SummaryConvective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and subsequent impacts on the hydrologic cycle. Global observation and accurate representation of these processes in numerical models is vital to improving our current understanding and future simulations of Earth's climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales that are associated with convective and stratiform precipitation processes; therefore, they must turn to parameterization schemes to represent these processes. In turn, the physical basis for these parameterization schemes needs to be evaluated for general application under a variety of atmospheric conditions. Analogously, space-based remote sensing algorithms designed to retrieve related cloud and precipitation information for use in hydrological, climate, and numerical weather prediction applications often rely on physical "parameterizations" that reliably translate indirectly related instrument measurements to the physical quantity of interest (e.g., precipitation rate). Importantly, both spaceborne retrieval algorithms and model convective parameterization schemes traditionally rely on field campaign data sets as a basis for evaluating and improving the physics of their respective approaches.The Midlatitude Continental Convective Clouds Experiment (MC3E) will take place in central Oklahoma during the April-May 2011 period. The experiment is a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The field campaign leverages the unprecedented observing infrastructure currently available in the central United States, combined with an extensive sounding array, remote sensing and in situ aircraft observations, NASA GPM ground validation remote sensors, and new ARM instrumentation purchased with American Recovery and Reinvestment Act funding. The overarching goal is to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land that have never before been available. Several different components of convective cloud and precipitation processes tangible to both the convective parameterization and precipitation retrieval algorithm problem are targeted, such as preconvective environment and convective initiation, updraft/downdraft dynamics, condensate transport and detrainment, precipitation and cloud microphysics, spatial and temporal variability of precipitation, influence on the environment and radiation, and a detailed description of the large-scale forcing.MC3E will use a new multi-scale ...

In situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in situ data collected during different field projects processed by different groups must be used. However, because of the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses. Methods used to process data from bulk water probes, single-particle light-scattering spectrometers and cloud-imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats, and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in situ data include the following: establishment of a common reference library of individual processing algorithms, better documentation of assumptions used in these algorithms, development and maintenance of sustainable community software for processing in situ observations, and more studies that compare different algorithms with the same benchmark datasets.

Abstract. Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here, we derive hygroscopic aerosol size distribution input profiles from ground-based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of an input profile in simulations of the only well-observed case study that produced extensive stratiform outflow on 20 May 2011. At well-sampled elevations between −11 and −23 °C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near ∼ 400 µm in randomly oriented maximum dimension (Dmax). The ice mass at −23 °C is primarily in a closely collocated mode, whereas a mass mode near Dmax ∼ 1000 µm becomes dominant with decreasing elevation to the −11 °C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak Dmax by a factor of 3–5 and underpredict ice number concentration by a factor of 4–10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that updraft microphysical pathways determining outflow properties in the 20 May case are similar to a tropical regime, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

Goodbye Intuition (2017 - 2020) is an artistic research project on improvisation. With Goodbye Intuition we seek to challenge our roles and artistic preferences as improvising musicians by improvising with "creative" machines. In our project the machines can both take the role of a performer for us to play with and they can be extensions of our own instruments. They can become both our duet partners and they can be additions, expansions or augmentations of our sound. Playing is core in our investigation, and it is based on this experience we try to articulate thoughts and answers to the following questions: - How do we improvise with "creative" machines, how do we listen, how do we play? - How will improvising within an interactive human-machine domain challenge our roles as improvisers? - What music emerge from the human-machine improvisatory dialogue? The project’s artists are Andrea Neumann (GER), Morten Qvenild (NOR) and Ivar Grydeland (NOR). The artist Sidsel Endresen (NOR) is our observer, commentator, critic and discussion partner. Additionally, musician, composer and researcher Henrik Frisk (SWE), writer, musician, composer David Toop (UK), musician and researcher Anna Lindal (S), musician Lasse Marhaug (N), Christian Wallumrød Ensemble (N), and director and writer Annie Dorsen (US) contributes to the project. Humans at Norwegian Center for Technology in Music and the Arts (NOTAM) are our technological collaborators.

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