POLARRIS A POLArimetric Radar Retrieval and Instrument Simulator

POLARRIS: A POLArimetric Radar Retrieval and Instrument Simulator Toshi Matsui, Code 612, NASA/GSFC and ESSIC/UMD Convective Stratiform Crystal Low density graupel Snow High-density graupel Wet snow Rain Hail Big drops Wet snow Drizzle Rain POLARRIS is a state-of-art polarimetric radar retrieval and instrumental simulator package that has been developed for evaluating CRMs and facilitates the analysis of observations and models of cloud and precipitation observing systems. It allows more comprehensive analysis of cloud, convection, and precipitation processes through better harnessing of ground-based polarimetric radars and cloud-resolving models (CRMs). The Stacked frequency by altitude diagrams shown above summarize the relative frequency of each identified hydrometeor type at each height in the convective (left) and stratiform (right) portions of deep convective systems beyond the capability of current space-born satellite observation.

Name: Toshi Matsui, NASA/GSFC, Code 612 and ESSIC UMD E-mail: Toshihisa. Matsui-1@nasa. gov Phone: 301 -614 -5658 References: Matsui, T. , Dolan, B. , Rutledge, S. A. , Tao, W. ‐K. , Iguchi, T. , Barnum, J. , & Lang, S. E. ( 2019). POLARRIS: A POLArimetric Radar Retrieval and Instrument Simulator. Journal of Geophysical Research: Atmospheres, 124. https: //doi. org/10. 1029/2018 JD 028317 Data Sources: the U. S. Department of Energy (DOE) C-band scanning precipitation radar (CSAPR) during the NASA-DOE Midlatitude Continental Convective Clouds Experiment (MC 3 E) field campaign Technical Description of Figures: Graphic 1: HID algorithms retrieve bulk hydrometeor classes for given ranges of polarimetric radar observables. Polariemtric radar HID classes include drizzle (DZ), rain (RN), ice crystals (IC), dry snow (DS), wet snow (WS), vertical ice (VI), low-density graupel (LDG), high-density graupel (HDG), hail (HA), and “big” drops (BD). The Stacked frequency by altitude diagrams” (SFADs) of the HID can summarize the relative frequency of each identified hydrometeor type at each height in the convective (left) and stratiform (right) portions of deep convective systems. Scientific significance, societal relevance, and relationships to future missions: Understanding of deep convective clouds is important for weather, climate, and society in terms of intense rainfall, water resource, agriculture, severe weather damages to name a few. Cloud-resolving models (CRMs) have been and will continue to be important tools to understand these process. Consequently, the establishment of robust frameworks to evaluate their dynamical and microphysical outputs is critical, and the widespread emergence of ground-based polarimetric radars hasprovided such an opportunity. Toward the goal of more comprehensive model evaluation, data assimilation, and polarimetric radar retrieval development, a systematicframework for a polarimetric simulator is required, including a fast and accurate forward model as well as a rigorous inverse component for linking polarimetric observables with retrieved geophysical parameters. To this end, a synthetic polarimetric radar simulator and retrieval package, POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS), has been developed for evaluating CRMs. The POLARRIS can be applied wide range of CRMs and polarimetric radars, such as NASA’s S-band dual-POLarimetric radar (N-POL) and the Dual-frequency Dual-polarized Doppler Radar (D 3 R) at the Wallops Flight Facility (WFF). These are the first steps to establishing a semi-permanent super site for cloud, convection, and precipitation processes at WFF with polarimetric radar measurements, operational CRM simulations, and the POLARRIS package in order to support the Aerosols – Clouds, Convection, and Precipitation (ACCP) satellite mission. Earth Sciences Division - Atmospheres

Understanding Volcanic Plume Evolution from Space V. Flower, Code 613, USRA, NASA/GSFC; R. Kahn Code 613, NASA/GSFC Satellite Retrievals of Volcanic Plumes Plume Concentration (AOD) and Particle Size (REPS) Particle Dispersion Processes Regime 1 - Uniform particle deposition Total-AOD: Decreasing REPS: Relatively constant Regime 2 – Size-selective particle deposition Total-AOD: Decreasing REPS : Decreasing Regime 3 - Particle aggregation Regime 4 - Particle deposition with aggregation Total-AOD: Constant Total-AOD: Decreasing REPS : Increasing Key: Formation of ‘small’ size aerosols Small from gaseous emissions Medium Small particles aggregated to ‘medium’ size Large Medium particles aggregated to ‘large’ size As they are transported downwind, the particles in volcanic plumes are affected by processes such as deposition, aggregation and new particle formation. Analyzing changes in Aerosol Optical Depth (AOD) and Retrieved Effective Particle Size (REPS) from Multi-angle Imaging Spectro. Radiometer (MISR) observations allows us to infer the characteristics of plume dispersion. We find that MISR-derived dispersion regimes correlate with meteorological conditions (e. g. wind shear, atmospheric stability), underscoring the influence of environmental factors on plume dispersion.

Name: Verity J. B. Flower, NASA/GSFC, Code 613, USRA/GESTAR E-mail: verity. j. flower@nasa. gov Phone: 301 -614 -6236 References: Flower, V. J. , & Kahn, R. A. (2020). Interpreting the volcanological processes of Kamchatka, based on multi-sensor satellite observations. Remote Sensing of Environment, 237, 111585. DOI: 10. 1016/j. rse. 2019. 111585. Data Sources: MISR data available from NASA’s Langley Research Center (La. RC) Atmospheric Science Data Center (ASDC) data repository (https: //l 0 dup 05. larc. nasa. gov/MISR/cgi-bin/MISR/main. cgi). The MISR 774 -mixture Research Algorithm (RA) developed and provided by J. Limbacher and R. Kahn at NASA Goddard Space Flight Center. NCEP Reanalysis data was compiled for ambient meteorological assessment provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https: //www. esrl. noaa. gov/psd/. Technical Description of Figures: Graphic 1: MISR Research Aerosol (RA) retrieval algorithm results for the October 5, 2007 Karymsky volcano plume. A) Contour map of MISR-derived Aerosol Optical Depth (AOD) and B) Map of MISR RA Retrieved Effective Particle Size (REPS) fraction for aggregated ~10 km plume regions. This shows a preferential reduction in medium-large particles relative to small-medium particles as the plume is transported. Bubble size represents the number of MISR RA retrievals in each ~10 km region and color intensity indicates the fraction of that region attributed to the dominant component. Graphic 2: Schematic diagrams of processes influencing particle variations in volcanic plumes that can be inferred from remote sensing. Changes in Total. AOD (Aerosol Optical Depth) and corresponding changes in Retrieved Effective Particle Size (REPS) are detailed for each regime. Note: We designate plumes that show no apparent downwind change in Total-AOD or REPS as Regime 0 (not shown). Scientific significance, societal relevance, and relationships to future missions: Volcanic emissions represent a major source of atmospheric aerosols that can have regional to global impacts. The extent to which volcanic aerosols disperse depends on the size and composition of the eruption (e. g. Graphic 1). However, atmospheric dynamics can mitigate, or exacerbate, the impact of volcanic particles. Assessing particle dispersion with these remote sensing techniques can improve our understanding, and ability to predict, the hazards posed by volcanic eruptions. A comprehensive evaluation of volcanic eruptions in Kamchatka (Russia) identified that plumes with size-independent particle loss (Graphic 2: Regime 1) occurred in the least stable atmospheric conditions, suggesting that turbulent atmospheric conditions force particles out of suspension, irrespective of size. However, when plumes are lofted into a more stable layer, gravitational settling dominates, and preferentially removes larger particles (e. g. Graphic 2: Regime 2). Particle aggregation is inferred for plumes displaying a stable aerosol concentration and increasing particle size (e. g. Graphic 2: Regime 3). Aggregation can occur along with particle deposition (e. g. Graphic 2: Regime 4), indicated by an overall decrease in particle concentration as particle size increases. When aggregation is also occurring, we cannot distinguish from the space-borne observations alone between size-selective and size-independent particle deposition. However, the differences in meteorological conditions (e. g. atmospheric stability, wind shear) observed between Regime 1 and Regime 2 can be used as a guide to infer the likely depositional processes even when aggregation is also occurring. Shifting particle size and concentration in the initial volcanic emissions can be identified by wildly varying signals across the plume length, but can also be more subtle, masking plume evolution signals. Understanding the meteorological characteristics driving plume dispersion can help refine atmospheric dispersion models, which are used to track the geographic extent and regional or global impact of volcanic emissions, for use in air quality and other disaster-response applications. Improved understanding of the extent and persistence of airborne volcanic particles also helps global climate system and radiation budget modeling. Earth Sciences Division - Atmospheres