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Flow-dependent wind extraction in strong-constraint 4D-Var
ID
Zaplotnik, Žiga
(
Avtor
),
ID
Žagar, Nedjeljka
(
Avtor
),
ID
Semane, Noureddine
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(13,13 MB)
MD5: 13782FA5D69CC860D4112ADD12C0259B
URL - Izvorni URL, za dostop obiščite
https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4497
Galerija slik
Izvleček
In the process of data assimilation for numerical weather prediction, biases in the model and observations can induce spurious analysis increments, which degrade the quality of the analyses. For this reason, the feedback of atmospheric composition (e.g., ozone and aerosols) observations on winds through dynamical adjustment is typically disabled in operational 4D-Var assimilation. This study investigates whether an increasing number of tracer observations could be exploited to constrain winds better in 4D-Var. For an idealized case study using analytical strong-constraint 4D-Var with the 1D advection model of Allen et al. and accurate background tracer field, it is shown that coupled wind–tracer assimilation always improves the tracer analysis. It also improves the wind analysis, but only if the magnitude of the error associated with unrepresented or misrepresented tracer physical forcings is smaller than the magnitude of the difference between the nature-run advection and the background-tracer advection. In other words, the model needs to be a good approximation of the truth for successful wind extraction. Based on this criterion, a method for flow-dependent 4D-Var wind extraction is developed, which selectively activates the coupling between winds and tracers locally and temporally by altering the tangent-linear tracer equation and the adjoint wind equation. The new method is implemented in the intermediate-complexity incremental 4D-Var model Moist Atmosphere Dynamics Data Assimilation Model (MADDAM) of Zaplotnik et al. Numerical experiments with MADDAM show that the new approach diminishes the occurrence of spurious wind-analysis increments and can improve the accuracy of wind analyses.
Jezik:
Angleški jezik
Ključne besede:
meteorology
,
atmospheric physics
,
data assimilation
,
wind
,
4D-Var
,
ensemble
,
flow-dependent
,
model error
,
wind extraction
,
wind tracing
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FMF - Fakulteta za matematiko in fiziko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2023
Št. strani:
Str. 2107-2124
Številčenje:
Vol. 149, iss. 755
PID:
20.500.12556/RUL-152303
UDK:
551.501
ISSN pri članku:
0035-9009
DOI:
10.1002/qj.4497
COBISS.SI-ID:
154282755
Datum objave v RUL:
17.11.2023
Število ogledov:
886
Število prenosov:
45
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Quarterly Journal of the Royal Meteorological Society
Skrajšan naslov:
Q. J. R. Meteorol. Soc.
Založnik:
Royal Meteorological Society
ISSN:
0035-9009
COBISS.SI-ID:
26227200
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
meteorologija
,
atmosferska fizika
,
asimilacija podatkov
,
veter
,
4D-var
Projekti
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Deutsche Forschungsgemeinschaft
Številka projekta:
461186383
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
European Space Agency, PECS
Številka projekta:
4000106739/12/NL/KML
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P1-0188
Naslov:
Astrofizika in fizika atmosfere
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