NumLab 2013: 15 March

Jouni Räisänen (UH)

Laura Rontu (FMI)

-a

Musca domestica

(Wikimedia Commons)

Agenda 15 March

• Summary of half-course time use diaries

• Progress of course projects

Time use diaries (half-course)

• op01: mean = 27.0, range = 27 to 27 (N = 1)

• op02: mean = 25.25, range = 16 to 34.5 (N = 2)

• op03: mean = 24.0, range = 19 to 28 (N = 3)

• op04: mean = 35.5, range = 29 to 50 (N = 4)

• op05: mean = 24.8, range = 22.5 to 29 (N = 3)

• op06: mean = 22.8, range = 16.5 to 32 (N = 4)

• All: mean = 26.9, range = 16 to 50

• Nominally: 5 credit units = 133 hours of work.

Progress of course projects

• op01: Tiina Nygård, Ella-Maria Kyrö, Roberta Pirazzini, Klara

Finkele

• op02: Carl Fortelius, Evgeny Kadantsev, Irene Suomi

• op03: Laura Rojas, Roberto Cremonini, Pirkka Ollinaho

• op04: Olle Räty, Peter Ukkonen, Kaisa Ylinen, Viivi Kallio

• op05: Ditte Mogensen, Rosa Gierens, Siegfried Schobesberger

• op06: Ilari Lehtonen, Elina Riskilä, Virve Karsisto, Jussi Tiira

• Ukraine: Julia Palamarchuk, Olga Krukova, Anna Pavlova

• Ireland: Emily Gleeson, Noelle Gillespie, Sinead Duffy,

• Estonia 1: Velle Toll, Oleg Batrashev, Piia Post, Mattias Rennel

• Estonia 2: Marko Zirk, Hardi Teder, Hannes Keernik

Group 06 progress

• Made plan about what to do next – Search surface parameters for coniferous forest

and grass

– Do some test rounds

– Download soundings from spring and summer 2009

– Run the model with each day’s sounding trough the period

– Automatize this with script

– Calculate forest fire index using temperature, wind speed, humidity and precipitation

Surface parameters

• Coniferous forest

– Roughness: 0.5

– LAI: 3

– Albedo: 0.13

– Emissivity: 0.98

• Grass

– Roughness: 0.01

– LAI: 1

– Albedo: 0.2

– Emissivity: 0.98

Results from test rounds

• If sounding is from a dry day, there are

less clouds than if the day were a moist

day.

• We have not managed to get different

results when using different surface

parameters

– It is possible that the problem is in running

the model and not in the model itself

Script

• #!/bin/bash AROMdir="AROM_60s_L41_Jokioinen" gradspath="$MU_OUT/$AROMdir/grads" gfile="" cd $MU_IN/Jokioinen for soudate in `ls|grep html|tail -n 2|cut -c 11-21` do Sou2MUSC.sh Jokioinen $soudate Aca2MUSC.sh Jokioinen_L41 cp name* MUSCIN* $MU_WD cd $MU_WD ./Runmusc_acatest Jokioinen cd $MU_OUT lfa2ascii_hourly.sh $AROMdir convert_to_grads_hourly.sh $AROMdir cd $gradspath for luku in 1 41 42 do gfile="output_${luku}_hourly" mv ${gfile}.ctl ${gfile}_${soudate}.ctl mv ${gfile}.grads ${gfile}_${soudate}.grads done done

Script already runs without errors, but it gives same result for both test days, so it needs to be fixed.

Plans for future

• Do more test rounds

• Investigate the script

• Gather soundings

• Run model with different soundings, if everything goes well

Ukraine

Location: Jokioinen

Station latitude: 60.81

Station longitude: 23.50

Date: 04.12.12

Sunrise: ~09 a.m.

Sunset:~03 p.m.

Julia Palamarchuk Anna Pavlova Olga Krukova

White line - clim aerosol Red line - zero aerosol

S039HUMI.SPECIFI> 253:051- 039-109@20121204_00:00+000h00m tri:000 000 Specific Humidity (kg S040HUMI.SPECIFI> 253:051- 040-109@20121204_00:00+000h00m tri:000 000 Specific Humidity (kg S041HUMI.SPECIFI> 253:051- 041-109@20121204_00:00+000h00m tri:000 000 Specific Humidity (kg S001FORC001 > 20121204_00:00+000h00m 000 0.000E+000 S002FORC001 > 20121204_00:00+000h00m 000 0.000E+000 S003FORC001 > 20121204_00:00+000h00m 000 0.000E+000

list_data_MUSCIN_Jokioinen_L41.fa

list_data_ICMSHHARM+0006 S039RAIN > 253:181- 039-109@20080930_18:00+006h00m tri:000 000 Rain (kg m-2) S038SNOW > 253:184- 038-109@20080930_18:00+006h00m tri:000 000 Snow (kg mSURFACCNEIGE > 253:184- 000-105@20080930_18:00+006h00m tri:004 000 Accumulated snow (kg m

NO FORCING

Period 1 (10:00-13:00)

Period 2 (15:00-16:30)

Difference of precipitation as snow

Period 1 Period 2

White line - clim aerosol Red line - zero aerosol

Resolved precipitation as snow, [mm/hour]

Period 2 Period 1

Resolved precipitation as snow, [mm/hour]

White line - clim aerosol Red line - zero aerosol

Period 1 Period 2 Temperature, K

Period 1 Period 2

White line - clim aerosol Red line - zero aerosol

Specific humidity of snow, [kg/kg]

The use of MUSC to provide minute-resolution forecasts at some Irish locations

Sinéad Duffy, Noelle Gillespie, Emily Gleeson

Update Summary

1. In addition to Dublin Airport, we tested MUSC at other station sites to view whether bias in temperature also existed – we used hourly and minute observation data.

2. Found the source of the temperature bias.

3. Had a look at temperature profile data for Harmonie and MUSC.

4. Looked into the MUSCIN* files with a view to modifying them.

1. Gurteen Minute Temperature Analysis (degrees)

-6

-4

-2

0

2

4

6

8

10

12

14

0

16

6

33

2

49

8

66

4

83

0

99

6

11

62

13

28

14

94

16

60

18

26

19

92

21

58

23

24

24

90

26

56

28

22

29

88

31

54

33

20

34

86

36

52

38

18

39

84

41

50

43

16

44

82

46

48

48

14

49

80

51

46

53

12

54

78

56

44

58

10

MUSC (analy)

MUSC (3hr f/c)

MUSC (6hr f/c)

Temperature - Observed

RMSE (analy)= 1.9134 RMSE (3hr f/c) = 2.2679 RMSE (6hr f/c)= 1.8387

2. Dublin Airport: 2m Temperature (deg)

RMSE (analy)=1.72 deg RMSE (3hr f/c) =1.93 deg RMSE (6hr f/c)=1.72 deg RMSE (Harmonie 3D) = 0.63 deg

-4

-2

0

2

4

6

8

10

12

0 10 20 30 40 50 60 70 80 90 100

MUSC (analy)

MUSC (3hr f/c)

MUSC (6hr f/c)

Temperature - Observed

Harmonie data

Reason for the bias in the MUSC data – Observation and Harmonie data = 2m temperature but the MUSC PTS parameter is surface temperature and not the 2m temperature. *** We have not found a way to get T2M from MUSC yet but I guess gl can do it….

Dublin Airport: 2m Temperature (deg)

-2,00

0,00

2,00

4,00

6,00

8,00

10,00

0 10 20 30 40 50 60 70 80 90 100

Harmonie data

Obs

Just to show the good agreement between 3D Harmonie and Observations

3. Dublin Airport: Temperature Profiles

Left: Used Harmonie data (analysis and 3hr forecast files for 00,06,12,18 Feb 7-10th) for Dublin Airport location (x-axis = hours); right: MUSC forecast data generated by using the Harmonie files i.e. we ran a series of 3hr forecasts (minute resolution but hourly averages are plotted) using each Harmonie file.

4. Modifying MUSCIN* files

•In Har2MUSC.sh we changed the MUSC output format from OUTPUT_FORMAT = 'MUSC_FORCING_FA' to OUTPUT_FORMAT = 'MUSC_FORCING' as this yields an ASCII file in a similar format to the defatm* files in the test cases. &NAM1D • LMAP=.F. • ZDELY=250000., • LNHDYN=.FALSE., • LALAPHYS=.T., • LREASUR=.T., • NFORC= 3 -------------------- there is 3hr forcing in the file (previously we thought that there was none) • LQCGRP = T ----------------- cloud water is turned on • LQIGRP = .F. -- all of these (ice, snow, rain, graupel etc) should be T as data exists for them or else the arrays should not be included in the file • LQRGRP = .F. • LQSGRP = .F. • LQGGRP = .F. • LCFGRP = .F. ---- this should also be T when using ACAF1D.F90 to convert back to FA for MUSC experiments (i.e. that there are clouds) • LSRCGRP = .F. • LTKEGRP = .F.

4. Modifying MUSCIN* files

•In the test cases the defatm* files contained model level U, V, T, QV, then forcings and at the end aerosols, albedos etc.

• Our equivalent MUSCIN* file generated from a 3D Harmonie file is different and hence the Aca2MUSC.sh script will not work on it unless the data file is altered or the F90 code called within Aca2MUSC.sh is adjusted.

•Differences in our file: -extra parameters pressure departure, vertical div - cloud liquid, ice, rain, snow, graupel, TKE – all non-zero

Next Steps

• Consider MUSC minute data and generate temperature

profiles.

• Expand the model-level analysis to other parameters.

•Revisit comparisons to observations.

• Work on converting the MUSCIN* Ascii file back to FA

following edits.

Progress of Estonian group 1 Group members: Velle Toll, Oleg Batrashev, Piia Post, Mattias Rennel

2m Temperature in tropics, S =

1.2 S0

black line - grassland (original) red line - 30 m coniferous forest green line - peat bog (or something that should become one after modifications)

Difference from grassland

Modified surface from acatest.

Estonia group 2: Marko Zirk, Hardi Teder, Hannes Keernik (Andres Luhamaa)

Task - to find out, how much does surface type influence other parameters, specific attention on bog

Surprisingly, wind in forest is stronger than in bog, though slightly, or is it more like noise?

black line - grassland red line - 30 m coniferous forest green line - peat bog (or something that should become one)

black line - grassland red line - 30 m coniferous forest green line - peat bog (or something that should become one)

black line - grassland red line - 30 m coniferous forest green line - peat bog (or something that should become one)

To be continued ...

Next time = 22 Mar 14-16 Finnish time

(12-14 UTC)

• Be prepared to report your progress

– What has the group done?

– Any unsolved problems?

– What are you planning to do next?

• You can use slides, if you have something

interesting to show