Aerosol indirect effects in complex orography areas: a numerical study over the Great Alpine Region

Aerosols play a crucial role in climate through different feedback mechanisms, affecting radiation, clouds and air column stability. This study focuses on the altitude-dependence of the cloud mediated indirect effects of aerosols in the Great Alpine Region (GAR), an area characterised by high pollution levels from anthropic activities in the Po Valley and a complex orography with the highest mountains in Europe. Using a regional atmospheric model, 5-years long convective-permitting sensitivity experiments have been run with different surface aerosol fluxes. The results show that seasonal mean cloud cover, temperature, and precipitations are affected by the aerosol concentrations in the air column, and that the response to pollution is both elevation and season dependent. The overall cloud cover increase with aerosol levels leads to either surface cooling or warming depending on the surface albedo (snow covered or not). Furthermore, different types of clouds have a different sensitivity to aerosols: while the lifetime of low pressure system clouds and orographic clouds is generally increased at high levels of aerosols, convective clouds (typical of the summer season) can actually decrease at high levels of pollution, due to the reduction of strong updrafts.

Analysis of aerosol–cloud interactions and their implications for precipitation formation using aircraft observations over the United Arab Emirates

Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of scientific flights targeted summertime convection along the eastern Al Hajar Mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 ∘C) up to the capping inversion level (−12 ∘C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the subcloud region. Subcloud aerosol sizes are shown to extend from submicron to 100 µm sizes, with higher concentrations of ultra-giant CCN (d>10 µm) from local sources closer to the Saudi border, compared with the eastern orographic region where smaller CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision–coalescence (C–C) process is not observed within the limited depths of warm cloud (<1000 m). The state-of-the-art observations presented in this paper can be used to initialize modeling case studies to examine the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud seeding on these clouds.

Potential for ground-based glaciogenic cloud seeding over mountains in the interior western United States, and anticipated changes in a warmer climate

Glaciogenic cloud seeding has long been practiced as a way to increase water availability in arid regions, such as the interior western United States. Many seeding programs in this region target cold–season orographic clouds with ground–based silver iodide generators. Here, the “seedability” (defined as the fraction of time conditions are suitable for ground–based seeding) is evaluated in this region, based on 10 years of hourly output from a regional climate model with a horizontal resolution of 4 km. Seedability criteria are based on temperature, presence of supercooled liquid water, and Froude number, which is computed here as a continuous field relative to the local terrain. The model’s supercooled liquid water compares reasonably well against microwave radiometer observations.Seedability peaks at 20–30% for many mountain ranges in the cold season, with the best locations just upwind of crests, over the highest terrain in Colorado and Wyoming, as well as over ranges in the Northwest Interior. Mountains further south are less frequently seedable, due to warmer conditions, but when they are, cloud supercooled liquid water content tends to be relatively high.This analysis is extended into a future climate, anticipated for later this century, with a mean temperature 2.0 K warmer than the historical climate. Seedability generally will be lower in this future warmer climate, especially in the most seedable areas, but when seedable, clouds tend to contain slightly more supercooled liquid water.

Effects of Trade Wind Strength on Airflow and Cloudiness over O‘ahu

Satellite observations and high-resolution modeling during July–August 2013 are used to study the effects of trade wind strength on island wake circulations and cloudiness over O‘ahu, Hawai‘i. O‘ahu is composed of two northwest–southeast orientated mountain ranges: the Wai‘anae Range (~1227 m) along the western leeside coast and the Ko‘olau Range (~944 m) along the eastern windward coast. At night, the flow deceleration of the incoming northeasterly trade winds on the eastern windward side is more significant when trades are stronger.In the afternoon hours, effective albedo and simulated cloud water are greater over the Ko‘olau Range when trades are stronger, and clouds are advected downstream by the trade winds aloft. Over the Wai‘anae Range, orographic clouds are more significant when trades are weaker due to less moisture removal by orographic precipitation over the Ko‘olau Range and the development of both upslope flow on the eastern slope and upslope/sea-breeze flow along the western coast, the latter of which brings in warm, moist air from the ocean. When trades are weaker, cloudiness off the western leeside coast is more extensive and originates from orographic cloud development over the Wai‘anae Range, which drifts downstream due to a combination of trade winds and the easterly return flow aloft. The latter is associated with the low-level sea-breeze/upslope flow.

Microphysical characteristics and evolution of seeded orographic clouds

The spatial distribution and magnitude of snowfall resulting from cloud seeding with silver iodide (AgI) is closely linked to atmospheric conditions, seeding operations, and dynamical, thermodynamical, and microphysical processes. Here, microphysical processes leading to ice and snow production are analyzed in orographic clouds for three cloud seeding events, each with light or no natural precipitation and well-defined, traceable seeding lines. Airborne and ground-based radar observations are linked to in-situ cloud and precipitation measurements to determine the spatiotemporal evolution of ice initiation, particle growth, and snow fallout in seeded clouds. These processes and surface snow amounts are explored as particle plumes evolve from varying amounts of AgI released, and within changing environmental conditions, including changes in liquid water content (LWC) along and downwind of the seeding track, wind speed, and shear. More AgI did not necessarily produce more liquid equivalent snowfall (LESnow). The greatest amount of LESnow, largest area covered by snowfall, and highest peak snowfall produced through seeding occurred on the day with the largest and most widespread occurrence of supercooled drizzle, highest wind shear, and greater LWC along and downwind of the seeding track. The day with the least supercooled drizzle and the lowest LWC downwind of the seeding track produced the smallest amount of LESnow through seeding. The stronger the wind, the farther away the snowfall occurred from the seeding track.

Analysis of aerosol-cloud interactions and their implications for precipitation formation using aircraft observations over the United Arab Emirates

Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of science flights targeted summertime convection along the eastern Hajar mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 °C) up to the capping inversion level (−12 °C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the sub-cloud region. Sub-cloud aerosol sizes are shown to extend from submicron to 100 µm sizes, with higher concentrations of ultra-giant CCN (d >10 µm) from local sources closer to the Saudi border, compared to the eastern orographic region where smaller size CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision-coalescence (C-C) process is not observed within the limited depths of warm cloud (< 1000 m). The state-of-the-art observations presented in this paper can be used to initialize modelling case studies to study the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud-seeding on these clouds.

Employing a shadowgraph imaging technique for cloud microphysical measurements on a mountain observatory

&lt;p&gt;Microphysical properties of cloud droplets, such as droplet size distribution and droplet&lt;br&gt;number concentration have been studied after performing a series of field experiments in&lt;br&gt;summer 2019 at Umweltforschungsstation Schneefernerhaus (UFS), an environmental&lt;br&gt;research station located just below the peak of Zugspitze in the German Alps.&lt;br&gt;&amp;#8220;VisiSize D30&amp;#8221; manufactured by Oxford Laser Ltd. is a shadowgraph imaging instrument&lt;br&gt;utilized for the first time to measure the size and velocity of cloud droplets during this&lt;br&gt;campaign. It applies a method called &amp;#8220;Particle/Droplet Image Analysis&amp;#8221; (PDIA) which&lt;br&gt;involves illuminating the region of interest from behind with an infrared pulse laser whilst&lt;br&gt;collecting shadow images of droplets passing through the measurement volume with a&lt;br&gt;high-resolution camera. Droplets detected inside the depth of field are then measured&lt;br&gt;based on their shadow images, and size distribution is built by analyzing a series of&lt;br&gt;images. Furthermore, while turbulent orographic clouds passing our measurement site&lt;br&gt;at UFS observatory during the campaign, a Phase Doppler Interferometer (PDI) device,&lt;br&gt;manufactured by Artium Tech. Inc., was also constantly measuring droplets passing&lt;br&gt;through its probe volume.&lt;br&gt;Analysis of simultaneously collected data from the two instruments, and applying&lt;br&gt;modifications to the original algorithms illustrate a reasonable agreement regarding the&lt;br&gt;droplet sizing and velocimetry between VisiSize D30 and PDI, at least for diameters&lt;br&gt;larger than 13 &amp;#956;m. Moreover, discrepancies have been observed concerning the&lt;br&gt;droplet number concentration results, especially in smaller sizes. Further investigation&lt;br&gt;by applying appropriate filters on data has allowed the attribution of discrepancies to&lt;br&gt;the different optical performance of the sensors regarding small droplets, and to high&lt;br&gt;turbulent velocity fluctuations relative to the mean flow resulting in an uncertain estimate&lt;br&gt;of the volume of air passing through the PDI probe volume.&lt;/p&gt;

Evaluation of Orographic Cloud Seeding Using a Bin Microphysics Scheme: Three-Dimensional Simulation of Real Cases

The University of Pécs and NCAR Bin (UPNB) microphysical scheme was implemented into the mesoscale Weather Research and Forecast (WRF) Model that was used to study the impact of silver iodide (AgI) seeding on precipitation formation in winter orographic clouds. Four different experimental units were chosen from the Wyoming Weather Modification Pilot Project to simulate the seeding effect. The results of the numerical experiments show the following: (i) Comparisons with the soundings, snow gauges, and microwave radiometer data indicate that the three-dimensional simulations with detailed microphysics reasonably represent both the dynamics and the microphysics of real clouds. (ii) The dispersion of the AgI particles from the simulated ground-based seeding was effective because of turbulent mixing. (iii) In the investigated cases (surface temperature is less than 0°C), surface precipitation and precipitation efficiency show low susceptibility to the concentrations of cloud condensation nuclei and natural ice nucleating particles. (iv) If the available liquid water content promotes the enhancement of the number of snowflakes by diffusional growth, the surface precipitation can be increased by more than 5%. A novel parameter relevant to orographic clouds, horizontally integrated liquid water path (LWP), was evaluated to find the relation between seeding efficiency and liquid water content. The impact of seeding is negligible if the horizontal LWP is less than 0.1 mm and is apparent if the horizontal LWP is larger than 1 mm, as based on the cases investigated in this study.

Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.