1st Chimere workshop E.Minguzzi, G.Bonafe, M.Deserti, S.Jongen, M.Stortini, ARPA-SIM Palaiseau, March 21-22, 2005 1 st Chimere workshop USE OF LOKAL MODELL

E.Minguzzi, G.Bonafe, M.Deserti, S.Jongen, M.Stortini, ARPA-SIM

Palaiseau, March 21-22, 20051st Chimere workshop

11stst Chimere workshop Chimere workshop

USE OF LOKAL MODELL FOR THE METEOROLOGICAL INPUT OF CHIMERE

Palaiseau, FranceMarch 21-22, 2005

Enrico Minguzzi, Giovanni Bonafè, Marco Deserti, Suzanne Jongen, Michele Stortini

HydroMeteorological Service of Emilia Romagna Region (SIM), Bologna, Italy



OverviewOverview

Objectives:

•Use Lokal Modell (our operational meteorological model) as input for Chimere

•Analysis (and, wherever possible, verification) of LM outputs relevant for this application

•Tuning Chimere implementation for simulations over Northern Italy

Summary: (work in progress)

•LM and how we plan to use it

•LM verification (how LM errors will impact on Chimere performance?)

•PM underestimation: testing erosion/resuspension scheme (analysis of Qsoil and U*)

-Choice of operational time-step

-New scheme for nucleation routine

Future work/Open questions

•Looking for advices…



Lokal ModellLokal Modell

The model:

•Non-hydrostatic, limited area model (same class as MM5, Aladin,…)

•First designed by the German Weather Service, presently developed by the COSMO consortium (weather services of Germany, Switzerland, Italy, Greece, Poland)

•Used for operational forecasts and research programs (see: www.cosmo-model.org)

Operational domain of LM @ ARPA-SIM

Implementation at ARPA-SIM:

•7 km horizontal resolution

•35 vertical levels (first levels: 35, 110, 200 m)

•Two daily forecasts, lasting 72 hours

•Initial and boundary conditions by GME (German GCM)

•Data assimilation: 12 hours nudging of GTS data



Lokal Modell “re-analysis”Lokal Modell “re-analysis”

Applications:

•Long-term and scenario simulations

•Simulations whit simple dispersion models

•Meteorological characterisation of areas where no measurements are available

Features

•Available from April 2003

•10 parameters on model levels + 26 surface fields included; hourly resolution

-to prevent model drift, some surface fields are updated from GCM every 12 hours (so this is no exactly a continuous assimilation)

-presently only GTS data are included in assimilation cycle (i.e. relatively low resolution)

A “re-analysis” dataset is being built by storing LM fields during assimilation cycle.



LM – Chimere interface (1)LM – Chimere interface (1)

It does exactly the same job as interf-mm5

•Input: GRIB archive. Output: input for diagmet.f

•All “questionable” calculations are left to diagmet

Interface steps:

•Extraction form archive

•Horizontal interpolation (rotated coordinates)

•Temporal interpolation and de-cumulation

•Calculation of pressure and mixing ratio, units conversions

This is NOT a general GRIB-to-Chimere interface!! (sorry for that)

•GRIB format is very general – and not as standard as it pretends to be. It is very difficult (and probably not worth) to handle all possibilities:

-a lot of different options for validation times, geographic projections, vertical levels…

-different models store different parameters (ex. Humidity, pressure…)

•We have substantially modified the structure of Chimere calling scripts, to make it possible to prepare input files prior to model integration



LM – Chimere interface (2)LM – Chimere interface (2)

Chimere implementation at ARPA-SIM

• Input preparation split from model run

• All user modifications are in a single command file (keywords)

• All programs making calculations unchanged

• Prevent duplication of data

• When testing different model configurations, input files can be prepared only once

• Easier analysis of inputs

Commandfile

(keywords)



LM verificationLM verification

Background:

•Meteorological fields are a very critical input, especially In Po valley

•LM has hardly ever been used to drive a chemical model

Objectives

•Verification of operational forecasts focused on environmental applications (routinely LM verifications concentrated on precipitation)

•Find the best way to produce the input (select the most reliable parameters)

•How will LM errors affect Chimere performance? Are they common to most LAMs?

Some systematic errors found in LM output

•Temperature profiles

•10 meters wind



LM verification: winter temperatureLM verification: winter temperature

• PBL looks always too cold in LM

• During night, LM strongly underestimates the strength of surface inversion (a 6 to 8 degrees inversion is frequent in Po valley)

• Possible causes: surface fluxes (sensible vs latent heat?), turbulent diffusion in PBL…

• Effects on Chimere: wrong vertical mixing, high level emissions (stacks) not being considered above inversion…

Examples of winter Temperature profiles at S.P.Capofiume (rural site)Observations (black) and short term LM forecasts at different resolutions (colours)

Day (12Z)

Night (00Z)



LM verification: summer temperatureLM verification: summer temperature

T, dayT, night

• Temperature in the PBL is underestimated also in summer (known problem of LM), both in the diurnal mixed layer and in the nocturnal residual layer.

• No night-time inversion in LM (which often occurs in Po valley)

• Possible cause: LHF overestimated, SHF underestimated (errors in soil moisture, soil type…)

• Effects on Chimere ??

Examples of summer Temperature profiles at S.P.CapofiumeObservations (black) and short term LM forecasts at different resolutions (colours)



LM verification: 10 m windLM verification: 10 m wind

Verification dataset:•74 stations in Po valley (46 plain, 10 hills, 18

mount.)•Hourly values, 1 year (apr 2003 – mar 2004)

Wind speed:•Overestimated on plain and hills, esp. during

night•MAE similar in plains/hills errors are more

systematic•Errors do not grow with validation time

Wind direction:•Plains slightly better than mountains (MAE 60°

vs 75°)

Wind direction, MAE

Wind speed, Bias (left) and MAE (right) as a function of validation time.Stations on plains (blue), hills (purple) and mountains (green)



PM10 underestimation (1)PM10 underestimation (1)

Erosion emissions: (negligible in this case)

•Increase with u*

salt

•Decrease with Qsoil

•Switched off over sea and if Qsoil > 0.3

m3/m3

Resuspension emissions:

•Proportional to u*

1.43

•Decrease with Qsoil if Qsoil > 0.15 m3/m3


m3/m3

The most pressing problem is PM10 underestimation activate erosion/resuspension scheme

• Forced by u* and Soil Humidity (Qsoil)

- Qsoil from LM

- u* (and u

*salt) estimated by Chimere (diagmet.f) starting form LM values of

wind, , … (Note: thermal mixing is taken into account through a term proportional to w

*)

• Note: in the following, LM re-analysis were used




Erosion emissions: (negligible in this case)

•Increase with u*

salt

•Decrease with Qsoil


m3/m3

Resuspension emissions:

•Proportional to u*

1.43

•Decrease with Qsoil if Qsoil > 0.15 m3/m3


m3/m3

The most pressing problem is PM10 underestimation activate erosion/resuspension scheme

• Forced by u* and Soil Humidity (Qsoil)

- Qsoil from LM

- u* (and u

*salt) estimated by Chimere (diagmet.f) starting form LM values of

wind, , … (Note: thermal mixing is taken into account through a term proportional to w

*)

• Note: in the following, LM re-analysis were used




Resulting additional emissions are not exactly what we expected:

•“biogenic” PM emissions are comparable to anthropogenic in mountain areas

•but much smaller (at least 2 orders of magnitude) in Po valley

Analysis of Soil Moisture and Friction Velocity:



Soil moisture: analysisSoil moisture: analysis

• Horizontal distribution dominated by soil type (low resolution!)

• Little time variability (except annual cycle and precipitation events)

• May be sistematically overestimated• No measurements available (at this time) Erosion/resusp often switched off, especially in

winter days with no precipitation (where PM concentrations are higher!)



Friction velocity: analysisFriction velocity: analysis

• Pattern is similar

• LM values are almost double (0.2 vs 0.1)

• Chimere much lower during night

• Chimere diurnal cycle much stronger

Note: u* affects also dry deposition, Kz,

Zi…

Chimere with LM input (wind, , v…)

LM direct output (momentum flux)

0.5

0.3

0.2

0.15

0.10

0.05



Friction Velocity: validation (1)Friction Velocity: validation (1)

Metodology (preliminary…):

•U* measurements (sonic anemometer) available from a campaign held in winter 2002 at S.P.Capofiume (rural site in eastern Po Valley)

•U* estimated by meteorological pre-processor Calmet (forced by surface observations and radiosoundings; Holtslag and Van Ulden 1983) is available for both 2002 and 2004

•Calmet output for 2002 is in good agreement with observations; we suppose that it is a good estimate also for 2004 data.

Note:

•In winter 2004 surface wind speed is significantly different from 2002, especially in afternoon hours

•Routine measurements of soil humidity and turbulence parameters at S.P.Capofiume will begin in the next months…



Friction velocity: “validation” (2)Friction velocity: “validation” (2)



Friction velocity: “validation” (3)Friction velocity: “validation” (3)

• During night:

-Chimere underestimates; very low in specific days (0.01)

-LM overestimates (by a factor of 2)

• During day:

-Chimere (probably) underestimates; very strong diurnal cycle because of W* term

(this will be even stronger in summer)

-LM looks good

• Further work required…



PM10 underestimationPM10 underestimationPossible solutions:

•Retuning the scheme in order to get higher additional emissions (soil type, salt. u* ...)

•if erosion/resuspension is really not important, try something else (ex. multiplying SOA)

•Take into account urban areas: -Approximately 10% of Po valley is urbanized (see pictures)-PM underestimation may not so large in “real” rural stations… A parameterisation for urban erosion/resuspension could be useful

Urbanized areas in Northern Italy (according to Corine 1990)

Nocturnal illumination in Northern Italy (satellite view)



Time stepTime step10’

We have a problem with computer time looking for the longest possible time-step

• Chimere suggestion:- 60’ (step=1) for resolution > 0.25°- 15’ (step=4) for resolution 5-10 km

• If we could use 20’ (step=3):- CPU time reduced from 1h15’ to 55’ per

day - 1 hour saved in a 3 days forecast

• Test with 20’ and comparison with 10’ (control)

- Model did not explode - Errors are usually negligible- Errors do not accumulate during the

simulation

- Some differences in secondary species (PM10, PM25), where high concentrations predicted

- Local differences in primary pollutants (NH3, H2SO4, NO) close to strong emitting sources

Promising results; test with strong wind required



Nucleation schemeNucleation scheme

A new nucleation scheme is being tested

•Different formulation (Kulmala et. al 2002 instead of 1998)

•Allows description of very dry conditions (RH<10%)

•First test: there are some differences, but rather small

•Further investigations required…

Surface PM10 concentration, g/m3, 18/02/2004 h 22Z. Old (left) and new (right) nucleation scheme

Difference new-old



Recap.Recap.

LM-Chimere interface has been built

LM output looks promising, but it shows some systematic errors

•Wind speed overestimation

•Surface inversions

The erosion/resuspension scheme needs to be adapted to Northern Italy

•Either tune the scheme

•Or improve inputs (soil water)

•Or change approach (urban)

•Friction velocity deserves further investigations (it also affects dry deposition, Kz, PBL height, ...)



Future workFuture work

“Near future” work

•Test on a summer episode

•Operational simulations over Northern Italy

•Long-term verification of our regional forecasts (GEMS project)

•Extend LM verification (surface inversion, micromet. station at S.P.Capofiume...)

•Test direct use of other optional meteorological parameters (Zi, surf. fluxes, cloud water…)

•Analysis of wet/dry deposition (we have a monitoring network for wet dep.)

•Improve soil type dataset

“Far future” work (looking for advices, cooperation, common interest…)

?Treatment of point sources (stacks…)

?PM verification with satellite data

?Urban parameterisation for erosion/resuspension

?Measuring campaign for PM speciation

?Data assimilation of air quality monitoring data to initialize Chimere runs



ReferencesReferences

Vehkamäki, H.; Kulmala, M.; Napari, I.; Lehtinen, K. E. J.; Timmreck, C.; Noppel, M.; Laaksonen, A, 2002.: An improved parameterization for sulfuric acid-water nucleation rates for tropospheric and stratospheric conditions; Journal of Geophysical Research (Atmospheres), Volume 107, Issue D22, pp. AAC 3-1.

Kulmala, Markku; Laaksonen, Ari; Pirjola, Liisa, 1998: Parameterizations for sulfuric acid/water nucleation rates; Journal of Geophysical Research, Volume 103, Issue D7, 8301-8308.

Holtslag, Van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes from routine weather data; Journal of Climate and Applied Meteorology, Volume 22, 517-529



ExtraExtra



LM verification: 2m TemperatureLM verification: 2m Temperature•LM operational forecasts, 1year (apr 2003 – mar 2004), 284 stations in Northern

Italy

•Plains (blue lines):-diurnal cycle underestimated (positive bias in min, negative in max)-annual variability overestimated (positive bias in summer max, negative in

winter)-RMS 2-3 °C, better than in mountains

•Mountains (green lines):-Night and winter are too cold-A lot of possible sources of errors (altitude difference, extrapolation form 1st

model level

BIAS RMSE



LM verification: 3D temperature evolutionLM verification: 3D temperature evolution

• Although the surface daily temperature excursion is underestimated, in the 200-1500 m layer this could be correct or even overestimated…

• Further analysis required

Examples of time evolution of Temperature profile, winter (left) and summer (right)

LM forecast

Observations (twice daily

radiosoundings)



Time step (2)Time step (2)

Sensitivity to time step doubling: NH3 surface concentrations, in an area of large emissions.

This is one of the largest differences observed between 10’ and 20’ time-step simulations

Documents

1st Chimere workshop E.Minguzzi, G.Bonafe, M.Deserti, S.Jongen, M.Stortini, ARPA-SIM Palaiseau, March 21-22, 2005 1 st Chimere workshop USE OF LOKAL MODELL