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SpatialVx (version 0.8)

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) .

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Version

Install

install.packages('SpatialVx')

Monthly Downloads

366

Version

0.8

License

GPL (>= 2)

Maintainer

Eric Gilleland

Last Published

March 27th, 2021

Functions in SpatialVx (0.8)

Connectivity Index

ExampleSpatialVxSet

Simulated Spatial Verification Set

Forecast Quality Index

FeatureMatchAnalyzer

Analyze Features of a Verification Set

Major and Minor Axes of a Feature

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

Threshold-based Feature Finder

Force Merges in Matched Feature Objects

CircleHistogram

Circular Histogram

2-d Interpolation

calculate_FSSwind

Calculate FSSwind Score Value

calculate_FSSvector_from_binary_fields

Calculate FSS Values

Forecast Verification with Cluster Analysis: The Variation

Single Feature Properties

The Spatial Alignment Summary Measure Called G

SpatialVx-package

Spatial Forecast Verification

Feature-Based Contingency Table

Raw Image Moments.

Example Verification Set

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

S1 Score, Anomaly Correlation

Merge and/or Match Identified Features Within Two Fields

Optical Flow Verification

Identify Disjoint Sets of Connected Components

Geographic Box Plot

SpatialVx-internal

Internal functions for the SpatialVx package

Spatial-Alignment Summary Measures

Simulated Forecast and Verification Fields

Various Verification Statistics on Possibly Neighborhood-Smoothed Fields.

Temporal Block Bootstrap Keeping Locations in Space Constant

2-d Gaussian Mixture Models Verification

Exponential Variogram

Create Several Graphics for List Objects Returned from hoods2d

Feature Interest

Binary Image Measures

Loss functions for the spatial prediction comparison test (SPCT)

Create Composite Features

Centroid Distance Between Two Identified Objects

Example Precipitation Rate Verification Set (NIMROD)

Practically Perfect Hindcast Neighborhood Verification Method

Combine Features/Matched Objects

Rigid Transformation

Spatial Verification Sets -- SpatialVx Object

Cluster Analysis Verification

Exponential Variogram

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

Apply a Threshold to a Field

Variograms for a Gridded Verification Set

Upscaling Neighborhood Verification on a 2-d Verification Set

Variography Score

Quilt Plot and a Matrix Plot.

Structure Function for Non-Gridded Spatial Fields.

Minimum Boundary Separation Feature Matching

Bearing from One Spatial Location to Another

Spatial Forecast Verification Shape Analysis

Neighborhood Verification Statistics for a Gridded Verification Set.

Monte Carlo Degrees of Freedom

Calculate dFSS Score Value

Spatial Prediction Comparison Test

High-Resolution Gridded Forecast Verification Using Discrete Wavelet Decomposition

Some Common Traditional Forecast Verification Statistics.

Feature-based Analysis of a Field (Image)

Create Binary Fields

Centered on Square Domain Baddeley's Delta Metric

structurogram.matrix

Structure Function for Gridded Fields

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

Localization Performance Measures

Intensity Scale (IS) Verification

Field Significance Method of Elmore et al. (2006)

Create Surrogate Fields

Apply Traditional Forecast Verification After Wavelet Denoising