Bias correction of numerical prediction model temperature forecast
Donated on 2/17/2020
It contains fourteen numerical weather prediction (NWP)'s meteorological forecast data, two in-situ observations, and five geographical auxiliary variables over Seoul, South Korea in the summer.
Dataset Characteristics
Multivariate
Subject Area
Climate and Environment
Associated Tasks
Regression
Feature Type
Real
# Instances
7750
# Features
7
Dataset Information
Additional Information
This data is for the purpose of bias correction of next-day maximum and minimum air temperatures forecast of the LDAPS model operated by the Korea Meteorological Administration over Seoul, South Korea. This data consists of summer data from 2013 to 2017. The input data is largely composed of the LDAPS model's next-day forecast data, in-situ maximum and minimum temperatures of present-day, and geographic auxiliary variables. There are two outputs (i.e. next-day maximum and minimum air temperatures) in this data. Hindcast validation was conducted for the period from 2015 to 2017.
Has Missing Values?
Yes
Variable Information
For more information, read [Cho et al, 2020]. 1. station - used weather station number: 1 to 25 2. Date - Present day: yyyy-mm-dd ('2013-06-30' to '2017-08-30') 3. Present_Tmax - Maximum air temperature between 0 and 21 h on the present day (°C): 20 to 37.6 4. Present_Tmin - Minimum air temperature between 0 and 21 h on the present day (°C): 11.3 to 29.9 5. LDAPS_RHmin - LDAPS model forecast of next-day minimum relative humidity (%): 19.8 to 98.5 6. LDAPS_RHmax - LDAPS model forecast of next-day maximum relative humidity (%): 58.9 to 100 7. LDAPS_Tmax_lapse - LDAPS model forecast of next-day maximum air temperature applied lapse rate (°C): 17.6 to 38.5 8. LDAPS_Tmin_lapse - LDAPS model forecast of next-day minimum air temperature applied lapse rate (°C): 14.3 to 29.6 9. LDAPS_WS - LDAPS model forecast of next-day average wind speed (m/s): 2.9 to 21.9 10. LDAPS_LH - LDAPS model forecast of next-day average latent heat flux (W/m2): -13.6 to 213.4 11. LDAPS_CC1 - LDAPS model forecast of next-day 1st 6-hour split average cloud cover (0-5 h) (%): 0 to 0.97 12. LDAPS_CC2 - LDAPS model forecast of next-day 2nd 6-hour split average cloud cover (6-11 h) (%): 0 to 0.97 13. LDAPS_CC3 - LDAPS model forecast of next-day 3rd 6-hour split average cloud cover (12-17 h) (%): 0 to 0.98 14. LDAPS_CC4 - LDAPS model forecast of next-day 4th 6-hour split average cloud cover (18-23 h) (%): 0 to 0.97 15. LDAPS_PPT1 - LDAPS model forecast of next-day 1st 6-hour split average precipitation (0-5 h) (%): 0 to 23.7 16. LDAPS_PPT2 - LDAPS model forecast of next-day 2nd 6-hour split average precipitation (6-11 h) (%): 0 to 21.6 17. LDAPS_PPT3 - LDAPS model forecast of next-day 3rd 6-hour split average precipitation (12-17 h) (%): 0 to 15.8 18. LDAPS_PPT4 - LDAPS model forecast of next-day 4th 6-hour split average precipitation (18-23 h) (%): 0 to 16.7 19. lat - Latitude (°): 37.456 to 37.645 20. lon - Longitude (°): 126.826 to 127.135 21. DEM - Elevation (m): 12.4 to 212.3 22. Slope - Slope (°): 0.1 to 5.2 23. Solar radiation - Daily incoming solar radiation (wh/m2): 4329.5 to 5992.9 24. Next_Tmax - The next-day maximum air temperature (°C): 17.4 to 38.9 25. Next_Tmin - The next-day minimum air temperature (°C): 11.3 to 29.8
Dataset Files
| File | Size |
|---|---|
| Bias_correction_ucl.csv | 1.6 MB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset bias_correction_of_numerical_prediction_model_temperature_forecast = fetch_ucirepo(id=514) # data (as pandas dataframes) X = bias_correction_of_numerical_prediction_model_temperature_forecast.data.features y = bias_correction_of_numerical_prediction_model_temperature_forecast.data.targets # metadata print(bias_correction_of_numerical_prediction_model_temperature_forecast.metadata) # variable information print(bias_correction_of_numerical_prediction_model_temperature_forecast.variables)
Bias correction of numerical prediction model temperature forecast [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C59K76.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.