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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true for areas with lower GVF in the SPoRT model runs. These differences in the heating and evaporation rates produced subtle yet quantifiable differences in the simulated convective precipitation systems for the selected severe weather case examined.

Bell, Jordan R.↗
NASA NTRS2003

Evaluation of Improved Pushback Forecasts Derived from Airline Ground Operations Data

Accurate and timely predictions of airline pushbacks can potentially lead to improved performance of automated decision-support tools for airport surface traffic, thus reducing the variability and average duration of costly airline delays. One factor which affects the realization of these benefits is the level of uncertainty inherent in the turn processes. To characterize this inherent uncertainty, three techniques are developed for predicting time-to-go until pushback as a function of available ground-time; elapsed ground-time; and the status (not-started/in-progress/completed) of individual turn processes (cleaning, fueling, etc.). These techniques are tested against a large and detailed dataset covering approximately l0(exp 4) real-world turn operations obtained through collaboration with Deutsche Lufthansa AG. Even after the dataset is filtered to obtain a sample of turn operations with minimal uncertainty, the standard deviation of forecast error for all three techniques is lower-bounded away from zero, indicating that turn operations have a significant stochastic component. This lower-bound result shows that decision-support tools must be designed to incorporate robust mechanisms for coping with pushback demand stochasticity, rather than treating the pushback demand process as a known deterministic input.

Carr, Francis↗
NASA NTRS2017

Transforming Satellite Data into Weather Forecasts

A NASA project spans the gap between research and operations, introducing new composites of satellite imagery to weather forecasters to prepare for the next generation of satellites. Satellite imagery has been an immense benefit to weather forecasters. With it, they can assess aviation hazards such as low clouds, monitor thunderstorms, and track the evolution of dust plumes. Satellite sensors are continually evolving to provide ever greater imaging capabilities, and researchers continue to develop advanced techniques to identify hazards in satellite imagery. However, barriers can prevent experimental products from reaching forecasters in the operational environment. A NASA project has developed an interactive process whereby weather forecasters learn to interpret the latest satellite imagery and apply it to their operations. Forecasters then provide feedback to the researchers who are developing algorithms and products to further improve future products. This effort has taken on greater importance with the anticipated launch of a new series of satellites that will provide substantially greater amounts of data than are currently available.

Emily Berndt↗
NASA NTRS2010

The Impact of Microphysical Schemes on Intensity and Track of Hurricane

During the past decade, both research and operational numerical weather prediction models [e.g. Weather Research and Forecasting Model (WRF)] have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. The WRF is a next-generation meso-scale forecast model and assimilation system that has incorporated a modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. The WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options. At Goddard, four different cloud microphysics schemes (warm rain only, two-class of ice, two three-class of ice with either graupel or hail) are implemented into the WRF. The performances of these schemes have been compared to those from other WRF microphysics scheme options for an Atlantic hurricane case. In addition, a brief review and comparison on the previous modeling studies on the impact of microphysics schemes and microphysical processes on intensity and track of hurricane will be presented. Generally, almost all modeling studies found that the microphysics schemes did not have major impacts on track forecast, but did have more effect on the intensity. All modeling studies found that the simulated hurricane has rapid deepening and/or intensification for the warm rain-only case. It is because all hydrometeors were very large raindrops, and they fell out quickly at and near the eye-wall region. This would hydrostatically produce the lowest pressure. In addition, these modeling studies suggested that the simulated hurricane becomes unrealistically strong by removing the evaporative cooling of cloud droplets and melting of ice particles. This is due to the much weaker downdraft simulated. However, there are many differences between different modeling studies and these differences were identified and discussed.

Tao, W. K.↗
NASA NTRS2010

Challenges in Transitioning Research Data to Operations: The SPoRT Paradigm

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS and planned NPP, JPSS, and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volume will likely increase by a factor of 5- 10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

Jedloved, Gary J.↗
NASA NTRS2013

SPoRT: Transitioning NASA and NOAA Experimental Data to the Operational Weather Community

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS, Suomi NPP, and planned JPSS and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volumes will likely increase by a factor of 5-10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.

Jedlovec, Gary J.↗
NASA NTRS1989

An integrated approach to the optimum design of actively controlled composite wings

The importance of interactions among the various disciplines in airplane wing design has been recognized for quite some time. With the introduction of high gain, high authority control systems and the design of thin, flexible, lightweight composite wings, the integrated treatment of control systems, flight mechanics and dynamic aeroelasticity became a necessity. A research program is underway now aimed at extending structural synthesis concepts and methods to the integrated synthesis of lifting surfaces, spanning the disciplines of structures, aerodynamics and control for both analysis and design. Mathematical modeling techniques are carefully selected to be accurate enough for preliminary design purposes of the complicated, built-up lifting surfaces of real aircraft with their multiple design criteria and tight constraints. The presentation opens with some observations on the multidisciplinary nature of wing design. A brief review of some available state of the art practical wing optimization programs and a brief review of current research effort in the field serve to illuminate the motivation and support the direction taken in our research. The goals of this research effort are presented, followed by a description of the analysis and behavior sensitivity techniques used. The presentation concludes with a status report and some forecast of upcoming progress.

Livne, E.↗
NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗
NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗
DOE OSTI2024

Assessing Effects of Climate Change on Legacy Waste at the Enewetak Atoll

The Republic of the Marshall Islands (RMI) is in the central Pacific Ocean ~4,500 km west of Hawaii. The Enewetak Atoll, located in the northwest part of the RMI, was the site for 43 nuclear weapon tests between 1948 and 1958. Fallout and deposition from the tests contaminated the island surfaces, lagoon waters and sediment, and nearby ocean waters at the atoll. In the 1970s, a cleanup effort collected radioactive waste and placed it in the Cactus Crater on Runit Island (also called the Runit Dome). In December 2021, Congress directed the U.S. Department of Energy to study the impacts of climate change on the Runit Dome nuclear waste disposal site. Pacific Northwest National Laboratory (PNNL) assembled a multidisciplinary team of climate scientists, ocean modelers, environmental scientists, and health physicists to assess the likely effects of remaining radionuclides at the Enewetak Atoll. PNNL’s approach focused on effects of tropical cyclones that were postulated to mobilize and transport contaminated lagoon sediments and result in human and biota exposure. PNNL’s study estimated (1) the radionuclide source term, (2) the effects of climate change on severe storms, (3) mobilization and transport of radionuclides, and (4) radiation dose to humans and biota. Radionuclides in the lagoon and/or ocean waters of the Enewetak Atoll were characterized by the U.S. Atomic Energy Commission (AEC) in 1972, Woods Hole Oceanographic Institution in 2015, and Lawrence Livermore National Laboratory in 2018. The RMI Nationwide Radiological Study was conducted in the early 1990s for radionuclides remaining in island soils. The 1972 AEC survey remains the most comprehensive source of radionuclide data on lagoon sediments. Climate change modeling at a regional scale in the central Pacific Ocean is limited. PNNL climate scientists simulated severe historical storms postulated to occur both in a recent climate (2015) and in the future (2090) using the Advanced Research Weather Research and Forecasting (WRF-ARW) model, employing a pseudo-global-warming technique. A postulated complete, future failure of the Runit Dome was also considered. PNNL developed a high-resolution regional ocean hydrodynamics model covering the entire RMI extended economic zone using the Finite Volume Coastal Ocean Model (FVCOM). The FVCOM model was run using global reanalysis data for current climate and WRF-ARW simulation for the future climate. PNNL also developed a radionuclide fate and transport model using the FVCOM Integrated Compartment Model (FVCOM-ICM) to simulate the current and future mobilization and transport of radionuclides sorbed to lagoon sediments and the exchange of radionuclides between the water and sediment. FVCOM-ICM-predicted radionuclide concentrations were then used to estimate radiation dose to humans and biota at all islands of the Enewetak Atoll. Under current climate conditions, annual radiation exposures for the southern islands including Enewetak (Fred) and Medren (Elmer) were below the current U.S. standards. Radiation doses were somewhat elevated starting at Runit Island northward and westward to Enjebi Island (Janet). The islands in the northwest quadrant, particularly Bokoluo (Alice) and Bokombako (Belle), remain relatively contaminated. The islands in the southwestern quadrant have low contamination. The highest contribution to radiation doses comes from consumption of locally grown foods. Two radionuclides, 90Sr and 137Cs, contributed the greatest fraction for most terrestrial foods. In current climate conditions, the storms temporarily increased radionuclide concentrations in the lagoon waters, increasing the radiation dose slightly. In future conditions, doses are expected to be smaller, primarily because of the radioactive decay of the shorter-lived radioisotopes of 90Sr and 137Cs. This could make all islands in the far northwest of the atoll – except Bokombako (Belle) and perhaps Bokoluo (Alice) – suitable for residency. For the f

Prasad, Rajiv↗
DOE OSTI2024

Temporal sequence transformer to advance long-term streamflow prediction

Accurate streamflow prediction is crucial for understanding climate change impacts on water resources and for effective management of extreme hydrological events. While Long Short-Term Memory (LSTM) networks have been the dominant data-driven approach for streamflow forecasting, recent advancements in transformer architectures for time series tasks have shown promise in outperforming traditional LSTM models. This study introduces a transformer-based model that integrates historical streamflow data with climatic variables to enhance streamflow prediction accuracy. We evaluated our transformer model against a benchmark LSTM across five diverse basins in the United States. Results demonstrate that the transformer architecture consistently outperforms the LSTM model across all evaluation metrics, highlighting its potential as a more effective tool for hydrological forecasting. This research contributes to the ongoing development of advanced AI techniques for improved water resource management and climate change adaptation strategies.

Singh, Ruhaan [Farragut High School]↗
DOE OSTI2022

A review on the application of machine learning for combustion in power generation applications

Abstract Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

Engineering↗
NASA NTRS1985

The use of a simplified isentropic model for short-term aviation forecasting

A simple, multiple-layer, isentropic potential vorticity forecast model is described for predicting short-term atmospheric changes between 12-hourly radiosonde observations. The model and the model initialization technique are described, and a case study with both convection and strong horizontal wind shear is discussed. The wind and stream function field forecasts and the forecast areas of convective instability are constrasted with observational results to assess the model's strengths and weaknesses. Excellent agreement is obtained between model predictions and observations.

Homan, J. H.↗
NASA NTRS1985

Validation of mesocale number sea breeze thunderstorm forecasts over Florida using LPATS - The Lightning Position and Tracking System

The research reported in the present paper has the objective to develop improved operational techniques for sea breeze (SB) initiated convective storms. It is pointed out that the sea breeze is a relatively simple mesoscale circulation. The considered studies are mainly concerned with detailed thunderstorm potential forecasts during the next several hours, taking into account real-time predictions using new supercomputer technology. Thunderstorm and coastal mesoscale circulations are discussed along with the P3DM model sea. The considered code represents the result of a further development of the NOAA Florida sea breeze model which was introduced by Pielke (1974). After its modificataion, the model was renamed the Prognostic Three Dimensional Mesoscale (P3DM) Model, with its 2-D connterpart the P2DM. Attention is also given to real time climatology, and a case study concerned with developments occurring on 5 May 1984 over the Florida Peninsula.

Lyons, W. A.↗
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗
NASA NTRS1993

Examples of data assimilation in mesoscale models

The keynote address was the problem of physical initialization of mesoscale models. The classic purpose of physical or diabatic initialization is to reduce or eliminate the spin-up error caused by the lack, at the initial time, of the fully developed vertical circulations required to support regions of large rainfall rates. However, even if a model has no spin-up problem, imposition of observed moisture and heating rate information during assimilation can improve quantitative precipitation forecasts, especially early in the forecast. The two key issues in physical initialization are the choice of assimilating technique and sources of hydrologic/hydrometeor data. Another example of data assimilation in mesoscale models was presented in a series of meso-beta scale model experiments with and 11 km version of the MASS model designed to investigate the sensitivity of convective initiation forced by thermally direct circulations resulting from differential surface heating to four dimensional assimilation of surface and radar data. The results of these simulations underscore the need to accurately initialize and simulate grid and sub-grid scale clouds in meso- beta scale models. The status of the application of the CSU-RAMS mesoscale model by the NOAA Forecast Systems Lab for producing real-time forecasts with 10-60 km mesh resolutions over (4000 km)(exp 2) domains for use by the aviation community was reported. Either MAPS or LAPS model data are used to initialize the RAMS model on a 12-h cycle. The use of MAPS (Mesoscale Analysis and Prediction System) model was discussed. Also discussed was the mesobeta-scale data assimilation using a triply-nested nonhydrostatic version of the MM5 model.

Carr, Fred↗
DOE OSTI2024

Tropical Cyclone Wind Shear-Relative Asymmetry in Reanalyses

Abstract While tropical cyclones (TCs) are axisymmetric vortices to the first order, they often exhibit noteworthy structural asymmetries. These often result from environmental vertical wind shear, which tilts the vortex and induces a wavenumber 1 pattern in the circulation and precipitation fields. Reanalyses and climate models have improved in representing the TC structure and climatology, but their relatively coarse resolution and dependence on parameterized physics cast doubt on their ability to capture the asymmetric TC structure. We perform the most comprehensive process-oriented assessment of TC asymmetry to date in reanalyses. Specifically, we analyze the composite shear-relative TC structure in ERA5 and Climate Forecast System Reanalysis (CFSR), which vary in their resolutions, physical parameterization suites, and data assimilation techniques. These structures are compared with aircraft reconnaissance radar observations. In agreement with the observations, the strongest tangential winds are usually found left-of-shear, while inner core rainfall, ascent, vortex tilt, and low-level inflow are favored directly downshear or in the downshear-left quadrant. Outer rainband convection generally peaks in the downshear-right quadrant. Thermodynamic asymmetries are also apparent, with anomalous low-level moisture right-of-shear, midlevel warmth in the upshear-right quadrant (uptilt), and cloud properties suggestive of a realistic precipitation life cycle from growth to fallout. We also decompose rainfall contributions from the convective parameterization and large-scale cloud schemes and highlight the roles of vorticity advection, buoyancy advection, and diabatic processes in driving asymmetric vertical motions in the inner core and outer rainband regions. Our results suggest that process-level studies of TC asymmetry and TC–wind shear interaction under future warming are viable using climate models. Significance Statement Asymmetries are common in tropical cyclones (TCs), influencing their intensity, track, and hazards. Vertical wind shear often plays a leading-order role in causing these asymmetries. It is uncertain how well asymmetric structures and processes are captured in reanalyses and global climate models (GCMs) with grid spacings of 0.25° and coarser. In this study, we first evaluate TC asymmetry in reanalyses, which have the benefit of being forced by observations. This helps to assess whether the resolutions associated with GCMs sufficiently capture asymmetric structures and processes and motivates upcoming work with free-running GCMs to study how TC asymmetry may change in a warming climate.

Carstens, Jacob D.↗