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 2020-04-28 00:00:05 2020 4 28 0 0 5.0004 280.8803 0.00496
## 2 2020-04-28 00:00:10 2020 4 28 0 0 10.0008 280.9404 0.00496
## 3 2020-04-28 00:00:15 2020 4 28 0 0 15.0012 280.9956 0.00496
## 4 2020-04-28 00:00:20 2020 4 28 0 0 20.0016 281.0460 0.00497
## 5 2020-04-28 00:00:24 2020 4 28 0 0 24.9984 281.0927 0.00497
## 6 2020-04-28 00:00:29 2020 4 28 0 0 29.9988 281.1365 0.00497
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 1.18013 -5.73527 5.855427 348.3727 JFK
## 2 1.21656 -5.44030 5.574664 347.3949 JFK
## 3 1.23473 -5.20785 5.352220 346.6620 JFK
## 4 1.24913 -5.02442 5.177366 346.0386 JFK
## 5 1.25819 -4.87098 5.030854 345.5169 JFK
## 6 1.25781 -4.74852 4.912283 345.1639 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-04-27 00:00:05 2020 4 27 0 0 5.0004 281.0728 0.00627
## 2 2020-04-27 00:00:10 2020 4 27 0 0 10.0008 281.1285 0.00627
## 3 2020-04-27 00:00:15 2020 4 27 0 0 15.0012 281.1789 0.00627
## 4 2020-04-27 00:00:20 2020 4 27 0 0 20.0016 281.2253 0.00627
## 5 2020-04-27 00:00:24 2020 4 27 0 0 24.9984 281.2687 0.00628
## 6 2020-04-27 00:00:29 2020 4 27 0 0 29.9988 281.3108 0.00628
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 -2.48566 -5.98303 6.478823 22.56052 JFK
## 2 -2.32374 -5.53625 6.004151 22.76934 JFK
## 3 -2.20656 -5.21192 5.659772 22.94624 JFK
## 4 -2.10938 -4.95568 5.385931 23.05698 JFK
## 5 -2.02726 -4.74365 5.158682 23.14007 JFK
## 6 -1.96089 -4.57175 4.974534 23.21520 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-04-26 00:00:05 2020 4 26 0 0 5.0004 283.1914 0.00451
## 2 2020-04-26 00:00:10 2020 4 26 0 0 10.0008 283.2469 0.00451
## 3 2020-04-26 00:00:15 2020 4 26 0 0 15.0012 283.2955 0.00451
## 4 2020-04-26 00:00:20 2020 4 26 0 0 20.0016 283.3401 0.00451
## 5 2020-04-26 00:00:24 2020 4 26 0 0 24.9984 283.3811 0.00451
## 6 2020-04-26 00:00:29 2020 4 26 0 0 29.9988 283.4196 0.00451
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 -1.39266 3.33559 3.614646 157.3387 JFK
## 2 -1.39505 3.24363 3.530906 156.7280 JFK
## 3 -1.40042 3.16880 3.464458 156.1574 JFK
## 4 -1.39328 3.10170 3.400261 155.8104 JFK
## 5 -1.38812 3.04252 3.344220 155.4756 JFK
## 6 -1.38458 2.99194 3.296781 155.1667 JFK## Station Date.Time Temperature Relative.Humidity Wind.Direction
## 1 JFK 2020-04-27 00:51:00 280.3722 100.00 30
## 2 JFK 2020-04-27 01:51:00 280.3722 96.63 10
## 3 JFK 2020-04-27 02:51:00 279.8722 95.88 10
## 4 JFK 2020-04-27 03:51:00 279.2611 100.00 10
## 5 JFK 2020-04-27 04:51:00 279.2611 100.00 350
## 6 JFK 2020-04-27 05:51:00 278.7611 100.00 10
## Wind.Speed year mon day hour min sec
## 1 9.773663 2020 4 27 0 51 0
## 2 7.716049 2020 4 27 1 51 0
## 3 6.687243 2020 4 27 2 51 0
## 4 5.144033 2020 4 27 3 51 0
## 5 6.172840 2020 4 27 4 51 0
## 6 6.687243 2020 4 27 5 51 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 | 1.490 | 2.790 | 1.682 |
| 2 | WRF D-1 | 0.543 | 2.498 | 1.529 |
| 3 | WRF D-2 | 0.660 | 2.224 | 1.264 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -2.333 | 2.843 | 2.542 |
| 2 | WRF D-1 | -2.284 | 2.740 | 2.482 |
| 3 | WRF D-2 | -1.240 | 2.350 | 2.104 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 47.777 | 26.709 |
| 2 | WRF D-1 | 18.555 | 15.048 |
| 3 | WRF D-2 | 65.948 | 40.197 |
Forecast Hour Evaluation for LGA
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 0.481 | 1.239 | 1.014 |
| 2 | WRF D-1 | 0.019 | 1.251 | 1.036 |
| 3 | WRF D-2 | -0.945 | 1.169 | 0.959 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -2.735 | 3.089 | 2.790 |
| 2 | WRF D-1 | -2.747 | 3.127 | 2.832 |
| 3 | WRF D-2 | -2.318 | 2.944 | 2.579 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 46.219 | 24.766 |
| 2 | WRF D-1 | 44.953 | 23.073 |
| 3 | WRF D-2 | 55.869 | 31.711 |
Forecast Hour Evaluation for NYC
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 0.784 | 1.133 | 0.892 |
| 2 | WRF D-1 | 0.545 | 1.014 | 0.746 |
| 3 | WRF D-2 | -0.603 | 1.403 | 1.085 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -1.371 | 1.813 | 1.600 |
| 2 | WRF D-1 | -1.371 | 1.807 | 1.593 |
| 3 | WRF D-2 | -1.324 | 1.824 | 1.584 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 37.422 | 36.411 |
| 2 | WRF D-1 | 33.632 | 32.480 |
| 3 | WRF D-2 | 29.001 | 27.605 |
uWRF and ASOS Time-Series Visualization
Temperature
## Warning: Removed 38 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_path).
Wind Speed
Wind Direction
