Changes between Version 9 and Version 10 of udg/ecoms/RPackage/examples/drift

Ignore:
Timestamp:
May 15, 2015 2:55:17 PM (7 years ago)
Comment:

--

Legend:

Unmodified
 v9 = Analysing model drift in South-western Iberia In this practice, we will analyse the model drift by using the forecast of daily mean (time = "DD") surface temperature (var = "tas") for July (season = 7) 2006 (years = 2006) considering 6 different forecast (lead) months, from January to June. The lead month 0 (i.e., the initialization of July itself) will be the reference from which we will compute the anomalies (leadMonth = 0). In this example, we will consider the first member (members = 1) of the CFSv2 hindcast as reference: In this practice, we will analyse the model drift by using the forecast of daily mean (time = "DD", aggr.d = "mean") surface temperature (var = "tas") for July (season = 7) 2006 (years = 2006) considering 6 different forecast (lead) months, from January to June. The lead month 0 (i.e., the initialization of July itself) will be the reference from which we will compute the anomalies (leadMonth = 0). In this example, we will consider the first member (members = 1) of the CFSv2 hindcast as reference: {{{#!text/R ref <- loadECOMS(dataset = "CFSv2_seasonal_16", ref <- loadECOMS(dataset = "CFSv2_seasonal", var = "tas", members = 1, years = 2006, leadMonth = 0, time = "DD", aggr.d = "mean") time = "DD") }}} Messages on-screen inform about the loading process. Note that the selection of leadMonth = 0 will give a NOTE on screen: Messages on-screen inform about the loading process. Note that the selection of leadMonth = 0 will give a NOTE: {{{ [2014-09-03 17:50:20] Defining homogeneization parameters for variable "tas" NOTE: daily mean will be calculated from the 6-h model output [2015-05-15 14:50:46] Defining homogeneization parameters for variable "tas" NOTE: 'leadMonth = 0' selected [2014-09-03 17:50:20] Defining geo-location parameters [2014-09-03 17:50:20] Defining initialization time parameters [2014-09-03 17:50:26] Retrieving data subset ... [2014-09-03 17:50:31] Done [2015-05-15 14:50:46] Defining geo-location parameters [2015-05-15 14:50:46] Defining initialization time parameters NOTE: Daily aggregation will be computed from 6-hourly data [2015-05-15 14:50:49] Retrieving data subset ... [2015-05-15 14:50:56] Done }}} {{{#!text/R cfs.list <- lapply(1:6, function(lead.month) { loadECOMS(dataset = "CFSv2_seasonal_16", loadECOMS(dataset = "CFSv2_seasonal", var = "tas", members = 1, years = 2006, leadMonth = lead.month, time = "DD") time = "DD", aggr.d = "mean") } ) [[Image(image-20150130-185813.png)]] The results show how a increasing lead month leads to a negative bias of the forecast, demonstrating that the mean state of a variable of a forecast is not stationary along the runtime dimension.