Learn R Programming

⚠️There's a newer version (1.0-3) of this package.Take me there.

SpatialVx (version 1.0-1)

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) .

Copy Link

Version

Install

install.packages('SpatialVx')

Monthly Downloads

366

Version

1.0-1

License

GPL (>= 2)

Maintainer

Eric Gilleland

Last Published

November 8th, 2022

Functions in SpatialVx (1.0-1)

Forecast Quality Index

Connectivity Index

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

Threshold-based Feature Finder

Forecast Verification with Cluster Analysis: The Variation

ExampleSpatialVxSet

Simulated Spatial Verification Set

CircleHistogram

Circular Histogram

Major and Minor Axes of a Feature

FeatureMatchAnalyzer

Analyze Features of a Verification Set

2-d Interpolation

Temporal Block Bootstrap Keeping Locations in Space Constant

Monte Carlo Degrees of Freedom

Example Verification Set

Geographic Box Plot

Spatial-Alignment Summary Measures

Raw Image Moments.

Force Merges in Matched Feature Objects

Single Feature Properties

Feature-Based Contingency Table

Bearing from One Spatial Location to Another

Example Precipitation Rate Verification Set (NIMROD)

Loss functions for the spatial prediction comparison test (SPCT)

Create Binary Fields

The Spatial Alignment Summary Measure Called G

SpatialVx-internal

Internal functions for the SpatialVx package

SpatialVx-package

Spatial Forecast Verification

Optical Flow Verification

S1 Score, Anomaly Correlation

Combine Features/Matched Objects

Create Composite Features

Centroid Distance Between Two Identified Objects

calculate_FSSvector_from_binary_fields

Calculate FSS Values

calculate_FSSwind

Calculate FSSwind Score Value

Cluster Analysis Verification

Merge and/or Match Identified Features Within Two Fields

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

Identify Disjoint Sets of Connected Components

Exponential Variogram

Calculate dFSS Score Value

Create Several Graphics for List Objects Returned from hoods2d

Centered on Square Domain Baddeley's Delta Metric

Exponential Variogram

Neighborhood Verification Statistics for a Gridded Verification Set.

Quilt Plot and a Matrix Plot.

Variograms for a Gridded Verification Set

Spatial Forecast Verification Shape Analysis

Various Verification Statistics on Possibly Neighborhood-Smoothed Fields.

2-d Gaussian Mixture Models Verification

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

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

Binary Image Measures

Feature Interest

Image Warping By Hand

Minimum Boundary Separation Feature Matching

Spatial Verification Sets -- SpatialVx Object

Simulated Forecast and Verification Fields

Localization Performance Measures

Spatial Prediction Comparison Test

Field Significance Method of Elmore et al. (2006)

Apply a Threshold to a Field

Create Surrogate Fields

Practically Perfect Hindcast Neighborhood Verification Method

Rigid Transformation

Feature-based Analysis of a Field (Image)

Structure Function for Non-Gridded Spatial Fields.

structurogram.matrix

Structure Function for Gridded Fields

Some Common Traditional Forecast Verification Statistics.

Apply Traditional Forecast Verification After Wavelet Denoising

Intensity Scale (IS) Verification

Upscaling Neighborhood Verification on a 2-d Verification Set

High-Resolution Gridded Forecast Verification Using Discrete Wavelet Decomposition

Variography Score