Finnmaid+GETM.Rmd

---
title: "Finnmaid and GETM"
author: "Jens Daniel Müller und Lara Sophie Burchardt"
date:  "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  workflowr::wflow_html:
    number_sections: true
    toc_depth: 3
    toc_float:
      collapsed: false
editor_options:
  chunk_output_type: console
---


```{r global_options, include = FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```

```{r packages}
library(tidyverse)
library(ncdf4)
library(vroom)
library(lubridate)
library(here)
library(seacarb)
library(oce)
library(patchwork)
```

```{r subsetting_criteria}

# route
select_route <- "E"

# latitude limits
low_lat <- 57.3
high_lat <- 57.5

#depth range to subset GETM 3d files
d1_shallow <- 0
d1_deep <- 30

# date limits
start_date <- "2018-06-20"
end_date <- "2018-08-25"

fixed_values <- 
  read_csv(here::here("Data/_summarized_data_files", "BloomSail_fixed_values.csv"))


```

# GETM Data preparation

## Salinity and temperature profiles

Mean salinity and temperature profiles within the BloomSail area were extracted from daily GETM transects beneath the Finnmaid track.

```{r read_getm_3d_sal_tem, eval=FALSE}

nc <- nc_open(here::here("data/GETM", "Finnmaid.E.3d.2018.nc"))

lat <- ncvar_get(nc, "latc")

time_units <- nc$dim$time$units %>%     #we read the time unit from the netcdf file to calibrate the time 
    substr(start = 15, stop = 33) %>%   #calculation, we take the relevant information from the string
    ymd_hms()                           # and transform it to the right format
t <- time_units + ncvar_get(nc, "time") # read time vector
rm(time_units)

d <- ncvar_get(nc, "zax") # read depths vector

for (var_3d in c("salt", "temp")) {
  
array <- ncvar_get(nc, var_3d) # store the data in a 3-dimensional array
#dim(array) # should be 3d with dimensions: 544 coordinates, 51 depths, and number of days of month

fillvalue <- ncatt_get(nc, var_3d, "_FillValue")

# Working with the data
array[array == fillvalue$value] <- NA

    for (i in seq(1,length(t),1)){
      
      # i <- 3
      array_slice <- array[, , i] # slices data from one day
      
      array_slice_df <- as.data.frame(t(array_slice))
      array_slice_df <- as_tibble(array_slice_df)
      
      gt_3d_part <- array_slice_df %>%
        set_names(as.character(lat)) %>%
        mutate(dep = -d) %>%
        gather("lat", "value", 1:length(lat)) %>%
        mutate(lat = as.numeric(lat)) %>%
        filter(lat > low_lat, lat < high_lat,
               dep >= d1_shallow, dep <= d1_deep) %>%
        #summarise_all("mean") %>%
        mutate(var = var_3d,
               date_time=t[i]) %>% 
        dplyr::select(date_time, dep, value, var) #%>% 
        #filter(date_time >= start_date, date_time <= end_date)

      
      if (exists("gt_3d")) {
        gt_3d <- bind_rows(gt_3d, gt_3d_part)
        } else {gt_3d <- gt_3d_part}
      
  rm(array_slice, array_slice_df, gt_3d_part)
      
    }
rm(array, fillvalue)

}

nc_close(nc)
rm(nc)

gt_3d_jun_aug <- gt_3d %>% 
  filter(date_time >= start_date & date_time <= end_date) %>% 
  group_by(dep,var,date_time ) %>% 
  summarise_all(list(value=~mean(.,na.rm=TRUE))) %>%
  ungroup()

gt_3d_jun_aug %>% 
  vroom_write((here::here("data/_summarized_data_files", file = "gt_3d_jun_aug_2018.csv")))


rm(gt_3d, gt_3d_jun_aug, d1_deep, d1_shallow, i, lat, d, t, var_3d)

```


## Mixed layer depth

Regional mean mixed layer depth estimates based on sewater density and windspeed within the BloomSail area were extracted from 3h GETM surface data along the Finnmaid track.

```{r read_getm_2d_mld_wind, eval=FALSE}

nc_2d <- nc_open(here("data/GETM", "Finnmaid.E.2d.2018.nc"))
#print(nc_2d)

lat <- ncvar_get(nc_2d, "latc")

time_units <- nc_2d$dim$time$units %>%     #we read the time unit from the netcdf file to calibrate the time 
    substr(start = 15, stop = 33) %>%   #calculation, we take the relevant information from the string
    ymd_hms()                           # and transform it to the right format
t <- time_units + ncvar_get(nc_2d, "time") # read time vector
rm(time_units)

for (var in names(nc_2d$var)[c(3,4,6:12)]) {
  
#var <- "mld_rho"

array <- ncvar_get(nc_2d, var) # store the data in a 3-dimensional array
fillvalue <- ncatt_get(nc_2d, var, "_FillValue")
array[array == fillvalue$value] <- NA

array <- as.data.frame(t(array), xy=TRUE)
array <- as_tibble(array)
      
  gt_2d_part <- array %>%
  set_names(as.character(lat)) %>%
  mutate(date_time = t) %>%
  filter(date_time >= start_date & date_time <= end_date) %>% 
  gather("lat", "value", 1:length(lat)) %>%
  mutate(lat = as.numeric(lat)) %>%
  filter(lat > low_lat, lat<high_lat) %>%
  select(-lat) %>% 
  group_by(date_time) %>% 
  summarise_all(list(value=~mean(.,na.rm=TRUE))) %>%
  ungroup() %>% 
  mutate(var = var)
     
  if (exists("gt_2d")) {
    gt_2d <- bind_rows(gt_2d, gt_2d_part)
    } else {gt_2d <- gt_2d_part} 

rm(array, fillvalue, gt_2d_part)

}

nc_close(nc_2d)
rm(nc_2d)

gt_2d <- gt_2d %>% 
  mutate(value = round(value, 3)) %>% 
  pivot_wider(values_from = value, names_from = var) %>% 
  mutate(U_10 = round(sqrt(u10^2 + v10^2), 3)) %>% 
  select(-c(u10, v10))


gt_2d %>% 
  vroom_write((here::here("data/_summarized_data_files", file = "gt_2d_jun_aug_2018.csv")))

rm(t, var, gt_2d, lat)

```

# Hydrography

## Read summary files

```{r read_gt_summary_files}

gt_3d_jun_aug <- 
  read_tsv((here::here("data/_summarized_data_files", file = "gt_3d_jun_aug_2018.csv")))

gt_2d_jun_aug <- 
  read_tsv((here::here("data/_summarized_data_files", file = "gt_2d_jun_aug_2018.csv")))

gt_3d_jun_aug <- gt_3d_jun_aug %>% 
  pivot_wider(values_from = value, names_from = var) %>% 
  mutate(rho = swSigma(salinity = salt, temperature = temp, pressure = dep/10))

gt_2d_jun_aug_daily <- gt_2d_jun_aug %>% 
  mutate(day = yday(date_time)) %>% 
  group_by(day) %>% 
  summarise_all(list(~mean(.,na.rm=TRUE))) %>%
  ungroup() %>% 
  select(-day)

```


## Hovmoeller Plots

```{r plot_hovmoeller_gt_salt_temp_rho}

p_sal <- gt_3d_jun_aug %>% 
  ggplot()+
  geom_raster(aes(date_time, dep, fill=salt))+
  geom_vline(data=fixed_values, aes(xintercept = start))+
  geom_vline(data=fixed_values, aes(xintercept = end))+
  scale_fill_viridis_c(name="Salinity ", direction = -1)+
  scale_y_reverse()+
  coord_cartesian(expand = 0)+
  labs(y="Depth [m]")+
  theme_bw()+
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())+
  geom_line(data= gt_2d_jun_aug_daily,
            aes(x = date_time, y = mld_rho), color = "white")+
  scale_color_discrete(name = "Legend", labels = c("MLD Rho"))


p_tem <- gt_3d_jun_aug %>% 
  ggplot()+
  geom_raster(aes(date_time, dep, fill=temp))+
  geom_vline(data=fixed_values, aes(xintercept = start))+
  geom_vline(data=fixed_values, aes(xintercept = end))+
  scale_fill_viridis_c(name="Temperature (°C)", option = "B")+
  scale_y_reverse()+
  coord_cartesian(expand = 0)+
  labs(y="Depth [m]", x="")+
  theme_bw()+
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank())+
  geom_line(data= gt_2d_jun_aug_daily,
            aes(x = date_time, y = mld_rho), color = "white")+
  scale_color_discrete(name = "Legend", labels = c("MLD Rho"))

p_rho <- gt_3d_jun_aug %>% 
  ggplot()+
  geom_raster(aes(date_time, dep, fill=rho))+
  geom_vline(data=fixed_values, aes(xintercept = start))+
  geom_vline(data=fixed_values, aes(xintercept = end))+
  scale_fill_viridis_c(name="d Rho (kg/m^3)", direction = -1)+
  scale_y_reverse()+
  coord_cartesian(expand = 0)+
  labs(y="Depth [m]", x="")+
  theme_bw()+
  theme(axis.title.x = element_blank())+
  geom_line(data= gt_2d_jun_aug_daily,
            aes(x = date_time, y = mld_rho), color = "white")+
  scale_color_discrete(name = "Legend", labels = c("MLD Rho"))

p_sal / p_tem / p_rho

rm(p_sal, p_tem, p_rho)

```

## Profiles

```{r plot_profiles_gt_salt_temp_rho}

gt_3d_jun_aug_long <- gt_3d_jun_aug %>% 
  pivot_longer(3:5, values_to = "value", names_to = "parameter")
#
lab_dates <- pretty(gt_3d_jun_aug_long$date_time)

gt_3d_jun_aug_long %>% 
  ggplot(aes(value, dep,
             col=as.numeric(date_time),
             group=as.factor(date_time)))+
  geom_path()+
  scale_y_reverse(expand = c(0,0))+
  scale_color_viridis_c(breaks = as.numeric(lab_dates),
                        labels = lab_dates,
                        name="Date")+
  theme_bw()+
  facet_grid(~parameter, scales = "free_x")

```


# Metrology

## Windspeeds

```{r plot_winspeed}

gt_2d_jun_aug %>% 
  ggplot()+
  geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
  geom_line(aes(x= date_time, y = U_10, col="3-hourly"))+
  geom_line(data = gt_2d_jun_aug_daily,
            aes(x= date_time, y = U_10, col="Daily mean"))+
  labs(y="U (m/s)", x = "Date")+
  scale_color_brewer(palette = "Set1", name="", direction = -1)+
  theme_bw()

```



# Finnmaid

## Read data

Finnmaid data, including reconstructed data during LICOS operation failure.

```{r read_Finnmaid}

df <-
 read_csv(here::here("Data/_summarized_data_files",
                      "Finnmaid.csv"))

df <- df %>% 
  filter(Area == "BS",
         date > start_date,
         date < end_date) %>% 
  select(-c(Lon, Lat, patm, Teq, xCO2, route, Area)) %>% 
  mutate(ID = as.factor(ID)) %>% 
  rename(tem=Tem,
         sal=Sal)

```

## C~T~ calculation

Calculate based on fixed A~T~ and salinity mean values. 

```{r CT_calculation}

df <- df %>% 
  mutate(CT = carb(24, var1=pCO2, var2=1720*1e-6,
                   S=6.9, T=tem, k1k2="m10", kf="dg", ks="d",
                   gas="insitu")[,16]*1e6)

```

## Timeseries

```{r timeseries_of_regional_averages}

FM_ts_mean <- df %>% 
  arrange(date) %>% 
  group_by(ID, sensor) %>% 
  summarise_all(list(~mean(.)), na.rm=TRUE) %>%
  ungroup() %>% 
  pivot_longer(4:8, values_to = "mean", names_to = "parameter")

FM_ts_sd <- df %>% 
  arrange(date) %>% 
  group_by(ID, sensor) %>% 
  summarise_all(list(~sd(.)), na.rm=TRUE) %>%
  ungroup() %>% 
  pivot_longer(4:8, values_to = "sd", names_to = "parameter") %>% 
  select(-date)

FM_ts <- inner_join(FM_ts_mean, FM_ts_sd)

rm(FM_ts_mean, FM_ts_sd)

```


```{r Plot_Finnmaid_crossings, fig.asp=1.5}

FM_ts %>% 
  ggplot()+
  geom_rect(data = fixed_values, aes(xmin=start, xmax=end, ymin=-Inf, ymax=Inf), alpha=0.2)+
  geom_path(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd))+
  geom_ribbon(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd), alpha=0.5)+
  geom_point(aes(x=date, y=mean, ymax=mean+sd, ymin=mean-sd, col=sensor))+
  facet_grid(parameter~., scales = "free_y")+
  scale_color_brewer(palette = "Set1", direction=-1)+
  theme_bw()

```


# C~T~ vs temperature

As primary production (negative changes in C~T~) and increase in seawater temperature have a common driver (light), the relation between both changes was investigated.


```{r CT_vs_temperature}

CT_tem <- FM_ts %>%
  filter(parameter %in% c("tem", "CT")) %>% 
  select(date, parameter, mean) %>% 
  pivot_wider(values_from = mean, names_from = parameter)

CT_tem <- CT_tem %>% 
  arrange(date) %>%
  mutate(dCT = CT - lag(CT),
         dtem = tem - lag(tem),
         sign = if_else(dCT < 0, "neg", "pos")) %>% 
  drop_na()


lab_dates <- pretty(CT_tem$date)

CT_tem %>% 
  filter(date > start_date,
         date < end_date) %>% 
  arrange(date) %>% 
  ggplot()+
  geom_hline(yintercept = 0)+
  geom_vline(xintercept = 0)+
  geom_smooth(aes(dtem, dCT), method = "lm", se=FALSE)+
  geom_point(aes(dtem, dCT, fill=as.numeric(date)), shape=21)+
  scale_fill_viridis_c(breaks = as.numeric(lab_dates),
                        labels = lab_dates,
                        name="Date")+
  guides(fill = guide_legend(override.aes=list(shape=21)))


```



# NCP

## MLD approach

Use dCT/dtem from Finnmaid, MLD from GETM

## delta T appraoch

Use dCT/dtem from Finnmaid/GETM, T-profiles from GETM