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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Effect of Ice Number Concentration on the Evolution of Boundary Layer Clouds During Arctic Marine Cold‐Air Outbreaks

Abstract Marine cold‐air outbreaks (MCAOs) are crucial for Arctic Ocean heat loss, featuring convective cloud rolls that transition into convection cells downstream. Understanding factors controlling this transformation is the key for improving MCAO cloud representation in climate models. This study employs large‐eddy simulations to investigate how cloud ice number concentrations () affect cloud evolution using a case from the Cold‐Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. The simulations, performed in a Lagrangian framework following an air mass trajectory, are driven by ERA5 reanalysis data. Initially, all simulations produce similar cloud patterns, but higher leads to earlier breakup of cloud rolls. Between 4 and 10 hr, surface precipitation rates are similar across simulations, but precipitation initiates earlier, and the cloud‐base precipitation rates are higher when is higher. The stronger precipitation evaporation leads to increased stability of the boundary layer and reduced intensity of vertical mixing between the surface and cloud layer. An increased sink of cloud layer moisture via precipitation and decreased source through diminished vertical transport result in earlier cloud breakup in higher conditions. Simulations with different sea surface temperatures (SST) indicate that this cloud breakup mechanism remains valid for MCAOs of different strengths, although the cloud organization is more sensitive to SST changes in low environments. This work highlights the importance of accurate representations of ice processes in simulating MCAO clouds and suggests the need for observational constraints of ice nucleating particles and over the mixed‐phase cloud regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2021

Climatology and Evolution of the Antarctic Peninsula Föhn Wind-Induced Melt Regime From 1979–2018

Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979–2018. We find that föhn wind-induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn-induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y-1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn-induced melt. Long-term föhn-induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluation of precipitation across the contiguous United States, Alaska, and Puerto Rico in multi-decadal convection-permitting simulations

Abstract This study is an early effort to generate a multi-decadal convection-permitting regional climate dataset that covers nearly the entire North American continent. We assessed a 20 year dynamically downscaled regional climate simulation at a 4 km spatial resolution with explicit convection across the contiguous United States (CONUS), Alaska, and Puerto Rico. Specifically, we evaluated the model’s performance in representing mean, 95th percentile, and extreme precipitation across regions. Our findings indicate that when compared with ERA5 reanalysis, the forcing data, convection-permitting simulation improves representations of seasonal, 95th percentile, and extreme precipitation over a large portion of the CONUS, Alaska, and Puerto Rico, particularly in areas where precipitation is heaviest. The simulation adds value over its forcing data (ERA5) in up to 53% of all grid cells in the CONUS, 68.8% in Alaska, and 84.0% in Puerto Rico. It is important to note that, however, despite improvements, model errors in Puerto Rico remain large. Similar improvements are observed in extreme indices, including consecutive dry days, maximum 5 days precipitation, and extreme precipitation. Analysis of the diurnal cycle of mean hourly precipitation suggests that representations of convective processes—including onset, dissipation, suppression, downstream propagation, and local circulation—improved overall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12 km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth-generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions. First, how well are the 3 and 24 h precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) 4 km Stage IV and Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data on 3 and 24 h timescales, respectively. Our analysis suggests that the 12 km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3 and 24 h precipitation maximum over the CONUS, in contrast to ERA5, which generally underestimates these quantities mainly over the eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3 and 24 h annual and seasonal maximum precipitation. WRF exhibits seasonally dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics but is susceptible to common regional climate model biases. Consequently, if used as input data for domain-specific models, we suggest moderate bias correction be applied to the dynamically downscaled product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Typological representation of the offshore oceanographic environment along the Alaskan North Slope

Erosion and flooding impacts to Arctic coastal environments are intensifying with nearshore oceanographic conditions acting as a key environmental driver. Robust and comprehensive assessment of the nearshore oceanographic conditions require knowledge of the following boundary conditions: incident wave energy, water level, incident wind energy, ocean temperature and salinity, bathymetry, and shoreline orientation. The number of offshore oceanographic boundary conditions can be large, requiring a significant computational investment to reproduce nearshore conditions. This present study develops location-independent typologies to reduce the number of boundary conditions needed to assess nearshore oceanographic environments in both a Historical (2007–2019) and Future (2020–2040) timespan along the Alaskan North Slope. We used WAVEWATCH III® and Delft3D Flexible Mesh model output from six oceanographic sites located along a constant ~50 m bathymetric line spanning the Chukchi to Beaufort Seas. K-means clustering was applied to the energy-weighted joint-probability distribution of significant wave height (H s ) and peak period (T p ). Distributions of wave and wind direction, wind speed, and water level associated with location-independent centroids were assigned single values to describe a reduced order, typological rendition of offshore oceanographic conditions. Reanalysis data (e.g., ASRv2, ERA5, and GOFS) grounded the historical simulations while projected conditions were obtained from downscaled GFDL-CM3 forced under RCP8.5 conditions. Location-dependence for each site is established through the occurrence joint-probability distribution in the form of unique scaling factors representing the fraction of time that the typology would occupy over a representative year. As anticipated, these typologies show increasingly energetic ocean conditions in the future. They also enable computationally efficient simulation of the nearshore oceanographic environment along the North Slope of Alaska for better characterization of coastal processes (e.g., erosion, flooding, or sediment transport).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Use of Shallow Ice Core Measurements to Evaluate and Constrain 1980–1990 Global Reanalyses of Ice Sheet Precipitation Rates

Abstract Sea‐level rise (SLR) projections by Earth System Models (ESMs) depend on ice sheet surface mass balances. Accurate, global atmosphere reanalyses would be ideal for providing equilibrated ice sheet model initial conditions in fully coupled ESM simulations. Here we present the first evaluation of 1980–1990 global reanalysis precipitation over Greenland and Antarctica that uses independent observations of net accumulation rates derived from shallow ice cores. Precipitation distributions from both the European Centre for Medium‐Range Weather Forecast's Reanalysis (ERA5) and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) are highly correlated with contemporaneous co‐located net accumulation rates from Greenland ( r 2 > 0.95) and West Antarctica ( r 2 > 0.7). Three other commonly used reanalyses (WFDE5, CRUNCEP, and GSWP3) exhibit significantly weaker correlations on one or both ice sheets. Our findings imply that ESMs should use ERA5 or MERRA‐2 in data‐forced simulations to validate ice sheet model dynamics and precondition firn for SLR projections.

Schneider, Adam↗
DOE OSTI2024

How well are hazards associated with derechos reproduced in regional climate simulations?

Abstract. A 15-member ensemble of convection-permitting regional simulations of the fast-moving and destructive derecho of 29–30 June 2012 that impacted the northeastern urban corridor of the USA is presented. This event generated 1100 reports of damaging winds, generated significant wind gusts over an extensive area of up to 500 000 km2, caused several fatalities, and resulted in widespread loss of electrical power. Extreme events such as this are increasingly being used within pseudo-global-warming experiments to examine the sensitivity of historical, societally important events to global climate non-stationarity and how they may evolve as a result of changing thermodynamic and dynamic contexts. As such it is important to examine the fidelity with which such events are described in hindcast experiments. The regional simulations presented herein are performed using the Weather Research and Forecasting (WRF) model. The resulting ensemble is used to explore simulation fidelity relative to observations for wind gust magnitudes, spatial scales of convection (as is manifest in high composite reflectivity, cREF), and both rainfall and hail production as a function of model configuration (microphysics parameterization, lateral boundary conditions (LBCs), start date, use of nudging, compiler choice, damping, and number of vertical levels). We also examine the degree to which each ensemble member differs with respect to key mesoscale drivers of convective systems (e.g., convective available potential energy and vertical wind shear) and critical manifestations of deep convection, e.g., vertical velocities, cold-pool generation, and how those properties relate to the correct characterization of the associated atmospheric hazards (wind gusts and hail). Use of a double-moment, seven-class scheme with number concentrations for all species (including hail and graupel) results in the greatest fidelity of model-simulated wind gusts and convective structure to the observations of this event. All ensemble members, however, fail to capture the intensity of the event in terms of the spatial extent of convection and the production of high near-surface wind gusts. We further show very high sensitivity to the LBCs employed and specifically that simulation fidelity is higher for simulations nested within ERA-Interim compared to ERA5. Excess convective available potential energy (CAPE) in all ensemble members after the derecho passage leads to excess production of convective cells, wind gusts, cREF > 40 dBZ, and precipitation during a frontal passage on the subsequent day. This event proved very challenging to forecast in real time and to reproduce in the 15-member hindcast simulation ensemble presented here. Future work could examine if simulations with other initial and lateral boundary conditions can achieve greater fidelity.

Shepherd, Tristan (ORCID:0000000186276419)↗
DOE OSTI2023

wrfhydro

This dataset contains a continuous WRF-Hydro simulation for the CACTI field campaign. The simulated period is 1-Aug-2018 through 21-Mar-2019 with hourly model output. The simulation is made with two grids that match the two outer LASSO-CACTI grids, with 7.5 km and 2.5 km grid spacings. Forcing data for WRF-Hydro was obtained from the ERA5-Land product. Values from this WRF-Hydro simulation were used to initialize the soil state for each LASSO-CACTI simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Gridded Sub-daily Climate Forcings for North America Based on Daymet and ERA5 (Daymet-ERA5)

To support high spatial and temporal resolution land surface modeling, this dataset provides hourly time step historic weather forcing at 1-km spatial resolution for the entire North America. The latest Daymet V4 data provides gridded historic daily weather observations at 1-km spatial resolution from 1980 to 2024. Using sub-daily temporal information from the ECMWF ERA5 reanalysis, Daymet was further temporally downscaled to hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each hourly time step from ERA5 while maintaining the total and average values from Daymet for each day. This results in a blended 1980-2024 Daymet-ERA5 dataset. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

A Synopsis of AIRS Global-Mean Clear-Sky Radiance Trends From 2003 to 2020

Atmospheric Infrared Sounder (AIRS) aboard the National Aeronautics and Space Administration (NASA) Aqua satellite has been operating since September 2002. Its information content, superb instrument performance, and dense sampling pattern make the AIRS radiances an invaluable data set for climate studies. The trends of global-mean, nadir-view, clear-sky AIRS radiances from 2003 to 2020 are studied here, together with the counterparts of synthetic radiances based on two reanalyzes, European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ECMWF ERA5) and NASA Goddard Earth Observing System V5.4.1 (GEOS-5.4.1; a reanalysis product without assimilation of hyperspectral radiances such as AIRS). The AIRS observation shows statistically significant negative trends in most of its CO2 channels, positive but non-significant trends in the channels over the window regions, and statistically significant positive trends in some of its H2O channels. The best agreements between observed and simulated radiance trends are seen over the CO2 tropospheric channels, while the observed and simulated trends over the CO2 stratospheric channels are opposite. ERA5 results largely agree with the AIRS observation over the H2O channels. The comparison in the H2O channels helps reveal a data continuity issue in the GEOS-5.4.1. Contributions from individual variables to the radiance trends are also assessed by performing separate simulations. This study provides the first synopsis of the global-mean trend of AIRS radiances over all its thermal-IR channels.

CO2↗
DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗
DOE OSTI2024

Modeled estimates of permafrost hydrology and related fields for pan-Arctic region over the period 1980-2100

This dataset contains gridded estimates of total runoff (mm/day); subsurface runoff (mm/day); snow water equivalent (SWE, mm/day); vertical upward water flux of combined surface evaporation, evapotranspiration, and snow sublimation (mm/day); and soil temperature (C) across the pan-Arctic basin from simulations of the Permafrost Water Balance Model (PWBM v4). These data were generated to allow researchers to advance understanding of the changes unfolding in the Arctic's terrestrial water cycle under the influences of climate warming, hydrological cycle intensification, and permafrost thaw. The dataset will enable researchers to address important questions spanning multiple disciplines. For example, how will rising river flows exported to the coast impact critical zone ecosystems, near-shore ice dynamics, and ocean biogeochemical processes. Spatial resolution is 25x25 km with N=39926 grid cells spanning the domain. Three forcing datasets were used to drive simulations at an implicit daily time step over the period 1980-2100. Daily air temperature, precipitation, and wind speed data from the WATCH Forcing Data methodology applied to ERA5 reanalysis (W5E5) were used for meteorological forcings over the period 1980-2019. Simulations for the period 1980-2100 used meteorological data from two global climate models; IPSL-CM6A-LR and MPI-ESM1-2-HR. The PWBM hydrological outputs are organized by grid and month in gzipped ASCII text files within each archived tar file. Soil temperature is organized by grid cell and day of year from DOY 205 to DOY 260. No special software is required to work with these data. The package contains spatial domain and river basin information files. These data are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and soil thermal dynamics. A manuscript describing the model simulations and associated analysis has been accepted for publication in The Cryosphere (Rawlins and Karmalkar, 2024).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Relationship between Precipitation and Precipitable Water in CMIP6 Simulations and Implications for Tropical Climatology and Change

It is well documented that over the tropical oceans, column-integrated precipitable water (pw) and precipitation (P) have a nonlinear relationship. In this study moisture budget analysis is used to examine this P–pw relationship in a normalized precipitable water framework. It is shown that the parameters of the nonlinear relationship depend on the vertical structure of moisture convergence. Specifically, the precipitable water values at which precipitation is balanced independently by evaporation versus by moisture convergence define a critical normalized precipitable water, pw nc . This is a measure of convective inhibition that separates tropical precipitation into two regimes: a local evaporation-controlled regime with widespread drizzle and a precipitable water–controlled regime. Most of the 17 CMIP6 historical simulations examined here have higher pw nc compared to ERA5, and more frequently they operate in the drizzle regime. When compared to observations, they overestimate precipitation over the high-evaporation oceanic regions off the equator, thereby producing a ‘‘double ITCZ’’ feature, while underestimating precipitation over the large tropical landmasses and over the climatologically moist oceanic regions near the equator. The responses to warming under the SSP585 scenario are also examined using the normalized precipitable water framework. It is shown that the critical normalized precipitable water value at which evaporation versus moisture convergence balance precipitation decreases as a result of the competing dynamic and thermodynamic responses to warming, resulting in an increase in drizzle and total precipitation. Statistically significant historical trends corresponding to the thermodynamic and dynamic changes are detected in ERA5 and in lowintensity drizzle precipitation in the PERSIANN precipitation dataset.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

High Latitude Atmospheric Rivers: Teleconnections and Impacts

Atmospheric rivers (ARs) are long, narrow synoptic scale features that act as moisture transport vehicles by moving water vapor poleward. They are typically found in storm tracks and often have profound implications for regional hydroclimate. For high latitude locales such as Antarctica and the Arctic, ARs are fewer in number but often have an outsized impact by either producing significant snow accumulation events, or, depending on their thermal characteristics, initiating melt events on ice sheets, ice shelves, and sea ice. In this study, we apply both reanalysis and climate model data to evaluate high latitude ARs for both the Arctic and Antarctic in the context of teleconnections and modes of variability. Specifically, we fold in both MERRA-2 and ERA5 reanalysis as well as historical and climate change simulations from the CESM2 (Community Earth System Model, Version 2) and E3SMv2 (Energy Exascale Earth System Model, v2) with multiple ensemble members to better understand natural variability versus climate change signals. An overview of AR climatology and impacts will be presented, including a baseline uncertainty quantification analysis using data from ARTMIP (the Atmospheric River Tracking Method Intercomparison Project). AR impacts during specific modes of variability will be characterized by diagnosing precipitation and boundary layer temperature associated with Ars influencing the cryosphere. Modes of variability investigated include the SAM (Southern Annular Mode), PDO (Pacific Decadal Oscillation), PSA2 (Pacific South American Mode 2), the IOD (Indian Ocean Dipole), and the AO (Arctic Oscillation).

Atmospheric Rivers↗
DOE OSTI2026

Performance of reanalysis and mesoscale models off the coast of Hawai'i

The eastern Hawai'i coast in the United States is characterized by considerable wind resource fuelled by persistent trade winds, making it an important area for energy research. The need is strong for reanalyses and higher-resolution regional simulations where observations have been historically limited, such as Hawai'i's offshore environments. However, studies using offshore observations in other parts of the world have shown that significant errors can occur in reanalyses and wind datasets, which can lead to inaccurate estimates of wind energy generation, payback periods, and extreme weather risks at project locations. The degree of such errors is influenced by a number of factors, including spatial resolution and the handling of processes within the planetary boundary layer (PBL). In this work, we provide a wind resource characterization from year-long lidar buoy measurements off the eastern coast of O'ahu, Hawai'i, an environment previously unobserved at the rotor level, and use the characterization to evaluate the performance of two simulation datasets. The O'ahu deployment location is meteorologically unique and less complex than land-based wind resource characterizations, being strongly characterized by trade winds with minimal land–atmosphere interaction influences. Despite the unique and fairly consistent meteorological conditions, we hypothesize that distinct simulation datasets will exhibit diverse ranges of errors similar to those that have been seen for other offshore locations. We find the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) to strongly underestimate observed wind speeds at the O'ahu location (bias = −1.54 m s −1 at a height of 140 m above sea level), while a regional Weather Research and Forecasting Model (WRF) simulation produced by the University of Hawai'i (UH-WRF) provides a significantly smaller wind speed bias (−0.25 m s −1 ), highlighting the value of running regional, higher-resolution simulations. The large bias noted for ERA5 is driven by significant underestimation of fast wind speeds (>9 m s −1 ), which the study site is largely characterized by, along with discontinuities in the ERA5 diurnal cycle. We also speculate that the relative sparsity of observations for data assimilation in this remote part of the world could influence the performance of ERA5 and that challenges with characterizing island effects could impact the performance of both datasets.

17 WIND ENERGY↗
DOE OSTI2022

Spatial heterogeneity effects on land surface modeling of water and energy partitioning

Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗