Cycling Variational Assimilation of Remotely Sensed Observations for Simulations of Hurricane Katrina
S.-H. Chen1 E. Lim2, W.-C. Lee3, C. Davis2, M. Bell3, Q. Xiao2, H.-C. Lin2, G. Holland2
Land, Air, and Water Resources, University of California, Davis, CA, USA
2 Mesoscale and Microscale Meteorology Division/NCAR, Boulder, CO, USA
3 Earth Observing Laboratory/NCAR, Boulder, CO, USA
1
Introduction
Results and Discussion
1000
990
SLP (hPa)
1000
950
990
980
940
970 0
6
12
960
18
Time (h)
24
30
36
950
940
(b)
920
OBS
ALL_2h
NORAD
910
900
0
6
CNTL
ALL_3h
12
▲ Fig. 4: 300-mb geopotential height and wind vectors from (a) AVN reanalysis,
(b) EXP1 ALL_3h, (c) EXP2 ALL_2h, and (c) EXP3 06h at 18Z 26 August 2005.
ALL
RAD
18
24
Time (h)
30
36
1000
990
SLP (hPa)
980
970
(c)
960
OBS
03h
09h
00h
06h
12h
Observations
0
6
12
18
Time (h)
24
30
36
(c)
(c)
▲ Fig. 3: Simulated tracks
from (a) EXP1, (b) EXP2,
and (3) EXP3.
Assimilated data
CNTL
CNTL
ALL
ALL
GTS + Sat + hourly Radar
ALL_2h
ALL_2h
GTS + Sat + 2-hly Radar
ALL_3h
ALL_3h
GTS + Sat + 3-hly Radar
NORAD
NORAD
RAD
RAD
(c)
1000
(d)
50
65
990
980
970
980
980
OBS
OBS
CNTL
CNTL
ALL
ALL
ALL_2h
ALL_2h
ALL_3h
ALL_3h
RAD
RAD
NORAD
NORAD
970
970
960
960
950
950
66
12
12
960
OBS
CNTL
ALL
ALL_2h
ALL_3h
RAD
NORAD
950
940
930
920
910
940
940
00
980
18
24
18
24
Time
(h)
Time (h)
30
30
36
36
970
960
OBS
03h
09h
950
00h
06h
12h
0
6
12
18
Time (h)
24
30
36
40
35
30
25
OBS
CNTL
ALL
ALL_2h
ALL_3h
RAD
NORAD
940
900
45
20
0
6
12
18
Time (h)
24
30
36
0
6
12
18
Time (h)
24
30
36
CNTL
ALL
ALL_2h
55
ALL_3h
RAD
50
NORAD
60
45
40
35
30
55
24
30
36
06h
09h
12h
6
12
35
30
20
18
Time (h)
03h
40
20
12
00h
45
25
6
OBS
50
25
0
(f)
65
OBS
60
10-m Maximum wind (m/s)
990
990
(e)
0
18
Time (h)
24
30
36
▲ Fig. 5: Simulated minimum sea level pressure (a-c) and maximum 10-m wind speed (d-f).
Experiment Design
EXP1
EXP2
(25/18Z-26/00Z) (26/00Z-26/06Z)
1000
SLP (hPa)
▲ Fig. 2: (a) The positions of Katrina with respected to
Miami radar, R, and the coverage of (b)
QuikSCAT and (c) Special Sensor Microwave/Imager
(SSM/I) satellite data from 18Z 25 August to 00Z 26
August, 2005 used in this study. The blue dot at the
tip of Florida is Katrina’s position at 0000 UTC 26
August, 2005.
(b)
990
SLP(hPa)
(hPa)
SLP
▲ Fig. 1: (a) Best track positions, (b) observations (pts)
and best track (line) maximum sustained surface
wind speed, and (c) pressure observations (pts) and
best track (line) minimum central pressure for
Hurricane Katrina, 23-30 August 2005. (Courtesy of
National Hurricane Center)
1000
1000
(a)
10-m Maximum wind (m/s)
(b)
SLP (hPa)
(a)
The simulated storms from EXP1 moved too slowly and meandered over southern
Florida. No improvement was made for simulated tracks after the use of any
observation (Fig. 3a). This is because the upper-level anticyclone did not intrude
southward into the northern Gulf of Mexico (Fig. 4a vs. 4b). The results were
greatly improved when the model started 6 h later (i.e., EXP2; Fig. 3b), in
particular with the assimilation of observations. The error was ~50 km throughout
the simulation period. The location of the upper-level anticyclone was
comparable with that from AVN reanalysis (Fig. 4a vs. 4c). Simulated tracks from
EXP3 were slightly deflected southward (Fig. 3c) because the upper-level
anticyclone intruded slightly too far south (Fig. 4a vs. 4d). However, these tracks
were much better than those from EXP1, which started data cycling with the
same first guess at 1800 UTC 25 August 2005. Results from EXP1-3 further illustrate
that the improvement to the simulated track was contributed by GTS data and/or
satellite data from 0000 UTC to 0600 UTC August 26.
10-m Maximum wind (m/s)
930
940
(b)
(d)
OBS
CNTL
ALL
ALL_2h
ALL_3h
RAD
NORAD
970
950
(a)
(c)
(b)
(a)
960
SLP (hPa)
Tropical cyclones (TCs) at landfall are one of the most dangerous natural disasters. Accurate TC data
analysis and forecasts are crucial for the protection of life and property. Despite recent progress in TC
track forecasting, intensity forecasting remains unsatisfactory primarily due to complicated processes
at multiple scales, including cloud-scale moist convection interacting with hurricane large-scale
environmental flows. To gain understanding of TC multi-scale processes and thus improve forecasts of
the rapid intensification or weakening of TCs, it is important to observe both the small-scale inner-core
structure and the large-scale environmental flows that have a profound impact on TC evolution.
Unfortunately, TC forecasting over the ocean remains a big challenge, especially during the period of
most rapid intensification and weakening, because of the lack of in-situ observations. Consequently,
specification of the model initial condition must rely on remotely-sensed techniques to retrieve the
critical parameters over both the inner-core and the vast area covered by TCs. This study assimilated
different scale observations from radar, satellite, and conventional instruments and assessed the
impact of these data on Hurricane Katrina (2005) simulations during one of its rapid intensification
periods (see the magenta lined box in Fig. 1) using the high-resolution Weather Research and
Forecasting (WRF) model.
(a)
980
No data
GTS + Sat
GTS + hourly Radar
EXP3
(25/18Z-26/06Z)
Assimilated data
00h
Sat + 25/18Z Radar
03h
GTS + Sat + 25/18Z & 25/21Z Radar
06h
GTS + Sat + 25/18Z, 25/21Z & 26/00Z Radar
09h
GTS + Sat + 3-hly Radar until 26/03Z
12h
GTS + Sat + 3-hly Radar until 26/06Z
Three sets of experiments, EXP1-3, with various initial times and data cycling periods were conducted.
The data cycling periods, which were applied to the coarsest three domains, were 1800 UTC August 25
to 0000 UTC August 26 (6 h), 0000 UTC to 0600 UTC August 26 (6 h), and 1800 UTC August 25 to 0600 UTC
August 26 (12 h) for EXP1-3, respectively. The different observations assimilated for each experiment
are shown in Table 1. The first two sets (i.e. EXP1 and EXP2) evaluated the impact of assimilating
different observations on Katrina simulations, while the last one (EXP3) examined the influence of
different assimilation periods for radar data. In EXP3, satellite and conventional data were assimilated
whenever data were available during the whole 12-h cycling period, but radar data were assimilated
only every three hours over different time periods (i.e., 0h, 3h, 6h, 9h, and 12h). Thirty-six hour model
integrations were then performed on all four domains after the data cycling.
Katrina was not only extremely intense but also exceptionally large. Unfortunately, the simulated Katrina was
smaller than observed for all experiments conducted here (figure not shown). Results from EXP1 show that the
assimilation of radar and conventional data had a positive impact on simulated storm intensities during the
first 24 hours. The influence of satellite data was also positive, though less significant, but was able to extend
over the whole 36-h simulation (Figs. 5a and 5d) since the coverage of satellite data was much larger than
that of radar. Results from EXP2 show great improvement in both simulated storm intensities (Figs. 5b and 5e)
and tracks after the use of observations. Finally, EXP1-3 (Fig. 5) indicated that the assimilation of radar data
at an interval of every three hours for a 6-h time period is optimal.
Summary
 Simulated Katrina was smaller than the observed size.
 The assimilation of observations improved simulated storm intensity for all three experiments and
improved simulated storm track for EXP2 and EXP3.
 Radar data influenced the first 24 hours of simulations, while the influence of satellite data lasted longer,
but was less significant.
 EXP3 confirmed that observations from 0000 UTC to 0600 UTC Aug 26 played a key role in improving
simulated tracks.
 Doppler radar data assimilation mainly contributed to the improvement of simulated hurricane intensity,
in particular during the early time period of simulations
 The assimilation of radar data at an interval of every three hours for a 6-h time period is close to an
optimal setting.