wsim lesson 1 updates

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.