scholarly journals Combining vLAPS and Nudging Data Assimilation

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 127
Author(s):  
Brian P. Reen ◽  
Huaqing Cai ◽  
Robert E. Dumais ◽  
Yuanfu Xie ◽  
Steve Albers ◽  
...  
Keyword(s):  
Single Case ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Local Analysis ◽  
Weather Research And Forecasting ◽  
Moist Convection ◽  
Subjective Analysis ◽  
Convective Structures ◽  
Series Of Experiments ◽  
Temporal And Spatial

The combination of techniques that incorporate observational data may improve numerical weather prediction forecasts; thus, in this study, the methodology and potential value of one such combination were investigated. A series of experiments on a single case day was used to explore a 3DVAR-based technique (the variational version of the Local Analysis and Prediction System; vLAPS) in combination with Newtonian relaxation (observation and analysis nudging) for simulating moist convection in the Advanced Research version of the Weather Research and Forecasting model. Experiments were carried out with various combinations of vLAPS and nudging for a series of forecast start times. A limited subjective analysis of reflectivity suggested all experiments generally performed similarly in reproducing the overall convective structures. Objective verification indicated that applying vLAPS analyses without nudging performs best during the 0–2 h forecast in terms of placement of moist convection but worst in the 3–5 h forecast and quickly develops the most substantial overforecast bias. The analyses used for analysis nudging were at much finer temporal and spatial scales than usually used in pre-forecast analysis nudging, and the results suggest that further research is needed on how to best apply analysis nudging of analyses at these scales.

scholarly journals Wavelet-Based Methodology for the Verification of Stochastic Submeso and Meso-Gamma Fluctuations

2015 ◽  
Vol 143 (10) ◽  
pp. 4220-4235 ◽  
Author(s):  
Astrid Suarez ◽  
David R. Stauffer ◽  
Brian J. Gaudet
Keyword(s):  
Single Case ◽  
Weather Prediction ◽  
Model Performance ◽  
Numerical Weather Prediction Model ◽  
Weather Research And Forecasting ◽  
New Approach ◽  
Frequency Distributions ◽  
Use Of Data ◽  
Rock Springs ◽  
Grid Length

Abstract Numerical weather prediction model skill is difficult to assess for transient, nonstationary, nondeterministic, or stochastic motions, like submeso and small meso-gamma motions. New approaches are needed to complement traditional methods and to quantify and evaluate the variability and the errors for these high-frequency, nondeterministic modes. A new verification technique that uses the wavelet transform as a bandpass filter to obtain scale-dependent frequency distributions of fluctuations is proposed for assessing model performance or accuracy. This new approach quantifies the nondeterministic variability independent of time while accounting for the time scale and amplitude of each fluctuation. The efficacy of this wavelet decomposition technique for the verification of submeso and meso-gamma motions is first illustrated for a single case before the analysis is extended to six cases. The sensitivity of subkilometer grid-length Weather Research and Forecasting Model forecasts to the choice of three initialization strategies is assessed for both deterministic and stochastic motions using observations from a special network located at Rock Springs, Pennsylvania. It is demonstrated that the use of data assimilation in a preforecast period results in improved temperature and wind speed statistics for deterministic motions and for nondeterministic fluctuations with periods greater than ~20 min. As expected, there is little-to-no accuracy forecasting the occurrence of variability for temperature and wind in the smaller-submeso range and greater accuracy in the larger-submeso and meso-gamma ranges. Nonetheless, the model has some difficulty reproducing the observed variability with the correct amplitude. It underestimates the amplitude of observed fluctuations even for larger time scales, where better model performance could be expected.

scholarly journals Bulk Convergence of Cloud-Resolving Simulations of Moist Convection over Complex Terrain

2012 ◽  
Vol 69 (7) ◽  
pp. 2207-2228 ◽  
Author(s):  
Wolfgang Langhans ◽  
Juerg Schmidli ◽  
Christoph Schär
Keyword(s):  
Control Volume ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Computational Domain ◽  
Moist Convection ◽  
Bulk Properties ◽  
Convective Systems ◽  
Convective Structures ◽  
Vertical Fluxes

Abstract The explicit treatment of moist convection in cloud-resolving models with kilometer-scale horizontal resolution is increasingly used for atmospheric research and numerical weather prediction purposes. However, several previous studies have implicitly questioned the physical validity of this approach, as the accurate representation of the structure and evolution of moist convective phenomena requires considerably higher resolution. Unlike these studies, which focused on single convective systems, here the convergence of bulk properties of an ensemble of moist convective cells in kilometer-scale simulations is considered. To address the convergence, the authors focus on the bulk net heating and moistening in a large control volume, the associated vertical fluxes, and the diurnal evolution of regionally averaged precipitation. Besides numerical convergence, “physical” convergence (Reynolds number increases with resolution) is addressed for two conceptually different subgrid-mixing approaches (1D mesoscale and 3D LES). Simulations are conducted for a 9-day period of diurnal summer convection over the Alps, using a large computational domain with grid spacings of 4.4, 2.2, 1.1, and 0.55 km and grid-independent topography. Results show that for the model and episode considered, the simulated bulk properties and vertical fluxes converge numerically toward the 0.55-km solution. In terms of bulk effects, differences between the simulations are surprisingly small, even within the physical convergence framework that exhibits a sensitivity of the small-scale dynamics and ensuing convective structures to the horizontal resolution. Despite some sensitivities related to the applied turbulence closure, the results support the feasibility of kilometer-scale models to appropriately represent the bulk feedbacks between moist convection and the larger-scale flow.

scholarly journals An Object-Oriented Verification of Three NWP Model Formulations via Cluster Analysis: An Objective and a Subjective Analysis

2008 ◽  
Vol 136 (9) ◽  
pp. 3392-3407 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
Hilary Lyons
Keyword(s):  
Spatial Information ◽  
Spatial Scales ◽  
Statistical Significance ◽  
Weather Prediction ◽  
Object Oriented ◽  
Objective Evaluation ◽  
Numerical Weather Prediction Model ◽  
Subjective Analysis ◽  
Timing Errors ◽  
Wide Range

Abstract Recently, an object-oriented verification scheme was developed for assessing errors in forecasts of spatial fields. The main goal of the scheme was to allow the automatic and objective evaluation of a large number of forecasts. However, processing speed was an obstacle. Here, it is shown that the methodology can be revised to increase efficiency, allowing for the evaluation of 32 days of reflectivity forecasts from three different mesoscale numerical weather prediction model formulations. It is demonstrated that the methodology can address not only spatial errors, but also intensity and timing errors. The results of the verification are compared with those performed by a human expert. For the case when the analysis involves only spatial information (and not intensity), although there exist variations from day to day, it is found that the three model formulations perform comparably, over the 32 days examined and across a wide range of spatial scales. However, the higher-resolution model formulation appears to have a slight edge over the other two; the statistical significance of that conclusion is weak but nontrivial. When intensity is included in the analysis, it is found that these conclusions are generally unaffected. As for timing errors, although for specific dates a model may have different timing errors on different spatial scales, over the 32-day period the three models are mostly “on time.” Moreover, although the method is nonsubjective, its results are shown to be consistent with an expert’s analysis of the 32 forecasts. This conclusion is tentative because of the focused nature of the data, spanning only one season in one year. But the proposed methodology now allows for the verification of many more forecasts.

scholarly journals Evaluation of high-resolution precipitation analyses using a dense station network

2015 ◽  
Vol 19 (3) ◽  
pp. 1547-1559 ◽  
Author(s):  
A. Kann ◽  
I. Meirold-Mautner ◽  
F. Schmid ◽  
G. Kirchengast ◽  
J. Fuchsberger ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Data Sets ◽  
Station Network ◽  
Temporal And Spatial ◽  
Categorical Verification

Abstract. The ability of radar–rain gauge merging algorithms to precisely analyse convective precipitation patterns is of high interest for many applications, e.g. hydrological modelling, thunderstorm warnings, and, as a reference, to spatially validate numerical weather prediction models. However, due to drawbacks of methods like cross-validation and due to the limited availability of reference data sets on high temporal and spatial scales, an adequate validation is usually hardly possible, especially on an operational basis. The present study evaluates the skill of very high-resolution and frequently updated precipitation analyses (rapid-INCA) by means of a very dense weather station network (WegenerNet), operated in a limited domain of the southeastern parts of Austria (Styria). Based on case studies and a longer-term validation over the convective season 2011, a general underestimation of the rapid-INCA precipitation amounts is shown by both continuous and categorical verification measures, although the temporal and spatial variability of the errors is – by convective nature – high. The contribution of the rain gauge measurements to the analysis skill is crucial. However, the capability of the analyses to precisely assess the convective precipitation distribution predominantly depends on the representativeness of the stations under the prevalent convective condition.

Larval Feeding: Mechanisms, Rates, and Performance in Nature

2018 ◽  
Keyword(s):  
Marine Invertebrates ◽  
Spatial Scales ◽  
Size Spectrum ◽  
Larval Feeding ◽  
Natural Habitats ◽  
Feeding Mechanism ◽  
Temporal And Spatial ◽  
And Performance ◽  
Bioenergetics Models ◽  
Many Particles

Larvae of many marine invertebrates must capture and ingest particulate food in order to develop to metamorphosis. These larvae use only a few physical processes to capture particles, but implement these processes using diverse morphologies and behaviors. Detailed understanding of larval feeding mechanism permits investigators to make predictions about feeding performance, including the size spectrum of particles larvae can capture and the rates at which they can capture them. In nature, larvae are immersed in complex mixtures of edible particles of varying size, density, flavor, and nutritional quality, as well as many particles that are too large to ingest. Concentrations of all of these components vary on fine temporal and spatial scales. Mechanistic models linking larval feeding mechanism to performance can be combined with data on food availability in nature and integrated into broader bioenergetics models to yield increased understanding of the biology of larvae in complex natural habitats.

Humans and the Environment

2013 ◽  
Keyword(s):  
Spatial Scales ◽  
Empirical Studies ◽  
Human Action ◽  
Cultural Practice ◽  
Environmental Archaeology ◽  
Archaeological Data ◽  
Temporal And Spatial ◽  
Global And Local ◽  
The Relationship ◽  
The Way

The environment has always been a central concept for archaeologists and, although it has been conceived in many ways, its role in archaeological explanation has fluctuated from a mere backdrop to human action, to a primary factor in the understanding of society and social change. Archaeology also has a unique position as its base of interest places it temporally between geological and ethnographic timescales, spatially between global and local dimensions, and epistemologically between empirical studies of environmental change and more heuristic studies of cultural practice. Drawing on data from across the globe at a variety of temporal and spatial scales, this volume resituates the way in which archaeologists use and apply the concept of the environment. Each chapter critically explores the potential for archaeological data and practice to contribute to modern environmental issues, including problems of climate change and environmental degradation. Overall the volume covers four basic themes: archaeological approaches to the way in which both scientists and locals conceive of the relationship between humans and their environment, applied environmental archaeology, the archaeology of disaster, and new interdisciplinary directions.The volume will be of interest to students and established archaeologists, as well as practitioners from a range of applied disciplines.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Sequential modelling of the Earth’s core magnetic field

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Guillaume Ropp ◽  
Vincent Lesur ◽  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Magnetic Field ◽  
Prior Information ◽  
Spatial Scales ◽  
Model Parameters ◽  
International Geomagnetic Reference Field ◽  
The Earth ◽  
Temporal And Spatial ◽  
Different Sources ◽  
Original Approach ◽  
Modelling Approach

Abstract We describe a new, original approach to the modelling of the Earth’s magnetic field. The overall objective of this study is to reliably render fast variations of the core field and its secular variation. This method combines a sequential modelling approach, a Kalman filter, and a correlation-based modelling step. Sources that most significantly contribute to the field measured at the surface of the Earth are modelled. Their separation is based on strong prior information on their spatial and temporal behaviours. We obtain a time series of model distributions which display behaviours similar to those of recent models based on more classic approaches, particularly at large temporal and spatial scales. Interesting new features and periodicities are visible in our models at smaller time and spatial scales. An important aspect of our method is to yield reliable error bars for all model parameters. These errors, however, are only as reliable as the description of the different sources and the prior information used are realistic. Finally, we used a slightly different version of our method to produce candidate models for the thirteenth edition of the International Geomagnetic Reference Field.

scholarly journals Dissecting the turbulent weather driven by mechanical AGN feedback

2020 ◽  
Vol 498 (4) ◽  
pp. 4983-5002
Author(s):  
D Wittor ◽  
M Gaspari
Keyword(s):  
Feedback Loop ◽  
Spatial Scales ◽  
Self Regulation ◽  
Cosmic Time ◽  
Massive Galaxies ◽  
Temporal And Spatial ◽  
Lagrangian Tracers ◽  
Field Unit ◽  
Integral Field ◽  
Gaseous Halo

ABSTRACT Turbulence in the intracluster, intragroup, and circumgalactic medium plays a crucial role in the self-regulated feeding and feedback loop of central supermassive black holes. We dissect the 3D turbulent ‘weather’ in a high-resolution Eulerian simulation of active galactic nucleus (AGN) feedback, shown to be consistent with multiple multiwavelength observables of massive galaxies. We carry out post-processing simulations of Lagrangian tracers to track the evolution of enstrophy, a proxy of turbulence, and its related sinks and sources. This allows us to isolate in depth the physical processes that determine the evolution of turbulence during the recurring strong and weak AGN feedback events, which repeat self-similarly over the Gyr evolution. We find that the evolution of enstrophy/turbulence in the gaseous halo is highly dynamic and variable over small temporal and spatial scales, similar to the chaotic weather processes on Earth. We observe major correlations between the enstrophy amplification and recurrent AGN activity, especially via its kinetic power. While advective and baroclinc motions are always subdominant, stretching motions are the key sources of the amplification of enstrophy, in particular along the jet/cocoon, while rarefactions decrease it throughout the bulk of the volume. This natural self-regulation is able to preserve, as ensemble, the typically observed subsonic turbulence during cosmic time, superposed by recurrent spikes via impulsive anisotropic AGN features (wide outflows, bubbles, cocoon shocks). This study facilitates the preparation and interpretation of the thermo-kinematical observations enabled by new revolutionary X-ray integral field unit telescopes, such as XRISM and Athena.

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