From a9d60a3a494703db2da93762f77da685f4625019 Mon Sep 17 00:00:00 2001 From: Wilmund Date: Mon, 14 Sep 2026 04:10:48 +0200 Subject: [PATCH] docs(vignette): fix code/prose mismatches in introductory exercises - Exercise 14: get_scan(elev = 1.0) returns the 0.5 degree scan on a 0.5/1.5/... volume (ties resolve to the lowest elevation), while the answer text describes the 1.5 degree PPI; use elev = 1.5 to match. - calculate_vp() call was missing mistnet = TRUE although the comment says MistNet is used (default is FALSE). - Fix layer arithmetic comment: 60*50=3000 m, not 30*100. - Replace dead S3 browse link (now AccessDenied) with the AWS open data registry page. --- vignettes/rad_aero_22.Rmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/vignettes/rad_aero_22.Rmd b/vignettes/rad_aero_22.Rmd index 5ddd4ada..b423c69c 100644 --- a/vignettes/rad_aero_22.Rmd +++ b/vignettes/rad_aero_22.Rmd @@ -497,8 +497,8 @@ my_pvolfiles <- list.files("./data_pvol", recursive = TRUE, full.names = TRUE, p # we will process the first one into a vp: my_pvolfile <- my_pvolfiles[1] # calculate the profile, using MistNet to remove precipitation: -# we calculate 60 layers of 50 meter width, so up to 30*100=3000 m. -my_vp <- calculate_vp(my_pvolfile, n_layer=60, h_layer=50, sd_vvp_threshold = 1) +# we calculate 60 layers of 50 meter width, so up to 60*50=3000 m. +my_vp <- calculate_vp(my_pvolfile, mistnet = TRUE, n_layer=60, h_layer=50, sd_vvp_threshold = 1) ``` **Exercise 13:** Plot the bird density in the vertical profile you just estimated and compare it with the plots above of the beam height and width of the lowest radar beam. At which approximate range do you expect the radar will no longer be able to resolve the altitude distribution of the migratory birds. And at which range will the radar start to overshoot the migration layer entirely? @@ -537,11 +537,11 @@ my_pvolfile %>% read_pvolfile() -> my_pvol my_ppi_integrated <- integrate_to_ppi(pvol=my_pvol,vp=my_vp, res=1000) ``` -**Exercise 14:** Visually compare the PPI for the 1.0 degree sweep and the vertically integrated PPI, and explain the difference in spatial pattern. (For the clearest comparison, make plots of comparable parameters that are either linear or logarithmic in bird density). +**Exercise 14:** Visually compare the PPI for the 1.5 degree sweep and the vertically integrated PPI, and explain the difference in spatial pattern. (For the clearest comparison, make plots of comparable parameters that are either linear or logarithmic in bird density). ```{r, echo=SHOW_ANSWERS, eval=SHOW_ANSWERS,warning=FALSE} # my_pvol %>% - get_scan(elev = 1.0) %>% + get_scan(elev = 1.5) %>% project_as_ppi(raster=raster::raster(my_ppi_integrated$data)) -> my_ppi # let's take the logarithm of the vertically integrated density, so @@ -569,7 +569,7 @@ plot(my_ppi_integrated, param="logVID", zlim=c(-10,10)) ## Accessing radar data -* US NEXRAD polar volume data can be accessed in the [Amazon cloud](https://s3.amazonaws.com/noaa-nexrad-level2/index.html). Use function `download_pvolfiles()` for local downloads +* US NEXRAD polar volume data can be accessed in the [Amazon cloud](https://registry.opendata.aws/noaa-nexrad/). Use function `download_pvolfiles()` for local downloads * European radar data can be accessed at https://aloftdata.eu. These are processed vertical profiles, the full polar volume data are not openly available for most countries. Use function `download_vpfiles()` for local downloads. The names of the radars in the networks can be found here: