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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 OSTI2026

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗
NASA NTRS2018

Using Reanalysis in Crop Monitoring and Forecasting Systems

Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980-2010 under potential and water-imited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time.

Reanalysis↗
NASA NTRS2018

Evaluation of Near-Surface Air Temperature from Reanalysis over the United States and Ukraine: Application to Winter Wheat Yield Forecasting

In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2>0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.

GSOD↗
NASA NTRS2023

M2-SCREAM: A Stratospheric Composition Reanalysis of Aura MLS Data With MERRA-2 Transport

MERRA-2 Stratospheric Composition Reanalysis of Aura Microwave Limb Sounder (M2-SCREAM) is a new reanalysis of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO 3 ) and nitrous oxide (N 2 O) between 2004 and the present (with a latency of several months). The assimilated fields are provided at a 50-km horizontal resolution and at a three-hourly frequency. M2-SCREAM assimilates version 4.2 Microwave Limb Sounder (MLS) profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. Dynamics and tropospheric water vapor are constrained by the MERRA-2 reanalysis. The assimilated species are in excellent agreement with the MLS observations, except for HNO 3 in polar night, where data are not assimilated. Comparisons against independent observations show that the reanalysis realistically captures the spatial and temporal variability of all the assimilated constituents. In particular, the standard deviations of the differences between M2-SCREAM and constituent mixing ratio data from The Atmospheric Chemistry Experiment Fourier Transform Spectrometer are much smaller than the standard deviations of the measured constituents. Evaluation of the reanalysis against aircraft data and balloon-borne frost point hygrometers indicates a faithful representation of small-scale structures in the assimilated water vapor, HNO 3 and ozone fields near the tropopause. Comparisons with independent observations and a process-based analysis of the consistency of the assimilated constituent fields with the MERRA-2 dynamics and with large-scale stratospheric processes demonstrate the utility of M2-SCREAM for scientific studies of chemical and transport variability on time scales ranging from hours to decades. Analysis uncertainties and guidelines for data usage are provided.

MERRA-2↗
DOE OSTI2023

Convectively coupled Rossby–Gravity waves in a field campaign: How they are captured in reanalysis products

Abstract Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM‐KWAJEX (Tropical Rainfall Measuring Mission‐Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55‐year Reanalysis (JRA‐55), Climate Forecast System Reanalysis (CFSR), and Modern‐Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid‐scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.

Wang, Xiaocong↗
NASA NTRS1990

Efficient eigensolution reanalysis of nonclassically damped structures

Effective methods of approximate eigensolution reanalysis of modified nonclassically damped structures are developed. For structures with passive or active discrete damping devices or with damping treatment, the system becomes nonproportionally damped and the computation of its dynamic responses may require the use of complex modes. For larger systems, the computation of complex modes is very expensive. Thus it is desirable to have approximate reanalysis techniques for the efficient evaluation of the effect of design changes. In recent years, the assumed mode reanalysis method was successfully applied to minimum weight design of undamped structures with natural frequency constraints. The accuracy of the assumed mode reanalysis method can be improved dramatically if the global approximation function includes the normal modes of the original system and their derivatives. This approach was demonstrated to be effective even for a system with shape changes. The approach used by Noor et al. for eigensolution reanalysis of undamped structures is extended to treat a nonclassically damped system.

Wang, Bo Ping↗
NASA NTRS2001

US Reanalysis for Climate and Chemistry Applications

The Data Assimilation Office will perform a short reanalysis with its next-generation data assimilation system. This reanalysis will start a few months prior to the eruption of El Chichon and continue to real time. It will cover the entire time span of the Upper Atmospheric Research Satellite mission, and it is expected to be used in chemistry and climate applications. The sorts of improvements that are expected with this system and the status will be presented. In addition there has been a call in the United States for a National Reanalysis Project. This is envisioned as a sustained multi-agency activity coordinated (staggered) with the ECMWF reanalysis. The plans for the National Reanalysis Project will be discussed.

Rood, Richard B.↗
NASA NTRS2010

The Sensitivity of Simulated Ocean Biogeochemistry to Forcing Fields Derived from NCEP and MERRA Reanalysis Products

Ocean biogeochemistry models are typically forced by atmospheric and oceanic data derived from reanalysis products. For the NASA Ocean Biogeochemistry Model (NOBM) such reanalysis forcing fields include: surface wind stress, sea surface temperature, ice distributions, shortwave radiation, surface wind speeds and surface atmospheric pressure. Additionally, proper computation of ocean irradiance requires reanalysis products of relative humidity and precipitable water (in addition to aerosol and cloud information which is derived from satellite data). The question posed here is, does the choice of reanalysis products make a difference in the representation of ocean biology and biogeochemistry? NOBM was forced by NCEP and MERRA reanalysis products for the period 2002-2009. We find that in 2009 global distributions and abundances of biological variables (total chlorophyll and nutrients) and carbon (dissolved inorganic and organic carbon and surface pCO2) were similar between the two different forcing fields. Global statistical comparisons with satellite and in situ data also showed negligible differences.

Gregg, Watson↗
NASA NTRS2018

Multidecadal Changes in the UTLS Ozone from the MERRA-2 Reanalysis and the GMI Chemistry Model

Long-term changes of ozone in the UTLS (Upper Troposphere / Lower Stratosphere) reflect the response to decreases in the stratospheric concentrations of ozone-depleting substances as well as changes in the stratospheric circulation induced by climate change. To date, studies of UTLS ozone changes and variability have relied mainly on satellite and in-situ observations as well as chemistry-climate model simulations. By comparison, the potential of reanalysis ozone data remains relatively untapped. This is despite evidence from recent studies, including detailed analyses conducted under SPARC (Scalable Processor Architecture) Reanalysis Intercomparison Project (S-RIP), that demonstrate that stratospheric ozone fields from modern atmospheric reanalyses exhibit good agreement with independent data while delineating issues related to inhomogeneities in the assimilated observations. In this presentation, we will explore the possibility of inferring long-term geographically and vertically resolved behavior of the lower stratospheric (LS) ozone from NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications -2) reanalysis after accounting for the few known discontinuities and gaps in its assimilated input data. This work builds upon previous studies that have documented excellent agreement between MERRA-2 ozone and ozonesonde observations in the LS. Of particular importance is a relatively good vertical resolution of MERRA-2 allowing precise separation of tropospheric and stratospheric ozone contents. We also compare the MERRA-2 LS ozone results with the recently completed 37-year simulation produced using Goddard Earth Observing System in "replay" mode coupled with the GMI (Global Modeling Initiative) chemistry mechanism. Replay mode dynamically constrains the model with the MERRA-2 reanalysis winds, temperature, and pressure. We will emphasize the areas of agreement of the reanalysis and replay and interpret differences between them in the context of our increasing understanding of model transport driven by assimilated winds.

Ozone↗
NASA NTRS2019

Summary and Highlights of the SPARC-Reanalysis Intercomparison Project

The climate research community uses global atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere; they are a particularly powerful tool for studying phenomena that cannot be directly observed. Different reanalyses may give very different results for the same diagnostics. The Stratosphere troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare key diagnostics that are important for stratospheric processes and their tropospheric connections among available reanalyses. S-RIP has been identifying differences among reanalyses and their underlying causes, providing guidance on appropriate usage of reanalysis products in scientific studies (particularly those of relevance to SPARC), and contributing to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. S-RIP emphasizes diagnostics of the upper troposphere, stratosphere, and lower mesosphere. The draft S-RIP final report is expected to be completed in 2018. This poster gives a summary of the S-RIP project and presents highlights including results on the Brewer-Dobson circulation, stratosphere/troposphere dynamical coupling, the extra-tropical upper troposphere / lower stratosphere, the tropical tropopause layer, the quasi-biennial oscillation, lower stratospheric polar processing, and the upper stratosphere/lower mesosphere.

Fujiwara, M.↗
NASA NTRSDate not supplied

Overview of the SPARC Reanalysis Intercomparison Project (S-RIP) during 2013-2020

The Stratosphere-troposphere Processes And their Role in Climate (SPARC) project is one of the four core projects of the World Climate Research Programme (WCRP). Researchers interested in SPARC use global atmospheric reanalysis products to understand a wide range of processes and variability in the atmosphere, to validate chemistry climate models, and to investigate and identify climate change. The SPARC Reanalysis Intercomparison Project (S-RIP) was initiated in 2011 and officially started in 2013 to conduct a coordinated intercomparison of all major global atmospheric reanalysis data sets. The S-RIP has been aiming at writing up an assessment report in the SPARC report series (to be published by the end of 2020) (1) on overall quality of temperature, winds, ozone, and water vapor data, (2) on more process- and region-oriented evaluation of the Brewer–Dobson circulation, extratropical stratosphere-troposphere coupling, extratropical upper troposphere and lower stratosphere, the tropical tropopause layer, the quasi-biennial oscillation, polar processes, and the upper stratosphere and lower mesosphere, and (3) with a coordinated description of the reanalysis systems. We also have an inter-journal special issue on "The SPARC Reanalysis Intercomparison Project (S-RIP)" in Atmospheric Chemistry and Physics (ACP) and Earth System Science Data (ESSD). In the presentation, we will discuss key findings and recommendations as well as the evaluation of this rst phase of the S-RIP activity.

Masatomo Fujiwara↗
NASA NTRSDate not supplied

M2-SCREAM: A Stratospheric Composition Reanalysis of Aura MLS

Stratospheric composition and transport patterns are changing because of increasing greenhouse gas concentrations and declining levels of ozone depleting substances. Superimposed on forced trends are: dynamical variability at a range of time scales and unforeseen perturbations such as those resulting from injections of volcanic material and smoke from PyroCb events. Chemical reanalyses can be powerful tools for studying the composition of the stratosphere and disentangling forced responses from internal variability. This presentation introduces a new chemical reanalysis of stratospheric constituents developed and produced at NASA’s Global Modeling and Assimilation Office (GMAO) using the recently developed GMAO Constituent Data Assimilation System. Called the MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM), this reanalysis consists of assimilated global three-dimensional fields of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) mixing ratios and covers the period since the beginning of MLS observations in September 2004 to the present. M2-SCREAM assimilates version 4.2 MLS profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. The dynamics and tropospheric water vapor are constrained by assimilated meteorological fields from MERRA-2. The assimilated constituent fields and meteorology are provided to users at a 50-km horizontal resolution and a three-hourly frequency. Monthly analysis uncertainties and flags are also provided to the users. M2-SCREAM is an accurate and dynamically consistent high-resolution data record of the five constituents, all of which are of primary importance to stratospheric chemistry and transport studies. We will present a description of M2-SCREAM and selected results of a process-based evaluation of this product using independent data. As an illustration, we will discuss the perturbation to stratospheric composition from the eruption of Hunga Tonga–Hunga Ha'apai in January 2022 as seen in the reanalysis. We will also propose potential scientific applications of M2-SCREAM and outline plans for an upcoming comprehensive composition reanalysis that is being developed at NASA GMAO.

MERRA-2↗
DOE OSTI2020

Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations

The offshore wind industry in the U.S. is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools in order to set performance expectations and quantify the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub-heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization.

lidar buoy, model validation, offshore wind energy↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
DOE OSTI2022

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Rapid iterative reanalysis for automated design

A method for iterative reanalysis in automated structural design is presented for a finite-element analysis using the direct stiffness approach. A basic feature of the method is that the generalized stiffness and inertia matrices are expressed as functions of structural design parameters, and these generalized matrices are expanded in Taylor series about the initial design. Only the linear terms are retained in the expansions. The method is approximate because it uses static condensation, modal reduction, and the linear Taylor series expansions. The exact linear representation of the expansions of the generalized matrices is also described and a basis for the present method is established. Results of applications of the present method to the recalculation of the natural frequencies of two simple platelike structural models are presented and compared with results obtained by using a commonly applied analysis procedure used as a reference. In general, the results are in good agreement. A comparison of the computer times required for the use of the present method and the reference method indicated that the present method required substantially less time for reanalysis. Although the results presented are for relatively small-order problems, the present method will become more efficient relative to the reference method as the problem size increases. An extension of the present method to static reanalysis is described, ana a basis for unifying the static and dynamic reanalysis procedures is presented.

Bhatia, K. G.↗
NASA NTRS1997

Technical Report Series on Global Modeling and Data Assimilation: Interannual Variability and Potential Predictability in Reanalysis Products - Volume 13

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Min, Wei↗
NASA NTRSDate not supplied

The SPARC-Reanalysis Intercomparison Project: Summary of Phase 1 and Plans for Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Jonathan S Wright↗