Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

[1] Different turbulent entrainment-mixing processes (e.g., homogeneous and inhomogeneous) occur in clouds; accurate representation of these processes is critical for improving cloud-related parameterizations in large-scale models, but poorly understood and quantified. Using in situ aircraft observations over the U. S. Department of Energy's Atmospheric Radiation Measurement Southern Great Plains site during the March 2000 Cloud Intensive Observation Period and numerical simulations with the Explicit Mixing Parcel Model (EMPM), here we explore the potential of using degree of homogeneous mixing as a measure to quantify these different mechanisms and examine various microphysical measures of homogeneous mixing degree and their relationships to entrainment-mixing dynamics as measured by transition scale numbers. Three different microphysical measures for the homogeneous mixing degree are newly defined and each is coupled with one of two different transition scale numbers. Both observations and simulations show that all the combinations have positive correlated relationships; simulations further show that the tightest relationship is between the measure of homogeneous mixing degree considering adiabatic number concentration and the transition scale number accounting for mixing fraction of dry air. A parameterization of the entrainment-mixing processes is advanced according to the relationships of homogeneous mixing degree measures to transition scale numbers.

CAPSULE SUMMARY A regional-scale observational experiment designed to address how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes.

38Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely 39 correspond to the limited capability of current earth system models to capture and simulate 40 land-atmosphere feedback. To facilitate its realistic simulation in next generation models, an 41 improved process understanding of the related complex interactions is essential. To this end, 42 accurate 3D observations of key variables in the land-atmosphere (L-A) system with high 43 vertical and temporal resolution from the surface to the free troposphere are indispensable. 44Recently, we developed a synergy of innovative ground-based, scanning active remote sens-45 ing systems for 2D to 3D measurements of wind, temperature, and water vapor from the sur-46 face to the lower troposphere that is able to provide comprehensive data sets for characteriz-47 Motivation 71The land-atmosphere (L-A) system includes the soil, the land cover such as vegetation, and 72 the overlying atmosphere. The interaction of variables, e.g. related to the water and energy 73 budgets, results in characteristic natural variabilities and regimes as well as their changes due 74 to anthropogenic influences. The planetary boundary layer (PBL) is part of the L-A system 75 and represents the interface between the land surface and the free troposphere. Through an 76 exchange of momentum, energy and water, the dynamics, the thermodynamic structure, and 77 the evolution of the PBL affect the formation of shallow and deep clouds, convection initia-78 tion, and thus precipitation (Sherwood et al. 2010, Behrendt et al. 2011, Santanello et al. 79 2011, van den Hurk et al. 2011. One of the most complex feedback 80 loops is between soil moisture and precipitation (Seneviratne et al. 2010, Guillod et al. 2015, 81 Santanello et al. 2017). Precipitation can be influenced directly by the surface fluxes (Ek and 82Holtslag 2004), and indirectly via PBL dynamics and mesoscale circulations (Taylor et al. 83 2012). 84The PBL state and its evolution are strongly influenced by non-linear feedbacks in the L-A 85 system. These are due to two-way interactions between radiation, soil, vegetation, and atmos-86 pheric variables, which result in the diurnal cycles of surface fluxes. The feedbacks are rele-87 vant from local to global scales (Mahmood et al. 2013, Stéfanon et al. 2014, and their 88 strength varies both regionally and seasonally in dependence of soil moisture, advection, and 89 climate regimes. In locations where these feedbacks play an important role, it is likely that 90 they will become even more important due to anthropogenic climate change (Dirmeyer et al. 91 2012). Thus, to improve our understanding of the state and the evolution of the L-A system as 92 well as the dynamics and thermodynamics of the PBL, it is critical that feedbacks and fluxes 93 between the different components, including entrainment at the top of the PBL, are well char-94 4 acterized and appropriately represented in weather, climate, and earth system models (e.g., 95 Se...

Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.