Boundary layer diagnosing for dispersion applications as part of meteo-to-dispersion model interface - M.Sofiev, Finnish Meteorological Institute ...
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Boundary layer diagnosing for dispersion applications as part of meteo-to-dispersion model interface M.Sofiev, Finnish Meteorological Institute E.Genikhovich, Main Geophysical Observatory NATO ARW, Dubrovnik, 18-22.4.2006
Content
• Dispersion models and dispersion applications: specific (sometimes
unique) users for meteorological models
¾ types of dispersion applications
¾ demand and supply of meteorological input
• Meteo-to-dispersion model interface
¾ on-line coupling vs off-line coupling
¾ required parameters
• Part of Met-PP: ABL parameterization (problem statement)
• A non-iterative solution based on basic meteorological variables
• Methodology verification
• Call for future developments
CS-137, kBq m-2 cumulative
Short- and long- term dispersion applications
• Short-term
¾ re-analysis of complicated and/or interesting/important episodes.
– As complicated and detailed chemistry-physics-dynamics as
computer allows
¾ forecasting the air quality..\silam.fmi.fi\index.htm
– compromise: level of details, uncertainty of input, and computer power
¾ emergency applications 1: pre-forecasting the consequences of
possible accidental releases.
– Compromise btw accuracy (explored variability) of the plume
dispersion and uncertainty of source characteristics
¾ emergency applications 2: real-time forecasting the consequences
of accidental releases.
– Fast but accurate dynamics, limited, if any, physics & chemistry
Short- and long- term dispersion applications (2)
• Long-term
¾ evaluation of cumulative pollution effect, “chemical climate” and
climate forcing, feedbacks, etc.
– Reduced level of details for short-term processes, rich list of
mechanisms important for longer scales. Main limitation is computer
power and uncertainty in description of slow processes and multi-
media interactions
¾ emergency applications 3: risk assessment.
– Maximum explored variability of plume dispersion under various
conditions and source features limited only by computer powerLow probability for severe pollution episode
PM budget
PM budget for 2002, DE_3
for 2002, DE_3
Demand and supply of the meteo input
20
50 Sea_salt_tot
Sea_salt_tot
PM2_5_10
PM2_5_10
• Dispersion
18
45
models need variables important for PM2_5
PM2_5
SO4_PM
SO4_PM
dispersion,
16
40
not NWP tasks PM10_corrected
SO4_PM_obs
SO4_PM_obs
14
35
¾ list
00
strongly depends on model, application, task, modeller,
24
phase
30 of the Moon, etc. Still, some coherence exists:
12
m-3
PM m-3
25 – always needed: 4-D wind, 4-D turbulence
ug PM
10
High probability for two severe pollution episodes
ug
20
8 – in most cases: 4-D temperature, 4-D precipitation flow, 3-D surface
15
6
state characteristics
10
4
– extra examples: 4-D cloud liquid water content, 4-D radiation
• As25 barely half of the list is of high interest for NWP, many
variables
00
are (i) often non-existent, (ii) never properly
11
12
23
33
14
25
88
19
30
10
21
22
13
24
44
15
26
77
18
29
99
20
00
11
22
33
14
25
55
16
27
88
19
30
validated
12
23
14
25
19
30
10
21
13
24
15
26
18
29
20
11
22
14
25
16
27
19
30
• Result: need for (sophisticated) NWP-to-DM interfaces
0
0 2
4Part of Met-PP: boundary layer parameters
• Available: profiles of basic meteorological variables: wind u,
temperature T, humidity q
• To find: basic ABL parameters: temperature, velocity and humidity
scales T, u*, q*, Monin-Obukhov length L, profile of some
characteristic of turbulence, e.g. KZ – if K-theory is used
• Verification possibility: mast data and consistency checking via
comparison of sensible and latent heat fluxes HS, Hl.
¾ These fluxes are NOT validated inside NWP and thus should not be used
as the input variables for the ABL re-stating.
¾ Deviation between NWP fluxes and dispersion model’s ones does not
mean that one of the models is wrong but rather points to differences in
the governing equations representationProblem solution ∂U
u* = K z ( z K ) ( zK )
∂z (u*)3 c p ρ
( )
2
∂U 2 ∂T
5/ 4
z K − σβ L=−
∂z ∂z cp ρ ∂T κβ H
K z ( zK ) = ∫ dz
0
( ∂U
∂
)
z
2
− 0.5σβ ∂T
∂ z
Hs = −
Pr
K z ( zK )
∂z
( zK )
Hs
T* = − Eρ ∂q
cpρ u * Hl = − K z ( zK ) ( zK )
Pr ∂z
Here all derivatives are NOT computed numerically
but rather taken from the analytical approximations of profiles.
Since zk~1m, these profiles can be taken purely logarithmic. Non-logarithmic
corrections start to play a strong role at |z/L|~0.5
Assuming the logarithmic shape, it is enough to have 2 values – at the
screening and the 1st model levels – to determine the profile.
All fluctuating and not well-defined parameters are inside the integral, thus
their effect is smoothed outEvaluation of the methodology
• Comparison with observations: good agreement
(Groisman & Genikhovich, 1997)
• Consistency checking against the driving NWP model:
must be similar but not exactly the same
¾ theoretical basis is more or less the same
¾ however, latent heat flux depends on surface moisture – a highly
uncertain parameter used as a “tunable variable” to meet overall
temperature profile
¾ still, there are differences in the computational algorithms
¾ HIRLAM & ECMWF provide accumulated fluxes e.g. for 3 hours,
while u,q,T are instant, thus re-stated fluxes will be instant tooVerification statistics:HIRLAM, Jan-March 2000, night
Sensible
Latent heatheat
flux: flux: re-stated
re-stated HIRLAM
HIRLAMVerification statistics: HIRLAM, May-Sep 2000, day
Latent
Sensible
heatheat
flux:flux:
re-stated
re-stated HIRLAM
HIRLAMVerification statistics: time correlation, quantile
charts Quantile chart for SILAM & HIRLAM sens. h_flux, [W m-2]
300
Correlation coefficient
250
Latent Sensible Total
200
Atlantic south 0.78 0.80 0.78
150
Atlantic north 0.86 0.91 0.89
100
Africa 0.67 0.82 0.82
SILAM
Helsinki terresrial 50 0.89 0.83 0.88
Gulf-200
of Finland
-100
0
0
0.69 100 0.70200 0.75
300
Sodankyla -50 0.90 0.82 0.88
Mediterranean -100 0.75 0.83 0.77
Moscow -150 0.85 0.74 0.82
-200
HIRLAMComparison of time series (sensible flux)
Helsinki terrestrial sensible hflux silam_sens_hfl
nwp_sens_hflux
500 North Atlantic sensible hflux silam_sens_hfl
nwp_sens_hflux
1000400
latent heat flux, W/m2
800300
latent heat flux, W/m2
200
600
100
400
0
200 1 1 3 3 4 5 6 7 8 9 10 11 12
-100
0 silam_sens_hfl
-200
1 1 3 3 4
Helsinki terrestrial sensible hflux
5 6 7 8 nwp_sens_hflux
9 10 11 12
-200 month
400 month
300
latent heat flux, W/m2
200
100
0
5 6
-100
-200
-300
monthDiscussion of comparison
• Re-stated and original NWP fluxes are close, often
surprisingly close
¾ Near-neutral and stable cases are re-stated practically 1:1
¾ Strongly unstable cases in re-stated fields are somewhat less
strong for terrestrial areas and more strong for marine ones
• Current methodology lacks the “non-classical” non-local
elements (as well as nearly all NWPs)
¾ It is rather a pre-requisite for introducing these extensions and
revisions of classical MO theoryCall for future developments
• Pre-requisite: universal approach for re-stating the main ABL
characteristics from the basic meteorological variables
¾ verified against observations
¾ cross-checked with NWPs
• Existing limitation
¾ no treatment of non-local features in strong stable/unstable cases
¾ certain deviations from HIRLAM are seen for some cases (not necessarily
a bad thing)
¾ comparisons with ECMWF model and modern mast measurements are
on-going
• Research planned
¾ finalize the comparison with independent datasets
¾ treatment of non-classical cases – advanced theory adaptation and
implementationThank you for your attention ! • P.S. SILAM is an open-code system (more at http://silam.fmi.fi) …
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