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 = T
)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
Note
the option wd = TRUE apply 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)
#> n Obs Sim r IOA FA2 RMSE MB ME
#> 1 100 50.79421 50.5 0.9992839 0.9996105 0.99 1.14183 -0.2942089 0.921157
#> NMB (%) NME (%)
#> 1 -0.5792175 1.813508