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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter

An accurate and reliable real-time flood forecast is crucial for mitigating flood disasters. The errors associated with the inflow boundary forcing data are considered as an important source of uncertainties in hydraulic model. In this paper, a real-time probabilistic channel flood forecasting model is developed with a novel function to incorporate the uncertainty of the forcing inflow. This new approach couples a hydraulic model with the particle filter (PF) data assimilation algorithm, a sequential Bayesian Monte Carlo method. The stage observations at hydrological stations are assimilated at each time step to update the model states in order to improve the next time step’s forecasting. This new approach is tested against a real flood event occurred in the upper Yangtze River. As compared with the open loop simulations, the evaluations of model performance with several deterministic and probabilistic metrics indicate that the accuracy of the ensemble mean prediction and the reliability of the uncertainty quantification are improved pronouncedly as a result of the PF assimilation. Further assessment of the prediction results at different lead times shows that the improvement of model performance deteriorates with the increase of the lead time due to the gradual diminishing of the updating effect for the initial conditions. Based on the analyses of the number of particles and the assimilation frequency, we find that the optimal number of particles can be determined by balancing the model performance and the computation cost, while a high assimilation frequency is preferred to incorporate the emerging observations to update the model states to match the real conditions.

Channel flood forecasting, probabilistic forecast,↗
DOE OSTI2021

Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage

Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.

42 ENGINEERING↗
NASA NTRS2013

Monte Carlo Bayesian Inference on a Statistical Model of Sub-gridcolumn Moisture Variability Using High-resolution Cloud Observations : Sensitivity Tests and Results - Part II

Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.

Monte Carlo Bayesian inference↗
NASA NTRSDate not supplied

GLDAS-2 Land Surface Model Data and Data Services at NASA GES DISC

The goal of the NASA Global Land Data Assimilation System (GLDAS, https://ldas.gsfc.nasa.gov/gldas(https://ldas.gsfc.nasa.gov/gldas)) is to generate optimal fields of land surface states and fluxes by ingesting satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques (Rodell et al., 2004).The GLDAS dataset currently archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/ (https://disc.gsfc.nasa.gov/)) is GLDAS Version 2 (GLDAS-2). It contains a series of output fields from the upgraded Noah-3.6, Catchment-F2.5, and VIC-4.1.2 Land Surface Models (LSMs) in the Land Information System (LIS-V7, https://lis.gsfc.nasa.gov/ (https://lis.gsfc.nasa.gov/)). GLDAS-2 has three components:GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the upgraded Princeton Meteorological ForcingV2.2 Dataset and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data, with data spanning from 2000 to the present. The GLDAS-2.2 product suite uses data assimilation(DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, varies for different GLDAS-2.2 products. The currently availableGLDAS-2.2 data contain a daily 0.25-degree output from the Catchment-F2.5 LSM in LIS-V7. The data are forced with the meteorological analysis fields from the operational European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) and assimilated with GRACE and GRACE-FO data, ranging from February 1, 2003 to the present. The current GLDAS-2.0 and 2.1 Noah LSM data were reprocessed in November 2019 and January 2020 respectively and their data from Catchment and VIC LSMs are new to the GLDAS-2 collection. This presentation provides a summary of theGLDAS-2 data products, their land surface fields, and their related data services at the GES DISC; and a description of the majorGLDAS-2 climatological characteristics as well as the intercomparison with the data of the previous version.

Hydrology↗
NASA NTRS2023

The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗
DOE OSTI2017

The potential value of early (1939-1967) upper-air data in atmospheric climate reanalysis

In recent years a number of reanalysis datasets have been published that cover the past century or more, including the ‘Twentieth Century Reanalysis’ 20CRv2 and the European Reanalysis of the twentieth century ERA-20C. These datasets are widely used, showing the need for, and possible benefit of, reanalysis data products designed for climate applications.The twentieth-century reanalyses so far have assimilated only surface observations, and rely on independent estimates of monthly averaged sea-surface temperatures and sea ice concentrations as boundary conditions. While 20CRv2 uses only observations of surface and sea-level pressure, ERA-20C additionally assimilates marine winds.Here we describe an experimental reanalysis, referred to as ERA-PreSAT, which covers the period 1939–1967 and also assimilates historical upper-air data. Assessments of this dataset including comparisons with independent data show that (i) temperature biases in the Northern Hemisphere are largely reduced compared to reanalyses that assimilate surface data only, (ii) concentration of 1940s upper-air data in the northern extra tropics created a strong interhemispheric asymmetry which is likely not realistic, (iii) the forecast skill in the Northern Hemisphere has increased substantially compared to reanalyses that assimilate surface data only, (iv) day-to-day and (in the northern extratropics) month-to-month correlations with independent observations (of total column ozone, upper-air data) increase over time, (v) interannual variability is well captured in the reanalysis, (vi) a signature of the stratospheric Quasi-Biennial Oscillation is present as far back as the 1940s,and (vii) tropical cyclones are not well represented. Furthermore, the generally encouraging results from the experimental ERA-PreSAT reanalysis underline that early upper-air data greatly contribute to our knowledge on the troposphere and lower stratosphere over the twentieth century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Phylogenetically conserved resource partitioning in the coastal microbial loop

Resource availability influences marine microbial community structure, suggesting that population-specific resource partitioning defines discrete niches. Identifying how resources are partitioned among populations, thereby characterizing functional guilds within the communities, remains a challenge for microbial ecologists. We used proteomic stable isotope probing (SIP) and NanoSIMS analysis of phylogenetic microarrays (Chip-SIP) along with 16S rRNA gene amplicon and metagenomic sequencing to characterize the assimilation of six 13 C-labeled common metabolic substrates and changes in the microbial community structure within surface water collected from Monterey Bay, CA. Both sequencing approaches indicated distinct substrate-specific community shifts. However, observed changes in relative abundance for individual populations did not correlate well with directly measured substrate assimilation. The complementary SIP techniques identified assimilation of all six substrates by diverse taxa, but also revealed differential assimilation of substrates into protein and ribonucleotide biomass between taxa. Substrate assimilation trends indicated significantly conserved resource partitioning among populations within the Flavobacteriia, Alphaproteobacteria and Gammaproteobacteria classes, suggesting that functional guilds within marine microbial communities are phylogenetically cohesive. However, populations within these classes exhibited heterogeneity in biosynthetic activity, which distinguished high-activity copiotrophs from low-activity oligotrophs. These results indicate distinct growth responses between populations that is not apparent by genome sequencing alone.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Global energy balance simulations of shattered pellet injection

The dynamics of shattered pellet injection (SPI) shutdowns are simulated using a time-dependent global energy balance model, based on a modification of the KPRAD framework. The new SPI particle source in the model calculates the ablation of individual pellet fragments that enter the plasma as a temporally resolved plume, thus capturing the effects of earlier fragments on the ablation of those that follow, which has a significant impact on the overall assimilation. Despite the reduced physics and the global averaging of all quantities, results from a large number of DIII-D, KSTAR, and JET experiments are well reproduced, including the plasma cooling timescales, particle assimilation, and current quench (CQ) rates. Cooling timescales and CQ rates are in good agreement for pellets containing as little as ∼1% neon, while particle assimilations are most accurate for neon fractions above ∼15% by number of atoms. Below this, the assimilation tends to be overestimated due to the lack of radial transport in the particle balance, which becomes important in the low-Z limit. Predictive simulations of mixed-composition dual-SPI shutdowns in ITER are compared against those with the 3D non-linear magnetohydrodynamic code JOREK and are found to reproduce overall trends observed in the higher-fidelity modeling across a range of injection scenarios. The general success of the model points to the critical role of energy balance in determining SPI particle assimilation and the subsequent disruption dynamics and highlights the value of these simulations for experimental interpretation and for optimizing the deployment of computationally expensive, higher-fidelity models.

energy balance model↗
DOE OSTI2022

Constraining the composition and quantity of organic matter used by abundant marine Thaumarchaeota

Marine Group I (MGI) Thaumarchaeota were originally described as chemoautotrophic nitrifiers, but molecular and isotopic evidence suggests heterotrophic and/or mixotrophic capabilities. Here, in this study, we investigated the quantity and composition of organic matter assimilated by individual, uncultured MGI cells from the Pacific Ocean to constrain their potential for mixotrophy and heterotrophy. We observed that most MGI cells did not assimilate carbon from any organic substrate provided (glucose, pyruvate, oxaloacetate, protein, urea, and amino acids). The minority of MGI cells that did assimilate it did so exclusively from nitrogenous substrates (urea, 15% of MGI and amino acids, 36% of MGI), and only as an auxiliary carbon source (<20% of that subset's total cellular carbon was derived from those substrates). At the population level, MGI assimilation of organic carbon comprised just 0.5%–11% of total biomass carbon. We observed extensive assimilation of inorganic carbon and urea- and amino acid-derived nitrogen (equal to that from ammonium), consistent with metagenomic and metatranscriptomic analyses performed here and previously showing a widespread potential for MGI to perform autotrophy and transport and degrade organic nitrogen. Our results constrain the quantity and composition of organic matter used by MGI and suggest they use it primarily to meet nitrogen demands for anabolism and nitrification.

13C↗
DOE OSTI2026

Biochemical parallels between catabolic pathways for lignin-associated aromatic dimers

Lignin is one of the most common biopolymers on Earth. In nature, lignin is primarily deconstructed by fungi into mixtures of aromatic compounds that are then assimilated by bacteria and fungi. Industrially, lignin is primarily generated as a byproduct of pulp and paper production and burned for process heat. However, if the appropriate assimilatory pathways were identified, deconstructed lignin could be funneled into value-added products using engineered bacteria. Foundational work has described pathways for assimilation of diverse monomeric aromatic compounds such as protocatechuate, ferulate, and syringate, as well as select dimers including those with β-O-4 and 5-5 interunit linkages. Recent advances have elucidated additional pathways for dimer assimilation, including pathways for new substrates as well as parallel pathways for previously characterized substrates. Comparing these dimer assimilation pathways can illuminate the underlying biochemical logic of assimilation for lignin-associated aromatic dimers and provide opportunities for metabolic engineering to enhance lignin valorization.

Sphingomonas↗
DOE OSTI2018

Improved Wave Energy Production Forecasts for Smart Grid Integration

Integration of renewable power sources into electrical grids remains an active research and development area, particularly for less developed renewable energy technologies, such as wave energy converters (WECs). High spatio-temporal resolution and accurate wave forecasts at a potential WEC (or WEC array) lease area are needed to improve WEC power prediction and to facilitate grid integration, particularly for microgrid locations. The availability of high quality measurement data from recently developed low-cost buoys allows for operational assimilation of wave data into forecast models at remote locations where real-time data have previously been unavailable. This work includes the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. Spoondrift wave measurement buoys were deployed off the coast of Yakutat, Alaska, a microgrid site with high wave energy resource potential. A wave modeling framework with data assimilation was developed and assessed, which was most effective when the incoming forecasted boundary conditions did not represent the observations well. For that case, assimilation of the wave height data using the ensemble Kalman filter resulted in a reduction of wave height forecast normalized root mean square error from 27% to an average of 16% over a 12-hour period. This results in reduction of wave power forecast error from 73% to 43%. In summary, the use of the low-cost wave buoy data assimilated into the wave modeling framework improved the forecast skill and will provide a useful development tool for the integration of WECs into electrical grids.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Improved Wave Energy Production Forecasts for Smart Grid Integration

Integration of renewable power sources into electrical grids remains an active research and development area, particularly for less developed renewable energy technologies, such as wave energy converters (WECs). High spatio-temporal resolution and accurate wave forecasts at a potential WEC (or WEC array) lease area are needed to improve WEC power prediction and to facilitate grid integration, particularly for microgrid locations. The availability of high quality measurement data from recently developed low-cost buoys allows for operational assimilation of wave data into forecast models at remote locations where real-time data have previously been unavailable. This work includes the development and assessment of a wave modeling framework with real-time data assimilation capabilities for WEC power prediction. Spoondrift wave measurement buoys were deployed off the coast of Yakutat, Alaska, a microgrid site with high wave energy resource potential. A wave modeling framework with data assimilation was developed and assessed, which was most effective when the incoming forecasted boundary conditions did not represent the observations well. For that case, assimilation of the wave height data using the ensemble Kalman filter resulted in a reduction of wave height forecast normalized root mean square error from 27% to an average of 16% over a 12-hour period. This results in reduction of wave power forecast error from 73% to 43%. In summary, the use of the low-cost wave buoy data assimilated into the wave modeling framework improved the forecast skill and will provide a useful development tool for the integration of WECs into electrical grids.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Cyanogen Metabolism in Cassava Roots: Impact on Protein Synthesis and Root Development

Cassava (Manihot esculenta Crantz), a staple crop for millions of sub-Saharan Africans, contains high levels of cyanogenic glycosides which protect it against herbivory. However, cyanogens have also been proposed to play a role in nitrogen transport from leaves to roots. Consistent with this hypothesis, analyses of the distribution and activities of enzymes involved in cyanide metabolism provides evidence for cyanide assimilation, derived from linamarin, into amino acids in cassava roots. Both β-cyanoalanine synthase (CAS) and nitrilase (NIT), two enzymes involved in cyanide assimilation to produce asparagine, were observed to have higher activities in roots compared to leaves, consistent with their proposed role in reduced nitrogen assimilation. In addition, rhodanese activity was not detected in cassava roots, indicating that this competing means for cyanide metabolism was not a factor in cyanide detoxification. In contrast, leaves had sufficient rhodanese activity to compete with cyanide assimilation into amino acids. Using transgenic low cyanogen plants, it was shown that reducing root cyanogen levels is associated with elevated root nitrate reductase activity, presumably to compensate for the loss of reduced nitrogen from cyanogens. Finally, we overexpressed Arabidopsis CAS and NIT4 genes in cassava roots to study the feasibility of enhancing root cyanide assimilation into protein. Optimal overexpression of CAS and NIT4 resulted in up to a 50% increase in root total amino acids and a 9% increase in root protein accumulation. However, plant growth and morphology was altered in plants overexpressing these enzymes, demonstrating a complex interaction between cyanide metabolism and hormonal regulation of plant growth.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1981

Numerical prediction of the monsoon depression of 5-7 July 1979

A well defined monsoon depression was used for two assimilation and forecast experiments: (1) using conventional surface and upper air data, (2) using these data plus Monex data. The data sets were assimilated and used with a general circulation model to make numerical predictions. The model, the analysis and assimilation procedure, the differences in the analyses due to different data inputs, and the differences in the numerical predictions are described. The MONEX data have a positive impact, although the differences after 24 hr are not significant. The MONEX assimilation does not agree with manual analysis location of depression center. The 2.5 x 3 deg horizontal resolution of the prediction model is too coarse. The assimilation of geopotential height data derived from satellite soundings generated gravity waves with amplitudes similar to the meteorologically significant features investigated.

Shukla, J.↗
NASA NTRS1987

A VAS-numerical model impact study using the Gal-Chen variational approach

A numerical study based on the use of a variational assimilation technique of Gal-Chen (1983, 1986) was conducted to assess the impact of incorporating temperature data from the VISSR Atmospheric Sounder (VAS) into a regional-scale numerical model. A comparison with the results of a control forecast using only conventional data indicated that the assimilation technique successfully combines actual VAS temperature observations with the dynamically balanced model fields without destabilizing the model during the assimilation cycle. Moreover, increasing the temporal frequency of VAS temperature insertions during the assimilation cycle was shown to enhance the impact on the model forecast through successively longer forecast periods. The incorporation of a nudging technique, whereby the model temperature field is constrained toward the VAS 'updated' values during the assimilation cycle, further enhances the impact of the VAS temperature data.

Aune, Robert M.↗
NASA NTRS1996

Experimental Constraints on the Origin of Lunar High-Ti Ultramafic Glasses

Phase equilibria and dissolution rate experiments are used to develop a petrogenetic model for the high-Ti lunar ultramafic glasses. Near-liquidus phase relations of the Apollo 14 black glass, the most Ti-rich lunar ultramafic glass, are determined to 2.2-GPa. The liquidus is saturated with Cr-spinel at 1-atm, olivine between approximately 0.5- and 1.5-GPa, and low-Ca pyroxene + Cr-spinel above 1.5-GPa. Ilmenite does not crystallize near the liquidus and implies that high-Ti ultramafic glasses are not produced by melting of an ilmenite-saturated source. We infer that high-Ti ultramafic magmas are derived from low-Ti ultramafic parent magmas by assimilation of ilmenite +/- clinopyroxene +/- urKREEP +/- pigeonite in the shallow lunar interior. Heat is provided by adiabatic ascent of the low-Ti ultramafic primary magmas from the deeper lunar interior and crystallization of olivine during assimilation. The assimilation reaction is modeled by mass balance and requires that ilmenite and high-Ca pyroxene are assimilated in a approximately 3:1 ratio, a much higher ratio than the proportion in which these minerals are thought to exist in the lunar interior. In an effort to understand the kinetic controls on this reaction, the dissolution of ilmenite is examined experimentally in both low- and high-Ti lunar magmas. We find that ilmenite dissolves incongruently to Cr-spinel and a high-Ti melt. The dissolution reaction proceeds by a diffusion-controlled mechanism. An assimilation model for the origin of high-Ti melts is developed that leaves the magma ocean cumulates in their initial stratigraphic positions and obviates source hybridization models that require lunar overturn.

Wagner, T. P.↗
NASA NTRS2000

Improving Global Analysis and Short-Range Forecast Using Rainfall and Moisture Observations Derived from TRMM and SSM/I Passive Microwave Instruments

The Global Precipitation Mission, a satellite project under consideration as a follow-on to the Tropical Rainfall Measuring Mission (TRMM) by the National Aeronautics and Space Agency (NASA) in the United States, the National Space Development Agency (NASDA) in Japan, and other international partners, comprises an improved TRMM-like satellite and a constellation of 8 satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-hour intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using, rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and 2 Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating the 6-h averaged TMI and SSM/I surface rainrate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System (CERES) instrument and brightness temperature observations by the TIROS Operational Vertical Sounder (TOVS) instruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the tropics. This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of 4-dimensional global datasets for climate analysis and weather forecasting applications.

Hou, Arthur Y.↗