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SpatialVx (version 1.0-2)

Spatial Forecast Verification

Description

Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) and for a tutorial on some of the main functions available here, see Gilleland (2022) .

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Version

Install

install.packages('SpatialVx')

Monthly Downloads

519

Version

1.0-2

License

GPL (>= 2)

Maintainer

Last Published

February 1st, 2024

Functions in SpatialVx (1.0-2)

Threshold-based Feature Finder

Forecast Quality Index

Major and Minor Axes of a Feature

2-d Interpolation

Single Feature Properties

Spatial-Alignment Summary Measures

Geographic Box Plot

Monte Carlo Degrees of Freedom

Temporal Block Bootstrap Keeping Locations in Space Constant

Example Verification Set

Force Merges in Matched Feature Objects

Feature-Based Contingency Table

Example Precipitation Rate Verification Set (NIMROD)

Raw Image Moments.

SpatialVx-internal

Internal functions for the SpatialVx package

Optical Flow Verification

Bearing from One Spatial Location to Another

Loss functions for the spatial prediction comparison test (SPCT)

Create Binary Fields

S1 Score, Anomaly Correlation

The Spatial Alignment Summary Measure Called G

Centroid Distance Between Two Identified Objects

Combine Features/Matched Objects

SpatialVx-package

Spatial Forecast Verification

calculate_FSSvector_from_binary_fields

Calculate FSS Values

Create Composite Features

Calculate dFSS Score Value

Centered on Square Domain Baddeley's Delta Metric

calculate_FSSwind

Calculate FSSwind Score Value

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

Cluster Analysis Verification

Variograms for a Gridded Verification Set

Merge and/or Match Identified Features Within Two Fields

Quilt Plot and a Matrix Plot.

Neighborhood Verification Statistics for a Gridded Verification Set.

Spatial Forecast Verification Shape Analysis

Exponential Variogram

Create Several Graphics for List Objects Returned from hoods2d

Identify Disjoint Sets of Connected Components

Various Verification Statistics on Possibly Neighborhood-Smoothed Fields.

Feature Interest

Exponential Variogram

Image Warping By Hand

Binary Image Measures

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

Localization Performance Measures

2-d Gaussian Mixture Models Verification

Spatial Verification Sets -- SpatialVx Object

Simulated Forecast and Verification Fields

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

Feature-based Analysis of a Field (Image)

Minimum Boundary Separation Feature Matching

Rigid Transformation

Create Surrogate Fields

Some Common Traditional Forecast Verification Statistics.

Field Significance Method of Elmore et al. (2006)

Practically Perfect Hindcast Neighborhood Verification Method

Structure Function for Non-Gridded Spatial Fields.

Apply a Threshold to a Field

structurogram.matrix

Structure Function for Gridded Fields

Intensity Scale (IS) Verification

Apply Traditional Forecast Verification After Wavelet Denoising

Spatial Prediction Comparison Test

High-Resolution Gridded Forecast Verification Using Discrete Wavelet Decomposition

Upscaling Neighborhood Verification on a 2-d Verification Set

Variography Score

ExampleSpatialVxSet

Simulated Spatial Verification Set

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

Connectivity Index

CircleHistogram

Circular Histogram

FeatureMatchAnalyzer

Analyze Features of a Verification Set

Forecast Verification with Cluster Analysis: The Variation