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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

John L. Schnase↗
NASA NTRS2008

Recent Studies of the Behavior of the Sun's White-Light Corona Over Time

Predictions of upcoming solar cycles are often related to the nature and dynamics of the Sun's polar magnetic field and its influence on the corona. For the past 30 years we have a more-or-less continuous record of the Sun's white-light corona from groundbased and spacebased coronagraphs. Over that interval, the large scale features of the corona have varied in what we now consider a 'predictable' fashion--complex, showing multiple streamers at all latitudes during solar activity maximum; and a simple dipolar shape aligned with the rotational pole during solar minimum. Over the past three decades the white-light corona appears to be a better indicator of 'true' solar minimum than sunspot number since sunspots disappear for months (even years) at solar minimum. Since almost all predictions of the timing of the next solar maximum depend on the timing of solar minimum, the white-light corona is a potentially important observational discriminator for future predictors. In this contribution we describe recent work quantifying the large-scale appearance of the Sun's corona to correlate it with the sunspot record, especially around solar minimum. These three decades can be expanded with the HAO archive of eclipse photographs which, although sparse compared to the coronagraphic coverage, extends back to 1869. A more extensive understanding of this proxy would give researchers confidence in using the white-light corona as an indicator of solar minimum conditions.

SaintCyr, O. C.↗
NASA NTRS2016

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders: Evidence Report

In April 2010, President Obama declared a space pioneering goal for the United States in general and NASA in particular. "Fifty years after the creation of NASA, our goal is no longer just a destination to reach. Our goal is the capacity for people to work and learn and operate and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite." Thus NASA's Strategic Objective 1.1 emerged as "expand human presence into the solar system and to the surface of Mars to advance exploration, science, innovation, benefits to humanity, and international collaboration" (NASA 2015b). Any space flight, be it of long or short duration, occurs in an extreme environment that has unique stressors. Even with excellent selection methods, the potential for behavioral problems among space flight crews remain a threat to mission success. Assessment of factors that are related to behavioral health can help minimize the chances of distress and, thus, reduce the likelihood of adverse cognitive or behavioral conditions and psychiatric disorders arising within a crew. Similarly, countermeasures that focus on prevention and treatment can mitigate the cognitive or behavioral conditions that, should they arise, would impact mission success. Given the general consensus that longer duration, isolation, and confined missions have a greater risk for behavioral health ensuring crew behavioral health over the long term is essential. Risk, which within the context of this report is assessed with respect to behavioral health and performance, is addressed to deter development of cognitive and behavioral degradations or psychiatric conditions in space flight and analog populations, and to monitor, detect, and treat early risk factors, predictors and other contributing factors. Based on space flight and analog evidence, the average incidence rate of an adverse behavioral health event occurring during a space mission is relatively low for the current conditions. While mood and anxiety disturbances have occurred, no behavioral emergencies have been reported to date in space flight. Anecdotal and empirical evidence indicate that the likelihood of an adverse cognitive or behavioral condition or psychiatric disorder occurring greatly increases with the length of a mission. Further, while cognitive, behavioral, or psychiatric conditions might not immediately and directly threaten mission success, such conditions can, and do, adversely impact individual and crew health, welfare, and performance.

Slack, Kelley J.↗
NASA NTRS1981

Predictor symbology in computer-generated pictorial displays

The display under investigation, is a tunnel display for the four-dimensional commercial aircraft approach-to-landing under instrument flight rules. It is investigated whether more complex predictive information such as a three-dimensional perspective vehicle symbol, predicting the future vehicle position as well as future vehicle attitude angles, contributes to a better system response, and suitable predictor laws for the predictor motions, are formulated. Methods for utilizing the predictor symbol in controlling the forward velocity of the aircraft in four-dimensional approaches, are investigated. The simulator tests show, that the complex perspective vehicle symbol yields improved damping in the lateral response as compared to a flat two-dimensional predictor cross, but yields generally larger vertical deviations. Methods of using the predictor symbol in controlling the forward velocity of the vehicle are shown to be effective. The tunnel display with superimposed perspective vehicle symbol yields very satisfactory results and pilot acceptance in the lateral control but is found to be unsatisfactory in the vertical control, as a result of too large vertical path-angle deviations.

Grunwald, A. J.↗
NASA NTRS2006

Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

This paper evaluates the use of precipitable water (PW) from Global Positioning System (GPS) in lightning prediction. Additional independent verification of an earlier model is performed. This earlier model used binary logistic regression with the following four predictor variables optimally selected from a candidate list of 23 candidate predictors: the current precipitable water value for a given time of the day, the change in GPS-PW over the past 9 hours, the KIndex, and the electric field mill value. This earlier model was not optimized for any specific forecast interval, but showed promise for 6 hour and 1.5 hour forecasts. Two new models were developed and verified. These new models were optimized for two operationally significant forecast intervals. The first model was optimized for the 0.5 hour lightning advisories issued by the 45th Weather Squadron. An additional 1.5 hours was allowed for sensor dwell, communication, calculation, analysis, and advisory decision by the forecaster. Therefore the 0.5 hour advisory model became a 2 hour forecast model for lightning within the 45th Weather Squadron advisory areas. The second model was optimized for major ground processing operations supported by the 45th Weather Squadron, which can require lightning forecasts with a lead-time of up to 7.5 hours. Using the same 1.5 lag as in the other new model, this became a 9 hour forecast model for lightning within 37 km (20 NM)) of the 45th Weather Squadron advisory areas. The two new models were built using binary logistic regression from a list of 26 candidate predictor variables: the current GPS-PW value, the change of GPS-PW over 0.5 hour increments from 0.5 to 12 hours, and the K-index. The new 2 hour model found the following for predictors to be statistically significant, listed in decreasing order of contribution to the forecast: the 0.5 hour change in GPS-PW, the 7.5 hour change in GPS-PW, the current GPS-PW value, and the KIndex. The new 9 hour forecast model found the following five independent variables to be statistically significant, listed in decreasing order of contribution to the forecast: the current GPSPW value, the 8.5 hour change in GPS-PW, the 3.5 hour change in GPS-PW, the 12 hour change in GPS-PW, and the K-Index. In both models, the GPS-PW parameters had better correlation to the lightning forecast than the K-Index, a widely used thunderstorm index. Possible future improvements to this study are discussed.

Kehrer, Kristen C.↗
NASA NTRS1992

In vivo assessment of forearm bone mass and ulnar bending stiffness in healthy men

The cross-sectional bending stiffness EI of the ulna was measured in vivo by mechanical resistance tissue analysis (MRTA) in 90 men aged 19-89 years. MRTA measures the impedance response of low-frequency vibrations to determine EI, which is a reflection of elastic modulus E and moment of inertia I for the whole ulna. EI was compared to conventional estimates of bone mineral content (BMC), bone width (BW), and BMC/BW, which were all measured by single-photon absorptiometry. Results obtained from the nondominant ulna indicate that BW increases (r = 0.27, p = 0.01) and ulnar BMC/BW decreases (r = -0.31, p < or = 0.005) with age. Neither BMC nor EI declined with age. The single best predictor of EI was BW (r2 = 0.47, p = 0.0001), and further small but significant contributions were made by BMC (r2 = 0.53, p = 0.0001) and grip strength (r2 = 0.55, p = 0.0001). These results suggest that the resistance of older men to forearm fracture is related to age-associated changes in the moment of inertia achieved by redistributing bone mineral farther from the bending axis. We conclude that the in vivo assessment of bone geometry offers important insights to the comprehensive evaluation of bone strength.

NASA Center ARC↗
NASA NTRS2010

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

Mascaro, Giuseppe↗
NASA NTRSDate not supplied

Surface Meets TOS Update & Potential Future Work

This briefing for the Surface CDM (Collaborative Decision Making) Team (SCT) & Flow Evaluation Team (FET) discussed the foundational data/information quality needs to enable a collaborative TOS concept that includes surface. The primary goal of this briefing was on soliciting broad feedback from the operator community. This discusses the importance of the Earliest Off Block Time (EOBT) as a predictor for improving NAS demand predictions as well as possible areas where SCT/FET could contribute to uses of Trajectory Option Sets in future FAA systems.

ATD-2, IADS, SCT/FET↗
NASA NTRS1992

The effect of cloud type on earth's energy balance - Results for selected regions

International Satellite Cloud Climatology Project (ISCCP) C1 cloud information is compared with planetary albedo, outgoing longwave radiation (OLR), and net radiation measured at the top of the atmosphere by the Earth Radiation Budget Experiment (ERBE). Principal component analysis indicates that the day-to-day variations of the abundances of the 35 cloud types of the C1 data are correlated with each other, so that for many purposes the data set can be well represented by about five cloud types. Using stepwise multiple regression, the ISCCP C1 data can be used to predict the day-to-day variations of the energy balance measured by ERBE for 2.5-deg regions. Total fractional area coverage of cloudiness is a relatively poor predictor of radiation budget quantities. If the total fractional area coverage by clouds is divided into contributions from several distinct cloud types, the fractional coverages by these several cloud types will together form a much better prediction of radiation budget quantities than the single variable of total fractional-area cloud coverage. The regression equations can be used to estimate the net effect of clouds on the radiation balance and the contributions from particular types of clouds to albedo, OLR, and net radiation.

Ockert-Bell, Maureen E.↗
NASA NTRS1996

Spray Combustion Modeling with VOF and Finite-Rate Chemistry

A spray atomization and combustion model is developed based on the volume-of-fluid (VOF) transport equation with finite-rate chemistry model. The gas-liquid interface mass, momentum and energy conservation laws are modeled by continuum surface force mechanisms. A new solution method is developed such that the present VOF model can be applied for all-speed range flows. The objectives of the present study are: (1) to develop and verify the fractional volume-of-fluid (VOF) cell partitioning approach into a predictor-corrector algorithm to deal with multiphase (gas-liquid) free surface flow problems; (2) to implement the developed unified algorithm in a general purpose computational fluid dynamics (CFD) code, Finite Difference Navier-Stokes (FDNS), with droplet dynamics and finite-rate chemistry models; and (3) to demonstrate the effectiveness of the present approach by simulating benchmark problems of jet breakup/spray atomization and combustion. Modeling multiphase fluid flows poses a significant challenge because a required boundary must be applied to a transient, irregular surface that is discontinuous, and the flow regimes considered can range from incompressible to highspeed compressible flows. The flow-process modeling is further complicated by surface tension, interfacial heat and mass transfer, spray formation and turbulence, and their interactions. The major contribution of the present method is to combine the novel feature of the Volume of Fluid (VOF) method and the Eulerian/Lagrangian method into a unified algorithm for efficient noniterative, time-accurate calculations of multiphase free surface flows valid at all speeds. The proposed method reformulated the VOF equation to strongly couple two distinct phases (liquid and gas), and tracks droplets on a Lagrangian frame when spray model is required, using a unified predictor-corrector technique to account for the non-linear linkages through the convective contributions of VOF. The discontinuities within the sharp interface will be modeled as a volume force to avoid stiffness. Formations of droplets, tracking of droplet dynamics and modeling of the droplet breakup/evaporation, are handled through the same unified predictor-corrector procedure. Thus the new algorithm is non-iterative and is flexible for general geometries with arbitrarily complex topology in free surfaces. The FDNS finite-difference Navier-Stokes code is employed as the baseline of the current development. Benchmark test cases of shear coaxial LOX/H2 liquid jet with atomization/combustion and impinging jet test cases are investigated in the present work. Preliminary data comparisons show good qualitative agreement between data and the present analysis. It is indicative from these results that the present method has great potential to become a general engineering design analysis and diagnostics tool for problems involving spray combustion.

Chen, Yen-Sen↗
NASA NTRS1992

Bone mineral density, muscle strength, and recreational exercise in men

Muscle strength has been shown to predict bone mineral density (BMD) in women. We examined this relationship in 50 healthy men who ranged in age from 28 to 51 years (average 38.3 years). BMD of the lumbar spine, proximal femur, whole body, and tibia were measured by dual-energy x-ray absorptiometry (Hologic QDR 1000W). Dynamic strength using one repetition maximum was assessed for the biceps, quadriceps, and back extensors and for the hip abductors, adductors, and flexors. Isometric grip strength was measured by dynamometry. Daily walking mileage was assessed by 9 week stepmeter records and kinematic analysis of video filming. Subjects were designated as exercisers and nonexercisers. Exercisers participated in recreational exercise at least two times each week. The results demonstrated that BMD at all sites correlated with back and biceps strength (p < 0.01 to p = 0.0001). Body weight correlated with tibia and whole-body BMD (p < 0.001); age negatively correlated with Ward's triangle BMD (p < 0.01). In stepwise multiple regressions, back strength was the only independent predictor of spine and femoral neck density (R2 = 0.27). Further, back strength was the most robust predictor of BMD at the trochanter, Ward's triangle, whole body, and tibia, although biceps strength, age, body weight, and leg strength contributed significantly to BMD at these skeletal sites, accounting for 35-52% of the variance in BMD. Exercisers and nonexercisers were similar for walking (3.97 versus 3.94 miles/day), age (37.8 versus 38.5) years, and weight (80.0 versus 77.7 kg). However, BMD and muscle strength were significantly greater in exercises than in nonexercisers.(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Center ARC↗
NASA NTRS2016

Risk of Visual Impairment and Intracranial Hypertension After Space Flight: Evaluation of the Role of Polymorphism of Enzymes Involved in One-Carbon Metabolism

Data from the Nutritional Status Assessment protocol provided biochemical evidence that the one-carbon metabolic pathway may be altered in individuals experiencing vision-related issues during and after space flight (1, 2). Briefly, serum concentrations of homocysteine, cystathionine, 2-methylcitric acid, and methylmalonic acid were significantly (P<0.001) higher (25-45%) in astronauts with ophthalmic changes than in those without such changes (1). These differences existed before, during, and after flight. Serum folate was lower (P<0.01) during flight in individuals with ophthalmic changes. Preflight serum concentrations of cystathionine and 2-methylcitric acid, and mean in-flight serum folate, were significantly (P<0.05) correlated with postflight changes in refraction (1). A follow-up study was conducted to evaluate a small number of known polymorphisms of enzymes in the one-carbon pathway, and to evaluate how these relate to vision and other medical aspects of the eye. Specifically, we investigated 5 polymorphisms in MTRR, MTHFR, SHMT, and CBS genes and their association with ophthalmic changes after flight in 49 astronauts. The number of G alleles of MTRR 66 and C alleles of SHMT1 1420 both contributed to the odds of visual disturbances (3). Block regression showed that B-vitamin status at landing and genetics were significant predictors for many of the ophthalmic outcomes studied (3). In conclusion, we document an association between MTRR 66 and SHMT1 1420 polymorphisms and space flightinduced vision changes. These data document that individuals with an altered 1-carbon metabolic pathway may be predisposed to anatomic and/or physiologic changes that render them susceptible to ophthalmic damage during space flight.

Smith, S. M.↗
NASA NTRS2016

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Model ensembles↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport (EWR)

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular perceived inequity that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based scheduling of internal departures by the Traffic Management Advisor (TMA) (now operationally superseded by the FAA's the Time-Based Flow Management system). Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot easily fit into the arrival flow at the runway. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. TMA scheduling delays are found to be generally higher than TMA airborne metering delays, regardless of prior GDP delays. Depending on how the double delay is defined, between 42 and 62 of all internal departures in GDP and TMA scheduling experienced double delays in this period. A deep dive into the data reveals that contributors to double delays include upstream flights departing before their Expect Departure Clearance Times (EDCTs); differences in the rates used for setting EDCTs and TMA Scheduled Times of Arrival; differences in the arrival demand expected based on EDCTs and the arrival demand entering TMA; and shorter en route times between takeoff and entry into TMA than assumed in the calculation of flight EDCTs, all of which undermine the sequencing and spacing underlying flight EDCTs. Double delays are also found to coincide with periods in which the virtual runway arrival queue being served by a TMA is large, there are periods of high demand relative to capacity, and there are high airborne metering delays. Data mining techniques are used to confirm that each of these factors contribute to the occurrence of double delay andor high internal departure scheduling delay across three months of data from June to August 2010. Predictors of the occurrence of double delay and high TMA scheduling delay are built using logistic regression, providing prediction accuracies of 69 and 73, respectively.

traffic flow management↗
NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗
NASA NTRS2018

Reliability Analysis in the Presence of Aleatory Uncertainty

This paper proposes a method for modeling a system’s response using data. In contrast to approaches that identify a limit state function, we focus on the case in which not all uncertain parameters affecting the response are observable and the measured response is corrupted by noise. To this end, the system response is not characterized by a limit state function but instead by a Random Predictor Model (RPM) having a nonparametric structure. Consequently, the resulting failure probability is not a scalar but a random variable. This variable accounts for the aleatory contributions of the model-form uncertainty and the measurement noise into the response. Furthermore, we propose a framework that enables trading off the predicted range of failure probabilities resulting from such an analysis with a measure of risk. In this context, risk is the percentage of all predicted outcomes the analyst is willing to ignore. The reliability analysis of an aeroelastic structure subject to flutter is used to illustrate the ideas proposed.

Crespo, L. G.↗
NASA NTRS2014

Space Mission Human Reliability Analysis (HRA) Project

The purpose of the Space Mission Human Reliability Analysis (HRA) Project is to extend current ground-based HRA risk prediction techniques to a long-duration, space-based tool. Ground-based HRA methodology has been shown to be a reasonable tool for short-duration space missions, such as Space Shuttle and lunar fly-bys. However, longer-duration deep-space missions, such as asteroid and Mars missions, will require the crew to be in space for as long as 400 to 900 day missions with periods of extended autonomy and self-sufficiency. Current indications show higher risk due to fatigue, physiological effects due to extended low gravity environments, and others, may impact HRA predictions. For this project, Safety & Mission Assurance (S&MA) will work with Human Health & Performance (HH&P) to establish what is currently used to assess human reliabiilty for human space programs, identify human performance factors that may be sensitive to long duration space flight, collect available historical data, and update current tools to account for performance shaping factors believed to be important to such missions. This effort will also contribute data to the Human Performance Data Repository and influence the Space Human Factors Engineering research risks and gaps (part of the HRP Program). An accurate risk predictor mitigates Loss of Crew (LOC) and Loss of Mission (LOM).The end result will be an updated HRA model that can effectively predict risk on long-duration missions.

Boyer, Roger↗
NASA NTRS2001

Ensemble Cannonical Correlation Prediction of Seasonal Precipitation Over the US

This paper presents preliminary results of an ensemble cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.

Lau, William K. M.↗