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1. A Case Study of Cloud-Top Kelvin–Helmholtz Instability Waves near the Dendritic Growth Zone

   https://doi.org/10.1175/JAS-D-21-0106.1  

   Cann, M. D. ; Friedrich, K. ; French, J. R. ; Behringer, D. ( February 2022 , Journal of the Atmospheric Sciences)

   Kelvin–Helmholtz instability (KH) waves have been broadly shown to affect the growth of hydrometeors within a region of falling precipitation, but formation and growth from KH waves at cloud top needs further attention. Here, we present detailed observations of cloud-top KH waves that produced a snow plume that extended to the surface. Airborne transects of cloud radar aligned with range height indicator scans from ground-based precipitation radar track the progression and intensity of the KH wave kinetics and precipitation. In situ cloud probes and surface disdrometer measurements are used to quantify the impact of the snow plume on the composition of an underlying supercooled liquid water (SLW) cloud and the snowfall observed at the surface. KH wavelengths of 1.5 km consisted of ∼750-m-wide up- and downdrafts. A distinct fluctus region appeared as a wave-breaking cloud top where the fastest updraft was observed to exceed 5 m s⁻¹. Relatively weaker updrafts of 0.5–1.5 m s⁻¹beneath the fluctus and partially overlapping the dendritic growth zone were associated with steep gradients in reflectivity of −5 to 20 dBZ_ein as little as 500-m depths due to rapid growth of pristine planar ice crystals. The falling snow removed ∼80% of the SLW content frommore »the underlying cloud and led to a twofold increase in surface liquid equivalent snowfall rate from 0.6 to 1.3 mm h⁻¹. This paper presents the first known study of cloud-top KH waves producing snowfall with observations of increased snowfall rates at the surface.

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2. The FARM (Flexible Array of Radars and Mesonets)

   https://doi.org/10.1175/BAMS-D-20-0285.1  

   Wurman, Joshua ; Kosiba, Karen ; Pereira, Brian ; Robinson, Paul ; Frambach, Andrew ; Gilliland, Alycia ; White, Trevor ; Aikins, Josh ; Trapp, Robert J. ; Nesbitt, Stephen ; et al ( April 2021 , Bulletin of the American Meteorological Society)

   Abstract The Flexible Array of Radars and Mesonets (FARM) Facility is an extensive mobile/quickly-deployable (MQD) multiple-Doppler radar and in-situ instrumentation network. The FARM includes four radars: two 3-cm dual-polarization, dual-frequency (DPDF), Doppler On Wheels DOW6/DOW7, the Rapid-Scan DOW (RSDOW), and a quickly-deployable (QD) DPDF 5-cm COW C-band On Wheels (COW). The FARM includes 3 mobile mesonet (MM) vehicles with 3.5-m masts, an array of rugged QD weather stations (PODNET), QD weather stations deployed on infrastructure such as light/power poles (POLENET), four disdrometers, six MQD upper air sounding systems and a Mobile Operations and Repair Center (MORC). The FARM serves a wide variety of research/educational uses. Components have deployed to >30 projects during 1995-2020 in the USA, Europe, and South America, obtaining pioneering observations of a myriad of small spatial and temporal scale phenomena including tornadoes, hurricanes, lake-effect snow storms, aircraft-affecting turbulence, convection initiation, microbursts, intense precipitation, boundary-layer structures and evolution, airborne hazardous substances, coastal storms, wildfires and wildfire suppression efforts, weather modification effects, and mountain/alpine winds and precipitation. The radars and other FARM systems support innovative educational efforts, deploying >40 times to universities/colleges, providing hands-on access to cutting-edge instrumentation for their students. The FARM provides integrated multiple radar, mesonet, sounding,more »and related capabilities enabling diverse and robust coordinated sampling of three-dimensional vector winds, precipitation, and thermodynamics increasingly central to a wide range of mesoscale research. Planned innovations include S-band On Wheels NETwork (SOWNET) and Bistatic Adaptable Radar Network (BARN), offering more qualitative improvements to the field project observational paradigm, providing broad, flexible, and inexpensive 10-cm radar coverage and vector windfield measurements.« less
3. Microphysical Characteristics and Evolution of Seeded Orographic Clouds

   https://doi.org/10.1175/JAMC-D-20-0206.1  

   Friedrich, Katja ; French, Jeffrey R. ; Tessendorf, Sarah A. ; Hatt, Melinda ; Weeks, Courtney ; Rauber, Robert M. ; Geerts, Bart ; Xue, Lulin ; Rasmussen, Roy M. ; Blestrud, Derek R. ; et al ( July 2021 , Journal of Applied Meteorology and Climatology)

   Abstract 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 themore »seeding track produced the smallest amount of LESnow through seeding. The stronger the wind was, the farther away the snowfall occurred from the seeding track.« less
4. Stochastic Spectral Method for Radar-Based Probabilistic Precipitation Nowcasting

   https://doi.org/10.1175/JTECH-D-18-0242.1  

   Pulkkinen, Seppo ; Chandrasekar, V. ; Harri, Ari-Matti ( June 2019 , Journal of Atmospheric and Oceanic Technology)

   Nowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppressing small-scale precipitation features that have low predictability. Building on the earlier work, Stochastic DARTS (S-DARTS), a probabilistic extension of DARTS, is developed and tested using the CASA DFW radar network. In this method, the nowcasts are stochastically perturbed in order to simulate uncertainties. Two novel features are introduced in S-DARTS. First, the scale filtering and perturbation based on an autoregressive model are done in the spectral domain in order to achieve high computational efficiency. Second, this methodology is extended to modeling the temporal evolution of the advection field. The performance and forecast skill of S-DARTS are evaluated with different precipitation intensity thresholds and ensemble sizes. It is shown that S-DARTS can producemore »reliable probabilistic nowcasts in the CASA DFW domain with 250-m spatial resolution up to 45 min for lower precipitation intensities (below 2 mm h⁻¹). For higher intensities (above 5 mm h⁻¹), adequate skill can be obtained up to 15 min.

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5. Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments

   https://doi.org/10.5194/amt-15-4931-2022  

   Oue, Mariko ; Saleeby, Stephen M. ; Marinescu, Peter J. ; Kollias, Pavlos ; van den Heever, Susan C. ( January 2022 , Atmospheric Measurement Techniques)

   Abstract. Optimizing radar observation strategies is one of the mostimportant considerations in pre-field campaign periods. This is especiallytrue for isolated convective clouds that typically evolve faster than theobservations captured by operational radar networks. This study investigatesuncertainties in radar observations of the evolution of the microphysicaland dynamical properties of isolated deep convective clouds developing inclean and polluted environments. It aims to optimize the radar observationstrategy for deep convection through the use of high-spatiotemporalcloud-resolving model simulations, which resolve the evolution of individualconvective cells every 1 min, coupled with a radar simulator and a celltracking algorithm. The radar simulation settings are based on the TrackingAerosol Convection Interactions ExpeRiment (TRACER) and Experiment of SeaBreeze Convection, Aerosols, Precipitation and Environment (ESCAPE) fieldcampaigns held in the Houston, TX, area but are generalizable to other fieldcampaigns focusing on isolated deep convection. Our analysis produces thefollowing four outcomes. First, a 5–7 m s−1 median difference inmaximum updrafts of tracked cells is shown between the clean and pollutedsimulations in the early stages of the cloud lifetimes. This demonstratesthe importance of obtaining accurate estimates of vertical velocity fromobservations if aerosol impacts are to be properly resolved. Second,tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitationmore »particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than oneradar in addition to the operational volume scans. We also propose the useof a single-RHI (range height indicator) updraft retrieval technique for cellsclose to the radars, for which multi-Doppler radar retrievals are stillchallenging. Finally, increasing the number of deep convective cells sampledby such observations better represents the median maximum updraft evolutionwith sample sizes of more than 10 deep cells, which decreases the errorassociated with sampling the true population to less than 3 m s−1.« less