Earth Science Data Research Articles

A user perspective on future cloud-based services for Big Earth data

ABSTRACT Cloud-based services introduce a paradigm shift in how users access, process and analyse Big Earth data. A key challenge is to align the current state of how users access, process and analyse the data with trends and roadmaps large data organisations layout. In addition, due to the increased availability of open data, a more diverse user base wants to take advantage of Earth science data leading to new user requirements. We run a web-based survey among Big Earth data users to better understand the motivation to migrate to cloud-based services as well as the challenges and opportunities that might arise. Results show an overall interest in moving to cloud-based services but air an insufficient literacy in cloud systems and a lack of trust due to security concerns and opacity of emerging costs. These gaps demand efforts on three levels. First, cloud services shall be targeted at intermediate users instead of policy- and decision-makers and over-engineered systems with a high level of abstraction should be avoided. Second, more substantial capacity-building efforts are required to decrease the existing gap in cloud skills and uptake. Third, a cloud certification mechanism could help in building up overall trust in cloud-based services.

Waterscape and Floods Management of Greek Selinus: The Cottone River Valley

Abstract This paper investigates water bodies in the Greek colony of Selinus, Western Sicily-Italy. It focuses especially on one of the two rivers of the city: the Cottone. The investigative strategy adopted in this study consists of an interdisciplinary approach based on the analysis of archaeological evidence, Earth Sciences data, and the study of historical cartography. Results indicate that the Cottone River was not a swampy and unhealthy intermittent stream as it was believed so far; it was instead a fully functional water body featuring an active floodplain. Most importantly, research presented in this article indicates several floods occurred in Selinus from the second or third quarter of the sixth century BC to the end of the fifth century BC. These floods, which occurred at the peak of Selinus’ cultural and economic life, were related to severe major events, rather than seasonal floods, as suggested by other scholars. The management of these floods and the waterscape was crucial to the city’s prosperity. This article also analyzes the relationship between the Cottone River and the fortification walls located at the Cottone River Valley. Results indicate that the fortification walls functioned not only as a defensive infrastructure, but also as a hydraulic engineering solution for flood retention. A better understanding of the environment in which Selinus was settled is now available, and knowledge on the importance of waterscapes and their management has been enhanced.

PBL Height From AIRS, GPS RO, and MERRA‐2 Products in NASA GES DISC and Their 10‐Year Seasonal Mean Intercomparison

AbstractWithin the planetary boundary layer (PBL), surface forcing response, drag, turbulence, and vertical mixing are important processes and play a more critical role here than in the overlying “free atmosphere.” The PBL height (PBLH) is an important parameter in climate models, weather forecasts, and air quality prediction. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides data processing, archiving, and distribution services for numerous Earth science products. PBLH is a parameter in three products served by GES DISC, which are from the atmospheric infrared sounder (AIRS), the global positioning system (GPS) radio occultation (RO) experiment, and the NASA reanalysis product Modern‐Era Retrospective analysis for Research and Applications‐2 (MERRA‐2). These products have different spatial and temporal resolutions and coverages, and their PBLH definitions are also different. To better serve the PBL research community, we have summarized the specifications of these products. A 10‐year seasonal mean intercomparison is also conducted to provide further guidance to users. The intercomparison results show that MERRA‐2 has a much shallower PBL than AIRS and GPS RO. An experimental study indicates that the different PBLH definition in MERRA‐2 caused smaller values of PBLH. The improvement of the water vapor retrieval in AIRS version 7 over version 6 results in the version 7 PBLH agreeing better with GPS RO and MERRA‐2 than version 6, especially near the equator and low latitudes.

Job Analyses of Earth Science Data Librarians and Data Managers

AbstractThis study’s purpose is to capture the skills of Earth science data managers and librarians through interviews with current job holders. Job analysis interviews were conducted of 14 participants—six librarians and eight data managers—to assess the types and frequencies of job tasks. Participants identified tasks related to communication, including collaboration, teaching, and project management activities. Data-specific tasks included data discovery, processing, and curation, which require an understanding of the data, technology, and information infrastructures to support data use, reuse, and preservation. Most respondents had formal science education and six had a master’s degree in Library and Information Sciences. Most of the knowledge, skills, and abilities for these workers were acquired through on-the-job experience, but future professionals in these careers may benefit from tailored education informed through job analyses.

Integrating Data to Find Links Between Environment and Health

Several obstacles stand in the way of integrating social, health, and Earth science data for vital geohealth studies, but there are tools and opportunities to overcome these obstacles.

Science Storms the Cloud

AbstractThe core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi‐petabyte data sets. Data sets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud‐based data science platforms, accessed through a web‐browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data‐driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

Land use-land cover anomalies and groundwater pattern with climate change for Western Vidarbha: a case study

The study investigates the land cover degradation over 30 years of period from 1985 to 2015 and its impact on the groundwater regime for the Western Vidarbha region of Central India. Land use/land cover (LULC) data for four different time spans via 1985, 1995, 2005, and 2015 were obtained from Goddard Earth Sciences Data and Information Services Center (GES DISC). Along with that, a time-series analysis was carried out using two data sets: historic gridded rainfall data (1964–2013) from India Meteorological Department (IMD), Pune, and future predicted data (2024–2095) from the Indian Institute of Tropical Meteorology (IITM), Pune. The increase in land use to cropland and a decrease in rainfall trend apparently mark the probability of groundwater exploitation of the region. Groundwater mapping of the study area has been evaluated using well data collected from the Central Ground Water Board (1996 and 2017). The non-parametric Mann-Kendall test is used for time series analysis and ArcGis 10.2 software was used for the generation of groundwater potential maps. The relation between changes in the land cover pattern, rainfall, and groundwater is identified and it is observed that there is a direct linkage between anthropogenic land degradation, erratic rainfall pattern, and depleted groundwater resources. A sustainable water supply plan, prioritizing the factors like rainfall pattern, nature of catchment, and land use/land cover characteristics should be defined to the region for overcoming future shortage and vulnerability.

STARE INTO THE FUTURE OF GEODATA INTEGRATIVE ANALYSIS.

Different kinds of observations feature different strengths, e.g. visible-infrared imagery for clouds and radar for precipitation, and when integrated better constrain scientific models and hypotheses. Even critical, fundamental operations such as cross-calibrations of related sensors operating on different platforms or orbits, e.g. spacecraft and aircraft, are integrative analyses. The great variety of Earth Science data types and the spatiotemporal irregularity of important low-level (ungridded) data has so far made their integration a customized, tedious process which scales in neither variety nor volume. Generic, higher-level (gridded) data products are easier to use, at the cost of being farther from the original observations and having to settle with grids, interpolation assumptions, and uncertainties that limit their applicability. The root cause of the difficulty in scalably bringing together diverse data is the current rectilinear geo-partitioning of Earth Science data into conventional arrays indexed using consecutive integer indices and then packaged into files. Such indices suffice for archival, search, and retrieval, but lack a common geospatial semantics, which is mitigated by adding on floating-point encoded longitude-latitude (lon-lat) information for registration. An alternative to floating-point lon-lat, the SpatioTemporal Adaptive Resolution Encoding (STARE) provides an encoding for geo-spatiotemporal location and neighborhood that transcends the use of files and native array indexing allowing diverse data to be organized on scalable, distributed computing and storage platforms.

Spatio-Temporal Dynamics of Plasmodium falciparum and Plasmodium vivax in French Guiana: 2005-2019.

Aims: This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. Methods: Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) (n = 17) from 2005–2019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The Box–Jenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysis—PCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. Results: The current sample includes 7050 eligible Plasmodium vivax (n = 4111) and Plasmodium falciparum (n = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de l’Oyapock and Antecume Pata for P. falciparum, and Saint Georges de l’Oyapock, Antecume Pata, Régina and Camopi for P. vivax. Conclusions: This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.

The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Spatial Data (BigSpatial 2020)

Since the "Big Data Research and Development Initiative" launched by the White House in 2012, big data has received great attention from industry and federal agencies alike emerging as an important area of research for scientists worldwide. Within the realm of big data, spatial and spatiotemporal data continues to be among the fastest-growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in the ground, air- and space-borne sensor technologies have led to unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Today, analyzing this data poses a massive challenge to researchers.

Ozone Monitoring Instrument (OMI) Aura nitrogen dioxide standard product version 4.0 with improved surface and cloud treatments

Abstract. We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for OMI NO2 products suggested by expert users and enhances the NO2 data quality in several ways through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on the geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O2–O2) absorption-based effective cloud fraction (ECF) and optical centroid pressure (OCP) provide improved information to the new NO2 AMF calculations. The new AMFs increase the retrieved tropospheric NO2 by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO2 product using independent observations from ground-based and airborne instruments. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020), and we encourage their use over previous versions of OMI NO2 products.

Identification of temperature anomalies in the western Caspian Sea in 2017 based on remote sensing data

Aim. The study of temperature anomalies in the western Caspian Sea based on space imagery materials in order to detect upwelling phenomena.Materials and Methods. We used temperature indicators of seawater for the summer season of 2017 when a sharp decrease by more than 2 °C in average daily temperature occurred. Space images were obtained from the specialized centres of Ocean Color NASA, Earth Science Data Systems NASA and SATIN. Remote sensing data were processed using SeaDAS and ArcGIS programs. Ground data were obtained from the resources of the Unified State System of Information about the Situation in the World Ocean (ESIMO). An ArcGIS database was created and maps compiled.Results. The first upwelling occurred on 9-17 June 2020. The minimum water temperature in the Makhachkala area was 14°C with an increase in salinity to 12%o over an area of 1,500 sq.km. An increase in the content of dissolved oxygen of up to 9.70 mg/l and pH 8.64 was recorded. A second upwelling of medium intensity occurred from 19 June-July 1 with a minimum temperature of 17.9C. The decrease in temperature was 2.8°C with an increase in salinity by 1%o. The surface area was 454 sq.km. A third case of upwelling was recorded from 26 August-September 1 and was characterised by a decrease in water temperature of 7.4C (near the coast, 17.1°C). The average salinity increase was 0.32%o while the 02 concentration was 8 mg/l over an area of 500 sq.km.Conclusion. Due to its large size, the Caspian Sea is characterised by spatial inhomogeneity of oceanological parameters, which can be recorded based on the results of processing satellite images and their verification using ground data. In the western part of the sea the upwelling is periodic and of different scales.

Long-Term Data Preservation Data Lifecycle, Standardisation Process, Implementation and Lessons Learned

Science and Earth Observation data represent today a unique and valuable asset for humankind that should be preserved without time constraints and kept accessible and exploitable by current and future generations. In Earth Science, knowledge of the past and tracking of the evolution are at the basis of our capability to effectively respond to the global changes that are putting increasing pressure on the environment, and on human society. This can only be achieved if long time series of data are properly preserved and made accessible to support international initiatives. Within ESA Member States and beyond, Earth Science data holders are increasingly coordinating data preservation efforts to ensure that the valuable data are safeguarded against loss and kept accessible and useable for current and future generations. This task becomes increasingly challenging in view of the existing 40 years’ worth of Earth Science data stored in archives around the world and the massive increase of data volumes expected over the next years from e.g., the European Copernicus Sentinel missions. Long Term Data Preservation (LTDP) aims at maintaining information discoverable and accessible in an independent and understandable way, with supporting information, which helps ensuring authenticity, over the long term. A focal aspect of LTDP is data Curation. Data Curation refers to the management of data throughout its life cycle. Data Curation activities enable data discovery and retrieval, maintain its quality, add value, and allow data re-use over time. It includes all the processes that involve data management, such as pre-ingest initiatives, ingest functions, archival storage and preservation, dissemination, and provision of access for a designated community.
 The paper presents specific aspects, of importance during the entire Earth observation data lifecycle, with respect to evolving data volumes and application scenarios. These particular issues are introduced in the section on 'Big Data' and LTDP. The Data Stewardship Reference lifecycle section describes how the data stewardship activities can be efficiently organised, while the following section addresses the overall preservation workflow and shows the technical steps to be taken during Data Curation. Earth Science Data Curation and preservation should be addressed during all mission stages - from the initial mission planning, throughout the entire mission lifetime, and during the post- mission phase. The Data Stewardship Reference Lifecycle gives a high-level overview of the steps useful for implementing Curation and preservation rules on mission data sets from initial conceptualisation or receipt through the iterative Curation cycle.

Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling

Due to their enhanced predictive capabilities, noninterpretable machine learning (ML) models (e.g. deep learning) have recently gained a growing interest in analyzing and modeling earth & planetary science data. However, noninterpretable ML models are often treated as “black boxes” by end-users, which could limit their applicability in critical decision making processes. In this paper, we compared the predictive capabilities of three interpretable ML models with three noninterpretable ML models to answer the overarching question: Is it essential to use noninterpretable ML models for enhanced model predictions from hydro-climatological datasets? The ML model development and comparative analysis were performed using measured climate data and synthetic reference crop evapotranspiration (ETo) data, with varying levels of missing values, from five weather stations across the karstic Edwards aquifer region in semi-arid south-central Texas. Our analysis revealed that interpretable tree-based ensemble models produce comparable results to noninterpretable deep learning models on structured hydro-climatological datasets. We showed that the tree-based ensemble model is also capable of imputing varying levels of missing climate data at the weather stations, employing the newly developed sequential transfer-learning technique. We applied an explainable machine learning (eXML) framework to quantify the global order of importance of hydro-climatic (predictor) variables on ETo, while highlighting the local dependencies and interactions amongst the predictors and ETo. The eXML framework also revealed the inflection points of the climate variables at which the transition from low to high daily ETo rates occur. The ancillary explainability of ML models are expected to increase users’ confidence and support any future decision-making process in water resource management.

The utility of earth science information in post-earthquake land-use decision-making: the 2010–2011 Canterbury earthquake sequence in Aotearoa New Zealand

Abstract. Earth science information (data, knowledge, advice) can enhance the evidence base for land-use decision-making. The utility of this information depends on factors including the context and objectives of land-use decisions, the timeliness and efficiency with which earth science information is delivered, and the strength, relevance, uncertainties, and risks assigned to earth science information relative to other inputs. We investigate land-use decision-making practices in Christchurch, New Zealand, and the surrounding region in response to mass movement (e.g., rockfall, cliff collapses) and ground-surface fault rupture hazards incurred during the 2010–2011 Canterbury earthquake sequence (CES). Rockfall fatality risk models combining hazard, exposure, and vulnerability data were co-produced by earth scientists and decision makers and formed primary evidence for risk-based land-use decision-making with adaptive capacity. A public consultation and submission process enabled consideration of additional earth science information primarily via stakeholder requests. For fault rupture hazards, pre-disaster geotechnical guidelines and collaboration networks enhanced the ability of earth scientists to rapidly acquire relevant observational data to meet the demands of decision makers. Expeditious decision-making granted permissive consent for rebuilding in the fault rupture zone based on preliminary scientific advice that was subsequently supported by more comprehensive geological investigations. Rapidly fluctuating and diverse demands for post-disaster earth science information may be best met through the prior establishment of (i) land-use policies and technical guidelines tailored for a variety of diverse disaster scenarios, (ii) hazard and risk analyses in land-use plans, including acquisition of geospatial and other earth science data, and (iii) coordinated scientific networks that may comprise subgroups with diverse goals, operational perspectives, and protocols which allow the many facets of scientific information acquisition and delivery to be successfully addressed. Despite the collective knowledge shared here, some recent land-use practices in New Zealand continue to prioritize other (e.g., socioeconomic) factors above earth science information, even in areas of extreme disaster risk.

Geospatial meteorological and climatic data services application programming interface

At present, it becomes necessary to develop software for Earth science data repositories instead of downloading data to computational servers and workstations. This is especially important taking into account the upcoming volume of big environmental datasets. WPS (Web Processing Service) is an OGC standard for distributed geospatial data processing services. The WPS services provide access to geospatial data processing packages including statistical processing software tools. The main goal of this paper is to present an approach that provides a unified access to the web processing services developed for climate and meteorological research based on the latest technological and scientific achievements. Based on the “OGC API-Processes” standard, a Web API Climate is proposed to provide a uniform access to the “Climate” platform (http://climate.scert.ru/climate/environment/v3.0a-demo/) geospatial services. The Climate Web API provides a software basis for an advanced development and use of the “Climate” platform web processing services aiming at climate change studies at regional and global levels. A correct implementation of the specification will form a solid foundation for further development of desktop client applications in the form of GIS plugins, as well as universal Web-GIS clients easily embedded into thematic virtual research environments.

Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

Severe Drought in the Spring of 2020 in Poland—More of the Same?

Two consecutive dry years, 2018 and 2019, a warm winter in 2019/20, and a very dry spring in 2020 led to the development of severe drought in Poland. In this paper, changes in the Standardized Precipitation-Evapotranspiration Index (SPEI) for the interval from 1971 to the end of May 2020 are examined. The values of SPEI (based on 12, 24 and 30 month windows, i.e., SPEI 12, SPEI 24 and SPEI 30) were calculated with the help of the Penman–Monteith equation. Changes in soil moisture contents were also examined from January 2000 to May 2020, based on data from the NASA Goddard Earth Sciences Data and Information Services Center, presenting increasing water shortages in a central belt of Poland. The study showed that the 2020 spring drought was among the most severe events in the analyzed period and presented decreasing trends of SPEI at most stations located in central Poland. This study also determined changes in soil moisture contents from January 2000 to May 2020 that indicate a decreasing tendency. Cumulative water shortages from year to year led to the development of severe drought in the spring of 2020, as reflected in very low SPEI values and low soil moisture.

OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals

NASA's Orbiting Carbon Observatory-3 (OCO-3) was installed on the International Space Station (ISS) on 10 May 2019. OCO-3 combines the flight spare spectrometer from the Orbiting Carbon Observatory-2 (OCO-2) mission, which has been in operation since 2014, with a new Pointing Mirror Assembly (PMA) that facilitates observations of non-nadir targets from the nadir-oriented ISS platform. The PMA is a new feature of OCO-3, which is being used to collect data in all science modes, including nadir (ND), sun-glint (GL), target (TG), and the new snapshot area mapping (SAM) mode.This work provides an initial assessment of the OCO-3 instrument and algorithm performance, highlighting results from the first 8 months of operations spanning August 2019 through March 2020. During the In-Orbit Checkout (IOC) phase, critical systems such as power and cooling were verified, after which the OCO-3 spectrometer and PMA were subjected to a series of rigorous tests. First light of the OCO-3 spectrometer was on 26 June 2019, with full science operations beginning on 6 August 2019. The OCO-3 spectrometer on-orbit performance is consistent with that seen during preflight testing. Signal to noise ratios are in the expected range needed for high quality retrievals of the column-averaged carbon dioxide (CO2) dry-air mole fraction (XCO2) and solar-induced chlorophyll fluorescence (SIF), which will be used to help quantify and constrain the global carbon cycle.The first public release of OCO-3 Level 2 (L2) data products, called “vEarly”, is being distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The intent of the vEarly product is to evaluate early mission performance, facilitate comparisons with OCO-2 products, and identify key areas to improve for the next data release. The vEarly XCO2 exhibits a root-mean-squared-error (RMSE) of ≃ 1, 1, 2 ppm versus a truth proxy for nadir-land, TG&SAM, and glint-water observations, respectively. The vEarly SIF shows a correlation with OCO-2 measurements of >0.9 for highly coincident soundings. Overall, the Level 2 SIF and XCO2 products look very promising, with performance comparable to OCO-2. A follow-on version of the OCO-3 L2 product containing a number of refinements, e.g., instrument calibration, pointing accuracy, and retrieval algorithm tuning, is anticipated by early in 2021.

NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.