Introduction
In this document I will explore how to create the first part of the evaluation system I proposed. The working title of this is the “Forecast-Hour Evaluation.” The idea here is that we are looking at the performance of the model by looking at how it performed with different start times (using the most recent 00-hr forecast as input).
## Warning in rgl.init(initValue, onlyNULL): RGL: unable to open X11 display## Warning: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.Read Model and Observation Data
Read WRF Data
For this evaluation system we need to look at three different output folders. Here we use the folders named, forecast_day_minus_0, forecast_day_minus_1, forecast_day_minus_2. The contents of each of these folders will be similar: wrfout files for 86 forecast hours and time-series data for different locations of interest. Here we will first read the forecast data.
Read the OBS Data
Now we will read the observation data from the ASOS stations. The script that downloads the data is in ./obs_station_day_minus_0/dl_ny_asos.py. The lines for the dates to download need to be changed before running it. Once the files are download, the lines below reads the data and adds column names.
Unit Conversion
Model and observation data do not share the same units for the same variable. For temperature, WRF is in Kelvin and ASOS is in degreesF. For winds, WRF is in m/s and ASOS is in knots. The formulas used to convert the numbers to a common system is shown here. For temperature I will use Kelvin, and m/s for wind speeds.
Combined Data Frames
Now we have one data frame for all the observations, and three (3) data frames of the WRF data (one data frame per forecast init time). The lines below provide a visual of the data frames.
## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-07-25 00:00:05 2020 7 25 0 0 5.0004 297.8785 0.01777
## 2 2020-07-25 00:00:10 2020 7 25 0 0 10.0008 297.8979 0.01777
## 3 2020-07-25 00:00:15 2020 7 25 0 0 15.0012 297.9153 0.01777
## 4 2020-07-25 00:00:20 2020 7 25 0 0 20.0016 297.9312 0.01777
## 5 2020-07-25 00:00:24 2020 7 25 0 0 24.9984 297.9457 0.01776
## 6 2020-07-25 00:00:29 2020 7 25 0 0 29.9988 297.9628 0.01777
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 -0.19366 0.90123 0.9218024 167.8725 JFK
## 2 -0.19436 0.89258 0.9134960 167.7156 JFK
## 3 -0.18742 0.89084 0.9103418 168.1191 JFK
## 4 -0.19043 0.88544 0.9056862 167.8624 JFK
## 5 -0.18519 0.87600 0.8953610 168.0632 JFK
## 6 -0.18203 0.86297 0.8819593 168.0890 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-07-24 00:00:05 2020 7 24 0 0 5.0004 300.0473 0.02035
## 2 2020-07-24 00:00:10 2020 7 24 0 0 10.0008 300.1220 0.02035
## 3 2020-07-24 00:00:15 2020 7 24 0 0 15.0012 300.1868 0.02035
## 4 2020-07-24 00:00:20 2020 7 24 0 0 20.0016 300.2448 0.02035
## 5 2020-07-24 00:00:24 2020 7 24 0 0 24.9984 300.2970 0.02035
## 6 2020-07-24 00:00:29 2020 7 24 0 0 29.9988 300.3450 0.02035
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 0.77760 3.93313 4.009261 191.1834 JFK
## 2 0.72123 3.74954 3.818275 190.8880 JFK
## 3 0.67933 3.61670 3.679947 190.6380 JFK
## 4 0.64965 3.50353 3.563252 190.5049 JFK
## 5 0.62527 3.40992 3.466773 190.3908 JFK
## 6 0.60323 3.32908 3.383291 190.2706 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-07-23 00:00:05 2020 7 23 0 0 5.0004 298.0579 0.01716
## 2 2020-07-23 00:00:10 2020 7 23 0 0 10.0008 298.1778 0.01716
## 3 2020-07-23 00:00:15 2020 7 23 0 0 15.0012 298.2868 0.01716
## 4 2020-07-23 00:00:20 2020 7 23 0 0 20.0016 298.3863 0.01716
## 5 2020-07-23 00:00:24 2020 7 23 0 0 24.9984 298.4786 0.01716
## 6 2020-07-23 00:00:29 2020 7 23 0 0 29.9988 298.5655 0.01716
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 3.07665 0.47349 3.112871 261.2509 JFK
## 2 2.99371 0.45817 3.028567 261.2987 JFK
## 3 2.92992 0.43680 2.962301 261.5206 JFK
## 4 2.88054 0.42196 2.911282 261.6662 JFK
## 5 2.84328 0.39998 2.871276 261.9924 JFK
## 6 2.80284 0.38832 2.829612 262.1122 JFK## Station Date.Time Temperature Relative.Humidity Wind.Direction
## 1 JFK 2020-07-24 00:00:00 NaN NaN 180
## 2 JFK 2020-07-24 00:05:00 NaN NaN 190
## 3 JFK 2020-07-24 00:10:00 NaN NaN 190
## 4 JFK 2020-07-24 00:15:00 NaN NaN 190
## 5 JFK 2020-07-24 00:20:00 NaN NaN 200
## 6 JFK 2020-07-24 00:25:00 NaN NaN 200
## Wind.Speed year mon day hour min sec
## 1 4.115226 2020 7 24 0 0 0
## 2 4.629630 2020 7 24 0 5 0
## 3 4.629630 2020 7 24 0 10 0
## 4 5.144033 2020 7 24 0 15 0
## 5 4.629630 2020 7 24 0 20 0
## 6 4.115226 2020 7 24 0 25 0Locations for Plots
Match Times for Model and Observations
Select Day of Interest
Time-matching is performed using a routine that can be found in Analysis01-Time_Matching_Problem.Rmd. The time matching will be done per variable. For the Forecast-Hour Evaluation product, we will focus on the temperature, wind speed and wind direction variables. Also, now that we have read all the TS data and ASOS data, we need to extract the day of interest, or doi for the time-series.
Note that for this product the “day of interest” will always be the UTC date of the day before.
We now have filtered data frames for the observations and model data for the day of interest.
Next, we will select only the temperature data for comparing the model and observations. This needs to be done on a per station basis. Note that we use the function drop_na() to drop rows which contain NaN or NA data. Since each variable is measured at different intervals, not all variables will have data available at every time step in the ASOS data. The functions may be too sensitive to missing data and thus we take care to remvove it here from the observations, after we have isolated a particular variable.
Temperature Time-Matching
Location: JFK
Location: LGA
Location: NYC
Wind Speed Time-Matching
Location: JFK
Location: LGA
Location: NYC
Wind Direction Time-Matching
Location: JFK
Location: LGA
Location: NYC
Forecast Hour Evaluation for JFK
For the temperature data I will use Bias, RMSE and MAE for the comparison statistics
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 0.225 | 1.034 | 0.765 |
| 2 | WRF D-1 | 0.467 | 0.978 | 0.816 |
| 3 | WRF D-2 | 1.427 | 1.705 | 1.460 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.341 | 1.242 | 1.025 |
| 2 | WRF D-1 | 0.204 | 1.434 | 1.162 |
| 3 | WRF D-2 | -0.255 | 1.253 | 0.975 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 62.346 | 45.778 |
| 2 | WRF D-1 | 99.745 | 78.451 |
| 3 | WRF D-2 | 85.578 | 61.385 |
Forecast Hour Evaluation for LGA
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.926 | 1.307 | 1.135 |
| 2 | WRF D-1 | -1.039 | 1.531 | 1.219 |
| 3 | WRF D-2 | 0.418 | 1.281 | 1.034 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.935 | 1.624 | 1.315 |
| 2 | WRF D-1 | -0.505 | 1.583 | 1.263 |
| 3 | WRF D-2 | -0.675 | 1.642 | 1.299 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 93.336 | 74.820 |
| 2 | WRF D-1 | 80.533 | 63.794 |
| 3 | WRF D-2 | 81.244 | 65.549 |
Forecast Hour Evaluation for NYC
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 1.334 | 1.748 | 1.521 |
| 2 | WRF D-1 | 1.543 | 1.929 | 1.639 |
| 3 | WRF D-2 | 3.061 | 3.399 | 3.061 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.572 | 1.228 | 1.017 |
| 2 | WRF D-1 | -0.509 | 1.271 | 1.102 |
| 3 | WRF D-2 | -0.521 | 1.300 | 1.111 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 105.530 | 91.513 |
| 2 | WRF D-1 | 97.965 | 89.049 |
| 3 | WRF D-2 | 57.945 | 46.257 |
uWRF and ASOS Time-Series Visualization
Temperature
## Warning: Removed 296 rows containing missing values (geom_point).## Warning: Removed 296 rows containing missing values (geom_path).
Wind Speed
Wind Direction
