Data Assimilation Of Observations Research Articles

Technical note: A prototype transparent-middle-layer data management and analysis infrastructure for cosmogenic-nuclide exposure dating

Abstract. Geologic dating methods for the most part do not directly measure ages. Instead, interpreting a geochemical observation as a geologically useful parameter – an age or a rate – requires an interpretive middle layer of calculations and supporting data sets. These are the subject of active research and evolve rapidly, so any synoptic analysis requires repeated recalculation of large numbers of ages from a growing data set of raw observations, using a constantly improving calculation method. Many important applications of geochronology involve regional or global analyses of large and growing data sets, so this characteristic is an obstacle to progress in these applications. This paper describes the ICE-D (Informal Cosmogenic-Nuclide Exposure-age Database) database project, a prototype computational infrastructure for dealing with this obstacle in one geochronological application – cosmogenic-nuclide exposure dating – that aims to enable visualization or analysis of diverse data sets by making middle-layer calculations dynamic and transparent to the user. An important aspect of this concept is that it is designed as a forward-looking research tool rather than a backward-looking archive: only observational data (which do not become obsolete) are stored, and derived data (which become obsolete as soon as the middle-layer calculations are improved) are not stored but instead calculated dynamically at the time data are needed by an analysis application. This minimizes “lock-in” effects associated with archiving derived results subject to rapid obsolescence and allows assimilation of both new observational data and improvements to middle-layer calculations without creating additional overhead at the level of the analysis application.

Observation of Jet Stream Winds during NAWDEX and Characterization of Systematic Meteorological Analysis Errors

Abstract Observations across the North Atlantic jet stream with high vertical resolution are used to explore the structure of the jet stream, including the sharpness of vertical wind shear changes across the tropopause and the wind speed. Data were obtained during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) by an airborne Doppler wind lidar, dropsondes, and a ground-based stratosphere–troposphere radar. During the campaign, small wind speed biases throughout the troposphere and lower stratosphere of only −0.41 and −0.15 m s−1 are found, respectively, in the ECMWF and Met Office analyses and short-term forecasts. However, this study finds large and spatially coherent wind errors up to ±10 m s−1 for individual cases, with the strongest errors occurring above the tropopause in upper-level ridges. ECMWF and Met Office analyses indicate similar spatial structures in wind errors, even though their forecast models and data assimilation schemes differ greatly. The assimilation of operational observational data brings the analyses closer to the independent verifying observations, but it cannot fully compensate for the forecast error. Models tend to underestimate the peak jet stream wind, the vertical wind shear (by a factor of 2–5), and the abruptness of the change in wind shear across the tropopause, which is a major contribution to the meridional potential vorticity gradient. The differences are large enough to influence forecasts of Rossby wave disturbances to the jet stream with an anticipated effect on weather forecast skill even on large scales.

Variational Data Assimilation in Problems of Modeling Hydrophysical Fields in Open Water Areas

The formulation of boundary conditions at liquid (open) boundaries is a topical problem in mathematically modeling the hydrothermodynamics of open water areas. Variational data assimilation is one method allowing one to take into account liquid boundaries in models. According to the approach considered in this paper, observational data at a certain time are given and the problem is treated as an inverse one with open boundary flows as additional unknowns. This paper presents a formulation of the general problem of the variational assimilation of observational data for a model of the hydrothermodynamics of open water areas based on the splitting method. Algorithms for the variational assimilation of temperature and sea-level data at the liquid boundary are formulated and the results of numerical experiments on the use of the algorithms in the Baltic Sea circulation model are presented.

On the assimilation of environmental tracer observations for model-based decision support

Abstract. It has been advocated that history matching numerical models to a diverse range of observation data types, particularly including environmental tracer concentrations and their interpretations and derivatives (e.g., mean age), constitutes an effective and appropriate means to improve model forecast reliability. This study presents two regional-scale modeling case studies that directly and rigorously assess the value of discrete tritium concentration observations and tritium-derived mean residence time (MRT) estimates in two decision-support contexts; “value” is measured herein as both the improvement (or otherwise) in the reliability of forecasts through uncertainty variance reduction and bias minimization as a result of assimilating tritium or tritium-derived MRT observations. The first case study (Heretaunga Plains, New Zealand) utilizes a suite of steady-state and transient flow models and an advection-only particle-tracking model to evaluate the worth of tritium-derived MRT estimates relative to hydraulic potential, spring discharge and river–aquifer exchange flux observations. The worth of MRT observations is quantified in terms of the change in the uncertainty surrounding ecologically sensitive spring discharge forecasts via first-order second-moment (FOSM) analyses. The second case study (Hauraki Plains, New Zealand) employs paired simple–complex transient flow and transport models to evaluate the potential for assimilation-induced bias in simulated surface-water nitrate discharge to an ecologically sensitive estuary system; formal data assimilation of tritium observations is undertaken using an iterative ensemble smoother. The results of these case studies indicate that, for the decision-relevant forecasts considered, tritium observations are of variable benefit and may induce damaging bias in forecasts; these biases are a result of an imperfect model's inability to properly and directly assimilate the rich information content of the tritium observations. The findings of this study challenge the advocacy of the increasing use of tracers, and of diverse data types more generally, whenever environmental model data assimilation is undertaken with imperfect models. This study also highlights the need for improved imperfect-model data assimilation strategies. While these strategies will likely require increased model complexity (including advanced discretization, processes and parameterization) to allow for appropriate assimilation of rich and diverse data types that operate across a range of spatial and temporal scales commensurate with a forecast of management interest, it is critical that increased model complexity does not preclude the application of formal data assimilation and uncertainty quantification techniques due to model instability and excessive run times.

Advances in Urban Meteorological Research in China

Over the past decades, a large number of studies have been carried out in the field of urban meteorology in China. This paper summarizes the main progress in urban meteorology research from four aspects: urban meteorological observation network and field campaign, multi-scale model of urban meteorology, interaction between urban meteorology and atmospheric environment, and the impacts of urbanization on weather and climate. Major advances are as follows. China’s major cities have established or are improving comprehensive urban meteorological observation networks characterized by multi-platform, multi-variable, multi-scale, multi-link, and multi-function. Beijing, Nanjing, Shanghai, and other cities carried out urban meteorological field campaigns, which were included in the WMO research demonstration project. Wind tunnel experiments and scale-model outdoor experiments were successfully conducted. Multi-scale urban meteorological and air quality prediction numerical model systems have been developed and put into operational use. The urban heat island effect; urban impacts on precipitation, regional climate, and air quality; urban planning; and interaction between urban meteorology and atmospheric environment are extensively investigated. Finally, efforts to improve observational technology, data assimilation, and urban system modeling, to explore the impacts of urbanization on environment and human health, and to provide integrated urban hydro-meteorological climate and environmental services are planned ahead.

Arctic Sea‐Ice Variability During the Instrumental Era

AbstractArctic sea‐ice extent (SIE) has declined drastically in recent decades, yet its evolution prior to the satellite era is highly uncertain. Studies using SIE observations find little variability prior to the 1970s; however, these reconstructions are based on limited data, especially prior to the 1950s. We use ensemble Kalman filter data assimilation of surface air temperature observations with Last Millennium climate model simulations to create a fully gridded Arctic sea‐ice concentration reconstruction from 1850 to 2018 and investigate the evolution of Arctic SIE during this period. We find a decline of ∼1.25×106 km2 during the early 20th‐century warming (1910–1940). The 25‐year trends during this period are ∼33–38% smaller than the satellite era (1979–2018) but almost twice as large as previous estimates. Additionally, we find that variability of SIE on decadal timescales prior to the satellite era is ∼40% greater than previously estimated.

Disentangling environmental and economic contributions to hydro-economic model output uncertainty: An example in the context of land-use change impact assessment

This paper presents a framework to systematically compare the contributions to uncertainty in hydro-economic simulated outputs from the uncertainty surrounding input parameters employed by the hydrologic and economic models independently. We consider an illustrative case study example. An integrated modeling framework is adopted, involving a surface-water/groundwater nitrate-transport model, and a multi-regional Computable General Equilibrium model. Environmental uncertainty contributions are determined by optimizing nitrate-loading under ecologically-relevant constraint uncertainty at varying risk stances—the results of which are mapped to economic outputs. Economic uncertainty contributions are quantified through Monte-Carlo sampling of variables associated with social-accounting matrices and substitution and transformation elasticities. Results indicate that, at the study-area scale, the environmental contribution to Gross-Regional Product uncertainty is generally larger compared to that of economic uncertainty. Nevertheless, the reliability of hydro-economic outputs is shown to be highly dependent on environmental and economic sources of uncertainty. On the basis of our case study findings, we recommend that commensurate effort be focused toward enhanced assimilation of observation data in both types of models to reduce their respective uncertainties.

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

AbstractThree types of observations, aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer, surface particulate matter with diameters less than 2.5 (PM2.5) and 10 μm (PM10), and aerosol extinction coefficient (AEXT) profiles from ground‐based lidars, were separately and simultaneously assimilated using the Weather Research and Forecasting Model with the Chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART). Two cases in June and November 2018 were selected over middle and eastern China. Experiments assimilating single‐type and multiple observations were evaluated by cross validating their analysis and forecast against the three observation types. Compared to the experiment without data assimilation (DA), DA of single‐type observations is always closer to the type of observations assimilated. However, DA of aerosol optical depth or AEXT sometimes significantly degraded the error performance for PM2.5. This problem is caused by the inconsistency of bias tendencies when modeling aerosol optical properties and surface aerosol mass. It is found that WRF‐Chem tends to predict dryer air within the boundary layer over eastern China, which may have played a role in the underestimation of AEXT even when PM2.5 was overestimated. After applying a simple bias correction (BC), the problem was alleviated. DA of multiple observations with BC gives the best overall error performance when validated against all types of observations and even performs better than any DA of single‐type observations experiments in reproducing AEXT profiles. The results illustrate that BC is important in DA of multiple observations and that the simultaneous DA of aerosol observations with different vertical information can work synergistically to improve aerosol forecasts.

A Fast Tunable Blurring Algorithm for Scattered Data

A blurring algorithm with linear time complexity can reduce the small-scale content of data observed at scattered locations in a spatially extended domain of arbitrary dimension. The method works by forming a Gaussian interpolant of the input data, and then convolving the interpolant with a multiresolution Gaussian approximation of the Green's function to a differential operator whose spectrum can be tuned for problem-specific considerations. Like conventional blurring algorithms, which the new algorithm generalizes to data measured at locations other than a uniform grid, applications include deblurring and separation of spatial scales. An example illustrates a possible application toward enabling importance sampling approaches to data assimilation of geophysical observations, which are often scattered over a spatial domain, since blurring observations can make particle filters more effective at state estimation of large scales. Another example, motivated by data analysis of dynamics like ocean eddies that have strong separation of spatial scales, uses the algorithm to decompose scattered oceanographic float measurements into large-scale and small-scale components.

Assimilation and processing of observation data obtained by satellite earth sensing for monitoring the current state of heterogeneous objects on the water surface

The study is devoted to the analysis of satellite observations data assimilation to discover the necessary information for developing and verifying mathematical models of hydrodynamics and biological shallowwater kinetics. The use of satellite earth sensing data is taken to enhance information base. The possible use of neural networks with optical flow computation is considered in the study. The objective of the study is to develop a software tool used to identify the initial conditions in mathematical modeling of hydrobilogical shallow-water processes.

Analysis of circulation near the coast of Western Crimea and the region of Sevastopol with assimilation of temperature and salinity observations

Hydrophysical fields which are continuous in time and space are reconstructed in the coastal zone of the Black Sea (near the coast of Western Crimea and the region of Sevastopol) by a three-dimensional nonlinear hydrodynamic model with assimilation of observational data of the 2007 hydrological survey. A sequential optimal interpolation of temperature and salinity observations is used as the assimilation procedure. We use real atmospheric forcing and a high resolution (a horizontal grid of ~1.6×1.6 km and 31 vertical layers from 1 m to 1300 m). Mesoscale features of the currents are obtained, and coastal upwelling in the Kalamitsky Bay is reconstructed and registered in satellite observations.

Impact of Ground-Based Remote Sensing Boundary Layer Observations on Short-Term Probabilistic Forecasts of a Tornadic Supercell Event

Abstract Due to lack of high spatial and temporal resolution boundary layer (BL) observations, the rapid changes in the near-storm environment are not well represented in current convective-scale numerical models. Better representation of the near-storm environment in model initial conditions will likely further improve the forecasts of severe convective weather. This study investigates the impact of assimilating high temporal resolution BL retrievals from two ground-based remote sensing instruments for short-term forecasts of a tornadic supercell event on 13 July 2015 during the Plains Elevated Convection At Night field campaign. The instruments are the Atmospheric Emitted Radiance Interferometer (AERI) that retrieves thermodynamic profiles and the Doppler lidar (DL) that measures horizontal wind profiles. Six sets of convective-scale ensemble data assimilation (DA) experiments are performed: two control experiments that assimilate conventional and WSR-88D radar observations using either relaxation-to-prior-spread (RTPS) or the adaptive inflation (AI) technique and four experiments similar to the control but that assimilate either DL or AERI or both observations in addition to all other observations that are in the control experiments. Results indicate a positive impact of AERI and DL observations in forecasting convective initiation (CI) and early evolution of the supercell storm. The experiment that employs the AI technique to assimilate BL observations in DA enhances the humidity in the near-storm environment and low-level convergence, which in turn helps forecasting CI. The forecast improvement is most pronounced during the first ~3 h. Results also indicate that the AERI observations have a larger impact compared to DL in predicting CI.

Meridional mass transport of bottom water in the South Atlantic

Estimates of the meridional mass transport of Antarctic Bottom Water, calculated using the coupled ocean- atmosphere model called “Earth System Model” in conjunction with the original data assimilation method are presented. We used the data of the latitudinal CTD sections of temperature and salinity of the WOCE international experiment in 1991-1995 for assimilation. Estimates of the current velocities of Antarctic Bottom Water with the assimilation of observational data are given. We used the author’s data assimilation method, which was previously referred as the generalized Kalman Filter (GKF) method. In the particular case it coincides with the classical Kalman method (EnKF). We also analyze the estimates of the mass transport by the standard dynamic scheme. It is shown that model calculations with data assimilation are qualitatively the same and are quantitatively close to the estimates of geostrophic flow transport based on the dynamic method.

A New Dynamic Emissivity-retrieval Scheme Based on Wavelet Atlas Constraints and Its Application into Land Surface Satellite Data Assimilation

Accurate description of surface information, including land surface emissivity, is a prerequisite for assimilating satellite observations over land. In this study, we used an emissivity atlas, essentially an image matrix containing noise from unknown sources, to improve the assimilation of observational data. First, we proposed a wavelet method for advanced image processing to denoise the emissivity atlas and developed a wavelet emissivity atlas suitable for the temperature sounder onboard the Chinese FY-3C satellite. Second, based on a real-time pixel dynamic emissivity retrieval scheme and using the wavelet atlas as the constraint set, we constrained dynamic retrieval emissivity values to eliminate retrieval errors. Finally, we designed and optimized a new assimilation system that can successfully utilize low-level microwave sounding channels of the FY-3C satellite. The results showed that the first-guess departures of the middle- and low-level channels were significantly improved as a result of the improved surface emissivity values. Compared with the emissivity atlas scheme, over-land observational data of the middle- and low-level channels could be increased by approximately 4–8%. Moreover, the addition of a large amount of new observational data did not negatively affect the analysis fields but rather improved short-term prediction results.

The Stability Problem for a Dynamic System with the Assimilation of Observational Data

The problem of stability of a dynamic system defined by a system of differential equations to the perturbation of the initial data and with the data assimilation is considered. The assimilation of observational data is realized by the previously published author’s method. The stability condition for this data assimilation method is formulated in the classical sense of Lyapunov, and the solution of the system is corrected using observational data for a given time interval. Necessary and sufficient conditions are proposed under which this system is stable as a function of the observed values. A possible numerical experiment to test and to apply this theory is discussed.

Meridional Mass Transport of Bottom Water in the South Atlantic

Estimates of the meridional mass transport of Antarctic Bottom Water, calculated using the coupled ocean-atmosphere Earth System Model on the basis of the original data assimilation method are presented. For assimilation, we use data of the latitudinal CTD sections of temperature and salinity of the WOCE international experiment in 1991–1995. Estimates of the current velocities of Antarctic Bottom Water with the assimilation of observational data are given. We used the author’s data-assimilation method, which was previously referred to as the generalized Kalman Filter (GKF) method. In this particular case, it coincides with the classical Kalman method (EnKF). We also present the estimates of mass transport based on a standard geostrophic dynamic scheme. It is shown that model calculations with data assimilation are qualitatively the same and are quantitatively close to the estimates of the geostrophic flow transport based on the dynamic method.

Analog Data Assimilation of Along-Track Nadir and Wide-Swath SWOT Altimetry Observations in the Western Mediterranean Sea

The growing availability of ocean data brought forth by recent advancements in remote sensing, in situ measurements, and numerical models supports the development of data-driven strategies as a powerful, computationally efficient alternative to model-based approaches for the interpolation of high-resolution, gap-free, regularly gridded sea surface geophysical fields from partial satellite-derived observations. In this paper, we investigate such data-driven strategies for the spatio-temporal interpolation of sea level anomaly (SLA) fields in the Western Mediterranean Sea from satellite-derived altimetry data. We introduce and evaluate the analog data assimilation (AnDA) framework, which exploits patch-based analog forecasting operators within a classic Kalman-based data assimilation scheme. With a view toward the upcoming wide-swath surface water and ocean topography (SWOT) mission, two different types of altimetry data are assimilated: along-track nadir data and wide-swath SWOT altimetry data. Using an observing system simulation experiment, we demonstrate the relevance of AnDA as an improved interpolation method, particularly for mesoscale features in the 20- to 100-km horizontal scale range. Results report an SLA reconstruction RMSE (correlation) improvement of 42% (14%) with respect to optimal interpolation, and show a clear gain when the joint assimilation of SWOT and along-track nadir observations are considered.

Methods for assimilation of observational data in problems of the physics of the atmosphere and the ocean

In this paper we review and analyze approaches to data assimilation in geophysical hydrodynamics problems, starting with the simplest successive schemes of assimilation and ending with modern variational methods. Special attention is paid to the the study of the problem of variational assimilation in the weak formulation and construction of covariance error matrices of the optimal solution. This is a new direction, to which the author made a contribution: an optimality system is formulated for the problem of variational data assimilation in a weak formulation and algorithms for deriving the covariance error matrices of the optimal solution are developed.

Model-data fusion of hydrologic simulations and GRACE terrestrial water storage observations to estimate changes in water table depth

Despite numerous advances in continental-scale hydrologic modeling and improvements in global Land Surface Models, an accurate representation of regional water table depth (WTD) remains a challenge. Data assimilation of observations from the Gravity Recovery and Climate Experiment (GRACE) mission leads to improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of lumped water storage. However, the usually shallow groundwater compartment of many models presents a problem with GRACE assimilation techniques, as these satellite observations also represent changes in deeper soils and aquifers. To improve the accuracy of modeled groundwater estimates and allow the representation of WTD at finer spatial scales, we implemented a simple, yet novel approach to integrate GRACE data, by augmenting the Variable Infiltration Capacity (VIC) hydrologic model. First, the subsurface model structural representation was modified by incorporating an additional (fourth) soil layer of varying depth (up to 1000 m) in VIC as the bottom ‘groundwater’ layer. This addition allows the model to reproduce water storage variability not only in shallow soils but also in deeper groundwater, in order to allow integration of the full GRACE-observed variability. Second, a Direct Insertion scheme was developed that integrates the high temporal (daily) and spatial (∼6.94 km) resolution model outputs to match the GRACE resolution, performs the integration, and then disaggregates the updated model state after the assimilation step. Simulations were performed with and without Direct Insertion over the three largest river basins in California and including the Central Valley, in order to test the augmented model's ability to capture seasonal and inter-annual trends in the water table. This is the first-ever fusion of GRACE total water storage change observations with hydrologic simulations aiming at the determination of water table depth dynamics, at spatial scales potentially useful for local water management.

Sensitivity of Functionals to Observation Data in a Variational Assimilation Problem for a Sea Thermodynamics Model

A mathematical model of sea thermodynamics, developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences, is considered. A problem of variational assimilation of sea surface temperature data is studied with allowance for observational error covariance matrices. Based on variational assimilation of satellite observation data, an inverse problem of restoring the heat flux on the sea surface is solved. The sensitivity of some functionals to observation data in the variational assimilation problem is investigated, and the results of numerical experiments with the model applied to Baltic Sea dynamics are presented.