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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Using Satellite Data to Characterize Land Surface Processes in Morocco

This study endeavors to produce a comprehensive land cover map for Morocco, addressing the absence of such a detailed map in the country. Our research encompasses ecological and climatic aspects specific to Morocco, while the methods used can be adapted to various regions and countries, considering their unique climatic conditions and land cover types. A combination of MODIS and Landsat datasets was employed to create a 5 km resolution Land Use and Land Cover (LULC) map for the entire nation. The process involved the aggregation and advanced processing of these datasets using surface processes algorithms. The resulting LULC map is the first of its kind for Morocco, shedding light on land cover distribution nationwide. It shows that approximately 13.5% of the country is covered by forests, predominantly in the Atlas and Rif mountains, Rabat–Sale, and the southern regions. Grasslands occupy over 16% of the study area, mainly in the north-east and west. Urban areas, including major cities like Casablanca, Rabat, and Marrakech, span nearly 3400 km². Moreover, large areas of shrublands and bare lands are evident across the country, while agricultural lands account for almost 20% of the national territory, mainly in the interior plains and north-western Atlantic coast. This study forms a crucial basis for ecological and climatic research in Morocco and serves as a valuable reference for various disciplines such as agriculture, natural resource management, and climate modeling. The mapping of biophysical parameters for each land cover class is a key feature of our research, and these parameters will be instrumental in a subsequent study examining the impact of urban development on surface climate in Morocco. Overall, our study underscores the importance of understanding biophysical parameters in addressing environmental and societal challenges.

Mohammed Thaiki↗
NASA NTRS1998

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗
NASA NTRSDate not supplied

TPSAS-NF1676L-21193-DND

Researchers at NASA Langley Research Center have been developing an automated pattern recognition algorithm to identify overshooting convective cloud tops (OTs) in support of the GOES-R satellite program. This algorithm identify regions of overshooting at the individual 1-4 km geostationary satellite pixel scale using visible (during daytime only) and infrared channel imagery and numerical weather analysis data. The algorithm has been developed based upon analysis of 0.25-1 km spatial resolution Aqua MODIS imagery, using a database of over 2000 manually identified OT features throughout the world in storms with varying intensity and morphology. The OT database includes storms ranging from small, warm topped cells in Alaska and Mongolia, tornadic supercells over the U.S. Central Plains and Europe, large tropical mesoscale convective systems, and overshooting in the eyewalls and spiral bands of category 5 tropical cyclones. This database is available for use by the research community. The algorithm is designed to operate on data from any current and historical satellite imager, allowing for development of a highly accurate global OT detection climatology that extends back into the 1990's at up to a 15-30 min temporal resolution throughout the diurnal cycle. As members of the McIDAS Users Group, NASA LaRC has immediate access to the full global archive of geostationary imager data which would allows rapid development of OT climatologies and short-term databases. This type of capability has never been available within the weather and climate research community. Regional geostationary OT databases have been already developed over CONUS during SEAC4RS, for 18-years over the Eastern U.S., and for 5-10 years over Australia, Europe, Southeast Asia, and East Africa among many other regions. Some of these datasets are being used by climate researchers and private industry to examine UTLS-penetrating storm spatial distributions and their temporal variability, in addition to weather hazards associated with these storms at unprecedented spatial detail. This presentation will describe the OT pattern recognition algorithm and highlight recent product applications.

Kristopher Bedka↗
NASA NTRS1990

Surface radiation budget - Current perspective

The surface radiation budget of the earth is important in climate research because it gives an understanding of the distribution of solar and IR radiation exchanges at the surface. Data on the distribution and exchange of these surface radiations are needed over the entire earth on a long-term basis. Global satellite data, coupled with highly developed models, now provide estimates of the surface radiation budget which are near the accuracy required for climate research. A description of surface radiation research, its current state, and planned programs are presented.

Darnell, Wayne L.↗
DOE OSTI2024

Research to Confront Climate Change Complexity: Intersectionality, Integration, and Innovative Governance

Abstract Climate impacts increasingly unfold in interlinked systems of people, nature, and infrastructure. The cascading consequences are revealing sometimes surprising connections across sectors and regions, and prospects for climate responses also depend on complex, difficult‐to‐understand interactions. In this commentary, we build on the innovations of the United States Fifth National Climate Assessment to suggest a framework for understanding and responding to complex climate challenges. This approach involves: (a) integration of disciplines and expertise to understand how intersectionality shapes complex climate impacts and the wide‐ranging effects of climate responses, (b) collaborations among diverse knowledge holders to improve responses and better encompass intersectionality, and (c) sustained experimentation with and learning about governance approaches capable of handling the complexity of climate change. Together, these three pillars underscore that usability of climate‐relevant knowledge requires transdisciplinary coordination of research and practice. We outline actionable steps for climate research to incorporate intersectionality, integration, and innovative governance, as is increasingly necessary for confronting climate complexity and sustaining equitable, ideally vibrant climate futures.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Status and Plans for Reanalysis at NASA/GMAO

Reanalysis plays a critical role in GMAOs goal to enhance NASA's program of Earth observations, providing vital data sets for climate research and the development of future missions. As the breadth of NASAs observations expands to include multiple components of the Earth system, so does the need to assimilate observations from currently uncoupled components of the system in a more physically consistent manner. GMAOs most recent reanalysis of the satellite era, MERRA-2, has completed the period 1980-present, and is now running as a continuing global climate analysis with two- to three-week latency. MERRA-2 assimilates meteorological and aerosol observations as a weakly coupled assimilation system as a first step toward GMAOs longer term goal of developing an integrated Earth system analysis (IESA) capability that will couple assimilation systems for the atmosphere, ocean, land and chemistry. The GMAO strategy is to progress incrementally toward an IESA through an evolving combination of coupled systems and offline component reanalyses driven by, for example, MERRA-2 atmospheric forcing. Most recently, the GMAO has implemented a weakly coupled assimilation scheme for analyzing ocean skin temperature within the existing atmospheric analysis. The scheme uses background fields from a near-surface ocean diurnal layer model to assimilate surface-sensitive radiances plus in-situ observations along with all other observations in the atmospheric assimilation system. In addition, MERRA-2-driven simulations of the ocean (plus sea ice) and atmospheric chemistry (for the EOS period) are currently underway, as is the development of a coupled atmosphere-ocean assimilation system. This talk will describe the status of these ongoing efforts and the planned steps toward an IESA capability for climate research.

MERRA-2↗
NASA NTRS2011

An Update on the VAMOS Extremes Working Group Activities

We review here the progress of the Variability of the American MOnsoon Systems (VAMOS) extremes working group since it was formed in February of 2010. The goals of the working group are to 1) develop an atlas of warm-season extremes over the Americas, 2) evaluate existing and planned simulations, and 3) suggest new model runs to address mechanisms and predictability of extremes. Substantial progress has been made in the development of an extremes atlas based on gridded observations and several reanalysis products including Modern Era Retrospective-Analysis for Research and Applications (MERRA) and Climate Forecast System Reanalysis (CFSR). The status of the atlas, remaining issues and plans for its expansion to include model data will be discussed. This includes the possibility of adding a companion atlas based on station observations based on the software developed under the World Climate Research Programme (WCRP) Expert Team on Climate Change. Detection and Indices (ETCCDI) activity. We will also review progress on relevant research and plans for the use and validation of the atlas results.

Schubert, Siegfried↗
NASA NTRS1994

Production of long-term global water vapor and liquid water data set using ultra-fast methods to assimilate multi-satellite and radiosonde observations

In recent years climate research scientists have recognized the need for increased time and space resolution precipitable and liquid water data sets. This project is designed to meet those needs. Specifically, NASA is funding STC-METSAT to develop a total integrated column and layered precipitable water data set. This is complemented by a total column liquid water data set. These data are global in extent, 1 deg x 1 deg in resolution, with daily grids produced. Precipitable water is measured by a combination of in situ radiosonde observations and satellite derived infrared and microwave retrievals from four satellites. This project combines these data into a coherent merged product for use in global climate research. This report is the Year 2 Annual Report from this NASA-sponsored project and includes progress-to-date on the assigned tasks.

Vonderhaar, Thomas H.↗
NASA NTRS2024

Celebrating 10 Years of the Sub-Seasonal to Seasonal Prediction Project and Looking to the Future

The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.

S. J. Woolnough↗
NASA NTRS2021

Research for Climate Adaptation

Adaptation to climate change must be ramped up urgently. We propose three avenues to transform ambition to action: improve tracking of actions and progress, upscale investment especially in critical areas, and accelerate learning through practice.

Climate-change adaptation↗
DOE OSTI2025

Towards provision of regularly updated climate data from the Coupled Model Intercomparison Project

The Coupled Model Intercomparison Project (CMIP) is a flagship of the World Climate Research Programme (WCRP). CMIP has become a recognised ‘brand’ in climate circles evolving over the last thirty years from a targeted research activity by a small number of climate modelling centres intercomparing their Earth System Model (ESM) simulations to a broad international coordinated research effort (Durack et al, 2025). CMIP is organized as a research activity leveraging funded and in-kind contributions from experts within modelling centres and the broader scientific community supported more recently by a fully-funded International Project Office. Within CMIP, Model Intercomparison Projects (MIPs) are community-designed to understand past, present and future climate. CMIP data provides a valuable resource for climate research and is routinely used to assess model representation of climate processes and test scientific hypotheses in the context of model uncertainty and (forced and internal) variability as evident from its prolific use in scientific publications1 . The impact relies on enabling infrastructure (most prominently via the Earth System Grid Federation (ESGF)), which allows sharing of simulation output, provision of the boundary conditions used in each simulation, and definition of the data standards that are essential to facilitating wide use of the data. The impact is supplemented by the wide-ranging scrutiny to which model simulations are subjected. Beyond its use in research, CMIP data is a key resource for communities producing derived climate information from downscaling and impact studies, such as the Coordinated Regional Downscaling Experiment (CORDEX; Gutowski et al., 2016) and the Intersectoral Impacts MIP (ISIMIP; Frieler et al., 2024). Government, academic and commercial entities also increasingly rely on CMIP and its downstream data for climate risk assessments and climate services (for example, Copernicus Climate Change Service and World Bank portal). This means that, although CMIP is a research activity, it increasingly serves a secondary and very relevant role as a provider of climate data – a long-recognised dichotomy (Stevens, 2024). Research and applications have distinct needs, with the former requiring flexibility and generality and the latter consistency. Here we explain how the design of the research activity has been adapted to reduce the burdens imposed by applications and how the research infrastructure might evolve to further enable scientific inquiry. We propose one possible approach to consistently providing model information and projections for applications in the future.

Environmental sciences↗
NASA NTRS2016

A Review of Recent Updates of Sea-Level Projections at Global and Regional Scales

Sea-level change (SLC) is a much-studied topic in the area of climate research, integrating a range of climate science disciplines, and is expected to impact coastal communities around the world. As a result, this field is rapidly moving, and the knowledge and understanding of processes contributing to SLC is increasing. Here, we discuss noteworthy recent developments in the projection of SLC contributions and in the global mean and regional sea-level projections. For the Greenland Ice Sheet contribution to SLC, earlier estimates have been confirmed in recent research, but part of the source of this contribution has shifted from dynamics to surface melting. New insights into dynamic discharge processes and the onset of marine ice sheet instability increase the projected range for the Antarctic contribution by the end of the century. The contribution from both ice sheets is projected to increase further in the coming centuries to millennia. Recent updates of the global glacier outline database and new global glacier models have led to slightly lower projections for the glacier contribution to SLC (7-17 cm by 2100), but still project the glaciers to be an important contribution. For global mean sea-level projections, the focus has shifted to better estimating the uncertainty distributions of the projection time series, which may not necessarily follow a normal distribution. Instead, recent studies use skewed distributions with longer tails to higher uncertainties. Regional projections have been used to study regional uncertainty distributions, and regional projections are increasingly being applied to specific regions, countries, and coastal areas.

glaciers↗