morphing_epw {epwshiftr}

Morphing EPW weather variables

Description

morphing_epw() takes an epw_cmip6_data object generated using extract_data() and calculates future core EPW weather variables using Morphing Method.

Usage

morphing_epw(
  data,
  years = NULL,
  labels = NULL,
  methods = NULL,
  warning = FALSE
)

Arguments

data

An epw_cmip6_dataobject generated using extract_data()

years

An integer vector indicating the target years to be considered. If NULL, all years in input data will be considered. Default: NULL.

labels

A character or factor vector used for grouping input years. Usually are the outputs of base::cut(). labels should have the same length as years. If given, climate data of years grouped by labels will be averaged. Default: NULL.

methods

A named character giving the methods of morphing procedures of each variables. Possible variable names are tdb, rh, p, hor_ir, glob_rad, wind. Possible values are: "stretch", "shift" and "combined". For example: c(tdb = "stretch", rh = "shift"). "combined" is only applicable to tdb. The default morphing method for each variable is listed in the Return section. If NULL, the default methods will be used. Default: NULL.

warning

If TRUE, warnings will be issued for cases with input data less than a decade (10 years). This is because using data that only covers a short period of time may not be able to capture the average of future climate. Default: FALSE.

Details

The EPW weather variables that get morphed are listed in details.

Value

An epw_cmip6_morphed object, which is basically a list of 12 elements:

No.	Element	Type	Morphing Method	Description
1	epw	eplusr::Epw	N/A	The original EPW file used for morphing
2	tdb	data.table::data.table()	Stretch	Data of dry-bulb temperature after morphing
3	tdew	data.table::data.table()	Derived	Data of dew-point temperature after morphing
4	rh	data.table::data.table()	Stretch	Data of relative humidity after morphing
5	p	data.table::data.table()	Stretch	Data of atmospheric pressure after morphing
6	hor_ir	data.table::data.table()	Stretch	Data of horizontal infrared radiation from the sky after morphing
7	glob_rad	data.table::data.table()	Stretch	Data of global horizontal radiation after morphing
8	norm_rad	data.table::data.table()	Derived	Data of direct normal radiation after morphing
9	diff_rad	data.table::data.table()	Stretch	Data of diffuse horizontal radiation after morphing
10	wind	data.table::data.table()	Stretch	Data of wind speed after morphing
11	total_cover	data.table::data.table()	Derived	Data of total sky cover after morphing
12	opaque_cover	data.table::data.table()	Derived	Data of opaque sky cover after morphing

Each data.table::data.table() listed above contains 19 columns below or an empty data.table::data.table() if the corresponding variables cannot be found in the input epw_cmip6_data object.

No.	Column	Type	Description
1	activity_drs	Character	Activity DRS (Data Reference Syntax)
2	institution_id	Character	Institution identifier
3	source_id	Character	Model identifier
4	experiment_id	Character	Root experiment identifier
5	member_id	Character	A compound construction from sub_experiment_id and variant_label
6	table_id	Character	Table identifier
7	lon	Double	The averaged values of input longitude
8	lat	Double	The averaged values of input latitude
9	dist	Double	The averaged spherical distances in km between EPW location and grid coordinates
10	interval	Factor	The label value used to average raw input data
11	datetime	POSIXct	The datetime value with fake year generated by calling the Epw$data() method with the input EPW
12	year	Integer	The original year of the raw EPW data
13	month	Integer	The month value of the morphed data
14	day	Integer	The day of the morphed data
15	hour	Integer	The hour of the morphed data
16	minute	Integer	The minute of the morphed data
17	Variable Name	Double	The morphed data, where ⁠Variable Name⁠ is the corresponding EPW weather variable name
18	delta	Double	The shift factor. Will be NA for derived values
19	alpha	Double	The stretch factor. Will be NA for derived values

The Morphing procedure

Here Morphing is an algorithm proposed by Belcher etc. 2005 used to morph the present-day observed weather files (here the EPWs) to produce future climate weather files. The EPW data is used as the 'baseline climate'.

The first step before morphing is to calculate the monthly means of climatological variables in the EPW file, denoted by <x_0>_m. The subscript '0' is to denote the present day weather record, and 'm' is to denote the month.

The morphing involves three generic operations, i.e. 1) a shift; 2) a linear stretch (scaling factor); and 3) a shift and a stretch:

\textrm{Shift: } x = x_0 + \Delta x_m

\textrm{Stretch: }x = \alpha_m x_m

\textrm{Shift + Stretch: } x = x_0 + \Delta x_m + \alpha_m (x_0 - <x_0>_m)

Shift

If using a shift, for each month, a shift \Delta x_m is applied to x_0. \Delta x_m is the absolute change in the monthly mean value of the variable for the month m, i.e. \Delta x_m = <x_0>_m - <x>_m. Here the monthly variance of the variable is unchanged.

Stretch

If using a stretch, for each month, a stretch \alpha _m is applied to x_0, where \alpha _m is the fractional change in the monthly-mean value of a variable, i.e. \alpha _m = <x>_m / <x_0>_m. In this case, the variance will be multiplied by to \alpha^2_m

Combined Shift and Stretch

When using a combined shift and stretch factor, both the mean and the variance will be switched off altogether.

For more details about morphing, please see (Belcher etc. 2005)

References

Belcher, S., Hacker, J., Powell, D., 2005. Constructing design weather data for future climates. Building Services Engineering Research and Technology 26, 49–61. https://doi.org/10.1191/0143624405bt112oa