1 =========> PRELIMINARES
2 =========> Identificando as variáveis do BAM x OBS
3 =========> Comparações iniciais
- 3.1 Latent heat flux ( LE_orig x lhtfl )
- 3.2 Sensible heat flux ( H_orig x shtfl )

First version: 17-02-2022

Pacotes e diretório

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

Diretório de trabalho e scripts

wd <- "/home/guilherme/Documents/BAM/R"
setwd(wd)

dir_out_bam <- "/home/guilherme/Documents/BAM/data_in/bam/"

# source("~/Inland/scriptsR/plot_GRID/plot_AUX_infile.R")
# source("~/Inland/scriptsR/plot_GRID/plot_soil_infile.R")
source("~/Documents/BAM/Rsource/plot_one_point_grid.R")

## ------------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## ------------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

source("~/Documents/BAM/Rsource/plot_ts_BAM.R")
# source("~/Inland/scriptsR/plot_GRID/plot_one_time_allgrid.R")
# source("~/Inland/scriptsR/plot_GRID/multiplot.R")

1 =========> PRELIMINARES

## este arquivo corresponde aos dados de JAN-2017, 3 em 3hs
file_out_day <- paste0(dir_out_bam, "SMA_2016010112-2017010112.nc4")
nc.d <- nc_open(filename = file_out_day, verbose = F)
#str(nc.d)
names(nc.d)

##  [1] "filename"    "writable"    "id"          "error"       "safemode"   
##  [6] "format"      "is_GMT"      "groups"      "fqgn2Rindex" "ndims"      
## [11] "natts"       "dim"         "unlimdimid"  "nvars"       "var"

names(nc.d$var)

##  [1] "sp"    "u"     "v"     "prmsl" "t"     "q"     "tp"    "acpcp" "pwat" 
## [10] "shtfl" "lhtfl" "dlwrf" "ulwrf" "nlwrt" "dswrf" "uswrf" "nswrt" "2t"   
## [19] "q_2"   "10u"   "10v"

names(nc.d$vars)

## NULL

names(nc.d$id)

## NULL

names(nc.d$ndims)

## NULL

names(nc.d$ndim)

## NULL

names(nc.d$filename)

## NULL

names(nc.d$writable)

## NULL

names(nc.d$id)

## NULL

## time?
time <- ncvar_get(nc.d, "time")
time #8*31

##   [1]   0   3   6   9  12  15  18  21  24  27  30  33  36  39  42  45  48  51
##  [19]  54  57  60  63  66  69  72  75  78  81  84  87  90  93  96  99 102 105
##  [37] 108 111 114 117 120 123 126 129 132 135 138 141 144 147 150 153 156 159
##  [55] 162 165 168 171 174 177 180 183 186 189 192 195 198 201 204 207 210 213
##  [73] 216 219 222 225 228 231 234 237 240 243 246 249 252 255 258 261 264 267
##  [91] 270 273 276 279 282 285 288 291 294 297 300 303 306 309 312 315 318 321
## [109] 324 327 330 333 336 339 342 345 348 351 354 357 360 363 366 369 372 375
## [127] 378 381 384 387 390 393 396 399 402 405 408 411 414 417 420 423 426 429
## [145] 432 435 438 441 444 447 450 453 456 459 462 465 468 471 474 477 480 483
## [163] 486 489 492 495 498 501 504 507 510 513 516 519 522 525 528 531 534 537
## [181] 540 543 546 549 552 555 558 561 564 567 570 573 576 579 582 585 588 591
## [199] 594 597 600 603 606 609 612 615 618 621 624 627 630 633 636 639 642 645
## [217] 648 651 654 657 660 663 666 669 672 675 678 681 684 687 690 693 696 699
## [235] 702 705 708 711 714 717 720 723 726 729 732 735 738 741

dim(time)

## [1] 248

nc.d$dim$time$units

## [1] "hours since 2017-1-1 15:00:00"

Os dados no arquivo “SMA_2016010112-2017010112.nc4” correspondem aos dados do BAM de JAN-2017, de 3 em 3hs, iniciando as 15:00:00hs.

gvar <- ncvar_get(nc.d, "lhtfl") #transposta array
gvar

##   [1] 132.0234833 149.6313782 139.2505951  46.3552246  34.4187775  22.8021088
##   [7]  15.8628693  96.1457214 201.6800385 213.0540924 135.9007263  58.7165222
##  [13]  35.8096313  23.3972168  19.3702850  55.3323669 139.8953400 168.8359528
##  [19] 105.4092102  46.8980713  29.4365692  14.8053131   5.2523651  41.8327484
##  [25] 119.8551788 160.0198212 106.2930756  44.4172058  29.4250793  17.0559540
##  [31]  11.8094177  39.9219971  99.4942474 143.7543182  96.5406799  47.0559998
##  [37]  36.0064087  23.7772675  10.9604950  41.4265747 106.7123718 134.5568390
##  [43]  88.0807648  45.1386108  34.2350616  22.7465210  11.6967621  37.2810974
##  [49] 100.4193878 129.2049255  86.4262695  43.4358368  31.2453461  24.3080597
##  [55]  14.0767517  42.1221466  99.0948639 105.8942871  69.6714325  33.3877258
##  [61]  27.6054382  19.9992981  13.5842285  41.3654938  84.4745483  93.7534332
##  [67]  64.8652039  34.4621582  27.0198975  23.0814362  13.0176392  41.6584778
##  [73]  77.7572479  98.7212830  71.4916229  38.0512848  36.9413452  24.9335480
##  [79]  19.8219299  44.1591949  88.2338867  94.7073669  65.4356232  42.2453766
##  [85]  40.6561279  26.8916626  29.8922882 103.4421387 137.7630768 207.6796722
##  [91] 186.0049286  53.5545044  37.4710693  20.8410492  14.8094330  75.7956848
##  [97] 232.4626923 178.6516113 172.5795135  53.5293579  14.9769440   6.8350067
## [103]  11.0957489  46.5904999 121.0740051 202.2311707 143.5193634  64.3280945
## [109]  30.2079315  16.2635803  16.4152069  57.7672882 138.5511322 183.7585602
## [115] 117.6403351  64.4586029  41.7089691  24.6871185  14.1631622  59.9015045
## [121] 141.5202942 144.5610352 104.7101288  73.1408081  49.9693146  30.7757721
## [127]  18.9707184  62.5002136 176.2383728 216.0911560 138.0605011  66.7271881
## [133]  38.9700775  22.1793518  15.8443604  67.2258759 169.8992157 195.9087372
## [139] 121.8094788  63.6152191  41.9447632  19.1229706   9.1528625  54.0086212
## [145] 147.7274170 169.3921356 103.5555267  50.5914917  40.2435455  25.5481262
## [151]  13.4019775  47.0207214 102.8509521 123.4288330  91.6858978  47.0477753
## [157]  37.1825867  22.8339081  12.5653229  43.9523468 108.2158508  97.5910645
## [163]  70.7715607  43.1634369  36.4530487  22.7598419  10.7365570  45.9875488
## [169] 117.1648865 111.8273468  75.1760559  47.1822357  43.8648987  29.1019745
## [175]  16.3334808 140.4855804 121.9693604 113.9630280  62.4591980  17.3670349
## [181]  33.5013428  24.3236542  24.9675446  52.5287781 132.7083435 127.4932709
## [187]  57.0056458  57.1818390  51.8772583  16.0643311  18.9747925 130.9260712
## [193] 247.7477722 304.3027649 121.0688629  52.6657410  28.2142334   8.1370697
## [199]   3.1428070 131.1781158 306.7114868 459.6167297 263.4349365  81.2991486
## [205]  41.9075928  30.3525085  29.7601166  86.5363617 225.1594238 240.1275482
## [211] 158.0039062  73.1315918  37.9156799  17.0321198   0.9420166  65.8371429
## [217] 187.9249420 214.7299347 139.4968872  62.0727539  41.7218475  26.7289734
## [223]  13.6646729  53.1247253 161.3610535 187.5287781 118.7520752  60.8440704
## [229]  41.4314117  26.9665680  15.3393250  59.1173096 155.5290222 172.8017883
## [235] 113.0414581  57.4247894  40.2044373  29.1970825  19.6564484  64.4403229
## [241] 160.1302795 155.2445221  75.3048401  44.5668182  41.8916168  30.5855713
## [247]  29.9526520  67.5719604

dim(gvar)

## [1] 248

2 =========> Identificando as variáveis do BAM x OBS

Temos as seguintes variáveis (conforme email do Dirceu):

vars_bam <- c("sp: surface pressure", 
              "u: U component of wind", 
              "v: V component of wind", 
              "prmsl: Pressure reduce to MSL", 
              "t: Temperature", 
              "q: Specific Humidity", 
              "tp: Total precipitation",
              "acpcp: Convective precipitation (water)", 
              "pwat: precipitable water",
              "shtfl: sensible heat net flux", 
              "lhtfl: latente heat net flux", 
              "dlwrf: downward long-wave radiation flux", 
              "ulwrf: upward long-wave radiation flux", 
              "nlwrt: net long-wave radiation flux (top of atmosphere)", 
              "dswrf: downward short-wave radiation flux", 
              "uswrf: upward short-wave radiation flux", 
              "nswrt: net short-wave radiation flux (top of atmosphere)", 
              "2t: ?",
              "q_2: ?", 
              "10u: ?", 
              "10v: ?" )

df_vars <- data.frame(BAM = vars_bam)
knitr::kable(df_vars)

BAM
sp: surface pressure
u: U component of wind
v: V component of wind
prmsl: Pressure reduce to MSL
t: Temperature
q: Specific Humidity
tp: Total precipitation
acpcp: Convective precipitation (water)
pwat: precipitable water
shtfl: sensible heat net flux
lhtfl: latente heat net flux
dlwrf: downward long-wave radiation flux
ulwrf: upward long-wave radiation flux
nlwrt: net long-wave radiation flux (top of atmosphere)
dswrf: downward short-wave radiation flux
uswrf: upward short-wave radiation flux
nswrt: net short-wave radiation flux (top of atmosphere)
2t: ?
q_2: ?
10u: ?
10v: ?

E as variávies observadas (torres):

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

## [1] "date"    "Rn_f"    "SWout"   "LE_orig" "H_orig"  "Fg_f"    "Rg_f"   
## [8] "Tair_f"  "VPD_f"

vars_torres <- c("date: data", 
                 "Rn_f: net radiation", 
                 "SWout: solar radiation reflected by the surface",
                 "LE_orig: latent heat flux",
                 "H_orig: sensible heat flux",
                 "Fg_f: soil heat flux",
                 "Rg_f: global radiation",
                 "Tair_f: air temperature",
                 "VPD_f: vapor pressure deficit " )

vars_torres

## [1] "date: data"                                     
## [2] "Rn_f: net radiation"                            
## [3] "SWout: solar radiation reflected by the surface"
## [4] "LE_orig: latent heat flux"                      
## [5] "H_orig: sensible heat flux"                     
## [6] "Fg_f: soil heat flux"                           
## [7] "Rg_f: global radiation"                         
## [8] "Tair_f: air temperature"                        
## [9] "VPD_f: vapor pressure deficit "

head(sm.torre)

## # A tibble: 6 × 9
##   date                 Rn_f SWout LE_orig H_orig   Fg_f    Rg_f Tair_f VPD_f
##   <dttm>              <dbl> <dbl>   <dbl>  <dbl>  <dbl>   <dbl>  <dbl> <dbl>
## 1 2017-01-01 00:00:00 -33.6   NaN    9.86   9.36 -0.916   0       22.4  2.07
## 2 2017-01-01 03:00:00 -35.9   NaN   -4.21 -17.2  -3.08    0.311   21.8  1.38
## 3 2017-01-01 06:00:00 107.    NaN   62.2   15.9  -2.19  257.      25.7  6.26
## 4 2017-01-01 09:00:00 494.    NaN  189.    88.8   6.25  716.      29.4 11.4 
## 5 2017-01-01 12:00:00 559.    NaN  295.    98.8  17.2   852.      32.2 16.9 
## 6 2017-01-01 15:00:00 290.    NaN  175.    36.7  18.7   461.      31.9 17.0

select(sm.torre, LE_orig)

## # A tibble: 14,600 × 1
##    LE_orig
##      <dbl>
##  1    9.86
##  2   -4.21
##  3   62.2 
##  4  189.  
##  5  295.  
##  6  175.  
##  7   31.8 
##  8   -7.78
##  9    1.98
## 10    9.65
## # … with 14,590 more rows

head(sm.torre, 30)

## # A tibble: 30 × 9
##    date                 Rn_f SWout LE_orig H_orig   Fg_f    Rg_f Tair_f  VPD_f
##    <dttm>              <dbl> <dbl>   <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
##  1 2017-01-01 00:00:00 -33.6   NaN    9.86   9.36 -0.916   0       22.4  2.07 
##  2 2017-01-01 03:00:00 -35.9   NaN   -4.21 -17.2  -3.08    0.311   21.8  1.38 
##  3 2017-01-01 06:00:00 107.    NaN   62.2   15.9  -2.19  257.      25.7  6.26 
##  4 2017-01-01 09:00:00 494.    NaN  189.    88.8   6.25  716.      29.4 11.4  
##  5 2017-01-01 12:00:00 559.    NaN  295.    98.8  17.2   852.      32.2 16.9  
##  6 2017-01-01 15:00:00 290.    NaN  175.    36.7  18.7   461.      31.9 17.0  
##  7 2017-01-01 18:00:00  49.4   NaN   31.8   -6.30 12.3    68.0     24.9  2.88 
##  8 2017-01-01 21:00:00 -31.8   NaN   -7.78 -14.9   3.89    1.63    24.4  0.766
##  9 2017-01-02 00:00:00 -28.1   NaN    1.98 -22.1   1.52    0       24.2  2.25 
## 10 2017-01-02 03:00:00 -26.7   NaN    9.65 -29.4   1.01    0.227   23.5  1.57 
## # … with 20 more rows

sm.torre[3:6, "LE_orig"]

## # A tibble: 4 × 1
##   LE_orig
##     <dbl>
## 1    62.2
## 2   189. 
## 3   295. 
## 4   175.

Função para plotar série temporal de UM ponto de grade

plot_point_bam(nc.path = file_out_day, var.name = "lhtfl")

## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## Please use `tibble()` instead.

plot_point_bam(nc.path = file_out_day, var.name = "shtfl")

plot_point_bam(nc.path = file_out_day, var.name = "nswrt")

3 =========> Comparações iniciais

3.1 Latent heat flux ( LE_orig x lhtfl )

nc.path = "~/Documents/BAM/data_in/bam/SMA_2016010112-2017010112.nc4"
var.name.bam= "lhtfl"
var.name.obs = "LE_orig"

nc.file <- nc_open(filename = nc.path, verbose = F)
time_3h <- seq(ymd_hm("2017-1-1 09:00"), ymd_hm("2017-2-1 06:00"), by = "3 hour")
#time_3h
length(time_3h)

## [1] 248

gvar <- ncvar_get(nc.file, var.name.bam) #transposta array
nc_close(nc.file)
df <- data.frame(date=time_3h, bam=gvar, torre = sm.torre[4:251, var.name.obs])
df

##                    date         bam       LE_orig
## 1   2017-01-01 09:00:00 132.0234833  189.33000000
## 2   2017-01-01 12:00:00 149.6313782  294.51000000
## 3   2017-01-01 15:00:00 139.2505951  174.53166667
## 4   2017-01-01 18:00:00  46.3552246   31.75291500
## 5   2017-01-01 21:00:00  34.4187775   -7.77804400
## 6   2017-01-02 00:00:00  22.8021088    1.97800000
## 7   2017-01-02 03:00:00  15.8628693    9.64616667
## 8   2017-01-02 06:00:00  96.1457214   66.17529000
## 9   2017-01-02 09:00:00 201.6800385  299.94333333
## 10  2017-01-02 12:00:00 213.0540924  265.70000000
## 11  2017-01-02 15:00:00 135.9007263  169.06666667
## 12  2017-01-02 18:00:00  58.7165222   57.09100000
## 13  2017-01-02 21:00:00  35.8096313   13.77175000
## 14  2017-01-03 00:00:00  23.3972168    4.60777833
## 15  2017-01-03 03:00:00  19.3702850   -6.95650000
## 16  2017-01-03 06:00:00  55.3323669   68.77670000
## 17  2017-01-03 09:00:00 139.8953400  283.80000000
## 18  2017-01-03 12:00:00 168.8359528  358.45166667
## 19  2017-01-03 15:00:00 105.4092102  224.05833333
## 20  2017-01-03 18:00:00  46.8980713   57.27375000
## 21  2017-01-03 21:00:00  29.4365692   62.56450000
## 22  2017-01-04 00:00:00  14.8053131   28.65083333
## 23  2017-01-04 03:00:00   5.2523651   23.30800000
## 24  2017-01-04 06:00:00  41.8327484   72.45066667
## 25  2017-01-04 09:00:00 119.8551788  210.25833333
## 26  2017-01-04 12:00:00 160.0198212  140.89600000
## 27  2017-01-04 15:00:00 106.2930756  198.28500000
## 28  2017-01-04 18:00:00  44.4172058   97.52025000
## 29  2017-01-04 21:00:00  29.4250793   -2.99431400
## 30  2017-01-05 00:00:00  17.0559540           NaN
## 31  2017-01-05 03:00:00  11.8094177  -31.84300000
## 32  2017-01-05 06:00:00  39.9219971   31.64368000
## 33  2017-01-05 09:00:00  99.4942474  139.42000000
## 34  2017-01-05 12:00:00 143.7543182  131.83700000
## 35  2017-01-05 15:00:00  96.5406799   88.82425000
## 36  2017-01-05 18:00:00  47.0559998   50.79410333
## 37  2017-01-05 21:00:00  36.0064087   14.41165000
## 38  2017-01-06 00:00:00  23.7772675   15.02694000
## 39  2017-01-06 03:00:00  10.9604950   53.26116667
## 40  2017-01-06 06:00:00  41.4265747   51.44283333
## 41  2017-01-06 09:00:00 106.7123718  191.43500000
## 42  2017-01-06 12:00:00 134.5568390  250.12666667
## 43  2017-01-06 15:00:00  88.0807648  167.65166667
## 44  2017-01-06 18:00:00  45.1386108   66.40066667
## 45  2017-01-06 21:00:00  34.2350616   27.14883333
## 46  2017-01-07 00:00:00  22.7465210   13.28991667
## 47  2017-01-07 03:00:00  11.6967621   10.08176667
## 48  2017-01-07 06:00:00  37.2810974   46.10850000
## 49  2017-01-07 09:00:00 100.4193878  126.83500000
## 50  2017-01-07 12:00:00 129.2049255  174.44666667
## 51  2017-01-07 15:00:00  86.4262695  178.92800000
## 52  2017-01-07 18:00:00  43.4358368   24.60090000
## 53  2017-01-07 21:00:00  31.2453461   -3.44047500
## 54  2017-01-08 00:00:00  24.3080597           NaN
## 55  2017-01-08 03:00:00  14.0767517  -15.01500000
## 56  2017-01-08 06:00:00  42.1221466   67.18880000
## 57  2017-01-08 09:00:00  99.0948639  174.42200000
## 58  2017-01-08 12:00:00 105.8942871  119.76775000
## 59  2017-01-08 15:00:00  69.6714325  123.95800000
## 60  2017-01-08 18:00:00  33.3877258   32.42501667
## 61  2017-01-08 21:00:00  27.6054382  116.35525000
## 62  2017-01-09 00:00:00  19.9992981    6.31445000
## 63  2017-01-09 03:00:00  13.5842285           NaN
## 64  2017-01-09 06:00:00  41.3654938   40.22720000
## 65  2017-01-09 09:00:00  84.4745483   77.82160000
## 66  2017-01-09 12:00:00  93.7534332  203.93733333
## 67  2017-01-09 15:00:00  64.8652039  277.36666667
## 68  2017-01-09 18:00:00  34.4621582   55.54862500
## 69  2017-01-09 21:00:00  27.0198975   26.94700000
## 70  2017-01-10 00:00:00  23.0814362   46.02140000
## 71  2017-01-10 03:00:00  13.0176392           NaN
## 72  2017-01-10 06:00:00  41.6584778  120.24000000
## 73  2017-01-10 09:00:00  77.7572479  161.36550000
## 74  2017-01-10 12:00:00  98.7212830  208.84666667
## 75  2017-01-10 15:00:00  71.4916229  181.28833333
## 76  2017-01-10 18:00:00  38.0512848   47.25285000
## 77  2017-01-10 21:00:00  36.9413452    5.49332500
## 78  2017-01-11 00:00:00  24.9335480 -197.23000000
## 79  2017-01-11 03:00:00  19.8219299   30.01880000
## 80  2017-01-11 06:00:00  44.1591949   77.96500000
## 81  2017-01-11 09:00:00  88.2338867  265.71833333
## 82  2017-01-11 12:00:00  94.7073669  445.54166667
## 83  2017-01-11 15:00:00  65.4356232  338.61833333
## 84  2017-01-11 18:00:00  42.2453766   92.91921667
## 85  2017-01-11 21:00:00  40.6561279   34.67293333
## 86  2017-01-12 00:00:00  26.8916626   11.92670000
## 87  2017-01-12 03:00:00  29.8922882    2.96636333
## 88  2017-01-12 06:00:00 103.4421387   70.07825000
## 89  2017-01-12 09:00:00 137.7630768  285.10000000
## 90  2017-01-12 12:00:00 207.6796722  475.23600000
## 91  2017-01-12 15:00:00 186.0049286  378.11000000
## 92  2017-01-12 18:00:00  53.5545044   76.49166667
## 93  2017-01-12 21:00:00  37.4710693    2.20802250
## 94  2017-01-13 00:00:00  20.8410492  -10.92283333
## 95  2017-01-13 03:00:00  14.8094330           NaN
## 96  2017-01-13 06:00:00  75.7956848   56.00740000
## 97  2017-01-13 09:00:00 232.4626923  256.21000000
## 98  2017-01-13 12:00:00 178.6516113  387.96333333
## 99  2017-01-13 15:00:00 172.5795135  274.72166667
## 100 2017-01-13 18:00:00  53.5293579   77.15752000
## 101 2017-01-13 21:00:00  14.9769440   -1.67703667
## 102 2017-01-14 00:00:00   6.8350067  -13.07850000
## 103 2017-01-14 03:00:00  11.0957489   22.70065000
## 104 2017-01-14 06:00:00  46.5904999   77.66025000
## 105 2017-01-14 09:00:00 121.0740051  200.40333333
## 106 2017-01-14 12:00:00 202.2311707  142.57500000
## 107 2017-01-14 15:00:00 143.5193634   78.18666667
## 108 2017-01-14 18:00:00  64.3280945   37.02535000
## 109 2017-01-14 21:00:00  30.2079315   -0.03783333
## 110 2017-01-15 00:00:00  16.2635803    9.31488000
## 111 2017-01-15 03:00:00  16.4152069    8.18627500
## 112 2017-01-15 06:00:00  57.7672882   49.58366667
## 113 2017-01-15 09:00:00 138.5511322  315.37666667
## 114 2017-01-15 12:00:00 183.7585602  416.54166667
## 115 2017-01-15 15:00:00 117.6403351  225.75166667
## 116 2017-01-15 18:00:00  64.4586029   51.50216667
## 117 2017-01-15 21:00:00  41.7089691   38.95050000
## 118 2017-01-16 00:00:00  24.6871185   19.50470000
## 119 2017-01-16 03:00:00  14.1631622   66.43033333
## 120 2017-01-16 06:00:00  59.9015045   36.20300000
## 121 2017-01-16 09:00:00 141.5202942  134.34325000
## 122 2017-01-16 12:00:00 144.5610352  254.62500000
## 123 2017-01-16 15:00:00 104.7101288  226.77500000
## 124 2017-01-16 18:00:00  73.1408081   56.76783333
## 125 2017-01-16 21:00:00  49.9693146   34.25900000
## 126 2017-01-17 00:00:00  30.7757721   20.54190000
## 127 2017-01-17 03:00:00  18.9707184   16.53052500
## 128 2017-01-17 06:00:00  62.5002136   58.82580000
## 129 2017-01-17 09:00:00 176.2383728  188.54500000
## 130 2017-01-17 12:00:00 216.0911560  228.76166667
## 131 2017-01-17 15:00:00 138.0605011  199.94833333
## 132 2017-01-17 18:00:00  66.7271881   57.91690000
## 133 2017-01-17 21:00:00  38.9700775    6.11166500
## 134 2017-01-18 00:00:00  22.1793518   -0.49382000
## 135 2017-01-18 03:00:00  15.8443604   -8.80560000
## 136 2017-01-18 06:00:00  67.2258759   85.82975000
## 137 2017-01-18 09:00:00 169.8992157  237.03333333
## 138 2017-01-18 12:00:00 195.9087372  434.75000000
## 139 2017-01-18 15:00:00 121.8094788  377.92500000
## 140 2017-01-18 18:00:00  63.6152191   87.77883333
## 141 2017-01-18 21:00:00  41.9447632  -13.26912000
## 142 2017-01-19 00:00:00  19.1229706   13.72937500
## 143 2017-01-19 03:00:00   9.1528625   -5.44596833
## 144 2017-01-19 06:00:00  54.0086212   56.03103333
## 145 2017-01-19 09:00:00 147.7274170  309.17166667
## 146 2017-01-19 12:00:00 169.3921356  520.49166667
## 147 2017-01-19 15:00:00 103.5555267  411.61666667
## 148 2017-01-19 18:00:00  50.5914917   84.51716667
## 149 2017-01-19 21:00:00  40.2435455   33.87950000
## 150 2017-01-20 00:00:00  25.5481262    5.14675000
## 151 2017-01-20 03:00:00  13.4019775  -13.67560000
## 152 2017-01-20 06:00:00  47.0207214   51.05950333
## 153 2017-01-20 09:00:00 102.8509521  275.65333333
## 154 2017-01-20 12:00:00 123.4288330  497.68333333
## 155 2017-01-20 15:00:00  91.6858978  364.92500000
## 156 2017-01-20 18:00:00  47.0477753   78.32073333
## 157 2017-01-20 21:00:00  37.1825867   26.03283333
## 158 2017-01-21 00:00:00  22.8339081    9.12419833
## 159 2017-01-21 03:00:00  12.5653229   -0.00480000
## 160 2017-01-21 06:00:00  43.9523468   66.22450000
## 161 2017-01-21 09:00:00 108.2158508  274.36000000
## 162 2017-01-21 12:00:00  97.5910645  440.11333333
## 163 2017-01-21 15:00:00  70.7715607  359.36500000
## 164 2017-01-21 18:00:00  43.1634369   77.81950000
## 165 2017-01-21 21:00:00  36.4530487   26.53500000
## 166 2017-01-22 00:00:00  22.7598419   -0.32543333
## 167 2017-01-22 03:00:00  10.7365570   17.53200000
## 168 2017-01-22 06:00:00  45.9875488   41.37733333
## 169 2017-01-22 09:00:00 117.1648865  248.43500000
## 170 2017-01-22 12:00:00 111.8273468  472.12333333
## 171 2017-01-22 15:00:00  75.1760559  339.07000000
## 172 2017-01-22 18:00:00  47.1822357   73.70190410
## 173 2017-01-22 21:00:00  43.8648987   25.52690000
## 174 2017-01-23 00:00:00  29.1019745    1.64925000
## 175 2017-01-23 03:00:00  16.3334808    3.74225000
## 176 2017-01-23 06:00:00 140.4855804   55.80300000
## 177 2017-01-23 09:00:00 121.9693604  255.76833333
## 178 2017-01-23 12:00:00 113.9630280  428.61666667
## 179 2017-01-23 15:00:00  62.4591980  211.20833333
## 180 2017-01-23 18:00:00  17.3670349   52.28152000
## 181 2017-01-23 21:00:00  33.5013428    0.00325000
## 182 2017-01-24 00:00:00  24.3236542  -14.37850200
## 183 2017-01-24 03:00:00  24.9675446  -59.32500000
## 184 2017-01-24 06:00:00  52.5287781   46.18718333
## 185 2017-01-24 09:00:00 132.7083435  264.58666667
## 186 2017-01-24 12:00:00 127.4932709  382.53666667
## 187 2017-01-24 15:00:00  57.0056458   17.98580000
## 188 2017-01-24 18:00:00  57.1818390   -9.15093333
## 189 2017-01-24 21:00:00  51.8772583    4.42492500
## 190 2017-01-25 00:00:00  16.0643311    1.63932500
## 191 2017-01-25 03:00:00  18.9747925           NaN
## 192 2017-01-25 06:00:00 130.9260712   88.57375000
## 193 2017-01-25 09:00:00 247.7477722  194.26000000
## 194 2017-01-25 12:00:00 304.3027649  267.69600000
## 195 2017-01-25 15:00:00 121.0688629  281.38150000
## 196 2017-01-25 18:00:00  52.6657410   73.76900000
## 197 2017-01-25 21:00:00  28.2142334   -2.34840000
## 198 2017-01-26 00:00:00   8.1370697   10.33023250
## 199 2017-01-26 03:00:00   3.1428070    2.04067820
## 200 2017-01-26 06:00:00 131.1781158   71.50875000
## 201 2017-01-26 09:00:00 306.7114868  161.96833333
## 202 2017-01-26 12:00:00 459.6167297  273.58333333
## 203 2017-01-26 15:00:00 263.4349365  214.37480000
## 204 2017-01-26 18:00:00  81.2991486   64.90033333
## 205 2017-01-26 21:00:00  41.9075928  -13.06428667
## 206 2017-01-27 00:00:00  30.3525085    2.11296000
## 207 2017-01-27 03:00:00  29.7601166           NaN
## 208 2017-01-27 06:00:00  86.5363617   68.13790000
## 209 2017-01-27 09:00:00 225.1594238  236.20833333
## 210 2017-01-27 12:00:00 240.1275482  309.09000000
## 211 2017-01-27 15:00:00 158.0039062  275.09833333
## 212 2017-01-27 18:00:00  73.1315918   81.34823333
## 213 2017-01-27 21:00:00  37.9156799    9.53345000
## 214 2017-01-28 00:00:00  17.0321198           NaN
## 215 2017-01-28 03:00:00   0.9420166    9.90680000
## 216 2017-01-28 06:00:00  65.8371429   47.06412000
## 217 2017-01-28 09:00:00 187.9249420  273.68666667
## 218 2017-01-28 12:00:00 214.7299347  453.88333333
## 219 2017-01-28 15:00:00 139.4968872  378.35833333
## 220 2017-01-28 18:00:00  62.0727539   65.77150000
## 221 2017-01-28 21:00:00  41.7218475    7.86260000
## 222 2017-01-29 00:00:00  26.7289734    0.58574050
## 223 2017-01-29 03:00:00  13.6646729   -9.17770000
## 224 2017-01-29 06:00:00  53.1247253   91.40975000
## 225 2017-01-29 09:00:00 161.3610535  261.85500000
## 226 2017-01-29 12:00:00 187.5287781  483.09800000
## 227 2017-01-29 15:00:00 118.7520752  309.96666667
## 228 2017-01-29 18:00:00  60.8440704   85.96233333
## 229 2017-01-29 21:00:00  41.4314117    9.98261667
## 230 2017-01-30 00:00:00  26.9665680    8.85790000
## 231 2017-01-30 03:00:00  15.3393250   -4.98636667
## 232 2017-01-30 06:00:00  59.1173096   17.11194000
## 233 2017-01-30 09:00:00 155.5290222  188.26333333
## 234 2017-01-30 12:00:00 172.8017883  287.17000000
## 235 2017-01-30 15:00:00 113.0414581  258.35750000
## 236 2017-01-30 18:00:00  57.4247894   64.52180000
## 237 2017-01-30 21:00:00  40.2044373   -3.31293400
## 238 2017-01-31 00:00:00  29.1970825   -9.97275000
## 239 2017-01-31 03:00:00  19.6564484   -0.12352800
## 240 2017-01-31 06:00:00  64.4403229   46.98005088
## 241 2017-01-31 09:00:00 160.1302795  254.19166667
## 242 2017-01-31 12:00:00 155.2445221  346.75333333
## 243 2017-01-31 15:00:00  75.3048401  165.47050000
## 244 2017-01-31 18:00:00  44.5668182   31.02202500
## 245 2017-01-31 21:00:00  41.8916168    3.90764000
## 246 2017-02-01 00:00:00  30.5855713    2.95126667
## 247 2017-02-01 03:00:00  29.9526520    2.65344000
## 248 2017-02-01 06:00:00  67.5719604   67.03240000

cores.basic <- c("blue", "red")
RMSE  <- hydroGOF::rmse(df$bam, df[, var.name.obs], na.rm=T) 
# plot time series   
df %>% gather(vars, value, -date) %>% 
    mutate(vars = factor(vars, levels = c(var.name.obs, "bam"))) %>%  #legenda na ordem correta!
    ggplot(aes(x = date, y = value, colour = vars)) +
    geom_line(aes(linetype=vars), size = 0.8) + theme_bw() + 
    scale_y_continuous(name = expression(W~m^{-2}))  +
    scale_x_datetime(name = element_blank(),
                     date_breaks = "day", date_labels = "%d\n%b", expand = c(0,0)) + #"%b\n%Y"
    scale_color_manual(values = cores.basic) +
    scale_linetype_manual(values=c("solid","longdash"))  + # Change linetypes
    theme(legend.title = element_blank(), legend.position = c(0.2, 0.85),
          legend.key=element_blank() , legend.direction = "horizontal",
          axis.text=element_text(size=12, face="bold"),
          axis.title=element_text(size=14,face="bold"),
          legend.text=element_text(size=14)) +
      annotate(geom = "text",x =as.POSIXct("2017-01-30"), y = 300, 
             label = paste0("RMSE=", round(RMSE, 2)), size = 5,col = "black" )

# plot.ts2v(dados = df, 
#           obs = "LE_orig", 
#           calib = "bam",
#           avg.time = "3 hour",
#           date.pos.stats = "2017-01-30")

3.2 Sensible heat flux ( H_orig x shtfl )

var.name.bam = "shtfl"
var.name.obs = "H_orig"

nc.file <- nc_open(filename = nc.path, verbose = F)
gvar <- ncvar_get(nc.file, var.name.bam) #transposta array
nc_close(nc.file)
df <- data.frame(date=time_3h, bam = gvar, torre = sm.torre[4:251, var.name.obs])
df

##                    date         bam      H_orig
## 1   2017-01-01 09:00:00 229.7846832  88.7978333
## 2   2017-01-01 12:00:00 254.1694336  98.7813333
## 3   2017-01-01 15:00:00 129.5083008  36.6571667
## 4   2017-01-01 18:00:00   2.7270508  -6.2959600
## 5   2017-01-01 21:00:00  -9.6328125 -14.9405400
## 6   2017-01-02 00:00:00  -8.6017609 -22.1188400
## 7   2017-01-02 03:00:00  -5.4406738 -29.3520333
## 8   2017-01-02 06:00:00  67.5069733  22.8266200
## 9   2017-01-02 09:00:00 277.0347290 117.5288333
## 10  2017-01-02 12:00:00 355.1858521  91.0358333
## 11  2017-01-02 15:00:00 218.5041046  58.0815000
## 12  2017-01-02 18:00:00   9.8925323 -15.3312000
## 13  2017-01-02 21:00:00  -8.2100983 -21.0065000
## 14  2017-01-03 00:00:00  -6.3421478 -22.3645000
## 15  2017-01-03 03:00:00  -4.3761902 -10.9734667
## 16  2017-01-03 06:00:00 104.3052216  29.7553667
## 17  2017-01-03 09:00:00 287.9510803 121.2195000
## 18  2017-01-03 12:00:00 341.3803711  99.4371667
## 19  2017-01-03 15:00:00 196.9227448  24.8322833
## 20  2017-01-03 18:00:00   0.1352692 -19.0703250
## 21  2017-01-03 21:00:00 -13.5226746 -17.6227500
## 22  2017-01-04 00:00:00  -9.6296082 -28.0193333
## 23  2017-01-04 03:00:00  -3.6340332 -31.5431000
## 24  2017-01-04 06:00:00  87.2514648  -4.1210167
## 25  2017-01-04 09:00:00 269.4631348  97.0883333
## 26  2017-01-04 12:00:00 335.0686035  -9.9265140
## 27  2017-01-04 15:00:00 195.9011230  23.5758667
## 28  2017-01-04 18:00:00  -6.0161133  17.4995600
## 29  2017-01-04 21:00:00 -17.8012695 -12.5703800
## 30  2017-01-05 00:00:00 -12.2050781 -49.7364667
## 31  2017-01-05 03:00:00  -8.6596680 -14.3103800
## 32  2017-01-05 06:00:00  82.1367188  19.0034600
## 33  2017-01-05 09:00:00 222.1665039  41.4621333
## 34  2017-01-05 12:00:00 315.7830811 -21.0832000
## 35  2017-01-05 15:00:00 191.2145996  -1.2064775
## 36  2017-01-05 18:00:00  -5.5847168  -6.6938000
## 37  2017-01-05 21:00:00 -20.5435791  -6.2302250
## 38  2017-01-06 00:00:00 -16.3532715  -9.5469667
## 39  2017-01-06 03:00:00  -7.9887543 -32.0593333
## 40  2017-01-06 06:00:00  85.7121124   1.7319600
## 41  2017-01-06 09:00:00 247.0288544  86.3738333
## 42  2017-01-06 12:00:00 310.7271423 138.7853333
## 43  2017-01-06 15:00:00 181.9912109  59.4320000
## 44  2017-01-06 18:00:00 -10.7397919 -13.3990000
## 45  2017-01-06 21:00:00 -28.3864594 -37.7885000
## 46  2017-01-07 00:00:00 -20.7478943 -19.8235000
## 47  2017-01-07 03:00:00 -10.4223785 -12.3633167
## 48  2017-01-07 06:00:00  91.2412872  20.6138500
## 49  2017-01-07 09:00:00 248.7508240  68.7351667
## 50  2017-01-07 12:00:00 310.2415161  71.2818333
## 51  2017-01-07 15:00:00 178.9218445  66.5211667
## 52  2017-01-07 18:00:00 -13.1195831   6.6873460
## 53  2017-01-07 21:00:00 -26.3465424 -25.7791833
## 54  2017-01-08 00:00:00 -21.7225342 -17.4034333
## 55  2017-01-08 03:00:00 -16.3312225  -1.8154000
## 56  2017-01-08 06:00:00 102.3172455   7.0732000
## 57  2017-01-08 09:00:00 257.5894775  43.9413333
## 58  2017-01-08 12:00:00 284.7452087  32.4685000
## 59  2017-01-08 15:00:00 171.1399231  47.6405000
## 60  2017-01-08 18:00:00  -7.8327789  -2.1006333
## 61  2017-01-08 21:00:00 -20.7008820  10.2712667
## 62  2017-01-09 00:00:00 -17.6315002 -11.2903667
## 63  2017-01-09 03:00:00 -16.8125000 -60.7352000
## 64  2017-01-09 06:00:00  93.5149536 -13.4761750
## 65  2017-01-09 09:00:00 218.8362732   7.3713160
## 66  2017-01-09 12:00:00 247.2500458 -18.0723800
## 67  2017-01-09 15:00:00 158.3486328  65.4648333
## 68  2017-01-09 18:00:00  -4.1814728  13.4883333
## 69  2017-01-09 21:00:00 -19.4866486 -27.5621833
## 70  2017-01-10 00:00:00 -16.3811340 -41.4052000
## 71  2017-01-10 03:00:00 -14.5082703 -10.0853750
## 72  2017-01-10 06:00:00  80.2249603 -14.3112120
## 73  2017-01-10 09:00:00 186.9731445  53.9734000
## 74  2017-01-10 12:00:00 250.5417480  68.0271667
## 75  2017-01-10 15:00:00 159.4123535  62.7463333
## 76  2017-01-10 18:00:00 -12.9327393   6.9668800
## 77  2017-01-10 21:00:00 -23.9676056 -10.1663500
## 78  2017-01-11 00:00:00 -22.0319061   2.0337750
## 79  2017-01-11 03:00:00 -24.8438721  -5.9594400
## 80  2017-01-11 06:00:00  84.2059479  16.0172500
## 81  2017-01-11 09:00:00 224.2965698 107.3725000
## 82  2017-01-11 12:00:00 260.7567749 168.5500000
## 83  2017-01-11 15:00:00 151.5749817 117.0008000
## 84  2017-01-11 18:00:00  -6.0394745   6.3356000
## 85  2017-01-11 21:00:00  -7.1403503 -40.7594167
## 86  2017-01-12 00:00:00 -18.3567657 -27.4505500
## 87  2017-01-12 03:00:00 -20.0596619 -18.3095000
## 88  2017-01-12 06:00:00  14.7952118  11.9779333
## 89  2017-01-12 09:00:00 114.4439392 168.7483333
## 90  2017-01-12 12:00:00  87.0587616 180.2766667
## 91  2017-01-12 15:00:00  24.0922699 127.6956667
## 92  2017-01-12 18:00:00  -2.1772614  -6.3353333
## 93  2017-01-12 21:00:00   1.4279633 -32.4688000
## 94  2017-01-13 00:00:00  -1.7067719 -18.5326667
## 95  2017-01-13 03:00:00  -3.3852692 -10.9074750
## 96  2017-01-13 06:00:00  10.6231232  30.5114333
## 97  2017-01-13 09:00:00  41.5465393 112.2780000
## 98  2017-01-13 12:00:00 190.7666931 106.1508333
## 99  2017-01-13 15:00:00  60.7743683  29.5122267
## 100 2017-01-13 18:00:00  -2.9436951 -12.0801400
## 101 2017-01-13 21:00:00  -2.7046051  -1.2872250
## 102 2017-01-14 00:00:00  -0.7140198  -3.0021000
## 103 2017-01-14 03:00:00  -0.1141815  -0.8809667
## 104 2017-01-14 06:00:00  58.6122894  20.5072600
## 105 2017-01-14 09:00:00 178.6743774  59.5096667
## 106 2017-01-14 12:00:00 260.5108337  21.2183000
## 107 2017-01-14 15:00:00 170.1315155  -6.1605333
## 108 2017-01-14 18:00:00  12.9259796  -9.2799600
## 109 2017-01-14 21:00:00  -4.7075958 -15.3049150
## 110 2017-01-15 00:00:00  -0.4315796  -9.2793333
## 111 2017-01-15 03:00:00   5.6847534  -8.4889617
## 112 2017-01-15 06:00:00  90.9924927  16.5123800
## 113 2017-01-15 09:00:00 185.5849304 147.6450000
## 114 2017-01-15 12:00:00 225.1601257 148.7650000
## 115 2017-01-15 15:00:00 121.4150085  52.4603333
## 116 2017-01-15 18:00:00  -3.4695129 -17.4978833
## 117 2017-01-15 21:00:00  -5.7335510 -29.8381667
## 118 2017-01-16 00:00:00  -3.6443176 -23.8252667
## 119 2017-01-16 03:00:00  -2.4931488 -21.2688600
## 120 2017-01-16 06:00:00  85.0923462  -4.2444167
## 121 2017-01-16 09:00:00 190.5852814  -6.4686750
## 122 2017-01-16 12:00:00 156.3361816   6.2629800
## 123 2017-01-16 15:00:00  97.0346680  31.4363333
## 124 2017-01-16 18:00:00   4.1055603   2.8100667
## 125 2017-01-16 21:00:00   3.1929932 -23.1854000
## 126 2017-01-17 00:00:00   2.1142578  -5.5955200
## 127 2017-01-17 03:00:00   1.4808350 -19.0802333
## 128 2017-01-17 06:00:00  93.6001892   6.3510000
## 129 2017-01-17 09:00:00 262.4610596  80.3488333
## 130 2017-01-17 12:00:00 315.8709106 112.8758333
## 131 2017-01-17 15:00:00 186.3731689  71.3523500
## 132 2017-01-17 18:00:00   2.8467255  -9.5358600
## 133 2017-01-17 21:00:00  -6.5149078  -1.2724540
## 134 2017-01-18 00:00:00  -3.3294678 -18.8169600
## 135 2017-01-18 03:00:00  -2.0212402 -13.9322400
## 136 2017-01-18 06:00:00 123.2717285  24.7650000
## 137 2017-01-18 09:00:00 297.2827454  95.5100000
## 138 2017-01-18 12:00:00 337.4480591 138.1733333
## 139 2017-01-18 15:00:00 188.7636719  92.9981667
## 140 2017-01-18 18:00:00  -0.9558105  -4.8510000
## 141 2017-01-18 21:00:00  -9.0765533 -22.5350000
## 142 2017-01-19 00:00:00 -11.1576538 -32.3625000
## 143 2017-01-19 03:00:00  -4.3723145 -29.1120000
## 144 2017-01-19 06:00:00 112.0641022  13.3185833
## 145 2017-01-19 09:00:00 280.6804199 161.4150000
## 146 2017-01-19 12:00:00 305.2058105 220.1983333
## 147 2017-01-19 15:00:00 160.1359863 121.3198333
## 148 2017-01-19 18:00:00 -14.6547852 -22.5541333
## 149 2017-01-19 21:00:00 -21.6320801 -58.5024000
## 150 2017-01-20 00:00:00 -18.3532715 -42.5840000
## 151 2017-01-20 03:00:00 -12.7029877 -23.0816667
## 152 2017-01-20 06:00:00  87.9159241  22.3382000
## 153 2017-01-20 09:00:00 219.1260834 165.0858333
## 154 2017-01-20 12:00:00 254.9318848 208.9033333
## 155 2017-01-20 15:00:00 137.9193420 126.4685000
## 156 2017-01-20 18:00:00 -20.2192383 -13.2567667
## 157 2017-01-20 21:00:00 -28.6074219 -52.2223333
## 158 2017-01-21 00:00:00 -22.2683105 -38.6266667
## 159 2017-01-21 03:00:00 -14.5720215 -17.8676667
## 160 2017-01-21 06:00:00  90.0351257  13.2532167
## 161 2017-01-21 09:00:00 227.6960754 169.3750000
## 162 2017-01-21 12:00:00 210.9308929 190.5850000
## 163 2017-01-21 15:00:00  89.9627838 117.7700000
## 164 2017-01-21 18:00:00 -19.5146790 -17.0278000
## 165 2017-01-21 21:00:00 -30.5303040 -51.2025000
## 166 2017-01-22 00:00:00 -20.6051941 -31.6590000
## 167 2017-01-22 03:00:00 -12.5573883  -7.5309000
## 168 2017-01-22 06:00:00  94.3617859  10.3884167
## 169 2017-01-22 09:00:00 237.4994202 154.1298333
## 170 2017-01-22 12:00:00 217.9649811 198.4733333
## 171 2017-01-22 15:00:00 135.0906677 108.1893333
## 172 2017-01-22 18:00:00  -8.3118134  -1.7739833
## 173 2017-01-22 21:00:00 -19.5141907 -41.1452833
## 174 2017-01-23 00:00:00 -14.2840118 -27.3169167
## 175 2017-01-23 03:00:00  -6.7483368  -8.1280333
## 176 2017-01-23 06:00:00  24.2196198  21.2046783
## 177 2017-01-23 09:00:00 237.1032410 140.2216667
## 178 2017-01-23 12:00:00 235.1357880 164.5015000
## 179 2017-01-23 15:00:00 101.5421295  39.9273333
## 180 2017-01-23 18:00:00  -1.6226349 -12.6722000
## 181 2017-01-23 21:00:00  -9.0006256 -31.5690000
## 182 2017-01-24 00:00:00 -16.3181000 -19.0982000
## 183 2017-01-24 03:00:00 -17.6876526  -8.3077400
## 184 2017-01-24 06:00:00  75.7718506  15.3968583
## 185 2017-01-24 09:00:00 257.2982178 126.6131667
## 186 2017-01-24 12:00:00 269.9943237  93.8955000
## 187 2017-01-24 15:00:00  46.3518372 -20.6317400
## 188 2017-01-24 18:00:00  -9.8899994  -7.9959000
## 189 2017-01-24 21:00:00  -9.8710938  -4.9289780
## 190 2017-01-25 00:00:00  -3.9138184  -8.6760800
## 191 2017-01-25 03:00:00  -4.3454590  -8.5837600
## 192 2017-01-25 06:00:00  50.3669586   0.6168000
## 193 2017-01-25 09:00:00 169.0460968  79.2655000
## 194 2017-01-25 12:00:00  56.3708496  22.1083333
## 195 2017-01-25 15:00:00  13.0478516  72.6492000
## 196 2017-01-25 18:00:00  -5.5075684  -6.2167500
## 197 2017-01-25 21:00:00  -1.0620117  -6.2107600
## 198 2017-01-26 00:00:00  -0.7343750  -2.5566600
## 199 2017-01-26 03:00:00  -3.3735352  -4.7701833
## 200 2017-01-26 06:00:00  24.0956726  16.9193750
## 201 2017-01-26 09:00:00  95.0229797  74.8778333
## 202 2017-01-26 12:00:00  83.9203033 141.1775000
## 203 2017-01-26 15:00:00  26.1781158  75.6532000
## 204 2017-01-26 18:00:00   4.6626740 -10.0295167
## 205 2017-01-26 21:00:00  11.7477875  -6.5416400
## 206 2017-01-27 00:00:00   3.1163025  -8.2598833
## 207 2017-01-27 03:00:00  10.3388214  -9.0303560
## 208 2017-01-27 06:00:00 113.3848114  15.4734942
## 209 2017-01-27 09:00:00 259.9184875 142.4566667
## 210 2017-01-27 12:00:00 259.8864746 168.2948333
## 211 2017-01-27 15:00:00 122.6867676 112.9218333
## 212 2017-01-27 18:00:00 -19.8710022  -2.3452167
## 213 2017-01-27 21:00:00 -20.4724121  -9.6746600
## 214 2017-01-28 00:00:00  -9.9420471  -3.0463650
## 215 2017-01-28 03:00:00  -5.0407867  -2.5679080
## 216 2017-01-28 06:00:00  97.7946014  22.9680483
## 217 2017-01-28 09:00:00 254.0809021 178.5850000
## 218 2017-01-28 12:00:00 270.5953979 183.7983333
## 219 2017-01-28 15:00:00 143.8586731 125.2718333
## 220 2017-01-28 18:00:00 -17.8778229   9.1858333
## 221 2017-01-28 21:00:00 -24.3664703 -25.1153260
## 222 2017-01-29 00:00:00 -15.4076080 -28.0396667
## 223 2017-01-29 03:00:00 -11.4948273 -12.6291000
## 224 2017-01-29 06:00:00  82.2763062  13.4006400
## 225 2017-01-29 09:00:00 230.9681854 152.5166667
## 226 2017-01-29 12:00:00 260.4782104 185.6933333
## 227 2017-01-29 15:00:00 137.1767273  67.3520000
## 228 2017-01-29 18:00:00 -16.3400879 -17.0188450
## 229 2017-01-29 21:00:00 -21.7989960 -23.7686667
## 230 2017-01-30 00:00:00 -16.3361053 -22.4596000
## 231 2017-01-30 03:00:00 -15.9437408 -14.3034667
## 232 2017-01-30 06:00:00  89.8936005  -2.1610000
## 233 2017-01-30 09:00:00 226.6159058  72.8858333
## 234 2017-01-30 12:00:00 248.5519714  87.7652500
## 235 2017-01-30 15:00:00 130.9089813 126.1850000
## 236 2017-01-30 18:00:00 -17.4060516 -16.1229500
## 237 2017-01-30 21:00:00 -16.8477783 -11.0456200
## 238 2017-01-31 00:00:00 -18.1329956  -7.3340500
## 239 2017-01-31 03:00:00 -17.7183533  -4.4956000
## 240 2017-01-31 06:00:00  86.0240021  -2.6641833
## 241 2017-01-31 09:00:00 239.5079803  88.8353333
## 242 2017-01-31 12:00:00 239.1917419  93.7423333
## 243 2017-01-31 15:00:00  85.7252808  42.7857500
## 244 2017-01-31 18:00:00  -9.5101929 -17.6791400
## 245 2017-01-31 21:00:00 -21.7984924 -12.5142750
## 246 2017-02-01 00:00:00 -16.8659515  -0.5957667
## 247 2017-02-01 03:00:00 -13.9810181 -14.7073600
## 248 2017-02-01 06:00:00  67.2657166 -11.8447667

RMSE  <- hydroGOF::rmse(df$bam, df[, var.name.obs], na.rm=T) 
# plot time series   
df %>% gather(vars, value, -date) %>% 
    mutate(vars = factor(vars, levels = c(var.name.obs, "bam"))) %>%  #legenda na ordem correta!
    ggplot(aes(x = date, y = value, colour = vars)) +
    geom_line(aes(linetype=vars), size = 0.8) + theme_bw() + 
    scale_y_continuous(name = expression(W~m^{-2}))  +
    scale_x_datetime(name = element_blank(),
                     date_breaks = "day", date_labels = "%d\n%b", expand = c(0,0)) + #"%b\n%Y"
    scale_color_manual(values = cores.basic) +
    scale_linetype_manual(values=c("solid","longdash"))  + # Change linetypes
    theme(legend.title = element_blank(), legend.position = c(0.2, 0.85),
          legend.key=element_blank() , legend.direction = "horizontal",
          axis.text=element_text(size=12, face="bold"),
          axis.title=element_text(size=14,face="bold"),
          legend.text=element_text(size=14)) +
      annotate(geom = "text",x =as.POSIXct("2017-01-30"), y = 300, 
             label = paste0("RMSE=", round(RMSE, 2)), size = 5,col = "black" )

!!!!!!! T H E E N D!!!!!!!!!!!

Diagnóstico saídas BAM x OBS - Preliminares

Goergen, G.

22 - setembro - 2022

1 =========> PRELIMINARES

2 =========> Identificando as variáveis do BAM x OBS

3 =========> Comparações iniciais

3.1 Latent heat flux ( LE_orig x lhtfl )

3.2 Sensible heat flux ( H_orig x shtfl )