Changes between Version 9 and Version 10 of udg/ecoms/RPackage/examples/drift
- Timestamp:
- May 15, 2015 2:55:17 PM (7 years ago)
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udg/ecoms/RPackage/examples/drift
v9 v10 1 1 = Analysing model drift in South-western Iberia 2 2 3 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:3 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: 4 4 5 5 6 6 {{{#!text/R 7 ref <- loadECOMS(dataset = "CFSv2_seasonal _16",7 ref <- loadECOMS(dataset = "CFSv2_seasonal", 8 8 var = "tas", 9 9 members = 1, … … 13 13 years = 2006, 14 14 leadMonth = 0, 15 time = "DD", 16 aggr.d = "mean") 15 17 time = "DD") 16 18 }}} 17 19 18 Messages on-screen inform about the loading process. Note that the selection of `leadMonth = 0` will give a NOTE on screen:20 Messages on-screen inform about the loading process. Note that the selection of `leadMonth = 0` will give a NOTE: 19 21 20 22 {{{ 21 [2014-09-03 17:50:20] Defining homogeneization parameters for variable "tas" 22 NOTE: daily mean will be calculated from the 6-h model output 23 [2015-05-15 14:50:46] Defining homogeneization parameters for variable "tas" 23 24 NOTE: 'leadMonth = 0' selected 24 [2014-09-03 17:50:20] Defining geo-location parameters 25 [2014-09-03 17:50:20] Defining initialization time parameters 26 [2014-09-03 17:50:26] Retrieving data subset ... 27 [2014-09-03 17:50:31] Done 25 [2015-05-15 14:50:46] Defining geo-location parameters 26 [2015-05-15 14:50:46] Defining initialization time parameters 27 NOTE: Daily aggregation will be computed from 6-hourly data 28 [2015-05-15 14:50:49] Retrieving data subset ... 29 [2015-05-15 14:50:56] Done 28 30 }}} 29 31 … … 43 45 {{{#!text/R 44 46 cfs.list <- lapply(1:6, function(lead.month) { 45 loadECOMS(dataset = "CFSv2_seasonal _16",47 loadECOMS(dataset = "CFSv2_seasonal", 46 48 var = "tas", 47 49 members = 1, … … 51 53 years = 2006, 52 54 leadMonth = lead.month, 53 time = "DD") 55 time = "DD", 56 aggr.d = "mean") 54 57 } 55 58 ) … … 110 113 [[Image(image-20150130-185813.png)]] 111 114 112 113 115 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. 114 116