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NASA NTRS2018

Examining Dense Data Usage near the Regions with Severe Storms in All-Sky Microwave Radiance Data Assimilation and Impacts on GEOS Hurricane Analyses

Many numerical weather prediction (NWP) centers assimilate radiances affected by clouds and precipitation from microwave sensors, with the expectation that these data can provide critical constraints on meteorological parameters in dynamically sensitive regions to make significant impacts on forecast accuracy for precipitation. The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center assimilates all-sky microwave radiance data from various microwave sensors such as all-sky GPM Microwave Imager (GMI) radiance in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS), which includes the GEOS atmospheric model, the Gridpoint Statistical Interpolation (GSI) atmospheric analysis system, and the Goddard Aerosol Assimilation System (GAAS). So far, most of NWP centers apply same large data thinning distances, that are used in clear-sky radiance data to avoid correlated observation errors, to all-sky microwave radiance data. For example, NASA GMAO is applying 145 km thinning distances for most of satellite radiance data including microwave radiance data in which all-sky approach is implemented. Even with these coarse observation data usage in all-sky assimilation approach, noticeable positive impacts from all-sky microwave data on hurricane track forecasts were identified in GEOS-5 system. The motivation of this study is based on the dynamic thinning distance method developed in our all-sky framework to use of denser data in cloudy and precipitating regions due to relatively small spatial correlations of observation errors. To investigate the benefits of all-sky microwave radiance on hurricane forecasts, several hurricane cases selected between 2016-2017 are examined. The dynamic thinning distance method is utilized in our all-sky approach to understand the sources and mechanisms to explain the benefits of all-sky microwave radiance data from various microwave radiance sensors like Advanced Microwave Sounder Unit (AMSU-A), Microwave Humidity Sounder (MHS), and GMI on GEOS-5 analyses and forecasts of various hurricanes.

Kim, Min-Jeong↗
NASA NTRS2018

Development of the GEOS-MIT gcm Atmosphere-Ocean Model for Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MIT-GCM (General Circulation Model)) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies. Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (approximately 1/8 degrees) configuration of the GEOS atmosphere to a latitude-longitude-cap-1080 (approximately 1/12 degrees) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8 deg) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12 deg) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Central Role of Air Quality Observations in NASA's GEOS Composition Forecasting Model

The NASA GEOS composition forecast model (GEOS-CF) provides global, high-resolution (25 km) air quality forecasts in near-real time. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest compared to current, publicly-available global composition forecasts.Air quality observations are an indispensable tool to evaluate the model's ability to capture the strong temporal and spatial gradients of air pollutants across the globe. We show how comparisons against near-real time observations available through OpenAQ (www.openaq.org) demonstrate the model's overall success in reproducing surface concentrations of ozone, nitrogen dioxide, and PM2.5. This analysis also helps identifying current limitations of the model, for example over South America. The model-observation mismatches are most likely caused by uncertainties in the emissions data. Using the example of Rio de Janeiro, we show how the model skill can be improved by using local, high-resolution emission inventories in combination with air quality data.

Keller, Christoph A.↗
NASA NTRS2019

Impact of GMI All-Sky Radiance Assimilation in the NASA GEOS Forecast System

The assimilation of cloud- and precipitation-affected ("all-sky") radiances has become an important focus of development at most numerical weather prediction centers. Efforts at the Global Modeling and Assimilation Office (GMAO) have focused on all-sky assimilation of GPM Microwave Imager (GMI) radiances, which became operational in the GEOS real-time production system in July 2018. Implementation of the all-sky capability required several upgrades to the GEOS hybrid 4D-EnVar assimilation infrastructure including the addition of control variables for cloud liquid, cloud ice, rain and snow, enhancements to the radiative transfer model, new hybrid background and observational error models, and modified quality control and bias correction procedures. This talk describes the impact of GMI all-sky radiance assimilation on GEOS analyses and forecasts as determined from examination of various metrics including statistics of background departures and analysis increments, forecast skill scores, and forecast sensitivity observation impact (FSOI) calculations. It is shown that in addition to the hydrometeors themselves, the initial wind, temperature and pressure fields all undergo significant dynamic adjustment in response to the analyzed cloud and precipitation features. Assimilation of GMI radiances leads to improved forecasts of lower tropospheric wind, temperature and humidity, especially in the tropics. The largest forecast improvements occur during the first 48 hours, with diminishing impact thereafter. However, combining GMI all-sky assimilation with improvements to the GEOS model physics as in the recent implementation of the real-time production system, extends these forecast improvements well in to the medium range. FSOI results based on a 24-hr moist global energy norm show that GMI radiances provide nearly uniform beneficial impact throughout the tropics, with more mixed impacts in the subtropics. While the overall impact of GMI is smaller than that of other, much more numerous microwave and hyperspectral infrared radiance types, its impact is among the largest of all radiance types on a per-observation basis.

Gelaro, Ron↗
NASA NTRS2019

Near Real-Time Air Quality Forecasts Using the NASA GEOS Model

We present a new high-resolution global composition forecast system produced by NASA's Global Modeling and Assimilation Office. The NASA Goddard Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) system combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke.

Knowland, K. Emma↗
NASA NTRS2019

Near Real-Time Air Quality Forecasts Using the NASA GEOS Model

We present a new high-resolution global composition forecast system produced by NASA's Global Modeling and Assimilation Office. The NASA Goddard Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) system combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke.

Knowland, K. Emma↗
NASA NTRS2019

Air Pollution Forecasts Using the NASA GEOS Model: A Unified Tool from Local to Global Scales

We present a new high-resolution global composition forecast system produced by NASA's Global Modeling and Assimilation Office. The NASA Goddard Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) system combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke.

Knowland, K. Emma↗
NASA NTRS2019

Convective Entrainment Rates Estimated from Aura CO and CloudSat/CALIPSO Observations and Comparison with GEOS-5

Entrainment rate (λ) in convective parameterizations remains a sensitive parameter with much uncertainty in model simulations. This study estimates λ using carbon monoxide (CO) measurements jointly from the Microwave Limb Sounder (MLS) and Tropospheric Emission Spectrometer (TES) onboard the Aura satellite, associated with deep convective cases identified by CloudSat and CALIPSO observations. CO is treated as a conserved quantity over convective transport time scales and a simple entraining-plume model is used to derive entrainment rates. The relationships of the observational estimates of λ as a function of convective height, environmental relative humidity and convective available potential energy (CAPE) derived from Atmospheric Infrared Sounder (AIRS) data are compared with those from Goddard Earth Observing System Model (GEOS-5) simulations. Bulk statistics of λ show that the values of λ are predominately below 20% km(exp-1) for deep convection and the occurrence frequency of any λ decreases with increasing λ. Composite λ values are generally lower in the tropics compared to northern mid-latitudes in both observations and the GEOS-5 model. A decrease of λ with increasing convective height is found in both observations and model simulations. We also find that λ tends to decrease with increasing CAPE in the observation-based λ’s and plume-based GEOS-5 λ’s, although the model given λ’s have a non-monotonic relation with CAPE. The observed λ’s have a weak relation with lower-to-mid tropospheric RH, while both GEOS-5 plume-based and given λ increases with increasing RH.

Ryan E Stanfield↗
NASA NTRS2019

Assimilation of Aerosol Observations in the NASA GEOS Model

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. The prognostic model is based on the GEOS model radiatively coupled to GOCART aerosol module and includes assimilation of bias-corrected Aerosol Optical Depth (AOD) at 550 nm from various space-based remote sensing platforms. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to a variational ensemble type of scheme. In this talk we will examine the impact of replacing the current analysis splitting scheme with this new approach. Starting with the assimilation of satellite based single-channel retrievals; we will discuss the impact of this aerosol data assimilation technique on the 3D aerosol distributions by means of innovation statistics and verification against independent datasets such as the Aerosol Robotic Network (AERONET) and surface PM2.5. We will also present preliminary results related to the introduction of new aerosol data types in GEOS, including multi-spectral AOD retrievals.

AERONET↗
NASA NTRSDate not supplied

Assessing the Viability of Using GEOS-Forecast Product for Landslides Forecasting: A Step Toward Early Warning System

Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time. Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system. This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS). GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.

Sana Khan↗
NASA NTRS2020

Atmospheric Carbon Cycle Dynamics Over the Above Domain: an Integrated Analysis Using Aircraft Observations (Arctic-CA) and Model Simulations (GEOS)

The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project conducted six airborne surveys of Alaska and northwestern Canada between April and November 2017 to capture the spatial and temporal gradients of northern high-latitude carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) as part of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4 and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. We observed vertical gradients of CO2, CH4 and CO that vary by eco-region and duration of the sampling period, which spanned the majority of the seasonal cycle. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons with GEOS simulations of atmospheric CO2, CH4 and CO highlight the potential of these multi-species observations to inform improvements in surface flux estimates and the representation of atmospheric transport. GEOS simulations provide estimates of the near surface average CO2 and CH4 enhancements that are well correlated with aircraft observations (R=0.74 and R=0.60 respectively), suggesting that GEOS has reasonable fidelity over this complex and heterogeneous region. This model-data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broadscale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are observed. The study also provides a framework for benchmarking a global model at regional scales, which is needed to use climate models as tools to investigate high-latitude carbon-climate feedbacks.

C Sweeney↗
NASA NTRS2021

Improving the Representation of Subtropical Boundary Layer Clouds in the NASA GEOS Model with the Eddy-Diffusivity/Mass-Flux Parameterization

A systematic underestimation of subtropical planetary boundary layer (PBL) stratocu-mulus clouds by the GEOS model has been significantly improved by a new Eddy-Diffusivity/Mass-Flux (EDMF) parameterization. The EDMF parameterization representsthe subgrid-scale transport in the dry and moist parts of the PBL in a unified mannerand it combines an adjusted eddy-diffusivity PBL scheme from GEOS with a stochasticmulti-plume mass-flux model. The new EDMF version of the GEOS model is first com-pared against the CONTROL version in a single-column model (SCM) framework for twobenchmark cases representing subtropical stratocumulus and shallow cumulus clouds, andvalidated against large eddy simulations. Global simulations are performed and comparedagainst observations and reanalysis data. The results show that the EDMF version of theGEOS model produces more realistic subtropical PBL clouds. Noteworthy, the EDMF im-provements first detected in the SCM framework translate into similar improvements of theglobal GEOS model.

Kay Suselj↗
NASA NTRSDate not supplied

Generating Global CH4 NASA GEOS Product by Assimilating TROPOMI

Radiative properties of methane (CH4) significantly contribute to climate change and the recent acceleration of global CH4 growth requires thorough investigation into its causes. Examination of temporal and spatial CH4 variability is crucial for better understanding the shifts that are currently taking place. Integrating different CH4 observations to create a reliable, easily to use global atmospheric CH4 product could contribute to assessment of emissions processes, but remains challenging due to the lack of long satellite data records. Here we present the NASA Goddard Earth Observing System (GEOS) based global CH4 product constrained by atmospheric transport from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and assimilated CH4 from the TROPOspheric Monitoring Instrument (TROPOMI). This product uses the Constituent Data Assimilation System (CoDAS) component of the GEOS capability, which has been expanded and generalized to assimilate any satellite, ground-based, or in situ observation of atmospheric composition that can be expressed as a vertical sounding or point sample. The product is similar to the one generated by assimilating carbon dioxide (CO2) data into GEOS from Orbiting Carbon Observatory 2 (OCO-2): OCO-2 GEOS Level 3 daily and monthly, 0.5x0.625 assimilated CO2 V10r. The preliminary product is investigated using a variety of quality check approaches with the help of in situ observations to examine bias correction, error inflation, and performance of the assimilation system. Potential applications of this approach include support for interpretation of high-resolution point source detection approaches, climate and greenhouse gas reanalyses, and boundary conditions for regional modeling approaches.

Nikolay Balashov↗
NASA NTRSDate not supplied

GEOS Constituent Data Assimilation Beyond Aura MLS: Assimilating NASA SAGE III/ISS Profiles of Stratospheric Water Vapor and Ozone

Ozone and water vapor in the lower stratosphere are important trace gases for atmospheric chemistry and radiative budget. The Stratospheric Aerosol and Gas Experiment (SAGE) missions have been crucial in monitoring the stratospheric ozone loss and the subsequent recovery as well as the trends in water vapor linked to surface temperature trends. The SAGE III instrument aboard the International Space Station (ISS) continues the SAGE mission record, with high vertical resolution profiles of ozone and water vapor available since mid 2017. The NASA GEOS Earth system model has the new capability to assimilate multi-constituents from ground and space-based instruments using the GEOS Constituent Data Assimilation System (CoDAS). The recently released MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM) assimilates version 4.2 MLS ozone, water vapor and other chemically-reactive species with the NASA GEOS model coupled to a stratospheric-only chemistry mechanism and transport constrained to the MERRA-2 reanalysis. While the number of solar occultation observations a day from SAGE III/ISS is about 1% of the total number of profiles observed globally by MLS, the chemical timescales of ozone and water vapor in the lower stratosphere are long enough that the SAGE III/ISS data may provide a useful constraint on the assimilated product. Using the same GEOS CoDAS configuration as M2-SCREAM, we will present a series of experiments to investigate if ozone and water vapor trends are consistent with the assimilation of SAGE observations with and without Aura retrievals, and to determine if the assimilation of SAGE observations produces a steady product for trend analysis, especially as the end of the Aura mission nears. In our experiments, assimilating only SAGE III/ISS water vapor profiles results in water vapor fields more consistent with experiments that assimilate MLS v5; however, in the polar regions SAGE III/ISS observations are not available and the values are unconstrained. We are encouraged by the positive benefit assimilating the less frequent SAGE III/ISS observations has on stratospheric composition. Sensitivity experiments such as these will allow us to assess the added value of SAGE data for continued monitoring of the stratospheric composition for climate and ozone recovery assessments.

SAGE↗
NASA NTRSDate not supplied

Status and Progress of Observation Usages in the GMAO GEOS Atmospheric Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) continuously works to enhance the use of observations in the Goddard Earth Observing System – Forward Processing (GEOS-FP) model and analysis system. Updates to GEOS-FP in March 2022 and February 2023 focused on the analysis component, adding new capabilities to ingest radiances and other new observing systems, such as SPIRE GNSS-RO data. Concurrent activities enhance aspects of the radiance assimilation, such as the use of hyperspectral radiances in the stratosphere and lower troposphere, the introduction of microwave radiances over land, and a revised treatment of GMI observations. Furthermore, GMAO has been contributing to the Joint Effort for Data assimilation Integration (JEDI), including development and testing of various components of the observing system and GEOS-specific background error covariances that will allow migration of GEOS-FP from a GSI- to a JEDI-based analysis system. A number of examples are shown.

Y Zhu↗
NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗