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NASA GES DISC Giovanni: Current and Future
Giovanni (Geospatial Interactive Online Visualization and Analysis Infrastructure), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), has established a reputation among NASA users for easy access, analysis, and visualization of NASA Earth science data. Currently, Giovanni supports over 1900 variables in eight disciplinary areas. Like any other enterprise application, Giovanni faces big data challenges, such as servicing increasingly large data volumes and more complex data types, while at the same time addressing the demands of a more diverse user community, e.g., placing requests for long-term time series from multiple spatially and temporally dense data records. I will present how Giovanni has been evolving from an on-premises, monolithic software application towards a cloud-enabled implementation to address these challenges.
Cloud Detection over Sunglint Regions with Observations from the Earth Polychromatic Imaging Camera (EPIC)
With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤ 25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764nm to 780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4~10% and 4~6% in the glint center and granule edges respectively. The false positive rate is reduced by 10~17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.
Developing the CRTM Active Sensor Module
Active sensors provide vertically resolved atmospheric and cloud information, however the assimilation of such observations into NWP models has been limited for several reasons including lack of reliable forward model. We present the development of CRTM active sensor module including its adjoint and tangent linear by taking advantage of current CRTM modules for calculating atmospheric transmittance and cloud absorption and scattering. Current CRTM cloud coefficients lack cloud backscattering information, thus we have implemented a new cloud scattering database generated using the discrete dipole technique that include backscattering coefficients. 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.
Sensitivity of the ACCP Value Framework Science Benefit Score
NASA’s priorities for Earth Science are informed by the 2017-2027 Decadal Survey for Earth Science and Applications from Space of the National Academies of Sciences, Engineering and Medicine. In that document, five Designated Observables are identified as priorities for implementation: Aerosols, Cloud-Convection-Precipitation, Surface Biology, Mass Change, and Surface Deformation Change. In 2019, NASA initiated four multi-year studies to formulate, assess, and recommend potential Observing System concepts that are responsive to the recommendations of the Earth Science Decadal Survey. The Aerosols, Clouds, Convection, and Precipitation (ACCP) study investigates opportunities for synergistic science resulting from implementations that combine both Aerosols science objectives and Clouds, Convection, and Precipitation science objectives. For the ACCP study, a Value Framework was designed to introduce structure, transparency, and traceability in the mission down-select decision process. This paper presents a sensitivity analysis that was conducted to characterize the behavior of the science benefit score of the ACCP Value Framework. it includes derivations of the sensitivity analysis equations, numerical analyses of the behavior of the combined benefit score, and the conclusions drawn from the results of the work that was conducted. The results of this analysis were key to ensuring that the aggregated science benefit scores would enable the team to differentiate and contrast across concepts.
OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds
Sea spray aerosol is one of the major sources of atmospheric particulate matter globally. It has increasingly been recognized that organic matter derived from ocean biological precursors contributes significantly to the composition of submicron sea spray and may modify sea spray aerosol impacts on clouds and climate. This paper describes the implementation of the OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) parameterization for sea spray organic aerosol emissions in a global Earth system model, the Energy Exascale Earth System Model (E3SM). OCEANFILMS is a physically based model that links sea spray chemistry with ocean biogeochemistry using a Langmuir partitioning approach. We describe the implementation details of OCEANFILMS within E3SM, compare simulated aerosol fields with observations, and investigate impacts on simulated clouds and climate. Four sensitivity cases are tested, in which organic emissions either strictly add to or strictly replace sea salt emissions (in mass and number) and are either fully internally or fully externally mixed with sea salt. The simulation with internally mixed, added organics agrees reasonably well with observed seasonal cycles of organic matter in marine aerosol and has been selected as the default configuration of the E3SM. In this configuration, sea spray organic aerosol contributes an additional source of cloud condensation nuclei, adding up to 30 cm -3 to Southern Ocean boundary-layer cloud condensation nuclei concentrations (supersaturation = 0.1 %). The addition of this new aerosol source strengthens shortwave radiative cooling by clouds by -0.36 W m -2 in the global annual mean and contributes more than -3.5 W m -2 to summertime zonal mean cloud forcing in the Southern Ocean, with maximum zonal mean impacts of about -4 W m -2 around 50–60°S. This is consistent with a previous top-down, satellite-based empirical estimate of the radiative forcing by sea spray organic aerosol over the Southern Ocean. Through its mechanistic approach, OCEANFILMS offers a path towards improved understanding of the feedbacks between ocean biology, sea spray organic matter, and climate.
The Implementation of Framework for Improvement by Vertical Enhancement Into Energy Exascale Earth System Model
Abstract The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to ameliorate the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the large eddy simulation‐like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Using FIVE to improve the representation of low‐level clouds does not come with any negative side effects associated with the simulation of mid‐ and high‐level cloud and precipitation, that can occur when running the full model at higher vertical resolution.
An ice-water saturation adjustment
A reasonably accurate and noniterative saturation adjustment scheme is proposed to calculate: (1) the amount of condensation and/or deposition necessary to remove any supersaturated vapor, or (2) the amount of evaporation and/or sublimation necessary to remove any subsaturation in the presence of cloud droplets and/or cloud ice. This proposed scheme can be implemented for a nonhydrostatic cloud model. The derivation of the scheme, an evaluation of its performance, and tests for sensitivity to variations in a few key parameters are presented.
Continuous thermostat setpoint monitoring and correction (Thermostat setpoint correction) v1.0
The Continuous Thermostat Setpoint Monitoring and Correction software is a set of fault detection and correction algorithms that can be implemented in thermostats with two-way OpenAPIs. It is written in the Python language. The algorithms aim to detect the most common and impactful efficiency problems associated with thermostat setpoints - overly aggressive heating or cooling setpoints, incorrect schedules/setbacks, and overly narrow deadbands. These algorithms can automatically detect faults, and implement associated corrective actions to bring the system back to a state of efficient operation. The algorithms can run remotely in the cloud, and directly implemented by connected thermostat manufacturers, or by third party service providers. The software enables a lightweight cost-effective energy management strategy for HVAC systems. The solution is specially viable for small and medium sized commercial buildings, where a full scale building automation system and fault detection and diagnostic tools are often unavailable.
Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer
Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.
Facilitating NASA Earth Science Data Processing Using Nebula Cloud Computing
Cloud Computing has been implemented in several commercial arenas. The NASA Nebula Cloud Computing platform is an Infrastructure as a Service (IaaS) built in 2008 at NASA Ames Research Center and 2010 at GSFC. Nebula is an open source Cloud platform intended to: a) Make NASA realize significant cost savings through efficient resource utilization, reduced energy consumption, and reduced labor costs. b) Provide an easier way for NASA scientists and researchers to efficiently explore and share large and complex data sets. c) Allow customers to provision, manage, and decommission computing capabilities on an as-needed bases
Gas-Particle Interaction Model Development in Plume Surface Interaction Erosion and Cratering
As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The GCD PSI Project, planned to be completed over a four year period, contains a Predictive Simulation Capability (PSC) Element focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the PSC Element, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper describes the activities and accomplishments of the past year in the gas-particle interaction modeling portion of the GCD PSI project. The objectives of this task are to investigate and construct models of particle drag and gas-particle cloud interactions leading to what is known as particle turbulent kinetic energy (PTKE). While drag on a lone particle suspended in a flow gas of gas is well-known, the drag and drag-induced dynamics of a cloud of particles in a gas flow are not. The PSC element of the PSI project requires models of gas-particle cloud interactions to implement in the Gas Granular Flow Solver (GGFS) in order to arrive at a predictive simulation capability for PSI-induced soil erosion, cratering and ejecta transport. Experiments of dilute mixtures of soil particles in supersonic gas flow are being conducted at Johns Hopkins University in order to provide a dataset describing gas-particle cloud interactions. Augmented by a separate funding source are efforts to perform small-scale particle-resolved direct numerical simulations (PR-DNS) and larger scale Eulerian-Lagrangian simulations. Together, these experiments and simulations are being used to devise models of particle cloud drag and particle cloud drag-induced dynamics. The final product of these efforts will be particle drag models suitable for implementation into the GGFS application and a PTKE model suitable for the same purpose.
Improving the Parameterization of Cloud and Rain Microphysics in E3SM using Novel Observationally-Constrained Bayesian Approach (Final Technical Report)
In this project, we sought to develop new cloud and rain microphysics frameworks within the Energy Exascale Earth System Model (E3SM). This work encompassed two primary avenues of research: 1) Further development of a Bayesian-based scheme called BOSS (Bayesian Observationally-constrained Statistical-physical Scheme) to represent cloud and rain microphysics, testing it in realistic high-resolution cloud models, and implementing it in E3SM; 2) Development of a methodology utilizing machine learning to enable computationally tractable use of tractable use of Markov chain Monte Carlo sampling for Bayesian parameter estimation in Earth system and cloud models. In this project, we adapted the BOSS microphysics scheme, originally formulated for rain-only, to include all liquid-phase microphysical processes for cloud and rain, in particular the processes that mediate between these two categories, for example the conversion from cloud to rain through collision and coalescence of drops. We constrained the scheme via comparison and testing against a detailed model that explicitly represents the evolution of cloud and rain particles, called a bin microphysics scheme.
Development of a Consistent Cross-Platform GEO-Satellite Cloud Mask to Support CERES
Geostationary satellites provide continuous cloud and meteorological observations over a fixed portion of the Earth’s surface, allowing them to monitor the movement of storm systems and their diurnal variation. For climate studies, geostationary observations provide valuable insight of cloud formation and evolution and how they influence the Earth’s radiation budget. The Geo-Satellite Edition 4 cloud mask (GEO Ed4) is used operationally in NASA’s Cloud and Earth’s Radiant Energy System (CERES) project to help account for diurnal variations in cloudiness on Earth’s radiation budget. The Ed4 cloud mask was applied to imager data on MSG (MeteoSat Second Generation), Himawari, GOES-West, and GOES-East satellites using as much spectral information as possible. That approach led to some discontinuities when a more modern satellite replaced an older satellite with less spectral information. A different strategy is being investigated for the CERES GEO Ed5 cloud mask that only uses spectral channels common on every satellite. Thus, a 3-channel (0.6, 3.9, 11 µm) algorithm for daytime cloud detection, and a 2-channels (3.9 and 11 µm) algorithm for nighttime cloud detection have been implemented and tested for Ed5. The goal of Ed5 cloud mask is to achieve global cloud amount consistency across five geo-satellites, and a smooth transition from an old satellite to a new satellite over each geo-location. This paper compares cloud mask results between Ed4 and a preliminary Ed5 version over the GOES-East region, using GOES-8, GOES-13, and GOES-16 satellites, each with a different spectral channel complement. Instantaneous and monthly regional mean inter-comparisons are performed and evaluated with CALIPSO cloud products to assess the accuracy and consistency of the Ed5 approach relative to that taken in Ed4. Results will be presented and discussed at the conference.
Description and Evaluation of a New Deep Convective Cloud Model Considering In-cloud Inhomogeneity
Convections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in-cloud inhomogeneities, a new convective cloud model explicitly considering in-cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub-plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single-column tests show a stronger mass flux profile and low-level detrainment with the new model. In global simulations, the long-lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is slightly over-enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.
Constraints on Mars Aphelion Cloud Belt phase function and ice crystal geometries
This study constrains the lower bound of the scattering phase function of Martian water ice clouds (WICs) throughthe implementation of a new observation aboard the Mars Science Laboratory (MSL). The Phase Function SkySurvey (PFSS) was a multiple pointing all-sky observation taken with the navigation cameras (Navcam) aboardMSL. The PFSS was executed 35 times during the Aphelion Cloud Belt (ACB) season of Mars Year 34 over a solarlongitude range of Ls = 61:4 156:5. Twenty observations occurred in the morning hours between 06:00 and 09:30 LTST, and 15 runs occurred in the evening hours between 14:30 and 18:00 LTST, with an operationallyrequired 2.5 h gap on either side of local noon due the sun being located near zenith. The resultant WIC phasefunction was derived over an observed scattering angle range of 18.3-152.61, normalized, and compared with 9modeled phase functions: seven ice crystal habits and two Martian WIC phase functions currently being implementedin models. Through statistical chi-squared probability tests, the five most probable ice crystal geometriesobserved in the ACB WICs were aggregates, hexagonal solid columns, hollow columns, plates, and bullet rosetteswith p-values greater than or equal to 0.60, 0.57,0.56,0.56, and 0.55, respectively. Droxtals and spheres had pvaluesof 0.35, and 0.2, making them less probable components of Martian WICs, but still statistically possibleones. Having a better understanding of the ice crystal habit and phase function of Martian water ice cloudsdirectly benefits Martian climate models which currently assume spherical and cylindrical particles.
Cybersecurity Standards, Certification, and Best Practices for DERs
Distributed energy resources (DERs) are becoming increasingly important to the electric grid, including solar energy systems. However, DERs also introduce new cybersecurity risks, including those posed by cloud computing. Standards harmonization is essential for ensuring that DERs are secure and can be safely integrated into the grid. This panel will discuss cyber standards harmonization for solar security. The panel will feature experts from the S2G Program, National Labs and Industry who will discuss the following topics: the cybersecurity risks and future benefits posed by ubiquitous solar energy systems, the development and implementation of cloud-based security solutions for DERs, including solar energy systems, the challenges and opportunities for harmonizing DER cybersecurity standards, and Cyber Informed Engineering and the solar security implementations The panel will also discuss the following specific initiatives: the S2G Program's DER Cybersecurity Framework, UL's DER Cybersecurity Certification Program, and IEEE 1547 Updates. The panel will conclude with a discussion of the future of standards harmonization for DER cybersecurity.
Implementation on Landsat Data of a Simple Cloud Mask Algorithm Developed for MODIS Land Bands
This letter assesses the performance on Landsat-7 images of a modified version of a cloud masking algorithm originally developed for clear-sky compositing of Moderate Resolution Imaging Spectroradiometer (MODIS) images at northern mid-latitudes. While data from recent Landsat missions include measurements at thermal wavelengths, and such measurements are also planned for the next mission, thermal tests are not included in the suggested algorithm in its present form to maintain greater versatility and ease of use. To evaluate the masking algorithm we take advantage of the availability of manual (visual) cloud masks developed at USGS for the collection of Landsat scenes used here. As part of our evaluation we also include the Automated Cloud Cover Assesment (ACCA) algorithm that includes thermal tests and is used operationally by the Landsat-7 mission to provide scene cloud fractions, but no cloud masks. We show that the suggested algorithm can perform about as well as ACCA both in terms of scene cloud fraction and pixel-level cloud identification. Specifically, we find that the algorithm gives an error of 1.3% for the scene cloud fraction of 156 scenes, and a root mean square error of 7.2%, while it agrees with the manual mask for 93% of the pixels, figures very similar to those from ACCA (1.2%, 7.1%, 93.7%).