stat: Calculate evaluation statistics from numerical vectors

In eva3dm: Evaluation of 3D Meteorological and Air Quality Models

Calculate evaluation statistics from numerical vectors

Description

Calculate statistical indexes (Number of pairs, observation average, model average, correlation, Index Of Agreement, Factor of 2, Root Mean Square Error, Mean Bias, Mean error, Normalized Mean Bias, and Normalized Mean Bias) for model evaluation

Usage

stat(
  model,
  observation,
  wd = FALSE,
  cutoff = NA,
  cutoff_NME = NA,
  nobs = 8,
  rname,
  verbose = TRUE
)

Arguments

model	numeric vector with paired model data
observation	numeric vector with paired observation data
wd	logical, set true to apply a rotation on wind direction, see notes
cutoff	(optionally the maximum) valid value for observation
cutoff_NME	(optionally the maximum) valid value for observation for NME, MFB and MFE
nobs	minimum number of observations
rname	row name
verbose	display additional information

Value

data.frame with calculated Number of pairs, observation average, model average, correlation, Index Of Agreement, Factor of 2, Root Mean Square Error, Mean Bias, Mean error, Normalized Mean Bias, and Normalized Mean Bias

Note

the option wd = TRUE applies a rotation of 360 on model wind direction to minimize the angular difference.

References

Emery, C. and Tai., E. 2001. Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes.

Monk, K. et al. 2019. Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison. Atmosphere 10(7), p. 374. doi: 10.3390/atmos10070374.

Ramboll. 2018. PacWest Newport Meteorological Performance Evaluation.

Zhang, Y. et al. 2019. Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions. Atmosphere 10(4), p. 189. doi: 10.3390/atmos10040189.

Emery, C., Liu, Z., Russell, A.G., Odman, M.T., Yarwood, G. and Kumar, N. 2017. Recommendations on statistics and benchmarks to assess photochemical model performance. Journal of the Air & Waste Management Association 67(5), pp. 582–598. doi: 10.1080/10962247.2016.1265027.

Zhai, H., Huang, L., Emery, C., Zhang, X., Wang, Y., Yarwood, G., ... & Li, L. (2024). Recommendations on benchmarks for photochemical air quality model applications in China—NO2, SO2, CO and PM10. Atmospheric Environment, 319, 120290.

Examples

model <- 1:100
data  <- model + rnorm(100,0.2)
stat(model = model, observation = data)

eva3dm documentation built on April 4, 2025, 5:11 a.m.