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24 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

On the Relation Between Sunspot Area and Sunspot Number

Often, the relation between monthly or yearly averages of total sunspot area, A, and sunspot number, R, has been described using the formula A = 16.7 R. Such a simple relation, however, is erroneous. The yearly ratio of A/R has varied between 5.3 in 1964 to 19.7 in 1926, having a mean of 13.1 with a standard deviation of 3.5. For 1875-1976 (corresponding to the Royal Greenwich Observatory timeframe), the yearly ratio of A/R has a mean of 14.1 with a standard deviation of 3.2, and it is found to differ significantly from the mean for 1977-2004 (corresponding to the United States Air Force/National Oceanic and Atmospheric Administration Solar Optical Observing Network timeframe), which equals 9.8 with a standard deviation of 2.1. Scatterplots of yearly values of A versus R are highly correlated for both timeframes and they suggest that a value of R = 100 implies A=1,538 +/- 174 during the first timeframe, but only A=1,076 +/- 123 for the second timeframe. Comparison of the yearly ratios adjusted for same day coverage against yearly ratios using Rome Observatory measures for the interval 1958-1998 indicates that sunspot areas during the second timeframe are inherently too low.

Wilson, Robert M.↗
NASA NTRS2008

GPM Pre-Launch Algorithm Development for Physically-Based Falling Snow Retrievals

In this work we compare and correlate the long time series (Nov.-March) n~easurements of precipitation rate from the Parsivels and 2DVD to the passive (89, 150, 183+/-1, +/-3, +/-7 GHz) observations of NOAA's AMSU-B radiometer. There are approximately 5-8 AMSU-B overpass views of the CARE site a day. We separate the comparisons into categories of no precipitation, liquid rain and falling snow precipitation. Scatterplots between the Parsivel snowfall rates and AMSU-B brightness temperatures (TBs) did not show an exploitable relationship for retrievals. We further compared and contrasted brightness temperatures to other surface measurements such as temperature and relative humidity with equally unsatisfying results. We found that there are similar TBs (especially at 89 and 150 GHz) for cases with falling snow and for non-precipitating cases. The comparisons indicate that surface emissivity contributions to the satellite observed TB over land can add uncertainty in detecting and estimating falling snow. The newest results show that the cloud icc scattering signal in the AMSU-B data call be detected by computing clear air TBs based on CARE radiosonde data and a rough estimate of surface emissivity. That is the differences in computed TI3 and AMSU-B TB for precipitating and nonprecipitating cases are unique such that the precipitating versus ~lon-precipitating cases can be identified. These results require that the radiosonde releases are within an hour of the AMSU-B data and allow for three surface types: no snow on the ground, less than 5 cm snow on the ground, and greater than 5 cm on the ground (as given by ground station data). Forest fraction and measured emissivities were combined to calculate the surface emissivities. The above work and future work to incorporate knowledge about falling snow retrievals into the framework of the expected GPM Bayesian retrievals will be described during this presentation.

Gail Skofronick-Jackson↗
NASA NTRS2007

Titan's Surface from Cassini RADAR SAR and High Resolution Radiometry Data of the First Five Flybys

The first five Titan flybys with Cassini's Synthetic Aperture RADAR (SAR) and radiometer are examined with emphasis on the calibration and interpretation of the high-resolution radiometry data acquired during the SAR mode (SAR-radiometry). Maps of the 2-cm wavelength brightness temperature are obtained coincident with the SAR swath imaging, with spatial resolution approaching 6 km. A preliminary calibration shows that brightness temperature in these maps varies from 64 to 89 K. Surface features and physical properties derived from the SAR-radiometry maps and SAR imaging are strongly correlated; in general, we find that surface features with high radar reflectivity are associated with radiometrically cold regions, while surface features with low radar reflectivity correlate with radiometrically warm regions. We examined scatterplots of the normalized radar cross-section sigma(exp o) versus brightness temperature, finding differing signatures that characterize various terrains and surface features. Implications for the physical and compositional properties of these features are discussed. The results indicate that volume scattering is important in many areas of Titan's surface, particularly Xanadu, while other areas exhibit complex brightness temperature variations consistent with variable slopes or surface material and compositional properties.

Titan↗
NASA NTRS2013

Estimated Probability of a Cervical Spine Injury During an ISS Mission

Introduction: The Integrated Medical Model (IMM) utilizes historical data, cohort data, and external simulations as input factors to provide estimates of crew health, resource utilization and mission outcomes. The Cervical Spine Injury Module (CSIM) is an external simulation designed to provide the IMM with parameter estimates for 1) a probability distribution function (PDF) of the incidence rate, 2) the mean incidence rate, and 3) the standard deviation associated with the mean resulting from injury/trauma of the neck. Methods: An injury mechanism based on an idealized low-velocity blunt impact to the superior posterior thorax of an ISS crewmember was used as the simulated mission environment. As a result of this impact, the cervical spine is inertially loaded from the mass of the head producing an extension-flexion motion deforming the soft tissues of the neck. A multibody biomechanical model was developed to estimate the kinematic and dynamic response of the head-neck system from a prescribed acceleration profile. Logistic regression was performed on a dataset containing AIS1 soft tissue neck injuries from rear-end automobile collisions with published Neck Injury Criterion values producing an injury transfer function (ITF). An injury event scenario (IES) was constructed such that crew 1 is moving through a primary or standard translation path transferring large volume equipment impacting stationary crew 2. The incidence rate for this IES was estimated from in-flight data and used to calculate the probability of occurrence. The uncertainty in the model input factors were estimated from representative datasets and expressed in terms of probability distributions. A Monte Carlo Method utilizing simple random sampling was employed to propagate both aleatory and epistemic uncertain factors. Scatterplots and partial correlation coefficients (PCC) were generated to determine input factor sensitivity. CSIM was developed in the SimMechanics/Simulink environment with a Monte Carlo wrapper (MATLAB) used to integrate the components of the module. Results: The probability of generating an AIS1 soft tissue neck injury from the extension/flexion motion induced by a low-velocity blunt impact to the superior posterior thorax was fitted with a lognormal PDF with mean 0.26409, standard deviation 0.11353, standard error of mean 0.00114, and 95% confidence interval [0.26186, 0.26631]. Combining the probability of an AIS1 injury with the probability of IES occurrence was fitted with a Johnson SI PDF with mean 0.02772, standard deviation 0.02012, standard error of mean 0.00020, and 95% confidence interval [0.02733, 0.02812]. The input factor sensitivity analysis in descending order was IES incidence rate, ITF regression coefficient 1, impactor initial velocity, ITF regression coefficient 2, and all others (equipment mass, crew 1 body mass, crew 2 body mass) insignificant. Verification and Validation (V&V): The IMM V&V, based upon NASA STD 7009, was implemented which included an assessment of the data sets used to build CSIM. The documentation maintained includes source code comments and a technical report. The software code and documentation is under Subversion configuration management. Kinematic validation was performed by comparing the biomechanical model output to established corridors.

Brooker, John E.↗
NASA NTRS2021

Comparison of Monthly IMERG Precipitation Estimates with PACRAIN Atoll Observations

Satellite-based precipitation estimates provide valuable information where surface observations are not readily available, especially over the large expanses of the ocean where in situ precipitation observations are very sparse. This study compares monthly precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG) with gauge observations from 37 low-lying atolls from the Pacific Rainfall Database for the period June 2000–August 2020. Over the analysis period, IMERG estimates are slightly higher than the atoll observations by 0.67% with a monthly correlation of 0.68. Seasonally, DJF shows excellent agreement with a near-zero bias, while MAM shows IMERG is low by 4.6%, and JJA is high by 1.2%. SON exhibits the worst performance, with IMERG overestimating by 6.5% compared to the atolls. The seasonal correlations are well contained in the range 0.67–0.72, with the exception of SON at 0.62. Furthermore, SON has the highest RMSE at 4.70 mm day^(−1), making it the worst season for all metrics. Scatterplots of IMERG versus atolls show IMERG, on average, is generally low for light precipitation accumulations and high for intense precipitation accumulations, with best agreement at intermediate rates. Seasonal variations exist at light and intermediate rate accumulations, but IMERG consistently overestimates at intense precipitation rates. The differences between IMERG and atolls vary over time but do not exhibit any discernible trend or dependence on atoll population. The PACRAIN atoll gauges are not wind-loss corrected, so application of an appropriate adjustment would increase the precipitation amounts compared to IMERG. These results provide useful insight to users as well as valuable information for future improvements to IMERG.

David T. Bolvin↗
NASA NTRS2021

Investigation on the Origins of Comets as Revealed through IR High Resolution Spectroscopy: I. Molecular Abundances

We report and analyze molecular abundances updatedin twenty comets by employingmodern data reduction procedures and molecular models. Using boxplots and scatterplots, we examine how the different molecular species are distributed among the comet population, while by means of pie charts we investigate the relative proportions of these molecular species in each comet. We compare these results with the orbital parametersof the selected targets in order to identify trends related to the dynamical history of each comet. In this way, we identify at least three chemical classes based mainly on relative abundances of CO, CH3OH, CH4, C2H6, HCN and NH3. The combination of relative abundances and orbital parameters is then compared with chemical models of planetary system formation and may offer an alternative approach to investigate the origin and evolution of the material in cometary nuclei.Our study also confirms the need to add more objects to our sample in order to improve the statistics, especially for hyper-volatiles (i.e. CH4and CO) in Jupiter Family comets.

M Lippi↗