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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Modification of a successive corrections objective analysis for improved higher order calculations

The use of objectively analyzed fields of meteorological data for the initialization of numerical prediction models and for complex diagnostic studies places the requirements upon the objective method that derivatives of the gridded fields be accurate and free from interpolation error. A modification was proposed for an objective analysis developed by Barnes that provides improvements in analysis of both the field and its derivatives. Theoretical comparisons, comparisons between analyses of analytical monochromatic waves, and comparisons between analyses of actual weather data are used to show the potential of the new method. The new method restores more of the amplitudes of desired wavelengths while simultaneously filtering more of the amplitudes of undesired wavelengths. These results also hold for the first and second derivatives calculated from the gridded fields. Greatest improvements were for the Laplacian of the height field; the new method reduced the variance of undesirable very short wavelengths by 72 percent. Other improvements were found in the divergence of the gridded wind field and near the boundaries of the field of data.

Achtemeier, Gary L.↗
NASA NTRS2018

Prediction Skill of U.S. Flash Droughts in Subseasonal Experiment (SubX) Model Hindcasts

Droughts that establish themselves over a short period of time (weeks to a few months), referred to as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict such droughts in advance would greatly enhance our preparation for them and potentially reduce their impacts. The sub-seasonal time scale at which flash droughts occur emphasizes the importance of producing forecasts at weekly or finer intervals that extend beyond the numerical weather prediction time frame. Here we assess the ability of eight global forecast systems, each participating in the Sub-seasonal Experiment project (SubX), to predict key features associated with rapidly developing droughts over the United States during the last two decades. MERRA2 reanalysis is used as observations. Prediction skill for temperature and precipitation anomalies during these events is limited to the first 1-2 weeks after initialization for most hindcasts. However, there are some hindcasts in which large anomalies are well predicted 3-4 weeks or more in advance. The physical mechanisms that are key to the development of surface anomalies, including quasi-stationary atmospheric waves, were also evaluated. Most hindcasts were unable to capture the development or progression of such drought-inducing circulation features more than 1-2 weeks in advance.

Deangelis, Anthony M.↗
NASA NTRS1995

Wavenumber selection and hysteresis in nonlinear baroclinic flow

Wavenumber transition and hysteresis in a highly unstable baroclinic flow are investigated using a high-resolution spectral numerical model. As the flow becomes more supercritical, the dominant wave gradually shifts from the most unstable wave predicted by the linear theory to a longer wave with a larger time-averaged amplitude, while the rectified mean flow attains a stronger shear at the center of the channel. The numerical results display a complex hysteresis behavior, which occurs not only between the states of different dominant wavenumbers, but also between the states of identical dominant wavenumber but of different dynamic characteristics. In a certain parameter range three stable states, each with different dominant wavenumber, are possible, and in another parameter range four stable states are possible, among them three stable states with an identical dominant wave. The numerical results suggest that a multiple weather regime exists even without external forcing in which the flow aperiodically varies between two distinct behaviors. The effects of stable higher harmonics are assessed and it is found that their presence contributes not only to the better approximation of the model solutions but also to the selection of the final equilibrium state, due to the chaotic nature of the initial transient period.

Chou, Shih-Hung↗
NASA NTRS1977

A balanced diagnostic system compatible with a barotropic prognostic model

A system of diagnostic equations for the velocity field, or wind laws, for a barotropic primitive-equation model of large-scale atmospheric flow is derived. Attention is given to the classical balance equation and its ellipticity condition. Numerical solutions of the diagnostic system are presented, including examples of cases of the mixed elliptic-hyperbolic type and cases with non-zero divergence. Procedures for implementing such a system are outlined, along with a review of factors in using the technique for operational numerical weather prediction.

Ghil, M.↗
NASA NTRS2005

Wake Vortex Tracking Using a 35 GHz Pulsed Doppler Radar

A 35 GHz, pulsed-Doppler radar system has been designed and assembled for wake vortex detection and tracking in low visibility conditions. Aircraft wake vortices continue to be an important factor in determining safe following distances or spacings for aircraft in the terminal area. Currently, under instrument meteorological conditions (IMC), aircraft adhere to conservative, fixed following-distance guidelines based primarily on aircraft weight classifications. When ambient conditions are such that vortices will either drift or dissipate, leaving the flight corridor clear, the prescribed spacings are unnecessarily long and result in decreased airport throughput. There is a potential for significant airport efficiency improvement, if a system can be employed to aid regulators and pilots in setting safe and efficient following distances based on airport conditions. The National Aeronautics and Space Administration (NASA), the Federal Aviation Agency, and Volpe National Transportation Systems Center have promoted and worked to develop systems that would increase airport capacity and provide for safe reductions in aircraft separation. The NASA Aircraft Vortex Spacing System (AVOSS), a wake vortex spacing system that can provide dynamic adjustment of spacings based on real-time airport weather conditions, has demonstrated that Lidar systems can be successfully used to detect and track vortices in clear air conditions. To fill the need for detection capability in low-visibility conditions, a 35 GHz, pulsed-Doppler radar system is being investigated for use as a complimentary, low-visibility sensor for wake vortices. The radar sensor provides spatial and temporal information similar to that provided by Lidar, but under weather conditions that a Lidar cannot penetrate. Currently, we are analyzing the radar design based upon the data and experience gained during the wake vortex Lidar deployment with AVOSS at Dallas/Fort Worth International Airport. As part of this study, two numerical models were utilized in system simulations. The results of this study improve our understanding of the method of detection, resolution requirements for range and azimuth, pulse compression, and performance prediction. Simulations applying pulse compression techniques show that detection is good in heavy fog to greater than 2000 m. Both compressed and uncompressed short pulses show the vortex structure. To explore operational challenges, siting and scanning strategies were also analyzed. Simulation results indicate that excellent wake vortex detection, tracking and classification is possible in drizzle (+15 dBZ) and heavy fog (- 13 dBZ) using short pulse techniques (<99ns) at ranges on the order of 900 m, with a modest power of 500 W output. At 1600 m, detection can be expected at reflectivities as low as -13 dBZ (heavy fog). The radar system, as designed and built, has the potential to support field studies of a wake vortex spacing system in low-visibility conditions ranging from heavy fog to rain, when sited within 2000m of the flight path.

Neece, Robert T.↗
NASA NTRS2012

Evaluation of Cloud Microphysics Simulated using a Meso-Scale Model Coupled with a Spectral Bin Microphysical Scheme through Comparison with Observation Data by Ship-Borne Doppler and Space-Borne W-Band Radars

Equivalent radar reflectivity factors (Ze) measured by W-band radars are directly compared with the corresponding values calculated from a three-dimensional non-hydrostatic meso-scale model coupled with a spectral-bin-microphysical (SBM) scheme for cloud. Three case studies are the objects of this research: one targets a part of ship-borne observation using 95 GHz Doppler radar over the Pacific Ocean near Japan in May 2001; other two are aimed at two short segments of space-borne observation by the cloud profiling radar on CloudSat in November 2006. The numerical weather prediction (NWP) simulations reproduce general features of vertical structures of Ze and Doppler velocity. A main problem in the reproducibility is an overestimation of Ze in ice cloud layers. A frequency analysis shows a strong correlation between ice water contents (IWC) and Ze in the simulation; this characteristic is similar to those shown in prior on-site studies. From comparing with the empirical correlations by the prior studies, the simulated Ze is overestimated than the corresponding values in the studies at the same IWC. Whereas the comparison of Doppler velocities suggests that large-size snowflakes are necessary for producing large velocities under the freezing level and hence rules out the possibility that an overestimation of snow size causes the overestimation of Ze. Based on the results of several sensitivity tests, we conclude that the source of the overestimation is a bias in the microphysical calculation of Ze or an overestimation of IWC. To identify the source of the problems needs further validation research with other follow-up observations.

Iguchi, T.↗
NASA NTRS1986

A case study of GWE satellite data impact on GLA assimilation analyses of two ocean cyclones

The effects of the Global Weather Experiment (GWE) data obtained on January 18-20, 1979 on Goddard Laboratory for Atmospheres assimilation analyses of simultaneous cyclones in the western Pacific and Atlantic oceans are examined. The ability of satellite data within assimilation models to determine the baroclinic structures of developing extratropical cyclones is evaluated. The impact of the satellite data on the amplitude and phase of the temperature structure within the storm domain, potential energy, and baroclinic growth rate is studied. The GWE data are compared with Data Systems Test results. It is noted that it is necessary to characterize satellite effects on the baroclinic structure of cyclone waves which degrade numerical weather predictions of cyclogenesis.

Gallimore, R. G.↗
DOE OSTI2021

Predicting Frigid Mixed-Phase Clouds for Pristine Coastal Antarctica

Supercooled water is common in the clouds near coastal Antarctica and occasionally occurs at temperatures at or below -30°C. Yet the ice physics in most regional and global numerical models will glaciate out these clouds. This presents a challenge for the simulation of highly supercooled clouds that were observed at McMurdo, Antarctica during the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) project during 2015–2017. The polar optimized version of the Weather Research and Forecasting model (Polar WRF) with the recently developed two-moment P3 microphysics scheme was used to simulate observed supercooled liquid water cases during March and November 2016. Nudging of the simulations to observed rawinsonde profiles and Antarctic automatic weather station observations provided increased realism and much greater cloud water amounts. Furthermore, sensitivity tests that adjust the ice physics for extremely low ice nucleating particle (INP) concentrations decrease cloud ice and increases the cloud liquid water closer to observed amounts. In these tests, a liquid layer near cloud top is simulated, in agreement with observations. Accurate representation of INP concentrations appears to be critical for the simulation of coastal Antarctic clouds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.

radar backscatter↗
NASA NTRS1999

Global Acceleration of Coronal Mass Ejections

Using the observed relation between speeds of coronal mass ejections (CMEs) near the Sun and in the solar wind, we estimate a global acceleration acting on the CMEs. Our study quantifies the qualitative results of Gosling [1997] and numerical simulations that CMEs at 1 AU with speeds closer to the solar wind. We found a linear relation between the global acceleration and the initial speed of the CMEs and the absolute value of the acceleration is similar to the slow solar wind acceleration. Our study naturally divides CMEs into fast and slow ones, the dividing line being the solar wind speed. Our results have important implications to space weather prediction models which need to incorporate this effect in estimating the CME arrival time at 1 AU. We show that the arrival times of CMEs at 1 AU are drastically different from the zero acceleration case.

Gopalswamy, Nat↗
DOE OSTI2025

Complex Dependence of Calcite Crack Kinetics on Salinity: The Role of DLVO and Hydration Forces

Abstract Subcritical crack growth (SCG) plays an important role in many geological processes such as delayed earth rupture and rock weathering. The complex dependency of SCG on the in‐crack fluid chemistry, however, is still poorly understood. In this study, we utilize the newly developed surface force‐based fracture theory (SFFT) to elucidate the relative contributions of surface forces and solute transport to the crack growth kinetics of calcite in NaCl solutions. Expanding on Barenblatt's cohesive crack model, SFFT introduces an effective stress intensity at the crack tip that encompasses all the relevant intermolecular forces across the crack in addition to the external far‐field stresses. The nonlinear system of equations portraying the crack opening profile, the solute distribution in a propagating crack, and the crack growth velocity are numerically solved via an implicit scheme. After carefully calibrating the model for calcite‐water systems, the SFFT is used to predict the SCG response of calcite at different NaCl concentrations, based on various hypotheses. These predictions are then compared to existing SCG data from the literature. We demonstrate that the experimentally observed variation of SCG rate with NaCl concentration cannot be explained solely by DLVO forces (electrostatic and Van der Waals interactions). This can be remediated by introducing an exponentially decaying hydration force with a nonlinear, nonmonotonic dependence on NaCl concentration. Furthermore, we demonstrate that accounting for both diffusive and advective transport of ions is important in explaining the absence of a stage‐II SCG response for calcite in electrolyte solutions. Plain Language Summary Subcritical crack growth (SCG) refers to the slow propagation of cracks in materials under a stress below the threshold for catastrophic failure. SCG is a key process in many geological events, for example, delayed earth ruptures and rock weathering. New initiatives such as underground CO 2 and H 2 storage in carbonate reservoirs further call for better understanding of SCG in carbonate minerals subjected to varying fluid chemistry. This study examines the SCG of calcite, a key mineral found in carbonate rocks, intergranular cement in sandstones, and filling material in mineral veins and faults, determining their deformation and strength. A mathematical model is developed to describe how the crack opens and propagates, how solutes (like salts) distribute within the crack, and how the crack surfaces interact with each other. We used the model to predict calcite SCG in water at different salt concentrations and compared it with experimental data. Our results revealed that the hydration force is the dominating factor in determining the complex, non‐linear dependency of SCG on salinity. We also found that both the movement of ions by diffusion and by bulk water flow are crucial for explaining the SCG rates, especially when the cracks grow quickly. Key Points Surface Force‐Based Fracture Theory predicts the complex subcritical crack growth patterns of calcite crystals immersed in NaCl solutions Results highlight the dominant role of hydration forces in altering the fracture behavior of calcite compared to VdW and electric double‐layer forces Advective solute transport explains the absence of stages‐II and ‐III subcritical crack growth responses in solid‐liquid systems

DLVO↗
NASA NTRS1985

The response of numerical weather prediction analysis systems to FGGE 2b data

An intercomparison of analyses of the main PGGE Level IIb data set is presented with three advanced analysis systems. The aims of the work are to estimate the extent and magnitude of the differences between the analyses, to identify the reasons for the differences, and finally to estimate the significance of the differences. Extratropical analyses only are considered. Objective evaluations of analysis quality, such as fit to observations, statistics of analysis differences, and mean fields are discussed. In addition, substantial emphasis is placed on subjective evaluation of a series of case studies that were selected to illustrate the importance of different aspects of the analysis procedures, such as quality control, data selection, resolution, dynamical balance, and the role of the assimilating forecast model. In some cases, the forecast models are used as selective amplifiers of analysis differences to assist in deciding which analysis was more nearly correct in the treatment of particular data.

Hollingsworth, A.↗
DOE OSTI2020

Empirically verified analysis of dual pre-cooling system for hydrogen refuelling station

The dual cooling system at the California State University Los Angeles Hydrogen Research and Fueling Facility is analysed to predict the output hydrogen temperature in different ambient conditions. Initially, the facility was built with a coil chiller T20 cooling, sufficient for occasional fuelings. A flat plate evaporator system was added in series to resolve any issues for multiple fuelings even in the hottest weather. This study was commenced to verify the set points and determine the control requirements for the dual setup in order to inform future designs. A numerical-empirical model of the cooling system was developed by reverse engineering, using the station experimental measurements and database records. The subsystems are characterized separately but validated as a whole system imposing specific and standard operating conditions. The cooling model is tested with different inputs to validate the pre-set parameter variation of the cooling components. Ten scenarios were investigated to evaluate the fueling output parameters, testing two hydrogen temperature input trends (according to the facility initial conditions) and five ambient temperatures (from 15 °C to 35 °C). In closing, the simulation results have confirmed that the hydrogen output temperature remains within the range imposed by the SAE J2601 for each scenario studied, around -22 °C and -25 °C.

08 HYDROGEN↗
DOE OSTI2024

Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics

One of the prominent effects of space weather is the formation of rapid geomagnetic field variations on Earth's surface driven by the magnetosphere-ionosphere system. These geomagnetic disturbances (GMDs) cause geomagnetically induced currents to run through ground conducting systems. In particular, localized GMDs (LGMDs) can be high amplitude and can have an effect on scale sizes less than 100 km, making them hazardous to power grids and difficult to predict. In this study, we examine the ability of the Space Weather Modeling Framework (SWMF) to reproduce LGMDs in the 7 September 2017 event using both existing and new metrics to quantify the success of the model against observation. We show that the high-resolution SWMF can reproduce LGMDs driven by ionospheric sources, but struggles to reproduce LGMDs driven by substorm effects. We calculate the global maxima of the magnetic fluctuations to show instances when the SWMF captures LGMDs at the correct times but not the correct locations. To remedy these shortcomings we suggest model developments that will directly impact the ability of the SWMF to reproduce LGMDs, most importantly updating the ionospheric conductance calculation from empirical to physics-based.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Understanding Relationships Between Satellite, Model, and Ground-Based Surface Temperature Characterizations From Overcast to Clear Conditions in Support of Satellite Remote Sensing of Clouds and Radiation

Accurate and consistent global estimates of cloud coverage and their properties are fundamental to long-term Earth radiation budget (ERB) monitoring efforts like the Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud detection algorithms often apply thresholding approaches to identify where clouds occur by comparing satellite-measured radiances with those that are expected under cloud-free conditions. In addition, once a cloud is detected, the derivation of cloud optical and microphysical properties also requires knowledge of the background radiances below the cloud. In the infrared, knowledge of the surface emissivity and the expected skin temperature under both cloudy and cloud-free conditions is needed. These traits are generally well known over the oceans. Over land, however, comparisons between satellite-derived land surface temperature (LST) with that characterized in numerical weather analyses reveal large differences in many parts of the world, often exceeding 5 K, which can lead to significant satellite cloud detection and cloud property retrieval errors. Furthermore, clouds have a dramatic influence on the LST, and therefore characterization of that model parameter also depends on the capability of the model to accurately resolve clouds. Thus, the LST characterized in models is, at times, a poor approximation for what would otherwise be observed, thereby impeding accurate satellite cloud retrievals. As a result, we seek to develop a more robust method for estimating the LST required for satellite cloud characterizations. This effort is accomplished through a combination of surface emission/air temperature relationship studies in all-sky conditions using ground measurement stations, along with deep neural network (DNN) estimates of expected LST under overcast and cloud-free conditions. We demonstrate that substituting DNN-predicted LST for that generated by numerical models can mitigate model-inherent diurnal dependencies and reduce overall bias and uncertainty relative to satellite/ground observations by 0.5–4 K and 0.5–2 K, respectively. It is expected that this work will lead to improved satellite cloud retrievals that enhance ERB monitoring efforts.

B Scarino↗
NASA NTRS1985

Sea breeze-induced mesoscale systems and severe weather

The relationship between thunderstorm activity during the summer months along coastal regions of the Atlantic and Gulf coasts and the dry sea breeze circulation was investigated. Satellite composites of thunderstorm activity for synoptically undisturbed conditions have been obtained for south Florida for a series of days in the summer of 1983. These data were catalogued into different low level synoptic flow regimes. Five synoptic flow regimes were found from the data. A three-dimensional mesoscale numerical model was used for each sysnoptic flow regime to quantitatively predict the location of enhanced thunderstorm activity. This model includes a parameterization of vegetation and soil moisture feedbacks as well as a sophisticated planetary boundary layer representation. Using the results of the satellite image composites, spatial and temporal characteristics of deep convective cloud patterns and their variation with synoptic flow are described. The results from the numerical model have provided explanations for the observed patterns.

Pielke, R. A.↗