diff --git a/.github/workflows/drivers_stage1.yaml b/.github/workflows/drivers_stage1.yaml
index 8e9140f947..0302ead383 100644
--- a/.github/workflows/drivers_stage1.yaml
+++ b/.github/workflows/drivers_stage1.yaml
@@ -31,7 +31,7 @@ jobs:
       - name: Update GEFS
         shell: Rscript {0}
         run: |
-          source("drivers/download_stage1_psuedo.R")
+          source("drivers/download_stage1_pseudo.R")
 
       - name: Generate stage 2
         shell: Rscript {0}
diff --git a/.github/workflows/drivers_stage3.yaml b/.github/workflows/drivers_stage3.yaml
index f1f6f78399..94c6542176 100644
--- a/.github/workflows/drivers_stage3.yaml
+++ b/.github/workflows/drivers_stage3.yaml
@@ -9,6 +9,7 @@ name: gefs_osn_stage3
 
 jobs:
   docker:
+    timeout-minutes: 2880
     runs-on: [self-hosted]
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
diff --git a/drivers/download_stage1_psuedo.R b/drivers/download_stage1_pseudo.R
similarity index 100%
rename from drivers/download_stage1_psuedo.R
rename to drivers/download_stage1_pseudo.R
diff --git a/drivers/generate_stage2.R b/drivers/generate_stage2.R
index 2d10efcf4b..7d23a2a3c9 100644
--- a/drivers/generate_stage2.R
+++ b/drivers/generate_stage2.R
@@ -67,7 +67,7 @@ if(length(missing_dates) > 0){
     hourly_df <- to_hourly(site_df,
                            site_list = dplyr::select(site_list, site_id, latitude, longitude),
                            use_solar_geom = TRUE,
-                           psuedo = FALSE) |>
+                           pseudo = FALSE) |>
       dplyr::mutate(ensemble = as.numeric(stringr::str_sub(ensemble, start = 4, end = 5)),
                     reference_datetime = lubridate::as_date(reference_datetime)) |>
       dplyr::rename(parameter = ensemble)
diff --git a/drivers/generate_stage3.R b/drivers/generate_stage3.R
index 7635372077..e4f588c379 100644
--- a/drivers/generate_stage3.R
+++ b/drivers/generate_stage3.R
@@ -47,8 +47,8 @@ furrr::future_walk(dplyr::pull(site_list, site_id), function(curr_site_id){
   df |>
     to_hourly(site_list = dplyr::select(site_list, site_id, latitude, longitude),
               use_solar_geom = TRUE,
-              psuedo = TRUE) |>
+              pseudo = TRUE) |>
     dplyr::mutate(ensemble = as.numeric(stringr::str_sub(ensemble, start = 4, end = 5))) |>
     dplyr::rename(parameter = ensemble) |>
-    arrow::write_dataset(path = s3, partitioning = "site_id")
+    arrow::write_dataset(path = s3_stage3, partitioning = "site_id")
 })
diff --git a/drivers/to_hourly.R b/drivers/to_hourly.R
index b79e609949..53442dbbdc 100644
--- a/drivers/to_hourly.R
+++ b/drivers/to_hourly.R
@@ -3,13 +3,13 @@
 #' @param df dataframe of stage1 NEON GEFS forecasts for sites to forecast at
 #' @param site_list a dataframe of the latitude and longitude for all site_ids in df
 #' @param use_solar_geom logical for using solar geometry for daily SW calculation
-#' @param psuedo logical for something...
+#' @param pseudo logical for something...
 to_hourly <- function(df,
                       site_list,
                       use_solar_geom = TRUE,
-                      psuedo = FALSE){
+                      pseudo = FALSE){
 
-  if(!psuedo){
+  if(!pseudo){
     reference_datetime <- lubridate::as_datetime(df$reference_datetime)[1]
   }else{
     reference_datetime <- NA
@@ -40,7 +40,7 @@ to_hourly <- function(df,
 
   states <- df |>
     dplyr::select(site_id, family, horizon, ensemble, datetime, variable, prediction) |>
-    dplyr::filter(!psuedo | (psuedo & horizon != "006") | (psuedo & datetime == max(df$datetime))) |>
+    dplyr::filter(!pseudo | (pseudo & horizon != "006") | (pseudo & datetime == max(df$datetime))) |>
     dplyr::select(-horizon) |>
     dplyr::group_by(site_id, family, ensemble, variable) |>
     dplyr::right_join(full_time, by = c("site_id", "ensemble", "datetime", "family", "variable")) |>
diff --git a/drivers/update_stage3.R b/drivers/update_stage3.R
index bd5625a0a1..51a6e2ad69 100644
--- a/drivers/update_stage3.R
+++ b/drivers/update_stage3.R
@@ -23,44 +23,65 @@ furrr::future_walk(dplyr::pull(site_list, site_id), function(curr_site_id){
                                       endpoint = config$endpoint,
                                       bucket = driver_bucket)
 
-  stage3_df <- arrow::open_dataset(s3_stage3) |>
-    dplyr::filter(site_id == curr_site_id) |>
-    dplyr::collect()
-
-  max_date <- stage3_df |>
-    dplyr::summarise(max = as.character(lubridate::as_date(max(datetime)))) |>
-    dplyr::pull(max)
+  # case for if this is the first time creating stage3 drivers
+  stage3_dataset <- arrow::open_dataset(s3_stage3)
+  if(length(stage3_dataset$files) > 0){
+    stage3_df <- stage3_dataset |>
+      dplyr::filter(site_id == curr_site_id) |>
+      dplyr::collect()
+
+    max_date <- stage3_df |>
+      dplyr::summarise(max = as.character(lubridate::as_date(max(datetime)))) |>
+      dplyr::pull(max)
+  }else{
+    max_date <- NA
+  }
 
   s3_pseudo <- gefs4cast::gefs_s3_dir(product = "pseudo",
                                       path = driver_path,
                                       endpoint = config$endpoint,
                                       bucket = driver_bucket)
 
-  vars <- names(stage3_df)
+  if(length(stage3_dataset$files) > 0){
+    cut_off <- as.character(lubridate::as_date(max_date) - lubridate::days(3))
+  }
 
-  cut_off <- as.character(lubridate::as_date(max_date) - lubridate::days(3))
+  if(length(stage3_dataset$files) > 0){
+    pseudo_df <- arrow::open_dataset(s3_pseudo) |>
+      dplyr::filter(variable %in% c("PRES","TMP","RH","UGRD","VGRD","APCP","DSWRF","DLWRF")) |>
+      dplyr::filter(site_id == curr_site_id,
+                    reference_datetime >= cut_off) |>
+      dplyr::collect()
+  }else{
+    pseudo_df <- arrow::open_dataset(s3_pseudo) |>
+      dplyr::filter(variable %in% c("PRES","TMP","RH","UGRD","VGRD","APCP","DSWRF","DLWRF")) |>
+      dplyr::filter(site_id == curr_site_id) |>
+      dplyr::collect()
+  }
 
-  pseudo_df <- arrow::open_dataset(s3_pseudo) |>
-    dplyr::filter(variable %in% c("PRES","TMP","RH","UGRD","VGRD","APCP","DSWRF","DLWRF")) |>
-    dplyr::filter(site_id == curr_site_id,
-                  reference_datetime >= cut_off) |>
-    dplyr::collect()
 
-  if(nrow(psuedo_df) > 0){
+  if(nrow(pseudo_df) > 0){
 
-    df2 <- psuedo_df |>
+    df2 <- pseudo_df |>
       to_hourly(site_list = dplyr::select(site_list, site_id, latitude, longitude),
                 use_solar_geom = TRUE,
-                psuedo = TRUE) |>
+                pseudo = TRUE) |>
       dplyr::mutate(ensemble = as.numeric(stringr::str_sub(ensemble, start = 4, end = 5))) |>
       dplyr::rename(parameter = ensemble)
 
-    stage3_df_update <- stage3_df |>
-      dplyr::filter(datetime < min(df2$datetime))
+    if(length(stage3_dataset$files) > 0){
+      stage3_df_update <- stage3_df |>
+        dplyr::filter(datetime < min(df2$datetime))
+
+      df2 |>
+        dplyr::bind_rows(stage3_df_update) |>
+        dplyr::arrange(variable, datetime, parameter) |>
+        arrow::write_dataset(path = s3_stage3, partitioning = "site_id")
+    }else{
+      df2 |>
+        dplyr::arrange(variable, datetime, parameter) |>
+        arrow::write_dataset(path = s3_stage3, partitioning = "site_id")
+    }
 
-    df2 |>
-      dplyr::bind_rows(stage3_df_update) |>
-      dplyr::arrange(variable, datetime, parameter) |>
-      arrow::write_dataset(path = s3_stage3, partitioning = "site_id")
   }
 })