Bancheri and Formetta

LINKERS

JGrass-NewAge: LWRB componentMarialaura Bancheri*†

and Giuseppe Formetta†

*Correspondence:

marialaura.bancheri@unitn.it

Dipartimento di Ingegneria Civile

Ambientale e Meccanica, Trento,

Mesiano di Povo, Trento, IT

Full list of author information is

available at the end of the article†Code Author

Abstract

These pages teach how to run the LongWave Radiation Balance (LWRB) componentinside the OMS 3 console. Some preliminary knowledge and installation of OMS ismandatory (see @Also useful). This component deals with the downwelling (L ↓) andupwelling (L ↑) longwave atmospheric radiation. Longwave radiation (1-100 µm) is animportant component of the radiation balance on Earth which affects many phenomenasuch as evapotranspiration, snow melt , glaciers evolution , vegetation dynamics, plantrespiration, and primary productivity. Many simplified models (SM) have been proposedin order to model L ↓ and L ↑ by using easily available meteorological observation suchas air temperature, relative humidity, incoming solar radiation, and cloud cover. TenSM for estimating L ↓ and one for L ↑ were integrated in the LWRB component. Thepackage is perfectly integrated in the JGrass-NewAge, and is fed by other components,like the one providing the shortwave radiation (SWRB, (1)). Once parameters areassigned according to the selected SM, it can be used for the forecasting longwaveradiation in the selected station.

@Version:0.1

@License:GPL v. 3

@Inputs:• Clearness index (-);• Relative humidity (%);• Air temperature (◦C);• Soil temperature (◦C);• Skyview factor (-);• X, Y, Z (-);• model (String);• A-Cloud, B-Cloud (-);• εs (-);

@Outputs:• L ↓ (W/m2)• L ↑ (W/m2)• longwave radiation (W/m2)

@Doc Author: Marialaura Bancheri

@References:• See References section below

Keywords: OMS; JGrass-NewAGE Component Description; Longwave radiationestimation

Bancheri and Formetta Page 2 of 9

Code Information

Executables

This link points to the jar file that, once downloaded can be used in the OMS console:

https://github.com/GEOframeOMSProjects/OMS_Project_LWRB/tree/master/lib

Developer Info

This link points to useful information for the developers, i.e. information about the code

internals, algorithms and the source code

https://github.com/geoframecomponents

Also useful

To run JGrass-NewAGE it is necessary to know how to use the OMS console. Information

at: ”How to install and run the OMS console”,

https://alm.engr.colostate.edu/cb/project/oms).

JGrasstools are required for preparing some input data (information at:

http://abouthydrology.blogspot.it/2012/11/udig-jgrasstools-resources-in-italian.

html

To visualize results you need a GIS. Use your preferred GIS, following its installation

instructions. To make statistics on the results, you can probably get benefits from R:

http://www.r-project.org/ and follow its installation instruction.

To whom address questions

marialaura.bancheri@unitn.it

Authors of documentation

Marialaura Bancheri (marialaura.bancheri@unitn.it)

This documentation is released under Creative Commons 4.0 Attribution International

Bancheri and Formetta Page 3 of 9

Component DescriptionSMs formulation for L ↑ [Wm−2] and L ↓ [Wm−2] are based on the Stefan-Boltzmann

equation:

L ↓= εall−sky · σ · T 4a (1)

L ↑= εs · σ · T 4s (2)

where σ = 5.670 · 10−8 is the Stefan-Boltzmann constant,Ta [K] is the near-surface air

temperature, εall−sky [-] is the atmosphere effective emissivity, εs [-] is the soil emissivity

and Ts is the surface soil temperature. In order to account for the increase of L ↓ in cloud

cover conditions is formulated according to eq. (3):

εall−sky = εclear · (1 + a · cb) (3)

where c [-] is the clearness index and a and b are two calibration coefficients. Ten

literature formulations were implemented for the computation of εclear. The complete

list of parameterizations used is presented in table 1 where it is specified: the component

number, the component name, the equation that defines it, and the reference to the paper

from which it is derived. X, Y and Z are the parameters provided in literature for each

model, table 2.

# Component Name Formulation Reference1 Angstrom εclear = X − Y · 10Ze Angstrom [1918] (2)2 Brunt’s εclear = X + Y · e0.5 Brunt’s [1932](3)3 Swinbank εclear = X · 10−13 · T 6

a Swinbank [1963](4)4 Idso and Jackson εclear = 1−X · exp(−Y · 10−4 · (273− Ta)2) Idso and Jackson [1969](5)5 Brutsaert εclear = X · (e/Ta)1/7 Brutsaert [1975] (6)6 Idso εclear = X + Y · 10−4 · e · exp(1500/Ta) Idso [1981] (7)7 Monteith and Unsworth εclear = X + Y · σ · T 4

a Monteith and Unsworth [1990] (8)8 Konzelmann εclear = X + Y · (e/Ta)1/8 Konzelmann et al [1994] (9)9 Prata εclear = [1− (X + w) · exp(−(Y + Z · w)1/2)] Prata [1996](10)10 Dilley and O’brien εclear = X + Y · (Ta/273.16)6 + Z · (w/25)1/2 Dilley and O’brien [1998] (11)

Table 1 Clear sky emissivity formulations: Ta is the air temperatue [K], w [kg/m2] is precipitable water= 4650 [e0/Ta] and e [kPa] screen-level water-vapour pressure.

Component Name X Y ZAngstrom 0.83 0.18 −0.07Brunt?s 0.52 0.21 [−]Swinbank 5.31 [−] [−]Idso and Jackson 0.26 −7.77 [−]Brutsaert 1.72 7 [−]Idso 0.70 5.95 [−]Monteith and Unsworth −119.00 1.06 [−]Konzelmann et al 0.23 0.48 [−]Prata 1.00 1.20 3.00Dilley and O’brien 59.38 113.70 96.96

Table 2 Models parameters values as presented in their literature formulation.

The formulation of the L ↑,eq.2, requires the soil emissivity, which usually is a property

of the nature of surface, and the surface soil temperature. Table 3 shows the literature

values of the soil emissivity for different types of surface: εs varies from its minimum of

0.95 for the bare soils up to its maximum of 0.99 for the fresh snow.

Bancheri and Formetta Page 4 of 9

Nature of surface EmissivityBare soil (mineral) 0.95− 0.97Bare soil (organic) 0.97− 0.98Grassy vegetation 0.97− 0.98Tree vegetation 0.96− 0.97Snow (old) 0.97Snow (fresh) 0.99

Table 3 Soil emissivity for each nature of surface (Brutsaert, 2005).

Detailed Inputs description

General description

The input file is a .csv file containing a header and one or more time series of input data,

depending on the number of stations involved. Each column of the file is associated to a

different station.

The file must have the following header:

• The first 3 rows with general information such as the date of the creation of the file

and the author;

• the fourth and fifth rows contain the IDs of the stations (e.g. station number 8:

value 8, ID, ,8);

• the sixth row contains the information about the type of the input data (in this

case, one column with the date and one column with double values);

• the seventh row specifies the date format (YYYY-MM-dd HH:mm).

All this information shown in the figure 1.

Figure 1 Heading of the .csv input file

Clearness index

The clearness index is the ratio between the measured incoming solar radiation (Im)

and the theoretical solar radiation computed at the top atmosphere (Itop). The clear-

ness index cab be computed using the relative component (see https://github.com/

geoframecomponents/ClearnessIndex) This quantity is given in time series or raster

maps of adimensional values between 0 and 1.

Relative humidity

The relative humidity is given in time series or raster maps of (% ) values. All the

conversions are made by the component.

Air temperature

The air temperature is given in time series or raster maps of (◦C) values. The conversion

in (◦K) is directly done by the component.

Bancheri and Formetta Page 5 of 9

Soil temperature

The soil temperature is given in time series or raster maps of (◦C) values.The conversion

in (◦K) is directly done by the component.

Skyview factor

The sky view factor is a raster map of adimensional values in the interval [0,1] at the

given point. It is the fraction of visible sky in the upper hemisphere and it is obtained

from the digital elevation model using the JGrasstools.

Model

The model is a String containing the number of the model chosen according to table 2.

X, Y, Z

X,Y, and Z are the double values of the model parameters chosen according to table 2.

Detailed Outputs descriptionThe output file will have exactly the same heading of the input file (see fig. 1).

L ↓The L ↓ output is given as a time series at a given point or as raster maps.The components

in the two cases are different (respectively LwrbPointCase and LwrbRasterCase) Its units

are (W/m2). Figure 2 shows the results of a L ↓ simulation obtained using the Angstrom

model, (2), and data from a station in Oklahoma.

Bancheri and Formetta Page 6 of 9

0 5000 10000 15000 20000 25000

200

300

400

500

Downwelling

Time [h]

Dow

nwel

ling

[W/m

^2]

Figure 2 Time series of downwelling radiation for the station ARM USDA UNL OSU WoodwardSwitchgrass 1 / US-AR1 in Oklahoma.

L ↑The L ↓ output is given as a time series at a given point or as raster maps. Its units are

(W/m2). Figure 3 shows the results of a L ↑ simulation obtained using the air temperature

data from a station in Oklahoma.

Bancheri and Formetta Page 7 of 9

0 5000 10000 15000 20000 25000

300

400

500

600

Upwelling

Time [h]

Upw

ellin

g [W

/m^2

]

Figure 3 Time series of upwelling radiation for the station ARM USDA UNL OSU WoodwardSwitchgrass 1 / US-AR1 in Oklahoma.

ExamplesThe following .sim file is customized for the use of the LWRB component. The .sim file

can be downloaded from here:

https://github.com/GEOframeOMSProjects/OMS_Project_LWRB/tree/master/simulation

import static oms3.SimBuilder.instance as OMS3def home = oms_prj

// start and end date of the simulationdef startDate= "1994 -01 -01 00:00"def endDate="1996 -01 -01 00:00"OMS3.sim {

resource "$oms_prj/lib"

model(while: "reader_data_airT.doProcess" ) {components {

// components to be called: reader input data , lwrb and writeroutput data

"reader_data_airT" "org.jgrasstools.gears.io.timedependent.OmsTimeSeriesIteratorReader"

"reader_data_soilT" "org.jgrasstools.gears.io.timedependent.OmsTimeSeriesIteratorReader"

"reader_data_CI" "org.jgrasstools.gears.io.timedependent.OmsTimeSeriesIteratorReader"

"vreader_station" "org.jgrasstools.gears.io.shapefile.OmsShapefileFeatureReader"

"l" "lwrbPointCase.Lwrb""writer_down" "org.jgrasstools.gears.io.

timedependent.OmsTimeSeriesIteratorWriter""writer_up" "org.jgrasstools.gears.io.

timedependent.OmsTimeSeriesIteratorWriter"

Bancheri and Formetta Page 8 of 9

"writer_long" "org.jgrasstools.gears.io.timedependent.OmsTimeSeriesIteratorWriter"

}

parameter{

// parameter of the reader components"reader_data_airT.file" "${home}/data/Airtemperature.

csv""reader_data_airT.idfield" "ID""reader_data_airT.tStart" "${startDate}""reader_data_airT.tEnd" "${endDate}""reader_data_airT.tTimestep" 60"reader_data_airT.fileNovalue" " -9999"

"reader_data_soilT.file" "${home}/data/SoilTemperature.csv"

"reader_data_soilT.idfield" "ID""reader_data_soilT.tStart" "${startDate}""reader_data_soilT.tEnd" "${endDate}""reader_data_soilT.tTimestep" 60"reader_data_soilT.fileNovalue" " -9999"

"reader_data_CI.file" "${home}/data/ClearnessIndex.csv"

"reader_data_CI.idfield" "ID""reader_data_CI.tStart" "${startDate}""reader_data_CI.tEnd" "${endDate}""reader_data_CI.tTimestep" 60"reader_data_CI.fileNovalue" " -9999"

"vreader_station.file" "${home}/data/stations.shp"

// parameter of the lwrb component , (see "Detailed inputdescription " section) for further datails

"l.X" 0.70"l.Y" 5.95"l.model" "6""l.epsilonS" 0.98"l.A_Cloud" 0"l.B_Cloud" 1"l.fStationsid" "netnum"

// parameter of the writing component"writer_down.file" "${home}/ output/downwelling.

csv""writer_down.tStart" "${startDate}""writer_down.tTimestep" 60

"writer_up.file" "${home}/ output/upwelling.csv"

"writer_up.tStart" "${startDate}""writer_up.tTimestep" 60

"writer_long.file" "${home}/ output/longwave.csv""writer_long.tStart" "${startDate}""writer_long.tTimestep" 60

}connect {

"reader_data_airT.outData" "l.inAirTemperatureValues""reader_data_soilT.outData" "l.inSoilTempratureValues""reader_data_CI.outData" "l.inClearnessIndexValues""vreader_station.geodata" "l.inStations""l.outHMlongwaveDownwelling" "writer_down.inData""l.outHMlongwaveUpwelling" "writer_up.inData""l.outHMlongwave" "writer_long.inData"

}

Bancheri and Formetta Page 9 of 9

}}

Data and ProjectThe following link is for the download of the input data necessaries to execute the LWRB

component (as shown in the .sim file in the previous section ) :

https://github.com/GEOframeOMSProjects/OMS_Project_LWRB/tree/master/data

The following link is for the download of the OMS project for LWRB component:

https://github.com/GEOframeOMSProjects/OMS_Project_LWRB

%

References1. Formetta, G., Rigon, R., Chavez, J., David, O.: Modeling shortwave solar radiation using the jgrass-newage system.

Geoscientific Model Development 6(4), 915–928 (2013)

2. Angstrom, A.K.: A Study of the Radiation of the Atmosphere: Based upon Observations of the Nocturnal Radiation

During Expeditions to Algeria and to California vol. 65. Smithsonian Institution, ??? (1915)

3. Brunt, D.: Notes on radiation in the atmosphere. i. Quarterly Journal of the Royal Meteorological Society 58(247),

389–420 (1932)

4. Swinbank, W.C.: Long-wave radiation from clear skies. Quarterly Journal of the Royal Meteorological Society 89(381),

339–348 (1963)

5. Idso, S.B., Jackson, R.D.: Thermal radiation from the atmosphere. Journal of Geophysical Research 74(23), 5397–5403

(1969)

6. Brutsaert, W.: On a derivable formula for long-wave radiation from clear skies. Water Resources Research 11(5),

742–744 (1975)

7. Idso, S.B.: A set of equations for full spectrum and 8-to 14-µm and 10.5-to 12.5-µm thermal radiation from cloudless

skies. Water resources research 17(2), 295–304 (1981)

8. Unsworth, M.H., Monteith, J.: Long-wave radiation at the ground i. angular distribution of incoming radiation.

Quarterly Journal of the Royal Meteorological Society 101(427), 13–24 (1975)

9. Konzelmann, T., van de Wal, R.S., Greuell, W., Bintanja, R., Henneken, E.A., Abe-Ouchi, A.: Parameterization of

global and longwave incoming radiation for the greenland ice sheet. Global and Planetary change 9(1), 143–164 (1994)

10. Prata, A.: A new long-wave formula for estimating downward clear-sky radiation at the surface. Quarterly Journal of the

Royal Meteorological Society 122(533), 1127–1151 (1996)

11. Dilley, A., O’brien, D.: Estimating downward clear sky long-wave irradiance at the surface from screen temperature and

precipitable water. Quarterly Journal of the Royal Meteorological Society 124(549), 1391–1401 (1998)