Model Data Assimilation Research Articles

Effect of interannual variations of Kuroshio–Tsushima Warm Current system on the transportation of juvenile Japanese jack mackerel (Trachurus japonicus) to the Pacific coast of Japan

AbstractRecruitment of Japanese jack mackerel (Trachurus japonicus) has been decreasing continuously since 2000 in the Pacific coastal waters of Japan. The reasons and mechanisms for this phenomenon are still unclear. Particle‐tracking experiments were performed using a data assimilation model to elucidate the effect of a current system on the transportation processes of T. japonicus from the main spawning ground in the southern East China Sea to the Pacific coastal waters. The experiments demonstrated that T. japonicus were transported from the southern East China Sea to the Pacific waters around western Japan through the Tokara Strait, and the number of particles transported to the Pacific decreased from 2000 to 2017. The particles passing through the northwest Tokara Strait tended to be transported to the Pacific side during 2000–2005 and to the Sea of Japan during 2006–2017. The bifurcation toward the Sea of Japan was due to an amplification of the northward current where the Tsushima Warm Current originates (west of Kyushu). This change was induced by a rising sea level west of Kyushu due to Kuroshio's northward shift along the western Pacific coast of Japan, which induced the Tsushima Strait through flow to strengthen. The decrease in larval transportation due to the current system change can be one of the causes for the recruitment decline of T. japonicus along the Pacific coast.

Impacts of 3DEnVar-Based FY-3D MWHS-2 Radiance Assimilation on Numerical Simulations of Landfalling Typhoon Ampil (2018)

The module for assimilating radiance data of the Microwave Humidity Sounder-2 (MWHS-2) onboard the Feng Yun 3D (FY-3D) satellite is built in the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system. The CONV, 3DVar, and EnVar experiments are conducted to investigate the impact of assimilating the new humidity sounder based on Typhoon Ampil (2018). Both the 3DVar and EnVar experiments assimilate FY-3D MWHS-2 radiance data on top of the conventional data, while the CONV experiment only applies conventional data. In the EnVar experiment, notable geopotential height increment is observed around the typhoon, leading the typhoon to move northeast. In addition, the moisture field is improved to some extent. Finally, from the analysis of the dynamic field of the typhoon, it can be found that the EnVar experiment can adjust the dynamic structure of the typhoon. Furthermore, the assimilation of FY-3D MWHS-2 radiance data reduces the forecast error of the typhoon track and intensity. Additionally, the precipitation skill is improved in terms of rainfall pattern and the verification score. This improvement in the precipitation may be closely related to the features of the circulation structure concerning the evolution of the typhoon. The improved prediction of the position and intensity of rainbands in the FY-3D MWHS-2 radiance data assimilation experiment corresponds to a better prediction of typhoon structure.

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting.

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models

A 15-year reanalysis (2007–2021) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf is described. The analysis uses the Regional Ocean Modeling System (ROMS) and four-dimensional variational (4D-Var) data assimilation (DA) of observations from in situ platforms, coastal radars, and satellites. The reanalysis downscales open boundary information from the Copernicus Marine Environmental Monitoring Service (CMEMS) global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions.A complementary analysis of the mean seasonal cycle of regional circulation, also computed using ROMS 4D-Var but with climatological mean observations and forcing, is used to reduce biases in the CMEMS boundary data and to provide a dynamically and kinematically constrained Mean Dynamic Topography to use in conjunction with the assimilation of satellite altimeter sea level anomaly observations.The configuration of ROMS 4D-Var used is described, presenting details of the comprehensive suite of observations assembled, data pre-processing and quality control procedures, and background and observation error hypotheses. Control variables of the DA are the initial conditions, surface forcing, and boundary conditions of a sequence of non-overlapping 3-day analysis cycles.Comparisons to a non-assimilative version of the same ROMS model configuration show the added skill brought by assimilation of local observations. The improvement that downscaling with assimilation achieves over ocean state estimates from CMEMS and the U.S. Naval Research Laboratory Global Ocean Forecast System (GOFS) is demonstrated by the reduction in residuals of the DA, and by comparison to independent (unassimilated) observations. Wherever data volumes allow, skill assessments are made with the respect to anomalies from the mean seasonal cycle to emphasize performance at the ocean mesoscale.To highlight the utility of the analysis to inform studies related to coastal sea level variability and marine ecosystems, comparisons are made to unassimilated coastal sea level gauges and novel observations from sensors on fishing gear. The assimilation of coastal satellite altimetry data produces coastal sea level results that are coherent with observations across all time scales from interannual to tidal, while bias and correlation metrics show that bottom temperatures in regions of commercial fishing activity in the Mid-Atlantic Bight and the Gulf of Maine are modeled well.

The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records

AbstractIn this paper, we introduce a relatively simple data‐driven method for the representation of the uncertainty in daily discharge records. The proposed method relies only on hourly discharge data and takes advantage of a nonparametric difference‐based estimator in the characterization of random errors in discharge time series. We illustrate with corrupted streamflow data that the nonparametric estimator provides an accurate characterization of the nature (homoscedastic or heteroscedastic) and magnitude of these errors. In addition, we demonstrate the practical usefulness of the estimator using discharge time series of 500+ watersheds of the Catchment Attributes and MEteorology for Large‐sample Studies data set. This analysis reveals that the magnitude of errors of aleatory nature in the investigated discharge records is rather small (less than 3% for 80% of the records). We then combine the effect of random errors and measurement frequency into a daily variance estimate, which serves as input to a streamflow generation approach. This procedure produces replicates of the discharge record which portray accurately the assigned streamflow uncertainty, preserve key statistical properties of the discharge record and are hydrologically realistic. The proposed method facilitates Bayesian analysis and supports tasks such as model diagnostics, data assimilation, uncertainty quantification and regionalization.

Lagrangian surface signatures reveal upper-ocean vertical displacement conduits near oceanic density fronts

Vertical transport in the ocean plays a critical role in the exchange of freshwater, heat, nutrients, and other biogeochemical tracers. While there are situations where vertical fluxes are important, studying the vertical transport and displacement of material requires analysis over a finite interval of time. One such example is the subduction of fluid from the mixed layer into the pycnocline, which is known to occur near density fronts. Divergence has been used to estimate vertical velocities indicating that surface measurements, where observational data is most widely available, can be used to locate these vertical transport conduits. We evaluate the correlation between surface signatures derived from Eulerian (horizontal divergence, density gradient, and vertical velocity) and Lagrangian (dilation rate and finite time Lyapunov exponent) metrics and vertical displacement conduits. Two submesoscale resolving models of density fronts and a data-assimilative model of the western Mediterranean were analyzed. The Lagrangian surface signatures locate significantly more of the strongest displacement features and the difference in the expected displacements relative to Eulerian ones increases with the length of the time interval considered. Ensemble analysis of forecasts from the Mediterranean model demonstrates that the Lagrangian surface signatures can be used to identify regions of strongest downward vertical displacement even without knowledge of the true ocean state.

Data assimilation in a regional high-resolution ocean model by using Ensemble Adjustment Kalman Filter and its application during 2020 cold spell event over Asia-Pacific region

Cold spell events can bring strong wind and low-temperature freezing and induce a significant rise of the water level, which have negative impacts on the economy. Based on a regional high-resolution ocean model, the satellite observations, including sea surface temperature (SST) and absolute dynamic topography (ADT), and the Argo data are assimilated to improve the modeling results over the Asia-Pacific area during a strong cold spell event of 2020 by using Ensemble Adjustment Kalman Filter (EAKF). To reduce the calculation cost, the ensemble for data assimilation is obtained by a dynamic sampling method which is based on the model forecasting biases and the EAKF is implemented by using an efficient parallelization scheme. The root mean square (RMS) error of SST decreased from 0.72 °C to 0.07 °C after assimilation, which was reduced by 90.28%. Besides, the RMS error of ADT was decreased from 0.28 m to 0.12 m, which was reduced by 57.14%. Responses of the ocean during the 2020 cold spell event were better reproduced using optimal EAKF configurations. As the speed of the wind increased and the air temperature dropped, the heat transported from the ocean to the atmosphere in the ocean model increased. Furthermore, the temporal evolutions of the ocean state were captured by data assimilation, which was the decrease of temperature and the increase of salinity and density. The mixed layers in the Kuroshio Extension were thicker due to excessive surface cooling. The seawater first increased and then overflowed in the Bohai Bay and Laizhou Bay. In conclusion, the EAKF data assimilation in this regional high-resolution ocean model has successfully reduced the model biases and the physical processes in reality can be well produced.

Medicane Ianos: 4D-Var Data Assimilation of Surface and Satellite Observations into the Numerical Weather Prediction Model WRF

This work investigates extreme weather events such as the onset of medicanes, which can cause severe socioeconomic impacts, along with their predictability. In order to accurately forecast such events, the Weather Research and Forecasting (WRF) model and its state-of-the-art data assimilation modeling framework (WRFDA) were set up to produce high-resolution forecasts for the case study of Medicane Ianos, which affected Greece between 17 and 19 September 2020. Information from weather stations and the satellite precipitation product IMERG was blended with the background model information from the Global Forecast System (GFS) using the 4D variational data assimilation (4D-Var) technique. New fields in an 18 km spatial resolution domain covering Europe were generated and utilized as improved initial conditions for the forecast model. Forecasts were issued based on these improved initial conditions at two nested domains of 6 km and 2 km spatial resolution, with the 2 km domain enclosing Greece. Denial experiments, where no observational data were assimilated in the initial boundary conditions, showed that the temperature fields benefited throughout the forecasting horizon from the assimilation (ranging from a 5 to 10% reduction in the average MAE values), while neutral to slightly positive (ranging from a 0.4 to 2% reduction in the average MAE values) improvement was found for wind, although not throughout the forecast horizon. The increase in spatial resolution did not significantly reduce the forecast error, but was kept at the same small order of magnitude. A tendency of the model to overpredict precipitation regardless of assimilation was observed. The assimilation of the IMERG data improved the precipitation forecasting ability up to the 18th hour of forecast. When compared to assimilation experiments that excluded IMERG data, the assimilation of IMERG data produced a better representation of the spatial distribution of the precipitation fields.

Higher-order perturbative parabolic-equation solutions for reduced-order sound propagation modeling

The parabolic-equation (PE) approximation to the Helmholtz equation has been shown to be one of the most effective and efficient numerical methods for sound propagation modeling. In this talk, a recursive and iterative algorithm will be introduced to compute the PE solutions due to perturbations in the medium wavenumber, which is a function of frequency and sound speed. This algorithm is developed based on perturbation theory with higher-order nonlinear terms, and it can efficiently compute higher-order derivatives of the sound pressure field with respect to changes in the local wavenumber. This higher-order perturbative PE (HOPPE) method can eventually lead to model order reduction for sound propagation simulations. An example of broadband computations with nonlinear interpolations between sampled frequencies will be presented to demonstrate the application of reduced-order modeling. A sensitivity kernel with higher-order nonlinear terms will also be introduced. Applications to data assimilation modeling and sensitivity tracking will be discussed. [Work supported by the Office of Naval Research.]

Modflow-setup: Robust automation of groundwater model construction

In an age of both big data and increasing strain on water resources, sound management decisions often rely on numerical models. Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connections between human activities and water resource impacts. However, despite many recent advances in model data assimilation and uncertainty quantification, the process of constructing numerical models remains laborious, expensive, and opaque, often precluding their use in decision making. Modflow-setup aims to provide rapid and consistent construction of MODFLOW groundwater models through robust and repeatable automation. Common model construction tasks are distilled in an open-source, online code base that is tested and extensible through collaborative version control. Input to Modflow-setup consists of a single configuration file that summarizes the workflow for building a model, including source data, construction options, and output packages. Source data providing model structure and parameter information including shapefiles, rasters, NetCDF files, tables, and other (geolocated) sources to MODFLOW models are read in and mapped to the model discretization, using Flopy and other general open-source scientific Python libraries. In a few minutes, an external array-based MODFLOW model amenable to parameter estimation and uncertainty quantification is produced. This paper describes the core functionality of Modflow-setup, including a worked example of a MODFLOW 6 model for evaluating pumping impacts to a lake in central Wisconsin, United States.

Spatiotemporal estimation of model error to improve soil moisture analysis in ensemble Kalman filter data assimilation

As a key variable in the surface hydrological cycle and energy balance, soil moisture is often analyzed using data assimilation to improve precise and high spatiotemporal resolution from multisource data. However, accurate estimation of model error in soil moisture data assimilation is not sufficient in spatial and temporal dimensions. This paper proposes a spatiotemporal error estimation method that integrates the spatial and temporal dimensions of the study area into model error estimation. The in-situ data was regarded as the “true value” for the temporal dimensions model error estimation. The triple collocation (TC) technique was used to estimate the spatial error of the model. Based on the aforementioned estimation, a linear method to construct the relationship between spatial and temporal information was proposed to fuse the spatiotemporal model error. Result from the proposed method was compared against the overall model error estimation and spatial estimation methods in ensemble Kalman filter (EnKF) data assimilation. The ERA-Interim reanalysis data and in-situ soil moisture data from Washington State in 2016 were used to evaluate the performance of EnKF data assimilation. Using statistical metrics, including the root mean square error (RMSE), the mean bias error (MBE), and the Pearson correlation coefficient (i.e., R-value), the results show that our proposed approach, which uses spatiotemporal approach to model error estimation in EnKF data assimilation, yielded more accurate soil moisture estimates. When ERA-Interim reanalysis soil moisture data was used as the reference data, compared with the overall model error estimation EnKF Data Assimilation Experiment, RMSE and MBE of spatiotemporal model error estimation EnKF Data Assimilation Experiment were reduced by 0.0062 and 0.0061 m3 / m3, respectively, and R remained basically unchanged. When using in-situ measurements as reference data, RMSE and MBE decreased by 0.0034 and 0.0038 m3 / m3, respectively, and R increased by 0.0365. Compared with the other estimation approaches in the experiments, the spatiotemporal error estimation approach greatly improved the estimation accuracy of soil moisture analysis, yielding the closest values and variation trends with the in-situ measurements. Fully considering the temporal and spatial variation of model error and improving the estimation accuracy of model error can provide more accurate model information for data assimilation. The proposed method and framework in this paper can be used in model error estimation and data assimilation, particularly in the fields of numerical weather forecasting, natural environment monitoring, and geographical environment research.

Environmental change and groundwater variability in South Bihar, India

In the last decade, researchers have underscored the need for carrying out climate change analysis at regional and sub-regional levels. Against this backdrop, we analyzed the climatic variability and environmental changes that occurred during the last seven decades (1958–2019) and their impact on groundwater variability in South Bihar, India. We employed a mixed-method approach and analyzed both the quantitative scientific data and qualitative ethnographic data collected from the local farmers.The scientific data was compiled for the climatic parameters (rainfall and temperature), hydrological parameters (runoff, actual evapotranspiration, potential evapotranspiration, and climate water deficit), terrestrial water storage, and groundwater storage capacity using TerraClimate model and Global Land Data Assimilation System. The public perception of climatic variability and environmental change was analyzed using farmers' understanding of environmental variability in selected villages of South Bihar. In terms of climatic variations, the historical climatic data showed rainfall variability over the last seven decades. Higher unpredictability in monsoon rainfall is evident from the 1990s onwards. The average temperature had gone up by 0.5 oC over the study period. The surface and groundwater deficit across South Bihar was relatively high (above 600 mm) with a significant sub-regional variation. From the public understanding perspective, the climatic variations were mostly narrated in terms of deficit and erratic rainfall, which increased in recent decades. Farmers explained the groundwater scarcity and associated environmental changes based on their lived experience. Both scientific and farmers' understanding of environmental variability explains the high climatic variability during the last three decades.

Overview of Arbitrarily High-Order Adjoint Sensitivity and Uncertainty Quantification Methodology for Large-Scale Systems

This work reviews from a unified viewpoint the concepts underlying the “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems” (nth-CASAM-L) and the “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) methodologies. The practical application of the nth-CASAM-L methodology is illustrated for an OECD/NEA reactor physics benchmark, while the practical application of the nth-CASAM-N methodology is illustrated for a nonlinear model of reactor dynamics that exhibits periodic and chaotic oscillations. As illustrated both by the general theory and by the examples reviewed in this work, both the nth-CASAM-L and nth-CASAM-N methodologies overcome the curse of dimensionality in sensitivity analysis. The availability of efficiently and exactly computed sensitivities of arbitrarily high order can lead to major advances in all areas that need such high-order sensitivities, including data assimilation, model calibration, uncertainty reduction, and predictive modeling.

Spatiotemporal Reconstruction of Antarctic Near-Surface Air Temperature from MODIS Observations

Abstract MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature measurements in combination with in situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05° × 0.05°, spanning from 2001 to 2018. Comparison with independent in situ air temperature measurements shows low uncertainty, with a mean bias of 0.09°C, a mean absolute error of 2.23°C, and a correlation coefficient of 97%. Furthermore, the performance of the reconstruction is better than ERA5 (the fifth-generation ECMWF reanalysis model) against in situ measurements. For the 2001–18 period, the MODIS-based near-surface air temperature product yields annual warming in the East Antarctica, but cooling in the Antarctic Peninsula and West Antarctica. However, they are not statistically significant. This product can also be used to investigate the impact of the Southern Hemisphere annual mode (SAM) on year-to-year variability of air temperature. The enhanced positive phase of SAM in recent decades in austral summer has a cooling effect on East and West Antarctica. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.

Sequential and batch data assimilation approaches to cope with groundwater model error: An empirical evaluation

Groundwater model data assimilation (DA) aims to reduce uncertainty in simulated outcomes of interest to resource management while minimizing the potential for predictive bias. Sequential DA, which can estimate model states along with properties and stresses dynamically in time, offers a potentially powerful alternative to batch DA (i.e., history matching) for reducing bias in decision-relevant predictions in the presence of incorrect model structure and/or processes. This study evaluates the ability of batch and sequential DA approaches to history match and forecast simulated quantities in the presence of model error using a novel ensemble-based paired complex–simple approach that enables the incorporation of stochastic uncertainty and a statistical evaluation of predictive bias. Our findings have implications for groundwater decision support modeling as they underscore the pitfalls of fixing parameters and forcing variables a priori and present a proof of concept for using adjustable model states to cope with model error.

Recent advances and opportunities in data assimilation for physics-based hydrological modeling

Data assimilation applications in integrated surface-subsurface hydrological models (ISSHMs) are generally limited to scales ranging from the hillslope to local or meso-scale catchments. This is because ISSHMs resolve hydrological processes in detail and in a physics-based fashion and therefore typically require intensive computational efforts and rely on ground-based observations with a small spatial support. At the other end of the spectrum, there is a vast body of literature on remote sensing data assimilation for land surface models (LSMs) at the continental or even global scale. In LSMs, some hydrological processes are usually represented with a coarse resolution and in empirical ways, especially groundwater lateral flows, which may be very important and yet often neglected. Starting from the review of some recent progress in data assimilation for physics-based hydrological models at multiple scales, we stress the need to find a common ground between ISSHMs and LSMs and suggest possible ways forward to advance the use of data assimilation in integrated hydrological models.

Quantifying biological carbon pump pathways with a data-constrained mechanistic model ensemble approach

Abstract. The ability to constrain the mechanisms that transport organic carbon into the deep ocean is complicated by the multiple physical, chemical, and ecological processes that intersect to create, transform, and transport particles in the ocean. In this paper we develop and parameterize a data-assimilative model of the multiple pathways of the biological carbon pump (NEMUROBCP). The mechanistic model is designed to represent sinking particle flux, active transport by vertically migrating zooplankton, and passive transport by subduction and vertical mixing, while also explicitly representing multiple biological and chemical properties measured directly in the field (including nutrients, phytoplankton and zooplankton taxa, carbon dioxide and oxygen, nitrogen isotopes, and 234Thorium). Using 30 different data types (including standing stock and rate measurements related to nutrients, phytoplankton, zooplankton, and non-living organic matter) from Lagrangian experiments conducted on 11 cruises from four ocean regions, we conduct an objective statistical parameterization of the model and generate 1 million different potential parameter sets that are used for ensemble model simulations. The model simulates in situ parameters that were assimilated (net primary production and gravitational particle flux) and parameters that were withheld (234Thorium and nitrogen isotopes) with reasonable accuracy. Model results show that gravitational flux of sinking particles and vertical mixing of organic matter from the euphotic zone are more important biological pump pathways than active transport by vertically migrating zooplankton. However, these processes are regionally variable, with sinking particles most important in oligotrophic areas of the Gulf of Mexico and California Current, sinking particles and vertical mixing roughly equivalent in productive coastal upwelling regions and the subtropical front in the Southern Ocean, and active transport an important contributor in the eastern tropical Pacific. We further find that mortality at depth is an important component of active transport when mesozooplankton biomass is high, but it is negligible in regions with low mesozooplankton biomass. Our results also highlight the high degree of uncertainty, particularly amongst mesozooplankton functional groups, that is derived from uncertainty in model parameters. Indeed, variability in BCP pathways between simulations for a specific location using different parameter sets (all with approximately equal misfit relative to observations) is comparable to variability in BCP pathways between regions. We discuss the implications of these results for other data-assimilation approaches and for studies that rely on non-ensemble model outputs.

The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part II: Forecast Performance

Abstract The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling. Significance Statement NOAA’s operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Adaptive Singular Evolutive Interpolated Kalman filter and its application to data assimilation in 2D Water pollution model

This study promotes a new algorithm for estimating the water pollution propagation with the primary goal of providing more reliable and high quality estimates to decision makers. To date, the widely used variational method suffers from the large computational burden which limits its application in practice. Moreover, this method, considering the initial state as a control variable, is very sensitive in specifying initial error, especially for unstable dynamical systems. The adaptive filter (AF), proposed in this paper, is aimed at overcoming these two drawbacks in the variational method: by its nature, the AF is sequential (no large batch assimilation window used) and stable even for unstable dynamics, with the gain parameters as control variables. The AF, developed in this paper, is an adaptive version of the Singular Evolutive Interpolated Kalman Filter (SEIKF). One of the new versions of this AF is that it uses a time-varying structure of the gain of SEIKF. To deal with the uncertainty of the system parameters and of the noise covariance, the proposed adaptive SEIKF (ASEIKF) makes use of the covariance of reduced rank iterated during assimilation process and of some pertinent gain parameters tuned adaptively to yield the minimum prediction error for the system output. The computational burden in implementation of the ASEIKF is reduced drastically due to applying the optimization tool known as a simultaneous perturbation stochastic approximation algorithm, which requires only two integrations of the numerical model. No iterative loop is required at each assimilation instant as usually happens with the standard gradient descent optimization algorithms. Data assimilation experiment, carried out by the SEIKF and ASEIKF, is implemented for the Thanh Nhan Lake in Hanoi and the performance comparison between the ASEIKF and SEIKF is given to show the high effectiveness of the proposed ASEIKF.

Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters.

This paper explores the use of a particle filter-a data assimilation method-to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents' choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.