Context

The dataset contains the results of two meteorological models and the real meteorological data from an airport. Our aim is to improve the accuracy of the meteorological model. Dependent variables are outputs of the meteorological model. Independent variables are measured by the sensors at the airport. Both models are the WRF model. Check this link to know more about the WRF project. The models have two different resolutions 4 Km and 36 Km. Extension of dependent variables will be _4K and _36K, respectively. We get the nearest point output provided by the model from the airport. The meteorological station is in the Vigo airport (LEVX by its ICAO indicative)

Content

The first column is a DateTime type. Times are in UTC units. As an example, the first row (index) 2011–08–22 20:00:00 means the variable predicted by the model for 2011–08–22 20:00:00 came from the model issued at time analysis 2011–08–21 00:00:00. It means that we see H+44 forecast. The dataset contains only the intervals forecasts from 24 hours to 47 hours. Rows with times 00:00:00 are H+24 forecast, and rows with times 23:00:00 are H+47 forecasts.

Columns are observed data (extension _o) or predicted values (extension _4K or _36K spatial resolution 4 Km or 36 Km, respectively). The observed values are:

metar_o: The raw meteorological report issued every 30 minutes at the Vigo station. We don´t use half hours because the model doesn’t report for half hours. You can see more information about the METAR report here.

dir_o: Observed wind direction. From North direction clockwise. Units are degrees. Value=-1 means variable direction.

mod_o: Wind intensity. Units are meters per second. All wind measurements are taken at 10 meters high.

wind_gust_o: Wind gust. Units are meters per second. Value=-1 means no winds gust reported.

visibility_o: Visibility in meters. Minimum visibility reported 48.280319 meters. Maximum visibility reported 9994.026301 (full visibility). Sorry for the decimal points. It´s a matter of changing units several times.

wxcodes_o: Present Weather Codes (space separated).

skyc1_o, skyc2_o, skyc3_o, skyc4_o: Are Sky Level Coverage at several levels. Amount of clouds, roughly speaking. Categorical data. M means no cloud coverage.

skyl1_o, skyl2_o, skyl3_o, skyl4_o: Sky Level Altitude of cloud cover in meters at several levels. Value=-1 means no clouds cover.

temp_o: Air Temperature in Kelvin at 2 meters.

dwp_o: Dew point temperature in Kelvins units at 2 meters.

rh_o: Relative Humidity.

mslp_o: Sea Level Pressure in pascals

Forecasted variables (with -4K or _36K extension) are:

lhflx: Surface downward latent heat flux. Units, watts per square meters.

dir: Predicted wind direction at 10 meters. From North direction clockwise. Units are degrees. Unlike dir_o no variable wind is forecasted (no -1 values)

mod: Wind intensity forecasted at 10 meters. Units are meters per second.

prec: Total accumulated rainfall between each model output. In our case, every hour. Units kilograms per meter squared.

rh: Relative Humidity. Units fraction

visibility: Visibility in air. Units meters. Minimum visibility 26.028316 meters. Maximum visibility 24235.000000

wind_gust: Wind gust at 10 meters. Units are meters per second. Unlike wind gust_o always forecasted (no -1 value)

mslp: Sea Level Pressure in pascals

temp: Air Temperature in Kelvin at 2 meters

cape: Convective available potential energy. Units: Jules per kilogram. Check this link for more information

cin: Convective inhibition. Click here for more information. Units Jules per Kilogram

cfl: Cloud area fraction at low atmosphere layer. I found 1251 samples with values higher than 1 !! Perhaps, we wouldn’t trust this feature so much.

cfm: Cloud area fraction at mid atmosphere layer. Also, I found 37 samples with values higher than 1.

conv_prec: Total accumulated convective rainfall between each model output. Every hour in our case.

HGT500: Geopotential height at 500mb. Units m

HGT850: Geopotential height at 850mb. Units m

HGTlev1: Geopotential height at model level 1. Units m

HGTlev2: Geopotential height at model level 2. Units m

HGTlev3: Geopotential height at model level 3. Units m

T500: Temperature at 500mb. Units Kelvin

T850: Temperature at 850mb. Units Kelvin

cfh: Cloud cover at high levels. Units fraction

cft: Cloud cover at low and mid-levels. Units fraction

lwflx: Surface downward latent heat flux. Units: W m-2

pbl_height: planetary boundary layer. Units meters. Information here

snow_prec: Total accumulated large scale snowfall between each model output. Units: kg m-2

snowlevel: Snow level. Units m

'sst: Sea surface temperature. Units Kelvin

swflx: Surface downwelling shortwave flux in air. Units: W m-2

u: Eastward wind at 10 m. Units m/s

ulev1: Eastward wind at level 1. Units m/s

ulev2: Eastward_wind at level 2. Units m/s

ulev3: Eastward_wind at level 3. Units m/s

v: Northward_wind at 10 m. Units m/s

vlev1: Northward_wind at level 1. Units m/s

vlev2: Northward_wind at level 2. Units m/s

vlev3: Northward_wind at level 3. Units m/s

Acknowledgements

The meteorological model that I used to build the database is maintained by Meteogalicia (a public meteorological service). Meteogalicia supplied a WRF model applied in the Galicia region (Northwest Spain). They use a THREDDS (Thematic Realtime Environmental Distributed Data Service), a connectivity tool between scientific data providers and end-users. We can get a historical model from Meteogalicia WRF archives. The actual meteorological data are obtained from the meteorological station at Vigo airport. Iowa State University provides a database with the meteorological airports’ reports, check this link.

Inspiration

I aim to find new algorithms that can improve meteorological model accuracy. We can set up supervised or unsupervised Machine learning algorithms to achieve our goal. The dataset would be a good start point