diff --git a/indicator_processing/automated_download/sst.R b/indicator_processing/automated_download/sst.R
index 153a01d..facffa9 100644
--- a/indicator_processing/automated_download/sst.R
+++ b/indicator_processing/automated_download/sst.R
@@ -1,4 +1,3 @@
-
 # fix for dealing with internet timeout error 
 # https://stackoverflow.com/questions/68666187/internetopenurl-failed-a-connection-with-the-server-could-not-be-established
 
@@ -12,9 +11,6 @@ library(rerddap)
 
 load("indicator_processing/spec_file.RData")
 
-# devtools::install_github("mdsumner/ncdf4")
-# library(ncdf4)
-
 # define years  --------------------------------
 styear <- 1982
 enyear <- terminal_year
@@ -23,8 +19,6 @@ enyear <- terminal_year
 sst <- info('ncdcOisst21Agg_LonPM180') # this may work better
 
 # empty data  -------------------------------------------------
-# dat <- data.frame(row.names = c("year", "mon", "PR_mean", "PR_min", "PR_max", "VI_mean", "VI_min", "VI_max"))
-
 dat <- setNames(data.frame(matrix(NA,length(styear:enyear)*12,5)),
                 c("year", "mon", 'mean', 'min', 'max'))
 m <- 1
@@ -46,35 +40,10 @@ for (yr in styear:enyear) {
   n <- n + 12
   dat[m:n,] <- data.frame(sst_agg[,1:2], unlist(sst_agg[,3]))
   m <- n + 1
-  
-# # url from ERDDAP for OISST, download and read ----------------
-#   url <- paste0("https://coastwatch.pfeg.noaa.gov/erddap/griddap/ncdcOisst21Agg.csv?sst[(", yr, "-01-01T12:00:00Z):1:(", yr, "-12-31T12:00:00Z)][(0.0):1:(0.0)][(17):1:(19)][(292):1:(296)]")
-# 
-#   #  url_vi <- paste0("https://coastwatch.pfeg.noaa.gov/erddap/griddap/ncdcOisst21Agg.csv?sst[(", yr, "-01-01T12:00:00Z):1:(", yr, "-12-31T12:00:00Z)][(0.0):1:(0.0)][(17):1:(19)][(294.75):1:(296)]")
-#   #  url_pr <- paste0("https://coastwatch.pfeg.noaa.gov/erddap/griddap/ncdcOisst21Agg.csv?sst[(", yr, "-01-01T12:00:00Z):1:(", yr, "-12-31T12:00:00Z)][(0.0):1:(0.0)][(17):1:(19)][(292):1:(294.75)]")
-#   #  download for entire Caribbean because USVI and PR highly correlated. 
-#     download.file(url = url, destfile = "st.csv")
-# 
-#   labs <- read.table("st.csv", sep = ",", header = T, skip = 0)
-#   sst_vi <- read.table("st.csv", sep = ",", header = T, skip = 1)
-#   names(sst_vi) <- names(labs)
-# 
-# # extract month -----------------------------------------------
-#   sst_vi$mon <- strftime(sst_vi$time, format = "%m")
-# 
-# # calculate mean, min, max and concatenate --------------------
-#   ind_vi <- as.numeric(tapply(sst_vi$sst, sst_vi$mon, mean, na.rm = T))
-#   min_vi <- as.numeric(tapply(sst_vi$sst, sst_vi$mon, min, na.rm = T))
-#   max_vi <- as.numeric(tapply(sst_vi$sst, sst_vi$mon, max, na.rm = T))
-# 
-#   tempdat <- data.frame(yr, unique(sst_vi$mon), ind_vi, min_vi, max_vi, stringsAsFactors = F)
-#   dat <- data.frame(rbind(dat, tempdat), stringsAsFactors = F)
-}
 
-# file.remove("st.csv")
+}
 
-# add row names and yearmonth column --------------------------
-# names(dat) <- c("year", "mon", "mean", "min", "max")
+# add yearmonth column --------------------------
 dat$yrmon <- paste0(dat$mon, "-", dat$year)
 dat
 head(dat)
@@ -83,7 +52,7 @@ tail(dat)
 # check outputs and look at correlations ---------------------
 table(dat$year)
 table(dat$mon)
-matplot(dat[3:5], type = "l") #, col = c(4, 4, 4, 2, 2, 2), lty = c(1, 2, 3, 1, 2, 3))
+matplot(dat[3:5], type = "l")
 cor(dat[3:5])
 plot(dat[3:5])
 
@@ -91,13 +60,12 @@ plot(dat[3:5])
 
 labs <- c(rep("U.S. Caribbean sea surface temperature", 3), rep("degrees Celsius", 3), 
           "monthly mean", "monthly minimum", "monthly maximum")
-          #          "Puerto Rico - mean", "USVI - mean", "Puerto Rico - monthly minimum", "USVI - monthly minimum", "Puerto Rico - monthly maximum", "USVI - monthly maximum")
 
 indnames <- data.frame(matrix(labs, nrow = 3, byrow = T))
 inddata <- data.frame(dat[c(3:5)])
 datdata <- dat$yrmon
 
-s <- list(labels = indnames, indicators = inddata, datelist = datdata) #, ulim = ulidata, llim = llidata)
+s <- list(labels = indnames, indicators = inddata, datelist = datdata)
 class(s) <- "indicatordata"
 s