diff --git a/_preview/168/_sources/ncl/ncl_entries/meteorology.ipynb b/_preview/168/_sources/ncl/ncl_entries/meteorology.ipynb index 2b038be6..dd576fdf 100644 --- a/_preview/168/_sources/ncl/ncl_entries/meteorology.ipynb +++ b/_preview/168/_sources/ncl/ncl_entries/meteorology.ipynb @@ -82,11 +82,11 @@ "\n", "Where the maximum number of daylight hours, N, is calculated as:\n", "\n", - "{math}`{\\omega}_{s} = arccos[-tan({\\phi})tan({\\delta})]`\n", + "{math}`N = \\frac{24}{{\\pi}} {\\omega}_{s}`\n", "\n", "Where {math}`{\\omega}_{s}` is the sunset hour angle in radians [(Chapter 3, Equation 25)](https://www.fao.org/4/X0490E/x0490e07.htm#chapter%203%20%20%20meteorological%20data) {footcite}`allan_fao_1998`\n", "\n", - "{math}`N = \\frac{24}{{\\pi}} {\\omega}_{s}`" + "{math}`{\\omega}_{s} = arccos[-tan({\\phi})tan({\\delta})]`" ] }, { @@ -135,7 +135,7 @@ "\n", "NCL's `satvpr_temp_fao56` calculates saturation vapor pressure using temperature as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 [(Chapter 3, Equation 11)](https://www.fao.org/4/x0490e/x0490e07.htm) {footcite}`allan_fao_1998`\n", "\n", - "Where the saturation vapor pressure, {math}`e^°` (kPa), at air temperature {math}`T` (°C) is calculated as:\n", + "Where the saturation vapor pressure, {math}`e^°` (kPa), at air temperature, {math}`T` (°C), is calculated as:\n", "\n", "{math}`e^°(T) = 0.6108 {\\exp}[\\frac{17.27T}{T + 237.3}]`" ] @@ -183,7 +183,7 @@ "\n", "NCL's `satvpr_tdew_fao56` calculates the actual saturation vapor pressure using dewpoint temperature as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 [(Chapter 3, Equation 14)](https://www.fao.org/4/x0490e/x0490e07.htm) {footcite}`allan_fao_1998`\n", "\n", - "Where the actual vapor pressure, {math}`e_{a}` (kPa), is saturation vapor pressure at a specific dewpoint temperature, {math}`T_{dew}` (°C) which is calculated as:\n", + "Where the actual vapor pressure, {math}`e_{a}` (kPa), is saturation vapor pressure at a specific dewpoint temperature, {math}`T_{dew}` (°C), which is calculated as:\n", "\n", "{math}`e_{a} = e^°(T_{dew}) = 0.6108 {\\exp}[\\frac{17.27 T_{dew}}{T_{dew} + 237.3}]`" ] @@ -226,7 +226,7 @@ "\n", "Where the slope of saturation vapor pressure curve, {math}`{\\Delta}` (kPa), at air temperature {math}`T` (°C) is calculated as:\n", "\n", - "{math}`{\\Delta} = \\frac{4098 [0.6108 {\\exp}[\\frac{17.27T}{T + 237.3}]}{(T + 237.3)^2}`" + "{math}`{\\Delta} = \\frac{4098 (0.6108 {\\exp}[\\frac{17.27T}{T + 237.3}])}{(T + 237.3)^2}`" ] }, { diff --git a/_preview/168/applications/climatology.html b/_preview/168/applications/climatology.html index 655ca557..04238054 100644 --- a/_preview/168/applications/climatology.html +++ b/_preview/168/applications/climatology.html @@ -861,7 +861,7 @@
array([[[ nan, nan, nan, ..., nan, + month (time) int64 1kB 1 2 3 4 5 6 7 8 9 10 11 ... 3 4 5 6 7 8 9 10 11 12
array([cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True), + 0.0133127 , 0.01326215]]], dtype=float32)
array([cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2000, 2, 14, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2000, 3, 15, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2000, 4, 15, 0, 0, 0, 0, has_year_zero=True), @@ -1742,7 +1742,7 @@Calculating Anomalies
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, + dtype=object)
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5, @@ -1759,7 +1759,7 @@Calculating Anomalies
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, + 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5])
array([ 0.5, 1.5, 2.5, ..., 357.5, 358.5, 359.5])
PandasIndex(CFTimeIndex([2000-01-15 12:00:00, 2000-02-14 00:00:00, 2000-03-15 12:00:00, + 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, + dtype='object', length=180, calendar='noleap', freq=None))
PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5, ... 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5], - dtype='float64', name='lat', length=180))
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, + dtype='float64', name='lat', length=180))
PandasIndex(Index([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, ... 350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5], - dtype='float64', name='lon', length=360))
<matplotlib.collections.QuadMesh at 0x7fd5343f8ad0>
+<matplotlib.collections.QuadMesh at 0x7f717808bf80>
@@ -2240,7 +2240,7 @@ Removing Annual Cyclexarray.DataArray'tos'- time: 180
- -0.1528 -0.1654 -0.1896 -0.1609 ... 0.1687 0.2035 0.1923 0.1621
array([-0.15282594, -0.16537704, -0.18962625, -0.16086924, -0.18127483,
+ month (time) int64 1kB 1 2 3 4 5 6 7 8 9 10 11 ... 3 4 5 6 7 8 9 10 11 12
xarray.DataArray'tos'- time: 180
- -0.1528 -0.1654 -0.1896 -0.1609 ... 0.1687 0.2035 0.1923 0.1621
array([-0.15282594, -0.16537704, -0.18962625, -0.16086924, -0.18127483,
-0.18800849, -0.2007687 , -0.20025028, -0.15869197, -0.14112295,
-0.13107252, -0.11059521, -0.10135283, -0.1312863 , -0.12155499,
-0.0912946 , -0.07899683, -0.05883313, -0.03680199, -0.01390849,
@@ -2276,7 +2276,7 @@ Removing Annual Cycle
- time(time)object2000-01-15 12:00:00 ... 2014-12-...
- axis :
- T
- bounds :
- time_bnds
- standard_name :
- time
- title :
- time
- type :
- double
array([cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True),
+ dtype=float32)
- time(time)object2000-01-15 12:00:00 ... 2014-12-...
- axis :
- T
- bounds :
- time_bnds
- standard_name :
- time
- title :
- time
- type :
- double
array([cftime.DatetimeNoLeap(2000, 1, 15, 12, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(2000, 2, 14, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(2000, 3, 15, 12, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(2000, 4, 15, 0, 0, 0, 0, has_year_zero=True),
@@ -2456,7 +2456,7 @@ Removing Annual Cycle
- month(time)int641 2 3 4 5 6 7 ... 6 7 8 9 10 11 12
- axis :
- T
- bounds :
- time_bnds
- standard_name :
- time
- title :
- time
- type :
- double
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
+ dtype=object)
- month(time)int641 2 3 4 5 6 7 ... 6 7 8 9 10 11 12
- axis :
- T
- bounds :
- time_bnds
- standard_name :
- time
- title :
- time
- type :
- double
- timePandasIndex
PandasIndex(CFTimeIndex([2000-01-15 12:00:00, 2000-02-14 00:00:00, 2000-03-15 12:00:00,
+ 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
+ dtype='object', length=180, calendar='noleap', freq=None))
<matplotlib.collections.QuadMesh at 0x7fd52fb99280>
+<matplotlib.collections.QuadMesh at 0x7f7173f35d90>
@@ -4031,7 +4031,7 @@ Finding The Standard Deviations of Monthly Means
Visualization#
@@ -4106,7 +4106,7 @@ Visualization
<matplotlib.collections.QuadMesh at 0x7fd52e311280>
+<matplotlib.collections.QuadMesh at 0x7f7173feb770>
diff --git a/_preview/168/applications/datetime.html b/_preview/168/applications/datetime.html
index 50bfc980..4b6bdf84 100644
--- a/_preview/168/applications/datetime.html
+++ b/_preview/168/applications/datetime.html
@@ -450,7 +450,7 @@ The datetime
datetime.date.today() -> 2025-01-06
-datetime.datetime.now() -> 2025-01-06 23:01:04.434005
+datetime.datetime.now() -> 2025-01-06 23:21:44.173662
@@ -481,10 +481,10 @@ strftime()
cosecant
<>:14: SyntaxWarning: invalid escape sequence '\M'
<>:14: SyntaxWarning: invalid escape sequence '\M'
-/tmp/ipykernel_2219/1093943048.py:14: SyntaxWarning: invalid escape sequence '\M'
+/tmp/ipykernel_2243/1093943048.py:14: SyntaxWarning: invalid escape sequence '\M'
print("\Multiple Value Input (array/list)")
[24885 values with dtype=float64]
array([-125. , -124.75, -124.5 , ..., -66.5 , -66.25, -66. ], - dtype=float32)
array([50. , 49.75, 49.5 , 49.25, 49. , 48.75, 48.5 , 48.25, 48. , 47.75, + long_name: 10 metre U wind component
[24885 values with dtype=float64]
array([-125. , -124.75, -124.5 , ..., -66.5 , -66.25, -66. ], + dtype=float32)
array([50. , 49.75, 49.5 , 49.25, 49. , 48.75, 48.5 , 48.25, 48. , 47.75, 47.5 , 47.25, 47. , 46.75, 46.5 , 46.25, 46. , 45.75, 45.5 , 45.25, 45. , 44.75, 44.5 , 44.25, 44. , 43.75, 43.5 , 43.25, 43. , 42.75, 42.5 , 42.25, 42. , 41.75, 41.5 , 41.25, 41. , 40.75, 40.5 , 40.25, @@ -851,15 +851,15 @@Overview 32.5 , 32.25, 32. , 31.75, 31.5 , 31.25, 31. , 30.75, 30.5 , 30.25, 30. , 29.75, 29.5 , 29.25, 29. , 28.75, 28.5 , 28.25, 28. , 27.75, 27.5 , 27.25, 27. , 26.75, 26.5 , 26.25, 26. , 25.75, 25.5 , 25.25, - 25. , 24.75, 24.5 , 24.25, 24. ], dtype=float32)
[1 values with dtype=datetime64[ns]]
PandasIndex(Index([ -125.0, -124.75, -124.5, -124.25, -124.0, -123.75, -123.5, -123.25, + 25. , 24.75, 24.5 , 24.25, 24. ], dtype=float32)
[1 values with dtype=datetime64[ns]]
PandasIndex(Index([ -125.0, -124.75, -124.5, -124.25, -124.0, -123.75, -123.5, -123.25, -123.0, -122.75, ... -68.25, -68.0, -67.75, -67.5, -67.25, -67.0, -66.75, -66.5, -66.25, -66.0], - dtype='float32', name='longitude', length=237))
PandasIndex(Index([ 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, + dtype='float32', name='longitude', length=237))
PandasIndex(Index([ 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, ... 26.25, 26.0, 25.75, 25.5, 25.25, 25.0, 24.75, 24.5, 24.25, 24.0], - dtype='float32', name='latitude', length=105))
array([[13.69517637, 11.8926209 , 10.1376864 , ..., 14.99941759, + time datetime64[ns] 8B 1995-07-14T12:00:00
array([[13.69517637, 11.8926209 , 10.1376864 , ..., 14.99941759, 14.13418851, 13.97024568], [14.41539187, 12.91478894, 13.15184086, ..., 14.35260232, 14.28806213, 14.3599888 ], @@ -1859,8 +1859,8 @@Bernard#< [17.73964687, 17.71867202, 17.71418707, ..., 25.05317386, 24.91487184, 24.82648568], [17.98647294, 17.94030902, 17.92631711, ..., 25.11072483, - 25.04516283, 24.94564003]])
array([-125. , -124.75, -124.5 , ..., -66.5 , -66.25, -66. ], - dtype=float32)
array([50. , 49.75, 49.5 , 49.25, 49. , 48.75, 48.5 , 48.25, 48. , 47.75, + 25.04516283, 24.94564003]])
array([-125. , -124.75, -124.5 , ..., -66.5 , -66.25, -66. ], + dtype=float32)
array([50. , 49.75, 49.5 , 49.25, 49. , 48.75, 48.5 , 48.25, 48. , 47.75, 47.5 , 47.25, 47. , 46.75, 46.5 , 46.25, 46. , 45.75, 45.5 , 45.25, 45. , 44.75, 44.5 , 44.25, 44. , 43.75, 43.5 , 43.25, 43. , 42.75, 42.5 , 42.25, 42. , 41.75, 41.5 , 41.25, 41. , 40.75, 40.5 , 40.25, @@ -1870,15 +1870,15 @@Bernard#< 32.5 , 32.25, 32. , 31.75, 31.5 , 31.25, 31. , 30.75, 30.5 , 30.25, 30. , 29.75, 29.5 , 29.25, 29. , 28.75, 28.5 , 28.25, 28. , 27.75, 27.5 , 27.25, 27. , 26.75, 26.5 , 26.25, 26. , 25.75, 25.5 , 25.25, - 25. , 24.75, 24.5 , 24.25, 24. ], dtype=float32)
array('1995-07-14T12:00:00.000000000', dtype='datetime64[ns]')
PandasIndex(Index([ -125.0, -124.75, -124.5, -124.25, -124.0, -123.75, -123.5, -123.25, + 25. , 24.75, 24.5 , 24.25, 24. ], dtype=float32)
array('1995-07-14T12:00:00.000000000', dtype='datetime64[ns]')
PandasIndex(Index([ -125.0, -124.75, -124.5, -124.25, -124.0, -123.75, -123.5, -123.25, -123.0, -122.75, ... -68.25, -68.0, -67.75, -67.5, -67.25, -67.0, -66.75, -66.5, -66.25, -66.0], - dtype='float32', name='longitude', length=237))
PandasIndex(Index([ 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, + dtype='float32', name='longitude', length=237))
PandasIndex(Index([ 50.0, 49.75, 49.5, 49.25, 49.0, 48.75, 48.5, 48.25, 48.0, 47.75, ... 26.25, 26.0, 25.75, 25.5, 25.25, 25.0, 24.75, 24.5, 24.25, 24.0], - dtype='float32', name='latitude', length=105))
array(['1935-01-01T00:00:00.000000000', '1935-02-01T00:00:00.000000000', + conventions: None
array(['1935-01-01T00:00:00.000000000', '1935-02-01T00:00:00.000000000', '1935-03-01T00:00:00.000000000', ..., '1998-10-01T00:00:00.000000000', '1998-11-01T00:00:00.000000000', '1998-12-01T00:00:00.000000000'], - dtype='datetime64[ns]')
[768 values with dtype=float64]
[768 values with dtype=float32]
[768 values with dtype=float32]
PandasIndex(DatetimeIndex(['1935-01-01', '1935-02-01', '1935-03-01', '1935-04-01', + dtype='datetime64[ns]')
[768 values with dtype=float64]
[768 values with dtype=float32]
[768 values with dtype=float32]
PandasIndex(DatetimeIndex(['1935-01-01', '1935-02-01', '1935-03-01', '1935-04-01', '1935-05-01', '1935-06-01', '1935-07-01', '1935-08-01', '1935-09-01', '1935-10-01', ... '1998-03-01', '1998-04-01', '1998-05-01', '1998-06-01', '1998-07-01', '1998-08-01', '1998-09-01', '1998-10-01', '1998-11-01', '1998-12-01'], - dtype='datetime64[ns]', name='time', length=768, freq=None))
NCL’s daylight_fao56
calculates the maximum number of daylight hours as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 (Chapter 3, Equation 34) [2]
Where the maximum number of daylight hours, N, is calculated as:
-\({\omega}_{s} = arccos[-tan({\phi})tan({\delta})]\)
-Where \({\omega}_{s}\) is the sunset hour angle in radians (Chapter 3, Equation 25) [2]
\(N = \frac{24}{{\pi}} {\omega}_{s}\)
+Where \({\omega}_{s}\) is the sunset hour angle in radians (Chapter 3, Equation 25) [2]
+\({\omega}_{s} = arccos[-tan({\phi})tan({\delta})]\)
NCL’s satvpr_temp_fao56
calculates saturation vapor pressure using temperature as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 (Chapter 3, Equation 11) [2]
Where the saturation vapor pressure, \(e^°\) (kPa), at air temperature \(T\) (°C) is calculated as:
+Where the saturation vapor pressure, \(e^°\) (kPa), at air temperature, \(T\) (°C), is calculated as:
\(e^°(T) = 0.6108 {\exp}[\frac{17.27T}{T + 237.3}]\)
NCL’s satvpr_tdew_fao56
calculates the actual saturation vapor pressure using dewpoint temperature as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 (Chapter 3, Equation 14) [2]
Where the actual vapor pressure, \(e_{a}\) (kPa), is saturation vapor pressure at a specific dewpoint temperature, \(T_{dew}\) (°C) which is calculated as:
+Where the actual vapor pressure, \(e_{a}\) (kPa), is saturation vapor pressure at a specific dewpoint temperature, \(T_{dew}\) (°C), which is calculated as:
\(e_{a} = e^°(T_{dew}) = 0.6108 {\exp}[\frac{17.27 T_{dew}}{T_{dew} + 237.3}]\)
NCL’s satvpr_slope_fao56
calculates the slope of the saturation vapor pressure curve as described in the Food and Agriculture Organization (FAO) Irrigation and Drainage Paper 56 (Chapter 3, Equation 13) [2]
Where the slope of saturation vapor pressure curve, \({\Delta}\) (kPa), at air temperature \(T\) (°C) is calculated as:
-\({\Delta} = \frac{4098 [0.6108 {\exp}[\frac{17.27T}{T + 237.3}]}{(T + 237.3)^2}\)
+\({\Delta} = \frac{4098 (0.6108 {\exp}[\frac{17.27T}{T + 237.3}])}{(T + 237.3)^2}\)
# Input: Single Value
diff --git a/_preview/168/ncl/ncl_entries/spectral_analysis.html b/_preview/168/ncl/ncl_entries/spectral_analysis.html
index 61058b4f..5c94c837 100644
--- a/_preview/168/ncl/ncl_entries/spectral_analysis.html
+++ b/_preview/168/ncl/ncl_entries/spectral_analysis.html
@@ -863,10 +863,10 @@ Read in Data#
source: Climate Analysis Section, NCAR
history: \nCreated to test specxy routines
creation_date: Tue Mar 30 12:34:39 MST 1999
- conventions: None
array(['1935-01-01T00:00:00.000000000', '1935-02-01T00:00:00.000000000', + conventions: None
array(['1935-01-01T00:00:00.000000000', '1935-02-01T00:00:00.000000000', '1935-03-01T00:00:00.000000000', ..., '1998-10-01T00:00:00.000000000', '1998-11-01T00:00:00.000000000', '1998-12-01T00:00:00.000000000'], - dtype='datetime64[ns]')
[768 values with dtype=float64]
[768 values with dtype=float32]
[768 values with dtype=float32]
PandasIndex(DatetimeIndex(['1935-01-01', '1935-02-01', '1935-03-01', '1935-04-01', + dtype='datetime64[ns]')
[768 values with dtype=float64]
[768 values with dtype=float32]
[768 values with dtype=float32]
PandasIndex(DatetimeIndex(['1935-01-01', '1935-02-01', '1935-03-01', '1935-04-01', '1935-05-01', '1935-06-01', '1935-07-01', '1935-08-01', '1935-09-01', '1935-10-01', ... '1998-03-01', '1998-04-01', '1998-05-01', '1998-06-01', '1998-07-01', '1998-08-01', '1998-09-01', '1998-10-01', '1998-11-01', '1998-12-01'], - dtype='datetime64[ns]', name='time', length=768, freq=None))
rmMonAnnCycTLL:
- python: 0.0033192038536071777
- ncl: 0.003319263
-
-clmDayTLL:
- python: 0.7408185005187988
- ncl: 0.7408184
-
-clmMonTLL:
+clmMonTLL:
python: 0.8063018918037415
ncl: 0.8063018
-calcMonAnomTLL:
+rmMonAnnCycTLL:
python: 0.0033192038536071777
ncl: 0.003319263
@@ -614,13 +606,21 @@ Comparison
-rmMonAnnCycTLL:
- -5.9146392822079924e-08
-clmDayTLL:
- 1.0051879884009907e-07
-clmMonTLL:
+clmMonTLL:
9.180374149764248e-08
-calcMonAnomTLL:
+rmMonAnnCycTLL:
-5.9146392822079924e-08
stdMonTLL:
-6.103516303479495e-11
-month_to_season:
- -5.482101439469034e-08
calcDayAnomTLL:
-5.7166442871126044e-08
+clmDayTLL:
+ 1.0051879884009907e-07
+month_to_season:
+ -5.482101439469034e-08
+calcMonAnomTLL:
+ -5.9146392822079924e-08
diff --git a/_preview/168/ncl/receipts/days_in_month.html b/_preview/168/ncl/receipts/days_in_month.html
index ee387c1f..75a290d6 100644
--- a/_preview/168/ncl/receipts/days_in_month.html
+++ b/_preview/168/ncl/receipts/days_in_month.html
@@ -547,38 +547,38 @@ Comparison
-366_day:
- python: [29, 29, 31, 29]
- ncl: [29, 29, 31, 29]
+360_day:
+ python: [30, 30, 30, 30]
+ ncl: [30, 30, 30, 30]
-all_leap:
+366_day:
python: [29, 29, 31, 29]
ncl: [29, 29, 31, 29]
+standard:
+ python: [29, 28, 31, 29]
+ ncl: [29, 28, 31, 28]
+
365_day:
python: [28, 28, 31, 28]
ncl: [28, 28, 31, 28]
-standard:
- python: [29, 28, 31, 29]
- ncl: [29, 28, 31, 28]
+noleap:
+ python: [28, 28, 31, 28]
+ ncl: [28, 28, 31, 28]
gregorian:
python: [29, 28, 31, 29]
ncl: [29, 28, 31, 28]
-360_day:
- python: [30, 30, 30, 30]
- ncl: [30, 30, 30, 30]
+all_leap:
+ python: [29, 29, 31, 29]
+ ncl: [29, 29, 31, 29]
julian:
python: [29, 28, 31, 29]
ncl: [29, 28, 31, 29]
-noleap:
- python: [28, 28, 31, 28]
- ncl: [28, 28, 31, 28]
-
none:
ncl: [29, 28, 31, 28]
diff --git a/_preview/168/ncl/receipts/general_applied_math.html b/_preview/168/ncl/receipts/general_applied_math.html
index 574fd2eb..bb3f0bca 100644
--- a/_preview/168/ncl/receipts/general_applied_math.html
+++ b/_preview/168/ncl/receipts/general_applied_math.html
@@ -1017,60 +1017,60 @@ Differencesxarray.Dataset- time: 1404
- time(time)int320 1 2 3 4 ... 1400 1401 1402 1403
- long_name :
- Months Since January 1882
- units :
- months
array([ 0, 1, 2, ..., 1401, 1402, 1403], dtype=int32)
- date(time)float64...
- short_name :
- Yr-Mo
- long_name :
- Year-Month
- units :
- YYYYMM
[1404 values with dtype=float64]
- DSOI(time)float32...
- short_name :
- DSOI
- long_name :
- Darwin Southern Oscillation Index
- units :
- dimensionless
[1404 values with dtype=float32]
- timePandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+ conventions: None
xarray.Dataset- time: 1404
- time(time)int320 1 2 3 4 ... 1400 1401 1402 1403
- long_name :
- Months Since January 1882
- units :
- months
array([ 0, 1, 2, ..., 1401, 1402, 1403], dtype=int32)
- date(time)float64...
- short_name :
- Yr-Mo
- long_name :
- Year-Month
- units :
- YYYYMM
[1404 values with dtype=float64]
- DSOI(time)float32...
- short_name :
- DSOI
- long_name :
- Darwin Southern Oscillation Index
- units :
- dimensionless
[1404 values with dtype=float32]
- timePandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403],
- dtype='int32', name='time', length=1404))
- title :
- Darwin Southern Oscillation Index
- source :
- Climate Analysis Section, NCAR
- history :
-
+ dtype='int32', name='time', length=1404))
- title :
- Darwin Southern Oscillation Index
- source :
- Climate Analysis Section, NCAR
- history :
-
DSOI = - Normalized Darwin
Normalized sea level pressure is used.
The normalization is done using Trenberth's method.
diff --git a/_preview/168/ncl/receipts/trigonometric_functions.html b/_preview/168/ncl/receipts/trigonometric_functions.html
index 699a6243..6a0415f2 100644
--- a/_preview/168/ncl/receipts/trigonometric_functions.html
+++ b/_preview/168/ncl/receipts/trigonometric_functions.html
@@ -617,35 +617,35 @@
Comparison
-