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Pulmonary arterial hypertension (PAH) presents a significant challenge to right ventricular (RV) function due to progressive pressure overload, necessitating adaptive remodeling in the form of increased wall thickness, enhanced myocardial contractility and stiffness to maintain cardiac performance. However, the impact of these remodeling mechanisms on RV mechanics in not clearly understood. In addition, there is a lack of quantitative understanding of how each mechanism individually influences RV mechanics. Utilizing experimental data from a rat model of PAH at three distinct time points, we developed biventricular finite element models to investigate how RV stress and strain evolved with PAH progression. The finite element models were fitted to hemodynamic and morphological data to represent different disease stages and used to analyze the impact of RV remodeling as well as the altered RV pressure. Furthermore, we performed a number of theoretical simulation studies with different combinations of morphological and physiological remodeling, to assess and quantify their individual impact on overall RV load and function. Our findings revealed a substantial 4-fold increase in RV stiffness and a transient 2-fold rise in contractility, which returned to baseline by week 12. These changes in RV material properties in addition to the 2-fold increase in wall thickness significantly mitigated the increase in wall stress and strain caused by the progressive increase in RV afterload. Despite the PAH-induced cases showing increased wall stress and strain at end-diastole and end-systole compared to the control, our simulations suggest that without the observed remodeling mechanisms, the increase in stress and strain would have been much more pronounced. Our model analysis also indicated that while changes in the RV's material properties-particularly increased RV stiffness - have a notable effect on its mechanics, the primary compensatory factor limiting the stress and strain increase in the early stages of PAH was the significant increase in wall thickness. These findings underscore the importance of RV remodeling in managing the mechanical burden on the right ventricle due to pressure overload.

In data assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, however, while accurately estimating uncertainty, is computationally expensive and infeasible to run in real-time for complex systems. Here, we present a novel particle filter methodology, the Deep Latent Space Particle filter or D-LSPF, that uses neural network-based surrogate models to overcome this computational challenge. The D-LSPF enables filtering in the low-dimensional latent space obtained using Wasserstein AEs with modified vision transformer layers for dimensionality reduction and transformers for parameterized latent space time stepping. As we demonstrate on three test cases, including leak localization in multi-phase pipe flow and seabed identification for fully nonlinear water waves, the D-LSPF runs orders of magnitude faster than a high-fidelity particle filter and 3-5 times faster than alternative methods while being up to an order of magnitude more accurate. The D-LSPF thus enables real-time data assimilation with uncertainty quantification for the test cases demonstrated in this paper. [ABSTRACT FROM AUTHOR]

This study presents the implementation and evaluation of scale‐dependent localization (SDL) in the hurricane analysis and forecast system 4D Ensemble Variational (4DEnVar) Data Assimilation (DA) system. The SDL capability is compared with the traditional Single‐Scale Localization (SSL) method to assess its benefits and necessity for hurricane prediction. The experiments focus on Hurricane Laura (2020) and involve single observation experiments as well as real observation DA cycling experiments. The results indicate that the SDL experiment, which incorporates the Fast Almost Gaussian Filtering approach for scale decomposition, consistently outperforms the corresponding SSL configurations in almost all aspects. Further diagnostics show that due to its multiscale nature, the SDL approach demonstrates better track prediction over small‐scale SSL due to improved environmental analysis and better analyzed vortex position and structure, and superior intensity prediction during the rapid intensification over both the large‐scale SSL and the small‐scale SSL owing to enhanced inner‐core thermodynamic analysis. Plain Language Summary: This study focuses on improving hurricane forecasting through a simultaneous multiscale data assimilation (DA) technique called scale‐dependent localization (SDL). We compare SDL to the traditional method called Single‐Scale Localization (SSL) using single observation experiments and real observation DA cycling experiments during Hurricane Laura (2020). Our findings show that compared to SSL, SDL significantly outperforms the corresponding SSL in various aspects. It provides more accurate prediction of the hurricane's track by improving the analysis of the storm's surrounding environment and the initial vortex position. Additionally, SDL enhances intensity prediction during the storm's rapid intensification stages by analyzing the inner‐core structure more effectively. Key Points: Scale dependent localization (SDL) is studied for hurricane data assimilation in a convection allowing model for the first timeBenefiting from the small‐scale localization, SDL outperforms single large‐scale localization in the intensity predictionBenefiting from the large‐scale localization, SDL outperforms single small‐scale localization in both the intensity and track prediction [ABSTRACT FROM AUTHOR]

Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitime-scale high-efficiency filtering algorithm which includes a deep-ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously established CDA system within the Coupled Model, version 2.1 (CM2-MSHea-CDA), that is developed by the Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past 4 decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep-ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, El Niño–Southern Oscillation (ENSO), and Pacific decadal oscillation (PDO). Particularly, both CDA systems generate similar time mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations. Significance Statement: A coupled climate model simulates the interactions of the atmosphere, ocean, sea ice, and land processes and derives change and variations of the climate system by combining these components. Due to imperfect numerical schemes, a model usually has systematic biases to the real world. Coupled data assimilation (CDA) combines coupled model dynamics and physics with atmospheric and oceanic observations to reconstruct the historical states of Earth's climate system, forming the analysis of climate change and variability. However, because of the existence of model errors and individual data assimilation algorithm behaviors, currently, the ocean variability of reanalysis produced by different numerical systems is basically divergent. Here, we first apply an identical coupled data assimilation algorithm and effective model error relaxing scheme to two widely used IPCC coupled models to establish two CDA systems. Then, we use both CDA systems to assimilate historical atmospheric and oceanic observations to form two sets of climate reanalyses for the past 4 decades. We found, by some degree, that the results of these multimodel coupled climate reanalyses converged to what historical states ought to be in observational estimation. These convergent climate reanalyses are expected to significantly increase our understanding of CDA and climate assessment. [ABSTRACT FROM AUTHOR]

A workshop at MIT discussed the importance of data assimilation and Earth system reanalysis in improving climate models. The workshop emphasized the need to bring models and observations into consistency to reduce uncertainties in climate projections. Recommendations were made for actions and funding in areas such as exploiting Earth observations, developing infrastructure for data assimilation and machine learning, and performing Earth system reanalysis. The document highlights the significance of using observations to improve models and calls for collaboration between different scientific communities to achieve these goals. The recommendations aim to enhance the quality and usability of climate information. [Extracted from the article]

To improve the representation of all relevant scales in initial conditions for large-domain convection-allowing models, a new multiscale ensemble Kalman filter (MEnKF) algorithm is developed and implemented within the Gridpoint Statistical Interpolation analysis system (GSI) data assimilation framework coupled with the Finite-Volume Cubed-Sphere Dynamical Core (FV3) limited-area model. The algorithm utilizes ensemble background error covariances filtered to match the observations assimilated. This is realized in a sequential manner. 1) When assimilating coarse-resolution observations such as radiosondes, ensemble background perturbations are filtered to remove scales smaller than those the observations can represent, along with relatively large horizontal localization radii to ensure low-noise and balanced analysis increments. 2) The resulting ensemble analyses from the first step then serve as the background to assimilate denser observations such as radar data with smaller localization radii. Several passes can be taken to assimilate all observations. In this paper, vertically increasing horizontal filter scales are used when assimilating rawinsonde and surface observations together, while radar data are assimilated in the second step. The algorithm is evaluated through six convective storm cases during May 2021, with cycled assimilation of either conventional data only or with additional radar reflectivity followed by 24-h ensemble forecasts. Overall, positive impacts of the MEnKF on forecasts are obtained regardless of reflectivity data; its advantage over the single-scale EnKF is most significant in surface humidity and temperature forecasts up to at least 12 h. More accurate hourly precipitation forecasts with MEnKF can last up to 24 h for light rain. Furthermore, MEnKF forecasts higher ensemble probabilities for the observed hazardous events. [ABSTRACT FROM AUTHOR]

In this study, the predictability of ocean wave fields is considered based on marine radar measurement data. Phase-resolved components obtained by applying 3D FFT-based reconstruction to a sequence of radar images are utilized for wave field prediction, and two different prediction approaches are introduced: (i) snapshot data-based prediction through the adjustment of the frequency and phase of each component, and (ii) spatiotemporal data-based prediction through the data assimilation for reconstructed wave fields. Furthermore, the time evolution of a predictable zone is derived for different shapes of measurement domains including rectangular and ring-shaped domains. To validate the proposed wave propagation modeling method, numerical simulations are conducted on synthetic radar images created by reflecting geometrical shadowing effects, and the prediction accuracy is examined in relation to the derived predictable zone. Lastly, the forecasting performance, which is represented by the predictable time range at a radar location, is discussed with respect to the prediction techniques, specifications of the reconstruction domain, and moving measurements. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment's TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon's structure and providing a prediction of the typhoon's intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system. [ABSTRACT FROM AUTHOR]

The Great Arctic Cyclone 2012 (AC12) is used to understand the role of initial condition errors in the predictability at 2–3‐day forecast range of a high‐impact summer Arctic Cyclone (AC). Ensemble sensitivity analysis (ESA) is first performed to identify potentially sensitive regions of the cyclone evolution using an ensemble baseline forecast with conventional in situ observations assimilated. A pseudo‐observation method is then introduced to investigate impacts of hypothetical observations in these sensitive but unobserved regions. In the baseline experiments with in situ observations assimilated, the forecasted AC12 reaches its peak intensity 18 hr earlier than in the verifying Global Forecast System Analysis (GFS‐ANL) and the cyclone track is biased toward the southwest. Using ESA, the time of peak intensity and the cyclone track error are identified to be sensitive to the upstream trough, downstream ridge, and the tropopause polar vortex (TPV) to the northeast (NE TPV) of the AC12. These features were not observed by the in situ observation networks. To examine the impact of the observation gaps, pseudo‐observations drawn from GFS‐ANL are assimilated. Pseudo‐observations sample the three features separately to study the impact of the initial condition error on the predictability of AC12. The cyclone peak intensity timing error and track error are greatly reduced when the initial condition error is reduced near the NE TPV. A southward expansion of the NE TPV and the corresponding southward shifting low‐level front lead the forecasted AC12 to progress to the east, which better agrees with the verifying GFS‐ANL. Plain Language Summary: Accurate prediction of strong Arctic cyclones is critical to the scientific and economical activities in the Arctic. However, the sparse observation networks in the Arctic pose challenges to the data assimilation system to constrain the initial conditions in various features of the Arctic Cyclones. This paper used a case study approach to investigate the impact of initial condition errors in different features on the development of the 2012 Great Arctic Cyclone. Synoptic features that are sensitive to the track and timing of the cyclone were first identified using ensemble sensitivity analysis. Assimilating in situ conventional observations did not sufficiently constrain the initial conditions in the sensitive features. Hypothetical pseudo‐observations from an external analysis system were then designed and assimilated to study how each sensitive feature affects the prediction of the physical processes of cyclone development. The results showed that more observations inside the TPV to the northeast of the cyclone brought the most significant forecast improvements at a 2‐day forecast lead time. Key Points: Ensemble sensitivity analysis reveals track and deepening trend forecast biases of Arctic Cyclone 2012 are sensitive to unobserved synoptic scale featuresPseudo‐observation experiments show the northeast tropopause polar vortex (TPV) is the most impactful feature among sensitive featuresSouthward expansion of the northeast TPV impacts the cyclone evolution by shifting the low‐level front southward [ABSTRACT FROM AUTHOR]

Operational forecasts rely on accurate and timely observations and it is important that the ocean forecasting community demonstrates the impact of those observations to the observing community and its funders while providing feedback on requirements for the design of the ocean observing system. One way in which impact of new observations can be assessed is through Observing System Simulation Experiments (OSSEs). Various satellite missions are being proposed to measure Total Surface Current Velocities (TSCV). This study uses OSSEs to assess the potential impact of assimilating TSCV observations. OSSEs have been performed using two global ocean forecasting systems; the Met Office's (MetO) Forecasting Ocean Assimilation Model and the Mercator Ocean International (MOI) system. Developments to the individual systems, the design of the experiments and results have been described in two companion papers. This paper provides an intercomparison of the OSSEs results from the two systems. We show that global near surface velocity analysis root-mean-squared-errors (RMSE) are reduced by 20-30% and 10-15% in the MetO and MOI systems respectively, we also demonstrate that the percentage of particles forecast to be within 50 km of the true particle locations after drifting for 6 days has increased by 9%/7%. Furthermore, we show that the global subsurface velocities are improved down to 1500m in the MetO system and down to 400m in the MOI system. There are some regions where TSCV assimilation degrades the results, notably themiddle of the gyres in the MetOsystem and at depth in the MOI system. Further tuning of the background and observation error covariances are required to improve performance in these regions. We also provide some recommendations on TSCV observation requirements for future satellite missions. We recommend that at least 80% of the ocean surface is observed in less than 4 to 5 days with a horizontal resolution of 20 to 50 km. Observations should be provided within one day of measurement time to allow real time assimilation and should have an accuracy of 10 cm/s in the along and across track direction and uncertainty estimates should be provided with each measurement. [ABSTRACT FROM AUTHOR]

Convective‐scale ensembles are used routinely in operational centres around the world to produce probabilistic precipitation forecasts, but a lack of spread between members is providing forecasts that are frequently overconfident. This deficiency can be corrected by increasing spread, increasing forecast accuracy, or both. A recent development in the Met Office forecasting system is the inclusion of large‐scale blending (LSB) in the convective‐scale data assimilation scheme. This method aims to reduce the synoptic‐scale forecast error in the analysis by reducing the influence of the convective‐scale data assimilation at scales that are too large to be constrained by the limited domain. These scales are instead initialised using output from the global data assimilation scheme, which we expect to reduce the forecast error and thus improve the spread–skill relationship. In this study, we quantify the impact of LSB on the spread–skill relationship of hourly precipitation accumulations by comparing forecast ensembles with and without LSB over a 17‐day summer trial period. This trial found modest but significant improvements to the spread–skill relationship as calculated using metrics based on the Fractions Skill Score. Skill is improved for a lower precipitation centile by an average of 0.56% at the largest scales, but a corresponding degradation of spread limits the overall correction. The spread–skill disparity is reduced the most in the higher centiles due to a more muted spread response, with significant reductions of up to 0.40% obtained at larger scales. Case‐study analysis using a novel extension of the Localised Fractions Skill Score demonstrates how spread–skill improvements transfer to smaller‐scale features, not just the scales that have been blended. There are promising signs that further spread–skill improvements can be made by implementing LSB more fully within the ensemble, and we encourage the Met Office to continue developing this technique. [ABSTRACT FROM AUTHOR]

Specification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques. [ABSTRACT FROM AUTHOR]

A constellation of microwave sounders named the EUMETSAT Polar System–Sterna (EPS–Sterna) is under study at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), with the aim of complementing the backbone orbits of the global observing system in low Earth orbit. The satellites of this constellation would be similar to the Arctic Weather Satellite (AWS), which is being developed by the European Space Agency (ESA). The microwave sounder on board AWS is equipped with temperature sounding channels around the 50‐GHz oxygen absorption band, water‐vapour sounding channels around the 183‐ and 325‐GHz absorption bands, and also window channels at 89 and 165 GHz. An observing‐system simulation experiment (OSSE) has been conducted at the Centre National de Recherches Météorologiques (CNRM) to evaluate the impact of this constellation on numerical weather prediction (NWP) at the global scale with the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model. Two periods ranging from August–October 2021 and December 2021–February 2022 have been chosen to compute the nature run and to run 4D‐Var data assimilation experiments. As validation of the OSSE framework, the impact of a Metop‐B denial experiment in the OSSE is compared with the impact of a Metop‐B denial with real observations. This comparison shows that the Metop‐B denial impacts are very similar in the OSSE and with real observations, with the OSSE slightly overestimating the impact. Then, the impacts of various scenarios for the EPS–Sterna constellation are assessed by computing forecast errors, fractions skill scores, and moist global energy norms, and comparing these with the results of a baseline experiment without the EPS–Sterna constellation. Significant and positive improvements of the forecasts are found up to 96 h, for every variable tested, with an impact increasing with the number of satellites. [ABSTRACT FROM AUTHOR]

Quantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather prediction and data assimilation systems. Ensemble‐based data assimilation systems incorporate state‐dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work, a machine‐learning method is presented based on convolutional neural networks that estimates the state‐dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heteroscedastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman‐like analysis update and the machine‐learning‐based estimation of a state‐dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof‐of‐concept. The promising results show that the machine‐learning method is able to predict precise values of the forecast covariance matrix in relatively high‐dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter, outperforming it when the ensembles are relatively small. [ABSTRACT FROM AUTHOR]

The European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022. [ABSTRACT FROM AUTHOR]

In recent years discontinuous Galerkin (DG) methods have received increased interest from the geophysical community. In these methods the solution in each grid cell is approximated as a linear combination of basis functions. Ensemble data assimilation (DA) aims to approximate the true state by combining model outputs with observations using error statistics estimated from an ensemble of model runs. Ensemble data assimilation in geophysical models faces several well‐documented issues. In this work we exploit the expansion of the solution in DG basis functions to address some of these issues. Specifically, it is investigated whether a DA–DG combination (a) mitigates the need for observation thinning, (b) reduces errors in the field's gradients, and (c) can be used to set up scale‐dependent localisation. Numerical experiments are carried out using stochastically generated ensembles of model states, with different noise properties, and with Legendre polynomials as basis functions. It is found that strong reduction in the analysis error is achieved by using DA–DG and that the benefit increases with increasing DG order. This is especially the case when small scales dominate the background error. The DA improvement in the first derivative is, on the other hand, marginal. We think this to be a counter‐effect of the power of DG to fit the observations closely, which can deteriorate the estimates of the derivatives. Applying optimal localisation to the different polynomial orders, thus exploiting their different spatial length, is beneficial: it results in a covariance matrix closer to the true covariance than the matrix obtained using traditional optimal localisation in state space. [ABSTRACT FROM AUTHOR]

An ensemble three‐dimensional ensemble‐variational (3DEnVar) data assimilation (En3DA) approach that directly assimilates radar reflectivity was developed based on the Weather Research and Forecasting model data assimilation system. This system adopts radar reflectivity as the control variable to avoid the need for a tangent linear and adjoint of the observation operator. Flow‐dependent covariance was introduced via ensemble forecasts updated by a group of 3DEnVar. The performance of the En3DA system was examined for two selected cases of high‐impact severe tornadic supercells over China. Results for both cases indicated that the structure of the storms in terms of intensity, coverage, and associated low‐level mesocyclones were analysed more accurately when using the En3DA approach than when adopting the 3DVar method. Hydrometeor analysis showed that En3DA provided a more physically reasonable increment of hydrometeors compared to 3DVar, especially for the graupel mixing ratio. Furthermore, the En3DA forecast was better than the 3DVar forecast throughout the forecast period for both studied cases. En3DA produced smaller errors in terms of intensity and location for supercell forecasts with respect to reflectivity and reflectivity swaths. Furthermore, the quantitative forecast skill of radar reflectivity was improved using En3DA. Errors in the wind, temperature, and water vapor forecast fields produced by En3DA were also reduced compared to those of 3DVar. Diagnostics revealed that En3DA predicted an enhanced low‐level cold pool and stronger outflows in the forward‐flank downdraft and the rear‐flank downdraft regions, which are important for tornadogenesis. [ABSTRACT FROM AUTHOR]

To better promote the research and development of regional crop yield simulations, we review related research on regional crop yield simulations over the past ten years, summarize the research progress on regional crop yield simulations at home and abroad from the three aspects of crop growth models, remote sensing technology, and data assimilation technology, and propose three future development directions for regional crop yield simulations: (1) Based on the agronomic mechanism of crop yield estimation, it is necessary to fully consider crop growth characteristics and yield formation mechanisms. (2) With respect to the remote sensing mechanism of crop yield estimation, we can consider combining radar stereo lateral view measurement technology to determine crop characteristics and remote sensing information. (3) From the perspective of combining agronomy and remote sensing, the crop yield spatiotemporal simulation assimilation algorithm should be optimized, and the yield simulation unit should be extended. It is expected that these considerations can provide new ideas for regional crop yield simulations with high accuracy, a large scale, and full coverage. [ABSTRACT FROM AUTHOR]

Several configurations of the Canadian Precipitation Analysis system (CaPA) currently produce precipitation analyses at Environment and Climate Change Canada (ECCC). To improve CaPA's performance during the winter season, the impact of assimilating the IMERG V06 product (IMERG: Integrated Multi-satellitE Retrievals for GPM—Global Precipitation Measurement mission) into CaPA is examined in this study. Tests are conducted with CaPA's 10 km deterministic version, evaluated over Canada and the northern part of the United States (USA). Maps from a case study show that IMERG plays a contradictory role in the production of CaPA's precipitation analyses for a synoptic-scale winter storm over North America's eastern coast. While its contribution appears to be physically correct over southern portions of the meteorological system, and early in its intensification phase, IMERG displays unrealistic spatial structures over land later in the system's life cycle when it is located over northern (colder) areas. Objective evaluation of CaPA's analyses when IMERG is assimilated without any restrictions shows an overall decrease in precipitation, which has a mixed effect (positive and negative) on the bias indicators. But IMERG's influence on the Equitable Threat Score (ETS), a measure of CaPA's analyses accuracy, is clearly negative. Using IMERG's quality index (QI) to filter out areas where it is less accurate improves CaPA's objective evaluation, leading to better ETS versus the control experiment in which no IMERG data are assimilated. Several diagnostics provide insight into the nature of IMERG's contribution to CaPA. For the most successful configuration, with a QI threshold of 0.3, IMERG's impact is mostly found in the warmer parts of the domain, i.e., in northern US states and in British Columbia. Spatial means of the temporal sums of absolute differences between CaPA's analyses with and without IMERG indicate that this product also contributes meaningfully over land areas covered by snow, and areas where air temperature is below −2 °C (where precipitation is assumed to be in solid phase). [ABSTRACT FROM AUTHOR]

Disasters caused by heavy rainfall associated with quasi-stationary line-shaped mesoscale convective systems (MCSs) frequently occur in Japan. Thus, highly accurate quantitative precipitation forecast (QPF) information that contributes to decision-making by municipalities to issue evacuation orders is necessary. To this end, we developed a blending forecasting system (BFS) for predicting heavy rainfall associated with MCSs. The BFS blends 1-h observed rainfall and forecasts of extrapolation-based nowcasting (EXT) in the first hour and numerical weather prediction (NWP) in the second hour, predicting 3-h accumulated rainfall (P3h) and its return period (RP) of up to 2 h ahead with a higher horizontal resolution (1 km) and higher-frequency updates (every 10 min) compared to the current operational systems. A blending technique with a spatial maximum filter for tolerating forecast displacement errors (BLEDE) was applied to the predicted rainfall of EXT and NWP. To improve the accuracy of the NWP, vertical profiles of water vapor obtained with two water vapor lidars (WVLs) were assimilated into the NWP. This combination predicted rare heavy rainfall with an RP of more than 10 years in the same city where flooding occurred for a heavy rainfall event associated with quasi-stationary line-shaped MCSs in southern Kyushu on 10 July 2021. The BFS yielded such forecast information 40 min earlier than the existing warning information, indicating the potential for providing a longer lead time for evacuation. The improvement in forecast accuracy was due to both BLEDE and WVL data assimilation (WVL-DA); however, the contribution of BLEDE was more than five times that of WVL-DA in terms of predicting the P3h for the threshold of 80 mm. Additionally, the sensitivity of the predicted rainfall to the background error covariance matrix in WVL-DA is also discussed. [ABSTRACT FROM AUTHOR]

Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data assimilation, but their performance is limited by their underlying Gaussian approximation. A two-step framework for ensemble data assimilation allows this approximation to be relaxed: The first step updates the ensemble in observation space, while the second step regresses the observation state update back to the state variables. This paper develops a new quantile-conserving ensemble filter based on kernel-density estimation and quadrature for the scalar first step of the two-step framework. It is shown to perform well in idealized non-Gaussian problems, as well as in an idealized model of assimilating observations of sea-ice concentration. [ABSTRACT FROM AUTHOR]

Because it is generally impossible to completely characterize the uncertainty in complex model variables after assimilation of data, it is common to approximate the uncertainty by sampling from approximations of the posterior distribution for model variables. When minimization methods are used for the sampling, the weights on each of the samples depend on the magnitude of the data mismatch at the critical points and on the Jacobian of the transformation from the prior density to the sample proposal density. For standard iterative ensemble smoothers, the Jacobian is identical for all samples, and the weights depend only on the data mismatch. In this paper, a hybrid data assimilation method is proposed which makes it possible for each ensemble member to have a distinct Jacobian and for the approximation to the posterior density to be multimodal. For the proposed hybrid iterative ensemble smoother, it is necessary that a part of the mapping from the prior Gaussian random variable to the data be analytic. Examples might include analytic transformation from a latent Gaussian random variable to permeability followed by a black-box transformation from permeability to state variables in porous media flow, or a Gaussian hierarchical model for variables followed by a similar black-box transformation from permeability to state variables. In this paper, the application of weighting to both hybrid and standard iterative ensemble smoothers is investigated using a two-dimensional, two-phase flow problem in porous media with various degrees of nonlinearity. As expected, the weights in a standard iterative ensemble smoother become degenerate for problems with large amounts of data. In the examples, however, the weights for the hybrid iterative ensemble smoother were useful for improving forecast reliability. [ABSTRACT FROM AUTHOR]

With the limitation of the high sensitivity of nonlinear models to initial conditions, the accurate estimation of wave spatial evolution is difficult to perform at a long distance. At this stage, a helpful approach is to improve the accuracy and robustness of the model through data assimilation technique. A robust data assimilation framework is developed by coupling ensemble Kalman filtering (EnKF) with the nonlinear wave model. The spatial evolution is obtained by numerically integrating the viscous modified Nonlinear Schrödinger (MNLS) equation. The performance of the EnKF-MNLS coupled framework is tested using synthetic data and laboratory measurements. The synthetic data is generated by the MNLS simulation superposing the Gaussian noise. In the synthetic cases, the estimated wave envelopes agree well with the clean solution. The results of laboratory experiments indicate that the EnKF-MNLS framework can improve the accuracy of wave forecasts compared to noised MNLS simulations. This study aims to enhance the noise resistance of the nonlinear wave model in spatial evolution and improve the accuracy of the model forecast. [ABSTRACT FROM AUTHOR]

Nearshore bathymetry changes on scales of hours to months in ways that strongly impact coastal processes. However, even at the best-monitored sites, surveys are typically not conducted with sufficient frequency to capture important changes such as sandbar migration. As a result, nearshore models often rely on outdated bathymetric boundary conditions, which may introduce significant errors. In this study, we investigate ensemble optimal interpolation (EnOI) as a method to update survey-derived bathymetry with altimetric measurements that are spatially sparse but have high temporal availability. We present the results of two synthetic examples and two field data experiments that demonstrate the ability of the method to accurately track morphological change between surveys. The method reduces the RMSE relative to a static bathymetry (corresponding to the day before the first assimilation step) by 23% to 68%. When compared with an estimate linearly interpolated between survey-derived bathymetries, the EnOI analysis reduces the RMSE by 19% to 47% in three out of the four experiments. [ABSTRACT FROM AUTHOR]

Geological formations with fractures are frequently encountered in various research fields. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and solute transport within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observable state variables. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non‐Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non‐Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real‐world case. In these cases, we systematically compare PEDL with three widely used DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL‐based update (ESDL). Notably, in the problems characterized by highly non‐Gaussian features, ESMDA and ILUES produce significantly divergent results. Conversely, employing the DL‐based update, ESDL demonstrates improved performance. However, its estimation uncertainty remains high, potentially attributable to ESDL's updating mechanism. Comprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks. Key Points: A deep learning‐based data assimilation method, named parameter estimator with deep learning (PEDL), is proposed to characterize fractured media with highly non‐Gaussian featuresPEDL outperforms three popular data assimilation methods based on deep learning or the Kalman formulaComprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures [ABSTRACT FROM AUTHOR]

Seawater intrusion (SI) poses a substantial threat to water security in coastal regions, where numerical models play a pivotal role in supporting groundwater management and protection. However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into SI predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by integrating various types of observational data with the model to characterize heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ESK) and Markov chain Monte Carlo, face challenges when confronted with the non‐linearity, non‐Gaussianity, and high‐dimensionality issues commonly encountered in aquifer characterization. In this study, we introduce a novel DA approach rooted in deep learning (DL), referred to as ESDL, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ESDL, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers under non‐linear and non‐Gaussian conditions. Comparison between ESDL and ESK under different experimentation settings underscores the robustness of ESDL. Conversely, in certain scenarios, ESK displays noticeable biases in the characterization results, especially when measurement data from non‐linear and discontinuous processes are used. To optimize the efficacy of ESDL, attention must be given to the design of the DL model and the selection of observational data, which are crucial to ensure the universal applicability of this DA method. Key Points: Non‐linearity and non‐Gaussianity in coastal aquifer characterization problems pose challenges to traditional data assimilation (DA) methodsWe propose to address these issues with a deep learning‐based DA method called ESDLVarious factors influencing the performance of ESDL are systematically investigated [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Asynchronous observations refer to observations that are obtained at multiple moments. The observation moments of fire fronts may differ throughout an entire wildfire area. Asynchronous observations include historical data, which hinders the effectiveness of data assimilation due to the lack of timely updates on changing fire fronts. Aims: This paper proposed a distributed strategy combined with the Ensemble Transform Kalman filter (ETKF-distributed) for asynchronous observations. It can assimilate fire fronts immediately at any location by using new matching schemes between prediction and observation. Methods: The ETKF-distributed undergoes testing using a wildland fire generated based on real terrain, vegetation, and historical weather data from the local area. In addition, the ETKF and ETKF-centralised proposed in our previous work were employed as comparisons. Observing System Simulation Experiments were conducted to generate asynchronous observation fire fronts. Key results: The benefit of immediate assimilation enables the new method to maintain high accuracy predictions. Conclusions: The allocation of observation resources can be focused in regions with high rates of speed when employing ETKF-distributed. Implications: The ETKF-distributed has high efficiency and adaptability, making it highly promising for implementation in wildfire prediction. Observation composed of data from multiple moments is asynchronous observation, which will bring errors when performing data assimilation. This paper proposes a distributed strategy combined with the Ensemble Transform Kalman filter for asynchronous observation. It could conduct the analysis step immediately by a new matching scheme between prediction and observation. [ABSTRACT FROM AUTHOR]

The existing water vapor present in the lower regions of the atmosphere plays a pivotal role in both weather forecasting and the propagation of signals in satellite-based observations. This parameter introduces a delay in GNSS observations, known as tropospheric wet delay. Accurately predicting the spatial distribution of this parameter can significantly enhance our ability to forecast rainfall and floods. It can also improve satellite-based positioning techniques. One mathematical technique that proves invaluable in modeling various temporal aspects of a signal is the Dynamic Mode Decomposition (DMD) method. To construct the necessary snapshot matrix in the DMD method, we have opted to employ B-spline coefficient time series, computed by assimilating GNSS-derived Zenith et Delay (ZWD) values into the GPT3w model as a reference, with the Ensemble Kalman Filter (EnKF) method serving as the core of the assimilation process. In the DMD procedure, we have utilized a dataset spanning approximately 30 consecutive days, with a temporal resolution of roughly 5 min, to predict B-spline coefficients representing the spatial distribution of ZWD values for a 24-h period ahead. This dataset comprises ZWD values collected from 241 GNSS stations located in Germany and nearby regions throughout the year 2018. Comparative analysis has been performed, including 10 excluded GNSS stations from the assimilation and DMD procedure and 10 existing radiosonde stations within the study region. The results of the analysis step demonstrate the superiority of the proposed method over the ERA5, GFS, and GPT3w models, showcasing the Root Mean Squared Error (RMSE) of approximately 0.8 cm. This performance marks a substantial improvement, being approximately 51%, 57%, and 74% lower than each respective model. In the prediction phase, the proposed method outperforms the ERA5 and GFS models up to the 6-h and 24-h prediction windows in comparison with the GPT3w model. [ABSTRACT FROM AUTHOR]

Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor's precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors' precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links. [ABSTRACT FROM AUTHOR]

Decision makers must often choose how many sensors to deploy, of what types, and in what locations to meet a given operational or scientific outcome. An observation system simulation experiment (OSSE) is a numerical experiment that can provide critical decision support to these complex and expensive choices. An OSSE uses a "truth model" or "nature run" to simulate what an observation system would measure and then passes these measurements to an assimilation model. Then, the output of the assimilation model is compared to that of the truth model to assess improvement and the impact of the observation system. Orion Space Solutions has developed the OSSE tool (OSSET) to perform OSSEs for ionospheric electron density specification quickly and accurately. In this study, we use OSSET to predict the impact of adding commercial radio occultation total electron content (TEC) data to an assimilation model. We compare the OSSE's predictions to the real performance at a group of validation ionosondes and find good agreement. We also demonstrate the global assessments that are possible with the OSSET using the improvement in critical frequency specification as an example. From this, we find that commercial radio occultation data can improve the critical frequency specification by nearly 20% at high latitudes, which are not covered by COSMIC-2. The commercial satellites are in sun-synchronous orbits with constant local times, and this improvement is concentrated at these local times. [ABSTRACT FROM AUTHOR]

Satellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets. Plain Language Summary: The MOPITT instrument on the NASA/Terra satellite can detect carbon monoxide (CO) pollution in the lower and mid‐tropospheric atmosphere but cannot accurately differentiate small changes in the altitude of pollution plumes. Such satellite observations are assimilated in numerical model predictions to improve the spatial and temporal distribution of CO in the atmosphere and to estimate emission fluxes. We present a novel method that does not require assumptions about the model and the observations, leading to a more efficient and accurate assimilation of the satellite observations. Key Points: A novel non Gaussian and nonlinear ensemble data assimilation (DA) framework is applied to MOPITT joint state/flux optimizationThe new method performs better than the Ensemble Adjustment Kalman Filter in comparison to independent observationsMOPITT observations indicate that CAMS‐GLOB‐ANT_v5.3 emission fluxes are underestimated across the mid‐latitudes in May 2018 [ABSTRACT FROM AUTHOR]

Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current largescale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R² and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R² and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) integrates experimental measurements into computational models to enable high-fidelity predictions of dynamical systems. However, the cost associated with solving this inverse problem, from measurements to the state, can be prohibitive for complex systems such as transitional hypersonic flows. We introduce an accurate and efficient deep-learning approach that alleviates this computational burden, and that enables approximately three orders of magnitude computational acceleration relative to variational techniques. Our method pivots on the deployment of a deep operator network (DeepONet) as an accurate, parsimonious and efficient meta-model of the compressible Navier–Stokes equations. The approach involves two main steps, each addressing specific challenges. Firstly, we reduce the computational load by minimizing the number of costly direct numerical simulations to construct a comprehensive dataset for effective supervised learning. This is achieved by optimally sampling the space of possible solutions. Secondly, we expedite the computation of high-dimensional assimilated solutions by deploying the DeepONet. This entails efficiently navigating the DeepONet's approximation of the cost landscape using a gradient-free technique. We demonstrate the successful application of this method for data assimilation of wind-tunnel measurements of a Mach 6, transitional, boundary-layer flow over a 7-degree half-angle cone. [ABSTRACT FROM AUTHOR]

Initialization is essential for accurate seasonal-to-decadal (S2D) climate predictions. The initialization schemes used differ on the component initialized, the Data Assimilation method, or the technique. We compare five popular schemes within NorCPM following the same experimental protocol: reanalysis from 1980 to 2010 and seasonal and decadal predictions initialized from the reanalysis. We compare atmospheric initialization—Newtonian relaxation (nudging)—against ocean initialization—Ensemble Kalman Filter—(ODA). On the atmosphere, we explore the benefit of full-field (NudF-UVT) or anomaly (NudA-UVT) nudging of horizontal winds and temperature (U, V, and T) observations. The scheme NudA-UV nudges horizontal winds to disentangle the role of wind-driven variability. The ODA+NudA-UV scheme is a first attempt at joint initialization of ocean and atmospheric components in NorCPM. During the reanalysis, atmospheric nudging improves the synchronization of the atmosphere and land components with the observed data. Conversely, ODA is more effective at synchronizing the ocean component with observations. The atmospheric nudging schemes are better at reproducing specific events, such as the rapid North Atlantic subpolar gyre shift. An abrupt climatological change using the NudA-UV scheme demonstrates that energy conservation is crucial when only assimilating winds. ODA outperforms atmospheric-initialized versions for S2D global predictions, while atmospheric nudging is preferable for accurately initializing phenomena in specific regions, with the technique's benefit depending on the prediction's temporal scale. For instance, atmospheric full-field initialization benefits the tropical Atlantic Niño at 1-month lead time, and atmospheric anomaly initialization benefits longer lead times, reducing hindcast drift. Combining atmosphere and ocean initialization yields sub-optimal results, as sustaining the ensemble's reliability—required for ODA's performance—is challenging with atmospheric nudging. [ABSTRACT FROM AUTHOR]

We conduct a comprehensive analysis of two data assimilation methods: the first utilizes the discrete adjoint approach with a correction applied to the production term of the turbulence transport equation, preserving the Boussinesq approximation. The second is a state observer method that implements a correction in the momentum equations alongside a turbulence model, both applied to fluid dynamics simulations. We investigate the impact of varying computational mesh resolutions and experimental data resolutions on the performance of these methods within the context of a periodic hill test case. Our findings reveal the distinct strengths and limitations of both methods, which successfully assimilate data to improve the accuracy of a RANS simulation. The performance of the variational model correction method is independent of input data and computational mesh resolutions. The state observer method, on the other hand, is sensitive to the resolution of the input data and CFD mesh. [ABSTRACT FROM AUTHOR]

A combined data-assimilation and linear mean-flow analysis approach is developed to estimate coherent flow fluctuations from limited mean-flow measurements. It also involves Reynolds-Averaged Navier–Stokes (RANS) modelling to efficiently tackle turbulent flows. Considering time-averaged Particle Velocimetry Image (PIV) measurements of the near-stall flow past a NACA0012 airfoil at an angle of attack of 10 ∘ and in the chord-based Reynolds number range 4.3 · 10 4 ≤ R e ≤ 6.4 · 10 4 , data assimilation is first employed to correct RANS equations that are closed by the Spalart-Allmaras model. The outputs of this procedure are a full mean-flow description that matches the PIV data and a consistent turbulence model that provides not only a mean eddy-viscosity field but also the perturbations of the latter with respect to mean-flow modifications. Global stability and resolvent analyses are then performed based on the so-obtained mean flow and model to satisfactorily predict near-stall low-frequency phenomena, as confirmed through comparison with the Spectral Proper Orthogonal Decomposition (SPOD) of the PIV measurements. This comparison highlights the benefits in taking into account variations in the turbulent eddy-viscosity over a frozen approach for the correct estimation of the present coherent low-frequency oscillations. [ABSTRACT FROM AUTHOR]

This article describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to "enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization." This article provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package. [ABSTRACT FROM AUTHOR]

Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon "Chanba". The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon's barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons. [ABSTRACT FROM AUTHOR]

Between 2014 and 2018, the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by observing system simulation experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are 1) increasing the revisit rate for satellite radiance soundings produces the largest benefits but at a significant cost by requiring an increase in the number of polar-orbiting satellites from 2 to 12; 2) a large positive impact is found when the number of RO soundings per day is increased well beyond current values and other observations are held at current levels of performance; 3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and 4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits. SIGNIFICANCE STATEMENT: The results of this paper are significant because they inform decision-makers about the future configuration of the NOAA's environmental satellite constellation, which serves millions of diverse users. [ABSTRACT FROM AUTHOR]

Boundary conditions are important constraints when solving partial differential equations to obtain solutions for the interior. Global ocean prediction models have supplied such boundary conditions for regional ocean models. However, the biases present in these global ocean models can significantly affect the accuracy of predictions within nested regional ocean models. In the present study, open boundary bias correction and data assimilation were applied using the observed temperature and salinity data. The bias correction was conducted at the upstream open boundary, the Korea Strait, while the data assimilation was performed in the interior, including Ulleung Basin in the southwestern East Sea, to reduce the prediction errors in the nested regional model. The effects of each correction method were quantified through four numerical experiments. In the Korea Strait and Ulleung Basin, root mean square errors (RMSEs) in temperature decreased by 44% in February using data assimilation compared to free run, while RMSEs in salinity decreased by 31% in August using bias correction. Bias correction of open boundary data enhanced the simulation performance in salinity near the open boundary; however, it did not lead to improvements in temperature distribution within the interior. Conversely, data assimilation improved temperature distribution within the interior; however, salinity bias persisted near the open boundary. Notably, the combined use of the two correction methods resulted in a lower RMSE in temperature in the Ulleung Basin during winter, and a lower RMSE in salinity in the Korea Strait and Ulleung Basin during the summer. Hence, the simultaneous use of open boundary bias correction and data assimilation is expected to improve the interior solution of salinity and temperature in various other regional ocean models. [ABSTRACT FROM AUTHOR]

This research employs a continuous adjoint data assimilation (DA) algorithm to enhance the prediction of three-dimensional flow behavior in the inclined round jet in crossflow (JICF). To rectify model-form errors arising from the Boussinesq approximation, a force correction is implemented, and the linear part of the eddy viscosity is incorporated for numerical stability. The DA model is theoretically derived to minimize discrepancies between particle image velocimetry (PIV) measurement and the numerical predictions of the primary-adjoint system, thus enabling determination of the optimal contribution of the force correction. Observational data are acquired through planar PIV measurement in the centerplane of JICF with a fixed velocity ratio of 1.2 and a bulk Reynolds number of 150,000. Delayed detached eddy simulation is validated using PIV results and serves as supplementary data for evaluating the reconstruction capabilities of the DA method. The research explores various regularization parameters in the data assimilation procedure, emphasizing their impact on eddy viscosity, correction force, and overall flow prediction accuracy. The findings underscore the effectiveness of regularization in promoting smoothness in the optimized field, thereby mitigating overfitting and irregular solutions. In-depth analyses of critical flow features, including counter-rotating vortex pairs and Reynolds stress forcing, provide insights into the accuracy achieved by the data assimilation procedure. Despite limited measurement data, the study demonstrates the capability of the presented method to successfully recover global flow fields. [ABSTRACT FROM AUTHOR]

In this study, we have demonstrated that data assimilation (DA) using lead and bismuth sample reactivities measured in the Kyoto University Critical Assembly A-core can successfully reduce the uncertainty of the coolant void reactivity in accelerator-driven systems (ADSs) derived from inelastic scattering cross sections of lead and bismuth. We reevaluated and highlighted the experimental uncertainties and correlations of the sample reactivities for the DA formula. We used the MCNP6.2 code to evaluate the sample reactivities and their uncertainties and performed DA using the reactor analysis code system MARBLE. The high-sensitivity coefficients of the sample reactivities to lead and bismuth allowed us to reduce the cross-section–induced uncertainty of the void reactivity of the ADS from 6.3% to 4.8%, achieving a provisional target accuracy of 5% in this study. Furthermore, we demonstrated that the uncertainties arising from other dominant factors, such as minor actinides and steel, can be effectively reduced by using integral experimental data sets for the unified cross-section dataset ADJ2017. [ABSTRACT FROM AUTHOR]

The challenging task of early yield prediction is an essential problem for present-day agriculture. It is commonly solved with a crop model along with relevant observation data: field scouting, in-situ sensors, satellite imagery data and information from previous growing seasons. Crop growth simulation models benefit greatly from application of these data; however, only a limited number of established data assimilation procedures receive notable application. Most studies focus on model parameter calibration, machine learning, ensemble Kalman filters (EnKF) or particle filters. These methods are powerful yet computationally expensive, which limits their extensive application. In this study, we bring into consideration a modern KF variant – the unscented Kalman filter (UKF). We implement the UKF data assimilation for leaf area index (LAI) within WOFOST PCSE model. To demonstrate its efficiency, we conduct simulations with EnKF and UKF assimilation of Sentinel-2 LAI data and compare the results to actual historical yield data of five crops on 2740 fields. Also, a field-level numerical experiment is set up to demonstrate the influence of LAI assimilation on the predicted yield. The results indicate the proposed approach performs consistently and significantly improves the accuracy of predicted yields. [ABSTRACT FROM AUTHOR]

The recently deployed Surface Water and Ocean Topography (SWOT) mission for the first time has observed the ocean surface at a spatial resolution of 1 km, thus giving an opportunity to directly monitor submesoscale sea surface height (SSH) variations that have a typical magnitude of a few centimeters. This progress comes at the expense of the necessity to take into account numerous uncertainties in calibration of the quality-controlled altimeter data. Of particular importance is the proper filtering of spatially correlated errors caused by the uncertainties in geometry and orientation of the on-board interferometer. These "systematic" errors dominate the SWOT error budget and are likely to have a notable signature in the SSH products available to the oceanographic community. In this study, we explore the utility of the block-circulant (BC) approximation of the SWOT precision matrix developed by the Jet Propulsion Laboratory for assessment of a mission's accuracy, including the possible impact of the systematic errors on the assimilation of the wide-swath altimeter data into numerical models. It is found that BC approximation of the precision matrix has sufficient (90–99%) accuracy for a wide range of significant wave heights of the ocean surface, and, therefore, could potentially serve as an efficient preconditioner for data assimilation problems involving altimetry observations by space-borne interferometers. An extensive set of variational data assimilation (DA) experiments demonstrates that BC approximation provides more accurate SSH retrievals compared to approximations, assuming a spatially uncorrelated observation error field as is currently adopted in operational DA systems. [ABSTRACT FROM AUTHOR]

Several observation types (e.g., geostationary satellite and Doppler radar observations) have recently been found to exhibit strong spatial error correlations. Including these error statistics in data assimilation for numerical weather prediction can improve analysis quality and forecast skill. Moreover, it allows for increases in the spatial density of observations assimilated, which is needed for the provision of information on appropriate scales for high‐resolution forecasting. However, introducing correlated error statistics may increase the computational complexity and parallel communication costs of matrix‐vector products involving observation precision matrices (inverse observation error covariance matrices). Without new approaches, we cannot take full advantage of new observation uncertainty estimates. We develop a new numerical approximation method based on a particular type of fast multipole method and a domain localization approach. The basic idea is to divide the observation domain into boxes and then separate calculations of matrix‐vector products according to the partition. These calculations can be done in parallel with very low communication overheads. The new method is easy to implement and parallelize, and it is applicable to a wide variety of observation precision matrices. We applied the new method to a simple variational data assimilation problem and found that the computational cost of the variational minimization was dramatically reduced while preserving analysis accuracy across a range of scales. The new method has the potential to be used as an efficient technique for practical applications where a large number of observations with mutual error correlations need to be assimilated quickly. Plain Language Summary: Weather forecasts are generated by running a computational model from a starting point that describes the current weather conditions. These conditions are estimated using a data assimilation process that combines observations of the weather with computational predictions from a previous forecast. In the data assimilation process, the statistical weighting given to the observations is determined by our knowledge of the observation error statistics. Spatial relationships between errors, or spatial error correlations, are often found in remotely sensed observations, such as those from satellites. The inclusion of spatially correlated observation error statistics in the data assimilation process can substantially increase the time required to complete the process. We develop a novel method that can significantly reduce the time taken to assimilate observations with mutual spatial error correlations. Moreover, the accuracy of data assimilation can be maintained with this new method. Key Points: The new method has the potential to be used in practice to assimilate a large number of observations with mutual spatial error correlationsThe new method allows fast computation of matrix‐vector products involving full observation precision matrices in data assimilationThe new method can preserve the analysis accuracy at all spatial scales [ABSTRACT FROM AUTHOR]

The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models' parameters in real time and for improving data assimilation results. [ABSTRACT FROM AUTHOR]

With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various R matrices, the results in terms of analysis error are similar. Significance Statement: Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping. [ABSTRACT FROM AUTHOR]