Search for: All records

This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using a pseudo-global warming approach. Climate perturbations for the future climate are given by the CMIP5 ensemble-mean global climate models under the high-end emission scenario. The study analyzes the projected changes in spatial patterns of seasonal precipitation and snowpack, with particular emphasis on the effects of elevation on orographic precipitation and snowpack changes in four key mountain ranges: the Montana Rockies, Greater Yellowstone area, Wasatch Range, and Colorado Rockies. The IWUS simulations reveal an increase in annual precipitation across the majority of the IWUS in this warmer climate, driven by more frequent heavy to extreme precipitation events. Winter precipitation is projected to increase across the domain, while summer precipitation is expected to decrease, particularly in the High Plains. Snow-to-precipitation ratios and snow water equivalent are expected to decrease, especially at lower elevations, while snowpack melt is projected to occur earlier by up to 26 days in the ~2050 climate, highlighting significant impacts on regional water resources and hydrological management. 

Abstract It is not uncommon for layers within the warm conveyor belt in a frontal system to become potentially unstable, releasing elevated convection. The present study examines this destabilization process over complex terrain, and resulting precipitation, with a focus on the surface coupling, orographic ascent, and the initiation and evolution of convective cells. This study uses detailed observations combined with numerical modeling of a baroclinic system passing over the Idaho Central Mountains in the United States on 7 February 2017. The data were collected as part of the Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE). Specifically, observations from a ground-based scanning X-band radar and an airborne profiling Doppler W-band radar along ~100 km long flight tracks aligned with the wind describe the development and evolution of convective cells above shallow stratiform orographic clouds. Convection-permitting numerical simulations of this event, with an inner domain grid resolution of 0.9 km, capture the emergence and vertical structure of the convective cells. Therefore, they are used to describe the advection of warm, moist air over a retreating warm front, cold air pooling within the Snake River Basin and adjacent valleys, destabilization in a moist layer above this shallow stable layer, and instability release in orographic gravity wave updrafts. In this case, the convective cells topped out near 6 km ASL, and the resulting precipitation fell mostly leeward of the ridge where convection was triggered, on account of strong cross-barrier flow. Sequential convection initiation over terrain ridges and rapid downwind transport led to banded precipitation structures. 

Abstract Cloud seeding of wintertime orographic clouds in the western United States has been attempted to enhance snow production and snowpack. Due to the scarcity of long-term, high-resolution cloud and precipitation observations over complex terrain, few studies have explored variations in orographic snowfall amounts by comparing environmental conditions and cloud characteristics with surface snowfall distribution and quantity. This study analyzes the environmental conditions and cloud characteristics in relation to surface snowfall patterns for the 24 snowfall events observed during the 2017 Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The investigation aims to understand: 1) What is the influence, if any, of wind, turbulence, and updraft strength on snowfall amounts, rates, and distribution? 2) What is the relationship, if any, of cloud properties and precipitation-forming effectiveness? and 3) Can cloud seeding modify controlling cloud characteristics sufficiently to increase precipitation in otherwise inefficient orographic clouds? The analysis over a 7200-km²observational domain revealed that the accumulated liquid-equivalent snowfall was <0.9 × 10⁷m³and snowfall rates were <0.45 mm h⁻¹for about half of the events. Low snowfall events were characterized by cloud-top temperatures >−20°C, fewer larger droplets, higher liquid water content, and lower ice water content compared to the other events. Cases with minimal background natural snowfall also permitted radar observation of seeding lines. In these cases, cloud seeding was mainly responsible for snowfall. The amount of silver iodide (AgI) released during cloud seeding did not correlate well with snowfall amount and rate. Significance StatementThis study illustrates the complexities of estimating snowfall in wintertime orographic clouds, underscoring the frequent inefficiency of these clouds in generating snowfall—a pivotal concern for regions dependent on snowpack for water resources. By analyzing environmental and cloud characteristics against snowfall patterns during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE), the research provides critical insights into the complexities of precipitation formation. The findings, particularly on the impact of cloud seeding in enhancing snowfall under specific conditions, contribute significantly to our understanding of weather modification techniques. This research not only is vital for advancing scientific knowledge in understanding wintertime mountain cloud systems but also holds profound implications for water resource management, agriculture, and disaster preparedness in snow-dependent regions. 

Abstract This study examines the mesoscale structure and evolution of a polar low associated with a marine cold-air outbreak (MCAO) on 2 April 2024 over the Norwegian Sea. As part of the Cold-Air Outbreak Experiment in the Subarctic Region (CAESAR), the National Science Foundation National Center for Atmospheric Research C-130 aircraft equipped with an array of in situ and remote sensing instrumentation, including profiling radars and lidars, traversed this polar low five times, yielding detailed vertical transects of clouds and precipitation. This polar low was rather shallow and formed in the wake (not at the leading edge) of an MCAO. Observations and output from an operational convection-permitting model reveal that the polar low developed in the lee of an island, Svalbard, under deep northerly flow that roughly aligned with surface-driven baroclinicity. The polar low was marked by a region of surface-driven, mostly open-cellular precipitating convection, and a separate region of deeper stratiform clouds driven by moist-isentropic ascent in an area of suppressed surface heat fluxes. The confluence of a cold air mass from the northeast, only briefly exposed to open water, with a more mature, warmer MCAO air mass with a deeper well-mixed boundary layer previously exposed to high surface heat fluxes over the Fram Strait led to convergent, cyclonically sheared boundaries with enhanced convection. These convergent boundaries emerged as cyclonic potential vorticity streamers generated frictionally by Svalbard’s terrain, became more intense by diabatic heating in clouds, and were transported downstream into the polar low. Significance StatementPolar lows are small, intense cyclones that may cause havoc with maritime transport and may bring strong winds and heavy snowfall to coastal areas. While they are most common across the far northern Atlantic Ocean, they also occur over other high-latitude oceans. Their predictability is hampered not only by the lack of measurements at high latitudes but also by a poor understanding of their vertical structure. An airborne field campaign was conducted in early 2024 to better understand clouds in marine cold-air outbreaks, including polar lows. This study combines profiling radar and lidar data collected aboard an aircraft with operational high-resolution model data to unveil fine-scale cloud and precipitation structures of a polar low in unprecedented detail. 

Abstract Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance StatementUnderstanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies. 

Potential factors affecting the inland penetration and orographic modulation of lake-effect precipitation east of Lake Ontario include the environmental (lake, land, and atmospheric) conditions, mode of the lake-effect system, and orographic processes associated with flow across the downstream Tug Hill Plateau (herein Tug Hill), Black River valley, and Adirondack Mountains (herein Adirondacks). In this study we use data from the KTYX WSR-88D, ERA5 reanalysis, New York State Mesonet, and Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine how these factors influence lake-effect characteristics with emphasis on the region downstream of Tug Hill. During an eight-cool-season (16 November–15 April) study period (2012/13–2019/20), total radar-estimated precipitation during lake-effect periods increased gradually from Lake Ontario to upper Tug Hill and decreased abruptly where the Tug Hill escarpment drops into the Black River valley. The axis of maximum precipitation shifted poleward across the northern Black River valley and into the northwestern Adirondacks. In the western Adirondacks, the heaviest lake-effect snowfall periods featured strong, near-zonal boundary layer flow, a deep boundary layer, and a single precipitation band aligned along the long-lake axis. Airborne profiling radar observations collected during OWLeS IOP10 revealed precipitation enhancement over Tug Hill, spillover and shadowing in the Black River valley where a resonant lee wave was present, and precipitation invigoration over the western Adirondacks. These results illustrate the orographic modulation of inland-penetrating lake-effect systems downstream of Lake Ontario and the factors favoring heavy snowfall over the western Adirondacks. 

Abstract Precipitation enhancement over complex terrain is predominantly driven by quasi-stationary, terrain-tied vertical motions, making their variability a critical factor in shaping precipitation distributions and accumulation. This study quantifies the dominant modes of terrain-tied vertical motion variability over the Payette River basin of Idaho. Principal component analysis is applied to a seasonal simulation spanning November 2016–April 2017, which encompassed the Seeded and Natural Orographic Wintertime Clouds: the Idaho Experiment (SNOWIE) field campaign (January–March 2017). The first mode, accounting for more than 20% of the variance in vertical motion, captures ridge-tied updrafts and represents the primary pattern of terrain-induced ascent. The second mode (8%) reflects how synoptic-scale variations modulate updraft orientation, distinguishing between north–south and east–west ridgelines. The third mode (6%) isolates variability in updraft width and magnitude. These three dominant modes of variability, which explain over one-third of the vertical velocity variance in the seasonal simulation, strongly influence the distribution of supercooled liquid water (SLW) and precipitation over the terrain. Results show that the dominant modes of vertical motion variability were consistent with patterns commonly observed during SNOWIE research flights. Additionally, we quantified vertical motion, SLW, and precipitation means as a function of phase space between the modes, demonstrating that enhanced SLW and precipitation occurred when quasi-stationary waves were present over the terrain. 

Abstract Cold-air outbreaks over high-latitude oceans typically include mixed-phase clouds and precipitation, in particular supercooled liquid clouds that support snow and graupel through ice growth processes. The partitioning of the total water into the liquid and ice phases impacts both weather and climate prediction, but accurate measurements on the phase partitioning remain difficult to acquire, especially near–real time. Here, we present a machine learning approach to retrieve liquid water path (LWP) using passive microwave measurements combined with vertically integrated radar reflectivities. The approach is an extension of Cadeddu et al. (2009), with the novel addition of radar reflectivity. The machine learning models are trained using the Passive and Active Microwave Radiative Transfer (PAMTRA) code applied to output from numerical simulations of three independent cold-air outbreaks sampled during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. Brightness temperatures corresponding to the four sidebands of an upward-looking G-band (183 GHz) vapor radiometer, along with the vertically integrated reflectivity from a zenith-pointing 95-GHz Wyoming Cloud Radar, are simulated from the perspective of a near-surface aircraft track. The radar reflectivity helps discriminate the snow contribution to the brightness temperatures. The machine learning models are thereafter tested on a simulation of an independent cold-air outbreak during COMBLE and against measurements from the U.S. Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. This machine learning approach is shown to provide robust, computationally efficient, near-real-time measurements of LWP and water vapor path during the Cold-Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) campaign in February–April 2024. Significance StatementPrecipitation from mixed-phase clouds over the high-latitude open waters impacts shipping, fishing, and coastal communities. Mixed-phase clouds are also a modeling challenge, inhibiting weather and climate prediction skill. More comprehensive measurements of the cloud liquid and ice water path occupying the same vertical column improve our understanding and ability to represent this challenging cloud type. Here, we propose a new retrieval to determine the liquid water path within air columns that also contain ice. 

Abstract This essay is intended to provide stakeholders and news outlets with a plain-language summary of orographic cloud seeding research, new capabilities, and prospects. Specifically, we address the question of whether a widely practiced type of weather modification, glaciogenic seeding of orographic clouds throughout the cold season, can produce an economically useful increase in precipitation over a catchment-scale area. Our objective is to clarify current scientific understanding of how cloud seeding may affect precipitation, in terms that are more accessible than in the peer-reviewed literature. Public confidence that cloud seeding “works” is generally high in regions with operational seeding, notwithstanding decades of scientific reports indicating that the changes in precipitation are uncertain. Randomized seeding experiments have a solid statistical foundation and focus on the outcome, but, in light of the small seeding signal and the naturally noisy nature of precipitation, they generally require too many cases to be affordable, and therefore are discouraged. A complementary method, physical evaluation, examines changes in cloud and precipitation processes when seeding material is injected and yields insights into the most suitable ambient conditions. Recent physical evaluations have established a robust, well-documented scientific basis for glaciogenic seeding of cold-season orographic clouds to enhance precipitation. The challenge of seeding impact assessment remains, but evidence is provided that, thanks to recent significant progress in observational and computational capabilities, the research community is finally on track to be able to provide stakeholders with guidance on the likely quantitative precipitation impact of cloud seeding in their region. We recommend further process-level evaluations combined with highly resolved, well-constrained numerical simulations of seasonal cloud seeding. 

Abstract The water vapor transport associated with latent heat flux (LE) in the planetary boundary layer (PBL) is critical for the atmospheric hydrological cycle, radiation balance, and cloud formation. The spatiotemporal variability of LE and water vapor mixing ratio (r_v) are poorly understood due to the scale‐dependent and nonlinear atmospheric transport responses to land surface heterogeneity. Here, airborne in situ measurements with the wavelet technique are utilized to investigate scale‐dependent relationships among LE, vertical velocity (w) variance (), andr_vvariance () over a heterogeneous surface during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, , and at 100 m height, with a majority scale range of 120 m–4 km in LE, 32 m–2 km in , and 200 m–8 km in . The scales are classified into three scale ranges, the turbulent scale (8–200 m), large‐eddy scale (200 m–2 km), and mesoscale (2–8 km) to evaluate scale‐resolved LE contributed by and . The large‐eddy scale in PBL contributes over 70% of the monthly mean total LE with equal parts (50%) of contributions from and . The monthly temporal variations mainly come from the first two major contributing classified scales in LE, , and . These results confirm the dominant role of the large‐eddy scale in the PBL in the vertical moisture transport from the surface to the PBL, while the mesoscale is shown to contribute an additional ∼20%. This analysis complements published scale‐dependent LE variations, which lack detailed scale‐dependent vertical velocity and moisture information.