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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Integrated and spectral energetics of the GLAS general circulation model

Integrated and spectral error energetics of the GLAS General circulation model are compared with observations for periods in January 1975, 1976, and 1977. For two cases the model shows significant skill in predicting integrated energetics quantities out to two weeks, and for all three cases, the integrated monthly mean energetics show qualitative improvements over previous versions of the model in eddy kinetic energy and barotropic conversions. Fundamental difficulties remain with leakage of energy to the stratospheric level, particularly above strong initial jet streams associated in part with regions of steep terrain. The spectral error growth study represents the first comparison of general circulation model spectral energetics predictions with the corresponding observational spectra on a day by day basis. The major conclusion is that eddy kinetics energy can be correct while significant errors occur in the kinetic energy of wavenumber 3. Both the model and observations show evidence of single wavenumber dominance in eddy kinetic energy and the correlation of spectral kinetics and potential energy.

Tenenbaum, J.↗
NASA NTRS1993

Investigation of the role of thermal boundary layer processes in initiating convection under the NASA SPACE Field Program

The current NWS ground based network is not sufficient to capture the dynamic or thermodynamic structure leading to the initiation and organization of air mass moist convective events. Under this investigation we intend to use boundary layer mesoscale models (McNider and Pielke, 1981) to examine the dynamic triggering of convection due to topography and surface thermal contrasts. VAS and MAN's estimates of moisture will be coupled with the dynamic solution to provide an estimate of the total convective potential. Visible GOES images will be used to specify incoming insolation which may lead to surface thermal contrasts and JR skin temperatures will be used to estimate surface moisture (via the surface thermal inertia) (Weizel and Chang, 1988) which can also induce surface thermal contrasts. We will use the SPACE-COHMEX data base to evaluate the ability of the joint mesoscale model satellite products to show skill in predicting the development of air mass convection. We will develop images of model vertical velocity and satellite thermodynamic measures to derive images of predicted convective potential. We will then after suitable geographic registration carry out a pixel by pixel correlation between the model/satellite convective potential and the 'truth' which are the visible images. During the first half of the first year of this investigation we have concentrated on two aspects of the project. The first has been in generating vertical velocity fields from the model for COHMEX case days. We have taken June 19 as the first case and have run the mesoscale model at several different grid resolutions. We are currently developing the composite model/satellite convective image. The second aspect has been the attempted calibration of the surface energy budget to provide the proper horizontal thermal contrasts for convective initiation. We have made extensive progress on this aspect using the FIFE data as a test data set. The calibration technique looks very promising.

Mcnider, Richard T.↗
NASA NTRS2013

Statistical and Hydrological Evaluation of TRMM-Based Multi-Satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the Latest Satellite Precipitation Products 3B42V7 Ready for Use in Ungauged Basins?

The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001-2010) daily rain gauge dataset reveals that: 1) 3B42V7 generally improves upon 3B42V6s underestimation both for the whole basin (bias from -41.15 to -8.38) and for a 0.250.25 grid cell with high-density gauges (bias from -40.25 to 0.04), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and 2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already well-performing 3B42V7 except for a reduction in bias (from -26.98 to -4.81). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in-situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins.

TRMM↗
NASA NTRS2019

Seasonal Forecasting of the Quasi-Biennial Oscillation

The ability to seasonally forecast the Quasi-Biennial Oscillation (QBO) was examined using NASA S2S (Sub-seasonal to Seasonal), 9-month, retrospective forecasts. Validation of these forecasts showed that the S2S retrospective QBO forecasts improved skill in predicting the QBO amplitude and phase over a simple QBO phase propagation model at forecast lead times of 1 to 3 months. Results from an initial assessment of whether more accurate QBO forecasts can improve Northern Hemisphere winter sea level pressure forecasts showed no significant forecast improvement at a 1-month lead time, indicating the need for improved stratosphere-troposphere QBO coupling metrics and pathway identification. Overall, these results suggest that future improvements in representing the QBO in global models can increase the ensemble fraction of valid 1 to 3 month QBO forecasts and potentially extend useful QBO forecasts beyond 3 months.

Coy, Lawrence↗
NASA NTRS1985

Analysis and forecast intercomparisons using the FGGE SOP-1 data base

The First GARP Global Experiment (FGGE) data base has provided the research community with the most complete global coverage of meteorological informaiton since measurements were first taken. It is important to understand the characteristics of the FGGE data base in terms of its accuracy and representativeness compared to the true state of the atmosphere. The uncertainty associated with the initial conditions used in a numerical model plays a significant role in subsequent error growth as shown by predictability theory. The ultimate limit of forecast skill is directly related to the initial uncertainty. The accuracy of analyses using FGGE data base coupled with the errors due to modeling imperfections determines the present skill of numerical models. An estimte of initial uncertainty is determined by examining differences in several FGGE analyses. The estimate is then used to determine an ultimate limit of forecast skill from derived predictability error growth rates. Several different calculations of predictability error growth are also compared. Finally, present-day forecast skill using FGGE analyses is contrasted to the estimates of ultimate skill, and the partition of model error versus initial uncertainty is discussed.

Baumhefner, D. P.↗
DOE OSTI2022

The effects of bias, drift, and trends in calculating anomalies for evaluating skill of seasonal-to-decadal initialized climate predictions

In initialized seasonal to decadal (S2D) predictions, model hindcasts rapidly drift away from the initial observed state and converge toward a preferred state characterized by systematic error, or bias. Bias and drift are among the greatest challenges facing initialized prediction today. Differences in trends between initial states and drifted states, combined with bias and drift, introduce complexities in calculating anomalies to assess skill of initialized predictions. We examine several methods of calculating anomalies using the Decadal Prediction Large Ensemble (DPLE) using the Community Earth System Model (CESM) initialized hindcasts and focus on Pacific and Atlantic SSTs to illustrate issues with anomaly calculations. Three methods of computing anomalies, one as differences from a long term model climatology, another as bias-adjusted differences from the previous 15 year average from observations, and a third as differences from the previous 15 year average from the model, are contrasted and each is shown to have limitations. For the first, trends in bias and drift introduce higher skill estimates earlier and later in the hindcast period due to the trends that contribute to skill. For the second, higher skill can be introduced in situations where low frequency variability in the observations is large compared to the hindcasts on timescales greater than 15 years, while lower skill can result if the predicted signal is small and the bias-correction itself produces a transition of SST anomalies to the opposite sign of those that are observed. The third method has somewhat lower skill compared to each of the others, but has less difficulties with not only the long term trends in the model climatology, but also with the unrealistic situational skill from using observations as a reference. However, the first 15 years of the hindcast period cannot be evaluated due to having to wait to accumulate the previous 15 year model climatology before the method can be applied. Here, the IPO transition in the 2014–2016 time frame from negative to positive (predicted by Meehl et al. in in Nat Commun, 10.1038/NCOMMS11718, 2016) did indeed verify using all three methods, though each provides somewhat different skill values as a result of the respective limitations. There is no clear best method, as all are roughly comparable, and each has its own set of limitations and caveats. However, all three methods show generally higher overall skill in the AMO region compared to the IPO region.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2 months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal time scales. It is also found that the skill of AO forecasts during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).

Arctic Oscillation↗
NASA NTRS2003

A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.

Chin, Mian↗
NASA NTRS1993

An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

Fiorino, Michael↗
DOE OSTI2023

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multimodel ensemble predictions of precipitation using bayesian neural networks

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.

Fan, Patrick↗
DOE OSTI2022

Multimodel Ensemble Predictions of Precipitation using Bayesian Neural Networks

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.

Fan, Patrick↗
NASA NTRS2010

The Contribution of Soil Moisture Information to Forecast Skill: Two Studies

This talk briefly describes two recent studies on the impact of soil moisture information on hydrological and meteorological prediction. While the studies utilize soil moisture derived from the integration of large-scale land surface models with observations-based meteorological data, the results directly illustrate the potential usefulness of satellite-derived soil moisture information (e.g., from SMOS and SMAP) for applications in prediction. The first study, the GEWEX- and ClIVAR-sponsored GLACE-2 project, quantifies the contribution of realistic soil moisture initialization to skill in subseasonal forecasts of precipitation and air temperature (out to two months). The multi-model study shows that soil moisture information does indeed contribute skill to the forecasts, particularly for air temperature, and particularly when the initial local soil moisture anomaly is large. Furthermore, the skill contributions tend to be larger where the soil moisture initialization is more accurate, as measured by the density of the observational network contributing to the initialization. The second study focuses on streamflow prediction. The relative contributions of snow and soil moisture initialization to skill in streamflow prediction at seasonal lead, in the absence of knowledge of meteorological anomalies during the forecast period, were quantified with several land surface models using uniquely designed numerical experiments and naturalized streamflow data covering mUltiple decades over the western United States. In several basins, accurate soil moisture initialization is found to contribute significant levels of predictive skill. Depending on the date of forecast issue, the contributions can be significant out to leads of six months. Both studies suggest that improvements in soil moisture initialization would lead to increases in predictive skill. The relevance of SMOS and SMAP satellite-based soil moisture information to prediction are discussed in the context of these studies.

Koster, Randal↗
NASA NTRSDate not supplied

Seasonal Prediction of an Extreme Temperature Index over the United States

Heat extremes have the potential to cause significant societal harm; advanced warming of the frequency of these events for a given season could help with planning and mitigation measures. While an individual temperature extreme event may not be predictable on seasonal time scales, the number of such events could be. In this study, we use an index representing the number of extreme days per summer to examine the ability of NASA’s Goddard Earth Observing System (GEOS) sub-seasonal to seasonal (S2S) prediction system to predict summertime temperature extremes over the continental United States. The index is defined as the number of days per summer (June-August) where the daily mean temperature is above the calendar-day 90th percentile and is computed for 1991-2020 using the GEOS-S2S seasonal retrospective forecasts as well as the Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2). Temporal correlations are assessed over this period at the grid point scale, indicating significant correlation between the retrospective forecasts and MERRA-2, particularly in the western half of the United States. Spatial correlations between the retrospective forecast and MERRA-2 indices are computed over the United States for each year revealing year-to-year variability in the forecast skill. We investigate local and remote processes associated with increased prediction skill of summertime temperature extremes in a given year.

Natalie Thomas↗
NASA NTRS2003

Modeling, Simulation, and Forecasting of Subseasonal Variability

A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

Waliser, Duane↗
NASA NTRS2001

Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

This paper presents preliminary results of an ensemble canonical 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 non-overlapping sectors. The canonical 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 the regions of the US in every season 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 to the 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 reduces the spring predictability barrier over all the 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 additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

Lau, William K. M.↗
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.↗