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NASA NTRS2014

Cross-Calibration of Earth Observing System Terra Satellite Sensors MODIS and ASTER

The Advanced Spaceborne Thermal Emissive and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectrometer (MODIS) are two of the five sensors onboard the Earth Observing System's Terra satellite. These sensors share many similar spectral channels while having much different spatial and operational parameters. ASTER is a tasked sensor and sometimes referred to a zoom camera of the MODIS that collects a full-earth image every one to two days. It is important that these sensors have a consistent characterization and calibration for continued development and use of their data products. This work uses a variety of test sites to retrieve and validate intercalibration results. The refined calibration of Collection 6 of the Terra MODIS data set is leveraged to provide the up-to-date reference for trending and validation of ASTER. Special attention is given to spatially matching radiance measurements using prelaunch spatial response characterization of MODIS. Despite differences in spectral band properties and spatial scales, ASTER-MODIS is an ideal case for intercomparison since the sensors have nearly identical views and acquisitions times and therefore can be used as a baseline of intercalibration performance of other satellite sensor pairs.

MODIS↗

NASA NTRS1984

ACARS wind measurements - An intercomparison with radiosonde, cloud motion and VAS thermally derived winds

Statistical comparisons between winds measured by ACARS and winds obtained from radiosondes, geostationary satellite image cloud motions, and VAS are presented. Observations from three separate comparisons reveal over 60 percent of wind vector magnitude differences are within 9 m/s, and 70 percent of the directional differences are within 15 deg. The comparisons indicate that the ACARS system provides an independent source of wind data that complements other sources of wind data for constructing composite wind field analyses.

Lord, R. J.↗

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↗

NASA NTRSDate not supplied

Broadening Systematic Reanalysis lntercomparisons in the SPARC-Reanalysis lntercomparison Project Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Sean M Davis↗

NASA NTRSDate not supplied

Broadening Systematic Reanalysis Intercomparisons in the SPARC-Reanalysis Intercomparison Project Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Sean M Davis↗

NASA NTRS1990

An intercomparison of airborne nitric acid measurements

Results are reported on the performance of (1) denuder-tube/chemiluminescent, (2) nylon-filter/ion-chromatography, and (3) tunable-diode-laser/multipath-absorption HNO3 measurement instruments during the NASA Global Tropospheric Experiment Chemical Instrumentation Test and Evaluation 2 (CITE 2) program in summer 1986. The instrument designs, test protocols, and CITE 2 flights are described, and the results are presented in extensive graphs and discussed in detail. The data obtained with the three instruments are shown to be in very poor agreement at mixing ratios below 150 parts per trillion by volume (pptv), with significant discrepancies even at higher levels. Instrument (3) gave systematically higher values than (1) and (2). It is concluded that none of the instruments is accurate enough for reliable use at normal atmospheric HNO3 levels (around 100 pptv).

Gregory, G. L.↗

NASA NTRS1999

An Intercomparison of Ground, Airborne, and Space Instrumentation Definitions of the Lightning Element

For the past century, scientists have made quantitative measurements of lightning discharges. In the process, they refined the definition of a lightning unit, or basic quantum of lightning, in order to base it on observable parameters. In this paper, we will use cluster analysis to derive a basic spatial and temporal definition or scale length for the unit of lightning. We will use data from three different systems that detected pulses from the same storm complex over Central Oklahoma during June, 1998. Since the different instruments detect lightning in different ways with different resolutions, there may not be a single definition of the unit of lightning that can be applied to all three systems. However, common components can be found since all instrumentation are detecting aspects of the same phenomenon.

Mach, Douglas↗

DOE OSTI2025

Evaluation of historical precipitation interannual variability in CMIP6 over the United States

Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use daily in situ precipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four key hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

ILAMBv2.7 benchmarking results comparing E3SMv2.1 land-atmosphere coupled (BGCv2LNDATM) and stand alone land (ELM) simulations with CMIP6 emission driven historical simulations

This dataset contains land model benchmarking results for the Energy Exascale Earth System Model version 2.1 (E3SMv2.1), including outputs from both coupled biogeochemistry simulations and stand-alone land model simulations. These results are compared against several emission-driven historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Benchmarking was conducted using the International Land Model Benchmarking (ILAMB) package, version 2.7 (ILAMBv2.7). CMIP6 model outputs were sourced from the Earth System Grid Federation (ESGF), while the E3SMv2.1 results were derived from raw model outputs. These outputs underwent processing steps such as time serialization, conservative regridding, and data standardization to ensure comparability. For spatial interpolation, the Earth System Modeling Framework (ESMF) tool, ESMF_RegridWeightGen, was employed to generate regridding weights, enabling the transformation of E3SM’s native cubed-sphere grid to a regular latitude-longitude grid.

Feng, Sha [PNNL]↗

NASA NTRS2020

The GGCMI Phase 2 Experiment: Global Gridded Crop Model Simulations Under Uniform Changes in CO2, Temperature, Water, and Nitrogen Levels (Protocol Version 1.0)

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

GGCMI↗

NASA NTRS2022

Global Gridded Crop Models Underestimate Yield Responses to Droughts and Heatwaves

Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models' capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models' capacity to adequately respond to extreme weather events.

extreme events↗

NASA NTRS1999

ER-2 Observations of Precipitation Systems During TRMM-LBA

The NASA ER-2 performed numerous flights over precipitation systems in Rondonia, Brazil. The ER-2 carried a payload including the ER-2 Doppler Radar (EDOP), the Advanced Microwave Precipitation Radiometer (AMPR), the Lightning Instrument Package, and other instruments. This presentation will overview the types of data sets collected during TRMM-LBA (Tropical Rainfall Measuring Mission Satellite-Large Scale Biosphere Atmosphere Experiment in Amazonia) with particular emphasis on EDOP measurements. Numerous cases of convection ranging from weak to very intense, were overflown by the ER-2. Two TRMM overpasses were coincident with ER-2 flights which allowed for intercomparisons between the Precipitation Radiometer (PR), EDOP, and the S-POL (S-band Polarimetric Radar) and TOGA (Tropical Oceans and Global Atmosphere) ground-based radars. Preliminary results from this comparison will be presented as well as initial selection of case studies and efforts involving vertical motions in convection.

Heymsfield, Gerald↗

NASA NTRS2023

Comment on “Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5-T2m” by N. Scafetta (2022)

Scafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that “all models with ECS > 3.0°C overestimate the observed global surface warming” is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion “that spatial t-statistics rejects the data-model agreement” is also not sustainable.

CMIP6↗

NASA NTRS2017

Answering the Call for Model-Relevant Observations of Aerosols and Clouds

We describe a technique for combining multiple A-Train aerosol data sets, namely MODIS spectral AOD (aerosol optical depth), OMI AAOD (absorption aerosol optical depth) and CALIOP aerosol backscatter retrievals (hereafter referred to as MOC retrievals) to estimate full spectral sets of aerosol radiative properties, and ultimately to calculate the 3-D distribution of direct aerosol radiative effects (DARE). We present MOC results using almost two years of data collected in 2007 and 2008, and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. We compare the spatio-temporal distribution of the MOC retrievals and MOC-based calculations of seasonal clear-sky DARE to values derived from four models that participated in the Phase II AeroCom model intercomparison initiative. Comparisons of seasonal aerosol property to AeroCom Phase II results show generally good agreement best agreement with forcing results at TOA is found with GMI-MerraV3.We discuss the challenges in making observations that really address deficiencies in models, with some of the more relevant aspects being representativeness of the observations for climatological states, and whether a given model-measurement difference addresses a sampling or a model error.

Redemann, J.↗

NASA NTRS2008

Evaluation of Near-Tropopause Ozone Distributions in the Global Modeling Initiative Combined Stratosphere/Troposphere Model with Ozonesonde Data

The NASA Global Modeling Initiative has developed a combined stratosphere/troposphere chemistry and transport model which fully represents the processes governing atmospheric composition near the tropopause. We evaluate model ozone distributions near the tropopause, using two high vertical resolution monthly mean ozone profile climatologies constructed with ozonesonde data, one by averaging on pressure levels and the other relative to the thermal tropopause. Model ozone is high biased at the SH tropical and NH midlatitude tropopause by approx. 45% in a 4 deg. latitude x 5 deg. longitude model simulation. Increasing the resolution to 2 deg. x 2.5 deg. increases the NH tropopause high bias to approx. 60%, but decreases the tropical tropopause bias to approx. 30%, an effect of a better-resolved residual circulation. The tropopause ozone biases appear not to be due to an overly vigorous residual circulation or excessive stratosphere/troposphere exchange, but are more likely due to insufficient vertical resolution or excessive vertical diffusion near the tropopause. In the upper troposphere and lower stratosphere, model/measurement intercomparisons are strongly affected by the averaging technique. NH and tropical mean model lower stratospheric biases are less than 20%. In the upper troposphere, the 2 deg. x 2.5 deg. simulation exhibits mean high biases of approx. 20% and approx. 35% during April in the tropics and NH midlatitudes, respectively, compared to the pressure averaged climatology. However, relative-to-tropopause averaging produces upper troposphere high biases of approx. 30% and 70% in the tropics and NH midlatitudes. This is because relative-to-tropopause averaging better preserves large cross-tropopause O3 gradients, which are seen in the daily sonde data, but not in daily model profiles. The relative annual cycle of ozone near the tropopause is reproduced very well in the model Northern Hemisphere midlatitudes. In the tropics, the model amplitude of the near tropopause annual cycle is weak. This is likely due to the annual amplitude of mean vertical upwelling near the tropopause, which analysis suggests is approx. 30% weaker than in the real atmosphere.

Considine, David B.↗

DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1987

Odd nitrogen during the MAP/GLOBUS 1983 campaign - Theoretical considerations

The paper reviews the chemical processes involved in the formation and destruction of active nitrogen in the stratosphere and indicates that transport processes play an important role in the behavior of these species. Vertical profiles measured during the MAP/GLOBUS 1983 campaign are compared to distributions calculated by a number of one-dimensional models. An intercomparison between six of these models shows that, if some quantitative differences attributed to different model input are noticeable, the same type of behavior is predicted for NO and NO2 by all models. Although the observed concentration of species belonging to the odd nitrogen family should be highly variable, as a result of dynamical disturbances, most data obtained during the campaign appear to be consistent with the chemical scheme currently used in photochemical models.

Brasseur, Guy↗

NASA NTRS2012

Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

Clouds cover about 70% of the Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the whole globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years in length. However, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel, provided the first coordinated intercomparison of publically available, standard global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (some with multiangle view and polarization capabilities), IR sounders and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature or altitude), cloud radiative properties (optical depth or emissivity), cloud thermodynamic phase and bulk microphysical properties (effective particle size and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. A monthly, gridded database, in common format, facilitates further assessments, climate studies and the evaluation of climate models.

Stubenrauch, C. J.↗