Ensemble MOS Test Data Set

Description

This data set gives 48-hour ahead forecasts for 2-m temperature over the last 24 hours at SeaTac (KSEA) and Portland (PDX) airports in 2007/2008 initialized at 00 hours UTC using a 12km grid. The forecasts are based on an 8 member version of the University of Washington mesoscale ensemble (Grimit and Mass 2002; Eckel and Mass 2005).

Format

A data frame with 66 rows and 16 columns:
idate the initialization date of each forecast/observation, format YYYYMMDDHH (categorical).
vdate the validation date of each forecast/observation, format YYYYMMDDHH (categorical).
latitude the latitude of each forecast/observation (numeric).
longitude the longitude of each forecast/observation (numeric).
elevation the elevation (in meters) above sea level (numeric).
station weather station identifier (categorical).
network weather network identifier (categorical). gfs,cmcg,eta,gasp,jma,ngps,tcwb forecasts from the 8 members of the ensemble (numeric). obs observed values for the weather parameters.

Details

Temperature is given in degrees Kelvin.
This is a small dataset provided for the purposes of testing. Typically forecasting would be performed on much larger datasets.

References

F. A. Eckel and C. F. Mass, Effective mesoscale, short-range ensemble forecasting, Weather and Forecasting 20:328–350, 2005.

E. P. Grimit and C. F. Mass, Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest, Weather and Forecasting 17:192–205, 2002.

Examples

  data(ensMOStest)

  ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")

  tempTestData <- ensembleData( forecasts = ensMOStest[,ensMemNames],
                                dates = ensMOStest[,"vdate"],
                                observations = ensMOStest[,"obs"],
                                station = ensMOStest[,"station"],
                                forecastHour = 48,
                                initializationTime = "00")

  tempTestFit <- ensembleMOSnormal( tempTestData, trainingDays = 30)

Results