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
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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 2020-07-24 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
##             Date.Time year mon day hour min     sec Temperature Mixing.Ratio
## 1 2020-07-22 00:00:05 2020   7  22    0   0  5.0004    299.2834       0.0164
## 2 2020-07-22 00:00:10 2020   7  22    0   0 10.0008    299.3737       0.0164
## 3 2020-07-22 00:00:15 2020   7  22    0   0 15.0012    299.4538       0.0164
## 4 2020-07-22 00:00:20 2020   7  22    0   0 20.0016    299.5265       0.0164
## 5 2020-07-22 00:00:24 2020   7  22    0   0 24.9984    299.5924       0.0164
## 6 2020-07-22 00:00:29 2020   7  22    0   0 29.9988    299.6529       0.0164
##     U_WIND  V_WIND Wind.Speed Wind.Direction Station
## 1 -0.30280 3.51635   3.529363       175.0783     JFK
## 2 -0.32375 3.37883   3.394305       174.5268     JFK
## 3 -0.33964 3.27984   3.297379       174.0879     JFK
## 4 -0.35301 3.18158   3.201104       173.6687     JFK
## 5 -0.36535 3.10078   3.122230       173.2801     JFK
## 6 -0.37027 3.02515   3.047726       173.0219     JFK
##   Station           Date.Time Temperature Relative.Humidity Wind.Direction
## 1     JFK 2020-07-23 00:00:00         NaN               NaN            320
## 2     JFK 2020-07-23 00:05:00         NaN               NaN            300
## 3     JFK 2020-07-23 00:07:00    297.0389             87.68            300
## 4     JFK 2020-07-23 00:10:00         NaN               NaN            300
## 5     JFK 2020-07-23 00:15:00         NaN               NaN            290
## 6     JFK 2020-07-23 00:20:00         NaN               NaN            300
##   Wind.Speed year mon day hour min sec
## 1   6.687243 2020   7  23    0   0   0
## 2   5.658436 2020   7  23    0   5   0
## 3   5.144033 2020   7  23    0   7   0
## 4   5.144033 2020   7  23    0  10   0
## 5   5.144033 2020   7  23    0  15   0
## 6   4.629630 2020   7  23    0  20   0

Locations 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

JFK - WRF 2-m Temperature (K) Performance
Forecast.Init BIAS RMSE MAE
1 WRF D-0 2.647 3.305 2.666
2 WRF D-1 2.623 3.976 3.177
3 WRF D-2 2.999 4.226 3.133
JFK - WRF 10-m Wind Speed (m/s) Performance
Forecast.Init BIAS RMSE MAE
1 WRF D-0 -0.161 2.648 2.063
2 WRF D-1 0.587 2.874 2.235
3 WRF D-2 0.365 2.607 1.960
JFK - WRF 10-m Wind Direction (degN) Performance
Forecast.Init RMSE MAE
1 WRF D-0 62.114 50.360
2 WRF D-1 79.035 65.422
3 WRF D-2 75.160 58.735

Forecast Hour Evaluation for LGA

LGA - WRF 2-m Temperature (K) Performance
Forecast.Init BIAS RMSE MAE
1 WRF D-0 2.413 3.407 2.599
2 WRF D-1 1.541 3.647 3.028
3 WRF D-2 3.004 4.429 3.480
LGA - WRF 10-m Wind Speed (m/s) Performance
Forecast.Init BIAS RMSE MAE
1 WRF D-0 -1.127 1.909 1.407
2 WRF D-1 -1.001 1.723 1.380
3 WRF D-2 -1.034 1.697 1.330
LGA - WRF 10-m Wind Direction (degN) Performance
Forecast.Init RMSE MAE
1 WRF D-0 50.558 37.743
2 WRF D-1 85.214 78.982
3 WRF D-2 76.459 67.484

Forecast Hour Evaluation for NYC

NYC - WRF 2-m Temperature (K) Performance
Forecast.Init BIAS RMSE MAE
1 WRF D-0 4.036 4.613 4.047
2 WRF D-1 3.000 4.430 3.430
3 WRF D-2 4.528 5.596 4.644
NYC - WRF 10-m Wind Speed (m/s) Performance
Forecast.Init BIAS RMSE MAE
1 WRF D-0 -1.141 1.703 1.482
2 WRF D-1 -1.120 1.705 1.485
3 WRF D-2 -1.116 1.681 1.444
NYC - WRF 10-m Wind Direction (degN) Performance
Forecast.Init RMSE MAE
1 WRF D-0 55.795 40.584
2 WRF D-1 86.740 77.996
3 WRF D-2 68.725 59.302

uWRF and ASOS Time-Series Visualization

Temperature

## Warning: Removed 408 rows containing missing values (geom_point).
## Warning: Removed 408 rows containing missing values (geom_path).

Wind Speed

Wind Direction