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.

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft.A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including: numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

Drought stress and hurricane defoliation influence mountain clouds and moisture recycling in a tropical forest

Mountain ranges generate clouds, precipitation, and perennial streamflow for water supplies, but the role of forest cover in mountain hydrometeorology and cloud formation is not well understood. In the Luquillo Experimental Forest of Puerto Rico, mountains are immersed in clouds nightly, providing a steady precipitation source to support the tropical forest ecosystems and human uses. A severe drought in 2015 and the removal of forest canopy (defoliation) by Hurricane Maria in 2017 created natural experiments to examine interactions between the living forest and hydroclimatic processes. These unprecedented land-based observations over 4.5 y revealed that the orographic cloud system was highly responsive to local land-surface moisture and energy balances moderated by the forest. Cloud layer thickness and immersion frequency on the mountain slope correlated with antecedent rainfall, linking recycled terrestrial moisture to the formation of mountain clouds; and cloud-base altitude rose during drought stress and posthurricane defoliation. Changes in diurnal cycles of temperature and vapor-pressure deficit and an increase in sensible versus latent heat flux quantified local meteorological response to forest disturbances. Temperature and water vapor anomalies along the mountain slope persisted for at least 12 mo posthurricane, showing that understory recovery did not replace intact forest canopy function. In many similar settings around the world, prolonged drought, increasing temperatures, and deforestation could affect orographic cloud precipitation and the humans and ecosystems that depend on it.

Numerical Simulations of Orographic Convection across Multiple Gray Zones

Idealized simulations are used to determine the sensitivity of moist orographic convection to horizontal grid spacing Δh. In simulated mechanically (MECH) and thermally (THERM) forced convection over an isolated ridge, Δh is varied systematically over both the deep-convection (Δh ~ 10–1 km) and turbulence (Δh ~ 1 km–100 m) gray zones. To aid physical interpretation, a new parcel-based bulk entrainment/detrainment diagnosis for horizontally heterogeneous flows is developed. Within the deep-convection gray zone, the Δh sensitivity is dominated by differences in parameterized versus explicit convection; the former initiates convection too far upstream of the ridge (MECH) and too early in the diurnal heating cycle (THERM). These errors stem in part from a large underprediction of parameterized entrainment and detrainment. Within the turbulence gray zone, sensitivities to Δh arise from the representation of both subcloud- and cloud-layer turbulence. As Δh is decreased, MECH exhibits stronger cloud-layer entrainment to enhance the convective mass flux Mco, while THERM exhibits stronger detrainment to suppress Mco and delay convection initiation. The latter is reinforced by increased subcloud turbulence at smaller Δh, which leads to drying and diffusion of the central updraft responsible for initiating moist convection. Numerical convergence to a robust solution occurs only in THERM, which develops a fully turbulent flow with a resolved inertial subrange (for Δh ≤ 250 m). In MECH, by contrast, turbulent transition occurs within the orographic cloud, the details of which depend on both physical location and Δh.

Quantifying snowfall from orographic cloud seeding

<p>Cloud seeding has been used as one water management strategy to overcome the increasing demand for water despite decades of inconclusive results on the efficacy of cloud seeding. In this study snowfall accumulation from glaciogenic cloud seeding is quantified based on snow gauge and radar observations from three days in January 2017, when orographic clouds in the absent of natural precipitation were seeded with silver iodide (AgI) in the Payette basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). On each day, a seeding aircraft equipped with AgI flares flew back and forth on a straight-line flight track producing a zig-zag pattern representing two to eight lines of clouds visible through enhancements in radar reflectivity. As these seeding lines started to form precipitation, they passed over several snow gauges and through the radar observational domain. For the three cases presented here, precipitation gauges measured increases between 0.05-0.3 mm as precipitation generated by cloud seeding pass over the instruments. A variety of relationships between radar reflectivity factor and liquid equivalent snowfall rate were used to quantify snowfall within the radar observation domain. For the three cases, snowfall occurred within the radar observational domain between 25 -160 min producing a total amount of water generated by cloud seeding ranging from 123,220 to 339,540 m3 using the best-match Ze-S relationship. Uncertainties in radar reflectivity estimated snowfall are provided by considering not only the best-match Ze-S relationship but also an ensemble of Ze-S relationships based on the range of coefficients published from previous studies and then examining the percentile of snowfall estimates based on all of the Ze-S relationships within the ensemble. Considering the interquartile range and 5<sup>th</sup>/95<sup>th</sup> percentiles, uncertainties in total amount of water generated by cloud seeding can range between 20-45% compared to the best-math estimates. These results provide new insights towards understanding how cloud seeding impacts precipitation and its distribution across a region.</p>

Effect of Aerosol Particles on Orographic Clouds: Sensitivity to Autoconversion Schemes

<p><span>Aerosol particles can serve as cloud condensation nuclei (CCN) to influence orographic clouds. Autoconversion, which describes the initial formation of raindrops from the collision of cloud droplets, is an important process for aerosol–cloud–precipitation systems. In this study, seven autoconversion schemes are used to investigate the impact of CCN on orographic warm-phase clouds. As the initial cloud droplet concentration is increased from 100 cm<sup>−3</sup> to 1000 cm<sup>−3</sup> (to represent an increase in CCN), the cloud water increases and then the rainwater is suppressed due to a decrease in the autoconversion rate, leading to a spatial shift in surface precipitation. Intercomparison of the results from the autoconversion schemes show that the sensitivity of cloud water, rainwater, and surface precipitation to a change in the concentration of CCN is different from scheme to scheme. In particular, the decrease in orographic precipitation due to increasing CCN is found to range from −87% to −10% depending on the autoconversion scheme. Moreover, the surface precipitation distribution also changes significantly by scheme or CCN concentration, and the increase in the spillover (ratio of precipitation on the leeward side to total precipitation) induced by increased CCN ranges from 10% to 55% under different autoconversion schemes. The simulations suggest that autoconversion parameterization schemes should not be ignored in the interaction of aerosol and orographic cloud.</span></p>

Quantifying snowfall from orographic cloud seeding

Climate change and population growth have increased demand for water in arid regions. For over half a century, cloud seeding has been evaluated as a technology to increase water supply; statistical approaches have compared seeded to nonseeded events through precipitation gauge analyses. Here, a physically based approach to quantify snowfall from cloud seeding in mountain cloud systems is presented. Areas of precipitation unambiguously attributed to cloud seeding are isolated from natural precipitation (<1 mm h−1). Spatial and temporal evolution of precipitation generated by cloud seeding is then quantified using radar observations and snow gauge measurements. This study uses the approach of combining radar technology and precipitation gauge measurements to quantify the spatial and temporal evolution of snowfall generated from glaciogenic cloud seeding of winter mountain cloud systems and its spatial and temporal evolution. The results represent a critical step toward quantifying cloud seeding impact. For the cases presented, precipitation gauges measured increases between 0.05 and 0.3 mm as precipitation generated by cloud seeding passed over the instruments. The total amount of water generated by cloud seeding ranged from 1.2 × 105 m3 (100 ac ft) for 20 min of cloud seeding, 2.4 × 105 m3 (196 ac ft) for 86 min of seeding to 3.4 x 105 m3 (275 ac ft) for 24 min of cloud seeding.