Research Testbed Research Articles

Report on the 16th Round of NII Testbeds and Community for Information Access Research (NTCIR-16)

This is a report on the NTCIR-16 conference held online in June 2022. NTCIR is a series of parallel and collective evaluation efforts designed to enhance research on diverse information access technologies, including, but not limited to, cross-language and multimedia information access, question-answering, text mining, and summarization. 53 active research groups from 20 countries/regions participated in one or more of the 10 different tasks in NTCIR-16. This report introduces the highlights of the conference and describes the scope and task designs of 10 tasks organized at NTCIR-16. Date: 14--17 June, 2022. Website: https://research.nii.ac.jp/ntcir/ntcir-16/.

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.

A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case

Abstract. Satellite visible radiance data that contain rich cloud and precipitation information are increasingly assimilated to improve the forecasts of numerical weather prediction models. This study evaluates the Data Assimilation Research Testbed (DART, Manhattan release v9.8.0), coupled with the Weather Research and Forecasting (WRF) model (ARW v4.1.1) and the Radiative Transfer for TOVS (RTTOV, v12.3) package, for assimilating the simulated visible imagery of the FY-4A geostationary satellite located over Asia in an Observing System Simulation Experiment (OSSE) framework. The OSSE was performed for the tropical storm Higos that occurred in 2020 and contains multi-layer mixed-phase cloud and precipitation processes. The advantages and limitations of DART for assimilating FY-4A visible imagery were evaluated. Both single-observation experiments and cycled data assimilation (DA) experiments were performed to study the impact of different filter algorithms available in DART, variables being cycled, observation outlier thresholds, observation errors, and observation thinning. The results show that assimilating visible radiance data significantly improves the analysis of the cloud water path (CWP) and cloud coverage (CFC) from first-guess forecasts. The rank histogram filter (RHF) allows WRF to more accurately simulate CWP and CFC compared with the ensemble adjustment Kalman filter (EAKF) although it took roughly twice as long as the latter. By cycling both cloud and non-cloud variables, specifying large outlier threshold values, or setting smaller observation errors without thinning of observations, WRF achieved a better simulation of CWP and CFC. With model integration, DA of the visible radiance data also generated a slightly positive impact on non-cloud variables as they were adjusted through the model dynamics and physics related to cloud processes. In addition, the DA improved the representation of precipitation. However, the impact on the rain rate is limited by the inabilities of the DA to improve cloud vertical structures and cloud phases. A negative impact of the DA on cloud variables was found due to the nature of the non-linear forward operator and the non-Gaussian distribution of the prior. Future works should explore faster and more accurate forward operators suitable for assimilating FY-4A visible imagery, techniques to reduce the non-linear and non-Gaussian errors, and methods to correct the location errors which correspond to the clouds underestimated by the first guess.

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.

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data analytics used to be mainly performed on cloud infrastructures, the rapid development of IoT infrastructures and the requirements for low-latency, secure processing has motivated the development of edge analytics. Today, to balance various trade-offs, ML-based analytics tends to increasingly leverage an interconnected ecosystem that allows complex applications to be executed on hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC systems in what is called the Computing Continuum, the Digital Continuum, or the Transcontinuum.Enabling learning-based analytics on such complex infrastructures is challenging. The large scale and optimized deployment of learning-based workflows across the Edge-to-Cloud Continuum requires extensive and reproducible experimental analysis of the application execution on representative testbeds. This is necessary to help understand the performance trade-offs that result from combining a variety of learning paradigms and supportive frameworks. A thorough experimental analysis requires the assessment of the impact of multiple factors, such as: model accuracy, training time, network overhead, energy consumption, processing latency, among others.This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. It describes the main learning paradigms enabling learning-based analytics on the Edge-to-Cloud Continuum. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed. Furthermore, we analyze how the selected systems provide support for experiment reproducibility. We conclude our review with a detailed discussion of relevant open research challenges and of future directions in this domain such as: holistic understanding of performance; performance optimization of applications; efficient deployment of Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures; and reproducible analysis of experiments on the Computing Continuum.

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.

NTCIR-16: NII Testbeds and Community for Information access Research

NTCIR-16: NII Testbeds and Community for Information access Research

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.

DELPHIN2: AN OVER ACTUATED AUTONOMOUS UNDERWATER VEHICLE FOR MANOEUVRING RESEARCH


 
 
 Delphin2 is a hover capable torpedo style Autonomous Underwater Vehicle (AUV), developed at the University of Southampton to provide a test bed for research in marine robotics, primarily to enhance the manoeuvring capability of AUVs. This paper describes the mechanical design of the vehicle and its software architecture. The performance of the vehicle is presented as well as preliminary findings from the vehicle’s first fully autonomous video survey missions in Lough Erne, Northern Ireland. It is interesting to note that the low-cost of the vehicle and its development using a succession of MEng and PhD students has provided an excellent training environment for specialists in the growing area of marine autonomous vehicles.
 
 

Developing an integrated platform to enable hardware-in-the-loop for synchronous VR, traffic simulation and sensor interactions

Hardware-in-the-loop (HIL) is a testing paradigm where physical sensors (e.g., monitoring sensors such as cameras and proximity sensors, and alarm sensors such as screaming traffic cones) are connected to a virtual test system that simulates reality (e.g., virtual work zone with simulated dangerous situations). This paradigm is well-suited for conducting user studies for work zone safety because virtual test systems can be implemented using virtual reality (VR), which allows for safe and realistic testing of a sensing system without putting workers in danger along with avoiding high upfront costs needed to generate physical research testbeds. However, when recreating physical work zones in VR, researchers face various challenges while representing traffic patterns in VR, such as the lack of bi-directional communication between traffic simulation platforms and user behaviors in VR, hardware compatibility and integration issues, and customization inflexibility during implementation. Researchers, who need to develop such platforms for research studies that involve high-risk exposure to participants, are in need of evaluating the options available for bringing together the components of such platforms. This study provides an overview of alternative ways to integrate components of platforms that enable hardware-in-the-loop for synchronous VR, traffic simulation, and sensor interactions to position researchers to make decisions based on the pros and cons of each alternative. This paper also presents the implementation of such an integrated platform that allows a two-way interface between traffic simulation and VR environments for work zone safety analysis. Outcomes of this work will lay out the steps in implementing the integrated and immersive platform to be used in work zone safety studies based on the guidance presented.

AI World Cup: Robot-Soccer-Based Competitions

Games have been used as excellent testbeds for research on artificial intelligence (AI) and computational intelligence for their diversity and complexity. In this article, we present AI World Cup, a set of AI competitions based on the game of soccer. We provide an introduction to the three challenges that concern a robot soccer match using both value-based and image-based state representations. AI Soccer runs the robot soccer match by participants managing each team of five two-wheeled robots. AI Commentator and AI Reporter observe the AI Soccer match and output real-time commentary and a summary article, respectively. Also, we introduce the AI World Cup platform along with rationale behind notable design choices. The official international AI World Cups held in 2018 and 2019 and the AI Masters competition held in 2019 as a part of the World Cyber Games are briefly discussed. Technical aspects of the strategies developed by participants are also discussed.

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.

Assessing the Impact of Non-Conventional Radar and Surface Observations on High-Resolution Analyses and Forecasts of a Severe Hailstorm

A 2009 National Research Council study recommended that new mesoscale observing networks be integrated with existing networks to form a nationwide “network of networks”. The report also recommended that research testbeds be established, such as the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) DFW Testbed, to ascertain the potential benefit of proposed observing systems. In this work, we use various conventional and non-conventional observing systems from the DFW Testbed in a series of observing system experiments (OSEs). Of special interest are radar data from Terminal Doppler Weather Radars and CASA X-band radars, as well as novel surface observations. The Advanced Regional Prediction System (ARPS) model is used to perform OSEs that are designed to assess the impact of these observing systems. A three-dimensional variational analysis system and companion complex cloud analysis are used to produce analysis increments, which are assimilated in ARPS using Incremental Analysis Updating. Experiments are performed on a supercell thunderstorm case from 11 April 2016 that produced large, damaging hail. The analysis includes quantitative comparisons of model-derived hail with radar-observed hail, along with verification of surface fields. The CASA radial velocity data benefited the forecasted storm structure, as it positively affected subsequent storm morphology and model-derived hail forecasts. Of note in surface observation impacts, the dewpoint measurements from the non-conventional Earth Networks and CWOP networks slightly degrade the forecasted dewpoint field compared to independent standard observations, but did not prevent the successful prediction of hail.

Data Discovery and Reuse in Data Service Practices: A Global Perspective

AbstractThe proposed panel will address the issues of the discovery and reuse of publicly available data on the web in the context of data service practices from a global perspective. Thousands of data discovery services have appeared around the world since the promotion of “open science”, reproducible research, and the FAIR (Findable, Accessible, Interoperable and Reusable) data principles in the research sector. However, there is also increasing demand for transparency of search algorithms, and in the design, development, evaluation, and deployment of current data search services; this requires a better understanding of how users approach data discovery and interact with data in search settings. From a global perspective, we will identify and discuss the specific system design issues in data discovery and reuse, drawing on our organization of the NTCIR (NII Testbeds and Community for Information access Research) project of Data Search track, the design and evaluation of the data discovery service of the Australian Research Data Commons (ARDC), and studies examining researchers' practices of data discovery and reuse.

A human brain test bed for research in large vessel occlusion stroke.

Endovascular removal of emboli causing large vessel occlusion (LVO)-related stroke utilizing suction catheter and/or stent retriever technologies or thrombectomy is a new standard of care. Despite high recanalization rates, 40% of stroke patients still experience poor neurological outcomes as many cases cannot be fully reopened after the first attempt. The development of new endovascular technologies and techniques for mechanical thrombectomy requires more sophisticated testing platforms that overcome the limitations of phantom-based simulators. The authors investigated the use of a hybrid platform for LVO stroke constructed with cadaveric human brains. A test bed for embolic occlusion of cerebrovascular arteries and mechanical thrombectomy was developed with cadaveric human brains, a customized hydraulic system to generate physiological flow rate and pressure, and three types of embolus analogs (elastic, stiff, and fragment-prone) engineered to match mechanically and phenotypically the emboli causing LVO strokes. LVO cases were replicated in the anterior and posterior circulation, and thrombectomy was attempted using suction catheters and/or stent retrievers. The test bed allowed radiation-free visualization of thrombectomy for LVO stroke in real cerebrovascular anatomy and flow conditions by transmural visualization of the intraluminal elements and procedures. The authors were able to successfully replicate 105 LVO cases with 184 passes in 12 brains (51 LVO cases and 82 passes in the anterior circulation, and 54 LVO cases and 102 passes in the posterior circulation). Observed recanalization rates in this model were graded using a Recanalization in LVO (RELVO) scale analogous to other measures of recanalization outcomes in clinical use. The human brain platform introduced and validated here enables the analysis of artery-embolus-device interaction under physiological hemodynamic conditions within the unmodified complexity of the cerebral vasculature inside the human brain.

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.