Search NASA⌕ Search

SEARCH · Search NASA

Results for “nonparametric testing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Downscaling↗
DOE OSTI2026

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Environmental sciences↗
DOE OSTI2023

Evaluating various composite sampling modes for detecting pathogenic SARS-CoV-2 virus in raw sewage

Inadequate sampling approaches to wastewater analyses can introduce biases, leading to inaccurate results such as false negatives and significant over- or underestimation of average daily viral concentrations, due to the sporadic nature of viral input. To address this challenge, we conducted a field trial within the University of Tennessee residence halls, employing different composite sampling modes that encompassed different time intervals (1 h, 2 h, 4 h, 6 h, and 24 h) across various time windows (morning, afternoon, evening, and late-night). Our primary objective was to identify the optimal approach for generating representative composite samples of SARS-CoV-2 from raw wastewater. Utilizing reverse transcription-quantitative polymerase chain reaction, we quantified the levels of SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA in raw sewage. Our findings consistently demonstrated that PMMoV RNA, an indicator virus of human fecal contamination in water environment, exhibited higher abundance and lower variability compared to pathogenic SARS-CoV-2 RNA. Significantly, both SARS-CoV-2 and PMMoV RNA exhibited greater variability in 1 h individual composite samples throughout the entire sampling period, contrasting with the stability observed in other time-based composite samples. Through a comprehensive analysis of various composite sampling modes using the Quade Nonparametric ANCOVA test with date, PMMoV concentration and site as covariates, we concluded that employing a composite sampler during a focused 6 h morning window for pathogenic SARS-CoV-2 RNA is a pragmatic and cost-effective strategy for achieving representative composite samples within a single day in wastewater-based epidemiology applications. This method has the potential to significantly enhance the accuracy and reliability of data collected at the community level, thereby contributing to more informed public health decision-making during a pandemic.

sampling timing↗
NASA NTRS1970

Relationship between vegetation reflectance spectra and soil geochemistry: New data from Catheart Mountain, Maine

Spectral reflectance curves for red spruce and balsam fir in the 350- to 1100-nanometer region showed differences between specimens growing over and away from a concealed copper deposit at Catheart Mountain, Maine. A sample group of 15 anomalous spruce, 9 background spruce, 10 anomalous fir, and 10 background fir was studied. Measurements of copper and molybdenum contents in the supporting soil were used to categorize the trees as anomalous or background. Analyses using parametric and nonparametric statistical tests were performed to establish whether significant differences existed between the anomalous and background groups. At the 95% confidence level, significant differences were found to exist for both species, generally in the chlorophyll band centered at about 550 nanometers and in the region from 700 to 900 nanometers.

Canney, F. C.↗
NASA NTRS1978

Use of change detection in assessing development plans - A Philippine example

An aircraft/Landsat change-detection study conducted 1948-1972 on Marinduque Province, Republic of the Philippines, is discussed, and a procedure using both remote sensing and information systems for collection, spatial analysis, and display of periodic data is described. Each of the 4,008 25-hectare cells representing Marinduque were observed, and changes in and between variables were measured and tested using nonparametric statistics to determine the effect of specific land cover changes. Procedures using Landsat data to obtain a more continuous updating of the data base are considered. The system permits storage and comparison of historical and current data.

Coiner, J. C.↗
NASA NTRS1990

Charge-coupled device spectra of asteroids. I - Near-earth and 3:1 resonance asteroids

Main-belt asteroids and cometary nuclei are both frequently suggested sources of the relatively short-lived near-earth asteroids (NEAs). The asteroidal origin hypothesis is studied by obtaining new high-quality reflectivity spectra of 19 NEAs and 23 asteroids near the 3:1 resonance, and by performing a systematic comparison of these two groups of spectra. Nonparametric statistical tests are used to check for consistency between the color distributions of the two populations. The tests indicate that the difference between the two distributions is generally not significant at the 3 sigma level. However, this conclusion is dependent on the (unknown) size distribution of the NEAs. The observations are compatible with the hypothesis that the NEAs are derived from the 3:1 resonance region, but do not imply that this is the only possible source region.

Luu, Jane X.↗
NASA NTRS1988

The effects of feedback lighting on the circadian drinking rhythm in the diurnal new world primate Saimiri sciureus

Feedback lighting provides illumination primarily during the subjective night (i.e., the photosensitive portion of the circadian cycle) in response to a given behavior. This technique has previously been used to test the nonparametric model of entrainment in nocturnal rodents. In three species (Rattus norvegicus, Mesocricetus auratus, and Mus musculus), the free-running period of the locomotor activity rhythm was similar whether the animals were exposed to continuous light or discrete light pulses occurring essentially only during the subjective night (i.e., feedback lighting). In the current experiments, feedback lighting was presented to squirrel monkeys so that light fell predominantly during the subjective night. Feedback lighting was linked to the drinking behavior in this diurnal primate so that when the animal drank, the lights went out. Despite the seemingly adverse predicament, the monkeys maintained regular circadian drinking rhythms. Furthermore, just as the period of the free-running activity rhythms of nocturnal rodents exposed to continuous light or feedback lighting were similar, the period of the drinking rhythms of the squirrel monkeys in continuous light and feedback lighting were comparable (25.6 +/- 0.1 and 25.9 +/- 0.1 hours, respectively), despite a substantial decrease in the total amount of light exposure associated with feedback lighting. The free-running period of monkeys exposed to continuous dark (24.5 +/- 0.1 hours) was significantly shorter than either of the two lighting conditions (P < 0.001). The results presented for the drinking rhythm were confirmed by examination of the temperature and activity rhythms. Therefore, discrete light pulses given predominately during the subjective night are capable of simulating the effects of continuous light on the free-running period of the circadian rhythms of a diurnal primate. The response of squirrel monkeys to feedback lighting thus lends further support for the model and suggests that the major entrainment mechanisms are similar in nocturnal rodents and diurnal primates.

NASA Discipline Number 40-30↗
NASA NTRS2016

Tropical Cyclone Activity in the North Atlantic Basin During the Weather Satellite Era, 1960-2014

This Technical Publication (TP) represents an extension of previous work concerning the tropical cyclone activity in the North Atlantic basin during the weather satellite era, 1960-2014, in particular, that of an article published in The Journal of the Alabama Academy of Science. With the launch of the TIROS-1 polar-orbiting satellite in April 1960, a new era of global weather observation and monitoring began. Prior to this, the conditions of the North Atlantic basin were determined only from ship reports, island reports, and long-range aircraft reconnaissance. Consequently, storms that formed far from land, away from shipping lanes, and beyond the reach of aircraft possibly could be missed altogether, thereby leading to an underestimate of the true number of tropical cyclones forming in the basin. Additionally, new analysis techniques have come into use which sometimes has led to the inclusion of one or more storms at the end of a nominal hurricane season that otherwise would not have been included. In this TP, examined are the yearly (or seasonal) and 10-year moving average (10-year moving average) values of the (1) first storm day (FSD), last storm day (LSD), and length of season (LOS); (2) frequencies of tropical cyclones (by class); (3) average peak 1-minute sustained wind speed ( ) and average lowest pressure ( ); (4) average genesis location in terms of north latitudinal ( ) and west longitudinal ( ) positions; (5) sum and average power dissipation index ( ); (6) sum and average accumulated cyclone energy ( ); (7) sum and average number of storm days ( ); (8) sum of the number of hurricane days (NHD) and number of major hurricane days (NMHD); (9) net tropical cyclone activity index (NTCA); (10) largest individual storm (LIS) PWS, LP, PDI, ACE, NSD, NHD, NMHD; and (11) number of category 4 and 5 hurricanes (N4/5). Also examined are the December-May (D-M) and June-November (J-N) averages and 10-year moving average values of several climatic factors, including the (1) oceanic Nino index ( ); (2) Atlantic multi-decadal oscillation ( ) index; (3) Atlantic meridional mode ( ) index; (4) global land-ocean temperature index ( ); and (5) quasi-biennial oscillation ( ) index. Lastly, the associational aspects (using both linear and nonparametric statistical tests) between selected tropical cyclone parameters and the climatic factors are examined based on their 10-year moving average trend values.

satellite↗
DOE OSTI2019

Observed Spatiotemporal Changes in the Mechanisms of Extreme Water Available for Runoff in the Western United States

We used a quality-controlled Snowpack Telemetry (SNOTEL) data set (1979–2017) combined with the nonparametric regional Kendall test (RKT) to examine changes in annual maximum water available for runoff at the land surface under four hydrometeorological conditions (snowmelt only, rain-on-snow, all melt with or without rainfall, and all melt plus rainfall on snow-free ground) over the mountainous regions of the western U.S. Our RKT analyses indicated significant declining trends at point (SNOTEL) scale under all four conditions. The annual maximum of all melt plus rainfall decreased primarily in the southwestern U.S., while the frequency of rain-on-snow events increased significantly in the northwestern U.S. The rate of annual maximum snowmelt only decreased in 10 of 11 ecological regions across the western U.S. These results suggest that under a warming climate, the four types of annual maximum water available for runoff have been substantially decreasing at local scale across the western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events

The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this paper, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Lastly, supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

LANDSAT 4 investigation of thematic mapper and multispectral scanner applications

Six different band combinations of TM data were selected for evaluation by four experienced photointerpreters who were asked to rank the band combinations according to the ease with which the category of image feature designated for each set of combinations could be distinguished. There were four sets of combinations selected for each category of image feature. Scenes of Oklahoma City, Oklahoma and of the Sacramento Valley, California area were enlarged to 1:250,000 and 2 cm circular chips were cut from each print. A nonparametric rank order test was carried out on the data to determine if the interpreters found no difference among the band combinations in ranking for the designated categories. Kendall's coefficient of concordance (W) was calculated and the significance of the W value was determined by a chi-square test. A table summarizes the results of the evaluation and shows the rank order of band combinations indicated as valid by rejection of the null hypothesis.

Lauer, D. T.↗
NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980–2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125° 3 0.125° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann–Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the upper Great Plains and Northeast to -1 to -9 mm/yr in the West and South, net radiation flux trends range from +0.05 to +0.20 W/sq. m yr in the East to -0.05 to -0.20 W/sq. m yr in the West, and U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W/sq. m yr, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Michael F. Jasinski↗
DOE OSTI2019

Strong Influence of Irrigation on Water Budget and Land Surface Temperature in Indian Subcontinental River Basins

Irrigation modulates the terrestrial water budget, especially in intensively irrigated regions such as Indian sub-continent river basins, where 35% of agricultural land has been irrigated during 2010. Here, we investigate the impact of irrigation on water budget components and land surface temperature (LST), as well as seasonal and annual trends of irrigation water demand in response to climate variability over the Indian sub-continental region. We use the Variable Infiltration Capacity (VIC) model coupled with an irrigation scheme (VIC-IRR) to represent the irrigation activities. The VIC-IRR model was calibrated and evaluated against in-situ and remote sensing observations including streamflow from India-Water Resource Information System (WRIS), Terrestrial Water Storage Change (TWSC) from Gravity Recovery and Climate Experiment (GRACE), LST from Advanced Along-Track Scanning Radiometer (AATSR), and observed gravimetric soil moisture data from India Meteorology Department (IMD). We conducted two simulations (i.e., with and without irrigation) over 18 Indian sub-continental basins at 0.25° spatial resolution from 1951 to 2012. The comparison between the two simulations indicates that as a result of irrigation, evapotranspiration (ET) increases in all the basins. We find that mean annual ET of Indus and Ganges basins increases by 47% and 12%, respectively due to irrigation. After irrigation, basin averaged total runoff (TR) was increased in the most irrigated basins (i.e., Sabarmati, Ganges, and Indus) by 4.62%, 11.87% and 7.06% in the monsoon, pre-monsoon, and post-monsoon seasons, respectively. The land surface temperature was cooled down in range of 0.2-0.65 °C in the Indian sub-continental basins especially in Ganges and Indus basins. Compare to other basins; irrigation water demand was high in Indus and Ganges basins during the post-monsoon season. Using the nonparametric Mann–Kendall trend test we find that in the pre-monsoon and monsoon seasons over the study period, irrigation demand decreased by 8.25 mm/year in 13 basins. Whereas for the post-monsoon season, irrigation water demand increased in 12 out of the 18 basins. Changes in irrigation demands are largely driven by changes in soil moisture due to decline in the summer monsoon precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Quantifying Model Form Uncertainty of Epidemic Forecasting Models from Incidence Data

Objective We present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. The resulting estimates allow to assess how well simpler models, such as SEIR, fit the observed time series of incidence data.IntroductionUncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a "universal model" to which more restrictive parametric models are compared too.MethodsThis talk presents a useful modeling framework for time series of incidence data from contagious diseases that enables one to identify and quantify the impact of model form uncertainty. Specifically, we propose to focus on estimating the timedependent force of infection. The latter is a universal parameters for all contagious disease model. Using a machine learning technique for estimating monotone functions, i.e., isotonic regression and its variants, one can estimate the force ofinfection without addtional assumptions. We note that most contagious disease model do satisfy this monotonicity assumption, due to a combination of factors: depletion of susceptibles, implementation of mitigation strategies, behavior change, etc. Comparing the resulting "non-parametric" estimate with parametric estimates, obtained by fitting an SEIR for example, can reveal model deficiencies and help quantify model form uncertainties.Finally, we discuss how ideas from "strict bound theory" can be used to develop upper and lower uncertainty bands for force of infection that acknowledge the intrinsic stochasticity in the data.ResultsWe demonstrate the application of the methodology to weekly Influenza Like Illness (ILI) incidence data from France andcompare the results to fitted SIR and SEIR models. This comparison can be seen as a nonparametric goodness of fit test, providing one with tools to do simple model selection.ConclusionsWe present a novel and flexible model to statistically describe the force of infection as a function of time. Comparing the fit to incidence data of that model with the fit of simpler parametric models enables the quantification of model form uncertainty and associated model selection.

Hengartner, Nicholas↗
NASA NTRS1987

Variability analysis in low count rate sources

A method, based on the chi-square statistics, is described for detecting pulselike time variability in low count rate sources observed with photon-counting instruments. This method can be used even in the presence of observational gaps, takes full advantage of the filtering effect due to binning with different bin sizes, and takes into account the arbitrariness introduced by the binning phase. The procedure developed to limit the dependence of the results on the binning phase and ensure statistically correct results is described along with the application of the proposed procedure to a model of a variable source. Monte Carlo simulations are used to show how the method can be used to derive the characteristic variability time scales and that the method is more sensitive than the nonparametric Kolmogorov-Smirnov test in detecting variability to a given confidence level.

Collura, A.↗
NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-47 2015 using the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah Version 3.3 land surface model at 0.125 degreesx0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p<0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends, and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the Upper Great Plains and Northeast to -1 to -9 in the West and South; net radiation flux trends range from +0.05 to +0.20 W/m(exp2)/yr in the East to -0.05 to -0.20 in the West; U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from West to East. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%; Trends in latent heat flux were hardly affected, RMSE decreasing only 0.17 to 0.16 W/m(exp 2)/yr, while trend detection increased 2%; NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gaged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Snow depth↗