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 2020-05-06 00:00:05 2020 5 6 0 0 5.0004 284.9117 0.00619
## 2 2020-05-06 00:00:10 2020 5 6 0 0 10.0008 284.9834 0.00619
## 3 2020-05-06 00:00:15 2020 5 6 0 0 15.0012 285.0485 0.00619
## 4 2020-05-06 00:00:20 2020 5 6 0 0 20.0016 285.1081 0.00620
## 5 2020-05-06 00:00:24 2020 5 6 0 0 24.9984 285.1633 0.00620
## 6 2020-05-06 00:00:29 2020 5 6 0 0 29.9988 285.2150 0.00620
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 -0.08240 2.30482 2.306292 177.9525 JFK
## 2 -0.09377 2.24738 2.249335 177.6108 JFK
## 3 -0.10545 2.19877 2.201297 177.2543 JFK
## 4 -0.11260 2.15324 2.156182 177.0065 JFK
## 5 -0.12106 2.11108 2.114548 176.7180 JFK
## 6 -0.12704 2.07366 2.077548 176.4942 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-05-05 00:00:05 2020 5 5 0 0 5.0004 285.5814 0.00510
## 2 2020-05-05 00:00:10 2020 5 5 0 0 10.0008 285.7219 0.00510
## 3 2020-05-05 00:00:15 2020 5 5 0 0 15.0012 285.8377 0.00510
## 4 2020-05-05 00:00:20 2020 5 5 0 0 20.0016 285.9331 0.00510
## 5 2020-05-05 00:00:24 2020 5 5 0 0 24.9984 286.0137 0.00510
## 6 2020-05-05 00:00:29 2020 5 5 0 0 29.9988 286.0836 0.00509
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 5.18231 -5.70611 7.708179 317.7542 JFK
## 2 5.04464 -5.44770 7.424677 317.1999 JFK
## 3 4.92823 -5.24456 7.196726 316.7811 JFK
## 4 4.82157 -5.06860 6.995587 316.4308 JFK
## 5 4.71896 -4.91857 6.816224 316.1865 JFK
## 6 4.62266 -4.78693 6.654599 316.0002 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-05-04 00:00:05 2020 5 4 0 0 5.0004 287.7050 0.00871
## 2 2020-05-04 00:00:10 2020 5 4 0 0 10.0008 287.8161 0.00871
## 3 2020-05-04 00:00:15 2020 5 4 0 0 15.0012 287.9155 0.00871
## 4 2020-05-04 00:00:20 2020 5 4 0 0 20.0016 288.0055 0.00871
## 5 2020-05-04 00:00:24 2020 5 4 0 0 24.9984 288.0884 0.00871
## 6 2020-05-04 00:00:29 2020 5 4 0 0 29.9988 288.1658 0.00871
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 1.19663 3.46634 3.667075 199.0454 JFK
## 2 1.14768 3.31912 3.511941 199.0743 JFK
## 3 1.10558 3.20617 3.391435 199.0257 JFK
## 4 1.07122 3.10672 3.286217 199.0246 JFK
## 5 1.04305 3.02173 3.196687 199.0438 JFK
## 6 1.01926 2.95210 3.123105 199.0481 JFK## Station Date.Time Temperature Relative.Humidity Wind.Direction
## 1 JFK 2020-05-05 00:00:00 NaN NaN 310
## 2 JFK 2020-05-05 00:05:00 NaN NaN 310
## 3 JFK 2020-05-05 00:10:00 NaN NaN 310
## 4 JFK 2020-05-05 00:15:00 NaN NaN 320
## 5 JFK 2020-05-05 00:20:00 NaN NaN 320
## 6 JFK 2020-05-05 00:25:00 NaN NaN 310
## Wind.Speed year mon day hour min sec
## 1 11.31687 2020 5 5 0 0 0
## 2 11.83128 2020 5 5 0 5 0
## 3 13.37449 2020 5 5 0 10 0
## 4 NaN 2020 5 5 0 15 0
## 5 11.31687 2020 5 5 0 20 0
## 6 11.31687 2020 5 5 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 | 0.936 | 1.275 | 1.035 |
| 2 | WRF D-1 | 1.044 | 1.579 | 1.249 |
| 3 | WRF D-2 | 0.880 | 1.767 | 1.497 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.829 | 1.676 | 1.370 |
| 2 | WRF D-1 | -0.220 | 1.405 | 1.188 |
| 3 | WRF D-2 | -0.856 | 2.344 | 1.778 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 38.998 | 30.753 |
| 2 | WRF D-1 | 73.160 | 51.390 |
| 3 | WRF D-2 | 84.310 | 72.108 |
Forecast Hour Evaluation for LGA
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 1.140 | 1.954 | 1.393 |
| 2 | WRF D-1 | 0.475 | 2.470 | 2.287 |
| 3 | WRF D-2 | 0.909 | 3.504 | 3.234 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -2.696 | 3.132 | 2.719 |
| 2 | WRF D-1 | -2.514 | 3.070 | 2.591 |
| 3 | WRF D-2 | -2.897 | 3.652 | 3.001 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 53.410 | 40.064 |
| 2 | WRF D-1 | 42.406 | 31.125 |
| 3 | WRF D-2 | 69.139 | 55.904 |
Forecast Hour Evaluation for NYC
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | 1.644 | 1.971 | 1.665 |
| 2 | WRF D-1 | 1.277 | 2.395 | 2.106 |
| 3 | WRF D-2 | 1.800 | 3.475 | 3.014 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -1.795 | 2.521 | 2.156 |
| 2 | WRF D-1 | -1.760 | 2.494 | 2.112 |
| 3 | WRF D-2 | -1.870 | 2.663 | 2.240 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 67.909 | 55.896 |
| 2 | WRF D-1 | 67.513 | 44.325 |
| 3 | WRF D-2 | 87.584 | 68.116 |
uWRF and ASOS Time-Series Visualization
Temperature
Wind Speed
Wind Direction
