Earth Observing System Research Articles

Air quality and cancer risk in the All of Us Research Program

IntroductionThe NIH All of Us Research Program has enrolled over 544,000 participants across the US with unprecedented racial/ethnic diversity, offering opportunities to investigate myriad exposures and diseases. This paper aims to investigate the association between PM2.5 exposure and cancer risks.Materials and methodsThis work was performed on data from 409,876 All of Us Research Program participants using the All of Us Researcher Workbench. Cancer case ascertainment was performed using data from electronic health records and the self-reported Personal Medical History questionnaire. PM2.5 exposure was retrieved from NASA’s Earth Observing System Data and Information Center and assigned using participants’ 3-digit zip code prefixes. Multivariate logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Generalized additive models (GAMs) were used to investigate non-linear relationships.ResultsA total of 33,387 participants and 46,176 prevalent cancer cases were ascertained from participant EHR data, while 20,297 cases were ascertained from self-reported survey data from 18,133 participants; 9,502 cancer cases were captured in both the EHR and survey data. Average PM2.5 level from 2007 to 2016 was 8.90 μg/m3 (min 2.56, max 15.05). In analysis of cancer cases from EHR, an increased odds for breast cancer (OR 1.17, 95% CI 1.09–1.25), endometrial cancer (OR 1.33, 95% CI 1.09–1.62) and ovarian cancer (OR 1.20, 95% CI 1.01–1.42) in the 4th quartile of exposure compared to the 1st. In GAM, higher PM2.5 concentration was associated with increased odds for blood cancer, bone cancer, brain cancer, breast cancer, colon and rectum cancer, endocrine system cancer, lung cancer, pancreatic cancer, prostate cancer, and thyroid cancer.ConclusionsWe found evidence of an association of PM2.5 with breast, ovarian, and endometrial cancers. There is little to no prior evidence in the literature on the impact of PM2.5 on risk of these cancers, warranting further investigation.

ANALYSIS OF USE CASES TOWARDS THE EVOLUTION OF EUROPE’S CONTRIBUTION TO GEOSS

Abstract. The European Commission (EC), being the co-lead of the European Regional Group on Earth Observations (EuroGEO), is currently outlining an ecosystem that will interconnect European technologies, digital infrastructures and data and enable the implementation of policy-relevant use cases using shared standards. This ecosystem will in turn strengthen Europe’s contribution to GEOSS, which is the Global Earth Observation System of Systems. To evaluate and demonstrate the potential of this ecosystem, prioritization of use cases is necessary. This work presents a methodology for this prioritization, taking into consideration policy relevance, data and digital infrastructures and adherence to European approaches, principles and values. This methodology can also be used for identifying noteworthy research projects in the Earth observation (EO) domain that possess policy relevance and have the potential to be transformed into sustained and operational services beyond the funding period, focusing on research projects funded by Horizon 2020 (H2020) and Horizon Europe EU Framework Programmes for Research and Innovation. We applied this methodology to an H2020 project named “EuroGEO Showcases: Applications Powered by Europe (e-shape)”, composed of 37 pilots, and here we present the results. This process led to the identification of a priority pilot on solar energy potential assessment in urban areas, for which EC Knowledge Centre on Earth Observation (KCEO) will participate in co-design activities with ARMINES, the leader of this pilot. We finally present the pilot shortly and elaborate on the next steps.

Gap‐Filled Multivariate Observations of Global Land–Climate Interactions

AbstractThe volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap‐filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap‐filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state‐of‐the‐art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land–atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995–2020 globally with gap‐free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)‐Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate‐resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA‐CCI burned area, and snow cover fraction as well as 2‐m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state‐of‐the‐art interpolation. The gap‐filled data set shows high correlations with ERA5‐Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA‐CCI only agree with the reanalysis product ERA5‐Land trends after gap‐filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations.

Paired Satellite and NWP Precipitation for Global Flood Forecasting

Abstract Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.

Public Health Data Applications Using the CDC Tracking Network: Augmenting Environmental Hazard Information With Lower-Latency NASA Data.

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention's National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated data sets over the contiguous U.S. (CONUS) include: daily 5-day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast, daily historical (1980-present) concentrations of speciated PM2.5 based on the modern era retrospective analysis for research and applications, version 2, and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real-time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (a) describing temporal and spatial trends in disease and potential environmental exposures, (b) identifying populations most affected, (c) generating hypotheses about associations between health and environmental exposures, and (d) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

Greening of human-dominated ecosystems in India

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India’s agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India’s croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India’s agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.

Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions.

The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data

Abstract. The adjoint of the GEOS-Chem (Goddard Earth Observing System with Chemistry) model has been widely used to constrain the sources of atmospheric compositions. Here, we designed a new framework to facilitate emission inventory updates in the adjoint of the GEOS-Chem model. The major advantage of this new framework is good readability and extensibility, which allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) meteorological data, which allows us to perform long-term analyses with consistent meteorological data for the period 1979–present. The performances of the developed capabilities were evaluated with the following steps: (1) diagnostic outputs of carbon monoxide (CO) sources and sinks to ensure the correct reading and use of emission inventories, (2) forward simulations to compare the modeled surface and column CO concentrations among various model versions, (3) backward simulations to compare adjoint gradients of global CO concentrations to CO emissions with finite-difference gradients, and (4) observing system simulation experiments (OSSEs) to evaluate the model performance in 4D variational (4D-Var) assimilations. Finally, an example application of 4D-Var assimilation was presented to constrain anthropogenic CO emissions in 2015 by assimilating Measurement of Pollution in the Troposphere (MOPITT) CO observations. The capabilities developed in this work are important for better applications of the adjoint of the GEOS-Chem model in the future. These capabilities will be submitted to the standard GEOS-Chem adjoint code base for better development of the community of the adjoint of the GEOS-Chem model.

Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020

Abstract. A single ozone (O3) tracer mode was developed in this work to build the capability of the Goddard Earth Observing System model with Chemistry (GEOS-Chem) for rapid O3 simulation. The single O3 tracer simulation demonstrates consistency with the GEOS-Chem full chemistry simulation, with dramatic reductions in computational costs of approximately 91 %–94 %. The single O3 tracer simulation was combined with surface and Ozone Monitoring Instrument (OMI) O3 observations to investigate the changes in tropospheric O3 over eastern China in 2015–2020. The assimilated O3 concentrations demonstrate good agreement with O3 observations because surface O3 concentrations are 43.2, 41.8, and 42.1 ppb and tropospheric O3 columns are 37.1, 37.9, and 38.0 DU in the simulations, assimilations, and observations, respectively. The assimilations indicate rapid rises in surface O3 concentrations by 1.60 (spring), 1.16 (summer), 1.47 (autumn), and 0.80 ppb yr−1 (winter) over eastern China in 2015–2020, and the increasing trends are underestimated by the a priori simulations. More attention is suggested to the rapid increases in the O3 pollution in spring and autumn. We find stronger rises in tropospheric O3 columns over highly polluted areas due to larger local contributions, for example, 0.12 DU yr−1 (North China Plain) in contrast to −0.29 (Sichuan Basin) and −0.25 DU yr−1 (southern China). Furthermore, our analysis demonstrated noticeable contributions of the interannual variability in background O3 to the trends in surface O3 (particularly in the summer) and tropospheric O3 columns over eastern China in 2015–2020. This work highlights the importance of rapid simulations and assimilations to extend and interpret atmospheric O3 observations.

Synthetic Observations of the Planetary Boundary Layer from Space: A Retrieval Observing System Simulation Experiment Framework

Abstract To address critical gaps identified by the National Academies of Sciences, Engineering, and Medicine in the current Earth system observation strategy, the 2017–27 Decadal Survey for Earth Science and Applications from Space recommended incubating concepts for future targeted observables including the atmospheric planetary boundary layer (PBL). A subsequent NASA PBL Incubation Study Team Report identified measurement requirements and activities for advancing the maturity of the technologies applicable to the PBL targeted observables and their associated science and applications priorities. While the PBL is the critical layer where humans live and surface energy, moisture, and mass exchanges drive the Earth system, it is also the farthest and most inaccessible layer for spaceborne instruments. Here we document a PBL retrieval observing system simulation experiment (OSSE) framework suitable for assessing existing and new measurement techniques and determining their accuracy and improvements needed for addressing the elevated Decadal Survey requirements. In particular, the benefits of large-eddy simulation (LES) are emphasized as a key source of high-resolution synthetic observations for key PBL regimes: from the tropics, through subtropics and midlatitudes, to subpolar and polar regions. The potential of LES-based PBL retrieval OSSEs is explored using six instrument simulators: Global Navigation Satellite System–Radio Occultation, differential absorption radar, visible to shortwave infrared spectrometer, infrared sounder, Multi-angle Imaging SpectroRadiometer, and microwave sounder. The crucial role of LES in PBL retrieval OSSEs and some perspectives for instrument developments are discussed.

Expanding the coverage of Multi-angle Imaging SpectroRadiometer (MISR) aerosol retrievals over shallow, turbid, and eutrophic waters

Abstract. Shallow and coastal waters are often rich in nutrients (eutrophic), biologically productive, turbid from runoff, and located where the atmosphere above can be more aerosol-laden than over open-ocean waters due to proximity to aerosol sources on land. Although the NASA Earth Observing System's Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra satellite has been monitoring global aerosols for over 23 years, the current operational retrieval algorithm (V23) is not applied over waters less than 50 m in depth or within 5 km of land, designated as “shallow water.” This is due to the simplicity of the Dark Water algorithm, applied operationally over deep waters, which assumes the surface is essentially black in the primarily used red and near-infrared spectral bands. In this work, we describe the implementation and validation of a “Shallow Water” aerosol retrieval algorithm for MISR, which takes advantage of all four available spectral bands and includes a Lambertian surface reflectivity term to account for water-leaving radiance. This algorithm compares well to independent, surface-based observations and demonstrates better performance over shallow waters than the operational Dark Water retrieval algorithm. Globally, aerosol retrievals over shallow waters increase the total number of MISR over-water measurements by more than 7 %, including new retrievals made over some of the most biologically productive parts of the ocean.

Response of the ozone-related health burden in Europe to changes in local anthropogenic emissions of ozone precursors

Exposure to ozone (O3) is associated with many human health problems, resulting in tens of thousands of premature deaths annually in Europe. This study quantifies the impact of changes in anthropogenic emissions of O3 precursors on premature deaths from long-term O3 exposure in Europe and the impact of emissions changes during 2005–2015 using the nested-grid chemical transport model Goddard Earth Observing System (GEOS)-Chem and its adjoint. In 2015, it is estimated that a 20% decrease in total anthropogenic emissions in our modeled European domain could prevent 1576 (467–3252) premature deaths from respiratory disease (30 years of age), 70% of which is owing to the decrease in nitrogen oxides (NO x ) emissions. Underlying this aggregate effect is substantial spatial variation. In most of Europe, O3 formation is NO x -limited so that NO x emission reductions help to decrease premature deaths. Yet where O3 formation is NO x -saturated (as in parts of the United Kingdom, Benelux and Germany) emission reductions cause more premature deaths through increased ozone exposure. Despite the overall decreases in anthropogenic emissions, the marginal benefit, expressed as the avoided premature deaths per 1 kg km−2 yr−1 reduction in NO x emissions, is found to generally increase during 2005–2015, with a mean value more than doubling over Europe. This highlights the general trend that O3 formation becomes less sensitive to volatile organic compound emissions and more limited by NO x emissions. An important policy implication of increasing marginal benefits is that more costly regulations of NO x emissions are economically justified even as total anthropogenic emission are declining. NO x contributions from road transport, industry, energy, and residential sectors are most affected by the change in the O3 production regime. Consequently, European regulations of NO x emissions targeted at those sectors will yield the highest health benefits per unit NO x emission of all sources.

Exposure to greenness during pregnancy and the first three years after birth and autism spectrum disorder: A matched case-control study in shanghai, China

Causes of autism spectrum disorder (ASD) have not been fully understood. Previous studies have linked environmental factors with ASD. However, evidence for the greenness-ASD association is limited, especially in China. To fill this gap, we conducted a matched case-control study to examine the association between greenness and ASD in China. Participants in this study were 84,934 children aged 3−12 years in Shanghai, China, selected using a multi-stage cluster sampling method. ASD cases were firstly screened by questionnaires completed by both children's parents and teachers, and were then confirmed by clinical examinations. Further, 10 healthy controls were randomly selected to match each ASD case by age and sex. The final analyses included 146 ASD cases and 1460 healthy controls. Participants' exposure to greenness before and after birth was assessed by normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from NASA's Earth Observing System according to their residential locations. We used conditional logistic regression to examine the ASD-greenness association. Per interquartile range (IQR) increase in EVI500m and NDVI500m during the year before birth were associated with lower risks of ASD with adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of 0.96 (95%CI: 0.946, 0.975, IQR = 0.074) and 0.937 (95%CI: 0.915, 0.959, IQR = 0.101). Exposure to greenness during the first 3 years after birth was also significantly associated with lower risk of ASD [IQR ORs for EVI500m and NDVI500m were 0.935 (95%CI: 0.91, 0.962, IQR = 0.06) and 0.897 (95%CI: 0.861, 0.935, IQR = 0.09), respectively]. Air pollution showed mediation effects on thegreenness-ASD association. Greenness was observed to have stronger beneficial effects on children without historical diseases and term birth. More greenness exposure before and after birth were significantly associated with lower risks of ASD in children. Our results highlight the importance of greenness in urban planning.

Direct assimilation of AVHRR satellite radiance measurements in a reanalysis system

AbstractThe Advanced Very‐High‐Resolution Radiometer (AVHRR) is a broad‐band, five‐channel scanner sensing in the visible, near‐infrared and thermal infrared portions of the electromagnetic spectrum. AVHRR instruments onboard polar‐orbiting satellites have data records spanning 30 years. The radiances from the infrared channels of AVHRR have been directly assimilated over the ice‐free ocean in the NASA Goddard Earth Observing System (GEOS) since 2017 to constrain skin sea surface temperature (SST). The GEOS system already uses an advanced bulk SST and a skin SST model which incorporates diurnal warming and cool‐skin temperature on the surface of the ocean. The positive contribution of this effort to the Numerical Weather Prediction (NWP) makes it desirable to extend the skin SST data assimilation procedure to reanalysis applications. The AVHRR data from platforms that were previously unexplored for NWP applications are assimilated at the Global Modeling and Assimilation Office (GMAO) at NASA, for an upcoming reanalysis project. This study uses results from reanalysis experiments to assess the impact of assimilating AVHRR radiances over open ocean on the atmospheric state variables, focusing on the analyzed skin temperatures that include the active skin SST model. It is demonstrated that including the skin temperature variations in atmospheric analysis is generally beneficial. The addition of radiance measurements from AVHRR provides further improvements to the overall reanalysis system performance. by helping to correct the diurnal heating and reducing errors in the background departure of hyperspectral radiance observations, which are an essential component of atmospheric reanalyses.

Flood Impacts on Net Ecosystem Exchange in the Midwestern and Southern United States in 2019

AbstractClimate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year‐to‐year increase in atmospheric CO2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO2 vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO2 is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low‐order Flux Inversion, where fluxes of CO2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO2 tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non‐crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

Surface Radiative Forcing as a Climate-Change Indicator in North India due to the Combined Effects of Dust and Biomass Burning

This study estimates the radiative forcing by biomass burning and dust aerosols over the Indian subcontinent, with emphasis on the Indo-Gangetic Plains (IGP) during the period from January 2021 to April 2021, based on multiple satellite and reanalysis datasets. In this respect, we used retrievals from the Moderate Resolution Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) system, as well as reanalysis data from the Goddard Earth Observing System, version 5 (GEOS-5), the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), the Copernicus Atmosphere Monitoring Service (CAMS), and ERA-Interim. According to the MERRA-2 and the CAMS, the highest black carbon (BC) concentrations in January 2021 were 7–8 µg m−3, which were significantly lower than measurements performed in main cities along the IGP, such as Patiala, Delhi, and Kanpur. The meteorological data analysis accompanied by the CALIPSO lidar measurements showed that the vertical distribution of total attenuated backscatter (TAB) could reach altitudes of up to ~4–5 km and could be transported over the central Himalayan region. The spatial-averaged daily aerosol radiative forcing (ARF) values over the Indian subcontinent from January 2021 to April 2021 were found to range from −51.40 to −6.08 W m−2 (mean of −22.02 ± 9.19 W m−2), while on a monthly basis, the ARF values varied widely, from −146.24 to −1.63 W m−2 (mean of −45.56 ± 22.85 W m−2) over different parts of the study region. Furthermore, the spatial-averaged daily BC radiative forcing ranged from −2.23 to −0.35 (−1.01 ± 0.40 W m−2), while it varied from −15.29 to −0.31 W m−2 (−2.46 ± 2.32 W m−2) over different regions of southern Asia, indicating a rather small contribution to the total aerosol radiative effect and a large presence of highly scattering aerosols. Our findings highlight the importance of growing biomass burning, in light of recent climate change and the rapid decline in air quality over North India and the Indian Ocean.

Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications.

This study introduces a prototype end-to-end Simulator software tool for simulating two-dimensional satellite multispectral imagery for a variety of satellite instrument models in aquatic environments. Using case studies, the impact of variable sensor configurations on the performance of value-added products for challenging applications, such as coral reefs and cyanobacterial algal blooms, is assessed. This demonstrates how decisions regarding satellite sensor design, driven by cost constraints, directly influence the quality of value-added remote sensing products. Furthermore, the Simulator is used to identify situations where retrieval algorithms require further parameterization before application to unsimulated satellite data, where error sources cannot always be identified or isolated. The application of the Simulator can verify whether a given instrument design meets the performance requirements of end-users before build and launch, critically allowing for the justification of the cost and specifications for planned and future sensors. It is hoped that the Simulator will enable engineers and scientists to understand important design trade-offs in phase 0/A studies easily, quickly, reliably, and accurately in future Earth observation satellites and systems.

Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model‐Data Fusion

AbstractThe NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original design life. However, the era of NASA's Earth Observing System may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA‐20 and NOAA‐21 satellites enable potential continuity of long‐term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model‐data fusion, we combined 18 years of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from the future MOD17 Collection 7 product and new VNP17 product for 2012–2018 is 127 ± 2.8 Pg C year−1 (58 ± 1.1 Pg C year−1), which compares well with independent top‐down and bottom‐up estimates. MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000–2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year−2 but slowing to 0.35–0.44 (0.11–0.13) Pg C year−2 over 2012–2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these estimates of global, terrestrial primary productivity beyond 2030.

Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast modeling system (GEOS-CF v2.0) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI)

The NASA Goddard Earth Observing System Composition Forecast system (GEOS-CF) provides global near-real-time analyses and forecasts of atmospheric composition. The current version of GEOS-CF builds on the GEOS general circulation model with Forward Processing assimilation of meteorological data (GEOS-FP) and includes detailed GEOS-Chem tropospheric and stratospheric chemistry. Here we add 3D variational data assimilation in GEOS-CF to assimilate satellite observations of ozone including MLS vertical profiles, OMI total columns, and AIRS and IASI hyperspectral 9.6 μm radiances. We focus our evaluations on the troposphere. We find that the detailed tropospheric chemistry in GEOS-CF significantly improves the simulated background ozone fields relative to previous versions of the GEOS model, allowing for specification of smaller background errors in assimilation and resulting in smaller assimilation increments to correct the simulated ozone. Assimilation increments are largest in the upper troposphere and are consistent between satellite data sets. The OMI and MLS ozone data generally provide more information than the AIRS and IASI radiances except at high latitudes where the radiances provide more information. Comparisons to independent ozonesonde and aircraft (ATom-4) observations for 2018 show significant GEOS-CF improvement from the assimilation, particularly in the extratropical upper troposphere.

An Excel Template Generator for Darwin Core

Scientific data is diverse and can be complex, potentially including biotic or abiotic measurements, material samples, DNA derived data and more. Especially for researchers who are new to the Darwin Core standard (Darwin Core Task Group 2009), it is not always obvious what the best practice is for creating a Darwin Core Archive (GBIF 2021) for their data. Which core and extensions should they select? Which Darwin Core terms*1 should they include? We present the 'Learnings from Nansen Legacy template generator' (Marsden and Schneider 2023), a spreadsheet template generator to simplify the creation of Darwin Core Archives. It enables users to create a single Microsoft Excel file that includes one sheet per core or extension using a graphical user interface. The user can select from a complete list Darwin Core terms to use as column headers. There are requirements and recommendations for which terms are selected for each core and extension. Descriptions for all terms are displayed when one hovers over a Darwin Core term, and are stored as notes in the template when one select a relevant cell. The generated template includes cell restrictions to prevent one from inputting data in an incorrect format. A separate configuration is also available to aid researchers in creating CF-NetCDF*2 files for physical data, which are also compliant with the FAIR (Findable, Accessible, Interoperable, Resuable)*3 data management principles. The Learnings from Nansen Legacy template generator is published (Marsden and Schneider 2023) and can be installed for use on your website or computer by following the instructions on the software's GitHub repository*4. The template generator can be tested where it is currently hosted, by SIOS*5 (Svalbard Integrated Arctic Earth Observing System). One can also refer to a YouTube tutorial*6 on how the template generator works.