A linear relationship between vertical velocity and condensation processes in deep convection

Vertical velocities and microphysical processes within deep convection are intricately linked, having wide-ranging impacts on water and mass vertical transport, severe weather, extreme precipitation, and the global circulation. The goal of this research is to investigate the functional form of the relationship between vertical velocity, w, and microphysical processes that convert water vapor into condensed water, M, in deep convection. We examine an ensemble of high-resolution simulations spanning a range of tropical and midlatitude environments, a variety of convective organizational modes, and different model platforms and microphysics schemes. The results demonstrate that the relationship between w and M is robustly linear, with the slope of the linear fit being primarily a function of temperature and secondarily a function of supersaturation. The R2 of the linear fit is generally above 0.6 except near the freezing and homogeneous freezing levels. The linear fit is examined both as a function of local in-cloud temperature and environmental temperature. The results for in-cloud temperature are more consistent across the simulation suite, although environmental temperatures are more useful when considering potential observational applications. The linear relationship between w and M is substituted into the condensate tendency equation and rearranged to form a diagnostic equation for w. The performance of the diagnostic equation is tested in several simulations, and it is found to diagnose the storm-scale updraft speeds to within 1 m s−1 throughout the upper half of the clouds. Potential applications of the linear relationship between w and M and the diagnostic w equation are discussed.

Characteristics and Seasonal Variations of Cirrus Clouds from Polarization Lidar Observations at a 30°N Plain Site

Geometrical and optical characteristics of cirrus clouds were studied based on one year of polarization lidar measurements (3969 h on 228 different days between March 2019 and February 2020) at Wuhan (30.5°N, 114.4°E), China. The cirrus clouds showed an overall occurrence frequency of ~48% and occurrence mid-cloud altitude of ~8–16 km over the 30°N plain site. The mean values of their mid-cloud height and temperature were 11.5 ± 2.0 km and −46.5 ± 10.7 ℃, respectively. The cirrus geometrical thickness tended to decrease with decreasing mid-cloud temperature, with a mean value of 2.5 ± 1.1 km. With the decrease of mid-cloud temperature, the cirrus optical depth (COD) tended to decrease, but the depolarization ratio tended to increase. On average, the COD, lidar ratio, and particle depolarization ratio were respectively 0.30 ± 0.36, 21.6 ± 7.5 sr, and 0.30 ± 0.09 after multiple scattering correction. Out of a total of the observed cirrus events, sub-visual, thin, and dense cirrus clouds accounted for 18%, 51%, and 31%, respectively. The cirrus clouds showed seasonal variations with cloud altitude maximizing in a slightly-shifted summertime (July to September) where the southwesterly wind prevailed and minimizing in winter months. Seasonally-averaged lidar ratio and depolarization ratio showed maximum values in spring and summer, respectively. Furthermore, a positive correlation between the cirrus occurrence frequency and dust column mass density was found in other seasons except for summer, suggesting a heterogeneous ice formation therein. The cirrus cloud characteristics over the lidar site were compared with those observed at low and mid latitudes.

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

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.

Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products

Satellite-imager-based operational cloud property retrievals generally assume that a cloudy pixel can be treated as being plane-parallel with horizontally homogeneous properties. This assumption can lead to high uncertainties in cloud heights, particularly for the case of optically thin, but geometrically thick, clouds composed of ice particles. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature and heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is tested using MODIS observations over the western North Pacific Ocean during August 2015. We estimate minimum–maximum heights for three cloud regimes, i.e., single-layered optically thin ice clouds, single-layered optically thick ice clouds, and multilayered clouds. Our results are assessed through comparison with CALIOP version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layered optically thin clouds show good agreement with those from CALIOP; biases for maximum (minimum) heights versus the cloud-top (base) heights of CALIOP are 0.13 km (−1.01 km). For optically thick and multilayered clouds, the biases of the estimated cloud heights from the cloud top or cloud base become larger (0.30/−1.71 km, 1.41/−4.64 km). The vertically resolved boundaries for ice clouds can contribute new information for data assimilation efforts for weather prediction and radiation budget studies. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multichannel infrared imager.

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.

Use of spectral cloud emissivity to infer ice cloud boundaries: Methodology and assessment using CALIPSO cloud products

Satellite-based operational cloud height retrievals generally assume a plane-parallel homogeneous cloud exists in each field of regard, or pixel, but this assumption ignores vertical inhomogeneity, which is of particular importance for optically thin, but geometrically thick, ice clouds. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature/heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is tested using MODIS observations over the western North Pacific Ocean during August 2015. We estimate minimum/maximum heights for three cloud regimes, i.e., single-layer thin and thick ice clouds, and multi-layered clouds. Our results are assessed through comparison with CALIOP Version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layer optically thin clouds show good agreement with those from CALIOP; bias for maximum (minimum) heights versus the cloud top (base) heights of CALIOP are 0.13 km (−1.01 km). For optically thick and multi-layered clouds, the biases of the estimated cloud heights from the cloud top/base become larger. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multi-channel infrared imager. The vertically resolved heights for ice clouds can contribute new information for studies involving weather prediction and cloud radiative effects.

Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy

The French–German Arctic Research Base AWIPEV at Ny-Ålesund, Svalbard, is an unique station for monitoring cloud related processes in the Arctic. For the first time, data from a set of ground-based instruments at AWIPEV observatory are analyzed to characterize the vertical structure of clouds. For this study, a 14-month dataset from Cloudnet combining observations from a ceilometer, a 94 GHz cloud radar and a microwave radiometer, is used. The total cloud occurrence of 81 %, with 44.8 % of multi-layer and 36 % of single-layer clouds was found. Among single-layer clouds the occurrence of liquid, ice and mixed-phase clouds was 6.4 %, 9 % and 20.6 %, respectively. It was found, that more than 90 % of single-layer liquid and mixed-phase clouds have LWP values lower than 100 and 200 g m2, respectively. Mean values of IWP for ice and mixed-phase clouds were found to be 273 and 164 g m2, respectively. The different types of single-layer clouds are also related to in-cloud temperature and relative humidity under which they occur. Statistics based on observations are compared to the ICON model output. Distinct differences in liquid phase occurrence in observations and the model at different environmental temperatures leading to higher occurrence of pure ice clouds and lower occurrence of mixed-phase clouds in the model at temperatures between −20° and −5 °C become evident. The analyzed dataset is useful for satellite validation and model evaluation.