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NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗
NASA NTRSDate not supplied

Enhancing CRTM All-Sky Simulations and Implementation of A New Active Sensor Module

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. 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 promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. 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 are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator 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 NTRS2022

Polarimeter + Lidar–Derived Aerosol Particle Number Concentration

In this study, we propose a simple method to derive vertically resolved aerosol particle number concentration (Na) using combined polarimetric and lidar remote sensing observations. This method relies on accurate polarimeter retrievals of the fine-mode column-averaged aerosol particle extinction cross section and accurate lidar measurements of vertically resolved aerosol particle extinction coefficient such as those provided by multiwavelength high spectral resolution lidar. We compare the resulting lidar + polarimeter vertically resolved N(a) product to in situ N(a) data collected by airborne instruments during the NASA aerosol cloud meteorology interactions over the western Atlantic experiment (ACTIVATE). Based on all 35 joint ACTIVATE flights in 2020, we find a total of 32 collocated in situ and remote sensing profiles that occur on 11separate days, which contain a total of 322 cloud-free vertically resolved altitude bins of 150 m resolution. We demonstrate that the lidar + polarimeter N(a) agrees to within 106% for 90% of the 322 vertically resolved points. We also demonstrate similar agreement to within 121% for the polarimeter-derived column-averaged N(a). We find that the range-normalized mean absolute deviation (NMAD) for the polarimeter-derived column-averaged Na is 21%, and the NMAD for the lidar + polarimeter-derived vertically resolved Na is 16%. Taken together, these findings suggest that the error in the polarimeter-only column-averaged N(a) and the lidar + polarimeter vertically resolved N(a) are of similar magnitude and represent a significant improvement upon current remote sensing estimates of N(a).

RSP↗
NASA NTRSDate not supplied

Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales

In this study, we assimilate Soil Moisture Active Passive (SMAP) mission brightness temperature (Tb) observations into NASA’s Catchment Land Surface Model using an Ensemble Kalman filter to update surface and root-zone soil moisture simulations. Different time series components of the Tb observations are assimilated including anomalies, inter-annual variations, and high-frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the un-certainties of modeled and observed Tb is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. Results are compared to a benchmark experiment that mimics the operational SMAP Level-4 algorithm, which assimilates Tb observations using a spatially-constant 4 Kelvin (K) observation uncertainty. All DA experiments exhibit notable skill improvements in most regions. Improvements are greatest for the inter-annual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (-) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for sur-face soil moisture simulations (0.06). Strikingly, however, no notable difference in skill—neither improvement nor deterioration—is observed between the experiments that use optimized observation uncertainty parameters and the 4 K benchmark experiment. We show, analytically, that this may be explained by the presence of large observation operator errors, which have the potential to render post-update uncertainty insensitive to inaccuracies in the Kalman gain.

Alexander Gruber↗
NASA NTRSDate not supplied

Trajectory Optimization Methods and Applications at NASA Goddard

The process of spacecraft trajectory design frequently incorporates an optimization step in which one or more objectives, such as propellant consumption or time of flight, is minimized. Optimization is especially crucial for low-thrust mission design, due to the need to specify a thrust vector at every instant in time along a trajectory. At NASA's Goddard Space Flight Center (GSFC) a number of open-source tools have been developed for spacecraft trajectory optimization that utilize direct shooting and collocation methods. These tools have been effectively applied to cislunar, libration point, and interplanetary mission design for the Lunar IceCube, SWFO, and DAVINCI missions, among others. This presentation outlines the theory underlying these optimization tools along with the details of their application to several trajectory design problems, with a focus on the Lunar IceCube (LIC) mission. The LIC mission required a low-thrust trajectory from a high-energy deployment state to a lunar orbit, and its limited control authority necessitated the use of optimization tools and low-energy trajectory design techniques. The tools discussed demonstrate how the use of optimization methods expands mission capabilities and enables transformational science.

Robert Pritchett↗
NASA NTRSDate not supplied

CRTM Microwave Cloud Scattering Lookup Tables and Radar Simulator

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. 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 promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. 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 are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator 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 NTRSDate not supplied

Enhancing Radiative Transfer Models for Optimized Assimilation of Microwave and Radar Observations

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRS2020

Clouds Around the World: How a Simple Citizen Science Data Challenge Became a Worldwide Success

Citizen science is often recognized for its potential to directly engage the public in science, and is uniquely positioned to support and extend participants’ learning in science. In March 2018, the Global Learning and Observations to Benefit the Environment (GLOBE) Program, NASA’s largest and longest-lasting citizen science program about Earth, organized a month-long event that asked people around the world to contribute daily cloud observations and photographs of the sky (15 March–15 April 2018). What was considered a simple engagement activity turned into an unprecedented worldwide event that garnered major public interest and media recognition, collecting over 55,000 observations from 99 different countries, in more than 15,000 locations, on every continent including Antarctica. The event was called the “Spring Cloud Challenge” and was created to 1) engage the general public in the scientific process and promote the use of the GLOBE Observer app, 2) collect ground-based visual observations of varying cloud types during boreal spring, and 3) increase the number and locations of ground-based visual cloud observations collocated with cloud-observing satellites. The event resulted in roughly 3 times more observations than during the historic and highly publicized 2017 North American total solar eclipse. The dataset also includes observations over the Drake Passage in Antarctica and reports from intense Saharan dust events. This article describes how the challenge was crafted, outreach to volunteer scientists around the world, details of the data collected, and impact of the data.

GLOBE↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗
NASA NTRSDate not supplied

Optimizing Assimilation of Microwave and Radar Observations in the NWP Models

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David M. Giles↗
NASA NTRSDate not supplied

A Comprehensive Forward Model for Spaceborne Radar Instruments

We present the development and validation of a comprehensive forward model designed to enhance remote sensing capabilities of spaceborne radar instruments. To overcome limitations in existing models, we integrated a Discrete Dipole Approximation (DDA) cloud scattering database into our Radiative Transfer Model (RTM), focusing on microwave frequencies. By simulating the optical properties of non-spherical frozen hydrometeors, the DDA technique effectively reduced discrepancies between simulated and observed values, surpassing traditional Mie tables. The evaluation of DDA lookup tables involved comparisons with a collocated dataset comprising short-term forecasts and satellite microwave data, providing evidence of their superiority. Additionally, we address the challenges of assimilating active radar measurements, which offer vertically resolved insights into clouds and precipitation. We explored the assimilation of spaceborne radar measurements in Numerical Weather Prediction (NWP) models by integrating a forward radar model, along with its adjoint and tangent linear, into the data assimilation system. Evaluation using CloudSat measurements demonstrated promising agreement between simulations and observations, particularly when the input hydrometeor profiles aligned with the measured reflectivity profiles, showcasing the potential of the developed forward radar model. Moreover, we discuss other challenges in radar measurement assimilation within NWP models, including potential observation errors and biases.

Isaac Moradi↗
NASA NTRSDate not supplied

The effects of preconditioning on the summer sea ice thickness evolution during MOSAiC

The central role of sea ice within the Arctic climate system is clear, as is the importance of sea ice properties in the Arctic Ocean’s evolution and response to climate change. Less clear is how the history of the sea ice floe (in winter and spring) precondition the summer thickness evolution. Accurate accounting for the collective influences of processes that affect the summer sea ice thickness evolution is necessary for determining sea ice survivability and the rate of future sea ice loss. In this study, we use a satellite-derived sea ice parcel database co-located with the MOSAiC floe to explore the factors that precondition the summer sea ice thickness evolution. First. we compare the >500 collocated satellite-tracked sea ice parcels that intersected the MOSAiC drift track with observations to evaluate the satellite-derived surface energy budget and surface properties, finding agreement with correlations >0.9. Approximately 60% of the satellite tracked parcels survived the summer melt season. Secondly, we explore the spread in sea ice thickness and survivability across the satellite-tracked parcels, finding contributions from spring/summer surface albedo evolution, surface temperature, clouds, and parcel sea ice concentration. The satellite tracked sea ice parcel analysis provides insights into the spatial coherence of the factors that influence sea ice thickness evolution.

Arctic↗
NASA NTRSDate not supplied

Geodetic Reference Instrument Transponder for Small Satellites (Gritss)

The accuracy of the Terrestrial Reference Frame is limited by systematic errors in tying the contributions from the different geodetic techniques. Local survey uncertainties are limited due to the inaccessibility of the instrument reference points. NASA is implementing a technology demonstration mission called Geodetic Reference Instrument Transponder for Small Satellites(GRITSS) that uses a small satellite as a space-based ref-refence point for tying together collocated geodetic stations. The technology being demonstrated uses a novel idea of upconverting the Global Positioning System (GPS) signals received at the satellite and transponding them to a VLBI Global Observing System (VGOS) antenna ground station. This approach does not require the satellite to be in view of more than one VLBI station at a time, allowing the use of Low Earth Orbits and an inexpensive CubeSat. The demonstration will initially involve only the NASA VGOS stations in Maryland, Hawaii, and Texas, but may be expanded later in the mission to include other international VGOS stations. This presentation will provide an overview of the GRITSS mission and the measurement concept.

S M Merkowitz↗
NASA NTRSDate not supplied

Geodetic Reference Instrument Transponder for Small Satellites (GRITTS)

The accuracy of the Terrestrial Reference Frame is limited by systematic errors in tying the contributions from the different geodetic techniques. Local survey uncertainties are limited due to the inaccessibility of the instrument reference points. NASA is implementing a technology demonstration mission called Geodetic Reference Instrument Transponder for Small Satellites(GRITSS) that uses a small satellite as a space-based ref-refence point for tying together collocated geodetic stations. The technology being demonstrated uses a novel idea of upconverting the Global Positioning System (GPS) signals received at the satellite and transponding them to a VLBI Global Observing System (VGOS) antenna ground station. This approach does not require the satellite to be in view of more than one VLBI station at a time, allowing the use of Low Earth Orbits and an inexpensive CubeSat. The demonstration will initially involve only the NASA VGOS stations in Maryland, Hawaii, and Texas, but may be expanded later in the mission to include other international VGOS stations. This presentation will provide an overview of the GRITSS mission and the measurement concept.

S. M. Merkowitz↗
NASA NTRS2024

Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible-Near-Infrared to Thermal Infrared

Super-coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super-coarse dust accounts for less dust extinction in the visible-to-near-infrared (VIS-NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS-NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET-retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super-coarse dust in the AERONET retrievals due to the limited VIS-NIR sensitivity. An extra super-coarse mode included in the AERONET-retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS-NIR.

thermal infrared (TIR)↗
NASA NTRSDate not supplied

Assimilation of Spaceborne Microwave and Radar Observations

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRSDate not supplied

Open-Source Science-Driven Development of the Science Data System (SDS) for Earth System Observatory (ESO) Atmospheric Missions

The NASA Earth System Observatory (ESO) atmospheric missions will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The Science Data System (SDS) will deploy the adaptive processing system (APS) developed within the Cloud to manage the research and operational processing of ESO atmospheric mission orbital and suborbital sensors and curate these data for near real-time and collection reprocessing and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage and distribution. Further, the SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The SDS follows NASA’s commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the SDS system components will be developed with open-source concepts including components of APS itself as well as ESO atmospheric mission algorithms. This presentation describes the framework of the SDS and its integral part in facilitating OSS within the ESO atmospheric missions.

David M. Giles↗