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 2020-07-27 00:00:05 2020 7 27 0 0 5.0004 303.1856 0.01413
## 2 2020-07-27 00:00:10 2020 7 27 0 0 10.0008 303.2060 0.01413
## 3 2020-07-27 00:00:15 2020 7 27 0 0 15.0012 303.2241 0.01413
## 4 2020-07-27 00:00:20 2020 7 27 0 0 20.0016 303.2395 0.01413
## 5 2020-07-27 00:00:24 2020 7 27 0 0 24.9984 303.2527 0.01413
## 6 2020-07-27 00:00:29 2020 7 27 0 0 29.9988 303.2641 0.01413
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 3.01805 0.67541 3.092702 257.3856 JFK
## 2 2.98021 0.65114 3.050514 257.6752 JFK
## 3 2.93443 0.64468 3.004412 257.6092 JFK
## 4 2.90709 0.61946 2.972356 257.9710 JFK
## 5 2.87335 0.60611 2.936581 258.0885 JFK
## 6 2.84116 0.59524 2.902844 258.1673 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-07-26 00:00:05 2020 7 26 0 0 5.0004 299.7292 0.01889
## 2 2020-07-26 00:00:10 2020 7 26 0 0 10.0008 299.8017 0.01889
## 3 2020-07-26 00:00:15 2020 7 26 0 0 15.0012 299.8618 0.01889
## 4 2020-07-26 00:00:20 2020 7 26 0 0 20.0016 299.9129 0.01889
## 5 2020-07-26 00:00:24 2020 7 26 0 0 24.9984 299.9566 0.01889
## 6 2020-07-26 00:00:29 2020 7 26 0 0 29.9988 299.9955 0.01889
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 0.90864 4.83350 4.918165 190.6467 JFK
## 2 0.83358 4.60276 4.677634 190.2653 JFK
## 3 0.78935 4.43366 4.503378 190.0949 JFK
## 4 0.75183 4.29829 4.363547 189.9215 JFK
## 5 0.72729 4.18675 4.249450 189.8546 JFK
## 6 0.70042 4.09501 4.154479 189.7061 JFK## 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## Station Date.Time Temperature Relative.Humidity Wind.Direction
## 1 JFK 2020-07-26 00:00:00 NaN NaN 190
## 2 JFK 2020-07-26 00:05:00 NaN NaN 190
## 3 JFK 2020-07-26 00:10:00 NaN NaN 190
## 4 JFK 2020-07-26 00:15:00 NaN NaN 190
## 5 JFK 2020-07-26 00:20:00 NaN NaN 190
## 6 JFK 2020-07-26 00:25:00 NaN NaN 190
## Wind.Speed year mon day hour min sec
## 1 6.687243 2020 7 26 0 0 0
## 2 5.658436 2020 7 26 0 5 0
## 3 5.658436 2020 7 26 0 10 0
## 4 6.687243 2020 7 26 0 15 0
## 5 6.687243 2020 7 26 0 20 0
## 6 6.687243 2020 7 26 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 | 0.331 | 1.598 | 1.338 |
| 2 | WRF D-1 | 0.738 | 1.546 | 1.262 |
| 3 | WRF D-2 | 0.103 | 1.317 | 1.107 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -1.542 | 1.908 | 1.704 |
| 2 | WRF D-1 | -0.936 | 1.416 | 1.178 |
| 3 | WRF D-2 | -0.991 | 1.411 | 1.206 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 19.943 | 16.151 |
| 2 | WRF D-1 | 16.625 | 12.977 |
| 3 | WRF D-2 | 16.684 | 13.799 |
Forecast Hour Evaluation for LGA
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.620 | 1.553 | 1.420 |
| 2 | WRF D-1 | -0.649 | 1.517 | 1.431 |
| 3 | WRF D-2 | -1.164 | 1.818 | 1.633 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -2.138 | 2.559 | 2.220 |
| 2 | WRF D-1 | -2.058 | 2.555 | 2.199 |
| 3 | WRF D-2 | -2.183 | 2.617 | 2.258 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 26.064 | 18.511 |
| 2 | WRF D-1 | 29.093 | 21.056 |
| 3 | WRF D-2 | 29.773 | 21.170 |
Forecast Hour Evaluation for NYC
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 2.321 | 2.528 | 2.321 |
| 2 | WRF D-1 | 2.132 | 2.411 | 2.132 |
| 3 | WRF D-2 | 1.530 | 1.937 | 1.553 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -1.328 | 1.802 | 1.564 |
| 2 | WRF D-1 | -1.319 | 1.841 | 1.597 |
| 3 | WRF D-2 | -1.327 | 1.835 | 1.596 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 62.980 | 42.972 |
| 2 | WRF D-1 | 62.079 | 41.671 |
| 3 | WRF D-2 | 64.418 | 42.640 |
uWRF and ASOS Time-Series Visualization
Temperature
## Warning: Removed 288 rows containing missing values (geom_point).## Warning: Removed 288 rows containing missing values (geom_path).
Wind Speed
Wind Direction
