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DOE OSTI2026

High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations

High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.

17 WIND ENERGY↗

DOE OSTI2026

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (CHPS) with forecasters-in-the-loop. Results demonstrate that forecasters-in-the-loop systems consistently outperform ML models in both general forecasts and flood alerting across lead times up to 96 hr, even when ML models use observed forcings, while CNRFC operational process relies on biased weather forecasts. Our analysis reveals that forecaster expertise maintains forecast reliability despite inaccurate precipitation inputs, with human-guided systems showing superior performance degradation characteristics at extended lead times. These findings highlight the irreplaceable value of human expertise in operational forecasting and caution against overstating current ML capabilities in real-world applications.

Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI ↗

NASA NTRS2010

FASTSAT-HSV01 Synergistic Observations of the Magnetospheric Response During Active Periods: MINI-ME, PISA and TTI

Understanding the complex processes within the inner magnetosphere of Earth particularly during storm periods requires coordinated observations of the particle and field environment using both in-situ and remote sensing techniques. In fact in order to gain a better understanding of our Heliophysics and potentially improve our space weather forecasting capabilities, new observation mission approaches and new instrument technologies which can provide both cost effective and robust regular observations of magnetospheric activity and other space weather related phenomenon are necessary. As part of the effort to demonstrate new instrument techniques and achieve necessary coordinated observation missions, NASA's Fast Affordable Science and Technology Satellite Huntsville 01 mission (FASTSAT-HSVOI) scheduled for launch in 2010 will afford a highly synergistic solution which satisfies payload mission opportunities and launch requirements as well as contributing iri the near term to our improved understanding of Heliophysics. NASA's FASTSAT-HSV01 spacecraft on the DoD Space Test Program-S26 (STP-S26) Mission is a multi-payload mission executed by the DoD Space Test Program (STP) at the Space Development and Test Wing (SDTW), Kirtland AFB, NM. and is an example of a responsive and economical breakthrough in providing new possibilities for small space technology-driven and research missions. FASTSAT-HSV is a unique spacecraft platform that can carry multiple small instruments or experiments to low-Earth orbit on a wide range of expendable launch vehicles for a fraction of the cost traditionally required for such missions. The FASTSAT-HSV01 mission allows NASA to mature and transition a technical capability to industry while increasing low-cost access to space for small science and technology (ST) payloads. The FASTSAT-HSV01 payload includes three NASA Goddard Space Flight Center (GSFC) new technology built instruments that will study the terrestrial space environment and potentially contribute to space weather research in a synergistic manner. MINI-ME, a neutral atom imager, will observe the neutral atom inputs to ionospheric heating which can be important during high levels of magnetospheric activity. PISA, a plasma impedance spectrometer, will measure simultaneously the local electron densities and temperatures as well as measure small scale density structure (500 m spatial scale) during these active periods. TTI, a thermospheric imager, will remotely determine the thermospheric temperature response to this magnetospheric activity. Together, these observations will contribute significantly to a comprehensive understanding of the flow of energy through and the response of the storm-time terrestrial magnetosphere.

Casas, Joseph C.↗

NASA NTRS2021

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗

DOE OSTI2024

Incorrect computation of Madden-Julian oscillation prediction skill

The Madden–Julian oscillation (MJO) is a major tropical weather system and one of the largest sources of predictability for subseasonal-to-seasonal weather forecasts. Skillful prediction of the MJO has been a highly active area of research due to its large socio-economic impacts. Silini et al., herein S21, developed a machine learning model to predict the MJO, which they claimed to have an MJO prediction skill of 26–27 days over all seasons and 45 days for December–February (DJF) winter. If true, this would make the skill of their model competitive with that of the state-of-the-art dynamical MJO prediction systems at 20–35 days. However, here we show that the MJO prediction was calculated incorrectly in S21, which spuriously increased the performance of their model. Correctly computed skill of their model was substantially lower than that reported in S21; the skill for all seasons drops to 11–12 days and the skill for forecasts initialized during DJF drops to 15 days. Our findings clarify that the S21 machine learning model is not competitive with state-of-the-art numerical weather prediction models in predicting the MJO.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Science Education Supporting Weather Broadcasters On-Air and in the Classroom with NASA "Mini-Education Supplements"

NASA-Goddard Space Flight Center has initiated a new project designed to expand on existing news services and add value to classrooms through the development and distribution of two-minute 'mini-supplements' which give context and teach about current weather and Earth research phenomena. The innovative mini-supplements provide raw materials for weather forecasters to build news stories around NASA related missions without having to edit the more traditional and cumbersome long-form video format. The supplements cover different weather and climate topics and include NASA data, animations, video footage, and interviews with scientists. The supplements also include a curriculum package with educational lessons, educator guide, and hand-on activities. One goal is to give on-air broadcasters who are the primary science educators for the general public what they need to 'teach' about the science related to NASA research behind weather and climate news. This goal achieves increasing public literacy and assures higher accuracy and quality science reporting by the media. The other goal is to enable on-air broadcasters to serve as distributors of high quality, standards-based educational curricula and supplemental material when they visit 8-12 grade classrooms. The focus of 'pilot effort' centers around the success of NASA's Tropical Rainfall Measuring Mission (TRMM) but is likely expandable to other NASA earth or space science missions.

Shepherd, J. Marshall↗

NASA NTRS2011

Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data

nfrared (IR) images (approximately 11-micron channel) recorded by satellite sensors have been widely used in weather forecasting, research, and classroom education since the Nimbus program. Unlike visible images, IR imagery can reveal cloud features without sunlight illumination; therefore, they can be used to monitor weather phenomena day and night. With geostationary satellites deployed around the globe, it is possible to monitor weather events 24/7 at a temporal resolution that polar-orbiting satellites cannot achieve at the present time. When IR data from multiple geostationary satellites are merged to form a single product--also known as a merged product--it allows for observing weather on a global scale. Its high temporal resolution (e.g., every half hour) also makes it an ideal ancillary dataset for supporting other satellite missions, such as the Tropical Rainfall Measuring Mission (TRMM), etc., by providing additional background information about weather system evolution.

Liu, Zhong↗

NASA NTRS1984

Recent examples of mesoscale numerical forecasts of severe weather events along the east coast

Mesoscale numerical forecasts utilizing the Mesoscale Atmospheric Simulation System (MASS) are documented for two East Coast severe weather events. The two events are the thunderstorm and heavy snow bursts in the Washington, D.C. - Baltimore, MD region on 8 March 1984 and the devastating tornado outbreak across North and South Carolina on 28 March 1984. The forecasts are presented to demonstrate the ability of the model to simulate dynamical interactions and diabatic processes and to note some of the problems encountered when using mesoscale models for day-to-day forecasting.

Kocin, P. J.↗

NASA NTRS2009

Adjoint-Based Diagnostics in Atmospheric Data Assimilation

With the adjoint of a data assimilation system, the impact of any or all assimilated observations on measures of forecast or analysis skill can be estimated accurately and efficiently. The approach is especially well-suited for assessing the impact of hyper-spectral satellite instruments on numerical weather forecasts because it easily allows aggregation of results in terms of individual data types, channels or locations, all computed simultaneously based on a single pass of the Adjoint- system. This talk 'provides a general overview of the use of Adjoint-based diagnostic tools in data assimilation. We will focus on the theoretical basis and practical implementation of these tools, as well as their application to specific problems. Adjoint-based Impact calculations will be compared with results from standard observing system experiments (OSEs) and their application to related problems, such as the calibration of Observing System Simulation Experiments (OSSEs) will be discussed.

Gelaro, Ronald↗

NASA NTRS2002

Aircraft Icing Weather Data Reporting and Dissemination System

The long-term operational concept of this research is to develop an onboard aircraft system that assesses and reports atmospheric icing conditions automatically and in a timely manner in order to improve aviation safety and the efficiency of aircraft operations via improved real-time and forecast weather products. The idea is to use current measurement capabilities on aircraft equipped with icing sensors and in-flight data communication technologies as a reporting source. Without requiring expensive avionics upgrades, aircraft data must be processed and available for downlink. Ideally, the data from multiple aircraft can then be integrated (along with other real-time and modeled data) on the ground such that aviation-centered icing hazard metrics for volumes of airspace can be assessed. As the effect of icing on different aircraft types can vary, the information should be displayed in meaningful ways such that multiple types of users can understand the information. That is, information must be presented in a manner to allow users to understand the icing conditions with respect to individual concerns and aircraft capabilities. This research provides progress toward this operational concept by: identifying an aircraft platform capable of digitally capturing, processing, and downlinking icing data; identifying the required in situ icing data processing; investigating the requirements for routing the icing data for use by weather products; developing an icing case study in order to gain insight into major air carrier needs; developing and prototyping icing display concepts based on the National Center for Atmospheric Research's existing diagnostic and forecast experimental icing products; and conducting a usability study for the prototyped icing display concepts.

Bass, Ellen J.↗

NASA NTRS2007

Recent Theoretical Advances in Analysis of AIRS/AMSU Sounding Data

AIRS was launched on EOS Aqua on May 4,2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. This paper describes the AIRS Science Team Version 5.0 retrieval algorithm. Starting in early 2007, the Goddard DAAC will use this algorithm to analyze near real time AIRS/AMSU observations. These products are then made available to the scientific community for research purposes. The products include twice daily measurements of the Earth's three dimensional global temperature, water vapor, and ozone distribution as well as cloud cover. In addition, accurate twice daily measurements of the earth's land and ocean temperatures are derived and reported. Scientists use this important set of observations for two major applications. They provide important information for climate studies of global and regional variability and trends of different aspects of the earth's atmosphere. They also provide information for researchers to improve the skill of weather forecasting. A very important new product of the AIRS Version 5 algorithm is accurate case-by-case error estimates of the retrieved products. This heightens their utility for use in both weather and climate applications. These error estimates are also used directly for quality control of the retrieved products. Version 5 also allows for accurate quality controlled AIRS only retrievals, called "Version 5 AO retrievals" which can be used as a backup methodology if AMSU fails. Examples of the accuracy of error estimates and quality controlled retrieval products of the AIRS/AMSU Version 5 and Version 5 AO algorithms are given, and shown to be significantly better than the previously used Version 4 algorithm. Assimilation of Version 5 retrievals are also shown to significantly improve forecast skill, especially when the case-by-case error estimates are utilized in the data assimilation process.

Susskind, Joel↗

DOE OSTI2021

Attribution of the Australian bushfire risk to anthropogenic climate change

Abstract. Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically based index of fire weather, the Fire Weather Index; long-term observations of heat and drought; and 11 large ensembles of state-of-the-art climate models. We find large trends in the Fire Weather Index in the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) since 1979 and a smaller but significant increase by at least 30 % in the models. Therefore, we find that climate change has induced a higher weather-induced risk of such an extreme fire season. This trend is mainly driven by the increase of temperature extremes. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor of 2 due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger, suggesting that the attribution of the increased fire weather risk is a conservative estimate. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season, September–February. The observations, however, show a weak drying trend in the annual mean. For the 2019/20 season more than half of the July–December drought was driven by record excursions of the Indian Ocean Dipole and Southern Annular Mode, factors which are included in the analysis here. The study reveals the complexity of the 2019/20 bushfire event, with some but not all drivers showing an imprint of anthropogenic climate change. Finally, the study concludes with a qualitative review of various vulnerability and exposure factors that each play a role, along with the hazard in increasing or decreasing the overall impact of the bushfires.

van Oldenborgh, Geert Jan (ORCID:0000000268989535)↗

NASA NTRS1976

Interactive applications of satellite observations and mesoscale numerical models

The impact of numerical weather prediction (NWP) and satellite meteorology on operational weather forecasting has become overwhelming in the past few years. The paper looks toward the merger of these technologies in making short range 6-18 h forecasts through the use of mesoscale NWP models. A short-range (2-18 h) mesoscale forecast system envisioned for the near future is described that includes four components: hydrodynamic numerical models, large-scale and mesoscale; satellites, polar orbiting for high latitudes and geostationary for low latitudes; mesoclimatology, derived in large part from satellite data; and special-purpose simple models and empirical relations. It is important that the components of the forecast system be developed in parallel rather than in series if the system is to be completed within five years. There is enough evidence to substantiate the revolution in the mesoscale weather prediction in the next five years.

Kreitzberg, C. W.↗

NASA NTRS1985

Observing system simulation experiments at NASA. Goddard Space Flight Center

A series of realistic simulation studies is being conducted as a cooperative effort between the European Centre for Medium Range Weather Forecasts (ECMWF), the National Meteorological Center (NMC), and the Goddard Laboratory for Atmospheres (GLA), to provide a quantitative assessment of the potential impact of future observing systems on large scale numerical weather prediction. A special objective is to avoid the unrealistic character of earlier simulation studies. Following a brief review of previous simulation studies and real data impact tests, the methodology for the current simulation system will be described. Results from an assessment of the realism of the simulation system and of the potential impact of advanced observing systems on numerical weather prediction and preliminary results utilizing this system will be presented at the conference.

Atlas, R.↗

NASA NTRS2013

Applied Meteorology Unit (AMU) Quarterly Report Fourth Quarter FY-13

Ms. Shafer completed the task to determine relationships between pressure gradients and peak winds at Vandenberg Air Force Base (VAFB), and began developing a climatology for the VAFB wind towers; Dr. Huddleston completed the task to develop a tool to help forecast the time of the first lightning strike of the day in the Kennedy Space Center (KSC)/Cape Canaveral Air Force Station (CCAFS) area; Dr. Bauman completed work on a severe weather forecast tool focused on the Eastern Range (ER), and also developed upper-winds analysis tools for VAFB and Wallops Fl ight Facility (WFF); Ms. Crawford processed and displayed radar data in the software she will use to create a dual-Doppler analysis over the east-central Florida and KSC/CCAFS areas; Mr. Decker completed developing a wind pairs database for the Launch Services Program to use when evaluating upper-level winds for launch vehicles; Dr. Watson continued work to assimilate observational data into the high-resolution model configurations she created for WFF and the ER.

Bauman, William↗

NASA NTRS1984

Simulation studies of proposed observing systems and their impact on numerical weather prediction

Atlas, R.↗

NASA NTRS1990

Tropical Pacific moisture variability

The objectives are to describe synoptic scale variability of moisture over the tropical Pacific Ocean and the systems leading to this variability; implement satellite analysis procedures in support of this effort, and to incorporate additional satellite information into operational analysis forecast systems at the National Meteorological Center (NMC). Composite satellite radiance patterns describe features detectable well before the development of synoptic scale tropical plumes. These typical features were extracted from historical files of Tiros Operational Vertical Sounder (TOVS) radiance observations for a pair of tropical plumes which developed during January 1989. Signals were inserted into the NMC operational medium range forecast model and a suite of model integrations were conducted. Many of the 48 h model errors of the historical forecasts were eliminated by the inclusion of more complete satellite observations. Three studies in satellite radiance analysis progressed. An analysis which blended TOVS moisture channels, OLR observations and European Center for Medium Weather Forecasts (ECMWF) model analysis to generate fields of total precipitable water comparable to those estimated from Scanning Multichannel Microwave Radiometer (SMMR) mu-wave observations. This study demonstrated that a 10 y climatology of precipitable water over the oceans is feasible, using available infrared observations (OLR and TOVS) and model analysis (ECMWF, NMC or similar quality). The estimates are sensitive to model quality and the estimating model must be updated with operational model changes. Coe developed a set of tropical plume and ITCZ composites from TOVS observations, and from NMC and ECMWF analyses which had been passed through a radiative transfer model to simulate TOVS radiances. The composites have been completed as well as many statistical diagnostics of individual TOVS channels. Analysis of the computations is commencing. Chung has initiated a study of the differences between TOVS observed vapor structure during El Nino Southern Oscillation (ENSO) (1983) and non-ENSO (1984) years. Preliminary diagnosis demonstrates gross moisture changes between warm and cold sea surface temperature episodes.

Mcguirk, James P.↗

NASA NTRS2007

Improved AIRS/AMSU Surface and Atmospheric Soundings Under Partial Cloud Cover Using an AIRS Only Cloud Clearing Approach

AIRS was launched on EOS Aqua on May 4,2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. This paper describes the latest scientific advances made in the AIRS Science Team Version 5.0 retrieval algorithm. Starting in early 2007, the Goddard DAAC will use this algorithm to analyze near real time AIRS/AMSU observations. These products are then made available to the scientific community for research purposes. The products include twice daily measurements of the Earth's three dimensional global temperature, water vapor, and ozone distribution as well as cloud cover. In addition, accurate twice daily measurements of the earth's land and ocean temperatures are derived and reported. Scientists use this important set of observations for two major uses. They provide important information for climate studies of global and regional variability and trends of different aspects of the earth's atmosphere. They also provide information for researchers to improve the skill of weather forecasting. A very important new product of the AIRS Version 5 algorithm is accurate case-by-case error estimates of the retrieved products. This heightens their utility for use in both weather and climate applications. These error estimates are also used directly for quality control of the retrieved products.

Susskind, Dr. Joel↗