Local rainfall measurements include a wide range of methods with different concepts and assumptions. Understanding the differences enables researchers to determine the most reliable device for each rain condition. Here, we compare two such devices: Parsivel2 (an optical disdrometer by OTT) and mini Doppler radar (part of the mini Meteorological Station by Thies). The performance assessment followed two foci: evaluating the scaling features using the framework of universal multifractals (UMs), and analysing the influence of physical parameters (drop size, wind velocity and rainfall rate). The devices were mounted on a meteorological mast, in Pays d'Othe wind farm, 110 km southeast of Paris. The mini Doppler radar returned very low rain measurements during conditions with bigger drops, and showed non-detection in light rain involving heavy wind. UM analysis revealed similar scaling behaviour for both devices, which extended to finer scales of mini Doppler radar data until 4 s beyond which white noise was detected.

Below cloud scavenging by rain is known to be a very efficient sinking mechanism for aerosols in atmosphere. Since this scavenging depends on interaction between aerosol particles as well as the scavening raindrops, and notably their respective size ranges, it is interesting to examine both fields together across various size ranges and across temporal scales. Towards this, a 4 month long data was used from Cherbourg-Octeville, France from 01/11/2010 to 12/03/2011 from the experimental station managed by Institut de Radioprotection et de Sûreté Nucléaire (IRSN). Here, simultaneous and continuous measurement of size resolved particle concentration (14.6 to 478.3 nm and 0.523 to 19.81 µm) range has been done using Scanning Mobility Particle Sizer (SMPS) and Aerodynamic Particle Sizer (APS), and rain measurement using a disdrometer. Variation of total aerosol concentration in nm and µm range, as well as individual number concentration in small size bins were analyzed according to rain and dry events, using the framework of Universal Multifractals (UM). UM is widely used, as a physically based scale invariant framework, for characterizing and simulating extreme variability and intermittency in geophysical fields. From initial analysis, the total concentration showed scaling properties (1 min to 1 hr), in both rain and dry events, regardless the scavenging efficiency of event. This was further explored in individual concentration ranges and they showed similar scaling properties in different rain types. However, while considering the different stages of rain, say start and end, the values of UM parameters showed some variation. To understand the behavior more clearly, few sizes were selected from nm and µm range, and efforts were made to extract the field which is devoid of scavenging by rain. Understanding the correct transformation required to extract accurate UM values and comparing the scavenging and non scavenging fields will improve understanding of particle concentration variation, and eventually understanding of scavenging coefficient.

A disdrometer is an instrument designed to assess both the size and velocity of descending hydrometeors. The applications of rainfall measurements retrieved with the help of disdrometers are diverse, spanning areas such as traffic control, scientific research, airport observation systems, and hydrology. Modern disdrometers leverage microwave or laser technologies that have increased the accuracy of the measurements with each iteration. Still, the quality of measurements fluctuates depending on factors such as raindrop size, wind velocity, and rain rate. A comprehension of these variations is needed to better understand the level of reliability of each device depending on the specific rain conditions. In this study, we compare the performance of two optical disdrometers : 3D Stereo disdrometer (manufactured by Thies Clima) and Parsivel2 (manufactured by OTT). Both devices provide size resolved measurement of rainfall along with velocity of falling drops. Parsivel is set to record data every 30 seconds over a sampling area of 54 cm² and arranges the information in 32 x 32 classes of drop size and velocity. Unlike the Parsivel, 3D Stereo does not discretize measurements, and directly provides the diameter and velocity of each falling drop in a sampling area of 100 cm² with a measuring resolution of 0.08 mm and 0.2 m/s respectively, and a temporal resolution of 1 millisecond. This finer resolution data enables us to study rainfall variability at very small scales which are not usually available. Here, we used continuously and simultaneously measured data since 21/08/2023, from TARANIS observatory of ENPC (https://hmco.enpc.fr/portfolio-archive/taranis-observatory/). The initial comparison of the data was done using a time series of rain-rate for rainfall events in between a dry period of at least 15 minutes and total depth >0.7 mm. This revealed an unexpected disparity in the water volume collected between the devices. Parsivel collected more than 3D Stereo on every instance, and the disparity got bigger as the rain rate increased. With the purpose of studying the source of this disparity, the sampling area of the 3D Stereo was divided into 8 sections and compared with each other. This showed that the estimate of rainfall parameters such mean diameter, mean velocity of the drops (which were expected to be uniform over long periods regardless of the section where drops are measured) were not the same for the sections studied, and exhibited clear trends. To understand this discrepancy in a scale invariant way, and to evaluate the performance of devices across scales and not only at a single scale, the widely used framework for studying variability of geophysical fields – Universal Multifractals (UM) was employed for assessing the scaling behavior of fields. Rainfall from both devices showed previously reported average scaling behavior from 30 s to 30 min. The difference between rain events and also the behavior at finer scales, which can be accessed from 3D stereo disdrometer were also studied using the UM framework and will be discussed. Authors acknowledge the Ra2DW project (supported by the French National Research Agency - ANR-23-CE01-0019), for partial financial support. Keywords: rainfall; disdrometer; multifractals

Scaling analysis of subjectively defined fields, which are often expressed as a range of values based on the field of application, is not straightforward. To avoid potential biases in statistical analysis, extracting the actual underlying field and understanding the effect of ranged values are important. One such example is atmospheric visibility which is often estimated as a range in meteorological context, as meteorological optical range (MOR). This is estimated from the extinction coefficient a-e, an objective measurement of light attenuation by constituent gases and aerosols in the atmosphere, and expressed as a range depending on the application needs. Accurate estimation of visibility and its variability is required for the safe functioning of various domains such as transport sectors and free optic communication or for understanding regional variations in air quality and climate. Since MOR is a subjective range, here we attempt to characterize it using the objectively measured a-e. In this context, we identify and illustrate the effect of a lower threshold in the data, which is not exclusive to the current problem, and examine its consequences in the multifractal characterization of the field. Here, a-e was extracted from visibility data by a present weather sensor located in the Paris region (France). This was then compared with a-e extracted from MOR measured at Paris Charles de Gaulle airport during the same period. Variability in a-e was investigated under the framework of universal multifractals (UMs), which is widely used to characterize geophysical fields that exhibit extreme variability across scales. With direct data analysis and numerical simulations mimicking the behavior, it was found that the multifractal properties exhibited by a-e are influenced by the upper limit of visibility range in the data. The biases are identified within the theoretical framework of UM, thus expanding the general understanding on the retrieval of the underlying unbiased stochastic field. SIGNIFICANCE STATEMENT: Data obtained from measurement campaigns are often influenced by instrumental limitations such as upper and lower detection limits. One such example is atmospheric visibility which is usually represented as a range, from objective measurement of light attenuation by constituent particles. Here, we examine the consequences of characterizing visibility using the extinction coefficient, by considering the presence of a lower limiting value, using two datasets in the Paris region forward scattering sensor and airport visibility measurement. Through data analysis, numerical simulation, and theoretical formulation, the biases are identified and the underlying unbiased stochastic field is retrieved. This enables a more accurate analysis and simulation of visibility, as well as other geophysical fields with similar instrumental or application limitations.

Rainfall and wind exhibit extreme variability over wide range of space-time scales. Such features are naturally transferred to wind turbine torque and ultimately to wind energy production. Improving our understanding of wind power production requires better accounting for the impact of these small scale fluctuations. This is much needed in order to achieve UN’s (United Nations) Sustainable Development Goal 7 (affordable and clean energy for all) and in a context of increasing global transition towards renewable and carbon neutral energy. The project RW-Turb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) was developed to address this challenge and to understand better the correlation across scales between rainfall and wind fields and its impact on wind power production. A high resolution measurement campaign was set up between 12/2020 and 07/2023 with two 3D sonic anemometers (manufactured by Thies), two mini meteorological stations (manufactured by Thies), and two disdrometers (Parsivel2, manufactured by OTT) installed on a meteorological mast at 75 and 45 m respectively in the wind farm of Pays d’Othe (110 km south-east of Paris, France; operated by Boralex). The framework of Universal Multifractals (UM) is used to carry out this analysis. It is a physically based and mathematically robust framework that enables to characterize and simulate the extreme variability of geophysical fields across scales. It is furthermore parsimonious since it relies on the use of only three parameters. In a first step multifractal analysis of the available fields (wind velocity, power available at the wind farm, power produced by wind turbines, air density, and rainfall) is implemented. Event based analysis enabled to observe differences in UM parameters depending on whether it is raining or not. In general, a slightly stronger variability is found when it rains. In a second step, a joint multifractal analysis is implemented to further quantify correlation across scales between the studied fields. An increase in correlation exponent of the various fields with increase in rain rate is found. Numerical simulations are then used as a complement to data analysis. More precisely, 3D space plus time vector fields which realistically reproduce observed spatial and temporal variability of wind fields are generated with multifractal tools. Then, they are used as input into three modeling chains of increasing complexity to simulate wind turbine torque. The simplest model uses average wind field over swept area, while a more realistic one computes the torque as an integral over the blades of the turbine enabling to account for the space-time variability of wind. Finally, OpenFAST, which is widely used by researchers and practitioners is implemented. UM analysis on the simulated torque time series were performed to quantify the impact of small scale fluctuations on wind power production, as well as the ability of the various models to account for it.

The inherent variability in atmospheric fields, which extends over a wide range of temporal and spatial scales, is also transferred to energy fields extracted from them. In the specific case of wind power generation, this can be seen in the theoretical power available for extraction and the empirical power produced by turbines. To model and analyse them, it is important to quantify their variability, intermittency, and correlations with other interacting fields across scales. To understand the uncertainties involved in power production, power outputs from four 2 MW turbines are analysed (from an operational wind farm at Pay d'Othe, 110 km south-east of Paris, France) using the scale-invariant framework of universal multifractals (UM). Their scaling properties were compared with power available at the same location from simultaneously measured wind velocity. While statistically analysing the turbine output, the rated power acts like an upper threshold that results in biased estimators. This is identified and quantified here using the theoretical framework of UM and validated using numerical simulations. Understanding the effect of instrumental thresholds in statistical analysis is important in retrieving actual fields and modelling them, more so in wind power production, where the uncertainties due to turbulence are already a leading challenge. This is expanded in Part 2, where the influence of rainfall on power production is studied across scales using UM and joint multifractals.

Abstract. Wind power production plays an important role in achieving UN’s (United nations) Sustainable development goal (SDG) 7 – affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding the uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the field becomes increasingly concentrated at smaller and smaller supports as the scale decreases. When it comes to power production by wind turbines, another complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long-term effects. To understand this, the project RWTurb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT) on a meteorological mast in the wind farm of Pays d’Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions. Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. Since rated power acts like an upper threshold in statistical analysis of empirical wind power, efforts were made to use the theoretical available power as a proxy to see the difference. From an event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analysis and an increase in correlation exponent was observed between various fields with an increase in rain rate. Here we also examine the possibility of differences in power production according to type of rain (convective or stratiform) as well as various regimes of wind velocity. While examining time steps according to wind velocity, power curves showed different regions of departure from state curve according to the rain rate.

Abstract. The inherent variability in atmospheric fields, which extends over a wide range of temporal and spatial scales, also gets transferred to energy fields extracted off them. In the specific case of wind power generation, this can be seen in the theoretical power available for extraction in the atmosphere as well as the empirical power produced by turbines. Further the power produced by turbines are affected by atmospheric turbulence as well as other fields it interact with. For modelling as well as analyzing them, quantification of their variability, intermittency and correlations with other interacting fields is important. To understand the uncertainties involved in power production, power outputs from four 2MW turbines are analyzed from an operational wind farm at Pay d’Othe, 110 km southeast of Paris, France. Using simultaneously measured wind velocity from the same location, the variability in power available at the wind farm, and power produced by wind turbines were analyzed. To account for the intermittency and variability in said fields, the framework of Universal Multifractals (UM) is used. UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over a wide range of scales. While statistically analysing the power produced by the turbine, rated power acts like an upper threshold resulting in biased estimators. This is identified and quantified here using the theoretical framework of UM along with the actual sampling resolution of instruments under study. The validity of this bias in framework is further tested and illustrated using numerical simulations of fields with the same multifractal properties. Understanding instrumental thresholds and their effect in analysis is important in retrieving actual fields and modelling them, more so, in the case of power production where the uncertainties due to turbulence are already a leading challenge. This is further expanded in the second part where the influence of rainfall in power production is studied using scale invariant tools of UM and joint multifractals.

In the increasing global transition towards renewable and carbon-neutral energy, understanding the uncertainties associated with wind power production is extremely important. In addition to the widely acknowledged uncertainties from turbulence and wind intermittency, further complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far. To understand this, multiple 3D sonic anemometers, mini meteorological stations, and optical disdrometers were employed on a meteorological mast on the Pays d'Othe wind farm (110 km south-east of Paris, France) in the framework of the Rainfall Wind Turbine or Turbulence (RW-Turb) project (https://hmco.enpc.fr/portfolio-archive/rw-turb/, last access: 26 November 2024). With these simultaneously measured data, wind power and its associated atmospheric fields were studied under various rainy conditions. Variations of the wind velocity, power available on the wind farm, power produced by wind turbines, and air density are examined here, under rainy and dry conditions, using the scale-invariant framework of universal multifractals (UM). Since rated power acts like an upper threshold in statistical analysis of turbine power (discussed in Part 1), theoretically available power was used as a proxy. From an event-based analysis, differences in UM parameters were observed between rainy and dry conditions for the fields. This is explored further using joint multifractal analysis, which revealed an increase in the correlation exponent between various fields with the rain rate. Here we also examine the possibility of variation in power production by rainy conditions (convective or stratiform) as well as by regimes of wind velocity. While examining time steps according to wind velocity, turbine power curves showed different regions of departure from the state curve according to the rain rate.

Wind power production plays an important role in achieving UN’s (United nations) Sustainable development goal (SDG) 7 - affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding the uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the field becomes increasingly concentrated at smaller and smaller supports as the scale decreases. When it comes to power production by wind turbines, another complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long term effects. To understand this, the project RW-Turb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT) on a meteorological mast in the wind farm of Pays d’Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions.Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. While statistically analysing the power produced by turbine, rated power acts like an upper threshold resulting in biased estimators. This is identified and quantified here using the theoretical framework of UM along with the actual sampling resolution of instruments under study. Further, from event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analysis and an increase in correlation exponent was observed between various fields with increase in rain rate.

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

Vertically constrained observations of aerosol size distribution and hygroscopicity using the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) measurements during the monsoon seasons of 2018 and 2019 over a typical global climate model grid area are presented. Two regimes of aerosol loading - low and high, were identified within the season. Low aerosol loading is associated with active monsoon conditions and strong westerlies, while high aerosol loading occurs when the westerly airmass weakens or becomes continental. Aerosol hygroscopicity was the lowest (∼0.08) during low aerosol loading days in 2019 and the highest (∼0.3) during high aerosol loading days in 2018. Aitken mode aerosols control the bulk hygroscopicity on high aerosol loading days and at high supersaturation. The refractory Black Carbon (rBC) aerosols accounted for nearly 10% of the total aerosol number concentration during the monsoon. The internally mixed rBC aerosols had thicker coatings for smaller rBC cores and vice-versa. The cloud condensation nuclei (CCN) closure at the cloud base is established from the in situ observations. These observations are first-of-its-kind from the Indian region, covering two contrasting monsoon seasons, and are useful for studying aerosol-cloud interactions and constraining models.

To calculate the effect of rainfall in detaching particles and initiating soil erosion, or in eroding wind turbine leading edge, it is important to measure recorded drop size distributions (DSD) and fall velocity over long period. Commonly used relationships between kinetic energy (KE) and rainfall rate (R) exhibit strong dependence on the temporal resolution at which the analysis is carried out. Here we aim at developing a new scale invariant relationship relying on the framework of Universal Multifractals (UM), which is widely used to analyze and characterize geophysical fields that exhibit extreme variability across wide range of scales. Rainfall data is collected using three optical disdrometers working on different underlying technologies (one Campbell Scientific PWS100 and two OTT Parsivel(2) instruments) and operated by the Hydrology Meteorology and Complexity laboratory of Ecole des Ponts ParisTech in Paris area (France). They provide access to the size and velocity of drops falling through sampling areas of few tens of cm2. Such data enables estimation of rainfall DSD, R and KE at various resolutions. The temporal variations of this geophysical data over wide range of scales are then characterized in the UM framework, which was never done for KE. A new power law relation is developed and tested against the theoretical framework assuming gamma DSD for describing the dependence between KE and R. The developed equation using scale invariant features of UM does not rely on gamma DSD assumption, performs as well as the existing tools, and is valid not only at a single scale, but also across scales.

Abstract. The Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (hmco.enpc.fr) has made a data set of atmospheric measurements available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the RW-Turb project (supported by the French National Research Agency – ANR-19- CE05-0022). Six devices are used : two 3D sonic anemometers (manufactured by Thies), two mini meteorological stations (manufactured by Thies), and two disdrometers (Parsivel2, manufactured by OTT). They are installed at two heights (approx. 45 m and 80 m), which enables to monitor potential effects of altitude will be used. The temporal resolution is of 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations and 30 s for the disdrometers. A multifractal analysis is implemented to assess the effective resolution of the devices and it suggested that anemometers and stations are able to measure expected variability only down to 1 s and 16 s respectively. Link to the data set: https://doi.org/10.5281/zenodo.5801900 (Gires et al., 2021).

<p>Atmospheric fields are known to exhibit extreme variability over wide range of temporal and spatial scales, which makes them complex to characterize. When it comes to wind power production, the power available at atmosphere and power extracted by turbines at multiple scales are affected by corresponding variations in coexisting fields. Understanding their variability and correlations helps in quantifying uncertainties in modeling as well as real data analysis. Here, we aim to characterize the variability and correlations across scales of wind power production, and atmospheric fields including 3D wind, rainfall and air density using simultaneous measurements in a wind farm relying on the framework of Universal Multifractal (UM) analysis. It is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales.</p><p>Towards this, high-resolution atmospheric data collected from a meteorological mast located in the wind farm of Pays d’Othe operated by Boralex (110 km south-east of Paris, France) is used. The data is being collected under the project RW-Turb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency (ANR-19-CE05-0022). The campaign utilizes multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT) installed at turbine hub height along with turbines in the wind farm. The temporal resolution is 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations and 30 s for the disdrometers. Variability in power production is examined according to different meteorological conditions using the framework of UM and consequences of their correlations are discussed. In the process we also make short commentary on the actual sampling resolution at which fields should be considered for extracting useful statistical information about their variability.</p>

The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (http://hmco.enpc.fr, last access: 16 August 2022) has made a data set of high-resolution atmospheric measurements available, which is of interest for the atmospheric science community. It comes from a campaign carried out in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency, grant no. ANR-19-CE05-0022) on a meteorological mast installed at a wind farm located approx. 110 km southeast of Paris in France. In total, 3 months of data, covering the spring period from 1 March to 1 June 2021, are made available. We used six devices, namely two 3D sonic anemometers (manufactured by Thies), two mini meteorological stations (manufactured by Thies), and two disdrometers (Parsivel2, manufactured by OTT). They are installed at two heights (approx. 45 and 80 m), which enables us to monitor potential effects of altitude. The temporal resolution is of 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations, and 30 s for the disdrometers. A multifractal analysis is implemented to assess the effective resolution of the devices, and it is suggested that the anemometers and stations are able to measure expected variability only down to 1 and 16 s, respectively. A link to the data set can be found at https://doi.org/10.5281/zenodo.5801900 (Gires et al., 2021)

Extinction coefficient (σe) is a measure of light attenuation in the atmosphere, due to absorption and scattering properties of constituent gases and aerosols. In meteorological context, σe is used to understand transparency of the atmosphere, by estimating visibility or meteorological observable range (MOR). An accurate representation of visibility is required for safe functioning of various domains such as transport sectors, free optic communication, etc., and for understanding regional variations in air quality and climate. As the measurement of visibility is subjective and dependent on the instrument and range of measurement, here we attempt to characterize the same using extinction coefficient. σe was investigated under the framework of universal multifractals (UM), which is widely used to analyze and characterize geophysical fields that exhibit extreme variability over measurement scales. For this study, σe was extracted from forward scattering visibility data by disdrometer (Campbell Scientific PWS100) located in the Paris area (France), operated by Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (HM&Co, ENPC). As governing nonlinear equations of the atmosphere such as Navier-Stokes possess scale invariance, it was assumed here that the behavior of light attenuating particles should inherit similar scaling properties and hence be treated as multifractal fields. σe extracted from MOR measured at Paris-Charles de Gaulle airport was also subjected to multifractal analysis during the same time period for comparison. With direct analysis and simulations, it was found that σe exhibits multifratcal properties but are influenced by upper limit of visibility range in the instrument used for measurement. From the study, we suggest usage of extinction coefficient (σe) for characterizing atmospheric visibility as the former is a more physically relevant quantity which is objectively measured by instruments and directly related to particles in the atmosphere; while emphasizing the need to consider biases from instrumental range.

Unique airborne observations of aerosol size spectral and chemical characteristics over the peninsular Indian region are illustrated in this case study. Multimodal lognormal distributions were required to fit the observed in situ aerosol size distribution. The aerosol composition and mixing state was deduced from the single-particle analyses of aerosols using the transmission electron microscope and soot photometer coupled with satellite retrieved aerosol classification, and back trajectory analyses. Organic carbon was the most prominent aerosol type found at all altitudes. Refractory black carbon aerosols which constituted about 10-12% of the aerosols in the boundary layer were primarily internally mixed with both inorganic and organic coating. Other major aerosol types were dust and sea salt, with the latter primarily found below 2 km. Further, the cloud forming ability of in-situ aerosols is tested through a cloud condensation nuclei closure analysis. The effective hygroscopicity decreased above cloud base due to the absence of sea salt aerosols. The change in large-scale winds with altitude affected the aerosol composition and hygroscopicity. The multimodal aerosol size distribution and hygroscopicity parameter (Kappa = 0.18) obtained for the cloud base aerosols over the rain shadow region are useful for studying aerosol-cloud interactions using regional cloud-resolving models.

In situ measurements of aerosol particle chemistry and cloud microphysics made during the Cloud–Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) in 2015 over the Western Ghats and its rain shadow region are presented in this study. The high ratio of cloud condensation nuclei (CCN) to large sized aerosol (above 0.1 µm) concentrations indicates Aitken mode aerosols as the major contributor towards the observed CCN. Morphology and chemical composition of airborne aerosol samples collected from different altitudes and the warm cloud layers indicated distinct particle chemistry on the dry and wet days. The majority of the particles sampled were heterogeneous and internally mixed with two or more aerosol species indicating multiple sources and atmospheric aging, even during the wet days. Si-rich particles (up to 73%) were the dominant species in samples collected during dry days when the shallow clouds with narrow drop size distribution were observed. A higher concentration of Na-rich particles (up to 50%) was observed on wet days when the clouds were majorly multiple layers of stratus, which had broader cloud droplet spectra. Internally mixed carbonaceous and iron/cobalt-rich aerosols from local pollution sources were found on both wet and dry days suggesting boundary layer venting of aerosols. Large droplets were observed near the cloud base over the Western Ghats compared to those formed over the rain shadow region.