage (MSG-1(IODC)) satellite over the Middle East region. For this purpose, the LibRadTran radiative transfer model was used. This approach is almost independent from other cloud properties, which makes this relationship more efficient for retrieving a cloud’s effective radius. To evaluate this approach, the results have been compared to the effective-radius product of the MODIS on board the Terra and Aqua satellites, and cloud effective radius parameter from the MSG-1 satellite’s optimal cloud analysis (OCA) data. The average of correlation coefficient, standard deviation, and RMSE (root mean square error) of this retrieved algorithm method for 29 randomly selected case studies, in comparison to the corresponding MODIS product, are 0.93, 3.093, and 3.639, and compared with OCA product, 0.88, 4.015, and 4.51, respectively. Therefore, the results of analysis in the Middle East region show that the retrieved effective particle radius from Meteosat satellites corresponds strongly with MODIS data from the Terra and Aqua satellites, and also with the OCA products of the MSG-1. Furthermore, using the algorithm that is presented in this paper, a nonlinear regression relationship can be made for retrieving cloud effective radius in the intended place.

Flux and polarization signals of Earth-like exoplanets covered by sub-solar clouds

We investigate the flux and polarization signals of Earth-like exoplanets covered by sub-solar clouds. These clouds occur at the region where solar zenith angle is smaller than a specific angle \(\sigma\). We calculate the flux and polarization of the light reflected by the planetary disk, and we include multiple scattering. For a given \(\sigma\), the sub-solar cloud coverage will change with the phase angle, corresponding to the various viewing geometries. We study the influences of cloud optical thicknesses, surface albedos, cloud particle sizes and wavelengths. Then we investigate how these parameters together with the variation of cloud coverage caused by viewing geometries leave traces on flux and polarization signals. The larger values of \(\sigma\) corresponding to larger cloud coverage before the cloud totally disappears, which increases flux and reduces polarization. For a given value of \(\sigma\), while considering the influences of parameters such as cloud optical thickness, surface albedo, cloud particle size and wavelength, the cloud coverage variation also should be taken into account. The location of a rainbow is not affected by \(\sigma\), cloud optical thickness and surface albedo. However, both the larger values of sub-solar cloud optical thicknesses and the larger surface albedos would reduce the polarization of the rainbow peaks. The location of a rainbow may be affected by cloud particle effective radius (\(r_{\mathrm{eff}}\)) and wavelength. As \(r_{\mathrm{eff}}\) and/or wavelength increases, the polarization signal shows a stronger primary rainbow feature. The location of the maximum polarization can be affected by \(\sigma\), cloud optical thickness, surface albedo and wavelength. But the \(r_{\mathrm{eff}}\) seems not to affect the location of maximum polarization. The longer wavelength (such as 865 nm) is suitable to derive sub-solar cloud coverage, and the rainbow feature can help to characterize cloud particles. All these results emphasize the importance of polarimetry for the characterization of exoplanets.

A study of some cloud microphysical parameters over Iraq using satellite data

Studying clouds is a top priority among many atmospheric scientists because clouds are one of the greatest unknown factors in predicting changes in the Earth’s climate. Clouds play an important role in maintaining the energy balance because they can reflect, absorb, and radiate energy. The aim of this research is to investigate the properties of clouds over Iraq using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS)on board Aqua Satellite for water and ice clouds. The results showed that daily mean cloud top pressure patterns during spring months are higher than other months and cloud top temperature patterns reached their highest values during summer months. The results also indicated that the ice cloud effective particle radius is relatively large during summer while cloud optical thickness assume its largest values in winter months. It was found that the highest values of precipitation rate over Iraq occurred during March to mid-April. Correlation aanalysis between optical thickness and liquid water path over Iraq that these two parameters are positively correlated and the correlation for water cloud was better that that for ice clouds. Case studies of heavy precipitation events over Iraq showed that the maximum values of the most cloud properties variables were located ahead of the storm center.

Optical Properties and Spatial Variation of Tropical Cyclone Cloud Systems From TRMM and MODIS in the East Asia Region: 2010–2014

AbstractThe tropical cyclone (TC) in East Asia varies strongly each year, indicating the different spectral characteristics of the TC cloud systems (TCCSs) with the distance from the center of TC. We report a new perspective of optical properties of TCCS by using the Moderate Resolution Imaging Spectroradiometer and Tropical Rainfall Measuring Mission data over the years from 2010 to 2014 for the 35 TCs in East Asia. We investigate the spatial distribution of the cloud‐top height, radar reflectivity, polarization corrected temperature in 85.5 GHz (PCT85.5), hydrometeor vertical profile, cloud water path, cloud optical thickness, and cloud particle effective radius of TCCS in the development, maturity, and decay stages of TC. The results indicate the significant differences of cloud characteristics in the maturity stage in comparison to other two stages, showing the higher radar reflectivity up to 30 dBZ and the PCT85.5 as low as 232.98 K in the TC eye wall. Meanwhile, the mean hydrometeor profile indicates more ice particles accumulating above the height of 10 km but less liquid droplets near 3 km. The peak of cloud water path and cloud optical thickness in the maturity stage can reach to 1,300 g/m2 and 75, respectively. The mean cloud particle effective radius and precipitation in the three stages could be influenced by aerosols. Based on these quantified results, several ideal fitting equations and the models are constructed to denote the properties of TCCS. Two cases are analyzed to examine the accuracy of the quantified mean state for different stages of TCCS and the relationship between aerosols and clouds.

Vertical Profiles of Ice Cloud Microphysical Properties and Their Impacts on Cloud Retrieval Using Thermal Infrared Measurements

AbstractDespite the vertically inhomogeneous (VIH) structure of ice clouds, current passive remote sensing methods assume plane‐parallel homogeneous (PPH) layers, which can lead to retrieval errors. An adequate VIH cloud model is required to improve retrieval performance. In this study, CloudSat and CALIPSO satellite measurements in a 1‐year period were analyzed to model cloud vertical inhomogeneity, and its impacts on cloud retrieval were assessed using thermal infrared (TIR) measurements. The satellite measurements revealed that the peak ice water content (IWC) located around the cloud vertical midpoint moved toward the cloud base as the ice water path (IWP) increased in clouds with small IWP values; thicker clouds exhibited a gradual shift in IWC peak location toward the cloud top as IWP increased. The vertical profiles of both the cloud‐particle effective radius (CER) and a proxy of cloud‐particle number concentration showed close associations with the vertical IWC profile. An empirical model linking cloud geometrical thickness to columnar optical properties (IWP and column‐mean CER) as a function of cloud‐top temperature was also proposed. Compared with a model assuming PPH clouds, the VIH cloud model improved retrieval performance by reducing the retrieval error of the TIR‐based passive remote sensing algorithm. Further, by increasing the retrieved values of cloud‐top height noticeably for high‐level clouds and column‐mean CER considerably, with minimal effects on cloud optical thickness retrieval, the VIH cloud model yielded results that were in better agreement with radar/lidar ice cloud products than those obtained under the assumption of PPH cloud layers.