diff --git a/codemeta.json b/codemeta.json
index 1a032ab..181ae07 100644
--- a/codemeta.json
+++ b/codemeta.json
@@ -7,7 +7,7 @@
   "codeRepository": "https://github.com/NIEHS/chopin",
   "issueTracker": "https://github.com/NIEHS/chopin/issues",
   "license": "https://spdx.org/licenses/MIT",
-  "version": "0.8.1",
+  "version": "0.8.2",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -366,9 +366,9 @@
       },
       "sameAs": "https://CRAN.R-project.org/package=lifecycle"
     },
-    "SystemRequirements": "NetCDF4"
+    "SystemRequirements": "netcdf"
   },
-  "fileSize": "27896.87KB",
+  "fileSize": "27888.915KB",
   "releaseNotes": "https://github.com/NIEHS/chopin/blob/master/NEWS.md",
   "readme": "https://github.com/NIEHS/chopin/blob/main/README.md",
   "contIntegration": ["https://github.com/NIEHS/chopin/actions", "https://github.com/NIEHS/chopin/actions/workflows/check-standard.yaml"],
diff --git a/vignettes/v04_climate_examples.Rmd b/vignettes/v04_climate_examples.Rmd
index 30ad95c..fe47b97 100644
--- a/vignettes/v04_climate_examples.Rmd
+++ b/vignettes/v04_climate_examples.Rmd
@@ -99,8 +99,14 @@ statemain <-
   state[!state$STUSPS %in% c("AK", "HI", "PR", "VI", "GU", "MP", "AS"), ]
 target_states <- statemain$GEOID
 
-# prepared populated places
-popplace <- readRDS("./input/populated_place_2022.rds")
+# download populated places
+options(tigris_use_cache = TRUE)
+popplace <-
+  lapply(target_states, function(x) {
+    tigris::places(year = 2022, state = x)
+  })
+popplace <- do.call(rbind, popplace)
+
 
 # generate circular buffers with 10 km radius
 popplacep <- sf::st_centroid(popplace, of_largest_polygon = TRUE) %>%
@@ -115,6 +121,27 @@ popplaceb <- sf::st_buffer(popplacep, dist = units::set_units(10, "km"))
 
 TerraClimate data are provided in yearly NetCDF files, each of which contains monthly layers. We will read the data with the [`terra`](https://cran.r-project.org/web/packages/terra/index.html) package and preprocess the data to extract the annual mean and sum of the bands by types of columns. 
 
+For reproducibility, run the code below with our companion package `amadeus` to download terraClimate data. Please note that it will take a while depending on the internet speed.
+
+```r
+rlang::check_installed("amadeus")
+
+tcli_variables <- c(
+  "aet", "def", "pet", "ppt", "q", "soil", "swe",
+  "PDSI", "srad", "tmax", "tmin", "vap", "vpd", "ws"
+)
+
+amadeus::download_terraclimate(
+  variables = tcli_variables,
+  year = c(2000, 2022),
+  directory_to_save = "./input/terraClimate",
+  acknowledgement = TRUE,
+  download = TRUE,
+  remove_command = TRUE
+)
+
+```
+
 
 ```r
 # wbd
@@ -248,7 +275,6 @@ We have 14 data elements for 23 years with 12 months each. Below demonstrates th
 
 ```r
 tic("multi threads (grid)")
-doFuture::registerDoFuture()
 future::plan(future::multicore, workers = 8L)
 grid_init <-
   chopin::par_pad_grid(
@@ -783,5 +809,3 @@ system.time(
 
 -   [Garnett, R. (2023). Geospatial distributed processing with furrr](https://posit.co/blog/geospatial-distributed-processing-with-furrr/)
 -   [Dyba, K. (n.d.). Parallel raster processing in *stars*](https://kadyb.github.io/stars-parallel/Tutorial.html)
-
-