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hv_snow_depth_qaqc.R
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hv_snow_depth_qaqc.R
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# jack Tarricone
# feb 24 2022
# hv jemez snowdepth data
# with influence from Dr. Keith's script
library(zoo)
library(lubridate)
library(dplyr)
library(ggplot2);theme_set(theme_classic(12))
# read back in the proper data!!!
depth_data <-read.csv("/Users/jacktarricone/ch1_jemez_data/climate_station_data/wrcc_depth_formatted.csv")
depth_data$date_time <-mdy_hm(depth_data$date_time)
depth_data$date <-as.Date(depth_data$date)
###############################################################################################
#4: Run the second QC
#4a: Remove values outside ±2 standard deviations (± 5 cm per 15 min)
#4b: Think about using more complex routine that identifies spikes
#based on rate of change and length (as in Lehning et al., 2002)
#this might be more accurate
#5 cm seems to be about the max of real snowfall events
del_thresh = 1.5 # 5mm
depth_data$delta_depth <- c(0, diff(depth_data$hv_snow_depth_cm))
depth_data$depth_lvl_2 <- ifelse(depth_data$delta_depth > del_thresh |
depth_data$delta_depth < (-1 * del_thresh) |
is.na(depth_data$delta_depth),
NA,
depth_data$hv_snow_depth_cm)
# test plot of first filter of depth data
ggplot(depth_data) +
geom_point(aes(x = date_time, y = hv_snow_depth_cm), size = .2)
ggplot(depth_data) +
geom_point(aes(x = date_time, y = depth_lvl_2), size = .1)
# filter off the bottom NaNs for interpolation funciton
# doesn't work with NaNs at ennd
depth_data <-filter(depth_data, X <= 1099)
# linearly interpolate between points to fill in NaNs
depth_data <-data.frame(X = seq(depth_data$X[1], depth_data$X[nrow(depth_data)], by = 1)) %>%
full_join(depth_data, by = "X") %>%
mutate(sd_interp = na.approx(depth_data$depth_lvl_2))
# test plot
ggplot(depth_data) +
# geom_line(aes(x = date_time, y = depth_lvl_2, col = "blue"))+
geom_line(aes(x = date_time, y = sd_interp), size = .3)
### filter again on the new data
del_thresh = 1 # 5mm
depth_data$delta_sd_interp <- c(0, diff(depth_data$sd_interp))
depth_data$depth_lvl_3 <- ifelse(depth_data$delta_sd_interp > abs(del_thresh) |
depth_data$delta_sd_interp < (-1 * del_thresh) |
is.na(depth_data$delta_sd_interp),
NA,
depth_data$sd_interp)
# linearly interpolate between interpolated data, lol
depth_data <-data.frame(X = seq(depth_data$X[1], depth_data$X[nrow(depth_data)], by = 1)) %>%
full_join(depth_data, by = "X") %>%
mutate(sd_interp_v2 = na.approx(depth_lvl_3))
# test plot with new data
ggplot(depth_data) +
# geom_line(aes(x = date_time, y = hv_snow_depth_cm))+
# geom_point(aes(x = date_time, y = sd_interp, col = "red"), size = .3) +
geom_line(aes(x = date_time, y = sd_interp_v2, col = "blue"), size = .3)
# write.csv(depth_data, "/Users/jacktarricone/ch1_jemez_data/climate_station_data/vg/snow_depth_qaqcv1.csv" )
#### third filter!
### filter again on the new data
del_thresh = .5 # 5mm
depth_data$delta_sd_interp_v2 <- c(0, diff(depth_data$sd_interp_v2))
depth_data$depth_lvl_4 <- ifelse(depth_data$delta_sd_interp_v2 > del_thresh |
depth_data$delta_sd_interp_v2 < (-1 * del_thresh) |
is.na(depth_data$delta_sd_interp_v2),
NA,
depth_data$sd_interp_v2)
# linearly interpolate between interpolated data, lol
depth_data <-filter(depth_data, X <= 1066)
depth_data <-data.frame(X = seq(depth_data$X[1], depth_data$X[nrow(depth_data)], by = 1)) %>%
full_join(depth_data, by = "X") %>%
mutate(sd_interp_v3 = na.approx(depth_lvl_4))
# test plot with new data
ggplot(depth_data) +
# geom_line(aes(x = date_time, y = hv_snow_depth_cm))+
geom_point(aes(x = date_time, y = sd_interp_v2, col = "red"), size = .3) +
geom_line(aes(x = date_time, y = sd_interp_v3, col = "blue"), size = .3)
######################
# compute rolling mean
######################
depth_data$sd_interp_v3_smooth3 <- rollmeanr(depth_data$sd_interp_v3, 3, fill = NA)
depth_data$sd_interp_v3_smooth10 <- rollmeanr(depth_data$sd_interp_v3, 10, fill = NA)
depth_data$sd_interp_v3_smooth20 <- rollmeanr(depth_data$sd_interp_v3, 30, fill = NA)
# test plot look good
ggplot(depth_data) +
geom_point(aes(x = date_time, y = sd_interp_v2, col = "red"), size = .3) +
geom_line(aes(x = date_time, y = sd_interp_v3_smooth20 ))
write.csv(depth_data, "/Users/jacktarricone/ch1_jemez_data/climate_station_data/hv/hv_snow_depth_qaqc_v2.csv" )
#### plot for ryan
insar <-filter(depth_data, date >= "2020-02-01" & date <="2020-02-26")
flight1 <-as.numeric(insar$date_time[58]) # row for correct date time
flight2 <-as.numeric(insar$date_time[226])
flight3 <-as.numeric(insar$date_time[394])
lims <- as.POSIXct(strptime(c("2020-02-01 00:00:00", "2020-02-27 00:00:00"),
format = "%Y-%m-%d %H:%M:%S"))
ggplot(insar) +
geom_point(aes(x = date_time, y = depth_lvl_3), col = "green", size = .3) +
geom_line(aes(x = date_time, y = sd_interp_v3_smooth10))+
geom_vline(xintercept = flight1, linetype=3, col = "red", alpha = .7) +
geom_vline(xintercept = flight2, linetype=3, col = "red", alpha = .7) +
geom_vline(xintercept = flight3, linetype=3, col = "red", alpha = .7) +
annotate("text", label = "Flight 1 (2/12)", x = insar$date_time[270], y = 700, col = "red") +
annotate("text", label = "Flight 2 (2/19)", x = insar$date_time[437], y = 700, col = "red") +
annotate("text", label = "Flight 3 (2/19)", x = insar$date_time[605], y = 700, col = "red") +
scale_x_datetime(limits = lims,
breaks = "1 week") +
labs(title = "HQ Met Snow Depth 2/1/20 - 2/27/20",
y = "Snow Depth [mm]",
x = "Date")
setwd("/Users/jacktarricone/ch1_jemez_data/plots")
ggsave("hq_met_sd_v2.png",
width = 8,
height = 5,
units = "in",
dpi = 300)