scholarly journals New Developments of the Intensity-Scale Technique within the Spatial Verification Methods Intercomparison Project

2010 ◽  
Vol 25 (1) ◽  
pp. 113-143 ◽  
Author(s):  
B. Casati
Keyword(s):  
Case Studies ◽  
Single Case ◽  
Spatial Scales ◽  
Forecast Model ◽  
Skill Score ◽  
Scale Structure ◽  
Discrete Wavelet ◽  
Intensity Scale ◽  
Verification Methods ◽  
Domain Constraints

Abstract The intensity-scale verification technique introduced in 2004 by Casati, Ross, and Stephenson is revisited and improved. Recalibration is no longer performed, and the intensity-scale skill score for biased forecasts is evaluated. Energy and its percentages are introduced in order to assess the bias on different scales and to characterize the overall scale structure of the precipitation fields. Aggregation of the intensity-scale statistics for multiple cases is performed, and confidence intervals are provided by bootstrapping. Four different approaches for addressing the dyadic domain constraints are illustrated and critically compared. The intensity-scale verification is applied to the case studies of the Intercomparison of Spatial Forecast Verification Methods Project. The geometric and synthetically perturbed cases show that the intensity-scale verification statistics are sensitive to displacement and bias errors. The intensity-scale skill score assesses the skill for different precipitation intensities and on different spatial scales, separately. The spatial scales of the error are attributed to both the size of the features and their displacement. The energy percentages allow one to objectively analyze the scale structure of the fields and to understand the intensity-scale relationship. Aggregated statistics for the Storm Prediction Center/National Severe Storms Laboratory (SPC/NSSL) 2005 Spring Program case studies show no significant differences among the models’ skill; however, the 4-km simulations of the NCEP version of the Weather Research and Forecast model (WRF4 NCEP) overforecast to a greater extent than the 2- and 4-km simulations of the NCAR version of the WRF (WRF2 and WRF4 NCAR). For the aggregated multiple cases, the different approaches addressing the dyadic domain constraints lead to similar results. On the other hand, for a single case, tiling provides the most robust and reliable approach, since it smoothes the effects of the discrete wavelet support and does not alter the original precipitation fields.

scholarly journals Intercomparison of Spatial Forecast Verification Methods: Identifying Skillful Spatial Scales Using the Fractions Skill Score

2010 ◽  
Vol 25 (1) ◽  
pp. 343-354 ◽  
Author(s):  
Marion Mittermaier ◽  
Nigel Roberts
Keyword(s):  
Spatial Scales ◽  
Wrf Model ◽  
Skill Score ◽  
Area Ratio ◽  
Forecast Verification ◽  
Care Needs ◽  
Forecast Performance ◽  
Verification Methods ◽  
Upper Level ◽  
Formed Part

Abstract The fractions skill score (FSS) was one of the measures that formed part of the Intercomparison of Spatial Forecast Verification Methods project. The FSS was used to assess a common dataset that consisted of real and perturbed Weather Research and Forecasting (WRF) model precipitation forecasts, as well as geometric cases. These datasets are all based on the NCEP 240 grid, which translates to approximately 4-km resolution over the contiguous United States. The geometric cases showed that the FSS can provide a truthful assessment of displacement errors and forecast skill. In addition, the FSS can be used to determine the scale at which an acceptable level of skill is reached and this usage is perhaps more helpful than interpreting the actual FSS value. This spatial-scale approach is becoming more popular for monitoring operational forecast performance. The study also shows how the FSS responds to forecast bias. A more biased forecast always gives lower FSS values at large scales and usually at smaller scales. It is possible, however, for a more biased forecast to give a higher score at smaller scales, when additional rain overlaps the observed rain. However, given a sufficiently large sample of forecasts, a more biased forecast system will score lower. The use of percentile thresholds can remove the impacts of the bias. When the proportion of the domain that is “wet” (the wet-area ratio) is small, subtle differences introduced through near-threshold misses can lead to large changes in FSS magnitude in individual cases (primarily because the bias is changed). Reliable statistics for small wet-area ratios require a larger sample of forecasts. Care needs to be taken in the choice of verification domain. For high-resolution models, the domain should be large enough to encompass the length scale of the typical mesoscale forcing (e.g., upper-level troughs or squall lines). If the domain is too large, the wet-area ratios will always be small. If the domain is too small, fluctuations in the wet-area ratio can be large and larger spatial errors may be missed. The FSS is a good measure of the spatial accuracy of precipitation forecasts. Different methods are needed to determine other patterns of behavior.

scholarly journals Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

2010 ◽  
Vol 138 (9) ◽  
pp. 3418-3433 ◽  
Author(s):  
Tanja Weusthoff ◽  
Felix Ament ◽  
Marco Arpagaus ◽  
Mathias W. Rotach
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Grid Point ◽  
Skill Score ◽  
Verification Methods ◽  
Driving Model ◽  
High Resolution Models ◽  
Precipitation Events ◽  
Better Than

Abstract High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods—upscaling (UP) and fractions skill score (FSS)—for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lower-resolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.

scholarly journals Making forecasters SAD: Verification of Scale, Anisotropy and Direction using wavelets.

2021 ◽  
Author(s):  
Sebastian Buschow ◽  
Petra Friederichs
Keyword(s):  
Wavelet Transform ◽  
Spatial Structure ◽  
Wavelet Transformation ◽  
Numerical Models ◽  
Spatial Scales ◽  
Forecast Model ◽  
Post Processing ◽  
Meteorological Field ◽  
Verification Methods ◽  
Atmospheric Phenomena

<p>Many atmospheric phenomena like fronts, convection and turbulence leave a distinct imprint on the spatial structure of meteorological fields such as precipitation, wind and temperature. Whether or not a forecast model is able to realistically simulate the resulting spatial correlation patterns is therefore a relevant question for model developers, forecasters and end users alike. Highly resolved numerical models have the potential to achieve this goal, but their realism is often difficult to assess objectively due to the sheer amount of data and wide variety of possible error contributions. </p><p>While some existing verification methods measure an overall “structure” error, most of these approaches are limited to precipitation fields and fail to produce specific, interpretable judgements. Here, we introduce a new structural verification technique based on the dual-tree complex wavelet transformation: The SAD-scores explicitly quantify how well the observed spatial Scales, degrees of Anisotropy and preferred Directions are represented by the simulation. Directional aspects in particular have previously often been neglected, but can be important in assessing the realism of predicted fronts, convergence lines and organized convection. </p><p>Unlike many established techniques, SAD is applicable not only to precipitation but to any meteorological field of interest. General verdicts like “the structure was predicted poorly” can be resolved into specific statements like “the modelled convection was too small in scale” or “the simulated front was too linear and rotated by an angle of X degrees”. The localized nature of the wavelets furthermore allows us to conveniently display the structural properties on a map. Lastly, making use of the inverse wavelet transform, we show how the detected structural errors can potentially be corrected, thereby leading the way towards future post-processing applications.</p>

scholarly journals Use of Multiple Verification Methods to Evaluate Forecasts of Convection from Hot- and Cold-Start Convection-Allowing Models

2013 ◽  
Vol 28 (1) ◽  
pp. 119-138 ◽  
Author(s):  
Derek R. Stratman ◽  
Michael C. Coniglio ◽  
Steven E. Koch ◽  
Ming Xue
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Spatial Scales ◽  
Cold Start ◽  
Skill Score ◽  
Test Bed ◽  
Hot Start ◽  
Verification Methods ◽  
Skill Scores ◽  
Convective Cells

Abstract This study uses both traditional and newer verification methods to evaluate two 4-km grid-spacing Weather Research and Forecasting Model (WRF) forecasts: a “cold start” forecast that uses the 12-km North American Mesoscale Model (NAM) analysis and forecast cycle to derive the initial and boundary conditions (C0) and a “hot start” forecast that adds radar data into the initial conditions using a three-dimensional variational data assimilation (3DVAR)/cloud analysis technique (CN). These forecasts were evaluated as part of 2009 and 2010 NOAA Hazardous Weather Test Bed (HWT) Spring Forecasting Experiments. The Spring Forecasting Experiment participants noted that the skill of CN’s explicit forecasts of convection estimated by some traditional objective metrics often seemed large compared to the subjectively determined skill. The Gilbert skill score (GSS) reveals CN scores higher than C0 at lower thresholds likely due to CN having higher-frequency biases than C0, but the difference is negligible at higher thresholds, where CN’s and C0’s frequency biases are similar. This suggests that if traditional skill scores are used to quantify convective forecasts, then higher (>35 dBZ) reflectivity thresholds should be used to be consistent with expert’s subjective assessments of the lack of forecast skill for individual convective cells. The spatial verification methods show that both CN and C0 generally have little to no skill at scales <8–12Δx starting at forecast hour 1, but CN has more skill at larger spatial scales (40–320 km) than C0 for the majority of the forecasting period. This indicates that the hot start provides little to no benefit for forecasts of convective cells, but that it has some benefit for larger mesoscale precipitation systems.

Comparing Post-communist Authoritarianism in Russia and China

2018 ◽  
Author(s):  
Cheng Chen
Keyword(s):  
Comparative Study ◽  
East Asia ◽  
Eastern Europe ◽  
Case Studies ◽  
Single Case ◽  
Full Range ◽  
Geographical Area ◽  
Context Sensitive ◽  
Elite Politics ◽  
Significant Divergence

The studies of post-communist Russia and China have traditionally been dominated by single-case studies and within-region comparisons. This chapter explores why the CAS of post-communist Russia and China is difficult, why it is rare, and how it could yield significant and unique intellectual payoffs. The cross-regional comparative study of anti-corruption campaigns in contemporary Russia and China is used as an example in this chapter to argue that a well-matched and context-sensitive comparison could reveal significant divergence in the elite politics and institutional capacities of these regimes that would otherwise likely be obscured by single-case studies or studies restricted to one single geographical area such as “Eastern Europe” or “East Asia.” By breaking Russia and China out of their respective “regions,” the CAS perspective thus enables us to better capture the full range of existing diversity of post-communist authoritarianism.

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

scholarly journals Can We Generalize from Case Studies?

2015 ◽  
Vol 15 (3) ◽  
pp. 152-175 ◽  
Author(s):  
Paul F. Steinberg
Keyword(s):  
Case Studies ◽  
Single Case ◽  
Case Study Research ◽  
Political Systems ◽  
Ceteris Paribus ◽  
Large N ◽  
Climate Change Politics ◽  
The Comparative Study

This article considers the role of generalization in comparative case studies, using as exemplars the contributions to this special issue on climate change politics. As a research practice, generalization is a logical argument for extending one’s claims beyond the data, positing a connection between events that were studied and those that were not. No methodological tradition is exempt from the requirement to demonstrate a compelling logic of generalization. The article presents a taxonomy of the logics of generalization underlying diverse research methodologies, which often go unstated and unexamined. I introduce the concept of resonance groups, which provide a causeway for cross-system generalization from single case studies. Overall the results suggest that in the comparative study of complex political systems, case study research is, ceteris paribus, on par with large-N research with respect to generalizability.

scholarly journals Coupling between Large-Scale Atmospheric Processes and Mesoscale Land–Atmosphere Interactions in the U.S. Southern Great Plains during Summer. Part II: Mean Impacts of the Mesoscale

2004 ◽  
Vol 5 (6) ◽  
pp. 1247-1258 ◽  
Author(s):  
Christopher P. Weaver
Keyword(s):  
Case Studies ◽  
Time Scales ◽  
Great Plains ◽  
Large Scale ◽  
Spatial Scales ◽  
Surface Heterogeneity ◽  
Atmospheric Modeling ◽  
Mesoscale Circulations ◽  
Southern Great Plains ◽  
Atmospheric Processes

Abstract This is Part II of a two-part study of mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. Part I focused on case studies drawn from monthlong (July 1995–97), high-resolution Regional Atmospheric Modeling System (RAMS) simulations carried out to investigate these interactions. These case studies were chosen to highlight key features of the lower-tropospheric mesoscale circulations that frequently arise in this region and season due to mesoscale heterogeneity in the surface fluxes. In this paper, Part II, the RAMS-simulated mesoscale dynamical processes described in the Part I case studies are examined from a domain-averaged perspective to assess their importance in the overall regional hydrometeorology. The spatial statistics of key simulated mesoscale variables—for example, vertical velocity and the vertical flux of water vapor—are quantified here. Composite averages of the mesoscale and large-scale-mean variables over different meteorological or dynamical regimes are also calculated. The main finding is that, during dry periods, or similarly, during periods characterized by large-scale-mean subsidence, the characteristic signature of surface-heterogeneity-forced mesoscale circulations, including enhanced vertical motion variability and enhanced mesoscale fluxes in the lowest few kilometers of the atmosphere, consistently emerges. Furthermore, the impact of these mesoscale circulations is nonnegligible compared to the large-scale dynamics at domain-averaged (200 km × 200 km) spatial scales and weekly to monthly time scales. These findings support the hypothesis that the land– atmosphere interactions associated with mesoscale surface heterogeneity can provide pathways whereby diurnal, mesoscale atmospheric processes can scale up to have more general impacts at larger spatial scales and over longer time scales.

The Phantom of Upgrading in Agricultural Supply Chains

2021 ◽  
Keyword(s):  
Collective Action ◽  
Case Studies ◽  
International Organizations ◽  
Single Case ◽  
Economic Conditions ◽  
Value Chains ◽  
Small Farmers ◽  
The Social ◽  
Political Economic ◽  
Social Upgrading

This book addresses the controversies surrounding smallholders’ opportunities for economic and social upgrading by joining global agricultural value chains (AVC). While international organizations encourage small farmers to become part of AVC, critics point out its risks. Unlike previous single case studies, researchers from three continents compared the influence of the characteristics of the crop (coffee, mango, rice), the end markets, and the national political economic contexts on the social and economic conditions for smallholders and agricultural workers. Their findings highlight the importance of collective action by smallholders and of a supportive state for economic and social upgrading. With contributions by Angela Dziedzim Akorsu, Do Quynh Chi; Francis Enu Kwesi, Daniel James Hawkins, Jakir Hossain, Khiddir Iddris, Clesio Marcelino de Jesus, Manish Kumar, Michele Lindner, Mubashir Mehdi, Rosa Maria Vieira Medeiros, Antonio Cesar Ortega, Thales Augusto Medeiros Penha, Bruno Perosa, Sérgio Schneider and Santosh Verma.

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