Cloud Microphysical Parameters Research Articles

Modeling analysis and experiments of dual-FOV multiple scattering Raman lidar for detecting water cloud microphysical properties

Clouds are the primary regulators of the Earth’s climate, but the mechanisms through which they influence climate are poorly understood. Probing the microphysical properties of liquid water clouds is an urgent need for research on the Earth’s atmospheric system and atmospheric physics. In this paper, an iterative inversion of water cloud microphysical properties for a multiple scattering Raman lidar is presented, which can enable continuous, vertical measurements of the extinction coefficient of water clouds, the liquid water content (LWC), the effective radius of the cloud droplets, and cloud droplet number concentration (CDNC). The technique based on a constructed dual-field-of-view (dual-FOV) device combined with a quasi-small-angle (QSA) approximation model was investigated. The determination method of the optimal field of view (FOV) for lidar detection was discussed to enhance the sensitivity of the measured signals to the observed cloud microphysical parameters. Field observations with a co-located microwave radiometer (MWR) were carried out to verify the effectiveness of the lidar system, also an error analysis of the measurement examples was performed. The dual FOV Raman lidar experimental LWC measurement has an average deviation of 0.034 g/m3 and the relative error is 27.2% relative to the MWR measurement. The system has important potential applications in aerosol-cloud interactions and provides new ideas and methods for studying water cloud microphysical properties, further studying aerosol indirect effects.

Toward the Direct Simulation of the Quasi‐Biennial Oscillation in a Global Storm‐Resolving Model

AbstractThis study presents the first attempt to simulate a full cycle of the quasi‐biennial oscillation (QBO) in a global storm‐resolving model (GSRM) that explicitly simulates deep convection and gravity waves instead of parameterizing them. Using the Icosahedral Nonhydrostatic (ICON) model with horizontal and vertical resolutions of about and , respectively, we show that an untuned state‐of‐the‐art GSRM is already on the verge of simulating a QBO‐like oscillation of the zonal wind in the tropical stratosphere for the right reasons. ICON shows overall good fidelity in simulating the QBO momentum budget and the downward propagation of the QBO jets in the upper QBO domain (25–35 km). In the lowermost stratosphere, however, ICON does not simulate the downward propagation of the QBO jets to the tropopause. This is the result of a pronounced lack of QBO wave forcing, mainly on planetary scales. The lack of planetary‐scale wave forcing in the lowermost stratosphere is caused by an underestimation of planetary‐scale wave momentum fluxes entering the stratosphere. We attribute this lack of planetary‐scale wave momentum fluxes to a substantial lack of convectively coupled equatorial waves (CCEWs) in the tropical troposphere. Therefore, we conclude that in ICON, simulating a realistic spatio‐temporal variability of tropical deep convection, in particular CCEWs, is currently the main roadblock toward simulating a reasonable QBO. To overcome this intermediate situation, we propose to aim at an improved explicit simulation of tropical deep convection by retuning the remaining parameterizations of cloud microphysics and vertical diffusion, and by increasing the horizontal resolution.

Vertical structures of typhoon cloud microphysical and radiative features associated with the precipitation type over the western North Pacific

Intensity of different precipitation types (convective, stratiform and shallow) and associated cloud vertical microphysical and radiative heating features are analyzed considering typhoon development, maturity, and decaying stages over the western North Pacific using the CloudSat Tropical Cyclone and China Meteorological Administration tropical cyclone best-track datasets from 2 June 2006 to 31 December 2015. At all three stages, the convective precipitation intensity, approximately twice that of stratiform precipitation, is the highest and peaks at development stage. The strongest stratiform precipitation occurs at typhoon maturity stage. Shallow precipitation is the weakest throughout the typhoon lifespan. Although the cloud microphysical parameters (radar reflectivity, cloud ice particle number concentration and effective radius) of both convective and stratiform precipitation tend to increase with precipitation intensity, convective precipitation contains more ice water of larger sizes in upper layers than stratiform precipitation. Unlike convective and stratiform precipitation, dominated by cold clouds, shallow precipitation is dominated by warm clouds with weak vertical contrast in the radiative distribution but strong radiation nearby 5 km. Our results show that cloud ice particle number concentration is important not only in precipitation intensity enhancement but also in determining the shortwave radiative heating center vertical location. More and larger ice particles in convective precipitation profiles result in stronger or comparable shortwave radiative heating than those in stratiform precipitation profiles, while the longwave radiative cooling rates in convective and stratiform precipitation profiles exhibit very similar features, likely attributable to similar infrared radiation levels due to comparable temperatures in these profiles.

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

Repurposing weather modification for cloud research showcased by ice crystal growth.

The representation of cloud processes in models is one of the largest sources of uncertainty in weather forecast and climate projections. While laboratory settings offer controlled conditions for studying cloud processes, they cannot reproduce the full range of conditions and interactions present in natural cloud systems. To bridge this gap, here we leverage weather modification, specifically glaciogenic cloud seeding, to investigate ice growth rates within natural clouds. Seeding experiments were conducted in supercooled stratus clouds (at to C) using an uncrewed aerial vehicle, and the created ice crystals were measured 4-10 min downwind by in situ and ground-based remote sensing instrumentation. We observed substantial variability in ice crystal growth rates within natural clouds, attributed to variations in ice crystal number concentrations and in the supersaturation, which is difficult to reproduce in the laboratory and which implies faster precipitation initiation than previously thought. We found that for the experiments conducted at C, the ice crystal populations grew nearly linearly during the time interval from 6 to 10 min. Our results demonstrate that the targeted use of weather modification techniques can be employed for fundamental cloud research (e.g. ice growth processes, aerosol-cloud interactions), helping to advance cloud microphysics parameterizations and to improve weather forecasts and climate projections.

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

Ground-Based Remote Sensing of Aerosol, Clouds, Dynamics, and Precipitation in Antarctica: First Results from the 1-Year COALA Campaign at Neumayer Station III in 2023

Abstract Novel observations of aerosol and clouds by means of ground-based remote sensing have been performed in Antarctica over the Ekström Ice Shelf on the coast of Dronning Maud Land at Neumayer Station III (70.67°S, 8.27°W) from January to December 2023. The deployment of the OCEANET-Atmosphere remote sensing observatory in the framework of the Continuous Observations of Aerosol-Cloud Interaction (COALA) campaign has brought Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) aerosol and cloud profiling capabilities next to meteorological and air chemistry in situ observations at the Antarctic station. We present an overview of the site, the instrumental setup, and data analysis strategy and introduce 3 scientific highlights from austral fall and winter, namely, 1) observations of a persistent mixed-phase cloud embedded in a plume of marine aerosol. Remote sensing–based retrievals of cloud-relevant aerosol properties and cloud microphysical parameters confirm that the free-tropospheric mixed-phase cloud layer formed in an aerosol-limited environment. 2) Two extraordinary warm air intrusions: one with intense snowfall produced the equivalent of 10% of the yearly snow accumulation and a second one with record-breaking maximum temperatures and heavy icing due to supercooled drizzle. 3) Omnipresent aerosol layers in the stratosphere. Our profiling capabilities could show that 50% of the 500-nm aerosol optical depth of 0.06 was caused by stratospheric aerosol, while the troposphere was usually pristine. As demonstrated by these highlights, the 1-yr COALA observations will serve as a reference dataset for the vertical structure of aerosol and clouds above the region, enabling future observational and modeling studies to advance understanding of atmospheric processes in Antarctica.

The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data

Abstract. This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration.

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

Improvement of cloud microphysical parameterization and its advantages in simulating precipitation along the Sichuan-Xizang Railway

Improvement of cloud microphysical parameterization and its advantages in simulating precipitation along the Sichuan-Xizang Railway

Combining effects of cloud microphysical and planetary boundary layer parameterization schemes on the prediction of tropical cyclone intensity

The numerical simulation of tropical cyclone (TC) can be affected by the choices in physics parameterization schemes. These effects can be from the single scheme or the combination of multiple ones due to closely linked physical processes. Previous studies have discussed the roles of different schemes in TC simulation, but the roles of scheme combinations still remain to be explored. To investigate the combining effects of cloud microphysical (MP) and planetary boundary layer (PBL) parameterization schemes on the prediction of TC intensity, two Single-Moment MP schemes, WRF 6-class (WSM6) and Goddard 4ice, and two PBL schemes, non-local closure Yonsei University (YSU) and local closure Quasi-normal Scale Elimination (QNSE), were selected for the cloud-resolving simulations of both real and idealized cases using the Weather Research and Forecasting (WRF) model.Results showed that the experiments combining the PBL scheme with different MP schemes had different performances in reproducing TC intensity. The local QNSE PBL scheme, compared with non-local YSU, simulated larger surface sensible, latent, and moisture fluxes, while not necessarily leading to a stronger TC. When used in conjunction with the Goddard MP scheme, the QNSE scheme can simulate a stronger TC than the YSU scheme, but when used with the WSM6 MP scheme, there was little difference in their intensity. Goddard 4ice scheme adding hail class, compared with WSM6, simulated a better-organized eyewall, more latent heat release and convective bursts, leading to the formation of a clear warm core in the upper troposphere and thus contributing to the intensification of TC. Only with the coupling of MP and PBL schemes, did the simulation generate a compact and erect eyewall, which further enhanced TC intensity via stronger convergence airflow and more water vapor supply at the lower layer. Idealized experiments were consistent with those in real cases, demonstrating the TC sensitivity responding to different combinations while the uncertainties remained in the selection of these schemes.

Development and Validation of Physical Retrieval Algorithm of Cloud Microphysical Parameters

Development and Validation of Physical Retrieval Algorithm of Cloud Microphysical Parameters

Assessment of future solar energy potential changes under the shared socio-economic pathways scenario 2–4.5 with WRF-chem: The roles of meteorology and emission

The 26th United Nations Climate Change Conference (COP26) proposes to limit global warming to <1.5 °C above pre-industrial levels until 2030 and aligns CO2 emissions with net-zero by 2050. To achieve this goal, it is crucial to replace fossil fuels with renewable and clean energy sources. Among the various renewable energy sources, solar energy undoubtedly stands out as an attractive option. Future meteorological conditions and emission reductions are expected to impact on solar energy potential. Consequently, the Weather Research and Forecasting model with chemistry (WRF-Chem) was applied to current (2016–2020) and future (2046–2050) under the shared socio-economic pathways (SSP) 2–4.5 scenario to investigate the impact of future meteorological conditions and emission reductions on solar energy potential. The evaluation of the WRF-Chem demonstrates satisfactory performance in capturing most meteorological and chemical variables at a climatological scale. However, the model underestimates 2 m temperature while overestimating 2 m specific humidity and 10 m wind speed, with mean bias (MB) of −0.1 °C, 1.4 g kg−1, and 0.8 m s−1, respectively. Additionally, the WRF-Chem overestimates PM2.5, with normalized mean bias (NMB) of −20%. The model underestimates cloud fraction and precipitation caused by the limitation in the cloud microphysical parameterization. The model well reproduced the solar energy distribution in China, with R of 0.76 and NMB of −3%. Looking ahead, the future annual average solar energy increases by 2.2, 0.1, 2, 4.1, 1.9, and 2.9 W m−2 for China, Beijing-Tianjin-Hebei, Fenwei Plain, Yangtze River Delta, Pearl River Delta, and Sichuan Basin, respectively. The future annual average photovoltaic potential increase by 1–4% in these regions. This increase is primarily attributed to the increase in solar energy resulting from emission reductions (4.7, 5.1, 5.0, 4.7, 3.2, and 6.1 W m−2), which outweighs the decrease caused by meteorological conditions (2.5, 5.0, 2, 4.1, 1.9, and 2.9 W m−2). Hence, emission reduction plays a vital role in promoting solar energy utilization.

Cloud characteristics over the Yunnan–Guizhou plateau as observed by MODIS and Himawari‐8

AbstractCloud descriptions and parameterizations are major sources of uncertainty in weather and climate modelling studies. In this study, we investigated the cloud properties over the Yunnan–Guizhou Plateau (YGP) by using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Himawari‐8 (H8_Japan and H8_Zhuge) cloud datasets. Corresponding to their distinct terrain features, the cloud climate patterns on the western and eastern sides of the YGP are different. Our results show that the spatial patterns of the cloud parameters in all three datasets are very similar over the YGP, especially the cloud cover and cloud effective radius. The high cloud frequency region is located to the east of Yunnan–Guizhou quasi‐stationary front in cold season, and the largest gradient is along front. On frontal days, the regional differences in the cloud properties over the YGP are much more obvious, with the cloud cover across the front increasing from ~20%–35% on its western side to more than 95% on its eastern side. Furthermore, higher thin clouds with larger cloud effective radius typically exist on the western side of the front, while lower thick clouds with smaller particles appear on its eastern side. This study enhances our understanding how cloud characteristics impact by the frontal system and provide a valuable insight for the design and improvement of cloud microphysics parameters in weather forecast models over the YGP region.

Sensitivity of cirrus and contrail radiative effect on cloud microphysical and environmental parameters

Abstract. Natural cirrus clouds and contrails cover about 30 % of the Earth's mid-latitudes and up to 70 % of the tropics. Due to their widespread occurrence, cirrus clouds have a considerable impact on the Earth energy budget, which, on average, leads to a warming net radiative effect (solar + thermal infrared). However, whether the instantaneous radiative effect (RE), which in some cases corresponds to a radiative forcing, of natural cirrus or contrails is positive or negative depends on their microphysical, macrophysical, and optical properties, as well as the radiative properties of the environment. This is further complicated by the fact that the actual ice crystal shape is often unknown, and thus, ice clouds remain one of the components that are least understood in the Earth's radiative budget. The present study aims to investigate the dependency of the effect on cirrus RE on eight parameters, namely solar zenith angle, ice water content, ice crystal effective radius, cirrus temperature, surface albedo, surface temperature, cloud optical thickness of an underlying liquid water cloud, and three ice crystal shapes. In total, 283 500 plane-parallel radiative transfer simulations have been performed, not including three-dimensional scattering effects. Parameter ranges are selected that are typically associated with natural cirrus and contrails. In addition, the effect of variations in the relative humidity profile and the ice cloud geometric thickness have been investigated for a sub-set of the simulations. The multi-dimensionality and complexity of the eight-dimensional parameter space makes it impractical to discuss all potential configurations in detail. Therefore, specific cases are selected and discussed. For a given parameter combination, the largest impact on solar, thermal-infrared (TIR), and net RE is related to the ice crystal effective radius. The second most important parameter is ice water content, which equally impacts the solar and terrestrial RE. The solar RE of cirrus is also determined by solar zenith angle, surface albedo, liquid cloud optical thickness, and ice crystal shape (in descending priority). RE in the TIR spectrum is dominated by surface temperature, ice cloud temperature, liquid water cloud optical thickness, and ice crystal shape. Net RE is controlled by surface albedo, solar zenith angle, and surface temperature in decreasing importance. The relative importance of the studied parameters differs, depending on the ambient conditions. Furthermore, and during nighttime the net RE is equal to the TIR RE. The data set generated in this work is publicly available. It can be used as a lookup table to extract the RE of cirrus clouds, contrails, and contrail cirrus instead of full radiative transfer calculations.

Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations

Abstract. Poor representations of aerosols, clouds, and aerosol–cloud interactions (ACIs) in Earth system models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and the key to these efforts is the evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they lack process-level understanding and representations of aerosols, clouds, and ACIs. In this study, we developed the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package to facilitate the routine evaluation of aerosols, clouds, and ACIs simulated the Energy Exascale Earth System Model (E3SM) from the US Department of Energy (DOE). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates compared with version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in situ and remote-sensing measurements from aircraft, ship, surface, and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: the eastern North Atlantic, the central US, the northeastern Pacific, and the Southern Ocean. These regions produce frequent liquid- or mixed-phase clouds, with extensive measurements available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multivariable diagnostics, including percentiles, histograms, joint histograms, and heatmaps, to evaluate the model representation of aerosols, clouds, and ACIs. Select examples highlighting the capabilities of ESMAC Diags are shown using E3SM version 2 (E3SMv2). In general, E3SMv2 can reasonably reproduce many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path response to a perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants further study to improve the cloud microphysics parameterizations in E3SMv2.

Sensitivities of Subseasonal Unified Forecast System Simulations to Changes in Parameterizations of Convection, Cloud Microphysics, and Planetary Boundary Layer

Abstract NOAA has been developing a fully coupled Earth system model under the Unified Forecast System framework that will be responsible for global (ensemble) predictions at lead times of 0–35 days. The development has involved several prototype runs consisting of bimonthly initializations over a 7-yr period for a total of 168 cases. This study leverages these existing (baseline) prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and planetary boundary layer on 35-day forecasts. Through these physics sensitivity experiments, it is found that no particular configuration of the subseasonal-length coupled model is uniformly better or worse, based on several metrics including mean-state biases and skill scores for the Madden–Julian oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3–4, indicating that some subseasonal biases may be mitigated through tuning at shorter time scales. This result, while shown for the first time in the context of subseasonal prediction with different physics schemes, is consistent with findings in climate models that some mean-state biases evident in multiyear averages can manifest in only a few days. An additional convective parameterization test using a different baseline shows that attempting to generalize results between or within modeling systems may be misguided. The limitations of generalizing results when testing physics schemes are most acute in modeling systems that undergo rapid, intense development from myriad contributors—as is the case in (quasi) operational environments.

A Comparative Investigation of Light Scattering and Digital Holographic Imaging to Measure Liquid Phase Cloud Droplets

The measurement of cloud microphysical parameters plays an important role in describing characteristics of liquid phase clouds and investigating mutual relationships between clouds and precipitation. In this paper, cloud microphysical parameters at Liupan Mountain Weather Station in Ningxia are measured with a high-resolution coaxial digital holographic imager and a fog monitor 120. There are differences in the measurement results between the two instruments. The number concentration measured by the digital holographic imager is about 1.5 times that of the fog monitor 120. However, their Pearson correlation coefficient is above 0.9. Through analysis, we found that the measurement results of the digital holographic imager and fog monitor 120 are differences in 2–4 µm and 7–50µm. For the droplets with the diameters of 4–7 µm, their measurement results have good consistency. By analyzing the influence of wind field and detection sensitivity on the measurement principle, the reasons which caused the difference are proposed. Advice is given to observe topographic clouds by using the above two instruments. In addition, the differences in liquid water content and visibility are analyzed due to the absence of small and large droplets. The study provides data support for improving the accuracy of instruments in measuring cloud droplets and is useful for research in the field of cloud microphysical processes.

Cascade sensitivity tests to model deep convective systems in complex orography with WRF

The goal of the present study is to improve the analysis of the performance of the Weather Research and Forecasting (WRF) model, Version 4.3.3, under different settings, in the prediction of precipitation in case of deep convective events. This is done through a wide cascade sensitivity test involving parameterizations expected to play a major role in the description of precipitation for deep convective events: cloud microphysics (CM), planetary boundary layer (PBL), surface layer (SL) and land-surface (L-S) model. Four significant precipitation events, which were associated to deep convective systems and occurred between 2019 and 2021 in Lombardia and Liguria regions (Northern Italy), have been simulated according to 45 different WRF settings. High resolution simulations are required when dealing with small scale deep convective systems as well as a suitable verification method. So, traditional statistical indexes have been integrated with new-generation verification methods, based on the identification of precipitation patterns in forecast and observed fields and on the evaluation of their similarity through the calculation of a coupling index. This analysis method has been exploited to evaluate the quality of the high-resolution simulations performed, and to individuate the best-performing WRF setting in simulating intense convective events. From the present work emerges how the unique CM parameterization characterized by a triple-moment treatment for cloud ice significantly outperforms all other CM schemes underlying the main role of cloud ice in the description of the precipitation case studies. Furthermore, PBL and L-S schemes show a leading role, at least as much as CM, in the description of the events.

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

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