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ORBIT-2 Weather and Climate Downscaling Software Repository
ORBIT-2 is a scalable foundation model for global, hyper-resolution climate and weather downscaling. ORBIT-2 incorporates two key innovations: (1) Residual Slim ViT (Reslim), a lightweight architecture with residual learning and Bayesian regularization for efficient, robust prediction; and (2) TILES, a tile-wise sequence scaling algorithm that reduces self-attention complexity from quadratic to linear, enabling long-sequence processing and massive parallelism. ORBIT-2 scales to 10 billion parameters across 65,536 GPUs, achieving up to 4.1 ExaFLOPS sustained throughput and 74–98% strong scaling efficiency. It supports downscaling to 0.9 km global resolution and processes sequences up to 4.2 billion tokens. On 7 km resolution benchmarks, ORBIT-2 achieves high accuracy with 𝑅2 scores in range of 0.98–0.99 against observation data.
Machine learning methods for weather forecasting
SAND2025-14466O This repository contains code for developing, training, and evaluating machine learning models for weather and climate forecasting, including forecast skill assessment, feature importance analysis, and reproducible workflows for model comparison. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning
SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.
The Life and Times of the Weather Risk Attribution Forecast
The Weather Risk Attribution Forecast (WRAF) provided a testbed for proactive estimation of the human role in extreme monthly events. Here, this paper looks back on issues that the WRAF highlighted.
Supporting Advancement in Weather and Water Prediction in the Upper Colorado River Basin: The SPLASH Campaign
Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a 2-yr deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and the surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry.
Understanding Observed Precipitation Change and the New Climate Normal from the Perspective of Daily Weather Types in the Southeast United States
Abstract Observed precipitation changes in the Southeast United States (SEUS) are spatially heterogeneous. Most of the inland SEUS and eastern Gulf Coast become drier, and the East Coast north of Charleston, South Carolina, and southern Florida become wetter from the old 30-yr period of 1961–90 to the recent period of 1991–2020. The observed climate change is examined from the perspective of daily weather types (WTs). A k -means clustering analysis has been conducted using daily 850-hPa circulation for 1948–2021. The obtained 10 WTs peak in different seasons, respectively. The frequencies and precipitation intensity of the WTs have been analyzed. A winter WT characterized by a western Appalachian trough (WAT) and a summer WT featuring North Atlantic subtropical high (NASH) have a rising trend of annual frequency from 1948 to 2021. An Appalachian high in the autumn has a decreasing frequency but becomes drier and stronger. Some precipitation intensity change and small location shift have also been observed. The drying up on the eastern Gulf Coast and the inland area of the SEUS is mainly caused by the weakened southwesterly low-level jet (LLJ) on the western flank of the NASH that reduces rain in the spring, the less frequent but stronger and drier Appalachian high in the summer and autumn, and the weaker and more western located Plains trough (PT) in the winter, spring, and autumn. The precipitation increase in the East Coast and southern Florida is majorly due to more frequent, stronger, and rainier troughs along the western Appalachian as well as the East Coast.
Long-Term Retrievals of Cloud Type and Fair-Weather Shallow Cumulus Events at the ARM SGP Site
Abstract A long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).
Assessment of Vertical Mesh Refinement in Concurrently Nested Large-Eddy Simulations Using the Weather Research and Forecasting Model
To facilitate multiscale simulation using the Weather Research and Forecasting Model, vertical mesh refinement for one-way concurrent nested simulation was recently introduced. Grid refinement in the vertical dimension removes the requirement of different grid aspect ratios on the bounding versus the nested domain, such that results from refinement are in the horizontal directions only, and thereby can also reduce numerical errors on the bounding domain arising from large aspect ratios in the presence of complex terrain. Herein, the impacts of vertical grid refinement on the evolving downstream flow in nested large-eddy simulations are evaluated in relation to other model configuration choices, including turbulence subfilter-scale (SFS) stress models, mesh configuration, and alternative methods for calculating several near-surface flow parameters. Although vertical nesting requires coarsening of the vertical grid on the bounding domain, leading to a smaller range of resolved turbulence scales in the nest’s lateral boundary conditions, parameter values within the nested domains are generally only minimally impacted, relative to nesting using the same vertical grid on each domain. Two dynamic SFS models examined herein generally improved the simulated mean wind speed, turbulence kinetic energy, stresses and spectra, on both domains, and accelerated equilibration rates within nested domains, relative to two constant coefficient models. A new method of extrapolating horizontal velocity components to near-surface locations at nested domain lateral boundaries, and a correction to the calculation of deformation elements near the surface, are each shown to slightly alter the mean parameter values, yet only minimally impact equilibration rates within the nested domain.
Using a Canopy Model Framework to Improve Large-Eddy Simulations of the Neutral Atmospheric Boundary Layer in the Weather Research and Forecasting Model
A canopy model framework is implemented in the Weather Research and Forecasting Model to improve the accuracy of large-eddy simulations (LES) of the atmospheric boundary layer (ABL). The model includes two options that depend on the scale of surface roughness elements. A resolved canopy model, typically used to model flow through vegetation canopies, is employed when roughness elements are resolved by the vertical LES grid. In the case of unresolved roughness, a modified “pseudocanopy model” is developed to distribute drag over a shallow layer above the surface. Both canopy model options are validated against idealized test cases in neutral stability conditions and are shown to improve surface layer velocity profiles relative to simulations employing Monin–Obukhov similarity theory (MOST), which is commonly used as a surface boundary condition in ABL models. Use of the canopy model framework also leads to increased levels of resolved turbulence kinetic energy and turbulent stresses. Because LES of the ABL has a well-known difficulty recovering the expected logarithmic velocity profile (log law) in the surface layer, particular focus is placed on using the pseudocanopy model to alleviate this issue over a range of model configurations. Tests with varying surface roughness values, LES closures, and grid aspect ratios confirm that the pseudocanopy model generally improves log-law agreement relative to simulations that employ a standard MOST boundary condition. The canopy model framework thus represents a low-cost, easy-to-implement method for improving LES of the ABL.
Mesoscale to Microscale Simulations over Complex Terrain with the Immersed Boundary Method in the Weather Research and Forecasting Model
Improvements to the Weather Research and Forecasting (WRF) Model are made to enable multiscale simulations over highly complex terrain with dynamically downscaled boundary conditions from the mesoscale to the microscale. Over steep terrain, the WRF Model develops numerical errors that are due to grid deformation of the terrain-following coordinates. An alternative coordinate system, the immersed boundary method (IBM), has been implemented into WRF, allowing for simulations over highly complex terrain; however, the new coordinate system precluded nesting within mesoscale simulations using WRF’s native terrain-following coordinates. Here, the immersed boundary method and WRF’s grid-nesting framework are modified to seamlessly work together. This improved framework for the first time allows for large-eddy simulation over complex (urban) terrain with IBM to be nested within a typical mesoscale WRF simulation. Simulations of the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma, are performed using a multiscale five-domain nested configuration, spanning horizontal grid resolutions from 6 km to 2 m. These are compared with microscale-only simulations with idealized lateral boundary conditions and with observations of wind speed/direction and SF 6 concentrations from a controlled release from intensive observation period 3. The multiscale simulation, which is configured independent of local observations, shows similar model skill predicting wind speed/direction and improved skill predicting SF 6 concentrations when compared with the idealized simulations, which require use of observations to set mean flow conditions. Use of this improved multiscale framework shows promise for enabling large-eddy simulation over highly complex terrain with dynamically downscaled boundary conditions from mesoscale models.
Evaluating Implementations of the Immersed Boundary Method in the Weather Research and Forecasting Model
The terrain-following coordinate system used by many atmospheric models can cause numerical instabilities due to discretization errors as resolved terrain slopes increase and the grid becomes highly skewed. The immersed boundary (IB) method, which does not require the grid to conform to the terrain, has been shown to alleviate these errors, and has been used successfully for high-resolution atmospheric simulations over steep terrain, including vertical building surfaces. Since many previous applications of IB methods to atmospheric models have used very fine grid resolution (5 m or less), the present study seeks to evaluate IB method performance over a range of grid resolutions and aspect ratios. Two classes of IB algorithms, velocity reconstruction and shear stress reconstruction, are tested within the common framework of the Weather Research and Forecasting (WRF) Model. Performance is evaluated in two test cases, one with flat terrain and the other with the topography of Askervein Hill, both under neutrally stratified conditions. WRF-IB results are compared to similarity theory, observations, and native WRF results. Despite sensitivity to the location at which the IB intersects the model grid, the velocity reconstruction IB method shows consistent performance when used with a hybrid RANS/LES surface scheme. The shear stress reconstruction IB method is not sensitive to the grid intersection, but is less consistent and near-surface velocity errors can occur at coarse resolutions. This study represents an initial investigation of IB method variability across grid resolutions in WRF. Future work will focus on improving IB method performance at intermediate to coarse resolutions.
Data for: Competitive and Cooperative Effects of Chloride on Palladium(II) Adsorption to Iron (Oxyhydr)oxides: Implications for Mobility During Weathering
Data package for manuscript "Competitive and Cooperative Effects of Chloride on Palladium(II) Adsorption to Iron (Oxyhydr)oxides: Implications for Mobility During Weathering" by Emily G. Wright, Xicheng He, Elaine D. Flynn, Daniel E. Giammar, and Jeffrey G. Catalano. The manuscript will be submitted to Geochimica et Cosmochimica Acta. This dataset contains adsorption results from experiments designed to investigate the effect of chloride on Pd(II) adsorption to hematite and 2-line ferrihydrite. This includes lab experiments reacting suspensions of the minerals with various amounts of Pd and chloride at pH 4. We also characterized adsorbed Pd with X-ray absorption fine structure spectroscopy.
naturf: a package for generating urban parameters for numerical weather modeling
The Neighborhood Adaptive Tissues for Urban Resilience Futures tool (NATURF) is a Python workflow that generates files readable by the Weather Research and Forecasting (WRF) model. NATURF uses geopandas and hamilton to calculate 132 building parameters from shapefiles with building footprint and height information. These parameters can be collected and used in many formats, and the primary output is a binary file configured for input to WRF. This workflow is a flexible adaptation of the National/World Urban Database and Access Portal Tool (NUDAPT/WUDAPT) that can be used with any study area at any spatial resolution. The climate modeling community and urban planners can identify the effects of building/neighborhood morphology on the microclimate using the urban parameters and WRF-readable files produced by NATURF. More information on the urban parameters calculated can be found in the documentation.
Advanced Vehicle Testing Activity Cold Weather On-road Testing of the Chevrolet Volt
This report details cold weather on-road testing of a Chevrolet Volt. It quantifies changes in efficiency and electric range as ambient temperature changes. It will be published to INL's AVTA website as an INL technical report and will be accessible to the general public.
Weather at LANL
This report gives general information about how to become a meteorologist and what kinds of jobs exist in that field. Then it goes into detail about why weather is monitored at LANL, how it is done, and where the data can be accessed online.
Introduction to Weather in Northern New Mexico
This is a presentation describing the weather in New Mexico for the World Wildlife Federation.
Gauging the Impact of Various Definitions of Low- and Moderate-Income Communities on Possible Electricity Savings From Weatherization
With rising interest in lowering energy costs for low- and moderate-income households, the U.S. Department of Energy (DOE) asked Lawrence Berkeley National Laboratory (LBNL) to assess the implications of pursuing energy efficiency neighborhood-by-neighborhood where those households are most prevalent. DOE provided certain scenarios for qualifying geographic areas as “low- and moderate-income communities,” and LBNL used data on demographics, housing types and recent savings from low-income retrofits or weatherization to provide rough electricity savings estimates under those scenarios.