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SpatialVx (version 0.6-2)

Spatial Forecast Verification

Description

Spatial forecast verification arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better. Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.

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Version

Install

install.packages('SpatialVx')

Monthly Downloads

344

Version

0.6-2

License

GPL (>= 2)

Maintainer

Eric Gilleland

Last Published

January 23rd, 2018

Functions in SpatialVx (0.6-2)

Forecast Verification with Cluster Analysis: The Variation

Connectivity Index

Elmore, Baldwin and Schultz Method for Field Significance for Spatial Bias Errors

FeatureMatchAnalyzer

Analyze Features of a Verification Set

Single Feature Properties

Major and Minor Axes of a Feature

Threshold-based Feature Finder

ExampleSpatialVxSet

Simulated Spatial Verification Set

Feature-Based Contingency Table

2-d Interpolation

Optical Flow Verification

S1 Score, Anomaly Correlation

Forecast Quality Index

SpatialVx-internal

Internal functions for the SpatialVx package

Force Merges in Matched Feature Objects

Raw Image Moments.

Loss functions for the spatial prediction comparison test (SPCT)

Bearing from One Spatial Location to Another

Exponential Variogram

Temporal Block Bootstrap Keeping Locations in Space Constant

Monte Carlo Degrees of Freedom

Create Several Graphics for List Objects Returned from hoods2d

Variograms for a Gridded Verification Set

Spatial Forecast Verification Shape Analysis

Centered on Square Domain Baddeley's Delta Metric

Centroid Distance Between Two Identified Objects

Spatial Verification Sets -- SpatialVx Object

Binary Location Metric Proposed in Zhu et al. (2011)

SpatialVx-package

Spatial Forecast Verification

Example Precipitation Rate Verification Set (NIMROD)

Example Verification Set

Geographic Box Plot

Identify Disjoint Sets of Connected Components

Exponential Variogram

structurogram.matrix

Structure Function for Gridded Fields

Create Surrogate Fields

Simulated Forecast and Verification Fields

Create Composite Features

Contiguous Rain Area

Cluster Analysis Verification

Combine Features/Matched Objects

Various Verification Statistics on Possibly Neighborhood-Smoothed Fields.

2-d Gaussian Mixture Models Verification

Neighborhood Verification Statistics for a Gridded Verification Set.

Spatial Forecast Verification Methods Inter-Comparison Project (ICP) Test Cases and other example verification sets

Merge and/or Match Identified Features Within Two Fields

Quilt Plot and a Matrix Plot.

Feature Interest

Binary Image Measures

Minimum Boundary Separation Feature Matching

Apply a Threshold to a Field

Upscaling Neighborhood Verification on a 2-d Verification Set

Intensity Scale (IS) Verification

Apply Traditional Forecast Verification After Wavelet Denoising

Feature-based Analysis of a Field (Image)

Field Significance Method of Elmore et al. (2006)

High-Resolution Gridded Forecast Verification Using Discrete Wavelet Decomposition

Spatial Prediction Comparison Test

Structure Function for Non-Gridded Spatial Fields.

Localization Performance Measures

Test for Equal Predictive Ability on Average Over a Regularly Gridded Space

Practically Perfect Hindcast Neighborhood Verification Method

Rigid Transformation

Variography Score

Some Common Traditional Forecast Verification Statistics.