Ensemble Kalman filter assimilation of Doppler radar data for the initialization and prediction of convective storms.

Finally, the EnSRF is applied to the May 29-30, 2004 central Oklahoma tornadic thunderstorm case. The initial storm environment is either horizontally homogeneous as defined by a single sounding or is three dimensional as obtained from a 3DVAR analysis of all available conventional observations. A full suite of model physics is employed in the latter case. Radial velocity and reflectivity from either one or two WSR-88D radars are assimilated. The results of the EnSRF analysis and the subsequent forecast are presented and a number of issues are discussed.For convective-scale prediction, microphyiscs parameterization is a major source of model error. Parameter estimation via state augmentation using an variant of EnKF, the ensemble square root filter (EnSRF), is applied to the correction of errors in fundamental parameters common in single-moment ice microphysics schemes, after parameter sensitivity and identifiability are examined. OSSEs are performed in which individual parameters are estimated separately or in different combinations. The estimation of individual parameters is successful while the level of difficulty increases as more parameters are estimated simultaneously. Explanations will be given as to why under certain circumstances the filter fails to estimate the correct values of parameters. Still, the state estimation is generally improved even when estimated parameters are inaccurate.As a first implementation, the simulated observations of radial velocity and reflectivity for a supercell thunderstorm are directly assimilated. The EnKF method is found to be able to retrieve accurately multiple microphysical species associated with a multi-class ice microphysics scheme. The relative role of radial velocity and reflectivity data as well as their spatial coverage in recovering the full flow and cloud fields are compared. The cross-covariance is shown to play an important role in retrieving variables indirectly related to the observations.Assimilation of Doppler radar data is very important for storm-scale NWP. To retrieve dynamically consistent wind, thermodynamic and microphysical fields from radar radial velocity and reflectivity, advanced data assimilation methods are required. This work explores the ability of the ensemble Kalman filter (EnKF) methods in assimilating Doppler radar data for thunderstorm initialization and prediction, as well as parameter estimation

The changes of cardiac energy metabolism with sodium-glucose transporter 2 inhibitor therapy

Background/aimsTo investigate the specific effects of s odium-glucose transporter 2 inhibitor (SGLT2i) on cardiac energy metabolism.MethodsA systematic literature search was conducted in eight databases. The retrieved studies were screened according to the inclusion and exclusion criteria, and relevant information was extracted according to the purpose of the study. Two researchers independently screened the studies, extracted information, and assessed article quality.ResultsThe results of the 34 included studies (including 10 clinical and 24 animal studies) showed that SGLT2i inhibited cardiac glucose uptake and glycolysis, but promoted fatty acid (FA) metabolism in most disease states. SGLT2i upregulated ketone metabolism, improved the structure and functions of myocardial mitochondria, alleviated oxidative stress of cardiomyocytes in all literatures. SGLT2i increased cardiac glucose oxidation in diabetes mellitus (DM) and cardiac FA metabolism in heart failure (HF). However, the regulatory effects of SGLT2i on cardiac FA metabolism in DM and cardiac glucose oxidation in HF varied with disease types, stages, and intervention duration of SGLT2i.ConclusionSGLT2i improved the efficiency of cardiac energy production by regulating FA, glucose and ketone metabolism, improving mitochondria structure and functions, and decreasing oxidative stress of cardiomyocytes under pathological conditions. Thus, SGLT2i is deemed to exert a benign regulatory effect on cardiac metabolic disorders in various diseases.Systematic review registrationhttps://www.crd.york.ac.uk/, PROSPERO (CRD42023484295)

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

VOLUME 136 MONTHLY WEATHER REVIEW S OMETIME

ABSTRACT The possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated, for a model-simulated supercell storm, by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values. The sensitivity of pure model forecast as well as model state estimation to the parameter values, and the solution uniqueness of the estimation problem are examined. The ensemble square-root filter (EnSRF) is employed for model state estimation. Both forecast and assimilation sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. Among the three intercept parameters, the pure forecast is most (least) sensitive to rain (snow) intercept while the sensitivity to hail density is generally lager than that to snow density. The analyzed model state in the assimilation sensitivity experiments is, however, found to be most (least) sensitive to hail (rain) intercept, and there is a larger sensitivity to hail density than to snow density. The time scales of analysis response to errors in individual parameters are also investigated. The results suggest that a successful estimation of the parameters can be expected within the typical lengths of assimilation window needed for state estimation. The response functions calculated for the forecast as well as assimilation sensitivity experiments for all five individual parameters show concave shapes, with unique minima occurring at or very close to the true values; therefore true values of these parameters can be retrieved at least in these cases where only one parameter contains error at a time. The identifiability of multiple parameters together is evaluated from their correlations with forecast reflectivity. Significant levels of correlations are found that can be interpreted physically. As the number of uncertain parameters increases, both the level and the area coverage of significant correlations decrease, implying increased difficulties with multiple-parameter estimation. The details of the estimation procedure and the results of a complete set of estimation experiments will be presented in Part II of this paper. ____________________________

Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments

A Doppler radar data assimilation system is developed based on an ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, it is assume

Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments

The ensemble Kalman filter method is applied to correct errors in five fundamental microphysical parameters that are closely involved in the definition of drop/particle size distributions of microphysical species in a commonly used single-moment ice microphysics scheme, for a model-simulated supercell storm, using radar data. The five parameters include the intercept parameters for rain, snow and hail/graupel, and the bulk densities of hail/graupel and snow. The ensemble square-root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation. The five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. A data selection procedure based on correlation information is introduced, which, combined with variance inflation, effectively avoids the collapse of the spread of parameter ensemble hence filter divergence. Our parameter estimation results demonstrate, for the first time, that the ensemble-based method can be used to correct model errors in microphysical parameters through simultaneous state and parameter estimation, using radar reflectivity observations. When error exists in only one of the microphysica