diff --git a/docs/Chapter_10_Comparing_Data_from_Different_Sources.html b/docs/Chapter_10_Comparing_Data_from_Different_Sources.html index 4621304..e3cfd23 100644 --- a/docs/Chapter_10_Comparing_Data_from_Different_Sources.html +++ b/docs/Chapter_10_Comparing_Data_from_Different_Sources.html @@ -300,7 +300,7 @@

- + @@ -318,25 +318,19 @@

In Barbados, Fig. 10.1a the point shown is a station called Husbands, the site of a regional climate centre. CIMH. The largest pixel is for the ERA5 reanalysis data and the smallest is for CHIRPS. This figure also shows that the pixel in coastal sites can sometimes be largely over the ocean and hence a neighbouring pixel may be more relevant.

--++ - - + + - - + +
Fig. 10.1a Pixel size for 3 methods in Barbados

Fig. 10.1b Difference between gridded and point data for rainfall

-

(Figure with permission from H. Greatrex)

Fig. 10.1a Pixel size for 3 methods in BarbadosFig. 10.1b Difference between gridded and point data for rainfall
-
-

-
Chart Description automatically generated
-
-
diff --git a/docs/Chapter_11_Drawing_Maps.html b/docs/Chapter_11_Drawing_Maps.html index 9807354..3135864 100644 --- a/docs/Chapter_11_Drawing_Maps.html +++ b/docs/Chapter_11_Drawing_Maps.html @@ -309,27 +309,26 @@

- +

C licking on country in Fig. 10.1a allows the input of a country name, as shown in Fig. 10.2b. For Kenya, as for most other formats, there are different ways to download the data.

--++ - - + + - - + +
Fig. 10.2b

Fig. 10.2c

-

Climatic > File > Open and Tidy Shapefile

Fig. 10.2bFig. 10.2c
{ width=“2.7050929571303586in” h eight=“2.183115704286964in”}
@@ -349,8 +348,8 @@

- - + + @@ -364,14 +363,14 @@

-Fig. 10.2fKenya county map -Fig. 10.2g +Fig. 10.2f Kenya county map +Fig. 10.2g - - + + @@ -389,8 +388,8 @@

- - + + @@ -408,7 +407,7 @@

- + @@ -426,8 +425,8 @@

- - + + @@ -446,8 +445,8 @@

- - + + @@ -469,8 +468,8 @@

- - + + @@ -479,20 +478,19 @@

This initial check is important, as any spelling mistakes in the county name, when entering the data, will invent a new county!

--++ - - + + - - + +

Fig. 10.3d Filter the map file

-

Right Click > Filter > Define New Filter

Fig. 10.3e Map file for Western KenyaFig. 10.3d Filter the map fileFig. 10.3e Map file for Western Kenya
h
@@ -509,7 +507,7 @@

- + @@ -527,8 +525,8 @@

- - + + diff --git a/docs/Chapter_12_Extremes.html b/docs/Chapter_12_Extremes.html index ad12139..c40edc4 100644 --- a/docs/Chapter_12_Extremes.html +++ b/docs/Chapter_12_Extremes.html @@ -307,27 +307,27 @@

- - + +

In the Climatic menu the data are already in the right “shape” and there is a date column, see Fig. 11.1a. So start by checking whether there are any missing dates to infill, Fig. 11.1b.

--++ - - + + - - + +
Fig. 11.1cFig. 11.1dFig. 11.1c Fig. 11.1d
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML177df15.PNG
@@ -347,8 +347,8 @@

- - + + @@ -366,8 +366,8 @@

- - + + @@ -391,8 +391,8 @@

- - + + @@ -410,8 +410,8 @@

- - + + @@ -430,7 +430,7 @@

- + @@ -456,8 +456,8 @@

- - + + @@ -475,8 +475,8 @@

- - + + @@ -495,8 +495,8 @@

- - + + @@ -514,8 +514,8 @@

- - + + @@ -531,7 +531,7 @@

- + @@ -631,29 +631,27 @@

- - + +

From the rain5 variable, the Climatic > Prepare > Extremes is an alternative dialogue to give the annual maxima, Fig. 11.3l. This gives the same results as the climdex Rx5day variable. It also gives a further the day in the year of the maximum. This could be used in a study to investigate whether there is any evidence for a trend in when the maximum occurs as well as its value.

--++ - - + + - - + +
Fig. 11.3l

Fig. 11.3m

-

Climatic > Prepare > Climatic Summaries
-with sub-dialogue

Fig. 11.3lFig. 11.3m
@@ -674,8 +672,8 @@

- - + + @@ -687,20 +685,18 @@

- + -Fig. 11.3p -Fig. 11.3q +Fig. 11.3p Fig. 11.3q + - -

-

Result in the output window

-

+ + @@ -721,8 +717,8 @@

- - + + @@ -738,7 +734,7 @@

- + @@ -850,20 +846,19 @@

They are again illustrated with the Dodoma data. Use Climatic > Prepare > Climdex, Fig. 11.4a and complete the Temperature sub-dialogue as shown in Fig. 11.4b.

--++ - - + + - - + +

Fig. 11.4a The Climdex dialogue

-

Climatic > Prepare > Climdex

Fig. 11.4b Climdex temperature sub-dialogueFig. 11.4a The Climdex dialogueFig. 11.4b Climdex temperature sub-dialogue
![] (media/image1381.png){wid th=“2.3313899825021873in” heig ht=“2.152358923884514in”} h
@@ -879,27 +874,26 @@

- +

As in Section 11.3, some of the temperature indices can be calculated through the Climatic > Prepare > Climatic Summaries dialogue. For example completing Fig. 11.4d and Fig. 11.4e as shown produces the indices TNn and TNx.

-+ - - + + - - + +

Fig. 11.4d

-

Climatic > Prepare > Climatic Summaries

Fig. 11.4e Choosing the max and minFig. 11.4dFig. 11.4e Choosing the max and min
h
diff --git a/docs/Chapter_13_PICSA__Long_Before_the_season.html b/docs/Chapter_13_PICSA__Long_Before_the_season.html index fbaaef4..b801a5f 100644 --- a/docs/Chapter_13_PICSA__Long_Before_the_season.html +++ b/docs/Chapter_13_PICSA__Long_Before_the_season.html @@ -306,7 +306,7 @@

- + @@ -323,7 +323,7 @@

- + @@ -331,8 +331,8 @@

Third is the emphasis on “options by context” that places the farmer (and not the “expert”) at the centre. Thus, PICSA does not make recommendations for the farmers. Instead it offers options, with the idea, shown in steps d and e, in Fig. 12.1b, that the farmers, or households, may wish to select those that particularly fit their circumstances. Fig. 12.1c shows an example of options related to livelihood activities.

--++ @@ -342,8 +342,8 @@

- - + +
0F15318A-1080-4C3C-AAB7-34B2C68B99B4 h
@@ -361,7 +361,7 @@

- + @@ -381,7 +381,7 @@

- + @@ -390,20 +390,19 @@

- + -Fig. 12.2b Tmax and Tmin, Dodoma -Fig. 12.2c Total annual rainfall, Dodoma +Fig. 12.2b Tmax and Tmin, Dodoma +Fig. 12.2c Total annual rainfall, Dodoma -

-

- + + @@ -530,8 +529,8 @@

- - + + @@ -540,20 +539,19 @@

- - + + -

Fig. 12.3c Importing from Excel

-

File > Open from File or Open from Library

-Fig. 12.3d Data imported incorrectly +Fig. 12.3c Importing from Excel +Fig. 12.3d Data imported incorrectly - -{ width=“2.6359897200349955in” h eight=“2.800184820647419in”} + h + @@ -564,20 +562,19 @@

- - + + -Fig. 12.3e The Tidy and Examine menu -

Fig. 12.3f One Variable Summarise

-

Climatic > Tidy and Examine > Summarise

+Fig. 12.3e The Tidy and Examine Menu +Fig. 12.3f One Variable Summarise - - + + @@ -600,8 +597,8 @@

- - + + @@ -619,29 +616,27 @@

- - + +

The Climatic > Date menu, Fig. 12.3i is used for Steps 3 to 5 in the checklist, Table 12.3a. First calculate a single Date variable, i.e. a Variable of Type (D). Here it is calculated from the 3 variables, giving the YEAR, MONTH and DAY as shown in Fig. 12.3j.

--++ - - + + - - + +

Fig. 12.3k Infilling where dates are omitted

-

Climatic > Dates > Infill Missing Dates

Fig. 13 Adding variables from the date

-

Climatic > Dates > Use Date

Fig. 12.3k Infilling where dates are omittedFig. 12.3l Adding variables from the date
@@ -661,29 +656,27 @@

- - + +

The data are shown in Fig. 12.3n. They are now ready for Step 7, which is to define the data as climatic, as shown in Fig. 12.3o.

--++ - - + + - - + +

Fig. 12.3o Defining a data frame as climatic

-

Climatic > Define Climatic Data

Fig. 12.3p The Check Data menu

-

Climatic > Check Data

Fig. 12.3o Defining a data frame as climaticFig. 12.3p The Check Data Menu
@@ -703,8 +696,8 @@

- - + + @@ -712,20 +705,19 @@

- - + + -Fig. 12.3s Saving the data -

Fig. 12.3t Save the RDS file

-

File > Save As >Save Data As

+Fig. 12.3s Saving the data +Fig. 12.3t Save the RDS file - - + + @@ -735,20 +727,19 @@

- - + + -

Fig. 12.3u Rainfall and temperature summaries

-

Climatic > Prepare > Climatic Summaries

-Fig. 12.3v Day range sub-dialogue +Fig. 12.3u Rainfall and temperature summaries +Fig. 12.3v Day range sub-dialogue - - + h + @@ -771,8 +762,8 @@

- - + + @@ -790,8 +781,8 @@

- - + + @@ -805,20 +796,19 @@

- - + + -

Fig. 12.3aa

-

Describe > Specific > Line Plot

-Fig. 12.3ab +Fig. 12.3aa +Fig. 12.3ab - - + h + @@ -837,8 +827,8 @@

- - + + @@ -846,20 +836,19 @@

- - + + -

Fig. 12.3ae

-

Climatic > PICSA > Rainfall Graph

-Fig. 12.3af +Fig. 12.3ae ***Fig. 12.3af +*** - - +{width=“2.8456485126859143in height=”2.7895516185476814in”} +” @@ -875,10 +864,7 @@

- - - - + @@ -894,20 +880,19 @@

We continue with the data from Dodoma, used in Section 12.3.

--++ - - + + - - + +

Fig. 12.4a Start of the rains

-

Climatic > Prepare > Start of the Rains

Fig. 12.4b Adding a dry-spell conditionFig. 12.4a Start of the rainsFig. 12.4b Adding a dry-spell condition
{ width=“2.573474409448819in” hei ght=“2.9682469378827645in”} h
@@ -921,6 +906,24 @@

Return to the Climatic > Prepare > Start of the Rains dialogue and add the dry-spells condition, Fig. 12.4b. Change the default of 9 days to 10 days as the maximum allowable spell[^50]. Also change the names of the resulting variables, or the events produced before will be overwritten.

+++ + + + + + + + + + + + + +
Fig. 12.4c End of the rainsFig. 12.4d
he
+ + @@ -957,8 +960,8 @@

- - + +
@@ -976,8 +979,8 @@

- - +{width=“2.811955380577428in” ](figures/Fig 12.4f.png){width=“2.9353149606299214in” height=“2.2885793963254595in”} + @@ -995,8 +998,8 @@

- - + + @@ -1046,13 +1049,7 @@

- - - - - - - + @@ -1075,25 +1072,28 @@

- - + +

Further “within the season” graphs can be given as needed. The season length was calculated earlier, Fig. 12.4e and is plotted in Fig. 12.4o. The median length at Dodoma was about 4 months and varies between 2 and 6 months.

The total rainfall within the season is sometimes requested. This is the rainfall between the start dates and the end dates. This again uses the Climatic > Prepare > Climatic Summaries, as shown in Fig. 12.4p.

++++ - - + + - - + +

Fig. 12.4p

-

Climatic > Prepare > Summaries

Fig. 12.4r Day Range from start to endFig. 12.4p Climatic > Prepare > SummariesFig. 12.4q Day Range from start to end
! {wi dth=“2.4361231408573927in” heig ht=“3.1956178915135607in”}
@@ -1130,7 +1130,7 @@

- + @@ -1141,20 +1141,19 @@

The Climatic > Prepare > Spells dialogue is shown in Fig. 12.4v. The Day Range is completed as shown earlier in Fig. 12.4r. The graph of the spell lengths, using Climatic > PICSA > Rainfall Graphs is then shown in Fig. 12.4 w. The median for the longest spell length in the season is 2.5 weeks and about 1 year in 7 has a dry spell of 25 days or more.

--++ - - + + - - + +

Fig. 12.4v Dry spells during the season

-

Climatic > Prepare > Spells

Fig. 12.4w Graph of maximum spell lengthsFig. 12.4v Dry spells during the seasonFig. 12.4w Graph of maximum spell lengths
{ width=“2.5910148731408573in” he ight=“3.0473589238845142in”}
@@ -1162,20 +1161,19 @@

The graph, in Fig. 12.4y shows the median is 66mm on a day and just a few years have a day with more than 100mm. The colours in Fig. 12.4y indicate which month the maximum value occurred, and indicate that it can be in any of the months of the rainy season[^53].

--++ - - + + - - + +

Fig. 12.4x

-

Climaitc > prepare > Extremes

Fig. 12.4yFig. 12.4xFig. 12.4y
{w idth=“2.415457130358705in” hei ght=“3.559381014873141in”} h
@@ -1188,9 +1186,14 @@

+ + +Fig. 12.5a + + - + @@ -1296,70 +1299,65 @@

- - + +

As a second preliminary, Fig. 12.7c uses Climatic > Prepare > Climatic Summaries to show the seasonal totals. The summary data are in Fig. 12.7d.

--++ - - + + - - + +

Fig. 12.7c

-

Climatic > Prepare > Climatic Summaries

-

Summaries sub-dialogue for total and missing values

Fig. 12.7d Summary DataFig. 12.7cFig. 12.7d Summary Data
{width=“2.008472222222222in” he ight=“3.5148261154855645in”} h

The Climatic > PICSA > Rainfall Graph, Fig. 12.5e is then used to show the resulting data. This is in Fig. 12.7f, and shows the seasonal mean is just over 550mm.

--++ - - + + - - + +

Fig. 12.7e

-

Climatic > PICSA > Rainfall Graph

Fig. 12.7fFig. 12.7e Climatic > PICSA > Rainfall GraphFig. 12.7f
{widt h=“2.2830610236220474in” height =“2.6302777777777777in”}

The start and end of the rains are two of the building blocks for the crops dialogue. Hence these are now found. They will usually already be available from earlier calculations, see Section 12.3, but are also shown here for completeness of this section.

--++ - - + + - - + +

Fig. 12.7g

-

Climatic > Prepare > Start of the Rains

Fig. 12.7h

-

Climatic > Prepare > End of the Rains

Fig. 12.7gFig. 12.7h
h
@@ -1378,28 +1376,27 @@

- - + +

Use Climatic > PICSA > Crops. For clarity the dialogue is shown twice. In Fig. 12.7k the top controls are completed with the data frame for the daily data, i.e. dodoma.

--++ - - + + - - + +

Fig. 12.7k Crops with daily data

-

Climatic > PICSA > Crops

Fig. 12.7l Crops with start and end dataFig. 12.7k Crops with daily dataFig. 12.7l Crops with start and end data
@@ -1409,20 +1406,19 @@

- - + + -Fig. 12.7m -

Fug 12.7n

-

Redo when no zero in first year

+Fig. 12.7m +Fig. 12.7n [Redo when no zero in first year] -{wi dth=“2.4111964129483816in” he ight=“2.10790135608049in”} - + h + @@ -1441,13 +1437,13 @@

- - + +

Return to the Climatic > PICSA > Crops dialogue and specify planting dates from 1st December to mid-January as shown in Fig. 12.7o. There is also a range of crop water requirements, from 250 to 400mm) and crop durations from 75 to 120 days. At other sites crops with a greater water requirement and with longer season lengths could also be considered. For Dodoma, the initial graphs in Fig. 12.7b, 12.7f and 12.7j suggest the range of values given in Fig. 12.7o.

-

The results, in the output window, are shown in Fig. 12.7p. They can be interpreted here, but for presentation they are copied into Excel or to Calc (in Open Office). Copied from Fig. 12.7p they can be pasted into Excel, using the Import wizard. The results are shown in Fig. 12.7r

+

The results, in the output window, are shown in Fig. 12.7p. They can be interpreted here, but for presentation they are copied into Excel or to Calc (in Open Office). Copied from Fig. 12.7p they can be pasted into Excel, using the Import wizard. The results are shown in Fig. 12.7q

@@ -1455,18 +1451,18 @@

+

- - - + +
Fig. 12.7q Fig. 12.7rFig. 12.7s
-

In Excel it is convenient to express the risks as fractions over 10 years as is shown in Fig. 12.7s. The results are in Fig. 12.7t. From Fig. 12.7t we see that a 75 day crop that needed 250mm and planted in 1st December would be OK in 6 years out of 10. Later planting would slightly increase the chance of success.

+

In Excel it is convenient to express the risks as fractions over 10 years as is shown in Fig. 12.7s. The results are in Fig. 12.7s. From Fig. 12.7t we see that a 75 day crop that needed 250mm and planted in 1st December would be OK in 6 years out of 10. Later planting would slightly increase the chance of success.

@@ -1474,14 +1470,14 @@

-

- + + - - + +
Fig. 12.7tFig. 12.7u Find the planting probabilitiesFig. 12.7sFig. 12.7t Find the planting probabilities
@@ -1494,14 +1490,14 @@

+Fig. 12.7u Fig. 12.7v -Fig. 12.7w - - + + diff --git a/docs/Chapter_14_The_Seasonal_forecast.html b/docs/Chapter_14_The_Seasonal_forecast.html index fb679da..75f8ae4 100644 --- a/docs/Chapter_14_The_Seasonal_forecast.html +++ b/docs/Chapter_14_The_Seasonal_forecast.html @@ -305,8 +305,8 @@

- - + + @@ -333,8 +333,8 @@

- - + + @@ -353,8 +353,8 @@

- - + + @@ -372,8 +372,8 @@

- - + + @@ -391,8 +391,8 @@

- - + + @@ -401,20 +401,19 @@

- - + + -

Fig. 13.2i

-

Climatic > Tidy and Examine > Stack

-Fig. 13.2j +Fig. 13.2i Climatic > Tidy and Examine >Stack +Fig. 13.2j - - + h + @@ -430,7 +429,7 @@

- + @@ -450,8 +449,8 @@

- - + + @@ -475,7 +474,7 @@

- + @@ -491,7 +490,7 @@

- + @@ -502,20 +501,19 @@

- - + + -Fig. 13.3a -

Fig. 13.3b

-

File > Open

+Fig. 13.3a +Fig. 13.3b File > Open - - + h + @@ -524,20 +522,19 @@

- - + + -Fig. 13.3c -

Fig. 13.3d Setting the filter

-

(Omit level 1 and from 54 onwards)

+Fig. 13.3c +Fig. 13.3d Setting the filter - - + + @@ -547,40 +544,38 @@

- - + + -Fig. 13.3e Making a subset -

Fig. 13.3f Adding a year variable

-

Prepare > Column: Generate > Regular Sequence

+Fig. 13.3e Making a subset +Fig. 13.3f Adding a year variable -{wi dth=“2.4592377515310586in” hei ght=“2.761431539807524in”} - + h +

Now all that remains is to stack the data, Climatic > Tidy and Examine > Stack, as shown in Fig. 13.3g. The re4sulting data are in Fig.13.3h. There are now 126984 rows of data, i.e. 37 years from 52 * 66 = 3432 equally spaced locations.

--++ - - + + - - + +

Fig. 13.3g

-

Climatic > Tidy and Examine > Stack

Fig. 13.3h The Rwanda ENACTS data so farFig. 13.3gFig. 13.3h The Rwanda ENACTS data so far
he
@@ -612,8 +607,8 @@

- - + + @@ -637,8 +632,8 @@

- - + + @@ -656,25 +651,27 @@

- +

This can be repeated for further parts of Rwanda. We suggest that exporting the data from individual “blocks” of 24 or usually 36 stations for CPT, may enable separate local forecasts to be given?

Change the filter to choose 12 blocks over the country, possibly Lat6S = (1, 5, 9) and Lon6E = (1, 4, 7, 10).

++++ - - + + - - + +

Fig. 13.3n Choosing 12 blocks as a filter

-

Right-Click > Filter > Define New Filter

Fig. 13.3oFacet with 2 variables

-

Describe > Specific > Line Plot Options > Facet

Fig. 13.3n Choosing 12 blocks as a filterFig. 13.3o Facet with 2 variables
! {wi dth=“2.402188320209974in” heigh t=“2.6810597112860894in”}
@@ -689,7 +686,7 @@

- + diff --git a/docs/Chapter_17_Circular_data_and_wind_roses.html b/docs/Chapter_17_Circular_data_and_wind_roses.html index 6d60c69..225d566 100644 --- a/docs/Chapter_17_Circular_data_and_wind_roses.html +++ b/docs/Chapter_17_Circular_data_and_wind_roses.html @@ -306,8 +306,8 @@

- - + + @@ -331,20 +331,19 @@

Fig. 16.2a shows the data in R-Instat. The wind directions (column wind_dir) are in radians, i.e. they are an angle between 0 and 2pi (i.e. 2 * 3.14 = 6.28).

--++ - - + + - - + +
Fig. 16.2a Data from circular package

Fig. 16.2b Adding a date variable

-

Climatic > Dates > Generate Dates

Fig. 16.2a Data from circular packageFig. 16.2b Adding a date variable
{ width=“2.5966491688538933in” h eight=“3.298924978127734in”}
@@ -365,8 +364,8 @@

- - + + @@ -374,20 +373,19 @@

The structure of the data is ignored initially, to concentrate on the new summary statistics for circular data. Graphs are discussed in Section 16.3. A preview, in Fig. 16.2e indicates the data are reasonably concentrated near zero. From the graph we expect a mean just above zero, i.e. perhaps North-North-East, (where East = pi/2 = 1.57 in radians) and a standard deviation that indicates a good level of concentration of the data. Thus, we don’t expect the circular mean to be as large as 2.36 radians, or 135 degrees, as shown in Fig. 16.2d

--++ - + - - + +
Fig. 16.2e A circular histogram of the wind data

Fig. 16.2f The circular keyboard

-

Prepare > Column: Calculate > Calculations

Fig. 16.2f The circular keyboard
{ width=“2.557014435695538in” hei ght=“2.6285608048993874in”}
@@ -406,12 +404,12 @@

- - + + -

It seems that nothing has happened. But now press the icon on the toolbar, Fig. 16.2i to confirm that the two variables are now defined as circular. Press to close the metadata window.

+

It seems that nothing has happened. But now press the icon on the toolbar, Fig. 16.2i to confirm that the two variables are now defined as circular. Press to close the metadata window.

@@ -423,7 +421,7 @@

- +
@@ -441,8 +439,8 @@

- - + + @@ -464,8 +462,8 @@

- - + + @@ -491,20 +489,19 @@

To help with the interpretation, of these summaries, take a very simple example with 5 observations. In degrees take 10, 15, 20, 25 30, Fig. 16.2n. Round the circle they are not very spread out.

--++ - - + + - - + +

Fig. 16.2n

-

File > New Data Frame

Fig. 16.2oFig. 16.2nFig. 16.2o
@@ -527,19 +524,18 @@

- + -Fig. 16.2k Examples of von Mises distributions -Fig. 16.2l +Fig. 16.2p Examples of von Mises distributions +Fig. 16.2q -

Prefer from R-Instat

-

- + h + @@ -549,24 +545,19 @@

Hence get the mean each day and then analyse the resulting daily data. This is the circular mean, and uses the Prepare > Column: Reshape > Column Summaries as shown in Fig. 16.2m and Fig. 16.2n.

-+ - - + + - - - - - - + +

Fig. 16.2m

-

Prepare > Column: Reshape > Column Summaries

Fig. 16.2n Circular sub-dialogueFig. 16.2rFig. 16.2s Circular sub-dialogue
{width=“2.9925721784776904in height=”3.2901760717410324in”}”
@@ -578,14 +569,14 @@

-Fig. 16.2o -Fig. 16.2p +Fig. 16.2t +Fig. 16.2u - - + + @@ -599,20 +590,19 @@

- + -Fig. 16.3a Wind and pollution data -

Fig. 16.3b Checking the wind data

-

Climatic > Tidy and Examine > One Variable Frequencies

+Fig. 16.3a Wind and pollution data +Fig. 16.3b Checking the wind data - - +{width=“2.9951706036745405in height=”3.0631321084864394in”} +” @@ -620,20 +610,18 @@

- + Fig. 16.3c Different values in the wd variable -

Fig. 16.3d Duplicating the wind variable

-

Right click (in wd variable) > Duplicate Column

-

Redo with new duplicate column dialogue

+Fig. 16.3d Duplicating the wind variable - - + h + @@ -649,21 +637,19 @@

- - + + -

Fig. 16.3e Replace 0 by missing

-

Climatic > Tidy and Examine > Replace

-

Fig. 16.3f Results from summary of 4 variables

-

Climatic > Tidy and Examine > One Variable Summarise

+Fig. 16.3e Replace 0 by missing +Fig. 16.3f Results from summary of 4 variables -![] (media/image872.png){wid th=“2.243173665791776in” heigh t=“2.243173665791776in”} - + h + @@ -683,50 +669,46 @@

- - + +

So return to the dialogue and click on the Histogram Options button to set the number of bins to 36, Fig. 16.3i. We make the histogram a little more colourful at the same time.

--++ - - + + - - + +

Fig. 16.3i Setting bins in the histogram

-

Describe > Specific > Histogram
-Then Histogram Options

Fig. 16.3j Ordinary histogramFig. 16.3i Setting bins in the histogramFig. 16.3j Ordinary histogram
{width=“2.63702646544182in” h eight=“3.539001531058618in”} h

This is now ready to produce a circular plot. Return to the dialogue, use the Plot Options and the Y-axis tab, Fig. 16.3k. Set the axis limits for the y-axis as shown in Fig. 16.3k. Also press on the Coordinates tab in Fig. 16.3k and specify polar coordinates.

--++ - - + + - - + +

Fig. 16.3k

-

Describe > Specific > Histogram
-Then Plot Options > Y-Axis and Coordinates

Fig. 16.3l Histogram as a circular plotFig. 16.3kFig. 16.3l Histogram as a circular plot
@@ -737,21 +719,19 @@

- - + + -

Fig. 16.3m Making an hour variable

-

Prepare > Column: Calculate > Calculations

-

Fig. 16.3n Making a 3-hour variable

-

Prepare > Column: Calculate > Calculations

+Fig. 16.3m Making an hour variable +Fig. 16.3n Making a 3-hour variable - - + + @@ -760,21 +740,19 @@

- - + + -

Fig. 16.3o

-

Describe > Specific > Histogram
-Plot Options > Facets and Y-axis

-Fig. 16.3p Circular histogram with facets +Fig. 16.3o +Fig. 16.3p Circular histogram with facets -{widt h=“2.160592738407699in” height =“2.357010061242345in”} - + h + @@ -782,49 +760,38 @@

- - + + -

Fig. 16.3q

-

Climatic > Dates > Make Date

-

Fig. 16.3r Defining a variable as circular

-

Prepare > Column: Define > Circular

+Fig. 16.3q +Fig. 16.3r Defining a variable as circular - -The new dialogue when ready + +

Now use the Prepare >Column: Define > Circular to set the wd variable as circular, Fig 16.4r.

--++ - - + + - - - - - - - - - - + +

Fig. 16.4s Calculating circular summaries

-

Prepare > Column: Reshape > Summarise

Fig. 16.4tFig. 16.3s Calculating circular summariesFig. 16.3t
@@ -832,8 +799,8 @@

- - + + @@ -843,20 +810,26 @@

- - + + --++ + + + + + + - - + +
Fig. 16.3wFig. 16.3x
@@ -867,12 +840,12 @@

-Fig. 16.3u +Fig. 16.3y - + @@ -885,21 +858,19 @@

Use Climatic > Describe > Wind Speed/Direction > Wind Rose, Fig. 16.4a. Ignore the Windrose Options to investigate the default results from the hourly data.

--++ - - + + - - + +

Fig. 16.4a Default wind rose

-

Climatic > Describe > Wind > Wind Rose

Fig. 16.4b Default wind-rose

-

(5 wind speeds, colours: Blues, Theme: Minimal)

Fig. 16.4a Default wind roseFig. 16.4b Default wind-rose
{width=“2.7000404636920385in” height=“2.970044838145232in”} h
@@ -918,8 +889,8 @@

- - + + @@ -937,8 +908,8 @@

- - + + @@ -955,7 +926,7 @@

- + @@ -974,8 +945,8 @@

- - + + @@ -990,7 +961,7 @@

- + @@ -1015,8 +986,8 @@

- - + + @@ -1035,8 +1006,8 @@

- - + + @@ -1054,8 +1025,8 @@

- - + + @@ -1074,8 +1045,8 @@

- - + + @@ -1091,7 +1062,7 @@

- + @@ -1107,7 +1078,7 @@

- + diff --git a/docs/Chapter_19_Drought_Indices__SPI.html b/docs/Chapter_19_Drought_Indices__SPI.html index 89362f1..c82e564 100644 --- a/docs/Chapter_19_Drought_Indices__SPI.html +++ b/docs/Chapter_19_Drought_Indices__SPI.html @@ -311,8 +311,8 @@

- - + + @@ -330,8 +330,8 @@

- - + + @@ -342,20 +342,19 @@

To help with the interpretation Fig. 18.2f shows boxplots of the monthly data.

--++ - - + + - - + +
Fig. 18.2e spi1, spi3 and spi12

Fig. 18.2f

-

Describe > Specific Boxplot: sum_rain by month

Fig. 18.2e spi1, spi3 and spi12Fig. 18.2f
h
@@ -369,21 +368,19 @@

The calculations to produce the spi index is relatively complex. A distribution is fitted to the data and then the equivalents from the normal distribution are found. For the annual totals, the distribution is already close to normal, as is shown by the density plot in Fig. 18.2g. Then the normal distribution is used, as in Fig. 18.2h, where the value for 1928 is indicated. The results are then standardised, which just changes the x-axis in Fig. 18.2h to go from roughly -2 to + 2. In fig 18.2h -2 corresponds to a value of 1068 – 2 * 178.5 = 711mm. A value of 711mm has a probability of 1 year in 40 (probability of about 0.025) of occurring and represents a severe drought. (Most readers will remember the value of ± 1.96, i.e. about ± 2 that gives the 5% points from the standard normal distribution.)

--++ - - + + - - + +

Fig. 18.2g Density graph of annual totals

-

Describe > Specific > Histogram with density

Fig. 18.2h Normal plot showing 1928 total

-

Model > Probability Distributions > Show Model

Fig. 18.2g Density graph of annual totalsFig. 18.2h Normal plot showing 1928 total
h
@@ -399,7 +396,7 @@

- + diff --git a/docs/Chapter_20_Climate_Normals.html b/docs/Chapter_20_Climate_Normals.html index f226f0a..bb976d1 100644 --- a/docs/Chapter_20_Climate_Normals.html +++ b/docs/Chapter_20_Climate_Normals.html @@ -614,21 +614,19 @@

For these reasons, we claim the proposed threshold value of 0.85mm is a practical was of implementing the WMO ≥1mm threshold.

--++ - - + + - - + +

Fig. 19.1a Inventory for Dodoma

-

Climatic > Check Data > Inventory

Fig. 19.1b Subset with 30 years

-

Right-click > Filter (to year from 1981 to 2010)

Fig. 19.1a Inventory for DodomaFig. 19.1b Subset with 30 years
@@ -645,20 +643,19 @@

<

The first 2 normals in Table 19.1a are the precipitation totals and the number of rain days. As preparation, calculate the rain days as shown in Fig. 19.2a. In Fig. 19.2a the 0.85mm threshold has been used, which we claimed above, is consistent with the WMO definition of rain ≥ 1mm. (Use 0.95mm is you don’t agree!)

--++ - - + + - - + +

Fig. 19.2a Add a variable for raindays

-

Climatic > Prepare > Transform

Fig. 19.2bFig. 19.2a Add a variable for rain daysFig. 19.2b
h
@@ -667,20 +664,19 @@

<

The first step uses the Climatic > Prepare > Climatic Summaries, as shown in Fig. 19.2c, to give the monthly rainfall totals for each year.

--++ - - + + - - + +

Fig. 19.2c

-

Climatic > Prepare > Climatic Summaries

Fig. 19.2dFig. 19.2cFig. 19.2d
h
@@ -699,8 +695,8 @@

< - - + + @@ -721,8 +717,8 @@

< - - + + @@ -740,8 +736,8 @@

< - - + + @@ -764,8 +760,8 @@

< - - + + @@ -781,7 +777,7 @@

< - + @@ -803,8 +799,8 @@

< - - + + @@ -815,20 +811,19 @@

<

The annual results, in Fig. 19.2p show the mean annual rainfall was 595mm from 43 rain days, of which, on average 7 days has 25mm or more. So, the mean rain per rain day was on average 14mm and about one rain day in six had 25mm or more. The largest ever daily rainfall was 113mm.

--++ - - + + - - + +
Fig. 19.2p

Fig. 19.2q

-

Prepare > Column: Reshape > Column Summaries (after making year a factor)

Fig. 19.2pFig. 19.2q
@@ -841,20 +836,19 @@

<

The results are in Fig. 19.2s. The first value simply confirms that the mean is the same, whichever way it is calculated. The lowest year had a total of 330mm and the highest was 864mm. The 20% point for the annual rainfall total was 487mm and the 80% point was 717mm.

--++ - - + + - - + +
Fig. 19.2r

Fig. 19.2s Annual results

-

From Prepare > Column: Reshape > Column Summaries

Fig. 19.2rFig. 19.2s Annual results
{wid th=“2.3395986439195102in” heig ht=“2.445317147856518in”} h
@@ -876,8 +870,8 @@

- - + + @@ -896,28 +890,27 @@

- - + +

The guidelines in (World Meteorological Organization (WMO), 2017) depend on what type of parameter you are calculating, i.e. sum, mean, count or extreme.

--++ - - + + - - + +
Fig. 19.3e

Fig. 19.3f

-

Redo when new option available

Fig. 19.3eFig. 19.3f
h
@@ -938,8 +931,8 @@

- - + + @@ -949,20 +942,19 @@

To handle this adjustment, use Climatic > Date > Use Date as shown in Fig. 19.3i. Just choose the check-box for Days in Month. The resulting column is also shown in Fig. 19.3h. Now use Prepare > Column: Calculate > Calculations and complete it as shown in Fig. 19.3j. The resulting variable is also shown in Fig. 19.3h.

--++ - - + + - - + +

Fig. 19.3i Number of days in each month

-

Climatic > Date > Use Date

Fig. 19.3jFig. 19.3i Number of days in each monthFig. 19.3j
h
@@ -974,17 +966,20 @@

In (World Meteorological Organization (WMO), 2017) the monthly (and annual) mean values of the (daily) Tmax, Tmin and Tmean are in the list of Principal normals, Table 19.1a, while the monthly extremes and the count of the number of days that Tmax exceeds 25˚C, 30˚C, 35˚C and 40˚C are listed as secondary. The only equivalent threshold for Tmin is the count less than 0˚C, which is rarely useful in Africa. In (Arguez, et al., 2012) the count less than 10˚C is used, see Table 19.1b and that may be more relevant.

There are missing values in the temperature record. The inventory in Fig. 19.1a indicated that there are not many missing values, but a more accurate check may be useful. One way again uses Climatic > Check Data > Inventory, as shown in Fig. 19.4a, but with a different layout of the data. The result is in Fig. 19.4b.

++++ - - + + - - + +

Fig. 19.4a Detailed inventory

-

Climatic > Check Data > Inventory

Fig. 19.4b Results for Tmax and Tmin 1981-2010Fig. 19.4a Detailed inventoryFig. 19.4b Results for Tmax and Tmin 1981-2010
! {wi dth=“2.362328302712161in” heig ht=“2.666806649168854in”}
@@ -1000,7 +995,7 @@

- + @@ -1018,8 +1013,8 @@

- - + + @@ -1036,12 +1031,8 @@

- - - - - - + + @@ -1062,8 +1053,8 @@

- - + + @@ -1080,8 +1071,8 @@

- - + + diff --git a/docs/Chapter_2_More_Practice_with_RInstat.html b/docs/Chapter_2_More_Practice_with_RInstat.html index a336b31..ce0ce70 100644 --- a/docs/Chapter_2_More_Practice_with_RInstat.html +++ b/docs/Chapter_2_More_Practice_with_RInstat.html @@ -313,20 +313,23 @@

From the opening screen in R-Instat, select File > Open From Library as shown in Fig. 2.2a. Choose Load From Instat Collection, Then Browse to the Climatic directory then to Zambia. Select the file called Moorings_July.rds to give the screen shown in Fig. 2.2b. Press Ok.

--++ - - + + - - + + + + + +

Fig. 2.2a File > Open from Library

-

(Climatic > Zambia > Moorings.RDS)

Fig. 2.2b Ready to import Moorings.RDSFig. 2.2a File > Open from LibraryFig. 2.2b Ready to import Moorings.RDS
{ width=“2.430002187226597in” hei ght=“2.7552777777777777in”}Climatic > Zambia > Moorings.RDS
Fig2.2aFig2.2b
@@ -334,20 +337,23 @@

Move to the second data frame as shown in Fig. 2.2c which shows the monthly totals. They are the total rainfall in mm and the total number of rain days. A rain day was defined as a day with more than 0.85mm[^1].

--++ - - + + - - + + + + + +
Fig. 2.2c The Moorings monthly data

Fig. 2.2d Boxplot dialogue on the Describe menu

-

Describe > Specific > Boxplot

Fig. 2.2c The Moorings monthly dataFig. 2.2d Boxplot dialogue on the Describe menu
Describe > Specific > Boxplot
Fig2.2cFig2.2d
@@ -356,20 +362,23 @@

Choose the Boxplot dialogue from the Describe menu, with Describe > Specific > Boxplot, as shown in Fog. 2.2d. Complete the dialogue as shown in Fig. 2.2e. The resulting graph is shown in Fig. 2.2f[^2]. This shows the total rainfall was typically 200mm in each of December to February. There was always some rain in each of these months, and the records were over 500mm.

--++ - - + + - - + + + + + +

Fig. 2.2e Completed boxplot dialogue

-

Describe > Specific > Boxplot

Fig. 2.2f Boxplot of monthly rainfall totalsFig. 2.2e Completed boxplot dialogueFig. 2.2f Boxplot of monthly rainfall totals
Describe > Specific > Boxplot
@@ -377,39 +386,42 @@

Boxplots are essentially a 5-number summary of the data, (with potential outliers also shown). The Prepare > Column: Reshape > Column Summaries, Fig. 2.2h, dialogue can provide the same summaries numerically.

--++ - - + + - - + +
Fig. 2.2g The number of rain daysFig. 2.2h Summary dialogue on the Prepare menuFig. 2.2g The number of rain daysFig. 2.2h Summary dialogue on the Prepare menu

Summarise both the monthly totals and the number of raindays, with the month as the factor, as shown in Fig. 2.2i. Then choose the Summaries button and complete the sub-dialogue as shown in Fig. 2.2j.

--++ - - + + - - + + + + + +

Fig. 2.2i The Summary dialogue

-

Prepare > Column: Reshape > Column Summaries

Fig. 2.2j Summaries sub-dialogueFig. 2.2i Summary dialogueFig. 2.2j Summaries sub-dialogue
Prepare > Column: Reshape > Column Summaries
@@ -417,19 +429,19 @@

Right-click in the name field of this data frame and choose the option to Reorder columns, Fig. 2.2l.

--++ - - + + - - + +
Fig. 2.2k Resulting summary dataFig 2.2l Right-click menu to reorder columnsFig. 2.2k Resulting summary dataFig. 2.2l Right-click menu to reorder columns
@@ -437,21 +449,23 @@

With the summaries in a sensible order, they are now transferred to the results (output) window.

--++ - - + + - - + + + + + +

Fig. 2.2m Reorder the resulting columns

-

Right-Click > Reorder Column(s)

Fig. 2.2n Simplify column names

-

Right-click > Rename Column

Fig. 2.2m Reorder the resulting columnsFig. 2.2n Simplify column names
Right-click > Reorder column(s)Right-click > Rename column(s)
@@ -459,19 +473,22 @@

- + -

Fig. 2.2o View Data dialogue

-

Prepare > Data Frame > View Data

-Fig. 2.2n The Monthly number of rain days +Fig. 2.2o View Data dialogue +Fig. 2.2p The Monthly number of rain days - {width=“2.7121620734908136in” height=“2.720417760279965in”} - +Prepare > Data Frame > View Data + + + + + @@ -484,63 +501,61 @@

Some “housekeeping” is a preliminary. The 3rd data-frame is no longer needed. Right-click on the bottom tab a and choose the option to delete, Fig. 2.3a. The dialogue shown in Fig. 2.3b opens. Just press ok.

--++ - - + + - - + +
Fig. 2.3a Right-click on the bottom tabFig. 2.3b Delete a data frameFig. 2.3a Right-click on the bottom tabFig. 2.3b Delete a data frame
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML1e674805.PNG

Use Prepare > Column: Reshape > Column Summaries and complete the dialogue and sub-dialogue as shown in Fig. 2.3c and Fig. 2.3d to produce the seasonal totals.

--++ - - + + - - + + + + + +

Fig. 2.3c Produce the annual totals

-

Prepare > Column: Reshape > Column Summaries

Fig. 2.3d The Summaries sub-dialogueFig. 2.3c Produce the annual totalsFig. 2.3d The Summaries sub-dialogue
-
-

-
C:\Users\R OGERS~1\AppData\Local\Temp\SN AGHTML1b0bb1d.PNG
-
-
Prepare > Column: Reshape > Column Summaries

The results are shown in Fig. 2.3 e after the steps explained below. First, notice in Fig. 2.3e that there were only 4 months in the first season, and the annual summary was therefore set to missing[^3].

--++ - - + + - - + +
Fig. 2.3e Resulting annual dataFig. 2.3f Menu for a text substringFig. 2.3e Resulting annual dataFig. 2.3f Menu for a text substring
@@ -548,20 +563,23 @@

Use Prepare > Column: Text > Transform, Fig. 2.3f. Complete the resulting dialogue, as shown in Fig. 2.3g, to give just the starting year of the season. The resulting variable is shown in Fig. 2.3e.

--++ - - + + - - + + + + + +

Fig. 2.3g The Substring Option

-

Prepare > Column:Text > Transform

Fig. 2.3h Convert Column to NumericFig. 2.3g The Substring OptionFig. 2.3h Convert Column to Numeric
{ width=“2.003675634295713in” hei ght=“2.8906255468066493in”}Prepare > Column: Text > Transform
@@ -571,39 +589,42 @@

Use Climatic > PICSA > Rainfall Graph. Complete as shown in Fig. 2.3i. Press the PICSA Options button and complete the Lines ab as shown in Fig. 2.3j to add (and label) a horizontal line for the mean.

--++ - - + + - - + + + + + +

Fig. 2.3i PICSA Rainfall graph dialogue

-

Climatic > PICSA > Rainfall Graph

Fig. 2.3j Add a line showing the meanFig. 2.3i PICSA Rainfall graph dialogueFig. 2.3j Add a line showing the mean
CLimatic > PICSA > Rainfall Graph

The resulting graph is shown in Fig 2.3k[^4]. Return to the dialogue and put raindays as the y-variable to give the results in Fig. 2.3l.

--++ - - + + - - + +
Fig. 2.3k Seasonal rainfall totalsFig. 2.3l Number of rain daysFig. 2.3k Seasonal rainfall totalsFig. 2.3l Number of rain days
@@ -614,6 +635,24 @@

Return to the monthly data frame and filter to examine just those months. So, make sure you are on the monthly data. Right click as usual and choose Filter, Fig. 2.3m

+++ + + + + + + + + + + + + +
Fig. 2.3m Right-click for FilterFig. 2.3n The filter dialogue
+ + @@ -639,19 +678,23 @@

--++ - - + + - - + + + + + +
Fig. 2.3o Defining the filterFig. 2.3p The filtered dataFig. 2.3o Define the filterFig. 2.3p The filtered data
Prepare > Column: Reshape > Column Summaries
@@ -660,19 +703,19 @@

Now it is quick to repeat the steps above for this analysis. It is simpler to recall the last dialogues as shown in Fig. 2.3q.

--++ - - + + - - + +
Fig. 2.3q Recall the last dialoguesFig. 2.3r The Column Statistics dialogueFig. 2.3q Recall the last dialoguesFig. 2.3r The Column Statistics dialogue
@@ -680,19 +723,19 @@

The new columns have added to the existing annual sheet. So, go straight to the PICSA Rainfall Graphs dialogue again. Choose the new variable for the November-December totals and press OK. The mean is now 286mm for the 2 months. Repeat for the number of rain days to give the graphs for the filtered data, see Fig. 2.3s and 2.3t.

--++ - - + + - - + +
Fig. 2.3s Rainfall totals Nov-DecFig. 2.3t Number of rain days Nov-DecFig. 2.3s Rainfall totals Nov-DecFig. 2.3t Number of rain days Nov-Dec
@@ -707,19 +750,19 @@

- - + + -Fig. 2.4a Importing the Dodoma data -Fig. 2.4b Add a Date column +Fig. 2.4a Importing the Dodoma data +Fig. 2.4b The Summaries sub-dialogue - - + + @@ -727,38 +770,38 @@

- - + + -Fig. 2.4c Import the CMSAF satellite data -Fig. 2.4d Change the name to Date +Fig. 2.4c Import the CMSAF Satellite data +Fig. 2.4d Change the name to Date -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML2bdf480.PNG - + +

Once imported, right-click to change the name of the last column from time_date to Date, i.e. to the same name as in the station data, Fig. 2.4d[^5].

--++ - - + + - - + +
Fig. 2.4e The Merge dialogueFig. 2.4f Sub-dialogue to add just SDUFig. 2.4e The Merge dialogueFig. 2.4f Sub-dialogue to add just SDU
@@ -767,21 +810,23 @@

- - + + -

Fig. 2.4g Results from One Variable Summarise

-

From Describe > One Variable > Summarise

-

Fig. 2.4h Generate columns from the Date

-

Prepare > Column: Date > Make Date

+Fig. 2.4g Results from One Variable Summarise +Fig. 2.4h Generate columns from the Date - - +Describe > One Variable > Summarise +Prepare > Column: Date > Make Date + + + + @@ -790,19 +835,19 @@

- - + + -Fig. 2.4i Resulting merged data -Fig. 2.4j Correlations dialogue +Fig. 2.4i Resulting merged data +Fig. 2.4j Correlations dialogue - - + + @@ -810,19 +855,19 @@

- - + + -Fig. 2.4k Correlations sub-dialogue -Fig. 2.4l Results +Fig. 2.4k Correlations sub-dialogue +Fig. 2.4l Results -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML3dae776.PNG - + + @@ -832,19 +877,19 @@

- - + + -Fig. 2.4m Including months in the analysis -Fig. 2.4n The Histogram dialogue +Fig. 2.4m Including months in the analysis +Fig. 2.4n The Histogram dialogue - - + + @@ -853,19 +898,19 @@

- - + + -Fig. 2.4o A set of density graphs -Fig. 2.4p Include facets in the graph +Fig. 2.4o A set of density graphs +Fig. 2.4p Include facets in the graph - - + + @@ -873,19 +918,23 @@

- - + + -Fig. 2.4q Multiple variables -Fig. 2.4r Resulting graphs +Fig. 2.4q Multiple variables +Fig. 2.4r Resulting graphs - - +Describe > One Variable > Summarise +Prepare > Column: Date > Make Date + + + + @@ -894,19 +943,19 @@

- - + + -Fig. 2.4s Scatterplot dialogue -Fig. 2.4t Plotting sub-dialogue +Fig. 2.4s Scatterplot dialogue +Fig. 2.4t Plotting sub-dialogue -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML6ce7e6e.PNG - + + @@ -922,7 +971,7 @@

- + diff --git a/docs/Chapter_3_Using_RInstat_effectively.html b/docs/Chapter_3_Using_RInstat_effectively.html index 049b55d..6a445c7 100644 --- a/docs/Chapter_3_Using_RInstat_effectively.html +++ b/docs/Chapter_3_Using_RInstat_effectively.html @@ -305,7 +305,10 @@

-C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLc875082.PNG +{width=“6.158751093613298in” + + +height=“3.271836176727909in”} @@ -325,8 +328,8 @@

- -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLc9d2c55.PNG + + @@ -344,8 +347,8 @@

- -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLc9b3538.PNG + + @@ -365,8 +368,8 @@

- - + + @@ -381,16 +384,16 @@

-Fig. 3.2aThe toolbar and View menu +Fig. 3.2a The toolbar and View menu - + -

Use the icon on the toolbar, (Fig. 3.2a) or View > Column Metadata to see the metadata currently associated with each open data frame. An example is in the top left in Fig. 3.2b.

+

Use the icon on the toolbar, (Fig. 3.2a) or View > Column Metadata to see the metadata currently associated with each open data frame. An example is in the top left in Fig. 3.2b.

@@ -402,7 +405,7 @@

-

+
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML5265ebf.PNG
@@ -423,8 +426,8 @@

- - + + @@ -442,8 +445,8 @@

- - + + @@ -471,8 +474,8 @@

- -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLa085dd4.PNG + + @@ -480,8 +483,8 @@

- - + + @@ -491,29 +494,29 @@

- - + h +

Double-click in the graph, Fig, 3.3d to turn it blue. This also produces the popup menu and the graph can be copied to the clipboard.

-

An alternative is shown in Fig. 3.3e. Click on graph icon in the toolbar to show the last graph in R’s viewer. This only shows a single graph, but the Window can be resized and, as shown in Fig. 3.3e, there are now many options to save the graph, or to copy it to the clipboard.

+

An alternative is shown in Fig. 3.3e. Click on graph icon in the toolbar to show the last graph in R’s viewer. This only shows a single graph, but the Window can be resized and, as shown in Fig. 3.3e, there are now many options to save the graph, or to copy it to the clipboard.

This toolbar option is only for the most recent graph. The Describe > View Graph dialogue, Fig. 3.3f provides the option to view any of the saved graphs in this way[^13].

--++ - + - - + +
Fig. 3.3e The R graph viewerFig. 3.3f Options for viewing graphsFig. 3.3f Options for reviewing
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLa31a91e.PNGC:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLa3be5f8.PNG
@@ -529,7 +532,7 @@

- + @@ -537,20 +540,19 @@

- - + + -

Fig. 3.3h Initial use of scatterplot (geom point)

-

Describe > Specific > Scatter Plot

-Fig. 3.3i Resulting graph +Fig. 3.3h Initial use of scatter plot (geom point) +Fig. 3.3i Resulting Graph - - + ight=“2.9195220909886266in”} +{width=“2.960900043744532in” @@ -570,8 +572,8 @@

- - + + @@ -588,7 +590,7 @@

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- + -Fig. 3.3o Correlations sub-dialogue -Fig. 3.3p Distributions, scatterplot and correlations +Fig. 3.3o Correlations subdialog +Fig. 3.3p Distributions, Scatter plot and Correlations -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLaa7a41f.PNG - + + @@ -647,8 +649,8 @@

- -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLb0219a7.PNG + + @@ -658,7 +660,7 @@

4.4 The log and script windows

The final two Windows in R-Instat are for the Log Window and the Script Window.

The R programming language is very powerful, but with a relatively steep learning curve. R-Instat gives easy access to a subset of R. However, a click-and-point system always has limitations. We consider here some options if these limitations are ever a constraint.

-

The log file keeps a record of all the R-commands that have been issued during a session of R-Instat. Use the toolbar option , Fig. 3.2e, or View > Log Window to open the log file.

+

The log file keeps a record of all the R-commands that have been issued during a session of R-Instat. Use the toolbar option , Fig. 3.2e, or View > Log Window to open the log file.

In the Log file, the right-click menu gives various options, Fig. 3.4a, including saving the log file. That action is the same as using File > Save As > Save Log As, shown earlier in Fig. 3.2c.

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- - + + -Fig. 3.4e The boxplot dialogue -Fig. 3.4f Boxplots with width proportional to the number of rain days +Fig. 3.4e The boxplot dialogue *** +Fig. 3.4f Boxplots with width proportional to number of raindays*** -C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML6cfc546.PNG - + + @@ -760,7 +762,7 @@

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The first example was previously prepared for the original Instat. It is 56 years of daily data from Samaru in Northern Nigeria. Use File > Open from Library. Choose Instat data, Browse to the Climatic directory, choose Original Climatic Guide Datasets. There untick the first option and choose the sheet called Samaru56, Fig. 4.4a.

--++ - - + + - - + +

Fig. 4.4a Open Samaru56

-

File > Open from Library > Instat > Browse > Climatic

Fig. 4.4b The “shape” of the Samaru56 dataFig. 4.4a Open Samaru56Fig. 4.4b The “shape” of the Samaru56 data

The data are shown in Fig. 4.4b. They are from 1928 and each year is in a separate column. All columns are of length 366, in Fig. 4.4b, and there is a special code for February 29th in non-leap years.

--++ - - + + - - + +

Fig. 4.4c Tidying the Samaru data

-

Climatic > Tidy and Examine > Tidy Daily Data

Fig. 4.4d Issues in the data – no surprisesFig. 4.4c Tidying the Samaru dataFig. 4.4d Issues in the data – no surprises
h
@@ -527,20 +525,19 @@

Return to the dialogue in Fig. 4.4c, tick the option above to ignore data on invalid dates. Press Ok again.

--++ - - + + - - + +
Fig. 4.4e Samaru data reshaped

Fig. 4.4f Using the date variable

-

Climatic > Dates > Use Date

Fig. 4.4e Samaru data reshapedFig. 4.4f Using the date variable
{width=“2.555686789151356in” he ight=“3.7988068678915137in”}
@@ -548,20 +545,19 @@

The date column has been included in the data in Fig. 4.4e. The next step is the Climatic > Dates > Use Date dialogue, completed as in Fig. 4.4f. This adds 4 new columns to the data file. We then do some housekeeping, with Right-Click and Reorder Columns to give the data as shown in Fig. 4.4g.

--++ - - + + - - + +
Fig. 4.4g Data ready to be defined as climatic

Fig. 4.4h Defining the data as climatic

-

Climatic > Define Climatic Data

Fig. 4.4g Data ready to be defined as ClimaticFig. 4.4h Defining the data as Climatic
{width=“2.641606517935258in” h eight=“2.5361253280839895in”}
@@ -579,7 +575,7 @@

- + @@ -606,8 +602,8 @@

- - + + @@ -615,20 +611,19 @@

The 15 Excel sheets have imported into 15 R data frames, Fig. 4.4l and we check quickly that each has the 31 rows and 13 columns. Also, that all the columns are numeric. All seems alright. The next step is to append the years together.

--++ - - + + - - + +
Fig. 4.4l The Garoua rainfall data imported

Fig. 4.4m Appending the years

-

Climatic > Tidy and Examine > Append

Fig. 4.4l The Garoua rainfall data importedFig. 4.4m Appending the years
@@ -637,71 +632,57 @@

Use Prepare > Column: Text > Transform and complete the dialogue as shown in Fig. 4.4o

--++ - - + + - - + +
Fig. 4.4n Data in a single data frame

Fig. 4.4o Extract the Year number

-

Prepare > Column: Text > Transform

Fig. 4.4n Data in a single data frame **Fig. 4.4o Extract the Year number**
-
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This adds a new column called yr, Fig. 4.4p.

--++ - - + + - - + +
Fig. 4.4p The year is now available

Fig. 4.4q Sort the data using the year column

-

Right-click > Sort or Prepare > Data Frame > Sort

Fig. 4.4p The year is now availableFig. 4.4q Sort the data using the year column
-
-

-
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-

Nearly there. Use Climatic > Tidy and Examine > Tidy Daily Data as shown in Fig. 4.4r. Earlier, in Fig. 4.4c, each year was in a column. Now each month is in its own column. Complete Fig. 4.4r as shown. As with the first run earlier (Fig. 4.4c) we first check on any errors, rather than simply ignoring them.

--++ - - + + - - + +

Fig. 4.4r Tidy the daily data

-

Climatic > Tidy and Examine > Tidy Daily Data

Fig. 4.4s An error is reported. The yr column should be numeric***Fig. 4.4r Tidy the daily data Climatic>Tidy and Examine>Tidy Daily Data*** Fig. 4.4s An error is reported The yr column should be numeric
{width=“2.59626968503937in” hei ght=“0.6789588801399825in”}
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- - + + @@ -740,8 +721,8 @@

- - + + @@ -766,7 +747,7 @@

- + @@ -774,37 +755,19 @@

- - + + -

Fig. 4.5b Import data from Climsoft

-

Climatic > File > Climsoft

-Fig. 4.5c Connect to the database +***Fig. 4.5b Import data from Climsoft Climatic > Fil +e > Climsoft*** Fig. 4.5c Connect to the database -
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- - + + @@ -842,8 +805,8 @@

- - + + @@ -851,20 +814,19 @@

- - + + -Fig. 4.5h The data are imported -

Fig. 4.5i Add a Date column

-

Climatic > Dates > Make Date

+Fig. 4.5h The data are imported +Fig. 4.5i Add a Date column - - + + @@ -875,26 +837,19 @@

- - + + -Fig. 4.5j Make the Element column a factor -

Fig. 4.5k Unstack the data on the 3 elements

-

Climatic > Tidy and Examine > Unstack

+Fig. 4.5j Make the Element column a factor *** +Fig. 4.5k Unstack the data on the 3 elements Climatic>Tidy and Examine>Unstack*** - {width=“2.6682010061242343in” h eight=“2.0810804899387576in”} -
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- + @@ -976,8 +931,8 @@

- - + + @@ -986,40 +941,38 @@

The data are shown in Fig. 4.7c. On row 120 make a deliberate mistake, i.e. double click on April 30 and change the 30 into 31. That’s not a possible day!

--++ - - + + - - + +
Fig. 4.7c Dodoma with an error added

Fig. 4.7d Add a date column

-

Climatic > Dates > Make Date

Fig. 4.7c Dodoma with an error added *Fig. 4.7d Add a date column Climatic > Dates > Make Date*
{w idth=“2.5625590551181103in” he ight=“2.680066710411199in”} h

Add a date column using Climatic > Dates > Make Date and complete the dialogue as shown in Fig. 4.7d.

--++ - - + + - - + +

Fig 4.7e Examine the data

-

Climatic > Tidy and Examine > One Variable Summarise

Fig. 4.7f Resulting summaries***Fig 4.7e Examine the data Climatic > Tidy and Examine > One Variable Summarise*** Fig. 4.7f Resulting summaries
h
@@ -1030,20 +983,19 @@

The first step in solving the problem (assuming we don’t know it) is to filter the data, choosing just the days when Date1 == NA. To do this, right-click in any column and choose Filter, Fig, 4.7g.

--++ - - + + - - + +

Fig. 4.7g Filter to locate the problem

-

Right-click > Filter

Fig. 4.7h The Filter sub-dialogueFig. 4.7g Filter to locate the problemFig. 4.7h The Filter subdialogue
@@ -1060,7 +1012,7 @@

- + @@ -1077,55 +1029,47 @@

- +

The data have been exported from a previous version of Climsoft (see Sections 1.6 and 4.5). They include a lot of station details but seem in exactly the right shape to make quick progress.

--++ - - + + - - + +

Fig. 4.7k Add a date column

-

Climatic > Dates > Make Date

Fig. 4.7l Define Climatic Data

-

Climatic > Define Climatic Data

Fig. 4.7k Add a date columnFig. 4.7l Define Climatic Data Climatic > Define Climatic Data
{ width=“2.5451115485564304in” h eight=“3.125343394575678in”} h

Define a date column from Climatic > Dates > Make Date, Fig. 4.7k, and then check, with Climatic > Tidy and Examine > One Variable Summarise, as in Fig. 4.7e. All seems fine so far.

So, the last step. Use Climatic > Define Climatic Data as in Fig. 4.7l. Complete the year, month, day fields, as the names are not recognised automatically. Also, obs is the rain column.

-

Finally check for uniqueness, Fig. 3.7m.

+

Finally check for uniqueness, Fig. 4.7m.

--++ - - + + - - + +
Fig. 4.7m A problem - duplicates

Fig. 4.7n Check on the Duplicates

-

Climatic > Tidy and Examine > Duplicates

Fig. 4.7m A problem - duplicatesFig. 4.7n Check on the Duplicates
-
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As suggested in Fig. 4.7m use Climatic > Tidy and Examine > Duplicates and complete as shown in Fig. 4.7n. This produces a new logical column, that is first examined through Climatic > Tidy and Explore > One Variable Summarise, Fig. 4.7o.

--++ - - + + - - + +

Fig. 4.7o Find how often there are duplicates

-

Climatic > Tidy and Examine > One Variable Summarise

Fig. 4.7p The results***Fig. 4.7o Find how often there are duplicates Climatic > Tidy and Examine> One Variable Summarise*** Fig. 4.7p Check on the Duplicates

If there were no duplicates, then this column would be FALSE all the time. We learn, from the result in Fig. 4.7p that it is TRUE on 364 rows. There are then at least 2 values for each day, so up to 182 days have duplicate values.

--++ - - + + - - + +

Fig. 4.7r Filter sub-dialogue

-

Right Click > Filter > Define New Filter

Fig. 4.7s The filtered data***Fig. 4.7q Filter sub-dialogue Right Click > Filter>Define New Filter*** Fig. 4.7r The filtered data
h
diff --git a/docs/Chapter_5_Quantity_and_quality.html b/docs/Chapter_5_Quantity_and_quality.html index 9b992b4..4e529a0 100644 --- a/docs/Chapter_5_Quantity_and_quality.html +++ b/docs/Chapter_5_Quantity_and_quality.html @@ -306,7 +306,7 @@

- + @@ -327,21 +327,19 @@

- - + + -

Fig. 5.2a Rwanda workshop data

-

File > Open from Library > Instat > Climatic > Rwanda > Rwanda Workshop Data.RDS

-

Fig. 5.2b Inventory Dialogue

-

Climatic > Check Data > Inventory

+Fig. 5.2a Rwanda workshop data +Fig. 5.2b Inventory Dialogue Climatic>Check Data>Inventory - - + + @@ -359,8 +357,8 @@

- - + + @@ -379,7 +377,7 @@

- + @@ -397,7 +395,7 @@

- + @@ -414,7 +412,7 @@

- + @@ -422,24 +420,23 @@

- - + + -

Fig. 5.2h Examining daily values

-

Climatic > Check Data > Display Daily

-Fig. 5.2i One year of the data +Fig. 5.2h Examining daily values +Fig. 5.2i One year of the data - {width=“2.6560739282589676in” height=“3.423170384951881in”} - + + -

. Complete the dialogue in Fig. 5.2h as shown. We have chosen to recode zero rainfalls to “—”. This is an option so the rainy days are displayed more clearly in Fig. 5.2i. It indicates the dry period in the year is usually June and July. The dry period was a feature of Fig. 5.2g.

+

Complete the dialogue in Fig. 5.2h as shown. We have chosen to recode zero rainfalls to “—”. This is an option so the rainy days are displayed more clearly in Fig. 5.2i. It indicates the dry period in the year is usually June and July. The dry period was a feature of Fig. 5.2g.

We have now almost come “full circle”. In Fig. 4.4i the rainfall data from Garoua, Cameroon, were supplied for analysis arranged in a similar way to the data now shown in Fig. 5.2i. This is a very clear and sensible layout for the data, but to examine, not to analyse.

The second tab in the same Climatic > Check Data > Display Daily dialogue gives a simple graphical display of the data “by year”. An example of the resulting display is in Fig. 5.2j. The results show both the missing periods as well as the dry parts of the year.

@@ -453,7 +450,7 @@

-

+
@@ -464,20 +461,19 @@

- - + + -

Fig. 5.3a Climatic boxplot dialogue

-

Climatic > Check Data > Boxplot

-Fig. 5.3b Seasonal pattern of sunshine hours +Fig. 5.3a Climatic boxplot dialogue +Fig. 5.3b Seasonal pattern of sunshine hours - - + + @@ -498,8 +494,8 @@

- - + + @@ -515,7 +511,7 @@

- + @@ -533,8 +529,8 @@

- - + + @@ -546,34 +542,33 @@

-Fig. 5.3hBoxplots for the rainfall -Fig. 5.3i Rainfall on rain days +Fig. 5.3h Boxplots for the rainfall +Fig. 5.3i Rainfall on rain days - - + +

Boxplots can be used for the rainfall data, as shown in Fig. 5.3i. The data plotted are just for rain days, with the variable width of each box indicating the number of rain days. The plot shows the unimodal pattern of the rainfall from November to April[^22].

--++ - - + + - - + +

Fig. 5.3j Adding a second layer to the plot

-

Plot Options > Layers > Add

Fig. 5.3k Tmax and Tmin plotted togetherFig. 5.3j Adding a second layer to the plotFig. 5.3k Tmax and Tmin plotted together
@@ -581,40 +576,38 @@

- - + + -

Fig. 5.3l Combining graphs

-

Describe > Combine Graphs > Options

-Fig. 5.3m The 4 elements in Dodoma +Fig. 5.3l Combining graphs ***Fig. +5.3m The 4 elements in Dodoma*** - - + h +

Other displays of these data are also possible. In Fig. 5.3k we observed the larger variation of the Tmax data in the rainy months (compared to both Tmin, and to Tmax in the dry months). This feature is shown more clearly through density plots as seen in Fig. 5.3p. This uses a dialogue from the ordinary Describe menu. The results in Fig. 5.3p indicate a roughly normal-type shape for the data, though with a slightly longer tail for Tmax in the rainy season[^24].

--++ - - + + - - + +

Fig. 5.3o Producing density plots

-

Describe > Specific > Histogram

Fig. 5.3p Density plots for Tmax and Tmin by monthFig. 5.3n Producing density plots *Fig. 5.3o Density plots for Tmax and Tmin by month*
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@@ -625,12 +618,12 @@

-Fig. 5.3q Example for multiple stations, Tmin for 4 stations in Rwanda +Fig. 5.3p Example for multiple stations, Tmin for 4 stations in Rwanda - + @@ -641,25 +634,19 @@

- - + + -

Fig. 5.4a

-

Climatic > Check Data > QC Temperatures

-Fig. 5.4b +**Fig. 5.4a Climatic > Check Data > QC Temperatures +** Fig. 5.4b -
-
-

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- - + + @@ -787,13 +774,13 @@

Fig. 5.5e -Fgi. 5.5f +Fig. 5.5f - - + + @@ -807,13 +794,13 @@

Fig. 5.5g It is a November/December problem -Fig. 5.5hThese are the years +Fig. 5.5h These are the years - - + + @@ -831,8 +818,8 @@

- - + + diff --git a/docs/Chapter_6_Preparing_summaries.html b/docs/Chapter_6_Preparing_summaries.html index 69b4a2c..274c543 100644 --- a/docs/Chapter_6_Preparing_summaries.html +++ b/docs/Chapter_6_Preparing_summaries.html @@ -302,14 +302,14 @@

-Fig. 6.1aThe main climatic summary menu -Fig. 6.1b Presenting the summary +Fig. 6.1a The main climatic summary menu +Fig. 6.1b Presenting the summary - - + + @@ -322,21 +322,19 @@

Use File > Open from Library > Instat > Browse > Climatic > Ghana and open the file called ghana_two_stations.rds, Fig. 6.2a. The data are from Saltpond, which is on the coast, with a bimodal pattern of rainfall, and Tamale, which is further North, and with unimodal rainfall. The rainfall starts in 1944 for each station. Other elements, Fig. 6.2a, start later.

--++ - - + + - - + +

Fig. 6.2a Data from 2 stations

-

File>Open from Library
->Instat>Browse>Climatic>Ghana

Fig. 6.2bFig. 6.2a Data from 2 stations ***Fig. 6.2b***
@@ -344,20 +342,19 @@

First use Climatic > Dates > Infill, Fig. 6.2b, to check there are no missing dates. Some files simply omit the days when all data are missing.

--++ - - + + - - + +

Fig. 6.2c Infill missing dates

-

Climatic > Date > Infill

Fig. 6.2d Results from infillingFig. 6.2c Infill missing dates ***Fig. 6.2d Results from infilling***
@@ -365,62 +362,57 @@

Use Climatic > Tidy and Examine > One Variable Summarise and complete the dialogue as shown in Fig. 6.2e. The results are in Fig. 6.2f. They show no missing values for the date column (which is good and a relief), and very few missing rainfall days. Most of those were infilled. The other variables have reasonable values. Hence we proceed.

--++ - - + + - - + +

Fig. 6.2e Checking the data

-

Climatic > Tidy and Examine > One Variable Summarise

Fig. 6.2f ResultsFig. 6.2e Checking the data ***Fig. 6.2fResults***
{ width=“2.6116010498687663in” h eight=“2.139025590551181in”}

Use Climatic > Date > Use Date, Fig. 6.2g. Then it is convenient to reorder the columns to put the date variables before the climatic data, Fig. 6.2h.

--++ - - + + - - + +

Fig. 6.2g Generate further date variables

-

Climatic > Date > Use Date

Fig. 6.2h Data

-

After Right-click > Reorder columns

Fig. 6.2g Generate further date variablesFig. 6.2h Data
{width=“2.626457786526684in” he ight=“4.1896883202099735in”}

Finally, in this preparation, use Climatic > Define Climatic Data. The dialogue should fill automatically as shown in Fig. 6.2i. Check that the data are unique and press Ok.

--++ - - + + - - + +

Fig. 6.2

-

Climatic > Define Climatic Datai

Fig. 6.2j A count column

-

Climatic > Prepare > Transform

Fig. 6.2i ***Fig. 6.2j A count column***
@@ -429,20 +421,19 @@

Now try the second method, which generates a very simple R command. It uses R-Instat’s powerful calculator, from Prepare > Column: Calculate > Calculations, Fig. 6.2k

--++ - - + + - - + +

Fig. 6.2k Using the calculator

-

Prepare > Column: Calculate > Calculations

Fig. 6.2l Using the additional logical keyboardFig. 6.2k Using the calculator ***Fig. 6.2l Using the additional logical keyboard***
@@ -450,17 +441,19 @@

They are now not both needed, so delete one of them. We have kept the logical column.

-++ + - +

Fig. 6.2m Data

After Right-click > Reorder columns
|
@@ -471,20 +464,19 @@

We are going to produce the annual totals. Fig. 6.3a also indicates it is equally easy to produce the totals for any subset of the year.

--++ - - + + - - + +

Fig. 6.3a The climatic summary dialogue

-

Climatic > Prepare > Climatic Summaries

Fig. 6.3b The summaries sub-dialogueFig. 6.3a The climatic summary dialogueFig. 6.3b The summaries sub-dialogue
@@ -504,8 +496,8 @@

- - + + @@ -516,20 +508,19 @@

In the sub-dialogue, click also on the Y-axis tab and set the lower limit to 0 (zero).

--++ - - + + - - + +

Fig. 6.3e PICSA-style rainfall graphs

-

Climatic > PICSA > Rainfall Graph

Fig. 6.3f Sub-dialogue to add linesFig. 6.3e PICSA-style rainfall graphsFig. 6.3f Sub-dialogue to add lines
h
@@ -544,7 +535,7 @@

- + @@ -552,7 +543,7 @@

-Fig. 6.3hPICSA-style rainfall graph for 12 stations from xxx +Fig. 6.3h PICSA-style rainfall graph for 12 stations from xxx @@ -562,20 +553,19 @@

Return to the previous PICSA dialogue and choose Data Options to give the sub-dialogue shown in Fig. 6.3i

--++ - - + + - - + +

Fig. 6.3i Define a filter

-

Climatic > PICSA > Rainfall Graph > Data Options

Fig. 6.3j Choose a single stationFig. 6.3i Define a filterFig. 6.3j Choose a single station
{width= “2.0657567804024497in” height=” 2.5604385389326336in”}
@@ -594,8 +584,8 @@

- - + + @@ -615,8 +605,8 @@

- - + + @@ -634,49 +624,46 @@

- - + +

Finally, in this section, we stress that the idea of the climatic menu is simply to make it even easier to do common climatic analyses. By “even easier” we mean easier than using the main dialogues in R-instat. If what you would like to do is not (yet) possible with the climatic menu it may still be possible with the ordinary use of R-Instat[^27]. It is important that you remain in charge and are not limited by the particular dialogues. As an example, suppose you would like to fit a trend line to the rainfall data. The PICSA graphs permit horizontal lines, but not trend lines. Perhaps the last graph, Fig. 6.3p has a downward slope?

--++ - - + + - - + +

Fig. 6.3q Graph with “ordinary” dialogue

-

Describe > Specific > Line Plot

Fig. 6.3r Saltpond annual rainfall with trend lineFig. 6.3q Graph with “ordinary” dialogueFig. 6.3r Saltpond annual rainfall with trend line

One way to check this possibility is through the “ordinary” graphics dialogues in R-Instat. So, use Describe > Specific > Line Plot and complete the dialogue as shown in Fig. 6.3q. The results are in Fig. 6.3t. They perhaps hint at a possible trend[^28]. If there is a trend

--++ - - + + - - + +

Fig. 6.3s Analysis for rainfall totals

-

(Change Variable to sum_rainfall in Fig. 6.3q)

Fig. 6.3t Cumulative or Exceedance Graph

-

Climatic > PICSA > Cumulative/Exceedance Graph

Fig. 6.3s Analysis for rainfall totalsFig. 6.3t Cumulative or Exceedance Graph
@@ -685,20 +672,19 @@

Time series are not the only way to display the annual summaries. Use Climatic > PICSA > Cumulative/Exceedance Graph, and complete the dialogue as shown in Fig. 6.3t. If the filter is still operating, then remove it by including Data Options and choosing no_filter in the resulting subdialogue, Fig. 6.3u. The resulting graph is in Fig. 6.3v.

--++ - - + + - - + +

Fig. 6.3u Removing a filter

-

(Data Options from dialogue)

Fig. 6.3v Cumulative distributionsFig. 6.3u Removing a filterFig. 6.3v Cumulative distributions
@@ -717,8 +703,8 @@

- - + + @@ -729,12 +715,12 @@

-Fig. 6.3y Numerical results +Fig. 6.3y Numerical results - + @@ -759,8 +745,8 @@

- - + + @@ -780,8 +766,8 @@

- - + + @@ -799,8 +785,8 @@

- - + + @@ -819,8 +805,8 @@

- - + + @@ -836,7 +822,7 @@

- + @@ -853,7 +839,7 @@

- + @@ -869,7 +855,7 @@

- + @@ -881,21 +867,19 @@

- - + + -

Fig. 6.5a

-

Climatic > Check Data Inventory

-

Fig. 6.5b Default annual summaries of rainfall

-

From > Climatic > Prepare > Climatic Summaries

+Fig. 6.5a +Fig. 6.5b Default annual summaries of rainfall - - + h + @@ -904,20 +888,19 @@

- - + + -Fig. 6.5c Data with both summaries -

Fig. 6.5d Years with missing values

-

Use Right-click > Filter with (Total == NA)

+Fig. 6.5c Data with both summaries +Fig. 6.5d Years with missing values - - + + @@ -944,8 +927,8 @@

- - + + @@ -956,20 +939,19 @@

- - + + -

Fig. 6.6a

-

Climatic > Prepare > Climatic Summaries

-Fig. 6.6bInclude temperature extremes +Fig. 6.6a +Fig. 6.6b Include temperature extremes - - + h + @@ -979,21 +961,19 @@

- - + + -

Fig. 6.6c Annual temperature data

-

Following Right-click > Filter > (Year > 1959)

-

Fig. 6.6d

-

Describe > Specific > Line Plot

+Fig. 6.6c Annual temperature data +Fig. 6.6d - - + h + @@ -1009,7 +989,7 @@

- + @@ -1026,27 +1006,26 @@

- +

In Fig. 6.6f (and earlier in Fig. 6.6e) the mean line for Saltpond looks odd. In this Chapter the quality control steps, discussed in Chapter 5, have been omitted and, as usual, that was not a good idea! Fortunately, the daily data are available, so we return to these and do a simple time series plot of the daily records, Fig. 6.6g. This indicates an oddity in the data in about 1974.

--++ - - + + - - + +

Fig. 6.6g Tmax for Saltpond daily data by Date

-

From Describe > Specific > Line Plot

Fig. 6.6h Monthly means for TmaxFig. 6.6g Tmax for Saltpond daily data by DateFig. 6.6h Monthly means for Tmax
@@ -1065,8 +1044,8 @@

- - + + @@ -1081,22 +1060,19 @@

- - + + -

Fig. 6.7a Filter the data (optional)

-

Right-click > Filter > Define new

-

Fig. 6.7bMonthly summaries

-

Climatic > Prepare > Climatic Summaries

-

Only summary is the mean

+Fig. 6.7a Filter the data (optional) +Fig. 6.7bMonthly summaries - - + + @@ -1104,21 +1080,19 @@

- + -Fig. 6.7c Monthly means for Tmin and Tmax -

Fig. 6.7d

-

Describe > Specific > Line Plot

-

(Also use Plot Options with Month a Facet)

+Fig. 6.7c Monthly means for Tmin and Tmax +Fig. 6.7d - - + + @@ -1134,7 +1108,7 @@

- + @@ -1142,20 +1116,19 @@

- - + + -Fig. 6.7f The Model menu -

Fig. 6.7g Filter by Station

-

Right-click > Filter > Define New

+Fig. 6.7f The Model menu +Fig. 6.7g Filter by Station -{widt h=“2.062606080489939in” height= “2.4237357830271216in”} - + + @@ -1167,19 +1140,18 @@

- + -Fig. 6.7h Make a new data frame -

Fig. 6.7i

-

Model > Three Variables > Fit Model

+Fig. 6.7h Make a new data frame +Fig. 6.7i - - + + @@ -1198,8 +1170,8 @@

- - + + @@ -1220,8 +1192,8 @@

- - + + @@ -1239,7 +1211,7 @@

- + diff --git a/docs/Chapter_7_Tailored_Products.html b/docs/Chapter_7_Tailored_Products.html index 82cfcd6..dad63ec 100644 --- a/docs/Chapter_7_Tailored_Products.html +++ b/docs/Chapter_7_Tailored_Products.html @@ -305,20 +305,19 @@

One of the examples used in this Chapter is from Moorings in Southern Zambia. This has rainfall data from early 1922. Use Open From Library > Instat > Browse > Climatic > Zambia and open the file called Moorings.rds.

--++ - - + + - - + +

Fig. 7.2a

-

Climatic > Dates > Use Date

Fig. 7.2bFig. 7.2aFig. 7.2b
{w idth=“2.5082042869641294in” he ight=“3.754517716535433in”}
@@ -340,28 +339,27 @@

- - + +

Now use the Climatic > Define Climatic Data dialogue.

--++ - - + + - - + +

Fig. 7.2e Define Moorings as climatic

-

Climatic > Define Climatic Data

Fig. 7.2f The Climatic > Prepare menuFig. 7.2e Define Moorings as ClimaticFig. 7.2f The Climatic > Prepare Menu
he
@@ -380,30 +378,27 @@

- - + +

Now use the Climatic > Prepare > Climatic Summaries dialogue, Fig. 7.i

--++ - - + + - - + +

Fig. 7.2i Getting annual summaries

-

Climatic > Prepare > Climatic Summaries

-

Summaries: N not missing and sum

Fig. 7.2j Annual level data frame

-

(Then also just sum for variable raindays)

Fig. 7.2i Getting annual summariesFig. 7.2j Annual level data frame
@@ -418,17 +413,19 @@

The dialogue is shown in Fig. 7.3a. If it is not automatically completed as in Fig. 7.2i, then either you did not define the data as climatic (Fig. 7.2e), or you are on the wrong data frame.

-++ + - +

Fig. 7.3a Start of the rains dialogue

Climatic > Prepare > Start of the Rains
|
@@ -453,8 +450,8 @@

- - + + @@ -473,8 +470,8 @@

- - + + @@ -496,8 +493,8 @@

- - + + @@ -521,8 +518,8 @@

- - + + @@ -531,21 +528,19 @@

Event 2 and Event 4 can be compared. Use Prepare > Column: Calculate > Calculations and subtract the date of Event 2 from Event 4, Fig. 7.3j.

--++ - - + + - - + +

Fig. 7.3j Difference from including a dry spell

-

Prepare > Column: Calculate > Calculation

Fig. 7.3k Exceedance graph for 3 definitions

-

Climatic > PICSA > Cumulative/Exceedance graph

Fig. 7.3j Difference from including a dry spellFig. 7.3k Exceedance graph for 3 definitions
@@ -561,22 +556,19 @@

Use File > Open from Library > Instat > Browse > Climatic > Nigeria and open the file called Samaru.rds. The data frame called Samaru56t has 56 years of daily data from 1928, Fig. 7.4a.

--++ - - + + - - + +

Fig. 7.4a Samaru daily data

-

File > Open from Library > Instat > Browse > Climatic > Nigeria > Samaru.rds

Fig. 7.4b Start of rains for Samaru

-

Climatic > Prepare > Start of the Rains

-

(Day range from 1 April to 30 June)

Fig. 7.4a Samaru daily dataFig. 7.4b Start of rains for Samaru
h
@@ -586,22 +578,19 @@

As in the previous section, get the starting day of year and the corresponding date.

--++ - - + + - - + +

Fig. 7.4c End of rains/Season dialogue

-

Climatic > Prepare > End of the Rains

-

(Day Range from 15 August to 15 December)

Fig. 7.4d The season length

-

Climatic > Prepare > Length

Fig. 7.4c End of rains/Season dialogueFig. 7.4d The season length
@@ -610,20 +599,19 @@

In the middle of the season (August), the bucket is usually quite full. The end of the season is defined, in Fig. 7.4c, as the first time, after 15th August, that the bucket is effectively empty.

--++ - - + + - - + +
Fig. 7.4e Samaru summary data

Fig. 7.4f Time-series graphs

-

Describe > Specific > Line

Fig. 7.4e Samaru summary dataFig. 7.4f Time-series graphs
h
@@ -631,20 +619,19 @@

The resulting annual data are shown in Fig. 7.4e. Various graphs are possible. The time series graph in Fig. 7.4f indicates clearly the smaller inter-annual variability of the end of the season, compared to the start. This graph was produced using Describe > Specific > Line Plot. An alternative would have been the Climatic > PICSA > Rainfall Graph.

--++ - - + + - - + +

Fig. 7.4g Exceedance plots

-

Climatic > PICSA > Cumulative/Exceedance

Fig. 7.4h Length of the season at SamaruFig. 7.4g Exceedance plotsFig. 7.4h Length of the season at Samaru
{width=“2.5142836832895887in” height=“3.160087489063867in”} h
@@ -653,20 +640,19 @@

Now return to the Moorings data in the Southern hemisphere[^37]. The start was considered in Section 7.3 and we choose the definition 4, i.e. from 1 November, but with no dry spell of more than 12 days. In principle it simply remains to get the end of the season, and then the length, as above.

--++ - - + + - - + +

Fig. 7.4i End of the season dialogue

-

Climatic > Prepare > End of the Rains

Fig. 7.4jFig. 7.4i End of the season dialogueFig. 7.4j
{wi dth=“2.5004582239720037in” heig ht=“2.9221620734908136in”}
@@ -680,20 +666,19 @@

In Fig. 7.4l choose Variable Day and select the end_rains variable from the summary data. Keep the end date as Fixed Day, and choose 30 June.

--++ - - + + - - + +

Fig. 7.4k

-

Climatic > Prepare > End of Rains

Fig. 7.4l Day Range on End of RainsFig. 7.4kFig. 7.4l Day Range on End of Rains
h
@@ -711,8 +696,8 @@

- - + + @@ -731,8 +716,8 @@

- - + + @@ -748,7 +733,7 @@

- + @@ -773,8 +758,8 @@

- - + + @@ -793,8 +778,8 @@

- - + + @@ -812,7 +797,7 @@

- + @@ -848,46 +833,38 @@

In Fig. 7.6a press Summaries and choose just the N non-missing and the Sum. Click on Day Range and choose 1 November to 30 April.

--++ - - + + - - + +

Fig. 7.6a Summaries for Moorings

-

Climatic > Prepare > Climatic Summaries

Fig. 7.6b Rename the resulting columns

-

Right-click > Rename Column

Fig. 7.6a Summaries for MooringsFig. 7.6b Rename the resulting columns
-
-

-
C:\Users\ ROGERS~1\AppData\Local\Temp\S NAGHTML59463e8.PNG
-
-
 h

Once run, change the rain variable in Fig. 7.6a, to raindays, and this time just get the Sum. You may have found that the variables overwrote the ones produced earlier for the full year. To ensure this does not happen in the future, rename the 3 columns, Fig. 7.6b. It is also useful to add an explanatory label as shown in Fig. 7.6b.

--++ - - + + - - + +

Fig. 7.6c Day Range sub-dialogue

-

From Climatic > Prepare > Climatic Summaries

Fig. 7.6d Main climatic summaries dialogueFig. 7.6c Day Range sub-dialogueFig. 7.6d Main climatic summaries dialogue
h
@@ -907,8 +884,8 @@

- - + + @@ -923,13 +900,13 @@

Fig. 7.6g Exceedance graphs of rainfall totals -Fig. 7.6hTime-series graphs +Fig. 7.6h Time-series graphs - - + + @@ -939,30 +916,19 @@

Use the Day Range sub-dialogue to specify January 1 to March 31 as the range of days. The results, shown in Fig. 7.6j give the longest dry-spell length within this 3-month period. It shows the longest number of consecutive days with rain less than 0.85mm on any day.

--++ - - + + - - + +

Fig. 7.6i Spells dialogue

-

Climatic > Prepare > Spells

-

(Day range from January 1 to March 31)

Fig. 7.6j Spell lengths Jan-Mar & Apr

-
    -
  • -
Fig. 7.6i Spells dialogue ***Fig. 7.6j Spell lengths Jan-Mar & Apr***
-
-

-
C:\User s\ROGERS~1\AppData\Local\Temp
-9f3a1.PNG
-
-
@@ -972,25 +938,16 @@

It sometimes causes confusion that the longest spell in a month can be longer than the month itself. We illustrate by calculating the longest spell length in April. The results are also given in the last column in Fig. 7.6j. They show that the longest dry spell in April 2024 was 43 days. This is confirmed from Fig. 7.6k, because 1st April 2024 “inherited” a dry spell from March, of 13 days, i.e. from 19th March. So, 1st April was already the 14th consecutive dry day and this had become 43 days by the ned of April. There is an option in the dialogue if you only wish to consider April itself. This option is used in Section 7.7.

----+ - - - - - - - +
Fig. 7.6k Data for January to April for 4 years
@@ -1010,8 +967,8 @@

- - + + @@ -1019,19 +976,18 @@

- + -

Fig. 7.6n Calculate the start of flowering

-

Prepare > Column: Calculate > Calculation

-Fig. 7.6o +Fig. 7.6n Calculate the start of floweing +Fig. 7.6o - - + he + @@ -1050,8 +1006,8 @@

- - + + @@ -1062,20 +1018,19 @@

Three day running totals were already used “behind the scenes” in calculating the start of the rains. Now these totals are needed explicitly. Use Climatic > Prepare > Transform, as shown in Fig. 7.6r.

--++ - - + + - - + +

Fig. 7.6r Transform dialogue

-

Climatic > Prepare > Transform

Fig. 7.6s Resulting data totalled over 3 daysFig. 7.6r Transform dialogueFig. 7.6s Resulting data totaled 3 days
@@ -1094,8 +1049,8 @@

- - + + @@ -1113,8 +1068,8 @@

- - + + @@ -1132,14 +1087,14 @@

-Fig. 7.7aDry spell risk after planting -Fig. 7.7b Result from 8 Nov and 8 Dec +Fig. 7.7a Dry spell risk after planting +Fig. 7.7b Result from 8 Nov and 8 Dec - - + + @@ -1164,8 +1119,8 @@

- - + + @@ -1178,14 +1133,14 @@

-Fig. 7.7e +Fig. 7.7e Fig. 7.7f - - + + @@ -1214,8 +1169,8 @@

- - + + @@ -1233,8 +1188,8 @@

- - + + @@ -1254,8 +1209,8 @@

- - + + @@ -1274,8 +1229,8 @@

- - + + @@ -1295,8 +1250,8 @@

- - + + @@ -1315,8 +1270,8 @@

- - + + @@ -1335,8 +1290,8 @@

- - + + @@ -1347,21 +1302,19 @@

Use the Climatic > Prepare > Transform dialogue again, with the Spell tab, as shown in Fig. 7.8o. Then, with the same dialogue, use the Moving tab, Fig. 7.8p, to get the maximum over 20 days. This assumes that the flowering period is of 20 day’s duration.

--++ - - + + - - + +

Fig. 7.8o Ordinary spell lengths

-

Climatic > Prepare > Transform

Fig. 7.8pMaximum over 20 days

-

Climatic > Prepare > Transform (again!)

Fig. 7.8o Ordinary spell lengthsFig. 7.8p Maximum over 20 days
@@ -1379,8 +1332,8 @@

- - + + @@ -1388,20 +1341,19 @@

The percentages are (as would be expected[^42]) slightly higher in Fig. 7.8r, compared with Fig. 7.8n. They show a minimum risk if the start of the flowering period is towards the end of January.

--++ - - + + - - + +
Fig. 7.8s

Fig. 7.8t Plot the cumulative data

-

Describe > Specific > Line Plot

Fig. 7.8s ***Fig. 7.8t Plot the cumulative data***
{w idth=“2.312619203849519in” heig ht=“3.1737740594925636in”}
@@ -1421,8 +1373,8 @@

- - + + @@ -1440,7 +1392,7 @@

- + @@ -1458,8 +1410,8 @@

- - + + @@ -1478,8 +1430,8 @@

- - + + @@ -1498,8 +1450,8 @@

- - + + @@ -1517,25 +1469,25 @@

- - + +

Fig. 7.8ad shows the progression of the cumulative rainfall for the season towards the final totals. These had a median of 800mm, with a minimum less than 500mm and a maximum of well over 1000mm.

-

One use of this type of plot is to monitor a new year. As an example, Fig. 7.8ae provides data, where the total has now risen to 340mm by the end of February. This information can now be super-imposed on the boxplots, as shown in Fig. 7.8ae with the blue points[^45]. This example shows a great cause for concern. The totals are very low, compared to the earlier 88 years of data.

+

One use of this type of plot is to monitor a new year. As an example, Fig. 7.8ae provides data, where the total has now risen to 340mm by the end of February. This information can now be super-imposed on the boxplots, as shown in Fig. 7.8af with the blue points[^45]. This example shows a great cause for concern. The totals are very low, compared to the earlier 88 years of data.

- + - +
Fig. 7.8ae Adding the current year to the boxplotsFig. 7.8af Adding the current year to the boxplots
diff --git a/docs/Chapter_8_Efficient_use_of_RInstat_and_R.html b/docs/Chapter_8_Efficient_use_of_RInstat_and_R.html index 4f57446..c827cda 100644 --- a/docs/Chapter_8_Efficient_use_of_RInstat_and_R.html +++ b/docs/Chapter_8_Efficient_use_of_RInstat_and_R.html @@ -306,8 +306,8 @@

- - + + diff --git a/docs/Chapter_9_Gridded_Data.html b/docs/Chapter_9_Gridded_Data.html index bf99fd7..11712c5 100644 --- a/docs/Chapter_9_Gridded_Data.html +++ b/docs/Chapter_9_Gridded_Data.html @@ -323,7 +323,7 @@

- + @@ -342,8 +342,8 @@

- - + + @@ -363,8 +363,8 @@

- - + + @@ -383,8 +383,8 @@

- - + + @@ -401,7 +401,7 @@

- + @@ -417,7 +417,7 @@

- + @@ -441,7 +441,7 @@

- + @@ -460,8 +460,8 @@

- - + + @@ -480,8 +480,8 @@

- - + + @@ -494,14 +494,14 @@

-Fig. 9.3g Generate a toolbox request -Fig. 9.3h Names for each element +Fig. 9.3f Generate a toolbox request +Fig. 9.3g Names for each element - - + + @@ -513,7 +513,7 @@

-Fig. 9.3i Sample toolbox code +Fig. 9.3h Sample toolbox code @@ -561,69 +561,69 @@

The second stage is to read the resulting data into R-Instat. For those who were not online, the six files have also been renamed and copied into the R-Instat library.

-

Go into R-Instat and use File > Open and Tidy NetCDF File Fig. 9.3j.

+

Go into R-Instat and use File > Open and Tidy NetCDF File Fig. 9.3i.

--++ - - + + - - + +
Fig. 9.3j Loading the data for 1981-5Fig. 9.3k Six files in R-InstatFig. 9.3i Loading the data for 1981-5Fig. 9.3j Six files in R-Instat
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML145541b7.PNG

In Fig. 9.3j, if you downloaded your own data, then choose Browse, otherwise choose From Library and use the file called cds.Rwanda_1981_5.nc.

Then recall the last dialogue and include the other five files, up to cds.Rwanda_2005_10. The resulting data are shown in Fig. 9.3k. There are about 394,416 rows of data in each file (i.e. roughly 9 * 24 * 365 * 5)

-

The next step is to append the files to give the 30-year record. Use Climatic >Tidy and Examine > Append, Fig. 9.3l. In Fig. 9.3l, include all 6 data frame, then the ID column isn’t needed and the resulting data frame is named better than Append1.

+

The next step is to append the files to give the 30-year record. Use Climatic >Tidy and Examine > Append, Fig. 9.3l. In Fig. 9.3k, include all 6 data frame, then the ID column isn’t needed and the resulting data frame is named better than Append1.

--++ - - + + - - + +
Fig. 9.3l Appending the 6 data framesFig. 9.3m Temperatures into centigradeFig. 9.3k Appending the 6 data framesFig. 9.3l Temperatures into centigrade
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML14701350.PNG

The data column in Fig. 9.3k, called tas, is in degrees Kelvin. Use Climatic > Prepare > Transform to change them into centigrade for comparison with the station data, Fig. 9.3m.

-

ECMWF provides the time variable always in GMT (Greenwich Mean Time). Rwanda is 2 hours ahead, so either, or both, the time variables in Fig. 9.3k need to be moved forward by 2 hours. To make this change the data should first be in “station” order. Hence first Right-Click and choose Sort (or use Prepare > Data Frame > Sort) to produce the dialogue in Fig. 9.3n.

-

Now use Prepare > Column: Calculate > Calculations as shown in Fig. 9.3o. In the calculator the Transform keyboard includes the lead function. The function, from the dplyr package is:

-

dplyr::lead(time_full,2), to move to Rwanda time. Pressing the Try button in Fig. 9.3o shows the first value is now 2am GMT[^47].

+

ECMWF provides the time variable always in GMT (Greenwich Mean Time). Rwanda is 2 hours ahead, so either, or both, the time variables in Fig. 9.3k need to be moved forward by 2 hours. To make this change the data should first be in “station” order. Hence first Right-Click and choose Sort (or use Prepare > Data Frame > Sort) to produce the dialogue in Fig. 9.3m.

+

Now use Prepare > Column: Calculate > Calculations as shown in Fig. 9.3n. In the calculator the Transform keyboard includes the lead function. The function, from the dplyr package is:

+

dplyr::lead(time_full,2), to move to Rwanda time. Pressing the Try button in Fig. 9.3n shows the first value is now 2am GMT[^47].

--++ + - - - + +
Fig. 9.3m Fig. 9.3nFig. 9.3o
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTML14c5504c.PNG
-

Now use Climatic > Dates > Make Date to make a new Date column from the Rwanda hourly column, Fig. 9.3p.

+

Now use Climatic > Dates > Make Date to make a new Date column from the Rwanda hourly column, Fig. 9.3o.

@@ -631,36 +631,34 @@

+ - - - + +
Fig. 9.3o Fig. 9.3pFig. 9.3q
-

Finally generate Tmax and Tmin, on a daily basis, from the hourly values, ready to use, or to compare with station data. First right-click and make the lon and lat columns into factors. The hourly data are now roughly as in Fig. 9.3q

-

Complete the Prepare > Column: Reshape > Column Summaries dialogue as shown in Fig. 9.3r. As these are temperatures the daily maximum and minimum are calculated. The resulting worksheet, Fig. 9.3s, has a more reasonable 100,000 rows of data at the 9 gridpoints.

+

Finally generate Tmax and Tmin, on a daily basis, from the hourly values, ready to use, or to compare with station data. First right-click and make the lon and lat columns into factors. The hourly data are now roughly as in Fig. 9.3p

+

Complete the Prepare > Column: Reshape > Column Summaries dialogue as shown in Fig. 9.3q. As these are temperatures the daily maximum and minimum are calculated. The resulting worksheet, Fig. 9.3r, has a more reasonable 100,000 rows of data at the 9 gridpoints.

--++ - - + + - - + +

Fig. 9.3r

-

Prepare > Column: Reshape > Column Summaries

-

(Make max and min the summaries)

Fig. 9.3s Resulting daily dataFig. 9.3qFig. 9.3r Resulting daily data
@@ -698,8 +696,8 @@

- - + + @@ -711,14 +709,14 @@

-Fig. 9.4c - +Fig. 9.4c ** +Fig. 9.4d** - - + + @@ -736,16 +734,16 @@

- - + +

Now, in R-Instat, use File > Open and Tidy NetCDF File. If you followed the screens above, then browse for the file that was downloaded. Otherwise there is a copy in the R-Instat library to March 2019, Fig. 9.4g.

--++ @@ -755,8 +753,8 @@

-

- + +
C:\Users\ROGERS~1\AppData\Local\Temp\SNAGHTMLda15af6.PNG
@@ -776,8 +774,8 @@

- - + + @@ -795,8 +793,8 @@

- - + + @@ -816,8 +814,8 @@

- - + + @@ -836,8 +834,8 @@

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