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3.3. Meteorological Data: MetPreprocessor

  • Contributor:
    Name Institution
    Fredrik Lindberg Gothenburg
  • Introduction

    MetPreprocessor can be used to transform required temporal meteorological data into the format used in UMEP. The following variables are usually required as a minimum: air temperature, relative humidity, barometric pressure, wind speed, incoming shortwave radiation and rainfall; if available, other variables can be supplied as well.

    Input data can include any number of header lines and should be separated by conventional separators (e.g. comma, space, tab, etc). The output format is space-separated and includes time-related variables of year, day of year, hour and minute. The plugin is able to process other input time formats including month, day of month, etc. Possibility to use an EnergyPlus Weather (.epw) is also available. Note that .epw-files rarely have precipitation data included. This is is required for SUEWS-modelling and need to be acquired from elsewhere (external dataset).

  • Dialog box


Fig. 3.4 Interface for inputting an ascii data file into the correct format for SUEWS

  • Dialog sections
top left Select an existing text file with meteorological data at a temporal resolution between 5 min and 180 min (3 hours) that is divisible by 5 min. Tick in EPW box to convert an EnergyPlus Weather file.
middle left Specify time-related columns in the imported data file.
lower left Perform quality control (recommended): Select to perform a simple quality control which will check the input data for unreasonable values of each variable.
right Choose columns from imported data file that correspond to the meteorological variables used in UMEP.
  • Variables included in UMEP meteorological input file
    if acceptable range is not reasonable (i.e. beyond the limits we have set) please contact
No. Header name Description Accepted range Comments
1 iy Year [YYYY] Not applicable  
2 id Day of year [DOY] 1 to 365 (366 if leap year)  
3 it Hour [H] 0 to 23  
4 imin Minute [M] 0 to 59  
5 qn Net all-wave radiation [W m-2] -200 to 800  
6 qh Sensible heat flux [W m-2] -200 to 750  
7 qe Latent heat flux [W m-2] -100 to 650  
8 qs Storage heat flux [W m-2] -200 to 650  
9 qf Anthropogenic heat flux [W m-2] 0 to 1500  
10 U Wind speed [m s-1] 0.001 to 60  
11 RH Relative Humidity [%] 5 to 100  
12 Tair Air temperature [°C] -30 to 55  
13 pres Surface barometric pressure [kPa] 90 to 107  
14 rain Rainfall [mm] 0 to 30 (per 5 min) this should be scaled based on time step used
15 kdown Incoming shortwave radiation [W m-2] 0 to 1200 Global irradiance on the horizontal plane
16 snow Snow [mm] 0 to 300 (per 5 min) this should be scaled based on time step used
17 ldown Incoming longwave radiation [W m-2] 100 to 600  
18 fcld Cloud fraction [tenths] 0 to 1  
19 wuh External water use [m3] 0 to 10 (per 5 min) scale based on time step being used
20 xsmd (Observed) soil moisture 0.01 to 0.5 [m3 m-3 or kg kg-1]
21 lai (Observed) leaf area index [m2 m-2] 0 to 15  
22 kdiff Diffuse shortwave radiation [W m-2] 0 to 600 Diffuse irradiance on the horizontal plane
23 kdir Direct shortwave radiation [W m-2] 0 to 1200 Beam/direct irradiance on a plane always normal to sun rays. One way to check this is to compare direct and global radiation and see if kdir is higher than global radiation during clear weather.
24 wdir Wind direction [°] 0 to 360  
  • Remarks
    1. If decimal time is ticked in, day of year column must be stated and the decimal time column should be numbers between 0 and 1.
    2. If you have problems with importing a dataset. Do a time series plot using small points. Check (1) are there any data gaps (there can be no gaps) (2) are the columns lined up throughout the data setes (e.g if variable suddenly changes incorrectly, you may have columns misaligned).
    3. Gapfilling - there are a number of techniques that can be used for this e.g.:
      1. Linear fit between one or two missing periods using the data on either
      2. Create diurnal average for each variabel for short periods (e.g. 2 weeks) and use these values to fill missing data