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NASA NTRS2013

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-cloud Aerosols over Ocean Using CALIOP and MODIS Data

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4%. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds.

CALIOP and MODIS↗

NASA NTRS2014

A Monte Carlo Analysis of the Thrust Imbalance for the RSRMV Booster During Both the Ignition Transient and Steady State Operation

This paper presents the results of statistical analyses performed to predict the thrust imbalance between two solid rocket motor boosters to be used on the Space Launch System (SLS) vehicle. Two legacy internal ballistics codes developed for the Space Shuttle program were coupled with a Monte Carlo analysis code to determine a thrust imbalance envelope for the SLS vehicle based on the performance of 1000 motor pairs. Thirty three variables which could impact the performance of the motors during the ignition transient and thirty eight variables which could impact the performance of the motors during steady state operation of the motor were identified and treated as statistical variables for the analyses. The effects of motor to motor variation as well as variations between motors of a single pair were included in the analyses. The statistical variations of the variables were defined based on data provided by NASA's Marshall Space Flight Center for the upgraded five segment booster and from the Space Shuttle booster when appropriate. The results obtained for the statistical envelope are compared with the design specification thrust imbalance limits for the SLS launch vehicle

Foster, Winfred A., Jr.↗

NASA NTRS2014

A Monte Carlo Analysis of the Thrust Imbalance for the Space Launch System Booster During Both the Ignition Transient and Steady State Operation

Foster, Winfred A., Jr.↗

NASA NTRS2011

Towards an Automated Classification of Transient Events in Synoptic Sky Surveys

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel featrue is the development of an automated follow-up recommendation engine, that suggest those measruements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost funcations. This illustrates the symbiotic relationship of astronomy and applied computer science through the emerging disciplne of AstroInformatics.

classification↗

NASA NTRS2016

On the Importance of High Frequency Gravity Waves for Ice Nucleation in the Tropical Tropopause Layer

Recent investigations of the influence of atmospheric waves on ice nucleation in cirrus have identified a number of key processes and sensitivities: (1) ice concentrations produced by homogeneous freezing are strongly dependent on cooling rates, with gravity waves dominating upper tropospheric cooling rates; (2) rapid cooling driven by high-frequency waves are likely responsible for the rare occurrences of very high ice concentrations in cirrus; (3) sedimentation and entrainment tend to decrease ice concentrations as cirrus age; and (4) in some situations, changes in temperature tendency driven by high-frequency waves can quench ice nucleation events and limit ice concentrations. Here we use parcel-model simulations of ice nucleation driven by long-duration, constant-pressure balloon temperature time series, along with an extensive dataset of cold cirrus microphysical properties from the recent ATTREX high-altitude aircraft campaign, to statistically examine the importance of high-frequency waves as well as the consistency between our theoretical understanding of ice nucleation and observed ice concentrations. The parcel-model simulations indicate common occurrence of peak ice concentrations exceeding several hundred per liter. Sedimentation and entrainment would reduce ice concentrations as clouds age, but 1-D simulations using a wave parameterization (which underestimates rapid cooling events) still produce ice concentrations higher than indicated by observations. We find that quenching of nucleation events by high-frequency waves occurs infrequently and does not prevent occurrences of large ice concentrations in parcel simulations of homogeneous freezing. In fact, the high-frequency variability in the balloon temperature data is entirely responsible for production of these high ice concentrations in the simulations.

simulation↗

NASA NTRS2016

Application of Interval Predictor Models to Space Radiation Shielding

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

Crespo, Luis G.↗

NASA NTRS2018

The Impact of Stellar Variability on the Detection of Transiting Earth-like Planets

Stellar variability represents a key limitation on the detectability of weak transit signatures of small, Earth-size planets with space-based missions such as NASA's Kepler Mission or ESA's PLATO Mission. The expectations for the performance of the Kepler Mission in the face of solar-like variability were predicated on observations of the Sun with the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) instrument aboard NASA and ESA's joint Solar and Heliospheric Observatory (SOHO) mission, which was launched in December 1995. Analyses of SOHO data indicated that solar-like variability would contribute approx.10 ppm to Kepler's noise budget at 6.5 ours, a typical grazing transit timescale. This proved to be optimistic as the typical 12th mag G2V star in the Kepler field of view exhibited 20 ppm of stellar variability-related noise at 6.5 hours, making the detection of true Earth-Sun analogs more difficult. While the consequent increase in the total noise at 6.5 hours from 20 ppm to 30 ppm could have been overcome by extending the mission to a total duration of 7 to 8 years. Unfortunately, a second reaction wheel failed in May 2013, ending the Kepler primary mission. Nevertheless, Kepler proved to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from less than 1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Kepler discovered over 2600 validated and confirmed exoplanets, and measured the pressure-mode oscillations of over 15000 stars over the course of its mission and the reconstituted K2 mission. We present the adaptive, wavelet-based matched filter used for both the Kepler and the TESS missions, and discuss the connections between this detection algorithm and fundamental detection theory, which allows the detector to function as a noise characterization engine. This provides a dynamical measurement of the photometric noise at transit timescales and thus, a key performance metric for missions like PLATO and Kepler. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale.

transit surveys↗

NASA NTRS2019

Evaluation of Seasonal Water Budget Components over the Major Drainage Basins of North America Using an Ensemble-Based Land Surface Model Approach

An ensemble of land surface models and forcing data was developed to assess variability in SWE estimation over North America. In this study, the ensemble output was used to assess how SWE uncertainty impacts stream flow estimation. The analysis was conducted by major basins of North America over the 2009-2017 time period.

Vuyovich, C.↗

NASA NTRS2018

The Impact of Stellar Variability on the Detection of Transiting Earth-Like Planets

Stellar variability represents a key limitation on the detectability of weak transit signatures of small, Earth-size planets with space-based missions such as NASA's Kepler Mission or ESA's PLATO Mission. The expectations for the performance of the Kepler Mission in the face of solar-like variability were predicated on observations of the Sun with the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) instrument aboard NASA and ESA's joint Solar and Heliospheric Observatory (SOHO) mission, which was launched in December 1995. Analyses of SOHO data indicated that solar-like variability would contribute ~10 ppm to Kepler's noise budget at 6.5 ours, a typical grazing transit timescale. This proved to be optimistic as the typical 12th mag G2V star in the Kepler field of view exhibited 20 ppm of stellar variability-related noise at 6.5 hours, making the detection of true Earth-Sun analogs more difficult. While the consequent increase in the total noise at 6.5 hours from 20 ppm to 30 ppm could have been overcome by extending the mission to a total duration of 7 to 8 years. Unfortunately, a second reaction wheel failed in May 2013, ending the Kepler primary mission. Nevertheless, Kepler proved to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from <1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Kepler discovered over 2600 validated and confirmed exoplanets, and measured the pressure-mode oscillations of over 15000 stars over the course of its mission and the reconstituted K2 mission. We present the adaptive, wavelet-based matched filter used for both the Kepler and the TESS missions, and discuss the connections between this detection algorithm and fundamental detection theory, which allows the detector to function as a noise characterization engine. This provides a dynamical measurement of the photometric noise at transit timescales and thus, a key performance metric for missions like PLATO and Kepler. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale.

exoplanets↗

NASA NTRSDate not supplied

Evaluating Retrieval Algorithm Climate Stability: Estimating 3D Optical Thickness Bias Distributions by Cloud Type

Detecting climate trends on large spatiotemporal scales requires accurate, stable measurements and stable retrieval algorithms. We strive to estimate how time-variant retrieval algorithm biases may impact trend detection. Here we focus on the 3D cloud optical thickness (τc) bias, which is among the largest in passive cloud retrieval algorithms. If this bias is time dependent, a possibility with potential decadal changes in cloud morphology, it may obscure genuine trends in τc. Although previous studies have evaluated the cloud- and sun-view geometry-dependent 3D τc bias on small spatial scales, before our current study none have evaluated the stability of this well-known bias on climate-relevant large spatiotemporal scales. These studies must estimate large scale distributions of the 3D τc bias by cloud type and estimate how cloud type amount may change between two climate states. We employ a novel approach to estimate large scale distributions of 3D τc using a proxy of the bias that quantifies the departure of clouds from satisfying the 1D radiative transfer assumption used in passive τc retrievals. This existing globally-distributed proxy is an angular consistency metric that was developed using fused Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) measurements. Calculating the 3D τc bias and the proxy, for known cloud fields enables us to establish statistical relationships between these two quantities, which can be used to calculate large-scale distributions of the 3D τc bias. This approach limits the number of 3D radiative transfer simulations required to only those needed to estimate a statistical relationship between the 3D τc bias for known cloud fields and a proxy of the bias. It is likely that future studies will be needed to evaluate retrieval algorithm bias stability for other geophysical variables as the community develops climate data records from satellite observations and their retrievals. This must be done in addition to monitoring and correcting measurement errors and uncertainties and understanding their impact on retrieved essential climate variables.

Yolanda Shea↗

NASA NTRS2020

Differences in Tropical High Clouds Among Reanalyses: Origins and Radiative Impacts

We examine differences among reanalysis highcloud products in the tropics, assess the impacts of these differences on radiation budgets at the top of the atmosphere and within the tropical upper troposphere and lower stratosphere (UTLS), and discuss their possible origins in the context of the reanalysis models. We focus on the ERA5 (fifthgeneration European Centre for Medium-range Weather Forecasts – ECMWF – reanalysis), ERA-Interim (ECMWF Interim Reanalysis), JRA-55 (Japanese 55-year Reanalysis), MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and CFSR/CFSv2 (Climate Forecast System Reanalysis/Climate Forecast System Version 2) reanalyses. As a general rule, JRA-55 produces the smallest tropical high-cloud fractions and cloud water contents among the reanalyses, while MERRA-2 produces the largest. Accordingly, long-wave cloud radiative effects are relatively weak in JRA-55 and relatively strong in MERRA-2. Only MERRA-2 and ERA5 among the reanalyses produce tropical-mean values of outgoing long-wave radiation (OLR) close to those observed, but ERA5 tends to underestimate cloud effects, while MERRA-2 tends to overestimate variability. ERA5 also produces distributions of longwave, short-wave, and total cloud radiative effects at the top of the atmosphere that are very consistent with those observed. The other reanalyses all exhibit substantial biases in at least one of these metrics, although compensation between the long-wave and short-wave effects helps to constrain biases in the total cloud radiative effect for most reanalyses. The vertical distribution of cloud water content emerges as a key difference between ERA-Interim and other reanalyses. Whereas ERA-Interim shows a monotonic decrease of cloud water content with increasing height, the other reanalyses all produce distinct anvil layers. The latter is in better agreement with observations and yields very different profiles of radiative heating in the UTLS. For example, whereas the altitude of the level of zero net radiative heating tends to be lower in convective regions than in the rest of the tropics in ERAInterim, the opposite is true for the other four reanalyses. Differences in cloud water content also help to explain systematic differences in radiative heating in the tropical lower stratosphere among the reanalyses. We discuss several ways in which aspects of the cloud and convection schemes impact the tropical environment. Discrepancies in the vertical profiles of temperature and specific humidity in convective regions are particularly noteworthy, as these variables are directly constrained by data assimilation, are widely used, and feed back to convective behaviour through their relationships with thermodynamic stability.

High Clouds↗

NASA NTRS2021

Relationship between circum-Arctic atmospheric wave patterns and large-scale wildfires in boreal summer

Long-term assessment of severe wildfires and associated air pollution and related climate patterns in and around the Arctic is essential for assessing healthy human life status. To examine the relationships, we analyzed the National Aeronautics and Space Administration (NASA) modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Our investigation based on this state-of-the-art atmospheric reanalysis data reveals that 13 out of the 20 months with the highest PM2.5 (corresponding to the highly elevated organic carbon in the particulate organic matter [POM] form) monthly mean mass concentration over the Arctic for 2003–2017 were all in summer (July and August), during which POM of ⩾0.5 μg/cu. m and PM2.5 were positively correlated. This correlation suggests that high PM2.5 in the Arctic is linked to large wildfire contributions and characterized by significant anticyclonic anomalies (i.e. clockwise atmospheric circulation) with anomalous surface warmth and drier conditions over Siberia and subpolar North America, in addition to Europe. A similar climate pattern was also identified through an independent regression analysis for the July and August mean data between the same atmospheric variables and the sign-reversed Scandinavian pattern index. We named this pattern of recent atmospheric circulation anomalies the circum-Arctic wave (CAW) pattern as a manifestation of eastward group-velocity propagation of stationary Rossby waves (i.e. large-scale atmospheric waves). The CAW induces concomitant development of warm anticyclonic anomalies over Europe, Siberia, Alaska, and Canada, as observed in late June 2019. Surprisingly, the extended regression analysis of the 1980–2017 period revealed that the CAW pattern was not prominent before 2003. Understanding the CAW pattern under future climate change and global warming would lead to better prediction of co-occurrences of European heatwaves and large-scale wildfires with air pollution over Siberia, Alaska, and Canada in and around the Arctic in summer.

wildfire↗

NASA NTRS2021

Methodology to Assess the Changing Risk of Yield Failure Due to Heat and Drought Stress Under Climate Change

While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.

Climate risk assessment↗

NASA NTRS2023

Implementing the GEOSS Water Strategy: From Observations to Decisions

In 2014, The GEOSS Water Strategy – From Observations to Decisions was published and steps were taken to implement the Strategy’s recommendations. The Strategy highlighted priority areas where the application of Earth observations to water research and water management decisions would have significant scientific and societal benefits. This article reviews the implementation of the Strategy’s recommendations over the past eight years. After a review of the Strategy’s assessment of the needs for water observations and their applications, it then reviews actions taken in response to the Strategy’s recommendations in its four major themes: improved data acquisition for Essential Water Variables, research and product development, interoperability, and capacity development. It highlights significant achievements in the implementation of the Strategy including some motivated by factors beyond this Strategy, reviews actions taken by participating agencies and programs for each of the main themes and summarizes the remaining challenges in achieving the Strategy’s full implementation. The article not only is an update for the Water Community regarding the GEOSS Water Strategy, but it is also an example of how other communities could develop and promulgate a set of strategic recommendations, monitor progress, and carry out assessments of their effectiveness.

Earth observations↗

NASA NTRSDate not supplied

A NASA GISTEMP Observational Uncertainty Ensemble: Regional and Monthly Uncertainty

The historical global temperature record is an essential data product for quantifying the variability and change of the Earth system. In recent years, better characterization of observational uncertainty in global and hemispheric trends has become available, but the methodologies are not necessarily applicable to analyses at smaller regional areas, or monthly means, where station sparsity and other systematic issues contribute to greater uncertainty. This work details a gridded uncertainty ensemble of historical temperature anomalies from the Goddard Institute for Space Studies (GISS) Surface Temperature product (GISTEMP) product. This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty in gridded historical temperature, enabling proper uncertainty propagation for climate and social science at regional and monthly scales. This work details the methodology for generating the uncertainty ensemble, key statistics of the uncertainty evolution over space and time, and provides best practices for using the uncertainty ensemble in future studies. Summary statistics from the uncertainty ensemble are in good agreement with production GISTEMP. Two applications of the uncertainty ensemble are also presented. First, the warmest year on record is shown to most likely be 2016 with a 53.2% chance and 2020 as the second most likely with a 44.4% chance. Second, it is shown that the arctic is warming 2.5 - 5 times faster than the globe, significantly faster than the regularly quoted twice as fast.

GISTEMP↗

NASA NTRS2020

Detecting Seasonal Ice Dynamics in Satellite Images

Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made more difficult by a spotty satellite record that contains no optical observations throughout the dark, polar winters. Furthermore, velocities obtained by feature tracking are marked by high noise when image pairs are separated by short time intervals and contain no direct insights into variability that occurs between images separated by long time intervals. In this paper, we describe a method of analyzing optical or SAR-derived feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr−1. We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice.

Chad A. Greene↗

NASA NTRS1982

Crop weather models of barley and spring wheat yield for agrophysical units in North Dakota

Models based on multiple regression were developed to estimate barley yield and spring wheat yield from weather data for Agrophysical units(APU) in North Dakota. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for Crop Reporting Districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU models show sight improvements in some of the statistics of the models, e.g., explained variation. These models are to be independently evaluated and compared to the previously evaluated CRD models. The comparison will indicate the preferred model area for this application, i.e., APU or CRD.

Leduc, S.↗

NASA NTRS2004

Perspectives on African Ozone from Sondes, Dobson and Aircraft Measurements

We have been studying variability in ozone over Africa using data from ozonesondes (vertical profiles from surface to stratosphere), aircraft (the MOZAIC dataset with cruise altitude and landing/takeoff profiles) and the ground (Dobson spectrophotometer total ozone column measurement). The following may give context for ozone investigations during AMMA: 1. Total ozone measurements since 1989 show considerable variability in mean value among the African stations in Algeria, Kenya, Egypt, South Africa, as well as in seasonal cycles and year-to-year. Trends are not evident. 2. The impacts of convection, stratospheric injection, biomass burning and lightning appear in ozone sounding profile data. Time-series analysis and case studies point to periodic influences of long-range interactions with the Atlantic ("ozone paradox," wave-one") and Indian Oceans. 3. Tropospheric ozone variations, observed in tropospheric profiles and integrated column amount, follow general seasonal patterns but short- term variability is so strong that simple averages are inadequate for describing "climatology" and statistical classification approaches may be required.

Thompson, A. M.↗