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 Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

scholarly journals Does the Representation of Flow Structure and Turbulence at a Cold Front Converge on Multiscale Observations with Model Resolution?

2017 ◽  
Vol 145 (11) ◽  
pp. 4345-4363 ◽  
Author(s):  
Ben Harvey ◽  
John Methven ◽  
Chloe Eagle ◽  
Humphrey Lean
Keyword(s):  
Flow Structure ◽  
Cold Front ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Turbulent Fluxes ◽  
Numerical Weather Prediction Model ◽  
Shear Instability ◽  
Small Scale ◽  
Convective Boundary ◽  
Direct Evaluation

In situ aircraft observations are used to interrogate the ability of a numerical weather prediction model to represent flow structure and turbulence at a narrow cold front. Simulations are performed at a range of nested resolutions with grid spacings of 12 km down to 100 m, and the convergence with resolution is investigated. The observations include the novel feature of a low-altitude circuit around the front that is closed in the frame of reference of the front, thus allowing the direct evaluation of area-average vorticity and divergence values from circuit integrals. As such, the observational strategy enables a comparison of flow structures over a broad range of spatial scales, from the size of the circuit itself ([Formula: see text]100 km) to small-scale turbulent fluctuations ([Formula: see text]10 m). It is found that many aspects of the resolved flow converge successfully toward the observations with resolution if sampling uncertainty is accounted for, including the area-average vorticity and divergence measures and the narrowest observed cross-frontal width. In addition, there is a gradual handover from parameterized to resolved turbulent fluxes of moisture and momentum as motions in the convective boundary layer behind the front become partially resolved in the highest-resolution simulations. In contrast, the parameterized turbulent fluxes associated with subgrid-scale shear-driven turbulence ahead of the front do not converge on the observations. The structure of frontal rainbands associated with a shear instability along the front also does not converge with resolution, indicating that the mechanism of the frontal instability may not be well represented in the simulations.

scholarly journals Cluster Analysis for Object-Oriented Verification of Fields: A Variation

2008 ◽  
Vol 136 (3) ◽  
pp. 1013-1025 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Mesoscale Model ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Model Performance ◽  
Object Oriented ◽  
Previous Method ◽  
The Other ◽  
False Alarms

Abstract In a recent paper, a statistical method referred to as cluster analysis was employed to identify clusters in forecast and observed fields. Further criteria were also proposed for matching the identified clusters in one field with those in the other. As such, the proposed methodology was designed to perform an automated form of what has been called object-oriented verification. Herein, a variation of that methodology is proposed that effectively avoids (or simplifies) the criteria for matching the objects. The basic idea is to perform cluster analysis on the combined set of observations and forecasts, rather than on the individual fields separately. This method will be referred to as combinative cluster analysis (CCA). CCA naturally lends itself to the computation of false alarms, hits, and misses, and therefore, to the critical success index (CSI). A desirable feature of the previous method—the ability to assess performance on different spatial scales—is maintained. The method is demonstrated on reflectivity data and corresponding forecasts for three dates using three mesoscale numerical weather prediction model formulations—the NCEP/NWS Nonhydrostatic Mesoscale Model (NMM) at 4-km resolution (nmm4), the University of Oklahoma’s Center for Analysis and Prediction of Storms (CAPS) Weather Research and Forecasting Model (WRF) at 2-km resolution (arw2), and the NCAR WRF at 4-km resolution (arw4). In the small demonstration sample herein, model forecast quality is efficiently differentiated when performance is assessed in terms of the CSI. In this sample, arw2 appears to outperform the other two model formulations across all scales when the cluster analysis is performed in the space of spatial coordinates and reflectivity. However, when the analysis is performed only on spatial data (i.e., when only the spatial placement of the reflectivity is assessed), the difference is not significant. This result has been verified both visually and using a standard gridpoint verification, and seems to provide a reasonable assessment of model performance. This demonstration of CCA indicates promise in quickly evaluating mesoscale model performance while avoiding the subjectivity and labor intensiveness of human evaluation or the pitfalls of non-object-oriented automated verification.

scholarly journals Multifractal evaluation of simulated precipitation intensities from the COSMO NWP model

2017 ◽  
Vol 17 (23) ◽  
pp. 14253-14273 ◽  
Author(s):  
Daniel Wolfensberger ◽  
Auguste Gires ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer ◽  
Alexis Berne
Keyword(s):  
Extreme Values ◽  
Weather Prediction ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Scale Invariant ◽  
Simulated Precipitation ◽  
Wide Range ◽  
Synoptic Conditions ◽  
Spatio Temporal ◽  
Nwp Model

Abstract. The framework of universal multifractals (UM) characterizes the spatio-temporal variability in geophysical data over a wide range of scales with only a limited number of scale-invariant parameters. This work aims to clarify the link between multifractals (MFs) and more conventional weather descriptors and to show how they can be used to perform a multi-scale evaluation of model data. The first part of this work focuses on a MF analysis of the climatology of precipitation intensities simulated by the COSMO numerical weather prediction model. Analysis of the spatial structure of the MF parameters, and their correlations with external meteorological and topographical descriptors, reveals that simulated precipitation tends to be smoother at higher altitudes, and that the mean intermittency is mostly influenced by the latitude. A hierarchical clustering was performed on the external descriptors, yielding three different clusters, which correspond roughly to Alpine/continental, Mediterranean and temperate regions. Distributions of MF parameters within these three clusters are shown to be statistically significantly different, indicating that the MF signature of rain is indeed geographically dependent. The second part of this work is event-based and focuses on the smaller scales. The MF parameters of precipitation intensities at the ground are compared with those obtained from the Swiss radar composite during three events corresponding to typical synoptic conditions over Switzerland. The results of this analysis show that the COSMO simulations exhibit spatial scaling breaks that are not present in the radar data, indicating that the model is not able to simulate the observed variability at all scales. A comparison of the operational one-moment microphysical parameterization scheme of COSMO with a more advanced two-moment scheme reveals that, while no scheme systematically outperforms the other, the two-moment scheme tends to produce larger extreme values and more discontinuous precipitation fields, which agree better with the radar composite.

scholarly journals Improving Precipitation Forecasts by Generating Ensembles through Postprocessing

2015 ◽  
Vol 143 (9) ◽  
pp. 3642-3663 ◽  
Author(s):  
Durga Lal Shrestha ◽  
David E. Robertson ◽  
James C. Bennett ◽  
Q. J. Wang
Keyword(s):  
Weather Prediction ◽  
Joint Probability ◽  
Numerical Weather Prediction Model ◽  
Climatic Conditions ◽  
Lead Times ◽  
Forecast Errors ◽  
Streamflow Forecasting ◽  
Ensemble Forecasts ◽  
Modeling Approach ◽  
Wide Range

Abstract This paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach. Calibrated QPFs are produced for 10 Australian catchments that cover a wide range of climatic conditions and hydrological characteristics. The calibrated QPFs are bias free, contain smaller forecast errors than that of the raw QPFs, reliably quantify the forecast uncertainty at a range of lead times, and successfully discriminate common and rare events of precipitation occurrences at shorter lead times. The postprocessing method is able to instill realistic within-catchment spatial variability in the QPFs, which is crucial for accurate and reliable streamflow forecasting.

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 COVID-19 and Its Symptoms’ Panoply: A Case-Control Study of 919 Suspected Cases in Locked-Down Ovar, Portugal

2021 ◽  
pp. 1-8
Author(s):  
Regina Sá ◽  
Tiago Pinho-Bandeira ◽  
Guilherme Queiroz ◽  
Joana Matos ◽  
João Duarte Ferreira ◽  
...  
Keyword(s):  
Large Scale ◽  
Case Control Study ◽  
Statistical Significance ◽  
Case Control ◽  
Case Definition ◽  
Suspected Case ◽  
Risk Characteristics ◽  
Increased Risk ◽  
Wide Range ◽  
Control Study

<b><i>Background:</i></b> Ovar was the first Portuguese municipality to declare active community transmission of SARS-CoV-2, with total lockdown decreed on March 17, 2020. This context provided conditions for a large-scale testing strategy, allowing a referral system considering other symptoms besides the ones that were part of the case definition (fever, cough, and dyspnea). This study aims to identify other symptoms associated with COVID-19 since it may clarify the pre-test probability of the occurrence of the disease. <b><i>Methods:</i></b> This case-control study uses primary care registers between March 29 and May 10, 2020 in Ovar municipality. Pre-test clinical and exposure-risk characteristics, reported by physicians, were collected through a form, and linked with their laboratory result. <b><i>Results:</i></b> The study population included a total of 919 patients, of whom 226 (24.6%) were COVID-19 cases and 693 were negative for SARS-CoV-2. Only 27.1% of the patients reporting contact with a confirmed or suspected case tested positive. In the multivariate analysis, statistical significance was obtained for headaches (OR 0.558), odynophagia (OR 0.273), anosmia (OR 2.360), and other symptoms (OR 2.157). The interaction of anosmia and odynophagia appeared as possibly relevant with a borderline statistically significant OR of 3.375. <b><i>Conclusion:</i></b> COVID-19 has a wide range of symptoms. Of the myriad described, the present study highlights anosmia itself and calls for additional studies on the interaction between anosmia and odynophagia. Headaches and odynophagia by themselves are not associated with an increased risk for the disease. These findings may help clinicians in deciding when to test, especially when other diseases with similar symptoms are more prevalent, namely in winter.

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