Cloud Top Temperature Research Articles

Unsupervised Clustering of Geostationary Satellite Cloud Properties for Estimating Precipitation Probabilities of Tropical Convective Clouds

Abstract Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties. Significance Statement We aim to identify the properties of tropical convective clouds (TCCs) that have a high precipitation probability. We designed a two-step framework to identify TCC objects and the conditions of cloud properties for TCCs to have a high precipitation probability. The TCCs with a precipitation probability > 70% tend to have a low cloud-top temperature and a cloud particle effective radius ≥ 20 μm. Cloud optical thicknesses are distributed over a wide range, but thinning requires a particle radius larger than 20 μm. These conditions of cloud properties appear to be unchanged under various spatial–temporal conditions over the tropics. This important observational finding advances our understanding of the cloud–precipitation relationship in TCCs and can be applied to satellite nowcasting of precipitation in the tropics, where numerical weather forecasts are limited.

On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain

Abstract Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective-permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including the following: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA); 2) kinematics via modification of cumulus parameterization; and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research and Forecasting (WRF) Model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain–Fritsch scheme and double-moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance. Significance Statement Forecasting thunderstorm clouds and rain over mountainous regions is challenging because of limitations in having radar and rain gauges and in resolving physical drivers in forecast models. We examine the value of considering all possible constraints by incorporating moisture into these models, and correcting physics in the model treatment of cumulus and cloud microphysics parameterizations. This study demonstrates that assimilating moisture and using modified Kain–Fritsch and double-moment microphysics schemes provides the best thunderstorm cloud and rain forecasts in terms of timing, location, and intensity. Each correction improves key properties of these storms such as vertical wind, along with distribution of water in various phases. We highlight the need to improve our efforts on effectively integrating these constraints into current and future forecasts.

Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Evaluating EAMv2 Simulated High Latitude Clouds Using ARM Measurements in the Northern and Southern Hemispheres

AbstractThis study evaluates high latitude stratiform mixed‐phase clouds (SMPC) in the atmosphere model of the Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one‐year‐long ground‐based remote sensing measurements from the Atmospheric Radiation and Measurement (ARM) program. A nudging approach is applied to model simulations for a constrained comparison with the ARM observations. Observed and modeled SMPCs are sampled and collocated to address the difference in data resolution, so that we can consistently evaluate their macrophysical properties at the North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates SMPC frequency of occurrence at both sites. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud top and cloud base and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phase is substantially biased. The model largely overestimates liquid water path, and the ice water path is underestimated at NSA, but at AWR, the liquid water path is frequently underestimated due to the dominance of snow in Antarctic SMPCs. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that additional model development is needed for high latitude mixed‐phase clouds.

Investigating the vertical extent and short-wave radiative effects of the ice phase in Arctic summertime low-level clouds

Abstract. Low-level (cloud tops below 2 km) mixed-phase clouds are important in amplifying warming in the Arctic region through positive feedback in cloud fraction, water content and phase. In order to understand the cloud feedbacks in the Arctic region, good knowledge of the vertical distribution of the cloud water content, particle size and phase is required. Here we investigate the vertical extent of the cloud-phase and ice-phase optical properties in six case studies measured in the European Arctic during the ACLOUD campaign. Late spring- and summertime stratiform clouds were sampled in situ over pack ice, marginal sea ice zone and open-ocean surface, with cloud top temperatures varying between −15 and −1.5 ∘C. The results show that, although the liquid phase dominates the upper parts of the clouds, the ice phase was frequently observed in the lower parts down to cloud top temperatures as warm as −3.8 ∘C. In the studied vertical cloud profiles, the maximum of average liquid phase microphysical properties, droplet number concentration, effective radius and liquid water content, varied between 23 and 152 cm−3, 19 and 26 µm, 0.09 and 0.63 g m−3, respectively. The maximum of average ice-phase microphysical properties varied between 0.1 and 57 L−1 for the ice number concentration, 40 and 70 µm for the effective radius, and 0.005 and 0.08 g m−3 for the ice water content. The elevated ice crystal number concentrations and ice water paths observed for clouds, with cloud top temperatures between −3.8 and −8.7 ∘C can be likely attributed to secondary ice production through rime splintering. Low asymmetry parameters between 0.69 and 0.76 were measured for the mixed-phase ice crystals with a mean value of 0.72. The effect of the ice-phase optical properties on the radiative transfer calculations was investigated for the four cloud cases potentially affected by secondary ice production. Generally the choice of ice-phase optical properties only has a minor effect on the cloud transmissivity and albedo, except in a case where the ice phase dominated the upper cloud layer extinction. In this case, cloud albedo at solar wavelengths was increased by 10 % when the ice phase was given its measured optical properties instead of treating it as liquid phase. The presented results highlight the importance of accurate vertical information on cloud phase for radiative transfer and provide a suitable data set for testing microphysical parameterizations in models.

Analysis of Cloud Growth Phases When Nocturnal Hail Occurs (Case Study: Sekadau, 22 August 2020)

On August 22 2020, hail was reported in Sekadau, West Kalimantan at 21:23 WIB. The phenomenon of hail does not usually occur at night. So, further research is needed to find out how the cloud growth phase and Cumulonimbus (Cb) cloud structure are in this phenomenon. This research utilizes remote sensing instruments in the form of a C-Band type weather radar for the Supadio Meteorological Station and the Himawari 8 Satellite, as well as a radiosonde instrument that is closest to the time and location of the hail events. The results of satellite imagery show that nocturnal hail occurs before the clouds reach the mature phase, with the cloud top temperature at the time of hail less than -60 ºC. The maximum reflectivity detected by the radar using CMAX products shows a less significant value with an intensity of up to 52 dBZ. The VCUT product shows the maximum intensity altitude is at an altitude of 5 – 8 km. This shows that there is a process of ice condensation on the freezing level layer. The ZHAIL product image shows that there is a potential for hail in the Sekadau area with a probability of more than 80%. The occurrence of ice rain at Sekadau at night is caused by the presence of Cumulonimbus clouds with very cold peak temperatures and the mixing process above the freezing level which supports the formation of ice condensation in Cb clouds.

Analysis of Atmospheric Conditions during the Occurrence of Mesoscale Convective Complex (MCC) in East Kalimantan and Surrounding Area (Case Study: December 26, 2021)

Mesoscale Convective Complex (MCC) was first introduced in 1980 through research on infrared (IR) satellite images by Maddox. The MCC phenomenon can caused rain with a long period of time. This study aims to determine the atmospheric conditions when MCC occurred in East Kalimantan and surroundings area on December 26, 2021. The research data used in this study is Himawari-8 satellite data to display cloud top temperatures and convective cloud distribution using the CCO method and image interpretation using RGB method. Analysis of atmospheric conditions was also carried out on air humidity, vertical velocity, and reflectivity in the atmosphere obtained from weather radar data from the Stasiun Meteorologi Sultan Aji Muhammad Sulaiman - Sepinggan. Based on the results of the analysis, MCC was identified as a mature phase on December 26, 2021 at 12.00 UTC. Airmass RGB analysis shows the presence of clouds which are known as Cb clouds through CCO analysis as well as warm air masses with high humidity in East Kalimantan. Negative value of vertical velocity also indicates an updraft of moist air masses and the maximum reflectivity value of the CMAX product, which is 53 dBz, indicates strong convection activity.

All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

The Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) allows for high-spatiotemporal-resolution observations of the local severe weather systems. The capabilities for assimilating the all-sky AGRI infrared radiances are explored with the WRF three-dimensional variational data assimilation system (WRF-3DVAR) to improve the forecast accuracy of an extremely heavy rainfall event over the Henan Province of China in this study. In particular, data assimilation experiments are conducted with different physical parameterization schemes, including microphysical schemes and cumulus parameterizations. The results show that the all-sky AGRI radiance data assimilation experiments based on the Purdue Lin microphysics scheme and the Kain–Fritsch cumulus scheme can lead to better forecasts of the rainfall intensity and location, respectively. Meanwhile, it is found that the analyzed cloud top temperature fits the observations better when cloudy radiances from the AGRI are assimilated using the Purdue Lin scheme. This finding suggests that proper physical processes could facilitate the effective use of AGRI cloud-affected observations. On the other hand, positive forecast impacts from assimilating the all-sky AGRI radiance data have been confirmed when compared to the experiments that assimilate only the clear-sky AGRI radiances or only conventional observations. The assimilation of the all-sky AGRI water vapor channel is conducive to humidifying the initial conditions in the middle and low troposphere with more realistic analysis increments of water vapor, cloud water, and cloud ice, resulting in improved intensity forecasts of the heavy rainfall system and better forecast skill scores.

Lightning-Based Tropical Cyclone Rapid Intensification Guidance

Abstract With several seasons of Geostationary Lightning Mapper (GLM) data, this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic Oceans. A long-term lightning dataset is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) dataset from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0–100-km inner core and 200–300-km rainband area for the preceding 6-h predictors and a temporal configuration with an inner core only for the preceding 0–1, 0–6, and 6–12 h. When tested on the 2018–21 seasons, the temporal configuration adds skill primarily to the 12–48-h forecasts when compared to the no-lightning version and rapid intensification operational consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids. Significance Statement The forecasting of rare, yet critical, tropical cyclone rapid intensification events continues to be challenging. The current operational tools to anticipate rapid intensity changes use a combination of numerical weather prediction–derived environmental conditions and satellite-based cloud top temperature variations of deep convection. Here, we use freely available Geostationary Lightning Mapper data, which provide independent information about convection, in similar intensity guidance frameworks using temporal and spatial aspects of lightning variability. Our results show an improvement in short-term (12–48 h) rapid intensification forecasts by using temporal lightning information, and our investigation highlights that users of Geostationary Lightning Mapper lightning information should be cognizant of the influence and impact of land on these observations.

Case Studies of Forest Windthrows and Mesoscale Convective Systems in Amazonia

AbstractThis study identifies 38 cases of windthrows in the Amazonia to explore the relationship between windthrows and the characteristics (storm passing time, cloud top temperature, and maximum precipitation) of mesoscale convective systems (MCSs) that produced them. Most of windthrow cases in this study occurred in August and September. The storm passing time is positively correlated with the size of windthrows. MCSs with colder cloud top temperature (with a mean at 206 K)—indicating deeper convection—resulted in large windthrows, while those with warm cloud top (with a mean above 230 K) resulted in relatively small windthrows except for windthrows in the western Amazonia. No significant relationship is found between maximum precipitation intensity and the area of windthrows.

Generation of global 1-km daily top-of-atmosphere outgoing longwave radiation product from 2000 to 2021 using machine learning

ABSTRACT Top-of-atmosphere (TOA) outgoing longwave radiation (OLR), a key component of the Earth’s energy budget, serves as a diagnostic of the Earth’s climate system response to incoming solar radiation. However, existing products are typically estimated using broadband sensors with coarse spatial resolutions. This paper presents a machine learning method to estimate TOA OLR by directly linking Moderate Resolution Imaging Spectroradiometer (MODIS) TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System (CERES) and other information, such as the viewing geometry, land surface temperature and cloud top temperature determined by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Models are built separately under clear- and cloudy-sky conditions using a gradient boosting regression tree. Independent test results show that the root mean square errors (RMSEs) of the clear-sky and cloudy-sky models for estimating instantaneous values are 4.1 and 7.8 W/m2, respectively. Real-time conversion ratios derived from CERES daily and hourly OLR data are used to convert the instantaneous MODIS OLR to daily results. Inter-comparisons of the daily results show that the RMSE of the estimated MODIS OLR is 8.9 W/m2 in East Asia. The developed high resolution dataset will be beneficial in analyzing the regional energy budget.

Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance

Abstract Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. Significance Statement This work presents a new method of short-term probabilistic forecasting for tropical cyclone (TC) convective structure and intensity using infrared geostationary satellite observations. Our prototype model’s performance indicates that there is some value in observed and simulated future cloud-top temperature radial profiles for short-term intensity forecasting. The nonlinear nature of machine learning tools can pose an interpretation challenge, but structural forecasts produced by our model can be directly evaluated and, thus, may offer helpful guidance to forecasters regarding short-term TC evolution. Since forecasters are time limited in producing each advisory package despite a growing wealth of satellite observations, a tool that captures recent TC convective evolution and potential future changes may support their assessment of TC behavior in crafting their forecasts.

The seasonal cycle of cloud radiative effects over Congo Basin based on CERES observation and comparison to CMIP6 models

This study investigates the seasonal variability of the cloud radiative effects (CREs) over Congo Basin (CB) using 15-year observations from Clouds and the Earth's Radiant Energy System (CERES) Energy Budget and Filled (EBAF) Ed4.1 level 3b dataset involving CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board Terra and Aqua satellites. The relationships between CREs and cloud properties such as total cloud fraction (TCF), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT) are checked. An evaluation of Coupled Model Intercomparison Project (CMIP) Phase 6 in capturing the seasonal cycle of CREs as well as the magnitudes of the CREs along the seasonal cycle is also performed. This study shows a net cloud cooling effect of −8.4 W/m2 and − 43.9 W/m2 respectively at the top of the atmosphere (TOA) and at the surface, leading to a net warming effect of 35.67 W/m2 in the atmosphere. This value implies a large energy source over the Central Africa (CA) atmospheric column. The associated relationships between CREs and cloud properties show that the shortwave CRE is more sensitive to TCF and optical thickness whereas its longwave counterparts is more sensitive to CTH, CTT and COT at the TOA and in the atmosphere. All of the four CMIP6 models used in this study can capture the spatial pattern of CREs as well as their seasonal cycle but misrepresent intensity of CREs. Results also show that a better-simulated TCF considerably reduces the intensity of the annual mean underestimation in both longwave and shortwave CRE for some CMIP6 models, but not for models with overestimated shortwave CRE.

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.

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part I: Observational Study

Abstract A study of the vertical structure of postfrontal shallow clouds in the marine boundary layer over the Southern Ocean is presented. The central question of this two-part study regards cloud phase (liquid/ice) of precipitation, and the associated growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-h case with continuous sea surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are mostly precipitating and shallow (tops mostly around 2 km above sea level), with weak up- and downdrafts, and with cloud-top temperatures generally around −18° to −10°C. The case study is extended to three other periods of postfrontal shallow clouds in MARCUS. While abundant supercooled liquid water is commonly present, an experimental cloud-phase algorithm classifies nearly two-thirds of clouds in the 0° to −5°C layer as containing ice (cloud ice, snow, or mixed phase), implying that much of the precipitation grows through cold-cloud processes. The best predictors of ice presence are cloud-top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment. The water-phase distribution in this cloud regime is explored through numerical simulations in Part II. Significance Statement Climate models generally predict a lower albedo than observed over the Southern Ocean, and this is largely attributed to a lack of cloudiness, especially in the postfrontal cold sector of midlatitude cyclones. This in turn may be due to an excess of ice in these simulated clouds, resulting in rapid precipitation fallout and an overly brief cloud lifespan. The objective of this study is to examine whether shallow postfrontal clouds over the Southern Ocean are dominated by supercooled drops, or by snow and ice, using data collected by a U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility deployed aboard an Australian Antarctic supply vessel. We find that these clouds contain much supercooled liquid, even though cloud-top temperatures generally are around −18° to −8°C, and that about two-thirds of the clouds just above the freezing level contain ice. Much of the precipitation appears to grow through cold-cloud processes above the freezing level, rather than drizzle/rain. Updrafts and/or turbulence in convection or in cloud-top generating cells do not initiate much ice, compared to observations elsewhere in a similar temperature range. This may be attributable to the extremely low concentration of ice nucleating particles in this environment. Ultimately, the deepest clouds with the coldest cloud tops are most likely to be ice dominated.

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Analysis Of Spearman Rank Correlation & Linear Regression Of Atmospheric Stability And Cloud Tops Temperature Of Himawari-8 IR Satellite Images (Case Study Of Hail On May 22, 2022)

This research is based on cases of hail that caused flooding in Binjai City on May 22, 2022. This study aims to see the correlation and regression of the Atmospheric Stability index value on cloud top temperature data from the Himawari-8 satellite imagery. So that the ERA-5 reanalysis data can be used to obtain cloud top temperatures in predicting hail events. The indices used in this study are CAPE, KI and TTI. The data used is located in Binjai City with correlation testing from 00UTC – 24UTC on May 22, 2022. Supported data is used in the form of sea surface temperature and wind conditions and looks at the causes of hail at the research location. The method used is Spearman's rank correlation with the results obtained in the form of a correlation value at the K index of -0.651, a correlation of the TT index of -0.563, and a correlation of the CAPE index of -0.348. The simple linear regression results also show a negative gradient line. This shows that the increase in the value of the stability index is in line with the decrease in cloud top temperature. So that the stability index data can be used as a benchmark in estimating cloud top temperatures in the case of convective cloud formation which causes hail.

The observational evidence of association between types of aerosol mode-cloud-precipitation interaction over Iran

The present study quantifies for the first time the statistical relationship between aerosol properties with Cloud microphysical Properties (CPs), and consequently effect on precipitation over Iran. The daily means of Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and CP including Cloud Fraction (CF), Cloud Top Temperature (CTT), Cloud Top Pressure (CTP), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER) were obtained from the MODerate resolution Imaging Spectroradiometer (MODIS). The precipitation was retrieved from Tropical Rainfall Measuring Mission (TRMM) during 2003–2019 for 156 1 × 1 geographical degree grid-points over Iran. The different types of aerosols including the dust and fine aerosols were separated using the AOD and AE values for each season. We identified grid points, which had statistically significant correlation (at the 5% level) between aerosol modes and at least one of CPs, the precipitation and one of CPs, and between the precipitation and aerosols modes, at the same time. With this approach, we identified 10 points showing a statistically significant relationship between AOD, CPs, and precipitation, stimulatingly. For instance, at a point in the southwest of Iran, the correlation coefficient between the AOD with precipitation (r = 0.54), AOD with CTP (r = −0.51), and precipitation with CTP (r = −0.61) were statistically significant simultaneously. Based on statistical calculations, the relationship between precipitation and AOD (for 10 points) is often positive, which indicates the positive effect of aerosols on precipitation. However, in two points, the relationship between precipitation and AOD is negative, and subsequently significant. In addition, a negative correlation coefficient is also observed between CTT and CTP with AOD, as well as a positive relationship between AOD and COT for both fine (black carbon) and coarse mode (desert dust) of aerosols over Iran. Due to the lack of ground station data in Iran, this study can serve as a starting point to better understanding the complex, and uncertain response to cloud, radiation and precipitation to aerosol over arid region of Iran.

In-orbit recalibration of Longwave Infrared Camera onboard Akatsuki

Akatsuki has been in operation since Venus orbit insertion-revenge 1 (VOI-R1) in December 2015 and has been making observations of Venus’ cloud-top temperature with Longwave Infrared Camera (LIR) since the start of nominal observations in April 2016. LIR was originally designed to maintain its performance for at least 4 years after the VOI originally planned in December 2010. Although the operation time of LIR has exceeded its designed lifetime as of August 2022, it is still functioning normally. The mechanical shutter plate has been kept at a normal temperature and used as a hot reference in determining the brightness temperature of objects when in the closed position. Since the observed temperature of the background deep space is merely a value representing the output for no radiation input, it should be the same in any observation. This was around 180 K just after the launch of Akatsuki in May 2010; however, it has gradually increased to approximately 200 K by February 2022. Average Venus disk temperatures also show a slight increasing trend. The increases of the background and Venus’ disk temperatures are most likely due to degradation of the sensitivity of the bolometer array used in LIR as an image sensor. These temperatures have apparently been increasing since LIR was activated in October 2016. While LIR is activated, the bolometer temperature is kept at 40 °C and a moderate baking effect may have accelerated degassing in the bolometer package, and the resulting increase of thermal conductivity or decrease of transmittance of the window contaminated by evaporated components may have degraded the sensitivity of the bolometer. A sensitivity degradation of 5% from October 2016 to February 2022 is estimated from the increasing trend of the background temperature. A correction has been made to the LIR data to keep the background temperature constant. The corrected data show no increasing trend in either the background or Venus’ disk temperature. The corrected data are open to the public as a more reliable dataset for investigating the long-term variability of thermal condition at cloud-top altitudes.Graphical

Study of the correlation between electrical activity and the microphysics and dynamsics of a real severe event using the WRF‐ELEC model

AbstractAtmospheric electrical activity is one of the most damaging meteorological phenomena. Studies suggest that storm electrical discharges are correlated with severe weather events such as hail, strong surface winds, etc. To study these correlations, in addition to real data, numerical simulations can be used. In this work, we investigated the electrical activity of simulated thunderstorms using the WRF‐ELEC model over a case study of the RELAMPAGO field campaign (11 December 2018) associated with the occurrence of generalized deep moist convection in central Argentina including some supercell storms. The WRF‐ELEC model successfully reproduced the convective event. A tracking algorithm was used to individualize different convective cells and to study the relationship between their electric activity and different microphysical and kinematic variables. The model reproduced the expected correlations and time lags between these variables. In particular, the model showed that the best correlation occurred between the graupel and hail mass and the electric activity of the storms; this is expected given the key role of these microphysical species in the charge separation processes. Also, 5 m·s−1 updraft volume and cloud top temperature (CTT) temporal evolutions presented good correlations with the electrical activity. Maximum updraft show a less but still good correlation with the electrical activity. We also investigated the correlation between the electrical activity and the hail and graupel concentration near the surface (as an indicator of hail precipitation) and the near‐surface winds and we found that correlation is strong. In particular, for near‐surface ice, we determined that peaks in electrical activity precede peaks in hail and graupel precipitation and that electrical activity could be used as a proxy of this high‐impact weather event.