USE OF LOKAL MODELL FOR THE METEOROLOGICAL INPUT OF CHIMERE

Transcription

1 1 st Chimere workshop USE OF LOKAL MODELL FOR THE METEOROLOGICAL INPUT OF CHIMERE Palaiseau, France March 21-22, 2005 Enrico Minguzzi, Giovanni Bonafè, Marco Deserti, Suzanne Jongen, Michele Stortini HydroMeteorological Service of Emilia Romagna Region (SIM), Bologna, Italy

2 Overview 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

3 Lokal 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: 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 Operational domain of ARPA-SIM

4 Lokal Modell re-analysis A re-analysis dataset is being built by storing LM fields during assimilation cycle. Applications: Long-term and scenario simulations Simulations whit simple dispersion models Meteorological characterisation of areas where no measurements are available Features Available from April 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)

5 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

6 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 Command file (keywords) Prevent duplication of data When testing different model configurations, input files can be prepared only once Easier analysis of inputs

7 LM 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

8 LM verification: winter temperature Day (12Z) Night (00Z) Examples of winter Temperature profiles at S.P.Capofiume (rural site) Observations (black) and short term LM forecasts at different resolutions (colours) 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

9 LM verification: summer temperature T, night T, day Examples of summer Temperature profiles at S.P.Capofiume Observations (black) and short term LM forecasts at different resolutions (colours) 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??

10 LM verification: 10 m wind Wind speed, Bias (left) and MAE (right) as a function of validation time. Stations on plains (blue), hills (purple) and mountains (green) 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

11 PM10 underestimation (1) The most pressing problem is PM10 underestimation activate erosion/resuspension scheme Forced by u * and Soil Humidity (Q soil ) - Q soil from LM - u * (and u * salt ) estimated by Chimere (diagmet.f) starting form LM values of wind, θ, (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 Q soil Switched off over sea and if Q soil > 0.3 m 3 /m 3 Resuspension emissions: Proportional to u * 1.43 Decrease with Q soil if Q soil > 0.15 m 3 /m 3 Switched off over sea and if Q soil > 0.3 m 3 /m 3

12 PM10 underestimation (1) The most pressing problem is PM10 underestimation activate erosion/resuspension scheme Forced by u * and Soil Humidity (Q soil ) - Q soil from LM - u * (and u * salt ) estimated by Chimere (diagmet.f) starting form LM values of wind, θ, (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 Q soil Switched off over sea and if Q soil > 0.3 m 3 /m 3 Resuspension emissions: Proportional to u * 1.43 Decrease with Q soil if Q soil > 0.15 m 3 /m 3 Switched off over sea and if Q soil > 0.3 m 3 /m 3

13 PM10 underestimation (2) 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:

14 Soil 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!)

15 Friction velocity: analysis Chimere with LM input (wind, θ, θ v ) LM direct output (momentum flux) 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

16 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

17 Friction velocity: : validation validation (2)

18 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

19 PM10 underestimation Possible 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)

20 Time step We have a problem with computer time looking for the longest possible time-step 10 Chimeresuggestion: - 60 (step=1) for resolution > (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

21 Nucleation scheme Surface PM10 concentration, µg/m 3, 18/02/2004 h 22Z. Old (left) and new (right) nucleation 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%) Difference new-old First test: there are some differences, but rather small Further investigations required

22 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,...)

23 Future 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

24 References 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, 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,

25 Extra

26 LM 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

27 LM verification: 3D temperature evolution LM forecast Observations (twice daily radiosoundings) Examples of time evolution of Temperature profile, winter (left) and summer (right) Although the surface daily temperature excursion is underestimated, in the m layer this could be correct or even overestimated Further analysis required

28 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