对于逐月及逐日资料的处理
❓什么时候需要去趋势，什么时候需要去季节循环？
对于气象资料的基本处理：
(1) 原始的逐日资料减去对应这一日的气候态，就去掉了季节循环信号（季节循环：90-365 day），只剩下低频信号。
(2) 原始的逐月资料减去对应月份的气候态，就去掉了循环信号，只剩下年际、年代际等异常信号。
气候态的选取根据你的研究内容，例如做季节内研究一般选择气候态为夏季平均，但有的时候J J A分别作为气候态。
去季节循环
1、去趋势 dtrend
2、daily climatology
3、FFT分解得到前4个波即为季节循环（90天以上）
4、数据-季节循环
clmDayTLLncl中求多年日平均的函数
Calculates long term daily means (daily climatology) from daily data.
💫具体见ncl官网例子：clmDayTLL
Compute the daily climatologies.
The input is mean daily 500 hPa heights spanning 1990-1999.
The values are packed as type short and the time is in units "hours since 1-1-1 00:00:0".
There was no calendar attribute associated with the 'time' dimension/variable.
Hence, a gregorian calendar is assumed.
Still, the example shows how a user might check if the calendar attribute exists and associate it with the required variables.
;;The following library is loaded by default in NCL V6.2.0 and newer
;;load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
diri = "./" ; input directory
fili = "HGT.nc" ; input file
f = addfile (diri+fili , "r")
;***********************************************************
; Read time and create required yyyyddd
; NOte the tests for a calendar attribute are necessary for non-gregorian calendars
;***********************************************************
time = f->time ; time:units = "hours since 1-1-1 00:00:0.0"
TIME = cd_calendar(time, 0) ; type float
year = toint( TIME(:,0) ) ; toint strips meta data
month = toint( TIME(:,1) )
day = toint( TIME(:,2) )
; check for calendar attribute
if (isatt(TIME,"calendar")) then ; default is gregorian
year@calendar = TIME@calendar
end if
ddd = day_of_year(year, month, day)
if (isatt(year,"calendar")) then ; default is gregorian
ddd@calendar = year@calendar
end if
yyyyddd = year*1000 + ddd ; needed for input
if (isatt(ddd,"calendar")) then ; default is gregorian
yyyyddd@calendar = ddd@calendar
end if
;***********************************************************
; Read data: short2flt
;***********************************************************
hgt = short2flt( f->hgt(:,0,:,:) ) ; convert to float
printVarSummary( hgt )
;***********************************************************
; Compute daily climatology: raw daily means
;***********************************************************
hClmDay = clmDayTLL(hgt, yyyyddd) ; daily climatology at each grid point
printVarSummary(hClmDay)
calcDayAnomTLL:
💫具体见ncl官网例子：calcDayAnomTLL
Calculates daily anomalies from a daily data climatology.
Compute daily anomalies using a climatology created by clmDayTLL or smthClmDayTLL. The input is daily 500 hPa heights spanning 1990-1999. The values are packed as type short and the time is in units "hours since 1-1-1 00:00:0".
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
diri = "./" ; input directory
fili = "HGT.nc" ; input file
f = addfile (diri+fili , "r")
;***********************************************************
; Read time and create required yyyyddd
;***********************************************************
time = f->time ; time:units = "hours since 1-1-1 00:00:0.0"
TIME = cd_calendar(time, 0) ; type float
year = toint( TIME(:,0) )
month = toint( TIME(:,1) )
day = toint( TIME(:,2) )
ddd = day_of_year(year, month, day)
yyyyddd = year*1000 + ddd ; needed for input
;***********************************************************
; Read data: short2flt
;***********************************************************
hgt = short2flt( f->hgt(:,0,:,:) ) ; convert to float
printVarSummary( hgt )
;***********************************************************
; Compute daily climatology: raw and then 'smoothed'
;***********************************************************
hClmDay = clmDayTLL(hgt, yyyyddd) ; daily climatology at each grid point
printVarSummary(hClmDay)
;***********************************************************
;Compute smoothed daily climatology using 2 harmonics
;***********************************************************
hClmDay_sm = smthClmDayTLL(hClmDay, 2)
printVarSummary(hClmDay_sm)
;***********************************************************
; Compute daily anomalies using raw and smoothed daily climatologies
;***********************************************************
hAnom = calcDayAnomTLL (hgt, yyyyddd, hClmDay)
printVarSummary(hAnom_sm)
printMinMax(hAnom, 0)
hAnom_sm = calcDayAnomTLL (hgt, yyyyddd, hClmDay_sm)
hAnom_sm@long_name = "Anomalies from Smooth Daily Climatology"
printVarSummary(hAnom_sm)
printMinMax(hAnom_sm, 0)
去趋势
去趋势是为了消除数据中的线性趋势或高阶趋势，去趋势后的时间序列反应的是气候内部变率。如果不去趋势，可能会得到一个“虚假的”全球变暖的模态，因此在分析前应去趋势，即去掉全球变暖的信号。去趋势和标准化是两个概念，如果要标准化序列是需要再进行标准化的。
可以利用cdo处理，
detrend ifile ofile
dtrend_msg_n
💫具体见ncl官网例子：calcDayAnomTLL
ncl中去趋势去掉的是线性趋势。