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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Contraceptive Sabotage and Contraceptive Use at the Time of Pregnancy: An Analysis of People with a Recent Live Birth in the United States

Contraceptive sabotage and other forms of intimate partner violence (IPV) can interfere with contraceptive use. We used 2012 to 2015 Pregnancy Risk Assessment Monitoring System data from 8,981 people residing in five states who reported that when they became pregnant, they were not trying to get pregnant. We assessed the relationships between ever experiencing contraceptive sabotage and physical IPV 12 months before pregnancy (both by the current partner) and contraceptive use at the time of pregnancy using multivariable logistic regression. We also assessed the joint associations between physical IPV 12 months before pregnancy and ever experienced contraceptive sabotage with contraceptive use at the time of pregnancy. Few people ever experienced contraceptive sabotage (1.8%; 95% confidence interval [CI]: 1.4, 2.3) or physical IPV 12 months before pregnancy (2.8%; 95% CI: 2.3, 3.3). In models adjusted for age, race/ethnicity, marital status, education, and state of residence, ever experiencing contraceptive sabotage was associated with contraceptive use at the time of pregnancy (adjusted odds ratio [aOR]: 1.73; 95% CI: 1.06, 2.82), but not with physical IPV 12 months before pregnancy (aOR: 0.69; 95% CI: 0.46, 1.02). When examining the joint association, compared to not ever experiencing contraceptive sabotage or physical IPV 12 months before pregnancy, ever experiencing contraceptive sabotage was significantly related to contraceptive use at the time of pregnancy (aOR: 1.72; 95% CI: 1.00, 2.95). However, it was not associated with experiencing physical IPV 12 months before pregnancy (aOR: 0.68; 95% CI: 0.45, 1.04) or with experiencing both contraceptive sabotage and physical IPV 12 months before pregnancy (aOR: 1.21; 95% CI: 0.42, 3.50), compared to not ever experiencing contraceptive sabotage or physical IPV 12 months before pregnancy. Our study highlights that current partner contraceptive sabotage may motivate those not trying to get pregnant to use contraception; however, all people in our sample still experienced a pregnancy.

Huber-Krum, Sarah↗
DOE OSTI2023

COVID-19 prevention at institutions of higher education, United States, 2020–2021: implementation of nonpharmaceutical interventions

Background, In early 2020, following the start of the coronavirus disease 2019 (COVID-19) pandemic, institutions of higher education (IHEs) across the United States rapidly pivoted to online learning to reduce the risk of on-campus virus transmission. We explored IHEs’ use of this and other nonpharmaceutical interventions (NPIs) during the subsequent pandemic-affected academic year 2020–2021. Methods, From December 2020 to June 2021, we collected publicly available data from official webpages of 847 IHEs, including all public (n = 547) and a stratified random sample of private four-year institutions (n = 300). Abstracted data included NPIs deployed during the academic year such as changes to the calendar, learning environment, housing, common areas, and dining; COVID-19 testing; and facemask protocols. We performed weighted analysis to assess congruence with the October 29, 2020, US Centers for Disease Control and Prevention (CDC) guidance for IHEs. For IHEs offering ≥50% of courses in person, we used weighted multivariable linear regression to explore the association between IHE characteristics and the summated number of implemented NPIs. Results, Overall, 20% of IHEs implemented all CDC-recommended NPIs. The most frequently utilized NPI was learning environment changes (91%), practiced as one or more of the following modalities: distance or hybrid learning opportunities (98%), 6-ft spacing (60%), and reduced class sizes (51%). Additionally, 88% of IHEs specified facemask protocols, 78% physically changed common areas, and 67% offered COVID-19 testing. Among the 33% of IHEs offering ≥50% of courses in person, having < 1000 students was associated with having implemented fewer NPIs than IHEs with ≥ 1000 students. Conclusions, Only 1 in 5 IHEs implemented all CDC recommendations, while a majority implemented a subset, most commonly changes to the classroom, facemask protocols, and COVID-19 testing. IHE enrollment size and location were associated with degree of NPI implementation. Additional research is needed to assess adherence to NPI implementation in IHE settings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2024

Relationship Between Radiation Dose and Markers of Insulin Resistance and Inflammation in Atomic Bomb Survivors

Abstract Context In recent studies of childhood cancer survivors, diabetes has been considered a late effect associated with high therapeutic doses of radiation therapy. Our recent study of atomic bomb (A-bomb) survivors also suggested an association between radiation dose and diabetes incidence, with exposure city and age at exposure as radiation dose effect modifiers. Insulin resistance mediated by systemic inflammation and abnormal body composition has been suggested as a possible primary mechanism for the incidence of diabetes after total body irradiation; however, no studies have examined low to moderate radiation exposure (<4 Gy) and insulin resistance in A-bomb survivors. Objective To examine the association between radiation dose and markers of inflammation and insulin resistance. Methods This study investigated 3152 survivors who underwent a health examination between 2008 and 2012 and who were younger than 15 years at exposure. Multivariate linear regression analyses were used to evaluate the radiation effects on levels of markers of inflammation and insulin resistance. Results Radiation dose was significantly and positively associated with levels of C-reactive protein, triglycerides, homeostasis model assessment of β-cell function (HOMA-β), and HOMA of insulin resistance (HOMA-IR) after adjustment for relevant covariates including sex, city, and age at exposure. Adiponectin and high-density lipoprotein cholesterol levels were also associated significantly and negatively with radiation dose. However, city was not a dose modifier of the radiation response on these markers of inflammation and insulin resistance. Conclusion Insulin resistance might be a possible factor in radiation-related diabetes incidence in A-bomb survivors.

Endocrinology & Metabolism↗
DOE OSTI2023

Willingness to Receive mHealth Services Among Patients with Diabetes on Chronic Follow-up in Public Hospitals in Eastern Ethiopia: Multicenter Mixed-Method Study

Background: Management of diabetes requires a long-term care strategy, including support for adherence to a healthy lifestyle and treatment. Exploring the willingness of patients with diabetes to receive mHealth services is essential for designing efficient and effective services. This study aimedto determine willingness to receive mHealth services and associated factors, as well as explore the barriers to receive mHealth services among patients with diabetes. Methods: A multicenter mixed-method study was employed from September 1 to November 30, 2022. For the quantitative part, a total of 365 patients with diabetes receiving chronic follow-up at three public hospitals were enrolled. Data were gathered using structured questionnaires administered by interviewers, entered into Epi-data version 4.6, and analyzed using Stata version 17. A binary and multivariable logistic regression model was computed to identify the associated factors. For qualitative, eight key informants and seven in-depth interviews were conducted. After verbatim transcription and translation, the data were thematically analyzed using ATLAS.ti V. 7.5. Results: Overall, 77.3% had access to a mobile phone, and 74.5% of them were willing to receive mHealth services. Higher odds of willingness to receive mHealth services were reported among patients with an age below 35 years [AOR = 4.11 (1.15– 14.71)], attended formal education [AOR = 2.63 (1.19– 5.77)], without comorbidity [AOR = 3.6 (1.54– 8.41)], < 1-hour travel to reach a health facility [AOR = 3.57 (1.03– 12.36)], answered unknown calls [AOR = 2.3 (1.04– 5.13)], and were satisfied with health-care provider service [AOR = 2.44 (1.04– 5.72)]. In the qualitative part, infrastructure, health facilities, socioeconomic factors, and patients’ behavioral factors were major identified barriers to receiving mHealth services. Conclusion: In this study, the willingness to receive mHealth services for those who have access to mobile phones increased. Additionally, the study highlighted common barriers to receiving mHealth services.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Determination of the uranium content of storage containers filled with waste from Fukushima using cosmic ray data

This report presents the results of an experimental study of cosmic ray tomography aimed at determining the uranium contents of waste containers filled with waste from Fukushima. Data, taken using the Giant Muon Tracker, were obtained on a set of scenes constructed from blocks of polyethylene, concrete, iron, and lead. The data have been provided to Toshiba and have been used to evaluate the composition of scenes using multivariate linear regression.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

COVID-19 vaccination status, side effects, and perceptions among breast cancer survivors: a cross-sectional study in China

Introduction Breast cancer is the most prevalent malignancy in patients with coronavirus disease 2019 (COVID-19). However, vaccination data of this population are limited. Methods A cross-sectional study of COVID-19 vaccination was conducted in China. Multivariate logistic regression models were used to assess factors associated with COVID-19 vaccination status. Results Of 2,904 participants, 50.2% were vaccinated with acceptable side effects. Most of the participants received inactivated virus vaccines. The most common reason for vaccination was “fear of infection” (56.2%) and “workplace/government requirement” (33.1%). While the most common reason for nonvaccination was “worry that vaccines cause breast cancer progression or interfere with treatment” (72.9%) and “have concerns about side effects or safety” (39.6%). Patients who were employed (odds ratio, OR = 1.783, p = 0.015), had stage I disease at diagnosis (OR = 2.008, p = 0.019), thought vaccines could provide protection (OR = 1.774, p = 0.007), thought COVID-19 vaccines were safe, very safe, not safe, and very unsafe (OR = 2.074, p < 0.001; OR = 4.251, p < 0.001; OR = 2.075, p = 0.011; OR = 5.609, p = 0.003, respectively) were more likely to receive vaccination. Patients who were 1–3 years, 3–5 years, and more than 5 years after surgery (OR = 0.277, p < 0.001; OR = 0.277, p < 0.001, OR = 0.282, p < 0.001, respectively), had a history of food or drug allergies (OR = 0.579, p = 0.001), had recently undergone endocrine therapy (OR = 0.531, p < 0.001) were less likely to receive vaccination. Conclusion COVID-19 vaccination gap exists in breast cancer survivors, which could be filled by raising awareness and increasing confidence in vaccine safety during cancer treatment, particularly for the unemployed individuals.

Xu, Yali↗
DOE OSTI2024

Sex Differences in Odds of Brain Metastasis and Outcomes by Brain Metastasis Status after Advanced Melanoma Diagnosis

Sex differences in cancer are well-established. However, less is known about sex differences in diagnosis of brain metastasis and outcomes among patients with advanced melanoma. Using a United States nationwide electronic health record-derived de-identified database, we evaluated patients diagnosed with advanced melanoma from 1 January 2011–30 July 2022 who received an oncologist-defined rule-based first line of therapy (n = 7969, 33% female according to EHR, 35% w/documentation of brain metastases). The odds of documented brain metastasis diagnosis were calculated using multivariable logistic regression adjusted for age, practice type, diagnosis period (pre/post-2017), ECOG performance status, anatomic site of melanoma, group stage, documentation of non-brain metastases prior to first-line of treatment, and BRAF positive status. Real-world overall survival (rwOS) and progression-free survival (rwPFS) starting from first-line initiation were assessed by sex, accounting for brain metastasis diagnosis as a time-varying covariate using the Cox proportional hazards model, with the same adjustments as the logistic model, excluding group stage, while also adjusting for race, socioeconomic status, and insurance status. Adjusted analysis revealed males with advanced melanoma were 22% more likely to receive a brain metastasis diagnosis compared to females (adjusted odds ratio [aOR]: 1.22, 95% confidence interval [CI]: 1.09, 1.36). Males with brain metastases had worse rwOS (aHR: 1.15, 95% CI: 1.04, 1.28) but not worse rwPFS (adjusted hazard ratio [aHR]: 1.04, 95% CI: 0.95, 1.14) following first-line treatment initiation. Among patients with advanced melanoma who were not diagnosed with brain metastases, survival was not different by sex (rwOS aHR: 1.06 [95% CI: 0.97, 1.16], rwPFS aHR: 1.02 [95% CI: 0.94, 1.1]). This study showed that males had greater odds of brain metastasis and, among those with brain metastasis, poorer rwOS compared to females, while there were no sex differences in clinical outcomes for those with advanced melanoma without brain metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Dilution impacts on smoke aging: evidence in Biomass Burning Observation Project (BBOP) data

Abstract. Biomass burning emits vapors and aerosols into the atmosphere that can rapidly evolve as smoke plumes travel downwind and dilute, affecting climate- and health-relevant properties of the smoke. To date, theory has been unable to explain observed variability in smoke evolution. Here, we use observational data from the Biomass Burning Observation Project (BBOP) field campaign and show that initial smoke organic aerosol mass concentrations can help predict changes in smoke aerosol aging markers, number concentration, and number mean diameter between 40–262 nm. Because initial field measurements of plumes are generally >10 min downwind, smaller plumes will have already undergone substantial dilution relative to larger plumes and have lower concentrations of smoke species at these observations closest to the fire. The extent to which dilution has occurred prior to the first observation is not a directly measurable quantity. We show that initial observed plume concentrations can serve as a rough indicator of the extent of dilution prior to the first measurement, which impacts photochemistry, aerosol evaporation, and coagulation. Cores of plumes have higher concentrations than edges. By segregating the observed plumes into cores and edges, we find evidence that particle aging, evaporation, and coagulation occurred before the first measurement. We further find that on the plume edges, the organic aerosol is more oxygenated, while a marker for primary biomass burning aerosol emissions has decreased in relative abundance compared to the plume cores. Finally, we attempt to decouple the roles of the initial concentrations and physical age since emission by performing multivariate linear regression of various aerosol properties (composition, size) on these two factors.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

The saturated activities of Na-22, Mn-54, and Co-56 and the depth of sampling of soils

A multivariate linear regression study shows that the saturated activities of Na-22, Co-56, and Mn-54 are functions of the chemical composition and the reciprocal of the cube root of the sample weight. A procedure for taking the temporal variation in these short-lived radionuclides into account is described. The regression results are used to estimate the average depths of sampling of soil material. The data indicate that all 28 samples were in the same conditions of exposure when recovered as they had been for the last approximately 10 years. The saturation activity of Mn-54 is a very weak function of time.

Keith, J. E.↗
NASA NTRS1979

Synthesis of rotor test data for real-time simulation

A mathematical model of a hingeless tilting rotor is presented. The model was obtained by a systematic curve fit procedure applied to an extensive set of model scale wind tunnel data. The math model equations were used in a real time flight simulation model of a hingeless tilt rotor XV-15 to assess changes in flying qualities compared to those obtained using a previous rotor model. Extensive plots of the rotor derivatives are given. Discussions of attempts to apply multivariable linear regression technqiues to the data and the use of an analytical rotor representation are included.

Mcveigh, M. A.↗
NASA NTRS1984

Soil temperature extrema recovery rates after precipitation cooling

From a one dimensional view of temperature alone variations at the Earth's surface manifest themselves in two cyclic patterns of diurnal and annual periods, due principally to the effects of diurnal and seasonal changes in solar heating as well as gains and losses of available moisture. Beside these two well known cyclic patterns, a third cycle has been identified which occurs in values of diurnal maxima and minima soil temperature extrema at 10 cm depth usually over a mesoscale period of roughly 3 to 14 days. This mesoscale period cycle starts with precipitation cooling of soil and is followed by a power curve temperature recovery. The temperature recovery clearly depends on solar heating of the soil with an increased soil moisture content from precipitation combined with evaporation cooling at soil temperatures lowered by precipitation cooling, but is quite regular and universal for vastly different geographical locations, and soil types and structures. The regularity of the power curve recovery allows a predictive model approach over the recovery period. Multivariable linear regression models alloy predictions of both the power of the temperature recovery curve as well as the total temperature recovery amplitude of the mesoscale temperature recovery, from data available one day after the temperature recovery begins.

Welker, J. E.↗
NASA NTRS1991

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

A new diagnostic tool is developed for examining relationships between the synoptic scale circulation and regional temperature distributions in GCMs. The 4 x 5 deg GISS GCM is shown to produce accurate simulations of the variance in the synoptic scale sea level pressure distribution over the U.S. An analysis of the observational data set from the National Meteorological Center (NMC) also shows a strong relationship between the synoptic circulation and grid point temperatures. This relationship is demonstrated by deriving transfer functions between a time-series of circulation parameters and temperatures at individual grid points. The circulation parameters are derived using rotated principal components analysis, and the temperature transfer functions are based on multivariate polynomial regression models. The application of these transfer functions to the GCM circulation indicates that there is considerable spatial bias present in the GCM temperature distributions. The transfer functions are also used to indicate the possible changes in U.S. regional temperatures that could result from differences in synoptic scale circulation between a 1XCO2 and a 2xCO2 climate, using a doubled CO2 version of the same GISS GCM.

Crane, Robert G.↗
NASA NTRS2002

Modeling and control for closed environment plant production systems

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

NASA Discipline Life Support Systems↗
NASA NTRS2003

Usefulness of microvolt T-wave alternans for prediction of ventricular tachyarrhythmic events in patients with dilated cardiomyopathy: results from a prospective observational study

OBJECTIVES: This study was designed to evaluate the ability of microvolt-level T-wave alternans (MTWA) to identify prospectively patients with idiopathic dilated cardiomyopathy (DCM) at risk of ventricular tachyarrhythmic events and to compare its predictive accuracy with that of conventional risk stratifiers. BACKGROUND: Patients with DCM are at increased risk of sudden death from ventricular tachyarrhythmias. At present, there are no established methods of assessing this risk. METHODS: A total of 137 patients with DCM underwent risk stratification through assessment of MTWA, left ventricular ejection fraction, baroreflex sensitivity (BRS), heart rate variability, presence of nonsustained ventricular tachycardia (VT), signal-averaged electrocardiogram, and presence of intraventricular conduction defect. The study end point was either sudden death, resuscitated ventricular fibrillation, or documented hemodynamically unstable VT. RESULTS: During an average follow-up of 14 +/- 6 months, MTWA and BRS were significant univariate predictors of ventricular tachyarrhythmic events (p < 0.035 and p < 0.015, respectively). Multivariate Cox regression analysis revealed that only MTWA was a significant predictor. CONCLUSIONS: Microvolt-level T-wave alternans is a powerful independent predictor of ventricular tachyarrhythmic events in patients with DCM.

Evaluation Studies↗
NASA NTRS1998

T wave alternans as a predictor of recurrent ventricular tachyarrhythmias in ICD recipients: prospective comparison with conventional risk markers

INTRODUCTION: The current standard for arrhythmic risk stratification is electrophysiologic (EP) testing, which, due to its invasive nature, is limited to patients already known to be at high risk. A number of noninvasive tests, such as determination of left ventricular ejection fraction (LVEF) or heart rate variability, have been evaluated as additional risk stratifiers. Microvolt T wave alternans (TWA) is a promising new risk marker. Prospective evaluation of noninvasive risk markers in low- or moderate-risk populations requires studies involving very large numbers of patients, and in such studies, documentation of the occurrence of ventricular tachyarrhythmias is difficult. In the present study, we identified a high-risk population, recipients of an implantable cardioverter defibrillator (ICD), and prospectively compared microvolt TWA with invasive EP testing and other risk markers with respect to their ability to predict recurrence of ventricular tachyarrhythmias as documented by ICD electrograms. METHODS AND RESULTS: Ninety-five patients with a history of ventricular tachyarrhythmias undergoing implantation of an ICD underwent EP testing, assessment of TWA, as well as determination of LVEF, baroreflex sensitivity, signal-averaged ECG, analysis of 24-hour Holter monitoring, and QT dispersion from the 12-lead surface ECG. The endpoint of the study was first appropriate ICD therapy for electrogram-documented ventricular fibrillation or tachycardia during follow-up. Kaplan-Meier survival analysis revealed that TWA (P < 0.006) and LVEF (P < 0.04) were the only significant univariate risk stratifiers. EP testing was not statistically significant (P < 0.2). Multivariate Cox regression analysis revealed that TWA was the only statistically significant independent risk factor. CONCLUSIONS: Measurement of microvolt TWA compared favorably with both invasive EP testing and other currently used noninvasive risk assessment methods in predicting recurrence of ventricular tachyarrhythmias in ICD recipients. This study suggests that TWA might also be a powerful tool for risk stratification in low- or moderate-risk patients, and needs to be prospectively evaluated in such populations.

Non-NASA Center↗
NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗