Global Modeling And Assimilation Office Research Articles

Using OSSEs to Evaluate GXS Impact in the Context of International Coordination

Abstract The Geostationary Extended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite, expected to launch in the mid-2030s. As part of the follow-on to the GOES program, the NOAA/NASA GeoXO Sounder (GXS) instrument will join several international counterparts in a geostationary orbit. In preparation, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring that includes international partners. Using a global observing system simulation experiment (OSSE) framework, GXS was assessed from a numerical weather prediction (NWP) perspective. Evaluation of the ability of GXS, both alone and as part of a global ring of GEO sounders, to improve weather prediction of thermodynamic variables was performed globally and regionally. GXS dominated regional analysis and forecast improvements and contributed significantly to global increases in forecast skill relative to a Control. However, more sustained global improvements, on the order of 4 days, relied on international partnerships. Additionally, GXS showed the capability to improve hurricane forecast track errors on the time scales necessary for evacuation warnings. The FSOI metric over CONUS showed that the GXS observations provided the largest radiance impact on the moist energy error norm reduction. The high-temporal-resolution atmospheric profile information over much of the Western Hemisphere from GXS provides an opportunity to improve the representation of weather systems and their forecasts. Significance Statement NOAA and NASA are currently planning the GeoXO mission as a follow-on to the GOES program. As part of this process, NASA’s Global Modeling and Assimilation Office has performed several experiments using an observing system simulation experiment (OSSE) framework to assess the potential impact of the GeoXO Sounder (GXS) on numerical weather prediction within the context of international partners launching similar instruments. As part of this assessment, it was found that assimilation of GXS data has the ability to improve both the model analyzed weather and forecasts of the weather, specifically over the domain that GXS observes. Global improvements relied more heavily on a solution consisting of multiple instruments to form a global ring.

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.

The GMAO Hybrid 4D-EnVar Observing System Simulation Experiment Framework

Abstract This work describes the extension of the Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework to use a hybrid four-dimensional ensemble–variational (4D-EnVar) scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short intercomparison of upgrading from a 3D to a 4D OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify particular features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western U.S.

AbstractSubseasonal‐to‐seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new metric, the accumulated water vapor transport associated with ARs, which is closely related to the winter precipitation over the western U.S., and provides a multi‐model S2S prediction skill assessment. The prediction skill is evaluated at lead time 1–4 weeks in four dynamical model hindcast data sets from National Centers for Environmental Prediction (NCEP), European Center for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration. Three reanalysis data sets are used to evaluate the uncertainty of prediction skills related to the choice of references. The AR‐related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at lead time 3–4 weeks. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both deterministic and probabilistic sense. At week‐3 lead time, the models have significant skill near the lower latitudes (<40°N) of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR cases than weak cases. The Madden–Julian Oscillation can modulate the prediction skill at week‐3 lead over central and Southern California, but with large uncertainties across models.

Using OSSEs to Evaluate the Impacts of Geostationary Infrared Sounders

Abstract An observing system simulation experiment (OSSE) was performed to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary orbit on numerical weather prediction, with a focus on the proposed sounder on board the Geostationary Extended Observations (GeoXO) program’s central satellite. Infrared sounders on a geostationary platform would fill several gaps left by IR sounders on polar-orbiting satellites, and the increased temporal resolution would allow the observation of weather phenomena evolution. The framework for this OSSE was the Global Modeling and Assimilation Office (GMAO) OSSE system, which includes a full suite of meteorological observations. The experiment additionally assimilated four identical IR sounders from geostationary orbit to create a “ring” of vertical profiling observations. Based on the experimentation, assimilation of the IR sounders provided a beneficial impact on the analyzed mass and wind fields, particularly in the tropics, and produced an error reduction in the initial 24–48 h of the subsequent forecasts. Specific attention was paid to the impact of the GeoXO Sounder (GXS) over the contiguous United States (CONUS) as this is a region that is well-observed and as such difficult to improve. The forecast sensitivity to observation impact (FSOI) metric, computed across all four synoptic times over the CONUS, reveals that the GXS had the largest impact on the 24-h forecast error of the assimilated hyperspectral infrared satellite radiances as measured using a moist energy error norm. Based on this analysis, the proposed GXS has the potential to improve numerical weather prediction globally and over the CONUS. Significance Statement The purpose of this study is to understand the impact of the proposed geostationary hyperspectral infrared sounder as part of the Geostationary Extended Observations (GeoXO) program on numerical weather prediction. The evaluation was done using a simulated environment, and showed a beneficial impact on the tropical mass and wind fields and an error reduction in the initial 24–48 h forecasts. Over the contiguous United States, the GeoXO Sounder (GXS) performed well and had the largest impact of the assimilated infrared satellite radiances on the 24 h forecast as measured by a moist energy error norm. Based on the results of this study, the proposed GXS has the potential to improve numerical weather prediction.

Brief Report of Possible Anticipation of Earthquakes from Geomagnetic Studies Water Level and Atmospheric Pressure in the South Atlantic

This paper presents a brief summary of studies carried out in the southwestern Atlantic, based on the analysis of disturbances in the earth’s Geomagnetic Field (GMF), Water Level (WL) and sea-level Atmospheric Pressure (AP) associated with seismic events. GMF records from observatories of the International Real-time Magnetic Observatory Network (INTERMAGNET), WL records from tide stations of the Intergovernmental Oceanographic Commission (IOC), and hourly AP records from databases of the Global Modeling and Assimilation Office (GMAO) of the National Aeronautics and Space Administration (NASA) have been analyzed in the area under study. A comparative table is presented between the frequencies of the GMF wavelet transform analysis, filtered WL records and variation in the AP grids at sea level before and after the earthquakes. It is possible to observe different degrees of approximation in each of the variables regarding the occurrence of earthquakes. For example, in the case of the CMF, some high-intensity seismic events show anomalous frequencies some hours before the earthquake. On the other hand, the WL filtered by astronomical tide, account for the seismic event along the seacoast with a correspondence between wave amplitude and distance to the nearest ports. AP grids account for a dipole type configuration in the distribution of the pressure difference around the earthquake. On the other hand, the time series of atmospheric pressure at ocean level show a local minimum in a range of hours around the occurrence of the event.

Airborne Lidar Measurements of XCO2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations.

Frontal boundaries have been shown to cause large changes in CO2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole-fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting--Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically-active weather systems.

Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia

Validated dust and aerosol datasets provide valuable information for dust and other atmospheric research. A reanalysis product called ‘MERRA-2’ developed by NASA's Global Modeling and Assimilation Office (GMAO) in recent years provides long-term, high frequency, global coverage aerosol simulations, in terms of AOD (aerosol optical depth) and dust AOD (DAOD). The AOD assimilation system including AERONET (Aerosol Robotic Network) as constraints has led to difficulties with independent validation of MERRA-2 AOD and DAOD. The existing validation and evaluation of MERRA-2 tend to focus on central inland Australia, and comparison of DAOD with MACC (Monitoring Atmospheric Composition and Climate) is insufficient to indicate which one is more appropriate for dust research in Australia. In this study, MERRA-2 output was compared with MODIS DeepBlue (MODIS-DB) (2002–2020) and AERONET (1998–2020) AOD and DAOD in Australia. The expected error (EE) for MERRA-2 AOD was estimated to be ±(0.03+0.15τA) containing 66% of data points. MERRA-2 is less well correlated with AERONET in terms of DAOD, but with a tighter distribution of data points (78%) within the EE. The EE for MODIS-DB AOD was found to be the same and the correlation between MODIS-DB DAOD and AERONET DAOD is stronger than that between MERRA-2 DAOD and AERONET DAOD. Comparison of MERRA-2 and MODIS-DB AOD showed that MERRA-2 AOD was 22.5% higher than MODIS-DB AOD at AERONET sites on average. MERRA-2 AOD was generally lower than MODIS-DB AOD away from AERONET sites when AOD >0.1, and the spatial distribution of the difference between the two is consistent with the spatial variation in AOD. Large differences between MODIS-DB AOD and MERRA-2 occurred in areas where AOD is greater than 0.3. AOD and DAOD based on MODIS-DB could be used for areas away from AERONET sites in Australia, as they are less dependent on AERONET data compared to MERRA-2. When compared to MODIS brightness temperature difference (BTD) for two known severe storm events in Australia, MODIS-DB product is better than MERRA-2 in terms of detecting the spatial extent of dust storms and estimating the storm intensity. MERRA-2 would underestimate the magnitude and spatial extent of AOD, especially for thick dust plumes, and AERONET was likely to have misclassified dust as cloud.

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0.

The Goddard Earth Observing System composition forecast (GEOS‐CF) system is a high‐resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS‐CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space‐based and in‐situ observations. GEOS‐CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS‐Chem chemistry module to provide hindcasts and 5‐days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS‐CF is identical to the offline GEOS‐Chem model and readily benefits from the innovations provided by the GEOS‐Chem community. Evaluation of GEOS‐CF against satellite, ozonesonde and surface observations for years 2018–2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of −0.1 to 0.3, normalized root mean square errors between 0.1–0.4, and correlations between 0.3–0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS‐CF v1.0 generally overestimates aerosols by 20%–50% due to known issues in GEOS‐Chem v12.0.1 that have been addressed in later versions. The 5‐days forecasts have skill scores comparable to the 1‐day hindcast. Model skills can be improved significantly by applying a bias‐correction to the surface model output using a machine‐learning approach.

SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data

The Soil Moisture Active Mission (SMAP) baseline V-pol single channel algorithm (SCA-V) retrieves soil moisture (SM) based on the 2000–2010 Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) climatology. This parameterization scheme might affect SM retrievals in the context of vegetation disturbances, e.g., as a result of drought. By referencing the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Data Assimilation System (GLDAS), the Soil Moisture and Ocean Salinity (SMOS) L3 (SMOS-L3) and the SMOS-IC SM datasets, this study investigated the effects of SMAP vegetation parameterization on SMAP–reference SM differences in global land areas that are subject to differing vegetation disturbances. The results show that SMAP might underestimate SM by ~0.007 m3·m−3 for a relative NDVI decrease of 10%. The underestimation might be primarily caused by low biases in the Global Modeling and Assimilation Office (GMAO) surface temperature. Although using NDVI climatology might cause an overestimation of SM (due to an overestimation of vegetation effects and an underestimation of surface roughness effects), the concurrent underestimation of the GMAO surface temperature might cause an even larger underestimation of SM. The vegetation biases, particularly the surface temperature biases, should be considered for SMAP SM retrieval. These results have implications for future updates of SMAP SCA-V and applications of time series SMAP SM data for hydroecological studies, especially in the future, which is projected to have strong and long-lasting droughts.

The application of elevation corrected MERRA2 reanalysis ground surface temperature in a permafrost model on the Qinghai-Tibet Plateau

There is an urgent need to evaluate the accuracy of reanalysis datasets to improve the reliability of climate research, hydrology, ecology, engineering work, and so on. The ground surface temperature (GST) is also regarded as a significant upper boundary condition when simulating permafrost distribution. In this study, the newest Modern-Era Retrospective Analysis for the Research and Applications Version 2 (MERRA2) reanalysis GST from the National Aeronautics and Space Administration's (NASA) Global Modeling and Assimilation Office (GMAO) is compared with GST data from 69 meteorological stations over the Qinghai-Tibet Plateau (QTP). Then, elevation correction of the MERRA2 GST was performed for different altitudes on seasonal scales. The correction indicates that the accuracy of raw MERRA2 GST data has been greatly improved after the elevation calibration. The decreased proportions of root mean square error (RMSE) for calibrated GST in winter, autumn, summer, and spring are 88.4, 92.8, 95.7, and 92.8%, respectively. The improvements in the mean bias error (MBE) in winter, autumn, summer, and spring are 88.4, 92.8, 95.7, and 92.8%, respectively. Most of the QTP presents a statistically increasing tendency of 0.2 to 0.5 °C/10a for GST at a significance level of 0.01 from 1980 to 2013. Then, the elevation corrected MERRA2 GST was applied in the surface frost number semi-physical permafrost model. The simulated results show that the permafrost area on the QTP is approximately 1.02 × 106 km2 in the early 21st century, excluding lakes and glaciers. The permafrost degradation was characterized by the disappearance of permafrost which mainly occurred in the eastern QTP.

Comparison of GTO-ECV and adjusted MERRA-2 total ozone columns from the last 2 decades and assessment of interannual variability

Abstract. In this study we compare the satellite-based Global Ozone Monitoring Experiment (GOME)-type Total Ozone Essential Climate Variable (GTO-ECV) record, generated as part of the European Space Agency's Climate Change Initiative (ESA-CCI) ozone project, with the adjusted total ozone product from the Modern Era Retrospective Analysis for Research and Applications version 2 (adjusted MERRA-2) reanalysis, produced at the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO). Total ozone columns and associated standard deviations show a very good agreement in terms of both spatial and temporal patterns during their 23-year overlap period from July 1995 to December 2018. The mean difference between adjusted MERRA-2 and GTO-ECV 5∘×5∘ monthly mean total ozone columns is -0.9±1.5 %. A small discontinuity in the deviations is detected in October 2004, when data from the Ozone Monitoring Instrument (OMI) were ingested in the GTO-ECV and adjusted MERRA-2 data records. This induces a small overall negative drift in the differences for almost all latitude bands, which, however, does not exceed 1 % per decade. The mean difference for the period prior to October 2004 is -0.5±1.7 %, whereas the difference is -1.0±1.1 % for the period from October 2004 to December 2018. The variability in the differences is considerably reduced in the period after 2004 due to a significant increase in data coverage and sampling. In the tropical region, the differences indicate a slight zonal variability with negative deviations over the Atlantic, Africa, and the Indian Ocean and positive deviations over the Pacific. Ozone anomalies and the distribution of their statistical moments indicate a very high correlation among both data records as to the temporal and spatial structures. Furthermore, we evaluate the consistency of the data sets by means of an empirical orthogonal function (EOF) analysis. The interannual variability is assessed in the tropics, and both GTO-ECV and adjusted MERRA-2 exhibit a remarkable agreement with respect to the derived patterns. The first four EOFs can be attributed to different modes of interannual climate variability, and correlations with the Quasi-Biennial Oscillation (QBO), the El Niño–Southern Oscillation (ENSO) signal, and the solar cycle were found.

GEOS-S2S Version 2: The GMAO High Resolution Coupled Model and Assimilation System for Seasonal Prediction.

The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Sub-seasonal to Seasonal prediction (S2S) system, GEOS-S2S-2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS-S2S-1. The GEOS-S2S-2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along-track Absolute Dynamic Topography. The forecast skill on subseasonal scales showed a much-improved prediction of the Madden-Julian Oscillation in GEOS-S2S-2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS-S2S-1 in the central Pacific. GEOS-S2S-2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS-S2S-1, and showed substantially reduced root mean square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS-S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state of the art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as to evaluate additional sources of predictability in the earth system through the expanded coupling of the earth system model and assimilation components.

A new method (M<sup>3</sup>Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

Abstract. We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database, which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8 h average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly spaced observations onto a fine-resolution grid by using integrated nested Laplace approximations and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of eight world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias corrected within 2∘ of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean, which is biased high in most regions of the world.

Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology.

Global simulations of atmospheric chemistry are commonly conducted with off-line chemical transport models (CTMs) driven by archived meteorological data from general circulation models (GCMs). The off-line approach has advantages of simplicity and expediency, but incurs errors due to temporal averaging in the meteorological archive and the inability to reproduce the GCM transport algorithms exactly. The CTM simulation is also often conducted at coarser grid resolution than the parent GCM. Here we investigate this cascade of CTM errors by using 222Rn-210Pb-7Be chemical tracer simulations offline in the GEOS-Chem CTM at rectilinear 0.25° ×0.3125° (≈25 km) and 2° ×2.5° (≈200 km) resolutions, and on-line in the parent GEOS-5 GCM at cubed-sphere c360 (≈25 km) and c48 (≈200 km) horizontal resolutions. The c360 GEOS-5 GCM meteorological archive, updated every 3 hours and remapped to 0.25° ×0.3125°, is the standard operational product generated by the NASA Global Modeling and Assimilation Office (GMAO) and used as input by GEOS-Chem. We find that the GEOS-Chem 222Rn simulation at native 0.25° ×0.3125° resolution is affected by vertical transport errors of up to 20% relative to the GEOS-5 c360 on-line simulation, in part due to loss of transient organized vertical motions in the GCM (resolved convection) that are temporally averaged out in the 3-hour meteorological archive. There is also significant error caused by operational remapping of the meteorological archive from cubed-sphere to rectilinear grid. Decreasing the GEOS-Chem resolution from 0.25°×0.3125° to 2°×2.5° induces further weakening of vertical transport as transient vertical motions are averaged out spatially as well as temporally. The resulting 222Rn concentrations simulated by the coarse-resolution GEOS-Chem are overestimated by up to 40% in surface air relative to the on-line c360 simulations, and underestimated by up to 40% in the upper troposphere, while the tropospheric lifetimes of 210Pb and 7Be against aerosol deposition are affected by 5-10%. The lost vertical transport in the coarse-resolution GEOS-Chem simulation can be partly restored by re-computing the convective mass fluxes at the appropriate resolution to replace the archived convective mass fluxes, and by correcting for bias 20 in spatial averaging of boundary layer mixing depths.

Evaluation of RapidScat Ocean Vector Winds for Data Assimilation and Reanalysis.

The RapidScat scatterometer was built as a low cost follow-on to the QuikSCAT mission. It flew on the International Space Station (ISS) and provided data from 3 October 2014 to 20 August 2016 and provided surface wind vectors retrieved from surface roughness estimates taken at multiple azimuth angles. These measurements were unique to the historical scatterometer record in that the ISS flies in a low inclination, non-sun-synchronous orbit. Scatterometry-derived wind vectors have been routinely assimilated in both forward processing and reanalysis systems run at the Global Modeling and Assimilation Office (GMAO). As the RapidScat retrievals were made available in near-real-time, they were assimilated in the forward processing system, and the methods to assimilate and evaluate these retrievals are described. Time series of data statistics are presented first for the near-real-time data assimilated in GMAO forward processing. Second, the full data products provided by the RapidScat team are compared passively to the MERRA-2 reanalysis. Both sets of results show that the root mean squared (RMS) difference of the observations and the GMAO model background fields increased over the course of the data record. Furthermore, the observations and the backgrounds are shown to be biased for both the zonal and meridional wind components. The retrievals are shown to have had a net forecast error reduction via the forecast sensitivity observation impact (FSOI) metric, which is a quantification of 24 hour forecast error reduction, though the impact became neutral as the signal to noise ratio of the instrument decreased over its lifespan.

Chemical Mechanisms and Their Applications in the Goddard Earth Observing System (GEOS) Earth System Model.

NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM), and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near‐real‐time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This paper describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)‐based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided.

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system, and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams, and converged to a single near-real time stream in mid 2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

Evaluation of the Ozone Fields in NASA's MERRA-2 Reanalysis.

We describe and assess the quality of the assimilated ozone product from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) produced at NASA's Global Modeling and Assimilation Office (GMAO) spanning the time period from 1980 to present. MERRA-2 assimilates partial column ozone retrievals from a series of Solar Backscatter Ultraviolet (SBUV) radiometers on NASA and NOAA spacecraft between January 1980 and September 2004; starting in October 2004 retrieved ozone profiles from the Microwave Limb Sounder (MLS) and total column ozone from the Ozone Monitoring Instrument on NASA's EOS Aura satellite are assimilated. We compare the MERRA-2 ozone with independent satellite and ozonesonde data focusing on the representation of the spatial and temporal variability of stratospheric and upper tropospheric ozone and on implications of the change in the observing system from SBUV to EOS Aura. The comparisons show agreement within 10 % (standard deviation of the difference) between MERRA-2 profiles and independent satellite data in most of the stratosphere. The agreement improves after 2004 when EOS Aura data are assimilated. The standard deviation of the differences between the lower stratospheric and upper tropospheric MERRA-2 ozone and ozonesondes is 11.2 % and 24.5 %, respectively, with correlations of 0.8 and above, indicative of a realistic representation of the near-tropopause ozone variability in MERRA-2. The agreement improves significantly in the EOS Aura period, however MERRA-2 is biased low in the upper troposphere with respect to the ozonesondes. Caution is recommended when using MERRA-2 ozone for decadal changes and trend studies.

The role of DYNAMO in situ observations in improving NASA CERES‐like daily surface and atmospheric radiative flux estimates

AbstractThe daily surface and atmospheric radiative fluxes from NASA Clouds and the Earth's Radiant Energy System (CERES) Synoptic 1 degree (SYN1deg) Ed3A are among the most widely used data to study cloud‐radiative feedback. The CERES SYN1deg data are based on Fu‐Liou radiative transfer computations that use specific humidity (Q) and air temperature (T) from NASA Global Modeling and Assimilation Office (GMAO) reanalyses as inputs and are therefore subject to the quality of those fields. This study uses in situ Q and T observations collected during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign to augment the input stream used in the NASA GMAO reanalysis and assess the impact on the CERES daily surface and atmospheric longwave estimates. The results show that the assimilation of DYNAMO observations considerably improves the vertical profiles of analyzed Q and T over and near DYNAMO stations by moistening and warming the lower troposphere and upper troposphere and drying and cooling the mid‐upper troposphere. As a result of these changes in Q and T, the computed CERES daily surface downward longwave flux increases by about 5 W m−2, due mainly to the warming and moistening in the lower troposphere; the computed daily top‐of‐atmosphere (TOA) outgoing longwave radiation increases by 2–3 W m−2 during dry periods only. Correspondingly, the estimated local atmospheric longwave radiative cooling enhances by about 5 W m−2 (7–8 W m−2) during wet (dry) periods. These changes reduce the bias in the CERES SYN1deg‐like daily longwave estimates at both the TOA and surface and represent an improvement over the DYNAMO region.