El presente reporte hace uso de la librería OpenAir (Carslaw and Ropkins 2012).
A continuación se importan los datos observados y modelados estructurados en una sola hoja de cálculo de Excel. Cabe indicar que previamente se realizó el cómputo de los promedios horarios de las variables observadas.
## Rows: 35,064
## Columns: 10
## $ anio <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 201…
## $ mes <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ dia <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ hora <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ T_obs <dbl> 26.3, 25.6, 25.2, 25.3, 25.3, 25.0, 25.1, 25.0, 25.5, 26.2, 27.…
## $ wd_obs <dbl> 220, 230, 230, 260, 230, 230, 240, 240, 270, 240, 230, 220, 210…
## $ ws_obs <dbl> 5.14, 5.14, 4.12, 3.09, 2.57, 3.09, 3.09, 3.09, 3.09, 2.06, 2.5…
## $ T_mod <dbl> 23.92, 23.76, 23.46, 23.52, 23.12, 22.54, 22.82, 23.05, 23.44, …
## $ wd_mod <int> 199, 193, 196, 183, 314, 252, 286, 105, 28, 45, 18, 27, 30, 33,…
## $ ws_mod <dbl> 3.42, 2.67, 2.15, 1.01, 0.60, 0.26, 0.51, 0.19, 0.82, 1.28, 1.8…## [1] 7.72 7.72 7.72 7.72 7.72 9.26 7.72 7.72 7.72 8.23 7.72 7.72
## [13] 13.38 8.75 8.23 8.23 7.72 8.75 8.75 8.23 7.72 8.23 7.72 7.72
## [25] 8.23 7.72 8.23 7.72 7.72 7.72 7.72 7.72 8.23 8.23 9.26 8.23
## [37] 7.72 7.72 7.72 7.72 7.72 7.72 8.23 8.23 7.72 7.72 7.72 7.72
## [49] 7.72 7.72 7.72 8.75 8.23 7.72 8.23 7.72 8.23 8.23 7.72 7.72
## [61] 7.72 7.72 7.72 7.72 7.72 7.72 7.72 7.72 8.23 7.72 7.72 7.72
## [73] 7.72 7.72 8.75 8.75 8.23 8.23 7.72 7.72 8.23 9.26 8.23 7.72
## [85] 7.72 7.72 7.72 7.72 7.72 7.72 7.72 8.75 8.23 7.72 7.72 7.72
## [97] 7.72 7.72 7.72 7.72 7.72 8.75 7.72 7.72 7.72 8.23 7.72 9.26
## [109] 7.72 7.72 8.23 7.72 8.23 8.23 7.72 7.72 7.72 7.72 7.72 8.23
## [121] 7.72 8.23 8.23 8.75 7.72 8.23 8.75 7.72 7.72 8.23 7.72 7.72
## [133] 7.72 7.72 8.23 7.72 7.72 7.72 8.23 8.23 7.72 7.72 7.72 7.72
## [145] 7.72 7.72 8.23 7.72 7.72 7.72 7.72 7.72 8.75 7.72 7.72 8.23
## [157] 7.72 7.72 7.72 8.23 7.72 7.72 7.72 7.72 7.72 8.23 8.23 8.23
## [169] 7.72 8.75 7.72 8.23 7.72 8.23 8.23 7.72 7.72 7.72 8.23 7.72
## [181] 7.72 8.23 7.72 7.72 7.72 7.72 8.23 8.75 8.23 7.72 8.23 7.72
## [193] 8.23 7.72 7.72 8.23 8.23 7.72 8.23 8.23 8.23 7.72 7.72 7.72
## [205] 7.72 7.72 8.23 8.75 7.72 8.23 7.72 7.72 8.23 8.23 7.72 7.72
## [217] 8.23 9.26 8.23 8.23 7.72 7.72 7.72 7.72 7.72 8.23 7.72 8.23
## [229] 8.75 7.72 9.26 7.72 7.72 7.72 7.72 7.72 7.72 8.75 7.72 7.72
## [241] 7.72 7.72 7.72 7.72 7.72 8.23 8.75 7.72 7.72 7.72 8.23 8.23
## [253] 7.72 7.72 7.72 7.72 7.72 7.72 7.72 8.75 7.72 7.72 7.72 8.23
## [265] 7.72 7.72 7.72 7.72 8.23 7.72 7.72 7.72 7.72 7.72 7.72 8.23
## [277] 7.72 7.72 7.72 7.72 7.72 7.72 7.72 8.23 7.72 7.72 8.23 8.23
## [289] 7.72 7.72 7.72 8.23 7.72 8.23 7.72 9.77 9.26 7.72 7.72 7.72
## [301] 7.72 7.72 8.75 8.23 7.72 7.72 7.72 7.72 8.75 8.23 8.23 9.26
## [313] 8.23 8.23 7.72 7.72 9.26 8.23 7.72 7.72 8.23 7.72 7.72 7.72
## [325] 7.72 7.72 7.72 8.23 7.72 8.23 7.72 8.23Cálculos considerando media.
Se calcula la rosa de los vientos por año.
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 35064 1 -0.0889 1.36 -0.00339 0.0519 1.73 0.832 0 0.424
## # ℹ 1 more variable: IOA <dbl>| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -0.0888587 | 1.360891 | 1.732451 | 0.8322945 | 0.4238666 |
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 8760 1 -0.378 1.35 -0.0141 0.0504 1.71 0.824 0 0.385 0.692
## 2 2016 8784 1 -0.253 1.28 -0.00953 0.0484 1.62 0.860 0 0.469 0.735
## 3 2017 8760 1 -0.0615 1.37 -0.00237 0.0528 1.74 0.823 0 0.402 0.701
## 4 2018 8760 1 0.337 1.44 0.0132 0.0563 1.86 0.828 0 0.408 0.704| year | MB | MGE | RMSE | r | COE |
|---|---|---|---|---|---|
| 2015 | -0.3780879 | 1.354860 | 1.705235 | 0.8242616 | 0.3848179 |
| 2016 | -0.2525512 | 1.281502 | 1.617104 | 0.8596207 | 0.4691749 |
| 2017 | -0.0614589 | 1.370888 | 1.741495 | 0.8232933 | 0.4023972 |
| 2018 | 0.3371119 | 1.436532 | 1.857674 | 0.8276776 | 0.4081535 |
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 11544 1 -0.969 1.52 -0.0355 0.0556 1.85 0.802 0 0.252 0.626| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -0.9691329 | 1.517128 | 1.853946 | 0.802301 | 0.25206 |
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 23520 1 0.343 1.28 0.0134 0.0500 1.67 0.868 0 0.475 0.737| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| 0.3431943 | 1.284207 | 1.669589 | 0.8677381 | 0.4749781 |
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 2880 1 -1.16 1.61 -0.0425 0.0587 1.93 0.816 0 0.216 0.608
## 2 2016 2904 1 -0.778 1.43 -0.0283 0.0518 1.75 0.783 0 0.229 0.614
## 3 2017 2880 1 -1.08 1.58 -0.0396 0.0579 1.93 0.772 0 0.183 0.591
## 4 2018 2880 1 -0.857 1.46 -0.0318 0.0541 1.79 0.830 0 0.359 0.679| year | MB | MGE | RMSE | r | COE |
|---|---|---|---|---|---|
| 2015 | -1.1637569 | 1.608819 | 1.932059 | 0.8164163 | 0.2160505 |
| 2016 | -0.7783953 | 1.425517 | 1.751158 | 0.7833214 | 0.2285100 |
| 2017 | -1.0792257 | 1.578566 | 1.934927 | 0.7721052 | 0.1829421 |
| 2018 | -0.8567431 | 1.456375 | 1.791144 | 0.8303856 | 0.3589090 |
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 5880 1 0.00673 1.23 0.000252 0.0462 1.58 0.853 0 0.455 0.728
## 2 2016 5880 1 0.00715 1.21 0.000275 0.0466 1.55 0.885 0 0.540 0.770
## 3 2017 5880 1 0.437 1.27 0.0172 0.0501 1.64 0.876 0 0.459 0.729
## 4 2018 5880 1 0.922 1.43 0.0371 0.0574 1.89 0.880 0 0.393 0.697| year | MB | MGE | RMSE | r | COE |
|---|---|---|---|---|---|
| 2015 | 0.0067296 | 1.230471 | 1.582319 | 0.8534161 | 0.4551721 |
| 2016 | 0.0071514 | 1.210376 | 1.546617 | 0.8852380 | 0.5396893 |
| 2017 | 0.4370391 | 1.269168 | 1.638441 | 0.8759938 | 0.4588580 |
| 2018 | 0.9218571 | 1.426813 | 1.889406 | 0.8803029 | 0.3933426 |
## # A tibble: 12 × 12
## month n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Janu… 2976 1 -0.811 1.43 -2.99e-2 0.0528 1.77 0.821 0 0.330 0.665
## 2 Febr… 2712 1 -0.856 1.51 -3.17e-2 0.0561 1.87 0.743 0 0.197 0.599
## 3 March 2976 1 -1.04 1.55 -3.79e-2 0.0566 1.88 0.797 0 0.193 0.596
## 4 April 2880 1 -1.17 1.57 -4.24e-2 0.0571 1.90 0.837 0 0.257 0.628
## 5 May 2976 1 -0.679 1.29 -2.50e-2 0.0474 1.58 0.861 0 0.400 0.700
## 6 June 2880 1 -0.0207 1.19 -8.01e-4 0.0462 1.54 0.858 0 0.457 0.728
## 7 July 2976 1 0.486 1.30 1.94e-2 0.0520 1.72 0.859 0 0.421 0.711
## 8 Augu… 2976 1 0.719 1.30 2.91e-2 0.0526 1.70 0.905 0 0.470 0.735
## 9 Sept… 2880 1 0.630 1.24 2.49e-2 0.0491 1.63 0.911 0 0.508 0.754
## 10 Octo… 2976 1 0.995 1.38 3.98e-2 0.0550 1.87 0.900 0 0.427 0.714
## 11 Nove… 2880 1 0.851 1.35 3.34e-2 0.0529 1.74 0.902 0 0.461 0.730
## 12 Dece… 2976 1 -0.221 1.22 -8.17e-3 0.0452 1.55 0.853 0 0.491 0.745| month | MB | MGE | RMSE | r | COE |
|---|---|---|---|---|---|
| January | -0.8109476 | 1.432997 | 1.770819 | 0.8206053 | 0.3300924 |
| February | -0.8560841 | 1.514897 | 1.872815 | 0.7433966 | 0.1972114 |
| March | -1.0370195 | 1.547819 | 1.875403 | 0.7971277 | 0.1926499 |
| April | -1.1688958 | 1.574451 | 1.897252 | 0.8371236 | 0.2568749 |
| May | -0.6793112 | 1.287577 | 1.584686 | 0.8611239 | 0.4001597 |
| June | -0.0207187 | 1.194816 | 1.536574 | 0.8577915 | 0.4566285 |
| July | 0.4856082 | 1.299351 | 1.715950 | 0.8587500 | 0.4213441 |
| August | 0.7188911 | 1.301304 | 1.695289 | 0.9053238 | 0.4698704 |
| September | 0.6298681 | 1.242826 | 1.629435 | 0.9111665 | 0.5080563 |
| October | 0.9954368 | 1.376371 | 1.869890 | 0.9003974 | 0.4272805 |
| November | 0.8506910 | 1.345892 | 1.741884 | 0.9018363 | 0.4609103 |
| December | -0.2210316 | 1.223290 | 1.553593 | 0.8532043 | 0.4905035 |
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 35064 0.614 -0.896 1.61 -0.265 0.478 1.99 0.401 0 -0.262 0.369| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -0.8955923 | 1.614137 | 1.989791 | 0.4008634 | -0.2616666 |
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 8760 0.613 -0.823 1.53 -0.260 0.484 1.91 0.374 1.47e-289 -0.195 0.403
## 2 2016 8784 0.607 -0.945 1.63 -0.281 0.484 2.01 0.393 1.98e-322 -0.286 0.357
## 3 2017 8760 0.629 -0.848 1.60 -0.250 0.471 1.97 0.422 0 -0.238 0.381
## 4 2018 8760 0.607 -0.966 1.70 -0.270 0.476 2.07 0.402 0 -0.378 0.311| year | MB | MGE | RMSE | r | COE |
|---|---|---|---|---|---|
| 2015 | -0.8228082 | 1.529808 | 1.908364 | 0.3743832 | -0.1947044 |
| 2016 | -0.9451082 | 1.625780 | 2.006626 | 0.3930714 | -0.2858153 |
| 2017 | -0.8483265 | 1.596747 | 1.973929 | 0.4222001 | -0.2377530 |
| 2018 | -0.9659909 | 1.704183 | 2.066906 | 0.4017199 | -0.3783189 |
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 11544 0.629 -0.466 1.28 -0.183 0.501 1.62 0.228 1.54e-135 -0.328
## # ℹ 1 more variable: IOA <dbl>| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -0.4664198 | 1.27874 | 1.617938 | 0.2276089 | -0.3276006 |
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 23520 0.607 -1.11 1.78 -0.293 0.471 2.15 0.410 0 -0.389 0.306| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -1.106237 | 1.778756 | 2.148889 | 0.410167 | -0.388928 |
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 2880 0.626 -0.124 1.21 -0.0557 0.544 1.55 0.213 8.93e-31 -0.263 0.369
## 2 2016 2904 0.632 -0.517 1.26 -0.201 0.490 1.59 0.195 2.80e-26 -0.396 0.302
## 3 2017 2880 0.638 -0.486 1.28 -0.188 0.496 1.63 0.247 2.62e-41 -0.339 0.331
## 4 2018 2880 0.620 -0.738 1.36 -0.261 0.481 1.70 0.269 7.84e-49 -0.368 0.316| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -0.4664198 | 1.27874 | 1.617938 | 0.2276089 | -0.3276006 |
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 5880 0.607 -1.16 1.68 -0.322 0.466 2.06 0.410 6.91e-237 -0.362 0.319
## 2 2016 5880 0.595 -1.16 1.81 -0.308 0.481 2.18 0.408 6.80e-235 -0.385 0.307
## 3 2017 5880 0.624 -1.03 1.75 -0.271 0.463 2.12 0.427 1.61e-259 -0.344 0.328
## 4 2018 5880 0.601 -1.08 1.87 -0.273 0.474 2.23 0.385 7.21e-207 -0.499 0.250| MB | MGE | RMSE | r | COE |
|---|---|---|---|---|
| -1.106237 | 1.778756 | 2.148889 | 0.410167 | -0.388928 |
## # A tibble: 12 × 12
## month n FAC2 MB MGE NMB NMGE RMSE r P COE
## <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 January 2976 0.644 -0.621 1.34 -0.218 0.473 1.69 0.290 1.10e- 58 -0.286
## 2 February 2712 0.631 -0.314 1.21 -0.132 0.509 1.54 0.226 8.29e- 33 -0.324
## 3 March 2976 0.612 -0.329 1.26 -0.140 0.538 1.61 0.141 8.88e- 15 -0.344
## 4 April 2880 0.629 -0.592 1.29 -0.225 0.491 1.62 0.219 1.23e- 32 -0.340
## 5 May 2976 0.608 -0.874 1.44 -0.299 0.492 1.80 0.249 2.06e- 43 -0.323
## 6 June 2880 0.578 -1.14 1.67 -0.335 0.490 2.03 0.287 1.36e- 55 -0.452
## 7 July 2976 0.579 -1.24 1.82 -0.329 0.485 2.19 0.313 1.63e- 68 -0.510
## 8 August 2976 0.539 -1.34 2.08 -0.325 0.504 2.43 0.325 2.89e- 74 -0.707
## 9 September 2880 0.590 -1.24 2.03 -0.294 0.483 2.37 0.401 9.10e-112 -0.542
## 10 October 2976 0.636 -1.03 1.87 -0.250 0.455 2.24 0.437 3.35e-139 -0.446
## 11 November 2880 0.650 -0.999 1.78 -0.244 0.435 2.14 0.480 5.50e-166 -0.398
## 12 December 2976 0.673 -0.992 1.53 -0.277 0.428 1.91 0.469 1.42e-162 -0.193
## # ℹ 1 more variable: IOA <dbl>| month | MB | MGE | RMSE | r | COE |
|---|---|---|---|---|---|
| January | -0.6211761 | 1.344919 | 1.691322 | 0.2898281 | -0.2857600 |
| February | -0.3137205 | 1.213824 | 1.539537 | 0.2262060 | -0.3244274 |
| March | -0.3292876 | 1.261989 | 1.613601 | 0.1414887 | -0.3436874 |
| April | -0.5920000 | 1.288792 | 1.616903 | 0.2190762 | -0.3399308 |
| May | -0.8737802 | 1.437913 | 1.797172 | 0.2493449 | -0.3234567 |
| June | -1.1435903 | 1.671736 | 2.034385 | 0.2866360 | -0.4516128 |
| July | -1.2357224 | 1.818088 | 2.194771 | 0.3127118 | -0.5096627 |
| August | -1.3433501 | 2.084029 | 2.434145 | 0.3252159 | -0.7072299 |
| September | -1.2375347 | 2.034965 | 2.372571 | 0.4010855 | -0.5420134 |
| October | -1.0263642 | 1.873528 | 2.238681 | 0.4372054 | -0.4456167 |
| November | -0.9991007 | 1.779698 | 2.138659 | 0.4801098 | -0.3982554 |
| December | -0.9924395 | 1.534933 | 1.905852 | 0.4688950 | -0.1926555 |
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 34869 0.717 1.52 71.4 0.00814 0.382 103. 0.274 0 -0.273 0.364Por años
## # A tibble: 4 × 12
## year n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 8678 0.688 -3.65 75.7 -0.0196 0.408 107. 0.274 5.97e-149 -0.278 0.361
## 2 2016 8741 0.707 4.32 72.5 0.0236 0.395 103. 0.303 1.61e-184 -0.266 0.367
## 3 2017 8716 0.722 2.59 71.5 0.0138 0.381 104. 0.265 3.39e-140 -0.261 0.369
## 4 2018 8734 0.752 2.80 65.8 0.0146 0.344 96.1 0.246 5.35e-121 -0.295 0.353Por épocas
## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all da… 11380 0.578 4.07 101. 0.0238 0.590 134. 0.181 5.56e-84 -0.245 0.378## # A tibble: 1 × 12
## default n FAC2 MB MGE NMB NMGE RMSE r P COE IOA
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 all data 23489 0.785 0.288 57.1 0.00148 0.293 83.7 0.357 0 -0.373 0.313Los estadísticos calculados son:
n, el número de pares de datos comparados.
FAC2, la fracción de las predicciones con un factor de 2.
MB, el sesgo medio.
MGE, el residual medio bruto.
NMB, el sesgo medio normalizado.
NMGE, el residual medio bruto normalizado.
RMSE, la raíz del error medio cuadrático.
r, el coeficiente de correlación de Pearson o de Spearman.
COE, el coficiente de eficiencia.
IOA, el índice de acuerdo.
Recordemos que automáticamente, las funciones de la librería Openair excluyen los registros con valores NA.
Por defecto presenta las unidades de hora y mes, con promedio.
Con variación en unidades temporales de ejes.