Comparações dados BAM x OBS

First version: 31-10-2022

##     dplyr lubridate   lattice  maptools     ncdf4   openair   ggplot2     knitr 
##      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE 
##     tidyr  magrittr   lattice 
##      TRUE      TRUE      TRUE

1 =========> Dados OBS

sm.torre <- readRDS("/home/guilherme/Documents/BAM/data_out/torre_SM_3hr_2017_2021.rds") 
ac.torre <- readRDS("/home/guilherme/Documents/BAM/data_out/torre_AC_3hr_2017_2021.rds")

2 =========> Dados BAM

## Santa Maria
sm.bam <- readRDS("/home/guilherme/Documents/BAM/data_out/BAM_SMA_3hr_2017_2021.rds")
##Aceguá
ac.bam <- readRDS("/home/guilherme/Documents/BAM/data_out/BAM_ACE_3hr_2018_2021.rds")

3 =========> Junta dados

-> SM

range(sm.bam$date)

## [1] "2017-01-01 09:00:00 UTC" "2022-02-04 06:00:00 UTC"

range(sm.torre$date)

## [1] "2017-01-01 00:00:00 GMT" "2021-12-31 21:00:00 GMT"

filt.sm.torre <- sm.torre %>% 
  mutate(LE_orig = ifelse(LE_orig <= -50, NA, LE_orig)) %>% 
  select(date, LE.obs = LE_orig, H.obs = H_orig)

#head(filt.sm.torre)
#head(sm.bam)
df.sm <- merge(x = filt.sm.torre, y = sm.bam, by = "date")

-> AC

filt.ac.torre <- ac.torre %>% 
  mutate(LE_orig = ifelse(LE_orig <= -50, NA, LE_orig)) %>% 
  select(date, LE.obs = LE_orig, H.obs = H_orig)

df.ac <- merge(x = filt.ac.torre, y = ac.bam, by = "date")

4 Análise exploratória dos dados

4.1 SANTA MARIA

str(df.sm)

## 'data.frame':    14597 obs. of  5 variables:
##  $ date  : POSIXct, format: "2017-01-01 09:00:00" "2017-01-01 12:00:00" ...
##  $ LE.obs: num  189.33 294.51 174.53 31.75 -7.78 ...
##  $ H.obs : num  88.8 98.8 36.7 -6.3 -14.9 ...
##  $ LE_bam: num  134.9 159.7 74.6 38.4 42.3 ...
##  $ H_bam : num  236.0432 263.8997 77.7508 0.0927 -11.4897 ...

summary(df.sm)

##       date                         LE.obs            H.obs         
##  Min.   :2017-01-01 09:00:00   Min.   :-49.901   Min.   :-146.073  
##  1st Qu.:2018-04-02 12:00:00   1st Qu.:  7.421   1st Qu.: -12.943  
##  Median :2019-07-02 15:00:00   Median : 36.888   Median :   1.118  
##  Mean   :2019-07-02 23:49:33   Mean   : 73.426   Mean   :  31.478  
##  3rd Qu.:2020-10-01 18:00:00   3rd Qu.:106.867   3rd Qu.:  62.599  
##  Max.   :2021-12-31 21:00:00   Max.   :541.952   Max.   : 359.208  
##                                NA's   :3858      NA's   :2709      
##      LE_bam           H_bam        
##  Min.   :-13.88   Min.   :-78.181  
##  1st Qu.: 11.37   1st Qu.: -8.761  
##  Median : 32.96   Median :  1.247  
##  Mean   : 57.05   Mean   : 43.065  
##  3rd Qu.: 85.50   3rd Qu.: 77.895  
##  Max.   :507.78   Max.   :396.830  
## 

##dados faltantes
colSums(is.na(df.sm))

##   date LE.obs  H.obs LE_bam  H_bam 
##      0   3858   2709      0      0

range(df.sm$date)

## [1] "2017-01-01 09:00:00 GMT" "2021-12-31 21:00:00 GMT"

4.1.1 LE e H 3hs

## Carregando pacotes exigidos: scales

4.1.2 LE e H diários

4.1.3 Panel plot

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.2 ACEGUÁ

str(df.ac)

## 'data.frame':    9193 obs. of  5 variables:
##  $ date  : POSIXct, format: "2018-09-06 00:00:00" "2018-09-06 03:00:00" ...
##  $ LE.obs: num  NA NA NA NA NA ...
##  $ H.obs : num  NaN NaN NaN NaN NaN ...
##  $ LE_bam: num  9.9 7.94 15.2 50.63 75.78 ...
##  $ H_bam : num  -7.34 -5.41 2.67 32.97 24.65 ...

summary(df.ac)

##       date                         LE.obs            H.obs        
##  Min.   :2018-09-06 00:00:00   Min.   :-47.720   Min.   :-93.365  
##  1st Qu.:2019-06-20 06:00:00   1st Qu.:  3.962   1st Qu.:-15.132  
##  Median :2020-04-03 12:00:00   Median : 32.949   Median : -3.627  
##  Mean   :2020-04-03 00:42:03   Mean   : 77.304   Mean   : 25.710  
##  3rd Qu.:2021-01-15 18:00:00   3rd Qu.:115.129   3rd Qu.: 58.422  
##  Max.   :2021-10-30 00:00:00   Max.   :603.010   Max.   :392.935  
##                                NA's   :882       NA's   :666      
##      LE_bam            H_bam        
##  Min.   :-24.833   Min.   :-82.034  
##  1st Qu.:  7.035   1st Qu.:-19.834  
##  Median : 30.427   Median : -4.133  
##  Mean   : 71.145   Mean   : 37.703  
##  3rd Qu.: 99.532   3rd Qu.: 68.696  
##  Max.   :696.891   Max.   :544.199  
## 

##dados faltantes
colSums(is.na(df.ac))

##   date LE.obs  H.obs LE_bam  H_bam 
##      0    882    666      0      0

range(df.ac$date)

## [1] "2018-09-06 GMT" "2021-10-30 GMT"

4.2.1 LE e H 3hs

4.2.2 LE e H diários

4.2.3 Panel plot

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Save figs

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