SEARCH · Search NASA
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.
Electron precipitation patterns and their relation to substorm increases in electrons at synchronous orbit
Abstract Atom‐ and energy‐efficient chemical separations are urgently needed to meet the surging demand for critical materials that has strained supply chains and threatened environmental damage. In this study, we used reaction‐diffusion coupling to separate iron, neodymium, and dysprosium ions from model feedstocks of permanent magnets, which are typically found in electronic wastes. Feedstock solutions were placed in contact with a hydrogel loaded with potassium hydroxide and/or dibutyl phosphate, resulting in complex precipitation patterns as the various metal ions diffused into the reaction medium. Specifically, we observed the precipitation of up to 40 mM of iron from the feedstock, followed by the enrichment of 73 % dysprosium, and the extraction of >95 % neodymium product at a further distance from the solution‐gel interface. We designed a series of experiments and simulations to determine the relevant ion diffusivities, D Nd =5.4×10 −10 and D Dy =5.1×10 −10 m 2 /s, and precipitation rates, k Nd =1.0×10 −5 and k Dy =5.0×10 −3 m 9 mol −3 s −1 , which enabled a numerical model to be established for predicting the distribution of products in the reaction medium. Our proof‐of‐concept study validates reaction‐diffusion coupling as an effective and versatile approach for critical materials separations, without relying on ligands, membranes, resins, or other specialty chemicals.
As shown from ground-based measurements and satellite-borne imagers, one type of global auroral pattern characteristic of quiet (usually northward IMF) intervals is that of a contracted but thickened emission region of a pattern referred to as 'horse-collar' aurora (Hones et al., 1989). In this report we use the Dynamics Explorer data set to examine a case in which this horse-collar pattern was observed by the DE-1 auroral imager, while at the same time DE-2, at lower altitude, measured precipitating particles, electric and magnetic fields, and plasma drifts. Our analysis shows that, in general, there is close agreement between the optical signatures and the particle precipitation patterns. In many instances, over scales ranging from tens to a few hundred kilometers, electron precipitation features and upward field-aligned currents are observed at locations where the plasma flow gradients indicate negative V-average x E. The particle, plasma, and field measurements made along the satellite track and the 2D perspective of the imager provide a means of determining the configuration of convective flows in the high-latitude ionosphere during this interval of northward IMF. Recent mapping studies are used to relate the low-altitude observations to possible magnetospheric source regions.
The 1997-1999 ENSO period was very powerful, but also well observed. Multiple satellite rainfall estimates combined with gauge observations allow for a quantitative analysis of precipitation anomalies in the tropics and elsewhere accompanying the 1997-99 ENSO cycle. An examination of the evolution of the El Nino and accompanying precipitation anomalies revealed that a dry Maritime Continent preceded the formation of positive SST anomalies in the eastern Pacific Ocean. 30-60 day oscillations in the winter of 1996/97 may have contributed to this lag relationship. Furthermore, westerly wind burst events may have maintained the drought over the Maritime Continent. The warming of the equatorial Pacific was then followed by an increase in convection. A rapid transition from El Nino to La Nina occurred in May 1998, but as early as October-November 1997 precipitation indices captured substantial changes in Pacific rainfall anomalies. The global precipitation patterns for this event were in good agreement with the strong consistent ENSO-related precipitation signals identified in earlier studies. Differences included a shift in precipitation anomalies over Africa during the 1997-98 El Nino and unusually wet conditions over northeast Australia during the later stages of the El Nino. Also, the typically wet region in the north tropical Pacific was mostly dry during the 1998-99 La Nina. Reanalysis precipitation was compared to observations during this time period and substantial differences were noted. In particular, the model had a bias towards positive precipitation anomalies and the magnitudes of the anomalies in the equatorial Pacific were small compared to the observations. Also, the evolution of the precipitation field, including the drying of the Maritime Continent and eastward progression of rainfall in the equatorial Pacific was less pronounced for the model compared to the observations.
Sensitivity experiments with an atmospheric general circulation model (AGCM) show that parameterized rain re-evaporation has a large impact on simulated precipitation patterns in the tropical Pacific, especially on the configuration of the model s intertropical convergence zone (ITCZ). Weak re-evaporation leads t o the formation of a "double ITCZ" during the northern warm season. The double ITCZ is accompanied by strong coupling between precipitation and high-frequency vertical motion in the planetary boundary layer (PBL). Strong reevaporation leads to a better overall agreement of simulated precipitation with observations. The model s double ITCZ bias is reduced. At the same time, correlation between high-frequency vertical motion in the PBL and precipitation is reduced. Experiments with modified physics suggest that evaporative cooling by rain near the PBL top weakens the coupling between precipitation and vertical motion. This may reduce the model s tendency to form double ITCZs. The strength of high-frequency vertical motions in the PBL was also reduced directly through the introduction of a diffusive cumulus momentum transport (DCMT) parameterization. The DCMT had a visible impact on simulated precipitation in the tropics, but did not reduce the model s double bias in all cases.
Abstract In this study, we compare the Energy Exascale Earth Systems Model (E3SM) multiscale modeling framework (MMF) with the cloud resolving model (CRM) configured in two (2dMMF) and three (3dMMF) dimensions. We explore how CRM dimensionality impacts the representation of mean and extreme precipitation characteristics. Our results show that tropical mean precipitation patterns are better represented in 3dMMF compared to observations (Integrated Multi‐satellitE Retrivals for GPM and Global Precipitation Climatology Project One Degree Daily products), while 2dMMF better captures extreme precipitation intensity, with systematic land‐ocean differences in precipitation and cloud‐associated variables. These differences are attributed to the co‐occurrence of CRM throttling (i.e., suppressed convection in due to smaller numbers of CRM columns and domain size) and dilution (i.e., 3‐D cloud circulations with increased entrainment and lower precipitation efficiency) effects. Overall, throttling results in more low‐level humidity in 2dMMF and dilution contributes to more high clouds with less precipitation efficiency in 3dMMF. Since throttling occurs more strongly over the ocean than land, the 3dMMF tends to have less cloud liquid and precipitation over the ocean and more cloud ice and precipitation over land. These results may serve as a guide for choosing the CRM structure to reduce precipitation and cloud‐related biases.
In this project, our overarching goal was to evaluate the skill and credibility of statistical and dynamical downscaling techniques for reconstructing the historical climate record and projecting future climate change. This report summarizes our approach and accomplishments. In the first year of the project, the priority was to generate new data sets. For Year 2, the overall goal was to focus comparisons during the historical period and for a subset of variables, especially temperature and precipitation. In Year 3 and beyond, the focus was mainly on comparisons and metrics for future climate change simulations. To quantify the uncertainty associated with gridded dataset choice, the UCLA group compared eight gridded temperature datasets, examining climatologies, variability, and trends. The Scripps group completed statistical downscaling of NARR with the LOCA method, and UCLA and Scripps collaborated on a comparison of historical and end-of-century temperature and precipitation patterns over California from WRF, LOCA-WRF, and LOCA-Livneh. In addition to downscaling temperature and precipitation, the Scripps group has spent considerable time working on near-surface wind and humidity downscaling. The Santa Clara group assembled the Variable Infiltration Capacity (VIC) for nine snow-dominated river basins draining the western slopes of the Sierra Nevada range and assessed the role of snow albedo feedback in their VIC simulations. The results will help inform future downscaling studies.
In this project, our overarching goal was to evaluate the skill and credibility of statistical and dynamical downscaling techniques for reconstructing the historical climate record and projecting future climate change. This report summarizes our approach and accomplishments. In the first year of the project, the priority was to generate new data sets. For Year 2, the overall goal was to focus comparisons during the historical period and for a subset of variables, especially temperature and precipitation. In Year 3 and beyond, the focus was mainly on comparisons and metrics for future climate change simulations. To quantify the uncertainty associated with gridded dataset choice, the UCLA group compared eight gridded temperature datasets, examining climatologies, variability, and trends. The Scripps group completed statistical downscaling of NARR with the LOCA method, and UCLA and Scripps collaborated on a comparison of historical and end-of-century temperature and precipitation patterns over California from WRF, LOCA-WRF, and LOCA-Livneh. In addition to downscaling temperature and precipitation, the Scripps group has spent considerable time working on near-surface wind and humidity downscaling. The Santa Clara group assembled the Variable Infiltration Capacity (VIC) for nine snow-dominated river basins draining the western slopes of the Sierra Nevada range and assessed the role of snow albedo feedback in their VIC simulations. The results will help inform future downscaling studies.
After more than a decade of moderate seasonal deviations from the expected climate, it is easy to forget that California is actually prone to instabilities in precipitation patterns that occur on various scales. Using modern satellite and reanalysis data we reassess certain aspects of the precipitation climate in California from the past three decades. California has a well-pronounced rain season that peaks in December-February. However, the 95% confidence interval around the climatological precipitation during these months imply that deviations on the order of 60% of the expected amounts are very likely during the most important period of the rain season. While these positive and negative anomalies alternate almost every year and tend to cancel each other, severe multi-year declines of precipitation in California seem to appear on decadal scales. The 1986-1994 decline of precipitation was similar to the current one that started in 2011, and is apparent in the reanalysis data. In terms of accumulated deficits of precipitation, that episode was no less severe than the current one. While El Niño (the warm phase of the El Niño Southern Oscillation, ENSO) is frequently cited as the natural forcing expected to bring a relief, our assessment is that ENSO has been driving at best only 6% of precipitation variability in California in the past three decades. It means El Niño needs to be stronger and longer, in order to have a higher likelihood of a positive impact, and the current one does not match these criteria. Using fractional risk analysis of precipitation populations during normal and dry periods, we show that the likelihood of losing the most intensive precipitation events drastically increases during the multi-year drying events. Since storms delivering up to 50% of precipitation in California are driven by atmospheric rivers making landfall, thus the importance of their suppression and blockage by persistent ridges of atmospheric pressure in the northeast Pacific.
Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to different drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle nor that from individual drivers is constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulfate have been perturbed separately. While the global-mean IWV is usually assumed to increase by 7% per kelvin of surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9%K(exp -1) for sulfate to 9.8±2%K(exp -1) for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7d between 1986-2005 and 2081-2100 under a high-emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the mean time, and therefore likely the distance, between evaporation and precipitation.
Earth system models (ESMs) bridge observationally based and theoretical understanding of the Earth system. They are among the most often used tools to study a variety of questions related to variability and changes in Earth’s climate. For many applications, ESMs must realistically simulate observed large-scale precipitation patterns and seasonal cycles that have a multitude of societal and national security implications. Despite steady improvement in the simulation of precipitation, model errors in many aspects of precipitation characteristics limit the use of ESMs both in understanding Earth system variability and change and for decision-making.
The Voyager Ultraviolet Spectrometer has made extensive observations of airglow from Jupiter's dark-side equatorial latitudes. The brightness of H Lyman alpha, the only emission detected, varies between 700 and 1000 rayleighs (R) as a function of longitude. The dark side of Jupiter is illuminated by sky background Lyman alpha arising from resonance scattering of the solar Lyman alpha line by the neutral hydrogen of the interstellar medium. Calculations show that resonance scattering of this sky background by hydrogen in Jupiter's thermosphere will produce about 300 R of Lyman alpha emission. The additional Lyman alpha observed is probably excited by electrons and protons precipitating at equatorial latitudes. Based on the 500-R upper limit set here on the dark-side H2 Lyman and Werner bands, and the Lyman alpha measurements, the exciting particles are thought to have a soft energy spectrum and deposit about 0.04 erg/sq cm per sec in the atmosphere. There is evidence for an asymmetrical precipitation pattern associated with the longitudinal variation in Lyman alpha emission, and a suggestion of a strong day-night difference in precipitation as well.
As shown from ground-based measurements and satellite-borne imagers, one type of global auroral pattern characteristic of quiet (usually northward IMF) intervals is that of a contracted but thickened emission region in which the dawn and dusk portions can spread poleward to very high latitudes, (the type of a pattern referred to as a 'horse-collar' aurora by Hones et al., 1989). In this report we use a DE data set to examine a case in which this horse-collar pattern was observed by the DE-1 auroral imager while at the same time the DE-2, at lower altitude, measured precipitating particles, electric and magnetic fields, and plasma drifts. There is close agreement between the optical signatures and the particle precipitation patterns. The particle, plasma, and field measurements made along the satellite track and the 2-D perspective of the imager provide a means of determining the configuration of convective flows in the high-latitude ionosphere during this interval of northward IMF. Recent mapping studies are used to relate the low-altitude observations to possible magnetospheric source regions.
The NASA Short ]term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real ]time configuration of the NASA Land Information System (LIS) with the Noah land surface model (LSM). Output from the SPoRT ]LIS run is used to initialize land surface variables for local modeling applications at select National Weather Service (NWS) partner offices, and can be displayed in decision support systems for situational awareness and drought monitoring. The SPoRT ]LIS is run over a domain covering the southern and eastern United States, fully nested within the National Centers for Environmental Prediction Stage IV precipitation analysis grid, which provides precipitation forcing to the offline LIS ]Noah runs. The SPoRT Center seeks to expand the real ]time LIS domain to the entire Continental U.S. (CONUS); however, geographical limitations with the Stage IV analysis product have inhibited this expansion. Therefore, a goal of this study is to test alternative precipitation forcing datasets that can enable the LIS expansion by improving upon the current geographical limitations of the Stage IV product. The four precipitation forcing datasets that are inter ]compared on a 4 ]km resolution CONUS domain include the Stage IV, an experimental GOES quantitative precipitation estimate (QPE) from NESDIS/STAR, the National Mosaic and QPE (NMQ) product from the National Severe Storms Laboratory, and the North American Land Data Assimilation System phase 2 (NLDAS ]2) analyses. The NLDAS ]2 dataset is used as the control run, with each of the other three datasets considered experimental runs compared against the control. The regional strengths, weaknesses, and biases of each precipitation analysis are identified relative to the NLDAS ]2 control in terms of accumulated precipitation pattern and amount, and the impacts on the subsequent LSM spin ]up simulations. The ultimate goal is to identify an alternative precipitation forcing dataset that can best support an expansion of the real ]time SPoRT ]LIS to a domain covering the entire CONUS.
Abstract We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.
This paper continues presentation and discussion of the results from our new global self-consistent theoretical model of interacting ring current ions and propagating electromagnetic ion cyclotron waves [Khazanov et al., 2006]. To study the effects of electromagnetic ion cyclotron wave propagation and refraction on the wave induced ring current precipitation and heating of the thermal plasmaspheric electrons, we simulate the May 1998 storm. The main findings after a simulation can be summarized as follows. Firstly, the wave induced ring current precipitation exhibits quite a lot of fine structure, and is highly organized by location of the plasmapause gradient. The strongest fluxes of about 4 x 10(exp 6) (cm(raised dot) s(raised dot) sr(raised dot) (sup -1)) are observed during the maill and early recovery phases of the storm. The very interesting and probably more important finding is that in a number of cases the most intense precipitating fluxes are not connected to the most intense waves in simple manner. The characteristics of the wave power spectral density distribution over the wave normal angle are extremely crucial for the effectiveness of the ring current ion scattering. Secondly, comparison of the global proton precipitating patterns with the results from RAM [Kozyra et al., 1997a] reveals that although we observe a qualitative agreement between the localizations of the wave induced precipitations in the models, there is no quantitative agreement between the magnitudes of the fluxes. The quantitative differences are mainly due to a qualitative difference between the characteristics of the wave power spectral density distributions over the wave normal angle in RAM and in our model. Thirdly, the heat fluxes to plasmaspheric electrons caused by Landau resonate energy absorption from electromagnetic ion cyclotron waves are observed in the postnoon-premidnight MLT sector, and can reach the magnitude of 10(exp 11) eV/(cm(sup 2)(raised dot)s). The Coulomb energy degradation of the RC H(+) and O(+) ions maximizes at about 10(exp 11) (eV/(cm(sup 2) (raised dot) s), and typically leads to electron energy deposition rates of about 2(raised dot) 10(exp 10) (eV/(cm(sup 2)(raised dot)s) which are observed during two periods; 32-48 hours, and 76-86 hours after 1 May, 0000 UT. The theoretically derived spatial structure of the thermal electron heating caused by interaction of the ring current with the plasmasphere is strongly supported by concurrent and conjugate plasma measurements from the plasmasphere, ring current, and topside ionosphere [Gurgiolo et al., 2005]. Finally, the wave induced intense electron heating has a structure of the spot-like patches along the most enhanced density gradients in the plasmasphere boundary layer and can be a possible driver to the observed but still not explained small-scale structures of enhanced emissions in the stable auroral red arcs.
Predicting how regional precipitation will respond to future warming is among the most challenging undertaking in climate change projection. Despite sustained efforts to improve modeling and understanding of precipitation, the overall uncertainty in projecting regional precipitation has not been reduced substantially. In this work, the potential for more robust precipitation projections is demonstrated through the use of discriminating metrics to subsample the multimodel ensemble. Using a two-dimensional metric of precipitation and its relationship with large-scale circulation indices in East Asia, 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are classified into three groups. Models in the top performing group projected statistically significant increasing trends in precipitation and the regional precipitation patterns are more similar to each other than to the patterns in the bottom performing group. In contrast, models in the bottom performing group projected diverse responses, with overall small drying or no significant trends in precipitation.
This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.