Note

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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

../_images/MetPreProcessor.jpg

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