Data Assimilation Research Research Articles

Comparison of Perturbation Strategies for the Initial Ensemble in Ocean Data Assimilation with a Fully Coupled Earth System Model

It is widely recognized that the initial ensemble describes the uncertainty of the variables and, thus, affects the performance of ensemble-based assimilation techniques, which is investigated in this paper with experiments using the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. Five perturbation strategies involving adding noises of different patterns and with/without extra integration are compared in the observation system simulation experiments framework, in which the SST is assimilated with the ensemble adjustment Kalman filter method. The comparison results show that for the observed variables (sea surface temperature), the differences in the initial ensemble lead to different rate of convergence in the assimilation, but all experiments reach convergence after three months. However, other variables (sea surface height and sea surface salinity) are more sensitive to the initial ensemble. The analysis of variance results reveal that the white-noise perturbation scheme has the largest RMSE. After excluding the effect of the white noise perturbation scheme, it can be found that the difference in the effect of different initial ensembles on the SSH with only assimilated SST is concentrated in the region of the Antarctic Circumpolar Current, which is related to the spread of the covariance between the SSH and the SST.

Improving the Thermosphere Ionosphere in a Whole Atmosphere Model by Assimilating GOLD Disk Temperatures

AbstractGlobal‐Scale Observations of Limb and Disk (GOLD) disk measurements of far ultraviolet molecular nitrogen band emissions are used to retrieve column integrated disk temperatures (Tdisk), which are representative of the lower‐and‐middle thermosphere. The present work develops a new approach to assimilate the Tdisk in the whole atmosphere community climate model with thermosphere‐ionosphere eXtension using the data assimilation research testbed ensemble adjustment Kalman filter. Nine days of data, 1–9 November 2018, are assimilated. Analysis state variables such as thermospheric effective temperature (Teff, airglow layer integrated temperature), ratio of atomic oxygen to molecular nitrogen column densities (O/N2), and column electron content are compared with a control simulation that is only constrained up to ∼50 km. It is observed that assimilation of the GOLD Tdisk improves the analysis states when compared with the control simulation. The analysis and model states, particularly, Teff, O/N2, and electron column density (ECD) are compared with their measurement counterparts for a validation of the assimilation. Teff and O/N2 are compared with GOLD Tdisk and O/N2. While, the ECD is compared with ground based total electron content measurements from global navigational satellite system receivers. Root mean square error (RMSE) improvements in Teff and O/N2 are about 10.8% and 22.6%, respectively. The RMSE improvement in analyses ECD is about 10% compared to the control simulation.

An Idealized 1½-Layer Isentropic Model with Convection and Precipitation for Satellite Data Assimilation Research. Part II: Model Derivation

AbstractIn this Part II paper we present a fully consistent analytical derivation of the “dry” isentropic 1½-layer shallow-water model described and used in Part I of this study, with no convection and precipitation. The mathematical derivation presented here is based on a combined asymptotic and slaved Hamiltonian analysis, which is used to resolve an apparent inconsistency arising from the application of a rigid-lid approximation to an isentropic two-layer shallow-water model. Real observations based on radiosonde data are used to justify the scaling assumptions used throughout the paper, as well as in Part I. Eventually, a fully consistent isentropic 1½-layer model emerges from imposing fluid at rest (v1= 0) and zero Montgomery potential (M1= 0) in the upper layer of an isentropic two-layer model.

An Idealized 1½-Layer Isentropic Model with Convection and Precipitation for Satellite Data Assimilation Research. Part I: Model Dynamics

Abstract An isentropic 1½-layer model based on modified shallow-water equations is presented, including terms mimicking convection and precipitation. This model is an updated version of the isopycnal single-layer modified rotating shallow water (modRSW) model. The clearer link between fluid temperature and model variables together with a double-layer structure make this revised, isentropic model a more suitable tool to achieve our future goal: to conduct idealized experiments for investigating satellite data assimilation. The numerical model implementation is verified against an analytical solution for stationary waves in a rotating fluid, based on Shrira’s methodology for the isopycnal case. Recovery of the equivalent isopycnal model is also verified, both analytically and numerically. With convection and precipitation added, we show how complex model dynamics can be achieved exploiting rotation and relaxation to a meridional jet in a periodic domain. This solution represents a useful reference simulation or “truth” in conducting future (satellite) data assimilation experiments, with additional atmospheric conditions and data. A formal analytical derivation of the isentropic 1½-layer model from an isentropic two-layer model without convection and precipitation is shown in a companion paper (Part II).

CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations

The study of anthropogenic carbon monoxide (CO) emissions is crucial to investigate anthropogenic activities. Assuming the anthropogenic CO emissions accounted for the super majority of the winter CO fluxes in western Europe, they could be roughly estimated by the inversion approach. The CO fluxes and concentrations of four consecutive winter seasons (i.e., December–February) in western Europe since 2017 were estimated by a regional CO flux inversion system based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART). The CO retrievals from the Measurements Of Pollution In The Troposphere instrument (MOPITT) version 8 level 2 multi-spectral Thermal InfraRed (TIR)/Near-InfraRed (NIR) CO retrieval data products were assimilated by the inversion system. The analyses of the MOPITT data used by the inversion system indicated that the mean averaging kernel row sums of the surface level was about 0.25, and the difference percentage of the surface-level retrievals relative to a priori CO-mixing ratios was 14.79%, which was similar to that of the other levels. These results suggested the MOPITT’s surface-level observations contained roughly the same amount of information as the other levels. The inverted CO fluxes of the four winter seasons were 6198.15 kilotons, 4939.72 kilotons, 4697.80 kilotons, and 5456.19 kilotons, respectively. Based on the assumption, the United Nations Framework Convention on Climate Change (UNFCCC) inventories were used to evaluate the accuracy of the inverted CO fluxes. The evaluation results indicated that the differences between the inverted CO fluxes and UNFCCC inventories of the three winter seasons of 2017–2019 were 13.36%, −4.59%, and −4.76%, respectively. Detailed surface-CO concentrations and XCO comparative analyses between the experimental results and the external Community Atmosphere Model with Chemistry (CAM-Chem) results and the MOPITT data were conducted. The comparative analysis results indicated that the experimental results of the winter season of 2017 were obviously affected by high boundary conditions. The CO concentrations results of the experiments were also evaluated by the CO observation data from Integrated Carbon Observation System (ICOS), the average Mean Bias Error (MBE), and the Root Mean Square Error (RMSE) between the CO concentrations results of the inversion system, and the ICOS observations were −22.43 ppb and 57.59 ppb, respectively. The MBE and RMSE of the inversion system were 17.53-ppb and 4.17-ppb better than those of the simulation-only parallel experiments, respectively.

The Impact of Assimilating COSMIC‐2 Observations of Electron Density in WACCMX

AbstractThe present study investigates the impact of assimilating electron density profiles from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2) mission in a whole atmosphere data assimilation system. The observations are assimilated into the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCMX) using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. Assimilation of the COSMIC‐2 electron density profiles during the evaluation time period of 25–30 April 2020 leads to improvement in both the 1 hr forecast and analysis electron densities in WACCMX + DART. Compared to a control experiment that does not assimilate COSMIC‐2 observations, the assimilation of the COSMIC‐2 electron density profiles reduces the 1 hr forecast root mean square error (RMSE) and bias with respect to COSMIC‐2 observations at 300 km by 6.76% and 24.91%, respectively. Assimilation of the COSMIC‐2 electron density profiles does not significantly influence the RMSE and bias with respect to ground‐based Global Navigation Satellite System vertical total electron content observations. The equatorial vertical plasma drift velocity in WACCMX + DART is changed by ±5–10 ms−1 due to the assimilation of the COSMIC‐2 electron density profiles, indicating that the model representation of the electrodynamics of the low latitude ionosphere are significantly impacted by the assimilation of COSMIC‐2 observations.

Impact of Thermospheric Wind Data Assimilation on Ionospheric Electrodynamics Using a Coupled Whole Atmosphere Data Assimilation System

AbstractThe upward plasma drift and equatorial ionization anomaly (EIA) in the Earth's ionosphere are strongly influenced by the zonal electric field, which is generated by the wind dynamo. Specification and forecasting of thermospheric winds thus plays an important role in ionospheric weather prediction. In this study, we assess the impact of assimilating thermospheric wind observations from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument on NASA's Ionospheric CONnection (ICON) explorer satellite on the ionospheric electrodynamics. Empirical localization functions (ELFs) of ICON/MIGHTI zonal and meridional winds are also computed and applied to our data assimilation experiments, enabling improved assimilation of the ICON/MIGHTI wind observations. A set of observing system simulation experiments (OSSEs) are performed using the National Center for Atmospheric Research (NCAR) Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCMX) with data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. The results show that assimilating the ICON/MIGHTI wind observations with the ELF improves the zonal and meridional wind root mean square error (RMSE) by 16%–18% and 7%–10%, respectively. The improved wind specification further improves the low‐latitude E × B vertical drift RMSE by about 18%. The response of electron densities is slower, and the overall impact is smaller. The improvement of the ionosphere F‐region maximum electron density (NmF2) between ±45° is about 8% after 18 days of data assimilation.

SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

Abstract. Recent research in data assimilation has led to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, whereby the covariance matrices are approximated by a parameterized covariance model. In the PKF, the dynamics of the covariance during the forecast step rely on the prediction of the covariance parameters. Hence, the design of the parameter dynamics is crucial, while it can be tedious to do this by hand. This contribution introduces a Python package, SymPKF, able to compute PKF dynamics for univariate statistics and when the covariance model is parameterized from the variance and the local anisotropy of the correlations. The ability of SymPKF to produce the PKF dynamics is shown on a nonlinear diffusive advection (the Burgers equation) over a 1D domain and the linear advection over a 2D domain. The computation of the PKF dynamics is performed at a symbolic level, but an automatic code generator is also introduced to perform numerical simulations. A final multivariate example illustrates the potential of SymPKF to go beyond the univariate case.

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, combined with multiple sources of model error, makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month. The data assimilation (DA) system developed in this paper explores two novel contributions, namely (1) along-the-stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases which often occur in flooding situations. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40 % more accurate (root mean square error) streamflow estimates than regular, Euclidean distance-based localization. An assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along the Pee Dee River in South Carolina, and many other locations in the domain, where inflation is able to avoid filter divergence and, thereby, assimilate significantly more observations.

Assessing the Impact of ICON/MIGHTI Zonal and Meridional Winds on Upper Atmosphere Weather Specification in a Whole Atmosphere Data Assimilation System: An Observing System Simulation Experiment

AbstractThermospheric data assimilation is limited due to the lack of continuous observation of the neutral state. Recently, the thermospheric wind data from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) on NASA's Ionospheric CONnection Explorer (ICON) became available. ICON/MIGHTI provides near‐continuous observations of the mid‐ and low‐latitude thermospheric meridional and zonal winds ICON/MIGHTI observes the thermosphere zonal and meridional winds at low‐middle latitudes between 90 and 300 km during the daytime, and 90–105 and above 210 km during the nighttime. This study assesses the impact of assimilating ICON/MIGHTI winds in the National Center for Atmospheric Research Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCM‐X) + Data Assimilation Research Testbed (DART) on the specification and short‐term forecasting of the thermosphere. Observing system simulation experiments of WACCM‐X + DART with and without assimilating synthetic ICON/MIGHTI meridional and zonal wind profiles are performed. The result shows that this new data set can correct the wind specification throughout the mid‐ and low‐latitude thermosphere, especially around the 90–160 km altitude region. A notable impact is also shown in the region above 300 km altitude, which is above the altitude of ICON/MIGHTI wind observations. The impact of ICON/MIGHTI data on the zonal wind field is larger than on the meridional wind field. The errors of the ensemble mean from the truth of meridional and zonal wind fields in the mid‐and low‐latitude region are reduced by 6% and 12%, respectively, with the help of ICON/MIGHTI wind data.

A new CAM6 + DART reanalysis with surface forcing from CAM6 to other CESM models

An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth System Model (CESM), several million observations per day, and the Data Assimilation Research Testbed (DART). The data saved from this global, sim 1^circ resolution, 80 member ensemble span 2011–2019. They include ensembles of: sub-daily, real world, atmospheric forcing for use by all of the nonatmospheric models of CESM; weekly, CAM6, restart file sets; 6 hourly, prior hindcast estimates of the assimilated observations; 6 hourly, land model, plant growth variables, and 6 hourly, ensemble mean, gridded, atmospheric analyses. This data can be used for hindcast studies and data assimilation using component models of CESM; CAM6, CLM5, CICE5, POP2. MOM6, MOSART, and CISM; and non-CESM Earth system models. This large dataset (~ 120 Tb) has a unique combination of a large ensemble, high frequency, and multiyear time span, which provides opportunities for robust statistical analysis and use as a machine learning training dataset.

Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System.

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite‐derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above‐ground biomass. The adjusted simulation significantly reduced the above‐ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation‐constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters

Ensemble-based Data Assimilation (EDA) has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. To facilitate the management of EDA workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a software framework linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. We verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed from continuous temperature measurements. Both cases yield accurate estimations of the parameters compared to their synthetic truth. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for large-scale inverse modeling using the sequential ES-MDA.

Tidal Variations in the Mesosphere and Lower Thermosphere Before, During, and After the 2009 Sudden Stratospheric Warming

AbstractThis work presents a comprehensive evaluation of tides in the mesosphere and lower thermosphere (MLT) of the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCMX) and data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A total of 26 diurnal and semidiurnal tidal components are calculated using the latest hourly data assimilation cycling during January 12–March 15, 2009 time period. When averaged over this entire yaw cycle, most tidal components display a latitudinal and vertical structures similar to that observed by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). However, WACCMX + DART amplitudes of some diurnal tidal components are a factor of ∼1.2–2 times smaller and the peak altitudes and latitudes are shifted compared to SABER. The amplitudes of semidiurnal tides are likewise underestimated in the model, as well as being shifted in altitude and latitude, and some wavenumbers are not captured. Daily tidal variations are calculated in the model and compared with SABER and Super Dual Auroral Radar Network (SuperDARN) radar observations. Both the model and the radars show an amplification of the migrating semidiurnal tide during the week following the peak SSW. Enhancements in nonmigrating diurnal tides are also shown around the SSW peak, which could be produced by the nonlinear interaction between migrating tides and planetary waves. This study finds that, despite using data assimilation to constrain model dynamics, more work is needed in order to accurately simulate the full tidal spectrum and responses to SSWs in the MLT region.

Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea

Ensemble optimal interpolation (EnOI) is a variant of the ensemble Kalman filter (EnKF) that operates with a static ensemble to drastically reduce its computational cost. The idea is to use a pre-selected ensemble to parameterize the background covariance matrix, which avoids the costly integration of the ensemble members with the dynamical model during the forecast step of the filtering process. To better represent the pronounced time-varying circulation of the Red Sea, we propose a new adaptive EnOI approach in which the ensemble members are adaptively selected at every assimilation cycle from a large dictionary of ocean states describing the Red Sea variability. We implement and test different schemes to select the ensemble members (i) based on the similarity to the forecast state according to some criteria, or (ii) in term of best representation of the forecast in an ensemble subspace using an Orthogonal Matching Pursuit (OMP) algorithm. The relevance of the schemes is first demonstrated with the Lorenz 63 and Lorenz 96 models. Then results of numerical experiments assimilating real remote sensing data into a high resolution MIT general circulation model (MITgcm) of the Red Sea using the Data Assimilation Research Testbed (DART) system are presented and discussed.

Mesoscale Prediction in the Antarctic Using Cycled Ensemble Data Assimilation

AbstractDue in part to sparse conventional observation coverage in the Antarctic region, atmospheric studies in this part of the globe often rely more heavily on numerical models. Model representation of atmospheric processes in the Antarctic remains inferior to representation in the Northern Hemisphere midlatitudes. Poor representation may be related to inaccurate model analyses that do not optimally utilize the limited observation network. Here, the ensemble Kalman filter (EnKF) data assimilation (DA) technique is employed in lieu of variational DA techniques to investigate impacts on model analysis accuracy. This DA technique [provided by the Data Assimilation Research Testbed (DART)] is coupled with a polar-modified, mesoscale numerical model that together compose Antarctic-DART (A-DART). A-DART is cycled with DA and run over a 1-month period, assimilating only conventional observations. Results show relatively good agreement between A-DART and observations. Comparison with radiosonde temperature and geostationary satellite wind observations shows large differences between RMSE and ensemble spread in the upper troposphere. The analysis increment shows large values in the eastern Atlantic–western Indian Oceans associated with geostationary satellite wind observations. Further evaluation determines that geostationary satellite wind observations may be biased in this region. Overall, this baseline demonstration of ensemble-based modeling applied in the Antarctic produced short-term forecasts that were competitive with two operational modeling systems while assimilating on the O(106) fewer observations. A-DART is capable of assimilating additional observations for a variety of applications. This study highlights the capability of applying this ensemble-based DA technique for process and forecast studies in an observation-sparse region.

Impact of GOLD Retrieved Thermospheric Temperatures on a Whole Atmosphere Data Assimilation Model

AbstractThe present investigation evaluates the assimilation of synthetic data which has properties similar to actual Global‐scale Observations of the Limb and Disk (GOLD) level‐2 (L2) temperatures and other conventional lower atmospheric observations. The lower atmospheric and GOLD L2 temperature (Tdisk) are assimilated in the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension using Data Assimilation Research Testbed. It is found that inclusion of the GOLD Tdisk improves the forecast root mean square error (RMSE) and bias by 5% and 71%. When compared to lower atmosphere only assimilation, the improvements in RMSE and bias are 20% and 94%. An investigation of the global diurnal westward‐propagating wavenumber 1 (DW1) and local diurnal tidal characteristics shows that inclusion of the GOLD temperatures improves the DW1 by about 8% and diurnal tide by more than 17%. Larger percentage improvements in the tides are seen in the lower thermosphere. Considerable improvements in the model state are also seen at times and locations where there are no GOLD observations available. These results and the background data assimilation procedure are presented here, which demonstrate that GOLD thermospheric temperature is an excellent data set that can be used for thermospheric assimilation studies and operational purposes.

Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ

Global coupled chemistry-climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea-United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42% in the control run and by 12% with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80% for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O3 datasets and observationally constrained box model simulations of OH and HO2. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29% for CO, 18% for ozone, 11% for HO2, and 27% for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O3, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NO x controls, can improve ozone pollution over East Asia.

Assimilation of OCO-2 retrievals with WRF-Chem/DART: A case study for the Midwestern United States

The Data Assimilation Research Testbed (DART) has been extended to be able to assimilate the column-average dry-air mole fraction of CO2 (XCO2) retrievals from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Atmospheric CO2 concentrations over the Midwestern United States were estimated by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), with and without assimilating OCO-2 retrievals by the extended-DART. To focus on evaluating the effect of the assimilation, the study period was deliberately set to January 2016, the coldest month in the dormant season, to minimize the influence of biogenic CO2 flux. Independent ground-based and flight observations, as well as the CarbonTracker 2017 products (CT2017), were used to evaluate the results of two distinct approaches. Comparing to the estimated CO2 concentration distribution without assimilating the OCO-2 retrievals, the overall root mean square error (RMSE) and mean bias error (MBE) between the results with the assimilation and the observations were averagely reduced by 20.65% and 78.49%, the overall difference in the RMSE and MBE with respect to CT2017 were averagely reduced by 48.29% and 28.61%, respectively. Experiments showed that the assimilation of OCO-2 retrievals by the extended-DART could make the estimated CO2 concentration distribution significantly more consistent with the observations and the CarbonTracker products.

An EnOI‐Based Data Assimilation System With DART for a High‐Resolution Version of the CESM2 Ocean Component

AbstractAn ensemble optimal interpolation (EnOI) data assimilation system for a high‐resolution (0.1° horizontal) version of the Community Earth System Model Version 2 (CESM2) ocean component is presented. For this purpose, a new version of the Data Assimilation Research Testbed (DART Manhattan) that enables large‐state data assimilation by distributing state vector information across multiple processors at high resolution is used. The EnOI scheme uses a static (but seasonally varying) 84‐member ensemble of precomputed perturbations to approximate samples from the forecast error covariance and utilizes a single model integration to estimate the forecast mean. Satellite altimetry and sea surface temperature observations along with in situ temperature and salinity observations are assimilated. This new data assimilation framework is then used to produce a global high‐resolution retrospective analysis for the 2005–2016 period. Not surprisingly, the assimilation is shown to generally improve the time‐mean ocean state estimate relative to an identically forced ocean model simulation where no observations are ingested. However, diminished improvements are found in undersampled regions. Lack of adequate salinity observations in the upper ocean actually results in deterioration of salinity there. The EnOI scheme is found to provide a practical and cost‐effective alternative to the use of an ensemble of forecasts.