Cloud Microphysical Parameters Research Articles

Delhi Model with Chemistry and aerosol framework (DM‐Chem) for high‐resolution fog forecasting

AbstractWe introduce here the Delhi Model with Chemistry and aerosol framework (DM‐Chem), a high‐resolution (330 m) visibility and fog forecasting model with the UK Chemistry and Aerosol scheme. DM‐Chem is developed at the National Centre for Medium‐Range Weather Forecasting (NCMRWF) in collaboration with UK Met Office and Carnegie Mellon University. We present a revised visibility scheme here which uses prognostic aerosols (mass and number concentration) from the two‐moment prognostic Global Model of Aerosol Processes (GLOMAP) ‐mode aerosol scheme. This scheme represents aerosol microphysical processes and the internal mixing of chemical components within five log‐normal size modes. The cloud microphysics parameterization also makes use of the GLOMAP scheme to predict cloud droplet number concentration (). Therefore we are able to evaluate the performance of the scheme indirectly by comparing the predicted with that derived from Moderate‐Resolution Imaging Spectroradiometer (MODIS) retrievals over the Indo‐Gangetic Plain. Aerosol emissions for the inner (330 m) nest are prescribed with a high‐resolution inventory for Delhi, from the System of Air Quality and Weather Forecasting project (SAFAR). Winter‐time stubble burning is represented using the MODIS‐based Fire Energetics and Emissions Research inventory, updated daily. In this article we consider case studies spanning both foggy and haze days of winter 2019–2020. The model compares well with the observed visibility and its spatial and diurnal patterns. The SAFAR emissions help improve the near‐surface model fields. For the “haze” case, the single scattering albedo (SSA) is high along the regions of high AOD, and the surface radiative cooling is enhanced. During the “foggy” case, the SSA is not clearly related to AOD, but aerosol absorption does lead to a minor surface warming via the semi‐direct effect at the time of onset of fog. The DM‐Chem entered operational mode at NCMRWF from winter 2020 and provides high‐resolution visibility forecast for Delhi and the adjoining region.

Demonstration of the Temporal Evolution of Tropical Cyclone “Phailin” Using Gray-Zone Simulations and Decadal Variability of Cyclones over the Bay of Bengal in a Warming Climate

The intensity and frequency variability of cyclones in the North Indian Ocean (NIO) have been amplified over the last few decades. The number of very severe cyclonic storms (VSCSs) over the North Indian Ocean has increased over recent decades. “Phailin”, an extreme severe cyclonic storm (ESCS), occurred during 8–13 October 2013 over the Bay of Bengal and made landfall near the Gopalpur coast of Odisha at 12 UTC on 12 October. It caused severe damage here, as well as in the coastal Odisha, Andhra Pradesh, and adjoining regions due to strong wind gusts (~115 knot/h), heavy precipitation, and devastating storm surges. The fidelity of the WRF model in simulating the track and intensity of tropical cyclones depends on different cloud microphysical parameterization schemes. Thus, four sensitivity simulations were conducted for Phailin using double-moment and single-moment microphysical (MP) parameterization schemes. The experiments were conducted to quantify and characterize the performance of such MP schemes for Phailin. The simulations were performed by the advanced weather research and forecasting (WRF-ARW) model. The model has two interactive domains covering the entire Bay of Bengal and adjoining coastal Odisha on 25 km and 8.333 km resolutions. Milbrandt–Yau (MY) double-moment and WRF single-moment microphysical schemes, with 6, 5, and 3 classes of hydrometeors, i.e., WSM6, WSM5, and WSM3, were used for the simulation. Experiments for Phailin were conducted for 126 h, starting from 00 UTC 08 October to 06 UTC 13 October 2013. It was found that the track, intensity, and structure of Phailin are highly sensitive to the different microphysical parameterization schemes. Further, the precipitation and cloud distribution were studied during the ESCS stage of Phailin. The microphysics schemes (MY, WSM3, WSM5, WSM6), along with Grell–Devenyi ensemble convection scheme predicted landfall of Phailin over the Odisha coast with significant track errors. Supply of moisture remains a more crucial component than SST and wind shear for rapid intensification of the Phailin 12 h before landfall over the Bay of Bengal. Finally, the comparison of cyclone formation between two decades 2001–2010 and 2011–2020 over the Bay of Bengal inferred that the increased numbers of VSCS are attributed to the supply of abundant moisture at low levels in the recent decade 2011–2020.

The Use of Weather Research and Forecasting Model to Predict Rainfall in Tropical Peatland: 1. Model Parameterization

Rainfall dynamics play a vital role in tropical peatland by providing sufficient water to keep peat moist throughout the year. Therefore, information of rainfall data either historical or forecasting data has risen in recent decades especially for an alert system of fire. Here the Weather and Research Forecasting (WRF) model may act as a tool to provide forecasting weather data. This study aims to do parameterization on WRF parameters for peatland in Sumatra, and to perform bias correction on the WRF’s rainfall output with observed data. We performed stepwise calibration to choose the best five physical schemes of WRF for use in the study area. The output WRF’s rainfall was bias corrected by spatially observed rainfall data for 2019 at day resolution. Our results showed the following schemes namely (i) Eta scheme for cloud microphysical parameters; (ii) GD scheme for cumulus cloud parameters, (iii) MYJ scheme for planetary boundary layer parameters; (iv) RRTM for longwave radiation; and (v) New Goddard schemes for shortwave radiation are best combination for being used to predict rainfall in maritime continent. The spatially interpolated observed rainfall with the Inverse Distance Weighting (IDW) was outperformed for calibration process of WRF’s rainfall as shown by statistical indicators used in this study. Further, the findings have contributed to advance knowledge of rainfall forecasting in maritime continent, particularly in providing data to support the development of fire danger rating system for Indonesian peatland.

Kerala floods in consecutive years - Its association with mesoscale cloudburst and structural changes in monsoon clouds over the west coast of India

Kerala, located at the southwest tip of India, witnessed disastrous floods during the monsoon seasons of two consecutive years, 2018 and 2019. This paper provides a detailed analysis of these two flood events using data from multiple sources. The unusually unstable and convective nature of the 2019 event, as detectable in its higher cloud tops and evidently fuelled by anomalously warm local sea temperatures, raises concerns regarding the changing patterns of rainfall over the southern parts of the west coast of India. Specifically, our analysis reveals that the flood of 2019 in Kerala satisfies the criteria for a mesoscale cloudburst (MsCB) event, more common in the north but a very rare and never before reported phenomenon in the Kerala region. Rainfall exceeding 50 mm in 2 h has been reported from many places between 8.00 and 22.00 UTC on the August 8, 2019. Satellite-derived rainfall and cloud microphysical parameters further reveal the uniqueness of the 2019 MsCB event. If 2019 is a harbinger of how global warming may continue to affect this region, transformations of the cloud structure and the recurrence and character of intense rainfall events could pose a major threat to the highly vulnerable Western Ghats ecosystems.

Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models

The improvement of the accuracy of simulated cloud-related variables, such as the cloud fraction, in global climate models (GCMs) is still a challenging problem in climate modeling. In this study, the influence of cloud microphysics schemes (one-moment versus two-moment schemes) and cloud overlap methods (observation-based versus a fixed vertical decorrelation length) on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero GCM. Compared with the fixed decorrelation length method, the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions. The utilization of a two-moment cloud microphysics scheme, on the other hand, notably improved the simulated cloud fraction compared with the one-moment scheme; specifically, the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%. Furthermore, the total cloud fraction bias decreased by 6.6% in the boreal winter (DJF) and 1.64% in the boreal summer (JJA). Cloud radiative forcing globally and in the four regions improved by 0.3%−1.2% and 0.2%−2.0%, respectively. Thus, our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.

Influence of cloud microphysical parameterization on the characteristics of monsoon depressions over the Indian region

AbstractIndian summer monsoon (ISM) low‐pressure systems are considered as the lifeline for seasonal monsoon rainfall over the Indian region. However, the current models have limitations in predicting their characteristics and rainfall. This study assesses the influence of five cloud microphysical parameterization Schemes (MP) on simulations of 14 monsoon deep depressions (DD) using the weather research and forecasting (WRF) model. The simulations are carried out with a lead time up to 96 hr in a nested domain resolution of 27, 9, and 3 km. The five cloud microphysical parameterizations, that is, WRF Double moment (WDM6), WRF single moment (WSM6), Milbrandt (MIL), Thompson (THOM), and Aerosol Aware Thompson (AAT), are considered in this study, leading to a total of 70 simulations. The results are validated through composite analysis at a radial distance of 300 km from the respective storm centres. The choice of MP significantly impacts the key characteristics of the monsoon DDs such as rainfall, wind, temperature, moisture, humidity, hydrometeors, and associated convective processes. In terms of rainfall, it is found that WDM6 (AAT) has the best (worst) performance with a lead time up to day‐4. Besides, WDM6 has simulated the best result in terms of temperature and specific humidity. Examining the hydrometeors distribution around the storm intense convective region (i.e., 300 km), it is noted that frozen hydrometeors (i.e., ice, snow, and graupel) are mainly modulating the rainfall. There is a general tendency to overestimate snow and graupel among MP except WDM6. Further, ice hydrometeors are well represented in WDM6 compared with others leading to better rainfall forecast. The moisture flux convergence and absolute vorticity are two major mechanisms determining the convection within the storm's core zone, and WDM6 stands out the best among these schemes. The findings of this study are relevant and have direct consequences to the operational applications.

Constraining Ice Water Content of Thin Antarctic Cirrus Clouds Using Ground-Based Lidar and Satellite Data

AbstractWe combine observations of optically thin cirrus clouds made by lidar at Davis, Antarctica (69°S, 78°E), during 14–15 June 2011 with a microphysical retrieval algorithm to constrain the ice water content (IWC) of these clouds. The cirrus clouds were embedded in a tropopause jet that flowed around a ridge of high pressure extending southward over Davis from the Southern Ocean. Cloud optical depths were 0.082 ± 0.001, and subvisual cirrus were present during 11% of the observation period. The macrophysical cirrus cloud properties obtained during this case study are consistent with those previously reported at lower latitudes. MODIS satellite imagery and AIRS surface temperature data are used as inputs into a radiative transfer model in order to constrain the IWC and ice water path of the cirrus. The derived cloud IWC is consistent with in situ observations made at other locations but at similarly cold temperatures. The optical depths derived from the model agree with those calculated directly from the lidar data. This study demonstrates the value of a combination of ground-based lidar observations and a radiative transfer model in constraining microphysical cloud parameters that could be utilized at locations where other lidar measurements are made.

Cloud resolving simulation of extremely heavy rainfall event over Kerala in August 2018 – Sensitivity to microphysics and aerosol feedback

The state of Kerala located in the southwestern part of India experienced heavy to extremely heavy rainfall events from 07 to 17 August 2018 during the southwest monsoon, leading to a devastating flood and extensive infrastructure damage. Rainfall analysis suggests that the prolonged heavy rainfall event between 14 and 17 August 2018, was responsible for the major devastation. In this work, the sensitivity of prediction of heavy rainfall during 14–17 August 2018 to cloud microphysics parameterization (CMP) is investigated using the Weather Research and Forecasting (WRF) model. High resolution (1-km) cloud resolving simulations are conducted to study the sensitivity of rainfall to the cloud microphysics schemes and cloud-aerosol interactions. The model results are compared with observations such as Automatic Weather Station, Tropical Rainfall Measuring Mission, radiosonde, Global Precipitation Mission precipitation estimates and Doppler weather radar products. Comparisons of simulated hydrometeor structure with DWR estimates and thermodynamic stability indices with radiosonde reveal that the differences in microphysics formulations play a vital role on the thermodynamic profile that in turn influence the intensity of convection and hydrometeor structure responsible for the extremely heavy rainfall. Comparison of simulated rainfall with IMD observational estimates suggests that the Thomson aerosol aware scheme, followed by Goddard microphysics captured the spatial and temporal distributions of observed heavy rainfall. The Thomson aerosol aware scheme followed by Goddard produced strong convective instability conditions for heavy rainfall due to the simulation of a strong westerly jet and the formation of an offshore vortex.

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

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

Characteristics of CCN activation and cloud microphysics over the east coast of India during the Northeast Monsoon onset

Airborne observations conducted during the Northeast Monsoon onset as part of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX 2011) are used to link the activation properties of aerosols with the observed cloud microphysics. This study illustrates the significant spatial variability in the aerosol spectrum, cloud condensation nuclei (CCN) activation characteristics, and the cloud droplet spectral properties over a coastal and an inland location. High concentrations of Aitken mode and black carbon aerosols were observed in the free troposphere and are attributed to the convectively transported smoke and aerosols from the coastal boundary layer. The assumption of an internally mixed organic and inorganic aerosol composition provided a better CCN closure over assumptions of either purely organic or purely inorganic aerosols. The coastal clouds were equally polluted as the inland clouds with high cloud droplet number concentrations near the cloud base. The vertical distribution of cloud droplet spectral characteristics was similar in both coastal and inland clouds. An increase in droplet number concentration up to 2 km above the cloud base indicated a prominent influence of submicron particles on the cloud microphysical parameters. Evidence for an enhanced concentration of supercooled drops above the freezing level up to temperatures below −12 °C is documented. The secondary ice production was evident through observations of graupel and snow particles. Heavy loading of aerosols near the cloud base leads to enhanced mixed-phase processes in these clouds.

Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston

Abstract. Aerosol–cloud interactions remain largely uncertain with respect to predicting their impacts on weather and climate. Cloud microphysics parameterization is one of the factors leading to large uncertainty. Here, we investigate the impacts of anthropogenic aerosols on the convective intensity and precipitation of a thunderstorm occurring on 19 June 2013 over Houston with the Chemistry version of Weather Research and Forecast model (WRF-Chem) using the Morrison two-moment bulk scheme and spectral bin microphysics (SBM) scheme. We find that the SBM predicts a deep convective cloud that shows better agreement with observations in terms of reflectivity and precipitation compared with the Morrison bulk scheme that has been used in many weather and climate models. With the SBM scheme, we see a significant invigoration effect on convective intensity and precipitation by anthropogenic aerosols, mainly through enhanced condensation latent heating. Such an effect is absent with the Morrison two-moment bulk microphysics, mainly because the saturation adjustment approach for droplet condensation and evaporation calculation limits the enhancement by aerosols in (1) condensation latent heat by removing the dependence of condensation on droplets and aerosols and (2) ice-related processes because the approach leads to stronger warm rain and weaker ice processes than the explicit supersaturation approach.

A stochastic closure for two-moment bulk microphysics of warm clouds: part I, derivations

We propose a mathematical methodology to two-moment parameterization of bulk warm cloud microphysics based on a stochastic representation of the high-order moment terms. Unlike previous bulk parameterizations, the stochastic closure approach proposed here does not assume any particular droplet size distribution, all parameters have physical meanings which are recoverable from data, and the resultant parameterization has the flexibility to utilize arbitrary collision kernels. Our strategy is a new twofold approach of approximating the kinetic collection equation (KCE). First, by partitioning the droplet spectrum into two large bins representing cloud and rain aggregate particles, we are able to represent droplet densities as the sum of mean and random fluctuation terms. Second, we use a Taylor approximation for the collision kernel around the centres of masses of bulk cloud and rain aggregates which allows the derivation of bulk rate equations for the cloud and rain aggregate droplet numbers and mixing ratios that are independent of the collision kernel. Detailed numerical simulations of the KCE demonstrate that the high-order (quadratic and cubic) stochastic fluctuation terms can be neglected which results in a closed set of equations for the mean bulk cloud and rain aggregates with only five parameters. Considerations of consistency of cloud number concentration and mass conservation constraints further reduce the parameter set to three key entities representing, respectively, the strength of cloud self-collection, the strength of auto-conversion, and the strength of rain self-collection, relative to auto-conversion. In an accompanying paper, bounds on the parameters’ space are derived and the mean bulk two-moment parameterization is validated against direct simulation of the KCE and compared to an existing competitor parameterization.

Evaluating the performances of cloud microphysical parameterizations in WRF for the heavy rainfall event of Kerala (2018)

This study evaluates the performances of four different cloud microphysical parameterization (CMP) schemes of the Weather Research and Forecasting (WRF) model at 3 km horizontal resolution (lead time up to 96 h) for the Heavy Rainfall Event (HRE) over Kerala in August 2018. The goal is to identify the major drivers for rain making mechanism and evaluate the ability of CMPs to accurately simulate the event with special emphasis on rainfall. It is found that the choice of CMP has a considerable impact on the rainfall forecast characteristics and associated convection. Results are validated against the India Meteorological Department (IMD) station data and Global Precipitation Measurement (GPM) observations and found that, among the four CMP schemes, viz., Milbrandt (MIL), Thompson Aerosol Aware (TAA), WRF double-moment 6-class scheme (WDM6) and WRF single-moment 6-class scheme (WSM6); WDM6 is the best performing scheme in terms of rainfall. It is noted that mixed phase processes are dominant in this scenario and the inability (ability) of MIL and TAA (WDM6 and WSM6) to predict the frozen hydrometeors, and thus simulating the cold rain processes realistically led to large (small) errors in the rainfall forecast. The moisture convergence was prominent in the foothills of the Western Ghats and highly influential in facilitating orography driven lifting of moisture. The moisture budget results suggest that horizontal moisture flux convergence (MFC) was the major driver of convection with WDM6 predicting the peaks of MFC most consistently with the observed Tropical Rainfall Measuring Mission (TRMM) rainfall product. Additionally, from Contiguous Rain Area analysis it is also found that the WDM6 has the least volumetric error. This is to highlight that hydrometeor distributions are strongly modulated by MFC, which further impacts the latent heat generation and rainfall over the region. Overall results infer the substantial influence of CMPs on the forecast of the Heavy Rainfall Event. The findings of this study will be highly useful for operational forecasting agencies and disaster management authorities for mitigation of damages caused by this kind of severe HREs in the future.

Processes controlling the vertical aerosol distribution in marine stratocumulus regions – a sensitivity study using the climate model NorESM1-M

Abstract. The vertical distribution of aerosols plays an important role in determining the effective radiative forcing from aerosol–radiation and aerosol–cloud interactions. Here, a number of processes controlling the vertical distribution of aerosol in five subtropical marine stratocumulus regions in the climate model NorESM1-M are investigated, with a focus on the total aerosol extinction. A comparison with satellite lidar data (CALIOP, Cloud–Aerosol Lidar with Orthogonal Polarization) shows that the model underestimates aerosol extinction throughout the troposphere, especially elevated aerosol layers in the two regions where they are seen in observations. It is found that the shape of the vertical aerosol distribution is largely determined by the aerosol emission and removal processes in the model, primarily through the injection height, emitted particle size, and wet scavenging. In addition, the representation of vertical transport related to shallow convection and entrainment is found to be important, whereas alterations in aerosol optical properties and cloud microphysics parameterizations have smaller effects on the vertical aerosol extinction distribution. However, none of the alterations made are sufficient for reproducing the observed vertical distribution of aerosol extinction, neither in magnitude nor in shape. Interpolating the vertical levels of CALIOP to the corresponding model levels leads to better agreement in the boundary layer and highlights the importance of the vertical resolution.

Impact of initial conditions and cloud parameterization on the heavy rainfall event of Kerala (2018)

In Aug 2018, the extreme precipitation event had created devastation over Kerala's state for 1 week and led to more than 483 deaths, and destructions of properties cost more than 50,000 Crore rupees. We simulated the event with the Advanced Research Weather Research and Forecasting model (WRF-ARW). Two most popular initial conditions (ICs), i.e., the National Centre for Environment Prediction (NCEP-FNL) and European Centre for Medium-range Weather Forecasting (ECMWF-ERA5), have been used to investigate the impacts of ICs on the forecast skills (up to day1) with particular reference to rainfall. Using these ICs, the study simulated the event with four cloud microphysics parameterization schemes (CMPS), namely, Milbrandt–Yau, Thompson, Aerosol aware Thompson, and WDM6. The study examined the role of ICs in modulating the cloud microphysical properties and associated mechanisms impacting the rainfall characteristics, hydrometeor formation, and related convective mechanism over the region. A total of 24 simulations (2 ICs × 4 CMPS × 3 initial days 13–15 Aug 2018) are conducted. Results show that the ERA5 IC with Milbrandt–Yau CMPS has performed best among all the simulations. Nevertheless, in general, there is an underestimation of rainfall in the model. It is found that though an ample amount of moisture available at the lower levels of the troposphere in the model, however, due to the lack of intense updraft, this moisture could not reach the middle levels, resulting in robust dryness at these levels and adversely impacting the convection and rainfall. This process shows a deficiency in the formation of frozen hydrometeors in the model's upper levels, leading to the lack of cloud heating and weakly developed vertical pressure gradient. These chain processes resulted in weak convection and reduced rainfall in the model experiments. These findings are highly relevant to understand the limitations of the model, influences of ICs, and cloud microphysical parameterizations to forecast extreme rainfall events accurately.

Phase Two of the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-II) over the Middle and Lower Reaches of the Yangtze River in 2020

Phase Two of the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-II) was conducted over the middle and lower reaches of the Yangtze River during the period 16 June to 19 July 2020. This paper provides a brief overview of the IMFRE-II field campaign, including the multiple ground-based remote sensors, aircraft probes, and their corresponding measurements during the 2020 mei-yu period, as well as how to use these numerous datasets to answer scientific questions. The highlights of IMFRE-II are: (1) to the best of our knowledge, IMFRE-II is the first field campaign in China to use ground-based, airborne, and spaceborne platforms to conduct comprehensive observations over the middle and lower reaches of the Yangtze River; and (2) seven aircraft flights were successfully carried out, and the spectra of ice particles, cloud droplets, and raindrops at different altitudes were obtained. These in-situ measurements will provide a “cloud truth” to validate the ground-based and satellite-retrieved cloud and precipitation properties and quantitatively estimate their retrieval uncertainties. They are also crucial for the development of a warm (and/or cold) rain conceptual model in order to better understand the cloud-to-rain conversion and accretion processes in mei-yu precipitation events. Through an integrative analysis of ground-based, aircraft, and satellite observations and model simulations, we can significantly improve our cloud and precipitation retrieval algorithms, investigate the microphysical properties of cloud and precipitation, understand in-depth the formation and dissipation mechanisms of mei-yu frontal systems, and improve cloud microphysics parameterization schemes and model simulations.

Simultaneous measurement of cloud microphysical parameters based on digital holographic interferometry

Investigation of cloud microphysical is of great significance in deepening the understanding of the radiation energy budget, water cycle process, and precipitation mechanism, and improving the scientificity and effectiveness of artificial precipitation. Especially under the action of turbulence, in addition to shear and inertia, the turbulence in the cloud will accelerate the collision of cloud droplets through vortex superposition. The above process will further complicate the cloud microphysical characters. At present, the methods of measuring cloud microphysical parameters based on light scattering, collision and imaging theories encounter bottlenecks: the inversion process needs to make the assumptions about cloud droplet spectrum and particle characteristics, the impact process will destroy particle characteristics, and the three-dimensional characteristics of cloud particles cannot be obtained. Because of its many advantages, such as fast, real-time, non-destructive, non-invasive, high-resolution, full-field optical measurement, etc., in-line digital holographic interferometry is considered as a new potential tool for the dynamical measurement of cloud microphysical property. In particular, the mutual interference between the particle image and twin image is small under far-field recording conditions. In this paper, the measurement method of the on-line digital holographic interferometry based on interference theory, combining optical information processing, depth of field compression, and gray gradient variance technology of fusion holograms, is investigated. This method, with a <i>z</i>-axis position accuracy of 0.01 mm and system resolution of 2 μm, is employed for simultaneously and finely detecting the cloud droplet spectrum, cloud particle diameter, and number concentration. In the experiment, the liquid droplet with a median diameter of 3.9 μm, produced by the ultrasonic atomizer, is used as an example of the cloud particle. The measurement results are consistent with realistic scenario. By using a high speed charge coupled device or complementary metal oxide semiconductor camera, this method can solve the technical bottleneck of three-dimensional fine characteristics of cloud particle in airborne measurement by using cloud droplet spectrometer. It can provide effective support for the research of liquid water in the cloud, entrainment, condensation, collision, and temporal and spatial evolution laws. In addition, it has reference significance for the study of particle dynamics. Simultaneously, this method provides a feasible solution for the measurement of cloud in land-based and airborne platforms.

Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes

AbstractThe use of machine learning based on neural networks for cloud microphysical parameterizations is investigated. As an example, we use the warm‐rain formation by collision‐coalescence, that is, the parameterization of autoconversion, accretion, and self‐collection of droplets in a two‐moment framework. Benchmark solutions of the kinetic collection equations are performed using a Monte Carlo superdroplet algorithm. The superdroplet method provides reliable but noisy estimates of the warm‐rain process rates. For each process rate, a neural network is trained using standard machine learning techniques. The resulting models make skillful predictions for the process rates when compared to the testing data. However, when solving the ordinary differential equations, the solutions are not as good as those of an established warm‐rain parameterization. This deficiency can be seen as a limitation of the machine learning methods that are applied, but at the same time, it points toward a fundamental ill‐posedness of the commonly used two‐moment warm‐rain schemes. More advanced machine learning methods that include a notion of time derivatives, therefore, have the potential to overcome these problems.

The evolution of cloud and aerosol microphysics at the summit of Mt. Tai, China

Abstract. The influence of aerosols, both natural and anthropogenic, remains a major area of uncertainty when predicting the properties and the behaviours of clouds and their influence on climate. In an attempt to better understand the microphysical properties of cloud droplets, the simultaneous variations in aerosol microphysics and their potential interactions during cloud life cycles in the North China Plain, an intensive observation took place from 17 June to 30 July 2018 at the summit of Mt. Tai. Cloud microphysical parameters were monitored simultaneously with number concentrations of cloud condensation nuclei (NCCN) at different supersaturations, PM2.5 mass concentrations, particle size distributions and meteorological parameters. Number concentrations of cloud droplets (NC), liquid water content (LWC) and effective radius of cloud droplets (reff) show large variations among 40 cloud events observed during the campaign. The low values of reff and LWC observed at Mt. Tai are comparable with urban fog. Clouds on clean days are more susceptible to the change in concentrations of particle number (NP), while clouds formed on polluted days might be more sensitive to meteorological parameters, such as updraft velocity and cloud base height. Through studying the size distributions of aerosol particles and cloud droplets, we find that particles larger than 150 nm play important roles in forming cloud droplets with the size of 5–10 µm. In general, LWC consistently varies with reff. As NC increases, reff changes from a trimodal distribution to a unimodal distribution and shifts to smaller size mode. By assuming a constant cloud thickness and ignoring any lifetime effects, increase in NC and decrease in reff would increase cloud albedo, which may induce a cooling effect on the local climate system. Our results contribute valuable information to enhance the understanding of cloud and aerosol properties, along with their potential interactions on the North China plain.

Evaluation of Simulated Winter Precipitation Using WRF-ARW during the ICE-POP 2018 Field Campaign

AbstractThis study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased . For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.