Cloud Base Height Research Articles

Enormous impulsive enhancement of particle fluxes observed on Aragats on May 23, 2023

An unprecedented thunderstorm ground enhancement (TGE) event was recorded on May 23, 2023, at Aragats Mountain, the highest peak in Armenia. This event showcased a maximum flux intensity surpassing 3 million particles per minute per square meter for energies above 0.4 MeV. Distinctly, the fluence of the event was measured at approximately ≈700 particles/cm2. The comprehensive instrumentation at the Aragats research station, including a suite of spectrometers and detectors, enabled precise cross-correlation of measurements. The electron flux at energies exceeding 10 MeV was observed at roughly ≈55,000 particles per minute per square meter. Additional measurements, including cloud base heights and corona discharge detections, validated the intensity of the electric field, reaching approximately 2.1 kV/cm at elevations 50–100 m above ground level. Our observations confirm that TGE is a universal and significant atmospheric event, contributing a substantial flux of high-energy electrons to the global electrical circuit. Integrating such TGE phenomena into Earth's numerical models is imperative, considering their impact on aviation and aerospace operation safety.

Tornadoes in Southeast South America: Mesoscale to Planetary-Scale Environments

Abstract A multiscale analysis of the environment supporting tornadoes in southeast South America (SESA) was conducted based on a self-constructed database of 74 reports. Composites of environmental and convective parameters from ERA5 were generated relative to tornado events. The distribution of the reported tornadoes maximizes over the Argentine plains, while events are rare close to the Andes and south of Sierras de Córdoba. Events are relatively common in all seasons except in winter. Proximity environment evolution shows enhanced instability, deep-layer vertical wind shear, storm-relative helicity, reduced convective inhibition, and a lowered lifting condensation level before or during the development of tornadic storms in SESA. No consistent signal in low-level wind shear is seen during tornado occurrence. However, a curved hodograph with counterclockwise rotation is present. The Significant Tornado Parameter (STP) is also maximized prior to tornadogenesis, most strongly associated with enhanced CAPE. Differences in the convective environment between tornadoes in SESA and the U.S. Great Plains are discussed. On the synoptic scale, tornado events are associated with a strong anomalous trough crossing the southern Andes that triggers lee cyclogenesis, subsequently enhancing the South American low-level jet (SALLJ) that increases moisture advection to support deep convection. This synoptic trough also enhances vertical shear that, along with enhanced instability, sustains organized convection capable of producing tornadic storms. At planetary scales, the tornadic environment is modulated by Rossby wave trains that appear to be forced by convection near northern Australia. Madden–Julian oscillation phase 3 preferentially occurs 1–2 weeks ahead of tornado occurrence. Significance Statement The main goal of this study is to describe what atmospheric conditions (from local to global scales) are present prior to and during tornadic storms impacting southeast South America (SESA). Increasing potential for deep convection, wind shear, and potential for rotating updrafts, as well as reducing convective inhibition and cloud-base height, are predominant a few hours before and during the events in connection to low-level northerly winds enhancing moisture transport to the region. Remote convective activity near northern Australia appears to influence large-scale atmospheric circulation that subsequently triggers convective storms supporting tornadogenesis 1–2 weeks later in SESA. Our findings highlight the importance of accounting for atmospheric processes occurring at different scales to understand and predict tornado occurrences.

A Method for Rapid Measurement of Cloud Base Height From a Pair of Sky Imagers

A Method for Rapid Measurement of Cloud Base Height From a Pair of Sky Imagers

A Statistical Study on Cloud Base Height Behavior and Cloud Types During Southwest Monsoon over a High-Altitude Site in Western Ghats, India

A Statistical Study on Cloud Base Height Behavior and Cloud Types During Southwest Monsoon over a High-Altitude Site in Western Ghats, India

Five-Year Analysis of Lightning Activities in Different Climatic Regions of Sichuan Province, China

Sichuan is a high-incidence area of thunderstorm activity in China. Based on the data of the total lightning location system from 2018 to 2022, the total lightning, cloud-to-ground (CG) lightning, and intracloud (IC) lightning activity regularity for the Sichuan province (SC) and its three climate subregions: Sichuan Basin (SB), Panxi district (PD), and West Sichuan Plateau (WSP) are analyzed, and the influences of different climate and topography conditions on lightning activities are also discussed. The results show that (1) for the whole province, the annual mean value of total lightning is about 850 thousand. The SB has the most lightning occurrences, and the WSP has the largest IC and +CG proportion. The southwest of PD, the north-center of PD, and the southeast of SB are the three high-value centers of lightning density. (2) For SB, the better thermodynamic and moisture conditions account for the most lightning occurrences. For PD, the lightning distribution is attributed to the joint effect of specific meteorological conditions and mountainous topography. For WSP, the convections are weak and shallow, which lead to high IC and +CG proportion. (3) The IC lightning mainly occurs below 12 km, and the IC height is much lower on WSP. The spatial and seasonal variation of IC height corresponds well to the cloud base height (CBH) and cloud top height (CTH). (4) The seasonal lightning frequency distribution in the three regions is similar, but the diurnal variation is quite different. The lightning activity mainly occurs between 1400 and 2200 LT on WSP but shows obvious nocturnal in SB. (5) Most CG intensity concentrates in the range below 50 kA, and IC concentrates in the range below 75 kA.

Characterizing the near-global cloud vertical structures over land using high-resolution radiosonde measurements

Abstract. Cloud remains one of the largest uncertainties in weather and climate research due to the lack of fine-resolution observations of cloud vertical structure (CVS) on a large scale. In this study, near-global CVS is characterized by high-vertical-resolution twice-daily radiosonde observations from 374 stations over land, which are distributed in Europe, North America, East Asia, Australia, the Pacific Ocean, and Antarctica. To this end, we initially develop a novel method to determine CVS, by combining both the vertical gradients of air temperature and relative humidity (RH) and the altitude-dependent thresholds of RH. It is found that the cloud base heights (CBHs) from radiosondes have a higher correlation coefficient (R= 0.91) with the CBHs from a millimeter-wave cloud radar than those from the ERA5 reanalysis (R= 0.49). Overall, cloudy skies occur 65.3 % (69.5 %) of the time, of which 55.4 % (53.8 %) are one-layer clouds at 00:00 (12:00) UTC. Most multi-layer clouds are two-layer clouds, accounting for 62.2 % (61.1 %) among multi-layer clouds at 00:00 (12:00) UTC. Geographically, one-layer clouds tend to occur over arid regions, whereas two-layer clouds do not show any clear spatial preference. The cloud bases and tops over arid regions are higher compared with humid regions albeit with smaller cloud thickness (CT). Clouds tend to have lower bases and thinner layer thicknesses as the number of cloud layer increases. The global-mean CT, CBH, and cloud top height (CTH) are 4.89 ± 1.36 (5.37 ± 1.58), 3.15 ± 1.15 (3.07 ± 1.06), and 8.04 ± 1.60 (8.44 ± 1.52) km above ground level (a.g.l.) at 00:00 (12:00) UTC, respectively. The occurrence frequency of clouds is bimodal, with lower peaks between 0.5 and 3 km a.g.l. and upper peaks between 6 and 10 km a.g.l. The CBH, CTH, and CT undergo almost the same seasonality; namely, their magnitudes in boreal summer are greater than in boreal winter. As expected, the occurrence frequencies of clouds exhibit pronounced diurnal cycles in different seasons. In boreal summer, clouds tend to form as the sun rises and the occurrence frequencies increase from morning to late afternoon, with the peak in the early afternoon at the altitude of 6–12 km a.g.l., while in boreal winter, clouds have peak occurrence frequencies in the morning. The relations between surface meteorological variables and moisture with CBH are investigated as well, showing that CBHs are generally more significantly correlated with 2 m relative humidity (RH2 m) and 2 m air temperature (T2 m) than with surface pressure and 10 m wind speed. Larger T2 m and smaller RH2 m always correspond to higher CBH. In most cases CBHs are negatively correlated to soil water content. The near-global CVS obtained from high-vertical-resolution radiosondes in this study can provide key data support for improving the accuracy of cloud radiative forcing simulation in climate models.

Variability of the Cloud Base Height over the Territory of Western Siberia Based on Laser Sounding Data for the Period 2010–2021

Variability of the Cloud Base Height over the Territory of Western Siberia Based on Laser Sounding Data for the Period 2010–2021

Cloud-Base Height Retrieval from MODIS Satellite Data Based on Self-Organizing Neural Networks

Cloud-Base Height Retrieval from MODIS Satellite Data Based on Self-Organizing Neural Networks

Factors governing the chemical composition of rain at a regional site in South Africa

Precipitation chemistry is influenced by a number of complex processes, which include the temporal and spatial evolution of air masses. Previous wet deposition studies in South Africa could not distinctly relate the influence of air mass history on rain chemistry in order to substantiate the influence of different sources. The Welgegund atmospheric monitoring station in South Africa measures several atmospheric parameters, which include vertical profiling of the atmosphere that could assist in relating rain chemistry to air mass history. Therefore, the aim of this study was to conduct an advanced assessment on factors influencing chemical composition of rain in the South African interior by relating individual rain events at Welgegund to air mass history at cloud base height (CBH) and arrival heights below clouds, as well as supplementary in situ measurements conducted at the site. Hierarchical cluster analysis was performed through two approaches, i.e. clustering based on the chemical composition of rain, as well as grouping based on air masses arriving at CBH and 100 m above ground level (a.g.l.). Although statistical analyses highlighted the complexity associated with correlating rain chemistry to sources of chemical species, it proved useful to determine some correlations between rain chemistry and air mass history. Clustering according to the chemical composition of rain events grouped rain events from high to low volume weighted mean (VWM) concentrations. Correlation of air mass histories to these clusters indicated that higher VWM concentrations were associated with air masses at 100 m a.g.l. passing over anthropogenic source regions. Air mass history clustering grouped air masses passing predominantly over predefined source regions. The rain chemistry of clusters determined for air masses at 100 m a.g.l. arrival heights could be related to the influence of different source regions, with the impact of large point sources, the clean western background region and oceans especially evident. No significant correlations between air mass histories at CBH and ionic composition were evident. It was concluded that below-cloud atmospheric chemical composition was more significant to the chemical composition of rain in this part of South Africa. A few case studies were also conducted in order to further explore factors influencing the chemical composition of rain.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers

Cloud base height (CBH) is a key parameter to characterize the cloud radiation effect. However, the CBH used in downwelling surface longwave radiation (DSLR) estimation is generally obtained indirectly through cloud top parameters retrieved by passive optical remote sensing instruments, which is of high uncertainty. At the same time, it is unreasonable to replace the effective radiation of the entire cloud layers by only using the cloud base radiation with single layer cloud model. This study proposes a new method to estimate cloudy-sky DSLR, which considers the radiation effect of the entire cloud layers from the cloud base to top. First, the CBH estimation model is established by the genetic algorithm-artificial neural network (GA-ANN) algorithm. The cloud top height and cloud attribute parameters (cloud optical depth, cloud water path, and cloud phase) from the passive remote sensing are used as the input features, and meanwhile CBH data from the active remote sensing are output features in the training and testing process of the model. Then, the cloud base temperature (CBT) is estimated based on the CBH combined with the temperature profile data in the EAR5 reanalysis data. Finally, the effective temperature of the entire cloud layers is calculated by using CBT and cloud top temperature. The verification results of CBH estimation showed that R2 is 0.83, the bias and root mean square error (RMSE) are 0.02 km and 1.56 km, respectively, which indicates a comparable accuracy and higher stability compared with the previous studies. The ground-based measurements in the SURFRAD network are used to validate the newly proposed DSLR estimation method, and the results showed that the bias and RMSE are 5.27 W/m2 and 28.48 W/m2, respectively. Additionally, this study found that although the effective temperature of the entire cloud layers has a weaker linear correlation with DSLR, the radiation contribution generated by cloud still occupies a certain weight, and the maximum ratio of cloud radiation in DSLR estimation can account for 30%. Therefore, the cloud radiation effect must be taken into account in the estimation of cloudy-sky DSLR.

Measuring nocturnal near-surface urban heat island intensity in the small, mid-latitude city of Inverness, Scotland

ABSTRACT Few publications have considered the urban heat island (UHI) effect in small settlements. This paper, therefore, presents the findings of a UHI study conducted in the small, mid-latitude city of Inverness, Scotland (population: 63,220). The study aimed to provide an initial appraisal of the scale of UHI phenomena in Inverness and to understand the factors associated with its presence. Mobile vehicular traverses of a study transect were conducted on 30 dates during June, July & August 2019. Measurements of near-surface air temperature were recorded at 19 local climate zone observation points between 20:00 and 23:00 BST. Daily mean UHI intensity (UHII) during the study ranged from 0.7 to 3.5°C, with an overall mean UHII of 1.6°C. Land use characteristics impacted the UHII, with areas with higher fractions of impervious surface cover returning significantly higher air temperatures. UHII increased with the onset of sunset, and the highest UHII occurred on dates with up to 1–2 oktas of cloud cover, low relative humidity, and high cloud base height. The results align with previous studies and comparisons are drawn between Inverness and other settlements globally. Further research in Inverness is recommended to better understand UHI effects and influence national planning policy.

Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

Abstract. This work introduces a model for all-sky-image-based cloud and direct irradiance nowcasting (MACIN), which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASIs). With a synthetic setup based on simulated cloud scenes, the model and its components are validated in depth. We train a convolutional neural network on real ASI images to identify clouds. Cloud masks are generated for the synthetic ASI images with this network. Cloud height and motion are derived using sparse matching. In contrast to other studies, all derived cloud information, from both ASIs and multiple time steps, is combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times of up to 20 min. For the cloud masks derived from the ASI images, we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud-base heights is 4 % and cloud motion error is in the range of ±0.1ms-1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than 1 min. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500 m×500 m. Errors for area nowcasts as required, e.g., for photovoltaic plants, are smaller compared with errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.

Surface warming from altitudinal and latitudinal amplification over Antarctica since the International Geophysical Year

Warming has been and is being enhanced at high latitudes or high elevations, whereas the quantitative estimation for warming from altitude and latitude effects has not been systematically investigated over Antarctic Ice Sheet, which covers more than 27 degrees of latitude and 4000 m altitude ranges. Based on the monthly surface air temperature data (1958–2020) from ERA5 reanalysis, this work aims to explore whether elevation-dependent warming (EDW) and latitude-dependent warming (LDW) exist. Results show that both EDW and LDW have the cooperative effect on Antarctic warming, and the magnitude of EDW is stronger than LDW. The negative EDW appears between 250 m and 2500 m except winter, and is strongest in autumn. The negative LDW occurs between 83 °S and 90 °S except in summer. Moreover, the surface downward long-wave radiation that related to the specific humidity, total cloud cover and cloud base height is a major contributor to the EDW over Antarctica. Further research on EDW and LDW should be anticipated to explore the future Antarctic amplification under different emission scenarios.

Definition of an ice index for wind turbines in cold climate

Icing losses encountered in a wind farm are very hard to assess as they can vary by a factor of three from year to year. This inter-annual variation is a critical issue from the assessment phase to the operation of wind turbines in cold climate. To mitigate the effects of this issue, a proposed solution is to define an ice index to yield a reference for icing conditions based on historical values. Knowing that icing frequency has an inter-annual variation 5 times greater than the wind speed, the definition of an ice index is long overdue. This paper proposes a simple and accessible method for defining an ice index based on cloud base height measured at a weather station. A similar strategy is already used in the industry for other purposes such as estimating the long-term average icing frequency in icing maps. The novelty of the proposed method lies in the description of the variation in icing frequency rather than its long-term average. The ice index proposed in this paper is shown to be representative of the icing losses in 6 wind farms located in eastern Canada. Moreover, it was also shown that the ice index is strongly correlated to the icing measurements made on wind turbine nacelle. In this paper, the yearly and monthly ice indexes, from September 2007 to December 2021, are shared. These indexes are based on the meteorological data from Valcartier weather station near Quebec City. This definition of an ice index directly responds to a critical need of the wind industry in cold climate, a common reference for icing frequency. Ice indexes could be publicly shared each month in cold climate areas to guide wind farm owners and operators in their decisions and analysis of performances. Such an index could also be used to asses the long-term variation of the ice index and the effect of climate change on icing frequency and impacts on the wind energy production in cold climate.

Retrieving cloud base height from passive radiometer observations via a systematic effective cloud water content table

Cloud base heights (CBHs) play an important role in weather and climate studies, but remain one of the most challenging properties to be inferred using passive satellite spectral instruments. This is because spectral signals detected from space are mainly from cloud tops and contain minimal information on cloud vertical structures owing to limited penetration. This paper presents a new algorithm for retrieving CBH from radiometer measurements, utilizing an improved characterization for mutable cloud water contents (CWCs). This was achieved by introducing a variable named effective CWC (ECWC) to represent the vertically varying CWC; and subsequently investigating the sensitivity of ECWC to both cloud and environmental properties. Considering these characteristics, a systematic ECWC lookup table (LUT) was created based on collocated active and passive satellite observations. We then converted the existing cloud water path (CWP; kg/m2) to cloud geometric thickness (CGT; km) via the developed LUT, and calculated CBH by subtracting the CGT estimates from conventional cloud top height (CTH) retrievals. The algorithm was tested using observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) as examples, and was evaluated using active measurements from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites and a ground-based Ka-band radar. Overall, our CBH results agreed closely with the active observations, with mean biases of 0.11 ± 1.93 km for MODIS and − 0.12 ± 2.34 km for AHI in single-layer cloud cases. Despite the natural limitations on retrieving CBHs using passive radiometers, the proposed algorithm achieves retrieval accuracies comparable to those of operational CTH products. Moreover, this algorithm works for both single-layer and overlapping clouds, and can be extended to other passive radiometers by reconstructing the LUT. Such accurate CBH information derived from high-resolution passive radiometers would greatly improve our understanding of cloud vertical distributions; and could play an important role in studies of cloud radiative effects and the aerosol-cloud-precipitation interactions.

Use of multiplatform in-situ observation to study vertical structure and microphysics of clouds during southwest monsoon over Western Ghats, India

Combination of radiosonde profiles with collocated in-situ ground and aircraft measurements is used for the first time to study the vertical structure and microphysics of clouds during southwest monsoon over the Western Ghats, India. The morphology of clouds is detailed with the help of radiosonde observations and classified as low, mid, and high-level clouds depending on the cloud base height. Radiosonde sounding profiles indicated occurrences of both single and multi-layered clouds with higher occurrences of single-layered (∼35%) clouds during monsoon transition period (June and September) and two-layered (∼42%) during core monsoon period (July and August). Dominance of low (∼30%) and high-level (∼60%) clouds were noticed compared to mid-level clouds over the observational site during the southwest monsoon.Warm cloud microphysics was investigated using collocated ground and airborne in situ measurements. Irrespective of the cloud type, the cloud liquid water content and the effective droplet diameter increased with altitude. One of the key results is the rapid broadening of the cloud droplet size distribution with height. The number concentration of droplets above 25 μm diameters showed a steep decrease at altitudes above 1800 m, suggesting active collision-coalescence.

Modeling cloud seeding technology for rain enhancement over the arid and semiarid areas of Ethiopia

The government of Ethiopia has started exploring different innovative approaches to tackle the scarcity of water in arid and semi-arid regions of the country. In line with this strategy, precipitation enhancement through weather modification technology is getting strong attention and some initial attempts have been made to assess its feasibility. Therefore, this paper aims to model cloud-seeding technology for rain enhancement and check its effectiveness in arid and semiarid regions of Ethiopia. Different relevant measurements including ground-based as well as reanalysis data from 2021 to 2022 are used to improve relevant cloud-seeded models. Reanalysis data are validated with ground-based data using different error metrics. The improved cloud-seeded modeling is developed for precipitation enhancement for the arid and semiarid regions of Ethiopia. An atmospheric moisture budget is used for improving the cloud-seeded model. The results indicated that the developed model and the direct operation are well agreed upon. The relative precipitation (RP) (after the application of cloud-seeded per before the application of cloud-seeded during spring, summer, and autumn is found 1.31, 0.98, and 1.03 respectively. The changing precipitation between cloud seeded and before seeded for spring, summer, and autumn is found at 1.38, −0.19, and 0.11 mm respectively; whereas changing temperature is found at 1.08, 1.78, and −1.06 k respectively. In general, the model result indicated that cloud-seeded technology is effective over Ethiopia when the daily resultant wind speed is less than 1.5 m/s and cloud base height (CBH) is less than 1700 m. Furthermore, by observing RP from the improved cloud-seeded model results, rain enhancement science is applicable for Ethiopia during the spring and slightly autumn seasons. Hence, before artificial aerosol is seeded into the cloud, the operators should be nowcast and forecast the daily wind speed and CBH of the target area unless an economic crisis will have happened.

The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations

Abstract. Continuous long-term ground-based remote-sensing observations combined with vertically pointing cloud radar and ceilometer measurements are well suited for identifying precipitation evaporation fall streaks (so-called virga). Here we introduce the functionality and workflow of a new open-source tool, the Virga-Sniffer, which was developed within the framework of RV Meteor observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment in January–February 2020 in the tropical western Atlantic. The Virga-Sniffer Python package is highly modular and configurable and can be applied to multilayer cloud situations. In the simplest approach, it detects virga from time–height fields of cloud radar reflectivity and time series of ceilometer cloud base height. In addition, optional parameters like lifting condensation level, a surface rain flag, and time–height fields of cloud radar mean Doppler velocity can be added to refine virga event identifications. The netCDF-output files consist of Boolean flags of virga and cloud detection, as well as base and top heights and depth for the detected clouds and virga. The sensitivity of the Virga-Sniffer results to different settings is explored (in the Appendix). The performance of the Virga-Sniffer was assessed by comparing its results to the CloudNet target classification resulting from using the CloudNet processing chain. A total of 86 % of pixels identified as virga correspond to CloudNet target classifications of precipitation. The remaining 14 % of virga pixels correspond to CloudNet target classifications of aerosols and insects (about 10 %), cloud droplets (about 2 %), or clear sky (2 %). Some discrepancies of the virga identification and the CloudNet target classification can be attributed to temporal smoothing that was applied. Additionally, it was found that CloudNet mostly classified aerosols and insects at virga edges, which points to a misclassification caused by CloudNet internal thresholds. For the RV Meteor observations in the downstream winter trades during EUREC4A, about 42 % of all detected clouds with bases below the trade inversion were found to produce precipitation that fully evaporates before reaching the ground. A proportion of 56 % of the detected virga originated from trade wind cumuli. Virga with depths less than 0.2 km most frequently occurred from shallow clouds with depths less than 0.5 km, while virga depths larger than 1 km were mainly associated with clouds of larger depths, ranging between 0.5 and 1 km. The presented results substantiate the importance of complete low-level precipitation evaporation in the downstream winter trades. Possible applications of the Virga-Sniffer within the framework of EUREC4A include detailed studies of precipitation evaporation with a focus on cold pools or cloud organization or distinguishing moist processes based on water vapor isotopic observations. However, we envision extended use of the Virga-Sniffer for other cloud regimes or scientific foci as well.

Estimation of Surface Downward Longwave Radiation and Cloud Base Height Based on Infrared Multichannel Data of Himawari-8

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m−2 for hourly surface downward longwave irradiance (SDLI).