1. Evaluating the impact of the COSMIC-RO
bending angle data on prediction
the heavy precipitation episode
on 16 June 2008 during SoWMEX-IOP8
Shu-Chih Yang1, Shu-Ya Chen2, Shu-Hua Chen3,
Ching-Yuang Huang1 and Ching-Sen Chen1
1 National Central University, Taiwan
2 NCAR, Boulder
3 University of California, Davis
Special Thanks to Dr. Y.-H. Kao (NCAR), NSPO (Taiwan),
SoWMEX/TiMREX team and ECMWF/ROPP

2. Motivations
• The radio occultation(RO) observations have the advantage of
improving the temperature and moisture fields of the analysis.
• Compared with the RO refractivity observations, bending angle is
the upstream observation with fewer assumptions.
• EnKF uses the “flow-dependent“ multivariate error covariance so
that the dynamical uncertainties in the underlying flow can be
better represented.
Q: With the regional EnKF, can the RO bending angle
provide additional benefits in predicting severe
precipitating systems?
• The location and intensity of heavy
rainfall in Taiwan during early summer
seasons are less predictable due to the
complex synoptic-, convective-scale
dynamics and topography.

3. Extreme heavy Rainfall event on June 16, 2008
Forecast
Observation
In observation:
•Large amount of rainfall near the coastal region
•Limited rainfall in the mountainous region
In forecast (initialized from the global analysis):
• Prediction for rainfall intensity is fine but poor
in representing the location.
• predicts “excessive rainfall amount”, even in
the mountainous region, leading false alarms
in many regions.

4. Observation operators for RO bending angle
Local RO operators are used to simulate the refractivity and
bending angle.
a(a) = -2a
d(lnn)/ dx
(x2
- a2
)1/2a
¥
ò dx, x = nrKursinski et al. (1997):
Tangent
point
• Local refractivity
• Local bending angle (Chen et al., 2010)
Below model top:
Above the model top: α is computed following Healy and Thépaut (2006)
Da = -2a
d lnn
dx
1
(x +a)
1
(x -a)
dx
xi
xi+1
ò
GPS
α

5. WRF model setup
DA system:
WRF-LETKF (Yang et al. 2012, 2013a)
Experiment setup
•Analysis is performed every 6-hour at largest
domain with the WRF-LETKF system
•Experiment period: 2008/06/13 00Z − 06/16 18Z
•Observations:
 Conventional: raob, upper air report,
dropsondes, surface stations
 COSMIC RO refractivity or bending angle
High resolution Forecast initialized at 06/15 12Z
• Nested domain 27km-9km-3km
27km
9km
3km
Observations
CNTL Convention
BND Convention + Bending angle
REF Convention + Refractivity

6. Error covariance of HPb=COV(Hεb, εb)
Bending angle vs. Refractivity
Limit the spread of
moisture gradient
Bending angle Refractivity
point error covariance: simulated COSMIC RO observation and Qv at 850hPa

7. Temperature and moisture fields
The bending angle data
1. increase the moisture in PBL
2. reflect the cooling effect induced by
the first heavy rainfall event.
★

8. Impact on the variables (direct vs. indirect)
The moisture content carried by southwesterly is strongest with BND!
Total precipitable water
2008/06/15 18Z
BND-CNTL
REF-CNTL
Difference in V
Direct impact Indirect impact
OBS( AIRS, AMSU, SOUND) CNTL
REFBND

9. Rain Forecast: total precipitation on 06/16
•In the CNTL forecast, heavy
rainfall locates mostly in the
mountainous region of southern
Taiwan.
•The location and intensity of the
heavy rainfall is improved with
the RO data.
•Particularly, the location and
intensity of the heavy rainfall in
the BND forecast are very similar
to the observations.
CNTL REF
BND OBS
Initialized at 12Z 15 June

10. Factors for predicting the locations and intensity of
heavy precipitation
Convergence
(Jun15 12Z, initial)
T (contour) vs. w (shading)
(Jun15 22Z, 9-hr fcst)Hourly rainfall rate
Convergence
+
Cooling from
land-breeze
(Xue et al. 2012,
Chen et al. 2012)
ocean-> coast-> mountain

11. Impact from Bending angle
Full effect Disable the impact on winds Disable the impact on moisture
Moisture (color) vs. convergence (contour)
• In BANGLE analysis, strong convergence and high moist region appear
offshore of southwestern Taiwan.
• Features related to heavy precipitation didn’t appear if the bending
angle doesn’t update the moisture field.
• The main improvement is in the moisture field and the wind adjustment
is the accompanied effect.
Detangle the RO effect with the variable localization
method (Kang et al. 2011)

12. Sensitivity experiments (I)
Total water vapor on Jun15 18Z
Assimilating the bending angle data
near Taiwan is able to reproduce the
local high moist region at
southwestern Taiwan.
A local high moist region at
southwestern Taiwan reflects the
condition before heavy precipitation
starts.
Exps Obs on Jun15 18Z
BANGLE all RO bending angle
BANGLE_NoTW Remove the RO profile near Taiwan
BANGLE_TW only the RO profile near Taiwan

13. Forecast sensitivity respect to bending angle
positive
impact
negative impact
Positive impact from RO
close to the heavy
precipitated region.
The low-level
observations
are important
for correcting
the moist error.
• Observation impact derived with
Ensemble-based Forecast
Sensitivity (EFSO, Kalnay et al.
2012)
• Forecast error is defined with the
moist energy norm.

14. Summary
The COSMIC RO data has positive impact to regional
NWP and heavy rainfall prediction!
• Bending angle is sensitive to vertical moisture gradient.
• Assimilation of bending angle improves moisture in lower
troposphere and has significant impact on improving the
heavy rainfall forecast for the SoWMEX-IOP8 event.
– The improvements include both the location and intensity of
the extreme heavy rainfall by providing a favorable condition
for the strong convective system with “local characteristics”.
• The positive impact can be confirmed with the EFSO
method.

15. Refractivity vs. Bending angle
moisture
Vertical gradient
of moisture
Spread of bending
angle represents the
uncertainties of the
vertical variations of
moisture
Qv
Ens spread
of Qv
Ens spread
of dQv/dz
Ens spread
of REF
Ens spread
of BND
dQv/dz