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At least 757 records · Page 42
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Community Radiative Transfer Model: Implementing A New Cloud Scattering Database and Developing the Forward Radar Module

The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promises in calculating the scattering properties of particles with different shapes in the microwave frequencies. The goal of the research was to enhance the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. Given that such optical properties cannot be practically calculated on the fly, pre-computed look-up tables need to be implemented into the fast RT models to calculate the scattering properties of these particles using the DDA technique and the inputs provided by the users. Therefore, we implemented such pre-computed DDA databases into CRTM. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system will be used to evaluate the scattering improvements. Additionally, we used the backscattering information from the DDA database to implement a radar simulator into CRTM. The radar operators takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗
NASA NTRS2023

Know Before You Go: A Community-Derived Approach to Planning for and Preventing Sexual Harassment at Oceanographic Field Sites

Sexual harassment is a pervasive problem on oceanographic research vessels and while conducting fieldwork in general. A variety of factors contribute to inadequate protection against sexual harassment, such as poor training in prevention, support, and response; the remoteness of field sites; academic hierarchies that reinforce uneven power dynamics that extend to fieldwork; and multi-institutional teams with distinct policies or reporting structures that can lead to confusion in reporting and responding to incidents in the field. In compromising individuals’ physical and mental health, sexual harassment can negatively affect research expeditions. For example, harassed individuals may decide to refrain from working on complicated team-based tasks, which can be a safety issue. A broader concern is that sexual harassment deters talented people from pursing or maintaining employment in ocean science. Harassment must be treated with the same gravity as research misconduct and safety policy infringements. When planning a research expedition, science team leaders are responsible for the safety of their team and other colleagues aboard and would benefit from resources aimed at helping team leadership create a plan to ensure safety and inclusivity. To address this resource gap and support consistent safety preparedness, 18 participants in the Workshop to Promote Field Safety in Ocean Sciences, convened by the Consortium for Ocean Leadership and held May 17-18, 2022, in Washington, D.C., developed a checklist for use by scientific leaders and others to assist in planning for participant safety and to prevent harassment the field. The checklist specifies the timing of, and who is responsible for, specific actions that should be taken to improve safety while conducting fieldwork, whether on a research vessel or on land. It also provides additional resources and suggestions for leaders on how to amend the checklist to address their specific fieldwork situations.

Oceanography↗
NASA NTRS2022

Determination of Chlorophyll Content in Selected Grass Communities of Krkonoše Mts. Tundra Based on Laboratory Spectroscopy and Aerial Hyperspectral Data

The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) – spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec® mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R 2 for all indices was 0.52 for all species together at leaf level and median R 2 = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R 2 was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.

L. Cervena↗