Properties Of Convective Clouds Research Articles

CAIPEEX: Indian Cloud Seeding Scientific Experiment

Abstract The demand for effective methods to augment precipitation over arid regions of India has been increasing over the past several decades as the changing climate brings warmer average temperatures. In the fourth phase of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX IV), a scientific investigation was conducted over a rain-shadow region of the Western Ghats mountains in India. The primary objective was to investigate the efficacy of hygroscopic seeding in convective clouds and to develop a cloud seeding protocol. CAIPEEX IV followed the World Meteorological Organization (WMO) recommendations in a peer-reviewed report with physical, statistical, and numerical investigations. The initial results of the campaign in the monsoon period of 2018 and 2019 with two instrumented aircraft, a ground-based dual-polarization C-band radar, a network of rain gauges, radiosondes, and surface aerosol measurements are reported here. The hygroscopic seeding material was detected in cloud droplets and key cloud microphysical processes in the seeding hypothesis were tracked. The formidable challenges of assessing seeding impacts in convective clouds and the results from 150 seed and 122 no-seed samples of randomized experiments are illustrated. Over 5,000 cloud passes from the airborne campaign provided details about the convective cloud properties as the key indicators for a seeding strategy and the evaluation protocol. The experimental results suggest that cloud seeding can be approached scientifically to reduce uncertainty. The results from this study should interest the scientific community and policymakers concerned with climate change’s impact on precipitation and how to mitigate rainfall deficiencies.

Cloud Properties Derived From Airborne Cloud Radar Observations Collected in Three Climatic Regions

AbstractClouds observed by the airborne High‐Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) were classified into twelve categories, based on their convective/stratiform nature. Dimensional and convective cloud properties were analyzed in three climatic regions: The subtropical easterlies off the coast of California, the Southern Ocean, and the tropics surrounding Central America. The convective properties of the stratocumulus clouds in the subtropical easterlies are closely related to the degree of boundary layer decoupling. In regions of strong boundary layer coupling, convectivity and updrafts in the clouds are weak and precipitation is light. In regions where the boundary layer is more decoupled, convective properties increase together with cloud top altitudes and cloud depth. Cloud properties of stratocumulus and cumulus clouds over the Southern Ocean show similarities to those observed in the subtropics, but overall they are less convective, indicating a strongly coupled boundary layer. Sea surface temperatures are much lower and the development of clouds is driven by transient synoptic conditions rather than zonal ocean temperature gradients. Clouds observed over the tropical oceans are much more convective in nature. As in the two other regions, they are mostly shallow, but clouds in regions with high sea surface temperatures have high convectivity and reflectivity values and stronger updrafts. Some of them grow to extreme depths (>14 km) and widths (>500 km). They have strong and large updraft regions and are heavily precipitating throughout their life cycle as they transition from the convective to the stratiform stage.

Impact of convection scheme on ENSO prediction of SINTEX-F2

A spectral cumulus parameterization (spectral scheme) is implemented in Scale Interaction Experiment Frontier version 2 (SINTEX-F2) seasonal prediction system, and the impact on the El Niño Southern Oscillation (ENSO) prediction is examined. By conducting hindcast experiments using the original convection scheme (Tiedtke scheme) and the spectral scheme, and comparing the ENSO prediction skill, the impact of the spectral scheme is analyzed in detail. It was found that prediction skill in terms of ENSO phase and the sea surface temperature (SST) persistence were improved by using the spectral scheme, but the root-mean-square error (RMSE) increased. The ENSO feedback was also changed by changing the convection scheme. The original scheme failed to predict the zonal wind stress anomaly toward the Niño 3.4 region, whereas the spectral scheme simulated it over the equatorial eastern Pacific with narrowing the meridional width, indicating that the spectral scheme strengthened the ENSO feedback. The spectral scheme also improved zonal-vertical atmospheric response to the Niño 3.4 index due to its advantageous features. Analysis of the ENSO feedback terms revealed that strengthened forcing in the eastern Pacific improved the thermocline feedback of ENSO, as its reversed timing of positive and negative tendencies for the mixed layer temperature matched that estimated from the reanalysis data. In conclusion, the spectral scheme can improve ENSO prediction through the atmospheric forcing and mean state in the eastern Pacific which impacted the ocean properties. It improved the phase error by improving thermocline feedback, but did not improve the RMSE. Tuning of the original scheme to obtain additional improvements to ENSO prediction would be difficult, since it requires modification of detailed convective cloud properties to correct the phase error. The spectral scheme tends to overestimate the ENSO amplitude, i.e., large RMSE, but this drawback can be mitigated by tuning the convection scheme so that it suppresses the warm SST climate drift, and this is considered the more promising method to further improve ENSO prediction.

Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation

The scale-aware convective parameterization for high resolution global climate models must satisfy the requirement that the parameterized subgrid convective transport diminishes as the model resolution increases to convection-resolving resolutions. A major assumption in current scale-aware convection schemes is that the differences between convective cloud properties and their environmental counterparts are independent of cloud fraction. This study examines convective cloud vertical velocity, moist static energy (MSE), moisture, and the vertical eddy transport of MSE and moisture for different averaging subdomain sizes and fractional convective cloudiness using a cloud resolving model simulation of a midlatitude mesoscale convective system. Results show that convective cloud fraction, mass flux, and vertical transport of MSE and moisture increase with decreasing subdomain size. The differences between convective cloud properties in both updrafts and downdrafts and their environment depend on both cloud fraction and the averaging subdomain size. For a given subdomain size, the differences increase with cloud fraction, in contrast to the assumption used in current scale-aware convection parameterization schemes. A consequence of this is that the parameterized convective eddy transport reaches maximum at a higher cloud fraction than believed in previous studies. This has implications on how fast the subgrid convective transport should diminish as GCM resolution increases.

Impacts of long-range-transported mineral dust on summertime convective cloud and precipitation: a case study over the Taiwan region

Abstract. As one of the most abundant atmospheric aerosols and effective ice nuclei, mineral dust affects clouds and precipitation in the Earth system. Here numerical experiments are carried out to investigate the impacts of dust aerosols on summertime convective clouds and precipitation over the mountainous region of Taiwan by acting as ice-nucleating particles. We run the Weather Research and Forecasting model (WRF) with the Morrison two-moment and spectral-bin microphysics (SBM) schemes at 3 km resolution, using dust number concentrations from a global chemical transport model (GEOS-Chem-APM). The case study indicates that the long-range-transported mineral dust, with relatively low number concentrations, can notably affect the properties of convective clouds (ice and liquid water contents, cloud top height, and cloud coverage) and precipitation (spatial pattern and intensity). The effects of dust are evident during strong convective periods, with significantly increased ice water contents in the mixed-phase regime via the enhanced heterogeneous freezing. With both the Morrison and SBM schemes, we see the invigoration effects of dust aerosols on the convective intensity through enhanced condensation and deposition latent heating. The low-altitude dust particles are uplifted to the freezing level by updrafts, which, in turn, enhance the convective cloud development through immersion freezing and convective invigoration. Compared to the Morrison scheme, the SBM scheme predicts more realistic precipitation and different invigoration effects of dust. The differences are partially attributed to the saturation adjustment approach utilized in the bulk scheme, which leads to a stronger enhancement of condensation at midlatitudes to low altitudes and a weaker deposition increase at the upper level.

Potential impact of aerosols on convective clouds revealed by Himawari-8 observations over different terrain types in eastern China

Abstract. Convective clouds are common and play a major role in Earth's water cycle and energy balance; they may even develop into storms and cause severe rainfall events. To understand the convective cloud development process, this study investigates the impact of aerosols on convective clouds by considering the influence of both topography and diurnal variation in radiation. By combining texture analysis, clustering, and thresholding methods, we identify all convective clouds in two warm seasons (May–September, 2016/17) in eastern China based on Himawari-8 Level 1 data. Having large diurnally resolved cloud data together with surface meteorological and environmental measurements, we investigate convective cloud properties and their variation, stratified by elevation and diurnal change. We then analyze the potential impact of aerosol on convective clouds under different meteorological conditions and topographies. In general, convective clouds tend to occur preferentially under polluted conditions in the morning, which reverses in the afternoon. Convective cloud fraction first increases then decreases with aerosol loading, which may contribute to this phenomenon. Topography and diurnal meteorological variations may affect the strength of aerosol microphysical and radiative effects. Updraft is always stronger along the windward slopes of mountains and plateaus, especially in northern China. The prevailing southerly wind near the foothills of mountains and plateaus is likely to contribute to this windward strengthening of updraft and to bring more pollutant into the mountains, thereby strengthening the microphysical effect, invigorating convective clouds. By comparison, over plain, aerosols decrease surface heating and suppress convection by blocking solar radiation reaching the surface.

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Abstract. An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Record‐Low Cloud Temperatures Associated With a Tropical Deep Convective Event

AbstractEarth‐orbiting satellites have long been used to examine meteorological processes. In the context of severe weather, brightness temperatures (BTs) at infrared wavelengths allow the determination of convective cloud properties. The anvils of cumulonimbus clouds, for example, typically produce BTs close to the tropopause temperature. Particularly severe storms generate overshoots that penetrate the stratosphere and are cooler than the anvil. In this study, we describe clustered storm overshoots in the tropical West Pacific on December 29, 2018 that resulted in the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard NOAA‐20 measuring a temperature of 161.96K (−111.2°C), which is, to our knowledge, the coldest on record. We describe the local meteorological conditions, examine the VIIRS overpass that produced the cold temperature, compare VIIRS with other sensors that observed the region and, finally, analyze the historical context provided by two other satellite instruments to show that such cold temperatures may be becoming more common.

Spatio-Temporal Analysis of Convective Cloud Properties Deriving from Weather Radar Reflectivity during the Decaying Stage of Tropical Storm over the Lower Northern Thailand

Disaster Management is one of the most important responsibilities of the governments all over the world. The systems of disaster prevention, preparedness and mitigation have been well established only in developed countries. For the developing countries including Thailand, people still have suffered from disaster since they are lacking of adequate information to cope with disaster. The weather radar is one of the tools that can provide spatio-temporal information for Nowcast which is useful for hydro-meteorological disasters warning and mitigation system. The extremes of precipitation are usually detected by convective cloud where the updrafts and downdrafts have been strengthened in vertical wind motions These extremes are the vital threat to people during wet season over Thailand. In this study, therefore, we have developed algorithm to extract convective cloud information that is necessary for prediction of precipitation extremes. The algorithm is constructed in Python script using OpenCV library to extract the radar reflectivity of the 1st Plan Position Indicator of radar image. The reflectivity data with the measurement frequency of once per hour has been obtained from the website of Thaiwater.net of which original data provided by Thai Meteorological Department (TMD). This study has been done by using reflectivity data from Phitsanulok radar station which locates in Lower Northern Thailand. The convective cloud regions have been detected by the developed algorithm. In addition, the fitting ellipses (Fitzgibbon and Fisher, 1995) have been applied to derive the properties of convective clouds during Sonca tropical storm in July of 2017. Results show that the number of convective cloud regions has abruptly increased during Sonca period compared to before the storm passing. The average number of convective cloud region has shown clearly of peak time at 17 LST. However, several peaks of total area on the convective region at hourly scale have also been found. The spatio-temporal analysis of the extracted storm information demonstrates the severe pattern of the high frequency convective storm during Sonca event over windward slope. The number of convective storm doubled increases comparing with period prior to Sonca storm.

Microphysical Properties of Convective Clouds in Summer over the Tibetan Plateau from SNPP/VIIRS Satellite Data

The Tibetan Plateau (TP) plays an important role in formation and development of the East Asian atmospheric circulation, climate variability, and disastrous weathers in China. Among the many topics on TP meteorology, it is critical to understand the microphysical characteristics of clouds over the TP; however, observations of the cloud micro-physics in this area are insufficient mainly due to sparse stations and limited cloud physical data. The Visible Infrared Imaging Radiometer Suite (VIIRS), onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, has an improved imaging spectroradiometer with 17 channels of 750-m moderate resolution and 5 channels of 375-m image resolution. The high-resolution instrument has an advantage for observing the small or initial convective clouds. Based on the methodologies that we proposed before for retrieving cloud microphysical properties from SNPP, an automated mapping software package named Automatic Mapping of Convective Clouds (AMCC) has been developed at the scale of satellite swath. The properties of convective clouds are retrieved by AMCC and their values are averaged over 0.33° × 0.33° grids based on the SNPP/VIIRS satellite data over the TP during the summers of 2013–17. The results show that: (1) the temperature of lifting condensation level (TLCL) at Naqu meteorological station and the cloud base temperature (Tb) retrieved from VIIRS are linearly correlated, with a correlation coefficient of 0.87 and standard deviation (STD) of 3.0°C; (2) convective clouds over the TP have the following macro- and micro-physical properties. First, the cloud base temperature (Tb) is about −5°C, the cloud base height above the ground (Hb) ranges between 1800 and 2200 m, and the cloud water content is low. Second, the cloud condensation nuclei concentration (NCCN) is between 200 and 400 mg−1 with 0.7% in maximum supersaturation (Smax); consequently, the condensation growth of water cloud droplet with less NCCN and higher Smax is fast. Third, because the precipitation initiation depth (D14) varies within 1500–2000 m and 500–1000 m at the Yarlung Zangbo River basin and southern Tibet, respectively, the clouds over these areas are more prone to precipitation. Fourth, mean height of the cloud top above sea level (Htop) is between 10 and 13 km, but the cloud depth (Dcld) is rather small, which is about 5000 m in southern TP and gradually reduces to 2500 m in northern TP. Fifth, the glaciation temperature (Tg) ranges from −30°C in central and southern TP to −25°C in northern TP, which, combined with the warmer Tg and the Tb less than 0°C, leads to the domination of ice process in the clouds; (3) the macro- and microphysical properties of convective clouds over the TP explain why rainfall there is frequent and lasts over a short time with small amount and large rain drops.

Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical, and CCN Properties from SNPP/VIIRS Satellite Data

AbstractThe advent of the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi NPP (SNPP) satellite made it possible to retrieve a new class of convective cloud properties and the aerosols that they ingest. An automated mapping system of retrieval of some properties of convective cloud fields over large areas at the scale of satellite coverage was developed and is presented here. The system is named Automated Mapping of Convective Clouds (AMCC). The input is level-1 VIIRS data and meteorological gridded data. AMCC identifies the cloudy pixels of convective elements; retrieves for each pixel its temperature T and cloud drop effective radius re; calculates cloud-base temperature Tb based on the warmest cloudy pixels; calculates cloud-base height Hb and pressure Pb based on Tb and meteorological data; calculates cloud-base updraft Wb based on Hb; calculates cloud-base adiabatic cloud drop concentrations Nd,a based on the T–re relationship, Tb, and Pb; calculates cloud-base maximum vapor supersaturation S based on Nd,a and Wb; and defines Nd,a/1.3 as the cloud condensation nuclei (CCN) concentration NCCN at that S. The results are gridded 36 km × 36 km data points at nadir, which are sufficiently large to capture the properties of a field of convective clouds and also sufficiently small to capture aerosol and dynamic perturbations at this scale, such as urban and land-use features. The results of AMCC are instrumental in observing spatial covariability in clouds and CCN properties and for obtaining insights from such observations for natural and man-made causes. AMCC-generated maps are also useful for applications from numerical weather forecasting to climate models.

Lagrangian Cloud Tracking and the Precipitation-Column Humidity Relationship

The tropical, oceanic mean relationship between column relative humidity and precipitation is highly non-linear. Mean precipitation remains weak until it rapidly picks up and grows at high column humidity. To investigate the origin of this relationship, a Lagrangian cloud tracking code, RAMStracks, is developed, which can follow the evolution of clouds. RAMStracks can record the morphological properties of convective clouds, the meteorological environment of clouds, and their effects. RAMStracks is applied to a large-domain radiative convective equilibrium simulation, which produces a complex population of convective clouds. RAMStracks records the lifecycle of 501 clouds through growth, splits, mergers, and decay. The mean evolution of all these clouds is examined. It is shown that the column humidity evolves non-monotonically, but that lower-level and upper-level contributions to total moisture do evolve monotonically. The precipitation efficiency of tropical storms tends to increase with cloud age. This is confirmed using a prototype testing method. The same method reveals that different tracked clouds with similar initial conditions evolve in very different ways. This makes drawing general conclusions from individual storms difficult. Finally, the causality of the precipitation-column humidity relationship is examined. A Granger Causality test, as well as regressions, suggest that moisture and precipitation are causally linked, but that the direction of causality is ambiguous. Much of this link appears to come from the lower-level moisture’s contribution to column humidity.

How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations

Current climate models cannot resolve individual convective clouds, and hence parameterizations are needed. The primary goal of convective parameterization is to represent the bulk impact of convection on the gridbox-scale variables. Spectral convective parameterizations also aim to represent the key features of the subgrid-scale convective cloud field such as cloud-top-height distribution and in-cloud vertical velocities in addition to precipitation rates. Ground-based radar retrievals of these quantities have been made available at Darwin, Australia, permitting direct comparisons of internal parameterization variables and providing new observational references for further model development. A spectral convective parameterization [the convective cloud field model (CCFM)] is discussed, and its internal equation of motion is improved. Results from the ECHAM–HAM model in single-column mode using the CCFM and the bulk mass flux Tiedtke–Nordeng scheme are compared with the radar retrievals at Darwin. The CCFM is found to outperform the Tiedtke–Nordeng scheme for cloud-top-height and precipitation-rate distributions. Radar observations are further used to propose a modified CCFM configuration with an aerodynamic drag and reduced entrainment parameter, further improving both the convective cloud-top-height distribution (important for large-scale impact of convection) and the in-cloud vertical velocities (important for aerosol activation). This study provides a new development in the CCFM, improving the representation of convective cloud spectrum characteristics observed in Darwin. This is a step toward an improved representation of convection and ultimately of aerosol effects on convection. It also shows how long-term radar observations of convective cloud properties can help constrain parameters of convective parameterization schemes.

Comparing parameterized versus measured microphysical properties of tropical convective cloud bases during the ACRIDICON–CHUVA campaign

Abstract. The objective of this study is to validate parameterizations that were recently developed for satellite retrievals of cloud condensation nuclei supersaturation spectra, NCCN(S), at cloud base alongside more traditional parameterizations connecting NCCN(S) with cloud base updrafts and drop concentrations. This was based on the HALO aircraft measurements during the ACRIDICON–CHUVA campaign over the Amazon region, which took place in September 2014. The properties of convective clouds were measured with a cloud combination probe (CCP), a cloud and aerosol spectrometer (CAS-DPOL), and a CCN counter onboard the HALO aircraft. An intercomparison of the cloud drop size distributions (DSDs) and the cloud water content (CWC) derived from the different instruments generally shows good agreement within the instrumental uncertainties. To this end, the directly measured cloud drop concentrations (Nd) near cloud base were compared with inferred values based on the measured cloud base updraft velocity (Wb) and NCCN(S) spectra. The measurements of Nd at cloud base were also compared with drop concentrations (Na) derived on the basis of an adiabatic assumption and obtained from the vertical evolution of cloud drop effective radius (re) above cloud base. The measurements of NCCN(S) and Wb reproduced the observed Nd within the measurements uncertainties when the old (1959) Twomey's parameterization was used. The agreement between the measured and calculated Nd was only within a factor of 2 with attempts to use cloud base S, as obtained from the measured Wb, Nd, and NCCN(S). This underscores the yet unresolved challenge of aircraft measurements of S in clouds. Importantly, the vertical evolution of re with height reproduced the observation-based nearly adiabatic cloud base drop concentrations, Na. The combination of these results provides aircraft observational support for the various components of the satellite-retrieved methodology that was recently developed to retrieve NCCN(S) under the base of convective clouds. This parameterization can now be applied with the proper qualifications to cloud simulations and satellite retrievals.

Evaluating uncertainty in convective cloud microphysics using statistical emulation

AbstractThe microphysical properties of convective clouds determine their radiative effects on climate, the amount and intensity of precipitation as well as dynamical features. Realistic simulation of these cloud properties presents a major challenge. In particular, because models are complex and slow to run, we have little understanding of how the considerable uncertainties in parameterized processes feed through to uncertainty in the cloud responses. Here we use statistical emulation to enable a Monte Carlo sampling of a convective cloud model to quantify the sensitivity of 12 cloud properties to aerosol concentrations and nine model parameters representing the main microphysical processes. We examine the response of liquid and ice‐phase hydrometeor concentrations, precipitation, and cloud dynamics for a deep convective cloud in a continental environment. Across all cloud responses, the concentration of the Aitken and accumulation aerosol modes and the collection efficiency of droplets by graupel particles have the most influence on the uncertainty. However, except at very high aerosol concentrations, uncertainties in precipitation intensity and amount are affected more by interactions between drops and graupel than by large variations in aerosol. The uncertainties in ice crystal mass and number are controlled primarily by the shape of the crystals, ice nucleation rates, and aerosol concentrations. Overall, although aerosol particle concentrations are an important factor in deep convective clouds, uncertainties in several processes significantly affect the reliability of complex microphysical models. The results suggest that our understanding of aerosol‐cloud interaction could be greatly advanced by extending the emulator approach to models of cloud systems.

On the Effects of Large-Scale Environment and Surface Types on Convective Cloud Characteristics over Darwin, Australia

Abstract Two seasons of Darwin, Australia, C-band polarimetric (CPOL) research radar, radiosoundings, and lightning data are examined to study the relative influence of the large-scale atmospheric regimes and the underlying surface types on tropical convective cloud properties and their diurnal evolution. The authors find that in the “deep westerly” regime, which corresponds to the monsoon period, the convective cloud occurrence rate is highest, consistent with its highest relative humidity. However, these convective clouds have relatively low cloud-top heights, smaller-than-average cell volumes, and are electrically least active. In this regime, the cloud cell volume does not vary significantly across different underlying surfaces and afternoon convective activity is suppressed. Thus, the picture emerging is that the convective cloud activity in the deep westerly regime is primarily regulated by the large-scale conditions. The remaining regimes (“easterly,” “shallow westerly,” and “moist easterly”) also demonstrate strong dependence on the large-scale forcing and a secondary dependence on the underlying surface type. The easterly regime has a small convective cloud occurrence rate and low cloud heights but higher lightning counts per convective cloud. The other two regimes have moderate convective cloud occurrence rates and larger cloud sizes. The easterly, shallow westerly, and moist easterly regimes exhibit a strong, clearly defined semidiurnal convective cloud occurrence pattern, with peaks in the early morning and afternoon periods. The cell onset times in these three regimes depend on the combination of local time and the underlying surface.

Parallax correction in collocating CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations: Method and application to convection study

[1] Parallax is associated with an apparent shift of the position of an object when viewed from different angles. For satellite observations, especially observations with clouds, it affects collocation of measurements from different platforms. In this study, we investigate how the parallax problem affects the collocation of CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of tropical convective clouds by examining the impact of parallax correction on statistics of convective cloud properties such as cloud top temperature (CTT) and buoyancy. Previous studies circumvented the parallax problem by imposing a “flat-top” condition on the selection of convective clouds, but it inadvertently biases the statistics toward convections at mature or dissipating stages when convective plumes cease to grow but flatten out to form cirrus anvils. The main findings of this study are the following: (1) Parallax correction reduces CTT of convective clouds; the magnitude of the reduction increases with cloud top height (CTH). (2) Parallax correction also reduces the spread of CTT estimates, making it more closely clustered around the corresponding CTH. (3) The fraction of convection with positive buoyancy decreases after the parallax correction. All these changes that are due to parallax correction are most pronounced for convections above 10–12 km, highlighting the importance of parallax correction in satellite-based study of deep convection. With parallax correction applied, we further examine the contrast in convective cloud buoyancy between land and ocean and day and night and the dependence on convective cloud size; results are consistent with our general understanding of tropical convection.

Cloud formation along mountain ridges on Titan

Cassini radar passes have shown a number of mountain ranges on Titan. Radar data covering approximately one quarter of Titan's surface places mountains in primarily equatorial regions with the mean height of about 900 m. The flow of air over topographic features can both trigger and enhance cloud formation. Orographically induced clouds near terrestrial mountain ranges include shallow wave clouds produced from upslope flow as well as precipitating stratus and cumulus type clouds; mountains can provide the perturbations needed to trigger convective clouds. The Titan regional atmospheric modeling system (TRAMS) has been used to explore a number of convective cloud properties and is now used to report on clouds formed when a mountain peak is placed within the model domain. Using a range of heights and surface winds compatible with Cassini/Huygens data, constraints can be placed on the scenarios in which clouds can be expected to form. Given sufficiently humid conditions (at least 50% humidity), convection is triggered. For drier environments similar to the Huygens landing site, short-lived, optically thin clouds form from air rising upslope. Precipitation is also seen in the cases of the convective clouds, which could have implications for the eroded appearance of Titan's mountains.

Assumed Probability Density Functions for Shallow and Deep Convection

The assumed joint probability density function (PDF) between vertical velocity and conserved temperature and total water scalars has been suggested to be a relatively computationally inexpensive and unified subgrid‐scale (SGS) parameterization for boundary layer clouds and turbulent moments. This paper analyzes the performance of five families of PDFs using large‐eddy simulations of deep convection, shallow convection, and a transition from stratocumulus to trade wind cumulus. Three of the PDF families are based on the double Gaussian form and the remaining two are the single Gaussian and a Double Delta Function (analogous to a mass flux model). The assumed PDF method is tested for grid sizes as small as 0.4 km to as large as 204.8 km. In addition, studies are performed for PDF sensitivity to errors in the input moments and for how well the PDFs diagnose some higher‐order moments. In general, the double Gaussian PDFs more accurately represent SGS cloud structure and turbulence moments in the boundary layer compared to the single Gaussian and Double Delta Function PDFs for the range of grid sizes tested. This is especially true for small SGS cloud fractions. While the most complex PDF, Lewellen‐Yoh, better represents shallow convective cloud properties (cloud fraction and liquid water mixing ratio) compared to the less complex Analytic Double Gaussian 1 PDF, there appears to be no advantage in implementing Lewellen‐Yoh for deep convection. However, the Analytic Double Gaussian 1 PDF better represents the liquid water flux, is less sensitive to errors in the input moments, and diagnoses higher order moments more accurately. Between the Lewellen‐Yoh and Analytic Double Gaussian 1 PDFs, it appears that neither family is distinctly better at representing cloudy layers. However, due to the reduced computational cost and fairly robust results, it appears that the Analytic Double Gaussian 1 PDF could be an ideal family for SGS cloud and turbulence representation in coarse‐grid CRMs, mesoscale models, and GCMs if the required input moments can be predicted or diagnosed accurately.

Aerosol invigoration and restructuring of Atlantic convective clouds

Clouds and precipitation play crucial roles in the Earth's energy balance, global atmospheric circulation and the availability of fresh water. Aerosols may modify cloud properties and precipitation formation by modifying the concentration and size of cloud droplets, and consequently the strength of cloud convection, and height of glaciation levels thus affecting precipitation patterns. Here we evaluate the aerosol effect on clouds, using large statistics of daily satellite data over the North Atlantic Ocean. We found a strong correlation between the presence of aerosols and the structural properties of convective clouds. These correlations suggest systematic invigoration of convective clouds by pollution, desert dust and biomass burning aerosols. On average increase in the aerosol concentration from a baseline to the average values is associated with a 0.05 ± 0.01 increase in the cloud fraction and a 40 ± 5mb decrease in the cloud top pressure.