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NASA NTRS2016

Safety Characteristics in System Application of Software for Human Rated Exploration Missions for the 8th IAASS Conference

NASA and its industry and international partners are embarking on a bold and inspiring development effort to design and build an exploration class space system. The space system is made up of the Orion system, the Space Launch System (SLS) and the Ground Systems Development and Operations (GSDO) system. All are highly coupled together and dependent on each other for the combined safety of the space system. A key area of system safety focus needs to be in the ground and flight application software system (GFAS). In the development, certification and operations of GFAS, there are a series of safety characteristics that define the approach to ensure mission success. This paper will explore and examine the safety characteristics of the GFAS development. The GFAS system integrates the flight software packages of the Orion and SLS with the ground systems and launch countdown sequencers through the 'agile' software development process. A unique approach is needed to develop the GFAS project capabilities within this agile process. NASA has defined the software development process through a set of standards. The standards were written during the infancy of the so-called industry 'agile development' movement and must be tailored to adapt to the highly integrated environment of human exploration systems. Safety of the space systems and the eventual crew on board is paramount during the preparation of the exploration flight systems. A series of software safety characteristics have been incorporated into the development and certification efforts to ensure readiness for use and compatibility with the space systems. Three underlining factors in the exploration architecture require the GFAS system to be unique in its approach to ensure safety for the space systems, both the flight as well as the ground systems. The first are the missions themselves, which are exploration in nature, and go far beyond the comfort of low Earth orbit operations. The second is the current exploration system will launch only one mission per year even less during its developmental phases. Finally, the third is the partnered approach through the use of many different prime contractors, including commercial and international partners, to design and build the exploration systems. These three factors make the challenges to meet the mission preparations and the safety expectations extremely difficult to implement. As NASA leads a team of partners in the exploration beyond earth's influence, it is a safety imperative that the application software used to test, checkout, prepare and launch the exploration systems put safety of the hardware and mission first. Software safety characteristics are built into the design and development process to enable the human rated systems to begin their missions safely and successfully. Exploration missions beyond Earth are inherently risky, however, with solid safety approaches in both hardware and software, the boldness of these missions can be realized for all on the home planet.

capability↗

NASA NTRS2016

Safety Characteristics in System Application Software for Human Rated Exploration

exploration↗

NASA NTRS2015

The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4: Technical Report Series on Global Modeling and Data Assimilation - Volume 38

Biomass burning is an important source of particulates and trace gases and a major element of the terrestrial carbon cycle. Well constrained emissions from vegetation fires are needed to model direct and indirect effects of biomass burning aerosols, to model homogeneous and heterogeneous chemistry in the atmosphere, and to perform credible Earth system analysis, and climate and air pollution studies. To improve the performance of NASA Goddard Earth Observing System Model (GEOS) in the areas of atmospheric constituent modeling with a focus on biomass burning we developed the Quick Fire Emissions Dataset (QFED). The QFED emissions are based on the fire radiative power (top-down) approach and draw on the cloud correction method developed in the Global Fire Assimilation System (GFAS). Location and fire radiative power of fires are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 fire products (MOD14 and MYD14) and the MODIS Geolocation products (MOD03 and MYD03). QFED strengths are high spatial and temporal resolutions and near-real time availability. Daily mean emissions are available at 0.3125 times 0.25 degrees and in recent versions also at 0.1 times 0.1 degrees. QFED provides emissions of black carbon, organic carbon, sulfur dioxide, carbon monoxide, carbon dioxide, PM2.5, ammonia, nitrogen oxides, methyl ethyl ketone, propylene, ethane, propane, n- and i-butane, acetaldehyde, formaldehyde, acetone and methane. Two QFED product systems are maintained by the NASA Global Modeling and Assimilation Office (GMAO): one that produces near real-time daily emissions used operationally in the GEOS-5 Data Assimilation System, and one that produces an extended historical dataset with daily emissions from March 2000 to the present. The historical dataset also provides monthly mean and monthly climatological emissions.

Koster, Randal D.↗

NASA NTRS2020

Ensemble PM2.5 Forecasting during the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model

Biomass burning releases a vast amount of aerosols into the atmosphere, often leading to severe air quality and health problems. Prediction of the air quality effects from biomass burning emissions is challenging due to uncertainties in fire emission, plume rise calculation, and other model inputs/processes. Ensemble forecasting is increasingly used to represent model uncertainties. In this paper, an ensemble forecast was conducted to predict surface PM2.5 during the 2018 California Camp Fire event using the NOAA HYSPLIT dispersion model at 0.1- degree horizontal resolution. Different combinations of four satellite-based fire emission datasets (FEER, FLAMBE, GBBEPx and GFAS), two plume rise schemes (Briggs and Sofiev), various meteorology inputs and model setup options were used to create the forecast ensemble, for a total of 112 experiments. The performance of each ensemble member and the ensemble mean were evaluated using ground-based observations, with four statistical metrics and an overall rank. The ensemble spread of the 112 members reached 1000 μg/m3, highlighting the large uncertainty in wildfire forecast. The ensemble mean displayed the best performance. Each fire emission product contributed to one or more members among the top ten performers, revealing the forecasting dependence on both the quality of fire emissions data and model representation of emission, transport and removal processes. In addition, an ensemble size reduction technique was introduced. With the help of this technique, the ensemble size was reduced from 112 to 28 members and still produced an ensemble mean that yielded comparable or even better performance to that of the full ensemble.

Biomass Burning↗