Investing in Rainfall Enhancement: An Innovative Plan for Arid Regions

Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (<span class="inline-formula"><i>N</i>_<i>t</i></span>), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm<span class="inline-formula">^−3</span> and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s<span class="inline-formula">^−1</span>. This scenario provided <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (<span class="inline-formula"><i>N</i>_<i>t</i></span>), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm<span class="inline-formula">⁻³</span> and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s<span class="inline-formula">⁻¹</span>. This scenario provided <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (<span class="inline-formula"><i>N</i>_<i>t</i></span>), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm<span class="inline-formula">^−3</span> and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s<span class="inline-formula">^−1</span>. This scenario provided <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

<strong class="journal-contentHeaderColor">Abstract.</strong> Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (<span class="inline-formula"><i>N</i>_<i>t</i></span>), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm<span class="inline-formula">⁻³</span> and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s<span class="inline-formula">⁻¹</span>. This scenario provided <span class="inline-formula"><i>N</i>_<i>t</i></span>, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

The Kingdom of Saudi Arabia (KSA) initiated a Regional Cloud Seeding Program (RCSP) in 2022 to enhance precipitation over the southwestern and central parts of the country. This study presents an overview of RCSP cloud seeding operations conducted from 2022 to 2024, along with preliminary results on precipitation enhancement during the autumn season of 2022.Precipitation data from the Global Interpolated Rainfall Estimation (GIRAFE) dataset, provided by EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF), were used to investigate the relationship between precipitation anomalies and cloud seeding operations. The analysis focused on identifying spatial patterns where high precipitation anomalies coincided with regions of elevated cloud seeding flare density. These regions were categorized to evaluate the potential influence of cloud seeding activities.This exploratory study highlights spatial correlations between cloud seeding operations and rainfall patterns, without accounting for additional meteorological or synoptic variables. The findings contribute to understanding the spatial dynamics of cloud seeding operations and their potential role in enhancing precipitation in arid regions, offering valuable insights for the optimization of weather modification strategies.

Aerosols affect development of clouds and precipitation by serving as cloud condensation nuclei (CCN) and ice nuclei (IN). Considering the dramatically changing ambient aerosol concentration, it is important to examine the potential “side effect” of aerosol pollution on precipitation enhancement by weather modification. In this study, the cloud seeding was performed on a precipitation event in Beijing in the summer of 2008, which is simulated by the NSSL two-moment cloud scheme of the Weather Research and Forecasting (WRF) model. Sensitivity tests were conducted by modifying the ambient aerosol concentration and the ice crystal seeding amount to investigate the cloud seeding efficacy in different CCN concentration scenarios. There was a slight difference in the precipitation distribution between the simulations with two ambient CCN concentrations: the northern precipitation center in polluted scenario was weaker and the southern center was stronger. Compared with normal CCN scenario, the cloud liquid water mass and ice crystal mass in the severe pollution scenario is larger, and the total contents of snow and graupel were not sensitive to the CCN concentration. With the same amount of man-made ice crystals seeding, the precipitation enhancement was quite different under different CCN conditions. The higher the CCN concentration usually leads to stronger precipitation suppression. As CCN concentration increase, the deposition growth of snow, auto-conversion and accretion of ice crystals to snow were weakened, as well as the conversion of melting snow and graupel into rainwater.

This study examines the influence of Purdue-Lin microphysical parameterization scheme (Lin et al.,1983) on quantitative precipitation for pre-monsoon/monsoon conditions over southern peninsular India in the Weather Research and Forecasting (WRF) model. An ideal microphysical scheme has to describe the formation, growth of cloud droplets and ice crystals and fall out as precipitation. Microphysics schemes can be broadly categorized into two types: bin and bulk particle size distribution (Morrison, 2010). Bulk schemes predict one or more bulk quantities and assume some functional form for the particle size distribution. For better parameterization, proper interpretation of these hydrometeors (Cloud Droplets, Raindrops, Ice Crystals and Aggregates, Rimed Ice Particles, Graupel, Hail) and non-hydrometeors (Aerosols vs. Condensation Nuclei vs. Cloud Condensation Nuclei vs. Ice Nuclei) is very important. The Purdue-Lin scheme is a commonly used microphysics scheme in WRF model utilizing the “bulk” particle size distribution, meaning that a particle size distribution is assumed. The intercept parameter ( N 0 ) is, in fact, turns out to be independent of the density. However, in situ observations suggest (Haddad et al., 1996, 1997) that the mass weighted mean diameter is correlated with water content per unit volume ( q ), leading to the fact that N0 depends on it. Here, in order to analyze the correlation of droplet size distribution with the convection, we have carried out simulations by implementing a consistent methodology to enforce a correlation between N0 and q in the Purdue-Lin microphysics scheme in WRF model. The effect of particles in Indian Summer Monsoon has been examined using frequency distribution of rainfall at surface, daily rainfall over the domain and convective available potential energy and convective inhibition. The simulations are conducted by analyzing the maximum rainfall days in the pre-monsoon/monsoon seasons using Tropical Rainfall Measuring Mission (TRMM) accumulated rainfall data for 24 hours.

This study incorporated the Weather Research and Forecasting (WRF) model double-moment 6-class (WDM6) microphysics scheme into the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_Meso). A rainfall event that occurred during 3–5 June 2015 around Beijing was simulated by using the WDM6, the WRF single-moment 6-class scheme (WSM6), and the NCEP 5-class scheme, respectively. The results show that both the distribution and magnitude of the rainfall simulated with WDM6 were more consistent with the observation. Compared with WDM6, WSM6 simulated larger cloud liquid water content, which provided more water vapor for graupel growth, leading to increased precipitation in the cold-rain processes. For areas with the warm-rain processes, the sensitivity experiments using WDM6 showed that an increase in cloud condensation nuclei (CCN) number concentration led to enhanced CCN activation ratio and larger cloud droplet number concentration (Nc) but decreased cloud droplet effective diameter. The formation of more small-size cloud droplets resulted in a decrease in raindrop number concentration (Nr), inhibiting the warm-rain processes, thus gradually decreasing the amount of precipitation. For areas mainly with the cold-rain processes, the overall amount of precipitation increased; however, it gradually decreased when the CCN number concentration reached a certain magnitude. Hence, the effect of CCN number concentration on precipitation exhibits significant differences in different rainfall areas of the same precipitation event.

The Weather Research and Forecasting (WRF) Model coupled with a spectral bin microphysics (SBM) scheme is used to investigate aerosol effects on cloud microphysics and precipitation over the Indian peninsular region. The main emphasis of the study is in comparing simulated cloud microphysical structure with in situ aircraft observations from the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX). Aerosol–cloud interaction over the rain-shadow region is investigated with observed and simulated size distribution spectra of cloud droplets and ice particles in monsoon clouds. It is shown that size distributions as well as other microphysical characteristics obtained from simulations such as liquid water content, cloud droplet effective radius, cloud droplet number concentration, and thermodynamic parameters are in good agreement with the observations. It is seen that in clouds with high cloud condensation nuclei (CCN) concentrations, snow and graupel size distribution spectra were broader compared to clouds with low concentrations of CCN, mainly because of enhanced riming in the presence of a large number of droplets with a diameter of 10–30 μm. The Hallett–Mossop ice multiplication process is illustrated to have an impact on snow and graupel mass. The changes in CCN concentrations have a strong effect on cloud properties over the domain, amounts of cloud water, and the glaciation of the clouds, but the effects on surface precipitation are small when averaged over a large area. Overall enhancement of cold-phase cloud processes in the high-CCN case contributed to slight enhancement (5%) in domain-averaged surface precipitation.

With the projected expansion of arid/semi‐arid regions in a warming world, precipitation enhancement activities such as cloud seeding will become increasingly popular and relied upon. Due to the inherent costs, a successful planning is crucial, which involves accurate model predictions. In this study, the usefulness of the Weather Research and Forecasting (WRF) model forecasts for guidance into seeding operations in the United Arab Emirates, where seeding activities have been conducted for more than two decades, is assessed. The WRF predictions are compared with ground‐based, satellite‐derived and radar reflectivity data, and in‐situ observations onboard the airplanes used to perform the seeding operations. WRF is found to have higher skill in simulating the observed cloud top pressure/temperature than the cloud fraction, with the model vertical velocity predictions also more skillful than those of the radar reflectivity. A stronger Arabian Heat Low (AHL) in the model leads to drier conditions which, together with a surface cold bias, limits the spatial extent and vertical depth of the simulated convective clouds. Development of convective rolls in the boundary layer is reported in both observations and simulations and their interaction with cold pools from convective clouds promote the development of secondary convection. Sensitivity to the choice of the Planetary Boundary Layer (PBL) scheme is also noticed, with the Yonsei University PBL scheme giving the best performance. When considering the two factors needed for a successful seeding operation that is, the presence of an updraft and clouds, the model‐predicted seeding regions largely match the areas where precipitation was observed. As the proposed WRF set up can be used operationally, the model forecasts will bring added value to the seeding activities in the country.

The Polarimetric Cloud Analysis and Seeding Test (POLCAST) was a cloud seeding research experiment that was conducted to help determine if hygroscopic seeding could be detected directly by polarimetric radar observations or through derived polarimetric parameters. The operational phase of the program, a cooperative study between the North Dakota Atmospheric Resource Board (NDARB), University of North Dakota, Weather Modification Inc., and Ice Crystal Engineering, was conducted from 10 July – 5 August 2006. The study was centered on the measurements recorded by the University of North (UND) C-Band polarimetric Doppler Radar (NorthPol). Hygroscopic flares, deployed on a Weather Modification Incorporation (WMI) aircraft, were used in the study. Ice Crystal Engineering provided these flares.From the polarimetric radar observations, liquid water content, rainfall rates and hydrometeor type were analyzed. The radar-estimated liquid water content retrievals indicated an increase in 7 of the 8 cases after hygroscopic seeding. In correspondence, the rainfall rates were also higher along with the durations of the cells in the seeded cases. The differential reflectivity decreased after seeding, which is anindication that rain drops were decreasing on average in size. The reflectivity also increased after seeding except at cloud base where there was a decrease in reflectivity after seeding. The hydrometeor identification results were in good agreement with the trends observed in reflectivity and differential reflectivity fields.

Abstract. Changes in aerosol amount and size distribution significantly impact cloud droplet size distribution, as aerosols act as cloud condensation nuclei (CCNs) and influence the relative dispersion (ε) of cloud droplet spectra. Relative dispersion plays a key role in parameterizing cloud processes in general circulation models (GCMs) and microphysical schemes, affecting precipitation estimates and climate predictions. However, the effects of varying aerosol modes on cloud microphysics remain debated, depending on thermodynamic conditions and cloud type. This study simulates a warm stratiform cloud in Jiangxi, China, using the Weather Research and Forecasting (WRF) Spectra–Bin Microphysics scheme (SBM-FAST) from 18:00 on 24 December 2014 to 06:00 on 25 December 2014 (UTC). Satellite and aircraft observations were used to validate the simulation, showing good agreement in cloud structure. Sensitivity experiments were conducted by increasing nucleation, accumulation, and coarse-mode aerosols 5-fold and by reducing the total aerosol concentration to 1/5 of the control. Results show that higher aerosol concentrations enhance cloud formation and broaden droplet spectra, while lower concentrations suppress cloud development. Accumulation-mode aerosols increase small-droplet concentrations, while nucleation- and coarse-mode aerosols favor larger droplets. The correlation between ε and volume-weighted radius (Rv) shifts from positive to negative as Rv increases. This transition is driven by cloud droplet collision–coalescence, condensation, and activation. Increased accumulation-mode aerosol concentrations shift the ε–Rv correlation from negative to positive in the Rv range of 4.5–8 µm, while reduced aerosol concentrations strengthen the negative correlation. Regardless of different coalescence intensities, ε converges with the increase in number concentration of cloud droplets (Nc).

Cloud condensation nuclei (CCN), the subset of atmospheric aerosol that nucleate cloud droplet formation, are a key component in cloud formation, and are an important factor in controlling climatically-relevant cloud properties such as cloud albedo, cloud lifetime, and precipitation rate. A CCN instrument that satisfies the constraints for small aircraft operation — minimum weight, volume, and power consumption, good robustness, and high frequency measurement — was constructed. The measurement technique was based on that of Hudson (1989) because it reportedly offered the ability to make measurements of CCN at all supersaturations simultaneously at high frequency and with good counting statistics. Modelling studies, and laboratory and field measurements, subsequently showed that this technique exhibits poor sensitivity. The CCN instrument was also studied in fixed supersaturation mode, where it is able to accurately measure CCN concentration at a fixed supersaturation, whose value ranges from 0.1 and 2%. The CCN instrument was flown during the 2nd Aerosol Characterization Experiment (ACE-2). The data were reported at a fixed supersaturation of 0.1%. Intercomparison of these measurements with those on two other aircraft shows good agreement. A sublinear relationship between measured CCN concentration and that predicted from aerosol size distribution and chemical composition measurements, Nmeas ~ N0.72pred. In-situ measurements of below-cloud CCN concentration and cloud droplet number concentration are compared. The results are in agreement with model predictions and with previous studies. Cloud droplet concentration is predicted to depend on the CCN spectrum and updraft velocity. Cloud droplet activation has often been assumed to be reasonably described by an equilibrium model. The error in calculated cloud droplet number concentration due to the influence of condensational growth kinetics was shown to be significant for some conditions. Such errors are estimated to lead to overestimates of indirect radiative climate forcing on the order of Wm-2. Accurate interpretation of measured CCN concentration may require consideration of activation kinetics associated with CCN instruments.

Effects of cloud condensation nuclei concentration on the evolution of severe convective storms