Joseph Hardin

The Green Ocean: Precipitation Insights from the GoAmazon2014/5 Experiment

This study summarizes the precipitation properties collected during the GoAmazon2014/5 campaign near Manaus in central Amazonia, Brazil. Precipitation breakdowns, summary radar rainfall relationships and self-consistency concepts from a coupled disdrometer and radar wind profiler measurements are presented. The properties of Amazon cumulus and associated stratiform precipitation are discussed, including segregations according to seasonal (wet or dry regime) variability, cloud echo-top height and possible aerosol influences on the apparent oceanic characteristics of the precipitation drop size distributions. Overall, we observe that the Amazon precipitation straddles behaviors found during previous U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program tropical deployments, with distributions favoring higher concentrations of smaller drops than ARM continental examples. Oceanic-type precipitation characteristics are predominantly observed during the Amazon wet seasons. An exploration of the controls on wet season precipitation properties reveals that wind direction, compared with other standard radiosonde thermodynamic parameters or aerosol count/regime classifications performed at the ARM site, provides a good indicator for those wet season Amazon events having an oceanic character for their precipitation drop size distributions.

An Analysis of Coordinated Observations from NOAA’s Ronald H. Brown Ship and G-IV Aircraft in a Landfalling Atmospheric River over the North Pacific during CalWater-2015

AbstractTo gain a more complete observational understanding of atmospheric rivers (ARs) over the data-sparse open ocean, a diverse suite of mobile observing platforms deployed on NOAA’s R/V Ronald H. Brown (RHB) and G-IV research aircraft during the CalWater-2015 field campaign was used to describe the structure and evolution of a long-lived AR modulated by six frontal waves over the northeastern Pacific during 20–25 January 2015. Satellite observations and reanalysis diagnostics provided synoptic-scale context, illustrating the warm, moist southwesterly airstream within the quasi-stationary AR situated between an upper-level trough and ridge. The AR remained offshore of the U.S. West Coast but made landfall across British Columbia where heavy precipitation fell. A total of 47 rawinsondes launched from the RHB provided a comprehensive thermodynamic and kinematic depiction of the AR, including uniquely documenting an upward intrusion of strong water vapor transport in the low-level moist southwesterly flow...

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

C louds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real-world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors that are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators,” which convert model variables into pseudoinstrument observations, has evolved with the goal to facilitate and improve the comparison of modeled clouds with observations. Many simulators have been (and continue to be) developed for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the GCM community to exploit satellite observations for model cloud evaluation (e.g., Kay et al. 2012; Klein et al. 2013; Suzuki et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to the radar measurements made by CloudSat [a satellite carrying the first spaceborne 94-GHz (3.2-mm wavelength) cloud radar], which provides near-global sampling of profiles of cloud condensate and precipitation with a vertical resolution of 500 m (Stephens et al. 2002), ARM radar measurements occur with higher temporal resolution (10 s) and finer vertical resolution (45 m). This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to both surface contamination (Mace et al. 2007; Marchand et al. 2008) and a radar sensitivity of approximately −28 dBZ near the surface. Therefore, the ARM ground-based cloud observations complement measurements from space.

Structure and Evolution of Mesoscale Convective Systems: Sensitivity to Cloud Microphysics in Convection‐Permitting Simulations Over the United States

Regional climate simulations over the continental United States were conducted for the 2011 warm season using the Weather Research and Forecasting model at convection-permitting resolution (4 km) with two commonly used microphysics parameterizations (Thompson and Morrison). Sensitivities of the simulated mesoscale convective system (MCS) properties and feedbacks to large-scale environments are systematically examined against high-resolution geostationary satellite and 3-D mosaic radar observations. MCS precipitation including precipitation amount, diurnal cycle, and distribution of hourly precipitation intensity are reasonably captured by the two simulations despite significant differences in their simulated MCS properties. In general, the Thompson simulation produces better agreement with observations for MCS upper level cloud shield and precipitation area, convective feature horizontal and vertical extents, and partitioning between convective and stratiform precipitation. More importantly, Thompson simulates more stratiform rainfall, which agrees better with observations and results in top-heavier heating profiles from robust MCSs compared to Morrison. A stronger dynamical feedback to the large-scale environment is therefore seen in Thompson, wherein an enhanced mesoscale vortex behind the MCS strengthens the synoptic-scale trough and promotes advection of cool and dry air into the rear of the MCS region. The latter prolongs the MCS lifetimes in the Thompson relative to the Morrison simulations. Hence, different treatment of cloud microphysics not only alters MCS convective-scale dynamics but also has significant impacts on their macrophysical properties such as lifetime and precipitation. As long-lived MCSs produced 2–3 times the amount of rainfall compared to short-lived ones, cloud microphysics parameterizations have profound impact in simulating extreme precipitation and the hydrologic cycle. Plain Language Summary Massive thunderstorms over the Great Plains of the United States have become more frequent and more intense in the past decades. As Earth continues to warm, changes in the characteristics of these massive thunderstorms, which often cause flooding and severe wind damage, have major societal implications. Climate models with spatial resolution comparable to weather forecasting models can now be used to simulate the complex physics in storms and reproduce their climatological properties. However, details of how to represent the cloud microphysical processes remain uncertain, with potential implications for long-term simulation of climate in regions of convective storms. This study examines the uncertainties associated with cloud microphysics of convective storms in the central United States by using two different microphysical representations and comparing results with a warm-season satellite and radar observations. Microphysical processes leading to a broader and more realistic storm rainfall areas favor prolonged lifetime of the storms and thus have greater effects on the evolution of the large-scale circulation and greater potential for storms producing floods, factors important for evaluating the effects of convective storms in a changing climate.

ARM: X-Band Scanning ARM Cloud Radar (XSACR) RHI Scan

X-Band Scanning ARM Cloud Radar (XSACR) RHI Scans, which can vary in elevation range and azimuth

ARM: X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scan

X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)

ARM: Ka-Band Scanning ARM Cloud Radar (KASACR) RHI scans, which can vary in elevation range and azimuth

Ka-Band Scanning ARM Cloud Radar (KASACR) RHI scans, which can vary in elevation range and azimuth

ARM: Ka-Band Scanning ARM Cloud Radar (KASACR) Hemispherical Sky RHI Scan

Ka-Band Scanning ARM Cloud Radar (KASACR) Hemispherical Sky RHI Scan (6 horizon-to-horizon scans at 30-degree azimuth intervals)

ARM: Ka ARM Zenith Radar (KAZR): filtered spectral data, moderate sensitivity mode, cross-polarized mode

Ka ARM Zenith Radar (KAZR): filtered spectral data, moderate sensitivity mode, cross-polarized mode

ARM: W-Band Scanning ARM Cloud Radar (W-SACR) Hemispherical Sky RHI Scan

W-Band Scanning ARM Cloud Radar (W-SACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)