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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
DOE OSTI2025

Maps of growing season gross primary production and net ecosystem exchange for Council Road Mile Marker 71, Seward Peninsula, Alaska, [2017-2023]

This data archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025a). Murphy et al. (2025a) evaluated whether incorporating observed Arctic vegetation heterogeneity into ELM, the land model of the Department of Energy’s Energy Exascale Earth System Model (E3SM), improved simulations of tundra carbon cycling. The associated model archive can be found at Murphy et al. (2025b). The study focused on the spatial patterns and net landscape-level growing season productivity and carbon uptake. As part of this evaluation, observationally derived maps of average growing season (June–August) net ecosystem exchange (NEE) and gross primary production (GPP) were developed for the same domain. These maps, which form the dataset described here, integrate eddy covariance flux tower, remote sensing, and vegetation community data to provide spatially explicit benchmarks for model evaluation. The maps provide spatially explicit estimates of average growing season NEE and GPP across 13 tundra vegetation communities within the study domain. By combining flux tower observations with Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral imagery and drone-based normalized difference vegetation index (NDVI), these maps capture the heterogeneity of carbon fluxes associated with different Arctic vegetation types. While they represent average seasonal conditions rather than interannual variability, the maps provide a unique dataset for evaluating model performance, comparing vegetation community contributions to landscape-scale carbon cycling, and supporting regional analyses of Arctic carbon dynamics. This data archive contains 5 m resolution maps of vegetation communities, vegetation community average growing season GPP, and vegetation community average growing season NEE (three *.tif files), a User’s Guide (*pdf file), and Table 1 of the User’s Guide displaying vegetation community coverage and average growing season NEE and GPP values (*.csv file).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2025

Wave energy in season: a comparative approach to feasibility of seasonal deployments for remote coastal communities

Remote coastal communities, which could be early adopters of wave energy projects, have concerns over costs, conflicts, and potential risks of development. Designers and developers are challenged to address these community concerns as they continue to develop wave energy technologies. One potential means of reducing costs, conflicts, and risks, especially for demonstration and pilot-scale projects, could be planning a deployment that operates for only a portion of the year—a seasonal deployment. Here, in this paper, we examine the impacts of a seasonal deployment in terms of cost, electricity production, operations and maintenance, environmental impacts, and community benefits. We take a holistic, comparative approach to feasibility that can be replicated for other comparative studies. We estimate electricity production using a point absorber WEC modeled near Sitka, AK, USA and optimized for the given sea conditions. We determine that, for remote community sized projects, seasonal deployments could result in small cost savings (less than 10 %), but larger decreases in annual energy production (around 30 % for our case study area). Seasonal deployments could be preferable in places with seasonal energy needs, if failures and device access become a major hindrance to wave energy technology development, or as a cautionary approach to introducing new technology to the oceans. We also determine that a highly seasonal wave resource is not necessarily a requirement for seasonal deployments to be considered. Seasonal deployments are an alternative to year-round deployments that can be considered in places where marine spatial conflict is a seasonal concern.

WEC optimizations↗
NASA NTRS2021

Developing a Hydrological Monitoring and Sub-Seasonal to Seasonal Forecasting System for South and Southeast Asian River Basins

South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

Yifan Zhou↗
NASA NTRS1999

Statistical Aspects of Major (Intense) Hurricanes in the Atlantic Basin During the Past 49 Hurricane Seasons (1950-1998): Implications for the Current Season

Statistical aspects of major (intense) hurricanes, those of category 3 or higher on the Saffir-Simpson scale (e.g., having a maximum sustained wind speed of greater than or equal to 50 M s (exp -1)), in the Atlantic basin during the interval of 1950-1998 are investigated in relation to the El Nino-Southern Oscillation cycle and to the postulated "more" versus "less" activity modes for intense hurricane activity. Based on Poisson statistics, when the hurricane season is simply classified as "non-El Nino-related" (NENR), the probability of having three or more intense hurricanes is approx. 53%, while it is only approx. 14% when it is classified as "El Nino-related" (ENR). Including the activity levels ("more" versus "less"), the probability of having three or more intense hurricanes is computed to be approx. 71% for the "more-NENR" season, 30% for the "less-NENR" season, 17% for the "more-ENR" season, and 12% for the "less-ENR" season. Because the 1999 hurricane season is believed to be a "more-NENR" season, the number of intense hurricanes forming in the Atlantic basin should be above average in number, probably about 4 plus or minus 1 or higher.

Wilson, Robert M.↗
NASA NTRS1999

Statistical Aspects of Intense Hurricanes in the Atlantic Basin during the Past 49 Hurricane Seasons (1950-1998): Implications for the Current Season

Statistical aspects of intense hurricanes (those of category 3 or higher) in the Atlantic basin during the interval of 1950-1998 are investigated in relation to the ENSO cycle and to the postulated 'more versus less' activity modes for intense hurricane activity. Because the 1999 hurricane season likely will be classified as a 'non-El Nino-related' (NENR) season and that the more active mode appears to be in vogue, an above average seasonal rate of greater than or equal to 2 intense hurricanes is to be expected (probably, about 4 +/- 1, or higher). Based on Poisson statistics, when the hurricane season is classified as NENR the probability of greater than or equal to 2 events is about 77%, whereas when the season is classified as NENR and the more active mode is operative it is about 87%. The probability of greater than or equal to 4 events is about 31 % and 48 %, respectively, for these two activity classes.

Wilson, Robert M.↗
NASA NTRS2019

Seasonal Variations of Arctic Low-Level Clouds and its Linkage to Sea Ice Seasonal Variations

Using CALIPSO-CloudSat-Clouds and the Earth's Radiant Energy System (CERES)-Moderate Resolution Imaging Spectrometer (MODIS) (C3M) dataset, this study documents the seasonal variation of sea ice, cloud, and related atmospheric properties in the Arctic region (70°N–82°N) during the period 2007-2010. The surface type stratification used reveals the surface type influence on the Arctic cloud liquid water path (LWP)seasonality. Four surface types are defined: i) Permanent Ocean, ii) Land, iii) Permanent Ice, and iv) Transient Sea Ice. The Transient Ice regions are further divided into sub-regions according to the onset dates of melt/freeze season. Results show that the sea ice cover has no significant direct influence on the seasonal variation of the water vapor in the lower troposphere and the low-level cloud LWP. Rather, the role played by sea ice appear indirect by modifying the surface temperature response to increased solar insolation and influencing lower tropospheric stability (LTS). The results suggest that the combined increase in atmospheric water vapor due to warming air temperatures and the weakening of the inversion strength that governs the seasonal structure of cloud LWP. Over Transient Ice regions, the results suggest that the May peak in LWP coincides with a decrease in LTS and that is insensitive to melt and freeze onset. Variations in melt and freeze onset set are found to influence probably distribution of cloud LWP where regions of earlier melt and later freeze onset how a higher likelihood of larger values.

Yueyue Yu↗
NASA NTRS2018

GEOS S2S-2_1 File Specification: GMAO Seasonal and Sub-Seasonal Forecast Output

The NASA GMAO seasonal (9 months) and subseasonal (45 days) forecasts are produced with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model and Data Assimilation System Version S2S-2_1. The new system replaces version S2S-1.0 described in Borovikov et al (2017), and includes upgrades to many components of the system. The atmospheric model includes an upgrade from a pre-MERRA-2 version running on a latitude-longitude grid at approx. 1 degree resolution to a current version running on a cubed sphere grid at approximately 1/2 degree resolution. The important developments are related to the dynamical core (Putman et al., 2011), the moist physics (''two-moment microphysics'' of Barahona et al., 2014) and the cryosphere (Cullather et al., 2014). As in the previous GMAO S2S system, the land model is that of Koster et al (2000). GMAO S2S-2_1 now includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART, Colarco et al., 2010) single moment interactive aerosol model that includes predictive aerosols including dust, sea salt and several species of carbon and sulfate. The previous version of GMAO S2S specified aerosol amounts from climatology, which were used to inform the atmospheric radiation only. The ocean model includes an upgrade from MOM4 to MOM5 (Griffies 2012), and continues to be run on the tripolar grid at approximately 1/2 degree resolution in the tropics with 40 vertical levels. As in S2S-1.0, the sea ice model is from the Los Alamos Sea Ice model (CICE4, Hunke and Lipscomb 2010). The Ocean Data Assimilation System (ODAS) has been upgraded from the one described in Borovikov et al., 2017 to one that uses a modified version of the Penny, 2014 Local Ensemble Transform Kalman Filter (LETKF), and now assimilates along-track altimetry. The ODAS also does a nudging to MERRA-2 SST and sea ice boundary conditions. The atmospheric data assimilation fields used to constrain the atmosphere in the ODAS have been upgraded from MERRA to a MERRA-2 like system. The system is initialized using a MERRA-2-like atmospheric reanalysis (Gelaro et al. 2017) and the GMAO S2S-2_1 ocean analysis. Additional ensemble members for forecasts are produced with initial states at 5-day intervals, with additional members based on perturbations of the atmospheric and ocean states. Both subseasonal and seasonal forecasts are submitted to the National MultiModel Ensemble (NMME) project, and are part of the US/Canada multimodel seasonal forecasts (http://www.cpc.ncep.noaa.gov/products/NMME/). A large suite of retrospective forecasts (''hindcasts'') have been completed, and contribute to the calculation of the model's baseline climatology and drift, anomalies from which are the basis of the seasonal forecasts.

Seasonal Forecast↗
DOE OSTI2025

Atmospheric River Detection Under Changing Seasonality and Mean-State Climate: ARTMIP Tier 2 Paleoclimate Experiments

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

Atmospheric river, paleoclimate↗
NASA NTRS1983

The simulation of the seasonal cycle of the Southern Hemispheric circulation by the GLAS Seasonal Cycle Model and a comparison to observations

The general circulation of the Southern Hemisphere is quite different from that of the Northern Hemisphere in many important ways. These include the barotropic nature of the stationary waves and the presence of a strong barotropic component to the mean zonal wind, the lack of a strong seasonal dependence of the transient eddies, and the dominant role played by eddies with periods less than 10 days compared to longer period fluctuations. Such differences attest to the importance of the altered nature of the orographic and thermal land-sea forcings in the Southern Hemisphere compared to the Northern Hemisphere. Some of the important features of the Southern Hemisphere circulation as simulated by the GLAS Seasonal Cycle Model (SCM) are presented. The geographical patterns of local variability and their seasonal shifts in the SCM are discussed and compared to observations.

Straus, D. M.↗
NASA NTRSDate not supplied

Isolating the Impacts of Soil Moisture Initialization on Land Carbon Fluxes at the Sub-Seasonal to Seasonal Scale

Soil moisture, through its impact on transpiration, plays a significant role in controlling the productivity of terrestrial vegetation. Initial soil moisture conditions in a seasonal forecast may therefore affect the forecast of land carbon uptake. Here we investigate the relative impacts of soil moisture initialization and carbon reservoir initialization on forecasts of land carbon fluxes at the sub-seasonal to seasonal (S2S) scale. The bias-corrected, retrospective meteorology of NASA Global Modeling and Assimilation Office (GMAO)’s S2S ensemble forecast was used to force the stand-alone Catchment-CN model and thereby estimate terrestrial carbon responses out to nine lead months. Our results show that soil moisture initialization is a major contributor (approximately 44%) to the high global carbon uptake forecast skill seen during the first three lead months. The carbon reservoir initialization explains roughly another half of the monthly carbon forecast skill during this period and becomes relatively more important at longer leads (while the overall forecast skill decreases after the 3 lead month), suggesting a slower but longer-lasting influence of carbon reservoir initialization on carbon fluxes. Our results highlight the significance of a good soil moisture initialization for improved forecasts of carbon fluxes at leads of several months, further support for the usefulness of assimilating the satellite based soil moisture information into terrestrial biosphere models and of a short term carbon forecast to understand current events with often a lag in the availability of flux estimates.

Land carbon↗
DOE OSTI2026

Post-fire soil respiration in late growing season (2023 and 2024), Kougarok Fire Complex, Seward Peninsula, Alaska

Field soil respiration data collected in 2023 and 2024 from burned and unburned tussock tundra sites in the Kougarok Fire Complex, near Nome, on the Seward Peninsula of Alaska. Specifically, we measured soil properties and late-growing season CO2 fluxes in patches of unique plant functional types (forbs, shrubs, and graminoids) across two years in tundra recovering from repeated wildfires over the decade. The goal was to identify the main drivers of soil respiration in Arctic tundra underlain by discontinuous permafrost that is recovering from two recent, repeated wildfires that differed in fire age and number of times burned, thereby resulting in different levels of vegetation and subsurface property changes (i.e., successional trajectories). There are five files in *.csv format with one data file and four data description files including data, dictionary, methods, terminology, and file-level metadata. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), is a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic Phase 3 project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Santos, Fernanda [ORNL] (ORCID:0000000191555623)↗
NASA NTRS1980

Seasonal variation of radiance variances from satellite observations Implication of seasonal variation of available potential energy in the stratosphere

Nimbus 5 satellite radiances for the period 1973-74 are used to examine the seasonal variation of available potential energy in the stratosphere in order to provide a further observational basis for a long-term numerical simulation of stratospheric circulation. The maximum value of stratospheric zonal available potential energy, A(Z), in the upper and middle stratosphere shows pronounced variations between winter and summer, while little variation occurs in the lower stratospheric A(Z). The aperiodic occurrence of sudden warmings complicates the seasonal variation of A(Z) and A(E) (eddy available potential energy) in the stratosphere, making the energetics irregular. Time-Fourier analysis reveals that the primary variation of A(Z) and A(E) in the stratosphere is annual and semiannual, respectively.

Chen, T.-C.↗
NASA NTRS1990

Ozone measurements in Amazonia - Dry season versus wet season

Recent ozone measurements taken in the Amazonian rain forest environment during the wet season (April-May 1987) are described, revealling new aspects of the regional atmospheric chemistry. The measurements were part of the Amazon Boundary Layer Experiment (ABLE 2B) mission and utilized UV absorption as a measurement technique to obtain surface ozone data; 20 ozonesondes were launched in order to obtain vertical ozone profiles used to describe the upper troposphere and stratosphere. The major differences in comparison to a previous dry season experiment, which found ozone concentrations to be lower in the whole troposphere by nearly a factor of 2, are stressed.

Kirchhoff, V. W. J. H.↗
NASA NTRS2004

Seasonal and Non-seasonal Sea Level Variations by Exchange of Water with Land Hydrology

The global ocean exchanges a large amount of water, seasonally or non-seasonally, with land hydrology. Apart from the long-term melting of ice sheets and glaciers, the water is exchanged directly as land runoff R, and indirectly via atmosphere in the form of precipitation minus evapo-transpiration P-E. On land, the hydrological budget balance is soil moisture S = P-E-R. The runoff R has been difficult to monitor; but now by combining the following two data sets one can obtain a global estimate, subject to the spatial and temporal resolutions afforded by the data: (1) The space gravity mission GRACE yields monthly S estimate on a spatial scale larger than approx. 1000 km over the last 2.5 years; (2) The atmospheric circulation model output, such as from NCEP, provides proxy estimates for P-E at monthly and approx. 200 km resolutions. We will discuss these estimates and the effects on the global ocean water budget and hence sea level.

Chao, Benjamin F.↗
NASA NTRS2012

Seasonal and Non-Seasonal Variations of Jupiter's Atmosphere from Observations of Thermal Emission, 1994-2011

We analyzed mid-infrared images of Jupiter's thermal emission, covering approx.1.5 Jovian years, acquired in discrete filters between 7.8 and 24.5 microns. The behavior of stratospheric (approx.10-mbar) and tropospheric (approx.100-400 mbar) temperatures is generally consistent with predictions of seasonal variability, with differences between 100-mbar temperatures +/-50-60deg from the equator on the order of +/-2. Removing this effect, there appear to be long-term quasi-periodic variability of tropospheric temperatures, whose amplitude, phase and period depend on latitude. The behavior of temperatures in the Equatorial Zone (EZ) suggests a approx.4-6-year period with amplitude of about +/-1-1.5 K in temperature. At mid-latitudes, the periodicity is more distinct with amplitudes around +/-1.5-2.5 K and 4-8 year periods. The 4.2-year variation of stratospheric temperatures known as the quasiquadrennial oscillation or "QQO" (Leovy et al. 1991, Nature 354, 380) continued during this period. There were no variations of zonal mean temperatures associated with any of the "global upheaval" events that have produced dramatic changes of jupiter's visible appearance and cloud cover, although there are colder discrete regions associated with updrafts, e.g. the early stages of the re-darkening ("revival") of the South Equatorial Belt (SEB) in late 2010. On the other hand increases in the visible albedos ("fades") of belts are accompanied by increases in the thickness of a 700-mbar cloud layer (most likely NH3 ice) and clouds at higher pressures, together with the mixing ratio of NH3 gas near 400 mbar (above its condensation level). These quantities decrease during re-darkening ("revival") episodes, during which we note discrete features that are exceptions to the general correlation between dark albedos and minimal cloudiness. In contrast to all these changes, the meridional distribution of the 240-mbar para-H2 fraction appears to be invariant in time.

Orton, G.↗
NASA NTRS2013

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

NOAA CPC↗
NASA NTRS2018

Nasa GEOS Model for Sub-Seasonal to Seasonal Predictions: The Major Teleconnections, Tropical Cyclone Activity, and ENSO

Ability of the NASA GEOS seasonal prediction model system (the latest version) in predicting the large-scale teleconnections, tropical cyclone (TC) activity, and ENSO are validated. The major large-scale teleconnections include the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the Pacific North American (PNA) that span vast geographical area across the North Pacific/Atlantic and North America. Predictive skill of TC activity is assessed by Genesis potential index (GPI). Anomaly correlations are greater than 0.5 for winter teleconnections at up to 2 month lead and for the first four month (June- July-August-September) GPIs over the North Atlantic and the Western Pacific. Correlation for the long-range prediction of the ENSO (Nino3.4 SST) maturity reaches 0.9 at 6 month lead and 0.8 at 9 month lead.

Lim, Young-Kwon↗