# proxy = TRUE will limit memory useage but does # not always work with certain downstream processing functions
-wsim_gldas_anoms <- stars::read_stars("composite_anom_1mo.nc", proxy =FALSE)
stars object with 3 dimensions and 5 attributes
attribute(s), summary of first 1e+05 cells:
- Min. 1st Qu. Median Mean 3rd Qu. Max.
-deficit -100 -1.8314584 -0.2373874 -1.26453645 -0.2373874 1.896493
-deficit_cause 1 129.0000000 129.0000000 112.90956000 129.0000000 129.000000
-surplus -100 -0.9671488 -0.7329655 -0.95631468 -0.6206152 2.384447
-surplus_cause 1 129.0000000 129.0000000 127.37130000 129.0000000 129.000000
-both 0 0.0000000 0.0000000 0.03784493 0.0000000 2.384447
- NA's
-deficit 87340
-deficit_cause 0
-surplus 98724
-surplus_cause 0
-both 98724
+ Min. 1st Qu. Median Mean 3rd Qu.
+deficit -100.000000 -2.366971 -1.9098018 -2.0387177 -1.171102
+deficit_cause 1.000000 129.000000 129.0000000 121.5036000 129.000000
+surplus -4.310967 -1.091849 -0.3321133 -0.3779968 0.224241
+surplus_cause 1.000000 129.000000 129.0000000 121.4569100 129.000000
+both 0.000000 0.000000 0.0000000 0.8073860 1.447755
+ Max. NA's
+deficit 1.977621 94083
+deficit_cause 129.000000 0
+surplus 3.547665 94088
+surplus_cause 129.000000 0
+both 100.000000 94088
dimension(s):
from to offset delta refsys values x/y
x 1 1440 -180 0.25 WGS 84 NULL [x]
y 1 600 90 -0.25 WGS 84 NULL [y]
-time 1 804 NA NA POSIXct 1948-01-01,...,2014-12-01
+time 1 793 NA NA POSIXct 1948-12-01,...,2014-12-01
The print command gives some basic information. The outputs tells us we have 5 attributes (deficit, deficit_cause, surplus, surplus_cause, both) and 3 dimensions. The first 2 dimensions are the spatial extents (x/y–longitude/latitude) and time is the 3rd dimension.
@@ -4588,24 +4768,24 @@
Reading the Data
Attribute Selection
-
We can start paring this down by subsetting for just the combined surplus/deficit anomaly (both).
+
We can start paring this down by subsetting for just deficits (drought).
Specifying a temporal range of interest will free up more space. We’ll grab every month for 2000-2014. This can be accomplished by generating a sequence for every month between January 2000 and December 2014, and then passing that vector of dates to filter.
+
Specifying a temporal range of interest will free up more space. We’ll grab every year for 2000-2014. This can be accomplished by generating a sequence for every year between December 2000 and December 2014, and then passing that vector of dates to filter.
# generate a vector of dates for subsetting
-keeps<-seq(lubridate::ymd("2000-01-01"),
+keeps<-seq(lubridate::ymd("2000-12-01"), lubridate::ymd("2014-12-01"),
-by ="month")
+by ="year")# filter using that vectorwsim_gldas_anoms <- dplyr::filter(wsim_gldas_anoms, time %in% keeps)
@@ -4613,25 +4793,29 @@
Time Selection
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
- Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-both 0 0 0 5.415477 1.631542 100 98724
+ Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
+deficit -3.140659 -1.491055 -1.092252 -1.037756 -0.5627083 0.8403344 94083
dimension(s):
from to offset delta refsys values x/y
x 1 1440 -180 0.25 WGS 84 NULL [x]
y 1 600 90 -0.25 WGS 84 NULL [y]
-time 1 180 NA NA POSIXct 2000-01-01,...,2014-12-01
+time 1 15 NA NA POSIXct 2000-12-01,...,2014-12-01
-
Now we’re down to a single attribute (“both”) with 180 time-steps. We can take a look at the first 6 months by passing the object through slice and then into plot.
+
Now we’re down to a single attribute (“both”) with 15 time-steps. We can take a look at the first 6 years by passing the object through slice and then into plot.
wsim_gldas_anoms |> dplyr::slice(index =1:6, along ="time") |>
-plot(key.pos =1)
Although we’ve pared it down to a single attribute with a restricted time period of interest, we can take it a step further and reduce the spatial extent to a country or state of interest.
@@ -4685,7 +4869,11 @@
Spatial Selection
plot(sf::st_geometry(usa))
-
+
+
+
+
+
This looks good, but it includes all United States territories. For simplicity, we can get it down to only the contiguous United States.
@@ -4702,7 +4890,11 @@
Spatial Selection
plot(sf::st_geometry(usa))
-
+
+
+
+
+
We can take this a step further and select a target state.
@@ -4711,7 +4903,11 @@
Spatial Selection
plot(sf::st_geometry(texas))
-
+
+
+
+
+
From here we can crop the WSIM GLDAS raster stack by indexing it with the stored boundary of Texas
@@ -4722,11 +4918,11 @@
Spatial Selection
and y do not overlap
-
For a final visual check we’ll take the last time-step in the WSIM-GLDAS dataset (180/December, 2014) and plot it with an overlay of the Texas boundary.
+
For a final visual check we’ll take the last time-step in the WSIM-GLDAS dataset (15/December, 2014) and plot it with an overlay of the Texas boundary.
});
selectedAnnoteEl = undefined;
};
+ // Handle positioning of the toggle
+ window.addEventListener(
+ "resize",
+ throttle(() => {
+ elRect = undefined;
+ if (selectedAnnoteEl) {
+ selectCodeLines(selectedAnnoteEl);
+ }
+ }, 10)
+ );
+ function throttle(fn, ms) {
+ let throttle = false;
+ let timer;
+ return (...args) => {
+ if(!throttle) { // first call gets through
+ fn.apply(this, args);
+ throttle = true;
+ } else { // all the others get throttled
+ if(timer) clearTimeout(timer); // cancel #2
+ timer = setTimeout(() => {
+ fn.apply(this, args);
+ timer = throttle = false;
+ }, ms);
+ }
+ };
+ }
// Attach click handler to the DT
const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
for (const annoteDlNode of annoteDls) {
@@ -4984,4 +5333,5 @@
Spatial Selection
+
\ No newline at end of file
diff --git a/wsim-gldas-acquisition.qmd b/wsim-gldas-acquisition.qmd
index dfbdabe..7f01f8f 100644
--- a/wsim-gldas-acquisition.qmd
+++ b/wsim-gldas-acquisition.qmd
@@ -30,7 +30,7 @@ Once you've completed the download and placed the .nc into your working director
# proxy = TRUE will limit memory useage but does
# not always work with certain downstream processing functions
-wsim_gldas_anoms <- stars::read_stars("composite_anom_1mo.nc", proxy = FALSE)
+wsim_gldas_anoms <- stars::read_stars("composite_anom_12mo.nc", proxy = FALSE)
print(wsim_gldas_anoms)
```
@@ -41,35 +41,35 @@ This means the total number of individual raster layers in this NetCDF is 4020 (
## Attribute Selection
-We can start paring this down by subsetting for just the combined surplus/deficit anomaly (both).
+We can start paring this down by subsetting for just deficits (drought).
```{r}
names(wsim_gldas_anoms)
-wsim_gldas_anoms <- wsim_gldas_anoms['both']
+wsim_gldas_anoms <- wsim_gldas_anoms['deficit']
```
## Time Selection
-Specifying a temporal range of interest will free up more space. We'll grab every month for 2000-2014. This can be accomplished by generating a sequence for every month between January 2000 and December 2014, and then passing that vector of dates to `filter`.
+Specifying a temporal range of interest will free up more space. We'll grab every year for 2000-2014. This can be accomplished by generating a sequence for every year between December 2000 and December 2014, and then passing that vector of dates to `filter`.
```{r}
# generate a vector of dates for subsetting
-keeps<-seq(lubridate::ymd("2000-01-01"),
+keeps<-seq(lubridate::ymd("2000-12-01"),
lubridate::ymd("2014-12-01"),
- by = "month")
+ by = "year")
# filter using that vector
wsim_gldas_anoms <- dplyr::filter(wsim_gldas_anoms, time %in% keeps)
print(wsim_gldas_anoms)
```
-Now we're down to a single attribute ("both") with 180 time-steps. We can take a look at the first 6 months by passing the object through `slice` and then into `plot`.
+Now we're down to a single attribute ("both") with 15 time-steps. We can take a look at the first 6 years by passing the object through `slice` and then into `plot`.
-```{r}
+```{r warning=FALSE}
wsim_gldas_anoms |>
dplyr::slice(index = 1:6, along = "time") |>
- plot(key.pos = 1)
+ plot(key.pos = 1, breaks = c(0, -5, -10, -20, -30, -50), key.lab = "Deficit")
```
Although we've pared it down to a single attribute with a restricted time period of interest, we can take it a step further and reduce the spatial extent to a country or state of interest.
@@ -137,13 +137,13 @@ From here we can crop the WSIM GLDAS raster stack by indexing it with the stored
wsim_gldas_anoms_tex <- wsim_gldas_anoms[texas]
```
-For a final visual check we'll take the last time-step in the WSIM-GLDAS dataset (180/December, 2014) and plot it with an overlay of the Texas boundary.
+For a final visual check we'll take the last time-step in the WSIM-GLDAS dataset (15/December, 2014) and plot it with an overlay of the Texas boundary.
-```{r}
+```{r warning = FALSE}
wsim_gldas_anoms_tex |>
- dplyr::slice(index = 180, along = "time") |>
- plot(reset = FALSE)
+ dplyr::slice(index = 15, along = "time") |>
+ plot(reset = FALSE, breaks = c(0,-1,-2,-3,-4,-5))
plot(sf::st_geometry(texas),
add = TRUE,
@@ -155,7 +155,7 @@ plot(sf::st_geometry(texas),
The subsetted dataset may be written to disk, and saved for future modules.
```{r}
-stars::write_stars(wsim_gldas_anoms_tex, "wsim_gldas_tex.nc")
+stars::write_mdim(wsim_gldas_anoms_tex, "wsim_gldas_tex.nc")
```
The size of the pre-processed dataset is 1.6 MB compared to the original dataset of 1.7 GB. This is much more manageable in cloud environments, workshops, and git platforms.
\ No newline at end of file
