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Abstract The Indian Ocean is a frequent site for the initiation of the Madden–Julian oscillation (MJO). The evolution of convection during MJO initiation is intimately linked to the subcloud atmospheric mixed layer (ML). Much of the air entering developing cumulus clouds passes through the cloud base; hence, the properties of the ML are critical in determining the nature of cloud development. The properties and depth of the ML are influenced by horizontal advection, precipitation-driven cold pools, and vertical motion. To address ML behavior during the initiation of the MJO, data from the 2011/12 Dynamics of the MJO Experiment (DYNAMO) are utilized. Observations from the research vesselRevelleare used to document the ML and its modification during the time leading up to the onset phase of the October MJO. The mixed layer depth increased from ∼500 to ∼700 m during the 1–12 October suppressed period, allowing a greater proportion of boundary layer thermals to reach the lifting condensation level and hence promote cloud growth. The ML heat budget defines an equilibrium mixed layer depth that accurately diagnoses the mixed layer depth over the DYNAMO convectively suppressed period, provided that horizontal advection is included. The advection at theRevelleis significantly influenced by low-level convective outflows from the southern ITCZ. The findings also demonstrate a connection between cirrus clouds and their remote impact on ML depth and convective development through a reduction in the ML radiative cooling rate. The emergent behavior of the equilibrium mixed layer has implications for simulating the MJO with models with parameterized cloud and turbulent-scale motions. 

Abstract The social, economic, and ecological impacts of wildfires are increasing over much of the United States and globally, partially due to changing climate and build-up of fuels from past forest management practices. This creates a need to improve coupled fire–atmosphere forecast models. However, model performance is difficult to evaluate due to scarcity of observations for many key fire–atmosphere interactions, including updrafts and plume injection height, plume entrainment processes, fire intensity and rate-of-spread, and plume chemistry. Intensive observations of such fire–atmosphere interactions during active wildfires are rare due to the logistical challenges and scales involved. The California Fire Dynamics Experiment (CalFiDE) was designed to address these observational needs, using Doppler lidar, high-resolution multispectral imaging, and in situ air quality instruments on a NOAA Twin Otter research aircraft, and Doppler lidars, radar, and other instrumentation on multiple ground-based mobile platforms. Five wildfires were studied across northern California and southern Oregon over 16 flight days from 28 August to 25 September 2022, including a breadth of fire stages from large blow-up days to smoldering air quality observations. Missions were designed to optimize the observation of the spatial structure and temporal evolution of each fire from early afternoon until sunset during multiple consecutive days. The coordination of the mobile platforms enabled four-dimensional sampling strategies during CalFiDE that will improve understanding of fire–atmosphere dynamics, aiding in model development and prediction capability. Satellite observations contributed aerosol measurements and regional context. This article summarizes the scientific objectives, platforms and instruments deployed, coordinated sampling strategies, and presents first results. 

Abstract Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s −1 and RMSEs of 2-3 m s −1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.