Data Assimilation Of Observations Research Articles

A New GRACE Downscaling Approach for Deriving High‐Resolution Groundwater Storage Changes Using Ground‐Based Scaling Factors

AbstractTo compensate for the coarse resolution of groundwater storage (GWS) estimation by the Gravity Recovery and Climate Experiment (GRACE) satellites and make better use of available observed groundwater‐level (GWL) data in some aquifers, a ground‐based scaling factor (SF) method is proposed here to derive high‐resolution GRACE GWS estimates. Improvement is achieved by using the gridded SF derived from assimilating ground‐based GWL observations. The proposed SF method is tested on the North China Plain (NCP, ∼140,000 km2), where a dense network of observation wells and a consistently estimated specific yield (SY) data set are available, to demonstrate its effectiveness and practical applications. The sensitivities of SF‐estimated GWS accuracy to the specification of SY and the assimilation of GWL observation data are explored through four designed numerical experiments. Results show that this novel ground‐based method can reduce the impact of SY uncertainty on GWS estimates, particularly in regions with more pronounced regional GWS trends. The accuracy of SF‐estimated GWS is primarily determined by whether the assimilated wells can reflect the regionally averaged GWS trend. GWS accuracy is less dependent on the number of available wells assimilated. The estimated GWS trend (2004–2015) in NCP is −32.6 ± 1.3 mm/yr (−4.6 ± 0.2 km3/yr), with contrasting GWS trends found in the west Piedmont Plain (∼54,000 km2, with a loss of −66.8 mm/yr) and the coastal Eastern Plain (∼20,000 km2, and a gain of +7.2 mm/yr). Despite the limitations of regional and time scale dependence inherent in SF method, this study highlights the benefits of assimilating in situ observed GWL data instead of using model simulations in estimating SF to downscale GRACE GWS to the higher‐resolution that is desired by local water resources management.

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without a Summer of 1816

Abstract. The “Year Without a Summer” in 1816 was characterized by extraordinarily cold and wet periods in central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective, and beyond its tragic consequences, the summer of 1816 represents a rare opportunity to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th century land use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g., regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, followed by a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing, and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

Application of Methods of Observational Data Assimilation to Model the Spread of Pollutants in a Reservoir and Manage Sustainable Development

Application of Methods of Observational Data Assimilation to Model the Spread of Pollutants in a Reservoir and Manage Sustainable Development

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part I: Background and Methods

Abstract Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm−1), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm−1) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are difficult to model, like non-Local Thermodynamic Equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In part one of this study, as presented here, the amount of unique information (as determined by Empirical Orthogonal Decomposition (EOD)) made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA’s Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part two of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.

Vertical distribution and transport characteristics of ozone pollution based on lidar observation network and data assimilation over the Pearl River Delta, China

In recent years, China has made certain progresses regarding the monitoring of ozone (O3) pollution. However, a comprehensive understanding of the three-dimensional distribution and evolutionary characteristics of O3 is still relatively lacking. This study analyzed an O3 pollution episode in the Pearl River Delta during September to October 2019 using data from five O3 lidars and air pollution monitoring stations combined with the Gridpoint Statistical Interpolation data assimilation system. The model's performance in the vertical direction improved after assimilation, with the correlation coefficient of model simulations improving by 87 %, errors decreasing by 30 %, and simulation accuracy increasing by 29 %. Results showed that O3 pollution persisted for more than a week due to high temperatures and the influence of Typhoon Mita. However, after October 2, the concentration of O3 decreased under the combined effects of southerly winds and reduced traffic emissions during the National Day period. Besides, lidar observations indicate that residual O3 at night can disrupt its typical diurnal variation patterns. Moreover, O3 pollution occurred both at ground level and greater heights. Overall, O3 is primarily produced by photochemical reactions. Nevertheless, the O3 concentration was also affected by pollutant transport from northeast to southwest at a height of 0–3 km during the pollution period, with an average transport flux of 366 μg·m−2·s−1. Anomalously high nighttime O3 concentrations (>100 μg·m−3) were observed in both coastal and inland cities. Residual O3 could lead to persistent pollution the next day. The results clarify the vertical distribution and evolutionary characteristics of O3, which is crucial for devising more targeted pollution control strategies in the Pearl River Delta.

Simultaneous Multiscale 4DEnVar With Scale Dependent Localization (SDL) in Hurricane Analysis and Forecast System (HAFS)

AbstractThis 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.

Assimilation of Observational Data in the COSMO-Ru Short-range Numerical Weather Prediction System of the Hydrometcenter of Russia

Assimilation of Observational Data in the COSMO-Ru Short-range Numerical Weather Prediction System of the Hydrometcenter of Russia

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Impact of Assimilation of the Tropical Cyclone Strong Winds Observed by Synthetic Aperture Radar on Analyses and Forecasts

Abstract A few high-wind observations have been obtained from satellites over the ocean around tropical cyclones (TCs), but the impact of data assimilation of such observations over the sea on forecasting has not been clear. The spaceborne synthetic aperture radar (SAR) provides high-resolution and wide-area ocean surface wind speed data around the center of a TC. In this study, the impact of data assimilation of the ocean surface wind speed of SAR (OWSAR) on regional model forecasts was investigated. The assimilated data were estimated from SAR on board Sentinel-1 and RADARSAT-2. The bias of OWSAR depends on wind speed, the observation error variance depends on wind speed and incidence angle, and the spatial observation error correlation depends on the incidence angle. The observed OWSAR is screened using the variational quality control method with the Huber norm. In the case of Typhoon Hagibis (2019), OWSAR assimilation modified the TC low-level inflow, which also modified the TC upper-level outflow. The propagation of this OWSAR assimilation effect from the surface to the upper troposphere was given by a four-dimensional variational method that searches for the optimal solution within strong constraints on the time evolution of the forecast model. Statistical validation confirmed that errors in the TC intensity forecast decreased over lead times of 15 h, but this was not statistically significant. The validation using wind profiler observations showed that OWSAR assimilation significantly improved the accuracy of wind speed predictions from the middle to the upper level of the troposphere. Significance Statement The purpose of this study was to demonstrate the impact of the assimilation of ocean surface wind speed by synthetic aperture radar (SAR) on regional model predictions. In the case of tropical cyclones, ocean surface wind speed assimilation modified inflows in the lower layer and outflows in the upper layer. The results indicate that the SAR assimilation improves the accuracy of wind speed forecasts in the middle to upper troposphere.

A new Wind Atlas to support the expansion of the Italian wind power fleet

AbstractAs a contribution to national strategic energy planning, recent developments in meteorological modeling and wind generation technologies have improved the representation of the spatio‐temporal features of wind. This paper describes an updated Italian Wind Atlas (Atlante EOLico ItaliANo [AEOLIAN]) released in the early 2000s. The objective of AEOLIAN is to guide future wind generation to accord with ambitious European greenhouse gas emission targets set for 2030 and 2050. AEOLIAN is the result of a collaboration effort between Ricerca sul Sistema Energetico (RSE) SpA and the National Center for Atmospheric Research (NCAR), which jointly developed a novel approach combining high‐resolution numerical weather modeling with the Analog Ensemble (AnEn) statistical technique. This paper uses dynamical model runs with hourly output for 1990–2019 with 4 km horizontal grid spacing. For 2015–2019, an inner grid nest with 1.33 km horizontal grid spacing is used. The AnEn is then employed to temporally extend the 5 years of high‐resolution runs back through 1990–2014 to create a 30‐year dataset for Italy and surrounding marine areas. A thorough verification is carried out using 104 observational stations homogeneously distributed throughout the territory. Compared with other state‐of‐the‐art products, AEOLIAN provides enhanced accuracy over complex terrain thanks to higher horizontal resolution and the assimilation of observational wind data over the domain, which result in a reduction of model bias on complex terrain and a better reconstruction of the wind distributions. Finally, a new WebGIS interface (https://atlanteeolico.rse-web.it/) to explore AEOLIAN data is described.

Including the Horizontal Observation Error Correlation in the Ensemble Kalman Filter: Idealized Experiments with NICAM-LETKF

Abstract Densely observed remote sensing data such as radars and satellites generally contain significant spatial error correlations. In data assimilation, the observation error covariance matrix is usually assumed to be diagonal, and the dense data are thinned or spatially averaged to compensate for neglecting the spatial observation error correlation. However, in theory, including the spatial observation error correlation in data assimilation can make better use of the dense data. This study performs perfect model observing system simulation experiments (OSSEs) using the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) to assess the impact of assimilating horizontally dense and error-correlated observations. The condition number of the observation error covariance matrix, defined as the ratio of the largest to smallest eigenvalues, is important for the numerical stability of the LETKF computation. A large condition number makes it difficult to compute the ensemble transform matrix correctly. Reducing the condition number by reconditioning is found effective for stable computation. The results show that including the horizontal observation error correlation with reconditioning makes the analysis more accurate but requires 6 times more computations than the case with the diagonal observation error covariance matrix. Significance Statement It is important to effectively utilize observations in data assimilation for more accurate weather prediction. Spatially dense observations are known to have an error correlation that is ignored in the data assimilation. This study explores assimilating dense observations by explicitly including observation error correlations with an idealized experiment. The results shows that the analysis is improved by including the observation error correlations. Also, the condition number of the observation error covariance matrix is essential for stable computations.

The study of the local sensitivity of functionals of the optimal solution to observational data and the heat flux input data in a variational assimilation problem for the sea thermodynamics model

Abstract The problem of variational assimilation of observational data for the sea thermodynamics model is considered with the aim to reconstruct heat fluxes on the sea surface. The local sensitivity of functionals of the solution to the observational data and input data for heat fluxes is studied in the considered problem and the results of numerical experiments are presented for the Black Sea dynamics model.

Data assimilation of PS-InSAR vertical deformation into a frost heave model to analyze subgrade deformation of high-speed railway in northwest China

Temperature changes may cause irregular soil uplift or thawing settlements in frozen soil areas, potentially affecting the safe operation of High-Speed Railways (HSR). Analyzing and predicting these deformation characteristics is thus critical. However, the conventional forecasting and analysis techniques rarely considered factors such as dynamic parameter variations, uncertainties, and measurement errors, which hinder accurate regional scale forecasting. To bridge this gap, this paper introduces a novel time-series coupling method, which integrates post-processing deformation from Interferometric Synthetic Aperture Radar (InSAR) with a frost heave model (FHM), facilitated by the ensemble Kalman filter (EnKF) assimilation algorithm. We obtained deformation observations along the HSR using Persistent Scatterer InSAR (PS-InSAR) technology in combination with time-series post-processing techniques. Considering the causative factors for deformation, we structured the FHM. By integrating FHM with observational data using the EnKF algorithm achieved an efficient upgrade of the posterior distribution of model parameters. This integration significantly improves the predictive accuracy, it facilitates an efficient update to the posterior distribution of model parameters, leading to enhanced prediction accuracy of our model. Our experimental results indicate that the effectiveness of this approach, with observational data assimilation into FHM reducing the average Root Mean Square Error (RMSE) to a mere 0.247 mm. Concurrently, both the Normalized Reduction Error Index (NER) and the Assimilation Efficiency Factor (EFF) values surpassed 0.60 and 0.84 respectively. These underlines signify a successful update of our model parameters, which in turn elevates the accuracy of future deformation predictions, thereby promising safer railway operations.

Variational Data Assimilation for Ocean Thermodynamics Model and Sensitivity of Marine Characteristics to Observation Errors

The methodology of variational assimilation of observational data for the restoration of the initial state and heat fluxes for the mathematical model of sea thermodynamics is presented. An algorithm is developed for estimating the sensitivity of a model solution to errors in observational data. Calculation of the gradient of the response function of the model solution is based on the use of the Hessian of the cost functional. The results of numerical experiments for the Black Sea dynamics model developed at INM RAS are presented.

Numerical Experiments with the Nemo Ocean Circulation Model and the Assimilation of Observational Data from Argo Drifters

Numerical Experiments with the Nemo Ocean Circulation Model and the Assimilation of Observational Data from Argo Drifters

Optimal parameters for the ocean's nutrient, carbon, and oxygen cycles compensate for circulation biases but replumb the biological pump

Abstract. Accurate predictive modeling of the ocean's global carbon and oxygen cycles is challenging because of uncertainties in both biogeochemistry and ocean circulation. Advances over the last decade have made parameter optimization feasible, allowing models to better match observed biogeochemical fields. However, does fitting a biogeochemical model to observed tracers using a circulation with known biases robustly capture the inner workings of the biological pump? Here we embed a mechanistic model of the ocean's coupled nutrient, carbon, and oxygen cycles into two circulations for the current climate. To assess the effects of biases, one circulation (ACCESS-M) is derived from a climate model and the other from data assimilation of observations (OCIM2). We find that parameter optimization compensates for circulation biases at the expense of altering how the biological pump operates. Tracer observations constrain pump strength and regenerated inventories for both circulations, but ACCESS-M export production optimizes to twice that of OCIM2 to compensate for ACCESS-M having lower sequestration efficiencies driven by less efficient particle transfer and shorter residence times. Idealized simulations forcing complete Southern Ocean nutrient utilization show that the response of the optimized system is sensitive to the embedding circulation. In ACCESS-M, Southern Ocean nutrient and dissolved inorganic carbon (DIC) trapping is partially short circuited by unrealistically deep mixed layers. For both circulations, intense Southern Ocean production deoxygenates Southern-Ocean-sourced deep waters, muting the imprint of circulation biases on oxygen. Our findings highlight that the biological pump's plumbing needs careful assessment to predict the biogeochemical response to ecological changes, even when optimally matching observations.

Assimilation of Expedition Observation Data on Water Temperature and Salinity in the Region of Drake Passage

Assimilation of Expedition Observation Data on Water Temperature and Salinity in the Region of Drake Passage

Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation

The main aim of this work is to study the spatial–temporal variability of the model’s physical and spectral characteristics in the process of assimilation of observed ocean surface height data from the AVISO (Archiving, Validating and Interpolation Satellite Observation) archive in combination with the NEMO (Nucleus for European Modeling of the Ocean) ocean circulation model for a period of two months. For data assimilation, the GKF (Generalized Kalman filter) method, previously developed by the authors, is used. The purpose of this work is to study the spatial–temporal structure of the simulated characteristics using decomposition into eigenvalues and eigenvectors (Karhunen–Loeve decomposition method). The feature of the GKF method is the fact that the constructed Kalman weight matrix multiplied by the vector of observational data can be represented as a weighted sum of eigenvectors and eigenvalues (spectral characteristics of the matrix), which describe the spatial and temporal structure of corrections to the model. The main investigations are focused on the North Atlantic. Their variability in time and space is estimated in this study. Calculations of the main ocean characteristics, such as the surface height, temperature, salinity, and the current velocities on the surface and in the depths, both with and without assimilation of observational data, over a time interval of 60 days, were performed by using a high-performance computing system. The calculation results have shown that the main spatial variability of characteristics after data assimilation is consistent with the localization of the currents in the North Atlantic.

Valid Time Shifting for an Experimental RRFS Convection-Allowing EnVar Data Assimilation and Forecast System: Description and Systematic Evaluation in Real Time

Abstract This study describes a real-time implementation of valid time shifting (VTS) within the Gridpoint Statistical Interpolation–based ensemble-variational (EnVar) data assimilation system, developed at the Multi-Scale Data Assimilation and Predictability Laboratory. This system, featuring data assimilation of mesoscale conventional observations and storm-scale radar reflectivity observations and interfaced with the next-generation Finite Volume Cubed Sphere Dynamical Core limited-area model (FV3-LAM), was run in real-time during the 2021 Hazardous Weather Testbed Spring Forecast Experiment. The VTS method efficiently increases ensemble size by incorporating ensemble forecast output before and after the central analysis. Two configurations were examined to systematically evaluate VTS: a baseline 36-member system with hourly data assimilation (NOVTS), and an experiment testing VTS for the radar analysis step. Verification across 22 cases shows statistically significant benefits of VTS to increase ensemble spread and better fit first guesses to observations. Control member forecasts launched at 0000 UTC have consistently higher skill, lower bias, and higher reliability in VTS than in NOVTS throughout the 18-h forecast evaluation period, especially from severe cases often featuring upscale growth into mesoscale convective systems. Verification of updraft helicity-based ensemble surrogate severe probabilistic forecasts against observed storm reports shows higher skill of VTS when verifying on finer scales, with benefits to constraining higher probabilities over report locations and reducing probabilities over no-report locations. This study is a first step toward the next-generation Rapid Refresh Forecast System (RRFS), demonstrating the feasibility of such a real-time system and the potential benefits of VTS implementation.

Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlight the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (atmosphere-wave-ocean).