Data Assimilation Research Testbed Research Articles

Advantages of assimilating multispectral satellite retrievals of atmospheric composition: a demonstration using MOPITT carbon monoxide products

Abstract. The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem+DART) to assimilate different MOPITT carbon monoxide (CO) products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral; near-IR and thermal IR) column product leads to better model–observation agreement at and near the surface than assimilating the MOPITT thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can outperform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral and joint products results in similar or slightly better agreement with observations compared to assimilating the single spectral products separately.

Investigating Arctic Cyclone-Tropopause Polar Vortex Interactions with Idealized Observing System Simulation Experiments

Abstract Arctic cyclones (ACs) are a primary driver of surface weather in the Arctic, contributing to heat and moisture transport and forcing short-term sea ice variability. Still, our understanding of the processes that drive ACs, particularly their large scales and long lifetimes, is limited. ACs are commonly associated with one or more cyclonic tropopause polar vortices (TPVs), potential vorticity (PV) anomalies in the upper troposphere and lower stratosphere that may spur baroclinic development in the surface system, though the exact processes that link the two have yet to be fully explored. In this study, we investigate physical links between TPVs, especially their mesoscale structure and moisture profiles, and ACs with idealized observing system simulation experiments (OSSEs). Starting with a nature run, we simulate different types of dropsonde observations over a TPV during the nascent phase of a nearby AC. The Model for Prediction Across Scales (MPAS) and the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter are then used to run experiments to test the impact of these detailed TPV observations. In addition to a control, five main experiments are conducted, assimilating new observations of temperature and humidity. All experiments reduce forecast errors at the surface and throughout the troposphere. Additional humidity observations alter vertical PV distributions, which in turn impact the development of the AC. Experiments with additional temperature observations exhibit improvements in TPV structure and surrounding PV features and produce stronger surface cyclones with skillful TPV forecasts for up to 36 hours longer than the control.

Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

AbstractThe global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space‐based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space‐based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm‐C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm‐B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE‐GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.

Mesospheric Water Vapor From SABER as a Tracer for the Residual Mean Circulation During SSW Events

AbstractThe sparse wind observations and reanalysis winds in the mesosphere make it challenging to accurately determine the residual mean meridional circulation (MMC). As winds distribute tracers, an alternative approach is to utilize long‐lived trace species such as water vapor (H2O). The recently released H2O data from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) version 2.07, combined with simulations by the Whole Atmosphere Community Climate Model eXtension with specified dynamics (SD‐WACCM‐X) and with the Data Assimilation Research Testbed (WACCMX + DART), provide an opportunity to assess the accuracy of using H2O isopleths to derive the vertical component of the residual MMC during the 2009 sudden stratospheric warming (SSW) event. In winter, the impact of photochemistry and diffusion on the distribution of mesospheric H2O is negligible compared to advective processes. H2O poleward of 70°N accurately captures the anomalous ascent that occurs a few days prior to the onset of the SSW and the subsequent descent. The derived vertical velocity in SD‐WACCM‐X and WACCMX + DART is qualitatively consistent with SABER observations. However, the derivation of vertical motion from isopleth analysis has limitations at the beginning of 2009 when the meridional transport is stronger than the vertical transport. While measuring winds in the mesosphere is challenging, satellite observations of mesospheric H2O prove to be an effective dynamical tracer during time periods characterized by strong vertical coupling.

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations

Abstract. An ensemble of 3D ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble-mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF). The experiment using En-3DEnVar produces a similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting, although there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance, and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.

Global Scale Inversions from MOPITT CO and MODIS AOD

Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors.

Potential impact of all‐sky assimilation of visible and infrared satellite observations compared with radar reflectivity for convective‐scale numerical weather prediction

AbstractAlthough cloud‐affected satellite observations are heavily used for nowcasting applications, their use in regional data assimilation is very limited despite possible benefits for convective‐scale forecasts. In this article, we estimate the potential impact of assimilating cloud‐affected satellite observations of visible (0.6 m) and near thermal infrared wavelengths (6.2 and 7.3 m) relative to the impact of assimilating radar reflectivity observations. We employed observing‐system simulation experiments with a perfect‐model forecast for two cases of strong convective summertime precipitation. Observations are simulated using the radiative transfer model RTTOV/MFASIS and assimilated by the ensemble adjustment Kalman filter in the Data Assimilation Research Testbed. The Weather Research and Forecasting model at 2‐km grid resolution was used for forecasts. Results show that satellite observations can be nearly as beneficial as three‐dimensional radar reflectivity observations. Under favorable conditions, where the prior contains no error in the stage of storm development but only in horizontal position and strength, the assimilation of visible observations leads to 88% of the radar impact. Under more difficult conditions, the impact of visible and infrared observations still reached 50 and 79%, respectively.

Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)

Abstract. To improve the initial condition (“analysis”) for numerical weather prediction, we attempt to assimilate observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the low-Earth-orbiting satellites. The data assimilation system, used in this study, consists of the Data Assimilation Research Testbed (DART) and the Community Earth System Model as the global forecast model. Based on the ensemble Kalman filter scheme, DART supports the radiative transfer model that is used to simulate the satellite radiances from the model state. To make the AMSU-A data available to be assimilated in DART, preprocessing modules are developed, which consist of quality control, spatial thinning, and bias correction processes. In the quality control, two sub-processes are included, outlier test and channel selection, depending on the cloud condition and surface type. The bias correction process is divided into scan-bias correction and air-mass-bias correction. Like input data used in DART, the observation errors are also estimated for the AMSU-A channels. In the trial experiments, a positive analysis impact is obtained by assimilating the AMSU-A observations on top of the DART data assimilation system that already makes use of the conventional measurements. In particular, the analysis errors are significantly reduced in the whole troposphere and lower stratosphere over the Northern Hemisphere. Overall, this study demonstrates a positive impact on the analysis when the AMSU-A observations are assimilated in the DART assimilation system.

Comparison of temperature and wind observations in the Tropics in a perfect‐model, global EnKF data assimilation system

Flow‐dependent errors in tropical analyses and short‐range forecasts are analysed using global observing‐system simulation experiments assimilating only temperature, only winds, and both data types using the ensemble Kalman filter (EnKF) Data Assimilation Research Testbed (DART) and a perfect model framework. The idealised, homogeneous observation network provides profiles of wind and temperature data from the nature run for January 2018 using the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) forced by the observed sea‐surface temperature. The results show that the assimilation of abundant wind observations in a perfect model makes the temperature data in the Tropics largely uninformative. Furthermore, the assimilation of wind data reduces the background errors in specific humidity twice as much as the assimilation of temperature observations. In all experiments, the largest analysis uncertainties and the largest short‐term forecast errors are found in regions of strong vertical and longitudinal gradients in the background wind, especially in the upper troposphere and lower stratosphere over the Indian Ocean and Maritime Continent. The horizontal error correlation scales are on average short throughout the troposphere, just several hundred km. The correlation scales of the wind variables in precipitating regions are half of those in nonprecipitating regions. In precipitating regions, the correlations are elongated vertically, especially for the wind variables. Strong positive cross‐correlations between temperature and specific humidity in the precipitating regions are explained using the Clausius–Clapeyron equation.

Demonstrating the Potential Impacts of Assimilating FY-4A Visible Radiances on Forecasts of Cloud and Precipitation with a Localized Particle Filter

Abstract The Advanced Geostationary Radiation Imager (AGRI) on board the Fengyun-4A (FY-4A) satellite provides visible radiances that contain critical information on clouds and precipitation. In this study, the impact of assimilating FY-4A/AGRI all-sky visible radiances on the simulation of a convective system was evaluated with an observing system simulation experiment (OSSE) using a localized particle filter (PF). The localized PF was implemented into the Data Assimilation Research Testbed (DART) coupled with the Weather Research and Forecasting (WRF) Model. The results of a 2-day data assimilation (DA) experiment generated encouraging outcome at a synoptic scale. Assimilating FY-4A/AGRI visible radiances with the localized PF significantly improved the analysis and forecast of cloud water path (CWP), cloud coverage, rain rate, and rainfall areas. In addition, some positive impacts were produced on the temperature and water vapor mixing ratio in the vicinity of cloudy regions. Sensitivity studies indicated that the best results were achieved by the localized PF configured with a localization distance that is equivalent to the model grid spacing (20 km) and with an adequately short cycling interval (30 min). However, the localized PF could not improve cloud vertical structures and cloud phases due to a lack of related information in the visible radiances. Moreover, the localized PF was compared with the ensemble adjustment Kalman filter (EAKF) and it was indicated that the localized PF outperformed EAKF even when the number of ensemble members was doubled for the latter, indicating a great potential of the localized PF in assimilating visible radiances.

On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model

AbstractIn the construction of an ensemble‐based data assimilation system for a complex fully coupled general circulation model (CGCM), the model state errors at initial time of assimilation have an important influence on assimilation quality. In this study, with the Community Earth System Model (CESM) and Data Assimilation Research Testbed (DART), we found that the influence of initial states errors persists throughout a vicious cycle and cannot be automatically remedied via consequent assimilations. As such, two strategies were applied to alleviate the initial state errors, and a reliable assimilation system was developed. Data assimilation experiments using oceanic observations were conducted over the period from 2005 to 2014 to investigate the impact of these different strategies. The evaluation revealed that the assimilation of observation‐derived climatological data is an effective approach to reduce initial state errors and preserve the balance between different variables to the largest extent, which significantly improved the performance of the assimilation system in the investigated time period. It was further found that the developed assimilation system can produce high‐quality oceanic analysis results comparable to the ECDA and GODAS, two widely used reanalysis products. Perspectives toward further improvement of coupled data assimilation are also outlined.

An OSSE Study of the Impact of Micropulse Differential Absorption Lidar (MPD) Water Vapor Profiles on Convective Weather Forecasting

Abstract The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor micropulse differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. MPD is designed to provide continuous vertical water vapor profiles with high vertical (150 m) and temporal resolution (5 min) in the lower troposphere. This study aims to investigate MPD observation impacts and the scientific significance of MPDs for convective weather analyses and predictions using observation system simulation experiments (OSSEs). In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to conduct OSSEs for a case study of a mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment. A poor-performing control simulation that was drawn from a 40-member ensemble at 3-km resolution is markedly improved by assimilation of simulated observations drawn from a more skillful simulation that served as the nature run at 1-km resolution. In particular, assimilating surface observations corrected surface warm front structure errors, while MPD observations remedied errors in low- to midlevel moisture ahead of the MCS. Collectively, these analyses changes led to markedly improved short-term predictions of convection initiation, evolution, and precipitation of the MCS in the simulations on 15 July 2015. For this case study, the OSSE results indicate that a more dense MPD network results in better prediction performance for convective precipitation while degrading light precipitation prediction performance due to an imbalance of the analysis at large scales.

Evaluation of Polar Winter Mesopause Wind in WACCMX+DART.

This work evaluates zonal winds in both hemispheres near the polar winter mesopause in the Whole Atmosphere Community Climate Model (WACCM) with thermosphere‐ionosphere eXtension combined with data assimilation using the Data Assimilation Research Testbed (DART) (WACCMX+DART). We compare 14 years (2006–2019) of WACCMX+DART zonal mean zonal winds near 90 km to zonal mean zonal winds derived from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) geopotential height measurements during Arctic mid‐winter. 10 years (2008–2017) of WACCMX+DART zonal mean zonal winds are compared to SABER in the Antarctic mid‐winter. It is well known that WACCM, and WACCM‐X, zonal winds at the polar winter mesopause exhibit a strong easterly (westward) bias. One explanation for this is that the models omit higher order gravity waves (GWs), and thus the eastward drag caused by these GWs. We show for the first time that the model winds near the polar winter mesopause are in closer agreement with SABER observations when the winds near the stratopause are weak or reversed. The model and observed mesosphere and lower thermosphere winds agree most during dynamically disturbed times often associated with minor or major sudden stratospheric warming events. Results show that the deceleration of the stratospheric and mesospheric polar night jet allows enough eastward GWs to propagate into the mesosphere, driving eastward zonal winds that are in agreement with the observations. Thus, in both hemispheres, the winter polar night jet speed and direction near the stratopause may be a useful proxy for model fidelity in the polar winter upper mesosphere.

Multivariate ensemble sensitivity analysis applied for an extreme rainfall over Indian subcontinent

Ensemble sensitivity analysis (ESA) uses sample statistics of ensemble forecasts to estimate relationships between forecast metrics and initial conditions. The ensemble sensitivity analysis is often considered as a simple univariate regression as it includes an approximation of analysis covariance matrix with corresponding diagonal elements. In this work, univariate ensemble sensitivity is extended to multivariate ensemble sensitivity that incorporates the contribution from the full covariance matrix in the sensitivity calculations. The performance of multivariate ensemble sensitivity over univariate is examined for meso- and convective scale ensemble forecasts of a heavy rainfall event that happened over the Chennai city in India in December 2015. The ensemble forecasts and analyses are generated using the Advanced Research - Weather Research and Forecasting (WRF) model Data Assimilation Research Testbed (DART) based Ensemble Kalman Filter (EnKF). Multivariate ensemble sensitivity shows organized sensitivity patterns, while the sensitivity values are found to be broadly distributed in univariate ensemble sensitivity. Both the methods are validated using a perturbed initial condition approach, and the results indicate that the multivariate ensemble sensitivity method is effective in predicting the forecast response closest to the actual model response compared to the univariate ensemble sensitivity. The impact of model error on sensitivity calculations is examined by generating a new set of ensembles that uses the Stochastic Kinetic Energy Backscatter Scheme (SKEBS). In the presence of added model error, the forecast response estimated by multivariate using SKEBS ensembles compares better with the actual response. It is found that the performance of the multivariate approach depends on the optimal choice of localization radius, and if insufficient localization is applied, the spurious long-distance correlation contaminates the performance of the multivariate ensemble sensitivity method. The impact of various forecast lead times on the univariate and multivariate ensemble sensitivity analysis indicates that responses using multivariate ensemble sensitivity are more accurate than univariate, especially at longer lead times when nonlinearity becomes significant. The performance of univariate and multivariate methods in convection-permitting scale is examined by using the high-resolution ensemble forecasts, and it is found that the multivariate sensitivity with localization substantially improves the estimates in finer scales.

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.

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.

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.