Assessing Impact of Joint Actuator Failure on Lunar Rover Mobility
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We used the EVAv6.0 system to estimate the present (2015) and future (2015–2050) global PM 2.5 and O 3 -related premature mortalities, using simulated surface concentrations from the GISS-E2.1-G Earth system model. The PM 2.5 -related global premature mortality is estimated to be 4.3 and 4.4 million by the non-linear and linear models, respectively. Ischemic heart diseases are found to be the leading cause of PM 2.5 -related premature deaths, contributing by 35% globally. Both long-term and short-term O 3 -related premature deaths are estimated to be around 1 million, globally. Overall, PM 2.5 and O 3 -related premature mortality leads to 5.3–5.4 million premature deaths, globally. The global burden of premature deaths is mainly driven by the Asian region, which in 2015 contributes by 75% of the total global premature deaths. An increase from 6.2% to 8% in the PM 2.5 relative risk as recommended by the WHO leads to an increase of PM 2.5 -related premature mortality by 28%, to 5.7 million. Finally, bias correcting the simulated PM 2.5 concentrations in 2015 leads to an increase of up to 73% in the global PM 2.5 -related premature mortality, leading to a total number of global premature deaths of up to 7.7 million, implying the necessity of bias correction to get more robust health burden estimates. PM 2.5 and O 3 -related premature mortality in 2050 decreases by up to 57% and 18%, respectively, due to emission reductions alone. However, the projected increase and aging of the population leads to increases of premature mortality by up to a factor of 2, showing that the population exposed to air pollution is more important than the level of air pollutants, highlighting that the population dynamics should be considered when setting up health assessment systems.
Climate induced sea-level rise poses a risk to coastal areas on the Island of Hawai’i, and many of the island’s historic cultural lands are in danger of becoming overtaken by wetlands or inundation. In partnership with the County of Hawai’i, State of Hawai’i Department of Land and Natural Resources, and Arizona State University, NASA DEVELOP mapped wetland extent and short-term sea-level rise inundation risk. We utilized Earth observations over a 10-year span (2013 – 2022) that included the NASA MEaSUREs Gridded Sea Surface Height Anomalies and MEaSUREs Group for High Resolution Sea Surface Temperature datasets, United States Geological Survey (USGS) Hawaii Digital Elevation Models (DEM), and in situ tidal gauge data. Flood risk index values were acquired for 5 known Hawai’i flood events between 2019 – 2021 from the Global Flood Mapper tool on Google Earth Engine. We used a random forest model to predict short-term sea-level rise inundation risk along the entire coast of Hawai’i. Current wetland extents and probabilistic locations of new wetlands were modeled with the most recently available data from PlanetScope Surface Reflectance optical imagery (2022), USGS 3D Elevation Program (3DEP) 10m DEM (2020), temperature and precipitation data from the Hawai’i Climate Atlas, and soils data from the Hawai’i Soil Atlas (2014) using the Wetland Intrinsic Potential tool. Results indicated locations that had the highest probability of wetland creation. The end products aimed to help the partners prioritize efforts to meeting regulation requirements for wetlands protection, evaluate the inundation risk to historical features, and support decision-making for their Shoreline Setback and Climate Adaption plans.
Satellite radar retrievals of clouds and precipitation rely on assumptions about the microphysical properties of hydrometeors such as particle size distributions (PSDs) and massdimensional relationships, which have been shown to vary in distinct environments. A deeper understanding of linkages between mass-dimensional relationships, ice crystal shape, and radar reflectivities across cloud and precipitation regimes is necessary for developing new retrievals for upcoming satellite radar missions, including the INvestigation of Convective UpdraftS (INCUS), as well as future missions that will feature space-borne radars observing clouds, convection, and precipitation. This study investigates ice microphysical characteristics through aircraft in-situ and remote sensing observations from two convective events during the Mid-latitude Continental Convective Clouds Experiment (MC3E) in the Southern Great Plains, and tests implications for ice particle habit in forward modeled radar observations. During MC3E, the Ka/Ku- band High Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) was onboard the NASA ER-2 aircraft, while in-situ measurements were taken onboard the University of North Dakota Citation. In-situ PSDs and particle shape information from optical array probe data are used to forward model radar reflectivities from particle scattering databases and are compared against HIWRAP reflectivities. Various particle combinations can be used to find agreement with HIWRAP observations, and the forward modeled reflectivities were sensitive to particle type. Varying the riming on the aggregate and graupel particles resulted in larger than 10 dB differences in reflectivities. The in-situ and radar observations imply the occurrence of aggregation, size sorting and lofting particles.
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To inhibit damage and limit the probability of penetration of the Space Station pressure wall by micrometeoroids and orbital debris, a shield placed away from the wall is used to form a double wall. To determine shield effectiveness and assess impact damage, existing test data were reviewed and additional testing was performed for Space Station double wall designs. Empirical spallation and penetration functions derived from the data show that shield thickness and impact angle affect the damage to the wall. Thick shields reduce wall damage for low angle impacts but increase damage for oblique impacts. Multilayer insulation between the shield and wall reduces impact damage to the wall. A relationship between impact velocity and spall damage to the wall is demonstrated. Preliminary test results on Li-Al shield material indicate possible improved effectiveness over Al shields.
This presentation summarizes impact risk assessment results for Epoch 3 of the 2023 PDC hypothetical asteroid impact scenario. Epoch 3 represents the assessment phase after data is received from a fast fly-by reconnaissance mission, which refines direct size estimates, asteroid type, and impact location range.
Performing impact risk assessment for the 2017 Planetary Defense Conference (PDC17) hypothetical impact exercise, to take place at the PDC17 conference, May 15-20, 2017. Impact scenarios and trajectories are developed and provided by NASA's Near Earth Objects Office at JPL (Paul Chodas). These results represent purely hypothetical impact scenarios, and do not reflect any known asteroid threat. Risk assessment was performed using the Probabilistic Asteroid Impact Risk (PAIR) model developed by the Asteroid Threat Assessment Project (ATAP) at NASA Ames Research Center. This presentation includes sample results that may be presented or used in discussions during the various stages of the impact exercisecenter dot Some cases represent alternate scenario options that may not be used during the actual impact exercise at the PDC17 conference. Updates to these initial assessments and/or additional scenario assessments may be performed throughout the impact exercise as different scenario options unfold.
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Climate change (CC) is likely to affect the thousands of bar-built or barrier estuaries (here referred to as Small tidal inlets - STIs) around the world. Any such CC impacts on the stability of STIs, which governs the dynamics of STIs as well as that of the inlet-adjacent coastline, can result in significant socio-economic consequences due to the heavy human utilisation of these systems and their surrounds. This article demonstrates the application of a process based snap-shot modelling approach, using the coastal morphodynamic model Delft3D, to 3 case study sites representing the 3 main STI types; Permanently open, locationally stable inlets (Type 1), Permanently open, alongshore migrating inlets (Type 2) and Seasonally/Intermittently open, locationally stable inlets (Type 3). The 3 case study sites (Negombo lagoon - Type 1, Kalutara lagoon - Type 2, and Maha Oya river - Type 3) are all located along the southwest coast of Sri Lanka. After successful hydrodynamic and morphodynamic model validation at the 3 case study sites, CC impact assessment are undertaken for a high end greenhouse gas emission scenario. Future CC modified wave and riverflow conditions are derived from a regional scale application of spectral wave models (WaveWatch III and SWAN) and catchment scale applications of a hydrologic model (CLSM) respectively, both of which are forced with IPCC Global Climate Model output dynamically downscaled to approximately 50 km resolution over the study area with the stretched grid Conformal Cubic Atmospheric Model CCAM. Results show that while all 3 case study STIs will experience significant CC driven variations in their level of stability, none of them will change Type by the year 2100. Specifically, the level of stability of the Type 1 inlet will decrease from 'Good' to 'Fair to poor' by 2100, while the level of (locational) stability of the Type 2 inlet will also decrease with a doubling of the annual migration distance. Conversely, the stability of the Type 3 inlet will increase, with the time till inlet closure increasing by approximately 75%. The main contributor to the overall CC effect on the stability of all 3 STIs is CC driven variations in wave conditions and resulting changes in longshore sediment transport, not Sea level rise as commonly believed.
Probabilistic Asteroid Impact Risk (PAIR) - PDC (Planetary Defense Conference) 2019 Hypothetical Exercise, NASA Ames, Asteroid Threat Assessment Project (ATAP) - Characterization Summary and Updates: Assessment date: 19 April 2027; Impact date: 29 April 2027 (10 days); Earth impact probability: 100 percent, New York Area; Diameter (meters): 60 plus or minus 10 (1 minus sigma), range 26–93; Energy: mean 11 megatons, range 650 kilotons to 46 megatons; Type: S class, remaining chunk of disrupted contact binary. Risk Summary: Affected population: mean 2.4 million, range zero to 10.2 million; Likely airburst at approximately16 kilometers altitude (8 to 29 kilometers); Blast overpressure is primary hazard; Damage out to approximately 70 kilometers if larger, lower burst; Little-to-no damage if burst is small and high.
The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.
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