Search NASA⌕ Search

SEARCH · Search NASA

Results for “trend estimation”

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 55 records · Page 3

Long-Term Changes in Lower Tropospheric Baseline Ozone Concentrations:

Two recent papers have quantified long-term ozone (O3) changes observed at northernmidlatitude sites that are believed to represent baseline (here understood as representative of continental to hemispheric scales) conditions. Three chemistry-climate models (NCAR CAM-chem, GFDL-CM3, and GISS-E2-R) have calculated retrospective tropospheric O3 concentrations as part of the Atmospheric Chemistry and Climate Model Intercomparison Project and Coupled Model Intercomparison Project Phase 5 model intercomparisons. We present an approach for quantitative comparisons of model results with measurements for seasonally averaged O3 concentrations. There is considerable qualitative agreement between the measurements and the models, but there are also substantial and consistent quantitative disagreements. Most notably, models (1) overestimate absolute O3 mixing ratios, on average by approximately 5 to 17 ppbv in the year 2000, (2) capture only approximately 50% of O3 changes observed over the past five to six decades, and little of observed seasonal differences, and (3) capture approximately 25 to 45% of the rate of change of the long-term changes. These disagreements are significant enough to indicate that only limited confidence can be placed on estimates of present-day radiative forcing of tropospheric O3 derived from modeled historic concentration changes and on predicted future O3 concentrations. Evidently our understanding of tropospheric O3, or the incorporation of chemistry and transport processes into current chemical climate models, is incomplete. Modeled O3 trends approximately parallel estimated trends in anthropogenic emissions of NO(sub x), an important O3 precursor, while measured O3 changes increase more rapidly than these emission estimates.

atmospheric composition↗

Changes in Vegetation Cover of the Arctic National Wildlife Refuge Estimated from MODIS Greenness Trends, 2000 to 2018

The Arctic National Wildlife Refuge (ANWR) was established in 1980 and covers 19 million acres (77,000 km2) in northeast Alaska. Wildlife habitats in the ANWR are vulnerable to long-lasting effects from any disturbance, in part because short growing seasons in the arctic provide limited time for species to recover. Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in all ecoregions of the ANWR. Statistical analysis of changes in each MODIS pixel NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST) to map regional change rates. Results suggested that most negative NDVI anomalies in the tundra-covered river drainages of the Brooks Range Mountains and coastal plain have been associated with early spring thawing and elevated levels of surface moisture in low elevation drainages of the northern ANWR ecoregions.

Arctic National Wildlife Refuge↗

Changes in Vegetation Cover of Yukon River Drainages in Interior Alaska Estimated from MODIS Greenness Trends, 2000 to 2018

Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Yukon River basin in interior Alaska. Statistical analysis of changes in the NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST). This structural change analysis indicated that NDVI breakpoints and negative 18-yr trends in vegetation greenness over the years since 2000 could be explained in large part by the impacts of severe wildfires, commonly affecting shrubland and forested ecosystems at relatively low elevations (< 300 m).

Potter, Christopher↗

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)

Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.

Melanie S. Hammer↗

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part II: The performances

In Part I (Lei et. al, submitted to J. of Appl. Rem. Sens.),we gave detailed reviews of the algorithms Version 2.0 used for the on-orbit radiometric calibration of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite instrument. These algorithms improve the accuracy of the measured on-orbit change factor of the solar diffuser bidirectional reflectance distribution function, the H-factor, and reveal that the H-factor is angle dependent. With the help of lunar observations and the improved H-factor, the algorithms give more accurate values for the RSB detector on-orbit F-factor changes (F-factor is a correction factor to the initially retrieved scene spectral radiance). In this paper we review the RSB radiometric calibration performances. We show the H-factor temporal trend, the estimated uncertainty of the retrieved H-factor, and the F-factor temporal trend. Additionally we show the detector signal-to-noise ratio (SNR), the estimated uncertainty of the top-of-the-atmosphere solar spectral reflectance, the reflectance temporal trend for the Libya 4 desert, and the differences in the reflectances among the SNPP and the NOAA-20 VIIRS, and the Aqua MODIS. Our results show that although the SNRs trend downwards, they exceed the requirements by large margins. The reflectances from the Libya 4 desert show that the SNPP VIIRS’ reflectance is higher than those of the Aqua MODIS and the NOAA-20 VIIRS.

on-orbit calibration↗

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part II: The performances

In Part I (Lei et. al, submitted to J. of Appl. Rem. Sens.),we gave detailed reviews of the algorithms Version 2.0 used for the on-orbit radiometric calibration of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite instrument. These algorithms improve the accuracy of the measured on-orbit change factor of the solar diffuser bidirectional reflectance distribution function, the H-factor, and reveal that the H-factor is angle dependent. With the help of lunar observations and the improved H-factor, the algorithms give more accurate values for the RSB detector on-orbit F-factor changes (F-factor is a correction factor to the initially retrieved scene spectral radiance). In this paper we review the RSB radiometric calibration performances. We show the H-factor temporal trend, the estimated uncertainty of the retrieved H-factor, and the F-factor temporal trend. Additionally we show the detector signal-to-noise ratio (SNR), the estimated uncertainty of the top-of-the-atmosphere solar spectral reflectance, the reflectance temporal trend for the Libya 4 desert, and the differences in the reflectances among the SNPP and the NOAA-20 VIIRS, and the Aqua MODIS. Our results show that although the SNRs trend downwards, they exceed the requirements by large margins. The reflectances from the Libya 4 desert show that the SNPP VIIRS’ reflectance is higher than those of the Aqua MODIS and the NOAA-20 VIIRS

Ning Lei↗

Optimal Estimation of Clock Values and Trends from Finite Data

We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation: with the help of the GACV, these principles lead to a set of linear equations for the regression coefficients and some auxiliary parameters. The mean square errors of the estimators are easily calculated. The method can be used to check the results of other methods and to find good suboptimal estimators based on a small subset of the available data.

stationary increments↗

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

An adaptive-performance-seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system is discussed. This paper presents flight- and ground-test evaluations of the propulsion system parameter-estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Kalman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation especially in trends between the performance seeking control estimated and measured thrust.

Orme, John S.↗

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Orme, John S.↗

20 Years of ClO Measurements in the Antarctic Lower Stratosphere

We present 20 years (1996-2015) of austral springtime measurements of chlorine monoxide (ClO) over Antarctica from the Chlorine Oxide Experiment (ChlOEl) ground-based millimeter wave spectrometer at Scott Base, Antarctica, as well 12 years (2004-2015) of ClO measurements from the Aura Microwave Limb Sounder (MLS). From August onwards we observe a strong increase in lower stratospheric ClO, with a peak column amount usually occurring in early September. From mid-September onwards we observe a strong decrease in ClO. In order to study interannual differences, we focus on a 3-week period from 28 August to 17 September for each year and compare the average column ClO anomalies. These column ClO anomalies are shown to be highly correlated with the average ozone mass deficit for September and October of each year. We also show that anomalies in column ClO are strongly anti-correlated with 30 hPa temperature anomalies, both on a daily and an interannual timescale. Making use of this anti-correlation we calculate the linear dependence of the interannual variations in column C1O on interannual variations in temperature. By making use of this relationship, we can better estimate the underlying trend in the total chlorine (Cl(sub y) = HCl + ClONO2 + HOCl + 2 x Cl2 + 2 x Cl2+ ClO + Cl). The resultant trends in Cl(sub y), which determine the long-term trend in ClO, are estimated to be -0.5 +/-0.2, -1.40.9, and -0.60.4% per year, for zonal MLS, Scott Base MLS (both 2004-2015), and ChlOE (1996-2015) respectively. These trends are within 1sigma of trends in stratospheric Cl(sub y) previously found at other latitudes. The decrease in ClO is consistent with the trend expected from regulations enacted under the Montreal Protocol.

temperature anomalies↗

Estimating 11-year solar UV variations using 27-day response as a guide to isolate trends in total column ozone

The total column ozone response to 11-year solar ultraviolet (UV) variations is estimated here from the observed response to 27-day solar variations adjusted for the theoretical difference between the 27-day response and 11-year response. The estimate is tested by comparing two data sets where long-term drifts have been removed, the Nimbus 7 TOMS Version 6 total column ozone and the 280 nm core-to-wing ratio (a proxy for solar UV variations). The 365-day running means of data area-weighted between 40 deg N to 40 deg S latitude give a 1.9% ozone variation related to the 11-year solar cycle compared with the estimate of 1.8%. Estimates of linear trends were reduced by a factor of 2 by including solar effects. The standard deviation from the empirical model was reduced from 1.0 to 0.6 Dobson Units, by including the quasi-biennial oscillation (QBO), but the QBO did not significantly alter trend estimates. Both the ozone responses to 27-day and 11-year solar variations were considerably stronger than predicted by a 2-D theoretical model.

Keating, G. M.↗

Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter estimation, and gas-path fault detection and isolation has been developed and evaluated in a simulation environment. The architecture incorporates two independent models: a realtime self-tuning performance model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting long-term engine degradation trends. This architecture was evaluated using flight profiles generated from a nonlinear model with realistic fleet engine health degradation distributions and sensor noise. The architecture was found to produce acceptable estimates of engine health and unmeasured parameters, and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 70 percent of the tested cases

Armstrong, Jeffrey B.↗

Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Trace gas↗

Total ozone seasonal and interannual variations in the principal air masses of the Northern Hemisphere in 1975-1990

The diurnally mean total ozone X from the Northern Hemisphere ground based 90 stations for 1975-1990 are averaged over the Arctic (bar X (sub A)) Intermediate (bar X (sub I)) and Tropical (bar X (sub T)) air mass areas, divided by the jet stream axes on the isobaric surfaces 300 and 200 mb. The mean square variations of the so averaged X are considerably smaller than of the X, averaged over the corresponding zonal belts. This property allows one to improve considerably the statistical significance of X trends and changes over various time periods, taking into account the time correlation of data for adjacent time intervals. Bar X (sub A), bar X (sub I), and bar X (sub T) trends are estimated over the periods of solar activity rise and fall in its 21st and rise in its 22nd 11 year cycles and over the periods of west and east phases of the known over the periods of west and east phases of the known quasibiennial oscillation (QBO). Solar activity variations affect mostly bar X (sub T), bar X (sub I) trends in summer months, while QBO phases influence the X changes mostly during the cold half year. X are lower in the west QBO phase and their trend is negative during almost all periods considered. The anthropogenic effects on the X is also estimated.

Karol, Igor L.↗

Antarctica, Greenland and Gulf of Alaska Land-ice Evolution from an Iterated GRACE Global Mascon Solution

We have determined the ice mass evolution of the Antarctica and Greenland ice sheets (AIS and GIS) and Gulf of Alaska (GOA) glaciers from a new GRACE global solution of equal-area surface mass concentration parcels (mascons) in equivalent height of water. The mascons were estimated directly from the reduction of the inter-satellite K-band range-rate (KBRR) observations, taking into account the full noise covariance, and formally iterating the solution. The new solution increases signal recovery while reducing the GRACE KBRR observation residuals. The mascons were estimated with 10 day and 1 arc degree equal-area sampling, applying anisotropic constraints. An ensemble empirical mode decomposition adaptive filter was applied to the mascon time series to compute annual mass balances. The details and causes of the spatial and temporal variability of the land-ice regions studied are discussed. The estimated mass trend over the total GIS, AIS and GOA glaciers for the time period 1 December 2003 to 1 December 2010 is -380 plus or minus 31 Gt a(exp -1), equivalent to -1.05 plus or minus 0.09 mma(exp -1) sea-level rise. Over the same time period we estimate the mass acceleration to be -41 plus or minus 27 Gt a(exp -2), equivalent to a 0.11 plus or minus 0.08 mm a(exp -2) rate of change in sea level. The trends and accelerations are dependent on significant seasonal and annual balance anomalies.

MASCON↗

Combining Satellite and in Situ Data with Models to Support Climate Data Records in Ocean Biology

The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.

Gregg, Watson↗

10 Yr Spatial and Temporal Trends of PM2.5 Concentrations in the Southeastern US Estimated Using High-resolution Satellite Data

Long-term PM2.5 exposure has been reported to be associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of the true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of spatiotemporally continuous distribution of PM2.5 concentrations are essential. Satellite-retrieved aerosol optical depth (AOD) has been widely used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, an inherent disadvantage of current AOD products is their coarse spatial resolutions. For instance, the spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) are 10 km and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5-AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US, centered at the Atlanta Metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted for each year individually, and we obtained model fitting R2 ranging from 0.71 to 0.85, MPE from 1.73 to 2.50 g m3, and RMSPE from 2.75 to 4.10 g m3. In addition, we found cross validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 g m3, and RMSPE from 3.12 to 5.00 g m3, indicating a good agreement between the estimated and observed values. Spatial trends show that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. A time series analysis was conducted to examine temporal trends of PM2.5 concentrations in the study area from 2001 to 2010. The results showed that the PM2.5 levels in the study area followed a generally declining trend from 2001 to 2010 and decreased about 20 during the period. However, there was an exception of an increase in year 2005, which is attributed to elevated sulfate concentrations in the study area in warm months of 2005. An investigation of the impact of wild and prescribed fires on PM2.5 levels in 2007 suggests a positive relationship between them.

particulate matter (PM2.5)↗