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).
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 2020-03-10 00:00:05 2020 3 10 0 0 5.0004 282.5070 0.00572
## 2 2020-03-10 00:00:10 2020 3 10 0 0 10.0008 282.5658 0.00572
## 3 2020-03-10 00:00:15 2020 3 10 0 0 15.0012 282.6164 0.00572
## 4 2020-03-10 00:00:20 2020 3 10 0 0 20.0016 282.6609 0.00572
## 5 2020-03-10 00:00:24 2020 3 10 0 0 24.9984 282.7015 0.00572
## 6 2020-03-10 00:00:29 2020 3 10 0 0 29.9988 282.7396 0.00573
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 1.18840 4.21625 4.380532 195.7411 JFK
## 2 1.11826 4.00461 4.157813 195.6020 JFK
## 3 1.07019 3.85333 3.999182 195.5216 JFK
## 4 1.02987 3.71346 3.853624 195.5005 JFK
## 5 1.01057 3.61113 3.749868 195.6342 JFK
## 6 0.98857 3.51797 3.654228 195.6957 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-03-09 00:00:05 2020 3 9 0 0 5.0004 282.5357 0.00414
## 2 2020-03-09 00:00:10 2020 3 9 0 0 10.0008 282.5535 0.00414
## 3 2020-03-09 00:00:15 2020 3 9 0 0 15.0012 282.5699 0.00415
## 4 2020-03-09 00:00:20 2020 3 9 0 0 20.0016 282.5848 0.00415
## 5 2020-03-09 00:00:24 2020 3 9 0 0 24.9984 282.5990 0.00415
## 6 2020-03-09 00:00:29 2020 3 9 0 0 29.9988 282.6132 0.00415
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 2.51192 2.63535 3.640716 223.6263 JFK
## 2 2.45090 2.52218 3.516860 224.1788 JFK
## 3 2.40120 2.43888 3.422557 224.5540 JFK
## 4 2.36938 2.35669 3.341848 225.1538 JFK
## 5 2.33325 2.28962 3.269008 225.5407 JFK
## 6 2.30780 2.23590 3.213283 225.9066 JFK## Date.Time year mon day hour min sec Temperature Mixing.Ratio
## 1 2020-03-08 00:00:05 2020 3 8 0 0 5.0004 279.1117 0.00229
## 2 2020-03-08 00:00:10 2020 3 8 0 0 10.0008 279.1327 0.00229
## 3 2020-03-08 00:00:15 2020 3 8 0 0 15.0012 279.1544 0.00230
## 4 2020-03-08 00:00:20 2020 3 8 0 0 20.0016 279.1753 0.00230
## 5 2020-03-08 00:00:24 2020 3 8 0 0 24.9984 279.1963 0.00230
## 6 2020-03-08 00:00:29 2020 3 8 0 0 29.9988 279.2176 0.00230
## U_WIND V_WIND Wind.Speed Wind.Direction Station
## 1 0.67758 -2.85109 2.930500 346.6313 JFK
## 2 0.69463 -2.78912 2.874318 346.0150 JFK
## 3 0.69894 -2.72728 2.815417 345.6258 JFK
## 4 0.70594 -2.67733 2.768835 345.2288 JFK
## 5 0.71077 -2.64125 2.735214 344.9383 JFK
## 6 0.70793 -2.60328 2.697820 344.7870 JFK## Station Date.Time Temperature Relative.Humidity Wind.Direction
## 1 JFK 2020-03-09 00:00:00 NaN NaN 240
## 2 JFK 2020-03-09 00:05:00 NaN NaN 240
## 3 JFK 2020-03-09 00:10:00 NaN NaN 240
## 4 JFK 2020-03-09 00:15:00 NaN NaN 240
## 5 JFK 2020-03-09 00:20:00 NaN NaN 240
## 6 JFK 2020-03-09 00:25:00 NaN NaN NaN
## Wind.Speed year mon day hour min sec
## 1 8.744856 2020 3 9 0 0 0
## 2 8.230453 2020 3 9 0 5 0
## 3 7.201646 2020 3 9 0 10 0
## 4 6.687243 2020 3 9 0 15 0
## 5 7.716049 2020 3 9 0 20 0
## 6 7.201646 2020 3 9 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 | 0.566 | 1.385 | 1.164 |
| 2 | WRF D-1 | 1.089 | 2.520 | 2.099 |
| 3 | WRF D-2 | 0.580 | 1.524 | 1.334 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -0.822 | 1.741 | 1.366 |
| 2 | WRF D-1 | -0.261 | 1.284 | 1.012 |
| 3 | WRF D-2 | -0.400 | 1.526 | 1.220 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 34.016 | 29.393 |
| 2 | WRF D-1 | 36.081 | 32.481 |
| 3 | WRF D-2 | 38.319 | 33.557 |
Forecast Hour Evaluation for LGA
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -1.461 | 1.873 | 1.614 |
| 2 | WRF D-1 | -1.503 | 1.637 | 1.509 |
| 3 | WRF D-2 | -2.236 | 2.533 | 2.386 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -2.937 | 3.271 | 2.940 |
| 2 | WRF D-1 | -2.907 | 3.251 | 2.927 |
| 3 | WRF D-2 | -2.924 | 3.240 | 2.924 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 15.868 | 12.840 |
| 2 | WRF D-1 | 19.950 | 14.902 |
| 3 | WRF D-2 | 17.245 | 13.064 |
Forecast Hour Evaluation for NYC
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -1.084 | 1.677 | 1.495 |
| 2 | WRF D-1 | -1.103 | 1.306 | 1.180 |
| 3 | WRF D-2 | -1.801 | 2.063 | 1.928 |
| Forecast.Init | BIAS | RMSE | MAE | |
| 1 | WRF D-0 | -2.356 | 2.739 | 2.356 |
| 2 | WRF D-1 | -2.312 | 2.679 | 2.312 |
| 3 | WRF D-2 | -2.243 | 2.619 | 2.243 |
| Forecast.Init | RMSE | MAE | |
| 1 | WRF D-0 | 16.180 | 13.835 |
| 2 | WRF D-1 | 11.908 | 9.458 |
| 3 | WRF D-2 | 10.080 | 8.146 |
uWRF and ASOS Time-Series Visualization
Temperature
## Warning: Removed 60 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_path).
Wind Speed
Wind Direction
