1 Institute of Meteorology and Climate Research, 2 Geodetic … · 2020-05-06 · EGU2020-18092...
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KIT – The Research University in the Helmholtz Association KIT – The Research University in the Helmholtz Association Clear improvements of water vapour simulations in regional climate models are mainly reported for highly convective rainfall. Though the potential of improvements is also valid for other weather conditions and seasons, but probably on a lower level. A variety of settings can be exploited to achieve this aim. Assimilation (3D-Var) Variational approach; update every hour. Parameters: SYNOP (T, RH, Pres), ZTD (GNSS) Validation: precipitation stations, GNSS, radiosonde Assimilation frequency Increase of mixing ratio for hourly assimilation below 1500 m and a decrease at about 2000 m (Fig. 3a). Differences are also obvious for PWV and its bias (Fig. 3a). Hourly assimilation leads to higher PWV, rainrate and correlation but lower KGE (Fig. 2a,b). www.kit.edu The sensitivity of data assimilation on water vapour fields on convection-permitting WRF simulations over the GNSS Upper Rhine Graben Network (GURN), Germany A. Wagner 1 , B. Fersch 1 , P. Yuan 2 and H. Kunstmann 1 ([email protected]) MOTIVATION RESULTS & DISCUSSION METHODS Fig. 1: WRF domain (650x670 km), evaluation area (440x460 km) with 70 GNSS stations (red) and 245 synop stations (black) and radiosonde Stuttgart (blue). WRF-ARW (v.3.9.1) Input: ECMWF-ERA5 (31 km, 1 h, 138 levels) Settings: One nest, 309x319 pixels, 2.1 km, 71 levels Physics: LW-radiation: RRTM, SW-radiation: Dudhia, Microphysics: WSM6, PBL: Shin-Hong, Surface Layer: MM5sim, no cumulus parametrization Fig. 3: Differences of mixing ratio with height (top), Precipitable Water Volume (PWV) (middle) and PWV-Bias compared to radiosonde data (Stuttgart) (bottom) for the comparison of simulations (first = red, second = green) with assimilation of a) ZTD and synop data (SYN) every 6 h vs. 1 h, b) SYN with a minimum station density of 10 km vs. 20 km, c) ZTDSYN with cv5 vs. cv6 background error (BE) option and d) ZTD vs. PWV, for the spring event 11th to 23rd April 2016 [c) 11th to 17th April 2016]. Fig. 4: Mean Bias and Standard deviation of mixing ratio for simulations with assimilation (ZTDSYN, green) and open cycle (red) compared to radiosonde for a) spring, b) summer, c) fall and d) winter. Conclusions No negative feedback due to a high density of assimilated stations (10 km) can be observed. Water vapour profiles can reveal significant differences which are not necessarily visible in PWV. The choice of the background error option influences assimilation results. Improvements are possible and are obtained for all seasons. The potential added value of assimilation depends also on the quality level of the open cycle simulation. 1 Institute of Meteorology and Climate Research, 2 Geodetic Institute – Karlsruhe Institute of Technology, Germany Acknowledgement The research was conducted in the framework of the AtmoWater project (KU 2090/10-1), with the financial support of the DFG (German Research Foundation). EGU2020-18092 Data-thinning Slightly better results for 10 km compared to radiosonde data (Fig. 3b) and station data (PWV, rainrate) (Fig. 2a,b). More information due to a higher station density outperforms the drawback of its correlation problem. Similar thinning tests with ZTD reveal the same results (not shown). Background error (BE) option Multivariate BE option cv6 allows a direct feedback of temperature on humidity. Clear differences for only certain timesteps (Fig. 3c). Slightly better results for cv6 for precipitation and the correlation of PWV (Fig. 2a,b). Assimilation parameter Small differences for all timesteps for PWV and mixing ratio (Fig. 3d). No clear systematic differences with altitude or with regard to over- and underestimations. Slightly better correlation for ZTD with stations (Fig. 2a,b) but a lower KGE with radiosonde (Fig. 3d). Seasonal variations Small improvements for spring, autumn and winter with differences below 3000 m altitude (Fig. 4). Larger improvements in summer and also for higher altitudes (Fig. 4). The open-cycle simulation in autumn is already very good. 209 x 219 pixels 309 x 319 pixels Stuttgart ZTDSYN6h vs. ZTDSYNsp (1 h) a) ZTD vs. PWV d) sZTDSYNcv5 vs. sZTDSYNcv6 c) SYN10 vs. SYN20 b) 11th to 23rd April 2016 a) 16th to 31st Octobre 2018 c) 6th to 21st January 2017 d) 13th to 24th August 2018 b) PWV (GNSS) a) -0.1 -0.3 0.1 0.0 Precipitation (synop) b) Fig. 2: Kling-Gupta-efficiency for a) PWV compared to 70 GNSS stations and b) precipi- tation (245 synop stations) for all simulations in spring 2016 (green), summer 2018 (red), fall 2018 (orange) and winter 2017 (blue). altitude [m] PWV-Bias [mm] PWV [mm] assimilation run assimilation run mixing ratio difference [kg/kg]
Transcript of 1 Institute of Meteorology and Climate Research, 2 Geodetic … · 2020-05-06 · EGU2020-18092...
KIT – The Research University in the Helmholtz AssociationKIT – The Research University in the Helmholtz Association
Clear improvements of water vapour
simulations in regional climate models are
mainly reported for highly convective rainfall.
Though the potential of improvements is also
valid for other weather conditions and
seasons, but probably on a lower level. A
variety of settings can be exploited to achieve
this aim.
Assimilation (3D-Var)
Variational approach; update every
hour.
Parameters: SYNOP (T, RH, Pres),
ZTD (GNSS)
Validation: precipitation stations,
GNSS, radiosonde
Assimilation frequency
Increase of mixing ratio for hourly
assimilation below 1500 m and a
decrease at about 2000 m (Fig. 3a).
Differences are also obvious for PWV
and its bias (Fig. 3a).
Hourly assimilation leads to higher
PWV, rainrate and correlation but
lower KGE (Fig. 2a,b).
www.kit.edu
The sensitivity of data assimilation on water vapour fields on convection-permitting
WRF simulations over the GNSS Upper Rhine Graben Network (GURN), GermanyA. Wagner1, B. Fersch1, P. Yuan2 and H. Kunstmann1 ([email protected])
MOTIVATION
RESULTS & DISCUSSION
METHODS
Fig. 1: WRF domain (650x670 km), evaluation area
(440x460 km) with 70 GNSS stations (red) and 245 synop
stations (black) and radiosonde Stuttgart (blue).
WRF-ARW (v.3.9.1)
Input: ECMWF-ERA5
(31 km, 1 h, 138 levels)
Settings: One nest, 309x319
pixels, 2.1 km, 71 levels
Physics: LW-radiation: RRTM,
SW-radiation: Dudhia,
Microphysics: WSM6,
PBL: Shin-Hong,
Surface Layer: MM5sim,
no cumulus parametrization
Fig. 3: Differences of mixing ratio with height (top), Precipitable Water Volume (PWV) (middle) and PWV-Bias compared to radiosonde data (Stuttgart) (bottom) for the
comparison of simulations (first = red, second = green) with assimilation of a) ZTD and synop data (SYN) every 6 h vs. 1 h, b) SYN with a minimum station density of 10 km
vs. 20 km, c) ZTDSYN with cv5 vs. cv6 background error (BE) option and d) ZTD vs. PWV, for the spring event 11th to 23rd April 2016 [c) 11th to 17th April 2016].
Fig. 4: Mean Bias and Standard deviation of mixing ratio for
simulations with assimilation (ZTDSYN, green) and open cycle (red)
compared to radiosonde for a) spring, b) summer, c) fall and d) winter.
Conclusions
No negative feedback due to a high density of
assimilated stations (10 km) can be observed.
Water vapour profiles can reveal significant
differences which are not necessarily visible
in PWV.
The choice of the background error option
influences assimilation results.
Improvements are possible and are obtained
for all seasons.
The potential added value of assimilation
depends also on the quality level of the open
cycle simulation.
1 Institute of Meteorology and Climate Research, 2 Geodetic Institute
– Karlsruhe Institute of Technology, Germany
AcknowledgementThe research was conducted in the framework of the
AtmoWater project (KU 2090/10-1), with the financial
support of the DFG (German Research Foundation).
EGU2020-18092
Data-thinning
Slightly better results for 10 km compared
to radiosonde data (Fig. 3b) and station
data (PWV, rainrate) (Fig. 2a,b).
More information due to a higher station
density outperforms the drawback of its
correlation problem.
Similar thinning tests with ZTD reveal the
same results (not shown).
Background error (BE) option
Multivariate BE option cv6 allows a
direct feedback of temperature on
humidity.
Clear differences for only certain
timesteps (Fig. 3c).
Slightly better results for cv6 for
precipitation and the correlation of
PWV (Fig. 2a,b).
Assimilation parameter
Small differences for all timesteps for
PWV and mixing ratio (Fig. 3d).
No clear systematic differences with
altitude or with regard to over- and
underestimations.
Slightly better correlation for ZTD with
stations (Fig. 2a,b) but a lower KGE
with radiosonde (Fig. 3d).
Seasonal variations
Small improvements for spring,
autumn and winter with differences
below 3000 m altitude (Fig. 4).
Larger improvements in summer
and also for higher altitudes (Fig. 4).
The open-cycle simulation in
autumn is already very good.
209 x 219 pixels
309 x 319 pixels
Stuttgart
ZTDSYN6h vs. ZTDSYNsp (1 h)a) ZTD vs. PWVd)sZTDSYNcv5 vs. sZTDSYNcv6c)SYN10 vs. SYN20b) 11th to 23rd April 2016a)
16th to 31st Octobre 2018c) 6th to 21st January 2017d)
13th to 24th August 2018b)PWV (GNSS)a)
-0.1-0.3
0.10.0
Precipitation (synop)b)
Fig. 2: Kling-Gupta-efficiency for a) PWV
compared to 70 GNSS stations and b) precipi-
tation (245 synop stations) for all simulations in
spring 2016 (green), summer 2018 (red), fall
2018 (orange) and winter 2017 (blue).
altitu
de
[m
]P
WV
-Bia
s [m
m]
PW
V [m
m]
assim
ilatio
n ru
na
ssim
ilatio
n ru
n
mixing ratio
difference
[kg/kg]
