Assimilation of GPS Radio Occultation Data for an Intense Atmospheric River with
the NCEP Regional GSI System
Zaizhong Ma1,2, Ying-Hwa Kuo2, Bin Wang1 , Wan-Shu Wu3, Sergey Sokolovskiy2,
Paul J. Neiman4 and F. Martin Ralph4
1
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and
Geophysical Fluid Dynamics (LASG), Institute of Atermospheric Physics (IAP), Chinese
Academy of Sciences (CAS), Beijing 10029, China
2
University Corporation for Atmospheric Research, Boulder, Colorado
3 NOAA/National Centers for Environmental Prediction, Washington D.C.
4
NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, CO
October 16, 2008
Submitted to Monthly Weather Review
Corresponding author address: Dr. Ying-Hwa Kuo, National Center for Atmospheric
Research, Mesoscale and Microscale Meteorology Division, P. O. Box 3000, Boulder,
CO 80307-3000. Email: kuo@ucar.edu.
1
Abstract
Atmospheric River (AR) that often takes place over the eastern North Pacific Ocean is a
type of intense storm characterized with a narrow region of strong horizontal water vapor
flux associated with polar cold fronts. Many studies show that AR is an important
contributor to extreme precipitation and flooding events of the west coast of the United
States. Except for integrated water vapor (IWV) observations gathered from special
sensor microwave/imager (SSM/I) and special field experiments, the ARs remain poorly
observed by the existing global observing system. With the launch of the Constellation
Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission in April
2006, ~2,000 GPS radio occultation (RO) near-real-time soundings per day uniformly
distributed around the globe are now routinely available in all weather conditions. Such
GPS RO observations will provide valuable observations for AR events.
In this paper, we examine the impact of COSMIC GPS RO observations on an intense
atmospheric river event that devastated portions of the Pacific Northwest with torrential
rains and severe flooding on 6-7 November 2006. We first conducted a one-week data
assimilation cycling experiment from 3 to 9 November 2006 using NCEP regional
Gridpoint Statistical Interpolation (GSI) analysis system coupled with Advanced
Research WRF (ARW) forecast model. GPS RO soundings from COSMIC and CHAMP
missions, in addition to all other observations routinely used by NCEP, were assimilated.
The assimilation of GPS RO profiles made use of a local refractivity operator as well as
an advanced non-local excess phase operator which considers the effects of atmospheric
horizontal gradients. The results show that the assimilation of GPS RO soundings
improved the moisture analysis and precipitation forecast for this AR event. The use of
nonlocal excess phase observation operator produced larger and more robust analysis
increments, and led to further improvement in precipitation forecasts.
2
1. Introduction
Water vapor is one of the most important variables of earth's atmosphere. Its distribution
and transport plays a crucial role in weather and climate. Because the saturation vapor
pressure is a function of temperature, the warmer the air temperature, the more it can hold
the water vapor. Therefore, most of earth’s water vapor is located over the equatorial
region. Atmospheric River (AR) is a narrow region of strong horizontal water vapor flux
associated with polar cold fronts. The AR over the eastern North Pacific Ocean often
brings a significant amount of water vapor, causing extreme precipitation and flooding
events over the west coast of the United States. The AR plays a significant role in
transporting moisture from the tropical reservoir to the middle latitudes (see Fig. 1a). The
AR can yield heavy rainfall and flooding when it moves inland. These types of storms
often take place over the east Pacific Ocean based on record of the past ten years. Many
studies have shown that ARs are critical contributors to extreme precipitation and
flooding events of the west coast of the U.S. (Zhu and Newell 1998; Ralph et al. 2004;
Bao et al. 2006; Ralph et al. 2006; Neiman et al. 2007). [Kuo: We need to make sure that
you don’t copy the entire section from other paper. If so, then it needs to be rewritten
using our own words.]
Despite the importance of AR to both weather and climate, its analysis and prediction
remain a challenge for operational numerical weather prediction (NWP) due to the lack
of traditional meteorological observations over the ocean. The optimal use of satellite
data is crucial for improving the skills of NWP models. The integrated water vapor
(IWV) observations gathered from special sensor microwave/imager (SSM/I) (Hollinger
et al. 1990) payloads aboard polar-orbiting satellites have proven crucial for monitoring
these transient features over oceanic regions (Ralph et al. 2004, 2006; Neiman et al.
2007). However, SSM/I observations contain no information on the vertical structure of
moisture distribution associated with AR events.
The atmospheric limb-sounding technique making use of radio signals transmitted by the
GPS satellites has emerged as a powerful and relatively inexpensive approach to sound
3
the global atmosphere in all weather (Ware et al. 1996; Kursinski et al. 1997). As
demonstrated by the proof-of-concept GPS Meteorology (GPS/MET) experiment and the
Challenging Minisatellite Payload (CHAMP; Wickert et al. 2001) and the Satellite de
Aplicaciones Cientificas-C (SAC-C; Hajj et al. 2004) missions, GPS radio occultation
(RO) sounding data are shown to be of high accuracy and vertical resolution. With the
launch of the joint U.S.-Taiwan COSMIC/FORMOSAT-3 (hereafter COSMIC, Anthes et
al. 2008) mission in April 2006, GPS RO soundings have played increasing role in
weather prediction and climate monitoring. Since August 2006, COSMIC has been
providing ~2,000 GPS RO profiles per day, uniformly distributed around the globe to
support operational and research communities.
An important advancement in COSMIC is the use of a new open-loop tracking technique
(Sokolovskiy 2006), which allows about 90% of the soundings to reach to within 1 km of
the surface (Anthes et al. 2008). As a result, COSMIC GPS RO soundings can be used
to observe the structure of the tropical atmospheric boundary layer (Sokolovskiy et al.
2008), providing valuable information on low-level atmospheric water vapor associated
with an AR event (Neiman et al. 2008; Wick et al. 2008). With uniform global coverage,
GPS RO data are extremely valuable as inputs to operational NWP systems. Kuo et al.
(2000) discussed various approaches to assimilate GPS RO soundings into weather
prediction models. Recent studies have shown that the assimilation of GPS RO soundings
has significant positive impact on the analysis and prediction of Hurricane Ernesto’s
genesis (Liu et al. 2008) and Typhoon Shanshan and Mei-yu precipitation systems (Kuo
et al. 2008). In this study, we examine the impact of GPS RO soundings on an intense AR
event formed over the eastern North Pacific Ocean and made landfall on the Pacific
Northwest of the U.S on 6-8 November 2006. Wick et al. (2008) compared integrated
water vapor retrievals from SSM/I and COSMIC and showed that COSMIC GPS RO
data can serve as an important new validation data source for SSM/I-derived products
over the ocean; Neiman et al. (2008) performed a detailed analysis of the moisture
structure associated with the November 6-7 AR event, using the moisture profiles retried
from twelve COSMIC GPS RO soundings that cut across the AR. This paper is a sequel
to these earlier work, with a focus on assessing the impacts of GPS RO soundings on the
4
analysis and prediction of AR and its associated precipitation. The assimilation is
performed with the NCEP regional GSI data assimilation system (Wu et al. 2002)
coupled with Advanced Research WRF (ARW) model. We also will make further
comparison of local refractivity and non-local excess phase observation operators which
were discussed in an observing system simulation experiment study (Ma et al. 2008).
The remainder of the paper is arranged as follows: Section 2 provides the synoptic
overview of the November 6-7, 2006 AR case; Section 3 discusses the configuration of
the numerical model and our experiments on the analysis and forecast of the AR event
are presented in Section 4; finally a summary and conclusions are given in Section 5.
2. Synoptic overview of the atmospheric river event (on 3-9 November
2006)
The case studied in this paper was an intense AR event that took place over the eastern
North Pacific Ocean in early November 2006. Neiman et al. (2008) provided a detailed
analysis of this event, using available satellite and other traditional observations. The
November 6-7 2006 AR event was ranked by Neiman et al. (2008) as the first- or secondmost intense storm among the 119 cases that occurred over the past nine years. Here, we
only provide a brief description of this event. The November 6-7 AR event is best
illustrated by the NOAA GOES infrared satellite images (Fig. 1), which shows the steady
stream of moisture moving into the Northwest of U.S. from the tropical Pacific. Unlike
most “Pineapple Express” cases, which last a day or so, the moist subtropical air in this
event remained over the Pacific Northwest for 3-4 days. As a result, heavy rainfall
occurred over the eastern Pacific Ocean and along the U.S. West Coast, causing major
flooding and damaging debris flows on 6-8 November 2006. According to Neiman et al.
(2008), numerous locations in northwest Oregon and southwest Washington reported
more than 7 inches precipitation in a single day (most of them on November 6). Lees
Camp in the Oregon Coast Range, a NOAA hourly rainfall station, recorded 14.3 inches
precipitation on the 6th, which broke Oregon’s 24-hour rainfall record. In summary, this
5
intense atmospheric river event was characterized by an extended period of heavy rainfall
that set records and, at some locations, exceeded 100-year precipitation thresholds for 24h periods. [Kuo: How did NAM perform in this case? This section needs to be beefed up.]
[Kuo: Zaizhong, did you get answer on this question? I remember that you have provided
some figures. Also, we need to make sure that you don’t copy things from Neiman paper,
word for word.]
3. Numerical aspects and experimental set-up
a. The GSI-ARW data assimilation and modeling system
In this paper, the assimilation of GPS data is performed using the NCEP regional Gridpoint Statistical Interpolation analysis system (GSI; Wu et al. 2002), coupled with the
Advanced Research WRF (ARW) model. The GSI system replaced the NCEP Spectral
Statistical Interpolation analysis system (SSI; Parish and Derber 1992) as the NCEP
operational data assimilation system in May 2007. The GSI has the advantage of
incorporating locally defined inhomogeneous anisotropic background errors. The GSI is
an incremental three-dimensional variational data assimilation system (3D-Var). Its
primary objective is to minimize the cost function, which measures the distance between
observations and a priori or forecast information (i.e., background). GSI can assimilate a
wide range of conventional observations, satellite retrieval products, and satellite
radiances data. With the use of Community Radiative Transfer Model (CRTM), GSI can
assimilate both clear and cloudy radiance data. In addition, the operational GSI system
also assimilates the GPS RO data using the local refractivity observation operator
(Cucurull et al. 2007).
The numerical model used is the non-hydrostatic, 36-km/12-km two-way nested grid,
mesoscale model WRF ARW, which has been developed primarily at the National Center
for Atmosphere Research (Skamarock et al. 2005). The model physics for both 36 km
and 12 km grids are the same, which include the Rapid Radiative Transfer Model
6
(RRTM) long wave radiation, the Dudhia shortwave radiation scheme, the YSU PBL
scheme, the Kain-Fritsch (new Eta) scheme cumulus parameterization, and the new
Thompson graupel microphysics scheme. Figure 2 shows the grid configuration used for
the simulations. The grid’s dimensions are 135x118 and 160x160 in east-west and northsouth directions in the 36-km and 12-km domains, respectively. The assimilation was
performed only on the coarse domain with 36 km resolution. The model has 38 vertical
levels with the top of the model located at 50 hPa.
b. GPS local and non-local operators
During an occultation event, the GPS receiver onboard a LEO satellite provides very
accurate measurements of the phase and amplitude of radio signals transmitted by the
GPS satellites. Based on these measurements together with the knowledge of the
occulting geometry (i.e., the precise positions and velocities of the GPS and LEO
satellites), vertical profiles of ray bending angle are derived under the assumption of a
spherically symmetric refractivity field. The atmospheric refractivity is then retrieved via
the Abel transform. Finally, the information on earth’s atmosphere can be inferred from
refractivity (N), which is a function of temperature (T; K), pressure (p; hPa), water vapor
pressure (e; hPa) after the ionospheric effect is removed in the neutral atmosphere (Smith
and Weintraub, 1953).
p
N 77.6 
T
e
3 .7 35 12 0
T
(1)
A detailed description of GPS local refractivity (Cucurull et al. 2007) and non-local
excess phase (Sokolovskiy et al. 2005a; Liu et al. 2008; Ma et al. 2008) operators in the
GSI system can be found in previous work and only a brief review is provided here. The
local refractivity operator is a simple and low-computational-cost approach to assimilate
the GPS RO observations in a data assimilation system. After the first guess fields of
temperature, pressure and water vapor on the model grid are interpolated to the RO
observations’ locations, the local refractivity is calculated based on Eq. (1). For the nonlocal excess phase operator, observed and modeled RO refractivities are integrated along
a fixed ray path that do not depend on refractivity. Therefore, one can calculate a new
7
observational “variable”, excess phase, with the non-local operator in the GSI system
(Ma et al. 2008).
Since the calculated local refractivity at the ray tangent point is a point value and Abelinverted GPS RO refractivity is a weighted average of the atmospheric refractivity along
the ray path and above (Ahmad and Tyler 1998), significant errors could arise over
regions with strong horizontal gradients of refractivity if the local refractivity operator is
used to assimilate the GPS RO refractivity (Sokolovskiy et al 2005).
c. GPS RO Observations
GPS RO soundings are obtained from the COSMIC Data Analysis and Archival Centre
(CDAAC) in BUFR (Binary Universal Format for data Representation) format
(www.cosmic.ucar.edu), which provides soundings of bending angle as a function of
impact parameter, refractivity as a function of geometric height, and other derived
atmospheric parameters as a function of the geometric height. The data also contains
additional information related to the geometry of the radio occultation. In addition to
COSMIC RO profiles, CHAMP RO profiles available for this case were also used in this
study. The distribution of GPS RO soundings used in this AR case is shown in Fig. 3.
The data are scattered geographically and homogeneously distributed over the entire
model domain (Fig. 2), with 370 soundings from COSMIC (Fig. 3a) and 63 from
CHAMP (Fig. 3b) during the study period of 3-9 November 2006. COSMIC soundings
are about 6 times as many as CHAMP. The total number of soundings available within a
6-h time window is showed in Fig. 4. We find that GPS soundings are not evenly
distributed in time. In general, more data are available centered at around 0600 UTC. The
main reason is that the COSMIC constellation of six satellites was still being deployed to
their final orbits in November 2006. In addition to the CDAAC quality control (QC,
Rocken et al. 2000; Kuo et al. 2004), all of the GPS RO observations used in this study
also had to pass two other QC procedures within the GSI data assimilation system. One is
the gross check which is the standard QC for all types of observations being assimilated
8
into the system, the other is special statistics for GPS refractivity based on one-month
comparison between GPS observations and model analyses (Cucurull et al. 2007). [Kuo: I
gave these comments earlier. However, I don’t think they have been improved.]
d. 3DVAR experiment set-up
To assess fully the impacts of GPS RO soundings on this intense atmospheric river event,
two sets of experiments are conducted: First, a cycling assimilation experiment from
0000 UTC 3 November to 1800 UTC 9 November 2006, in which GSI is used for the
analysis component and WRF ARW model is used for the 6-h forecast component.
Second, a 24-h forecast impact experiment from 1200 UTC 6 is carried out with the WRF
ARW model. The experiment design is shown in Fig. 5.
Three experiments have been performed in each set:
1) The “CTRL” experiment that assimilates the same conventional and satellite
measurements as used in the NCEP operations.
2) The “LOC” experiment is identical to the CTRL, except that GPS RO refractivity
profiles are assimilated with the local refractivity operator.
3) The “NON-LOC” run is identical to the CTRL, except that GPS RO refractivity
profiles are assimilated with the non-local excess phase operator.
All assimilation experiments are performed with continuous cycling with 6-h assimilation
window. The background fields are obtained from 6-h WRF-ARW forecasts initialized
with NCEP AVN analyses. However, the background fields in the subsequent cycling
assimilation are the 6-h WRF forecast initialized with GSI analysis. The background
error (BE) is estimated by the NMC method (Parrish and Derber 1992) via forecast
difference statistics. Figure 4 shows the availability of GPS RO soundings within each 6h window in the cycling experiment. The data volume at 0600 and 1800 UTC always is
much larger than those at 0000 and 1200 UTC every day. We perform continuous
assimilation up to one week, including a total of 28 analysis-update cycles. The CTRL,
LOC and NON-LOC runs are performed with the same cycling assimilations, each with
their own data sets, respectively. In order to evaluate the impact on forecast, one-day free
9
forecast run is carried out at 1200 UTC 6, after GSI assimilation is completed at that
time.
4. Results
a. Cycling experiments
The impact of GPS RO data assimilation using local refractivity operator with the global
GSI system has been evaluated by Cucurull et al. (2007). In this study we examine the
performance of the GPS non-local excess phase assimilation algorithm using the
fractional difference ( Sobs  Smod ) / Sobs (where S obs and S mod are the observational
variable, excess phase, and a corresponding model counterpart for NON-LOC) in Figure
6. In general, the statistics of the observation minus background (O-B) and observation
minus analysis (O-A) departure distributions provide useful diagnostic information on the
effectiveness of assimilation. The O-B departures represent the difference between the
real observations and 6-h model prediction (which is used as the background). Figure 6
presents a comparison of the fractional differences between O-B and O-A (where B and A
are background and analysis projected in the space of observation) in the one-week
cycling assimilation of refractivity profiles for the NON-LOC experiment. The statistics
(Fig. 6a, b and c) is calculated for model levels below 5 km and from 3 to 11 November
2006. A total of 4748 observations1 were used during this period. Figure 6a presents the
percentage differences between the observations and model forecasts of excess phase. We
note the maximum and minimum departure varies from approximately –5 to 4%. Figure
6b is the percentage differences between the observation and the analysis at the end of the
first outer loop iteration, [Kuo: Again, this comment is not taken care of.] and Fig. 6c is
between the observation and the analysis at the end of the second outer loop iteration, the
final analysis. Obviously, both Figs. 6b and c have smaller departure than O-B (Fig. 6a),
and Fig. 6c is slightly improved over Fig. 6b with the second minimization step, which is
1
Observation data at each level for each sounding is considered as one independent
observation, so the number is much higher than the number of soundings.
10
our expected result from 3D-Var data assimilation. The mean departure decreased about
two orders of magnitude from O-B (-0.1835) to O-A (-0.0546), while the standard
deviation also reduces about 3 times from O-B (1.01928) to O-A (0.34664). It is evident
that the analysis fits the observations better after the assimilation procedure. The fact that
the distance between the observation and analysis becomes smaller at the end of the first
and second minimizations suggests a good behavior of the minimization algorithm in the
3D-Var GSI system. [Kuo: One review would comment that if we really want to show
that the observation does have an impact, then we should see continued reduction of O-B
with time. This would mean earlier observations improve the quality of short-term
forecast.]
The same statistics are also presented for different heights (5~8 km and above 8 km) in
Figs. 6d~i. This allows us to examine the performance of GPS refractivity data
assimilation in the GSI system at different height. Figures 6d~f are the results for the
height of 5~8 km, and Figs. 6g~i for the height of above 8 km. Similarly, the statistics for
both 5~8 km and over 8 km give good performance of GPS refractivity profiles data
assimilation with non-local excess phase operator. Comparing with the behavior from
non-local at different height ranges shown in Fig. 6, it is obvious that the largest
improvements with GPS non-local operator are in the lower troposphere (below 5 km).
This suggests that the GPS RO provides most useful information in the lower troposphere
to improve the GSI analysis. Our explanation is that most of water vapor is located in the
lower level. On the other hand, model in general has poor performance in predicting the
distribution of water vapor in the lower troposphere. The fact that O-A is smaller than OB indicates that GPS RO refractivity observations have been assimilated successfully by
the GSI system with non-local excess phase operator.
In this section, we choose the analysis at 1200 UTC 6 November 2006 to illustrate the
influence of GPS RO profiles on the analysis and prediction of this event. This is the time
that we choose to initialize the WRF model for 24-h forecast experiments with and
without the assimilation of GPS RO data. This selected analysis is extracted from the
one-week cycling experiment starting from 0000 UTC 3 November 2006. Figure 7
11
shows the precipitable water vapor (PWV) at 1200 UTC 6 November 2006 derived from
the analysis with NON-LOC experiment. Comparing with the SSM/I satellite image (Fig.
1), the bend of atmospheric river shown in the PWV analysis is very similar to that of the
SSM/I satellite infrared water vapor (IWV) image. This similarity is reflected in both the
strength and range of PWV values associated with the atmosphere river.
We now compare the impact on the analyses with and without the assimilation of
GPS RO data in Fig. 8, using local and non-local observation operators. The difference
field in PWV between CTRL and LOC is presented in Fig. 8a, and the difference
between CTRL and NON-LOC in Fig. 8b. These two difference fields are both valid at
1200 UTC 6 November 2006. From the difference field, we can see the impacts of GPS
RO data assimilation on this event mainly occur primarily in the vicinity of the
atmospheric river using either local or non-local operator to assimilate the GPS RO
soundings, even though the distribution of GPS RO soundings is relatively uniform over
the assimilation domain in the cycling experiments (as shown in Fig. 3). Another
noticeable result is that the NON-LOC experiment produces larger changes (both in terms
of the amount and area) than LOC (comparing Fig. 8a with Fig. 8b). The mean absolute
impact of NON-LOC is about 1.5 times than Local, which is consistent with the results
from OSSEs (Ma et al. 2008). [Kuo: Did you actually calculate this number at 1200 UTC
6 November?] It is encouraging that the real data experiments give a similar conclusion
that non-local operator with the assimilation of excess phase produces more significant
changes on the moisture analysis than the simple refractivity operator, especially over the
region of the atmospheric river where there are strong moisture gradients.
To further analyze the impact of GPS RO data on the analysis of the atmospheric river,
we show in Fig. 9 the cross-section of the potential temperature and water vapor ratio
from the surface to 150 hPa at 1200 UTC 6 November along the line AB in Fig. 8b from
the NON-LOC expeirment. [Kuo: From which experiment? NON-LOC? We need to
specify.] This cross section cuts through the atmospheric river just prior to its making the
landfall over the west coast. The cross section is located at the tail end of an upper-level
front. A band of enhanced water vapor ratio (exceeding 13 g kg-1 near the surface)
coincides with the location of high IWV amount as seen from the SSM/I satellite image
12
(Fig. 1). Figure 10 shows the differences in water vapor ratio and potential temperature
between the LOC and NON-LOC with the control run at 1200 UTC 6 along the same
cross section. The assimilation of GPS RO data in both experiments adds considerable
more moisture in the lower atmosphere. Comparing with LOC, NON-LOC produces
larger changes in the moisture field. For the potential temperature field, the changes are
quite different. The LOC produces warming near the atmospheric river in the lower level,
while NON-LOC has very little changes. At this time, it is hard to say which experiment
produces better results. This will require a careful verification against observations,
which is the subject for the following section.
b. Verification of 6h Forecast
The basic analysis problem of the GSI system with incremental approach is to minimize
the cost function as following:


1 T
T

1

1
J
(
x
)

(
x

x
)

(
x

x
)

y

H
(
x
)
R
y

H
(
x
)




b
b
0
b
0
b
2

J
(
x
)

J
(
x
)
b
o
(2)
Where, J b ( x) is the background term, and J o ( x) is the observation term.
In order to conveniently evaluate the performance of GPS RO data assimilation, the
observational term of the cost function in Eq. 2 is written as:
Jx
()

J
(
x
)

J
(
x
)
o
G
P
S
o
t
h
e
r
s
(3)
The departure y0 H(x) between observations and modeled observation projected from
state vector is called innovations when state vector is x  xb , and is called analysis
residuals when the state vector is x  xa . Here, we make another state vector as x  x6h ,
the 6-h forecast initialized by each 3D-Var analysis in the cycling, which is to evaluate
the performance of 6-h model forecast after the assimilation of GPS RO data. Therefore,
J GPS ( x) here stands for the sum of departure between all GPS observations and 6-h
forecast GPS soundings. All of the J GPS ( x) in the cycling data assimilation experiments
13
are calculated to fit to observed refractivity ( N ). Actually, J GPS ( x) represents the sum of
departures between all observed GPS soundings and forecasted values, this provides an
assessment of the performance of GPS RO data assimilation.
A comparison of J GPS ( x) in terms of refractivity from CTRL, LOC and NON-LOC
experiments as a function of time is shown in Fig. 11. If we also look at Fig. 4 (the
distribution of GPS sounding with time), we can see that the value of cost function of
refractivity is larger when more GPS soundings are available. The value of cost function
for LOC or NON-LOC is smaller than that of CTRL, when the cost function is above
1000 at the time of 06 UTC for 3~6 November, and 00 UTC for 7 November. We also
want to compare the performance of GPS RO with the local and non-local operators.
Figure 11 shows that NON-LOC has a smaller value of J GPS ( x) , which mean it fits
better to the observed refractivity than LOC at most assimilation times. One may ask
what causes the LOC and NON-LOC experiments to have different performance, while
they assimilate the same data with the same data assimilation system? The only
difference between these two experiments is the different observation operators used for
the assimilation of GPS RO refractivity data. Obviously, there are many GPS RO
soundings located in and around the atmospheric river in every assimilation cycle, where
there are significant moisture horizontal gradients. Theoretically, the simple local
refractivity operator cannot consider properly account for effects of strong horizontal
gradients, while the non-local excess phase operator, which is a two-dimensional operator
which is capable of taking account of the along-track refractivity horizontal gradients.
This provides an explanation for the superior performance of the non-local excess phase
operator.
To gain insights on the impact of GPS RO data assimilation, we show in Fig. 12 the
vertical profiles of the mean and RMS errors of 6-h WRF forecasts (which serve as the
“background” for the data assimilation) verified against GPS RO refractivity
observations. It is clear that both the mean and RMS errors of LOC and NON-LOC with
GPS RO data are smaller than CTRL at all model levels, especially at the lower level
14
(below 5km). [Kuo: It would be good to show the data counts as a function of height.
This gives a level of “confidence” in the assessment of the data assimilation
performance.] The fact that 6-h forecasts based on analyses with the assimilation of GPS
RO data verified better with the observations indicates that these analysis are improved
with the assimilation of the GPS RO data (with either local or non-local observation
operators). For example, the assimilation of GPS RO measurements reduce a negative
bias in the layer between 2 to 5 km from ~ -1.2% (for CTRL) to -1.0% for LOC and
NON-LOC. A closer look shows that NON-LOC has superior performance (more
accurate 6-h forecast), particularly in terms of rms error, to the LOC experiment. Again,
giving support that the use of non-local excess phase observation operator can assimilate
the GPS RO data more effectively for this atmospheric river event with significant
horizontal gradients. [Kuo: We should show standard deviation instead of rms. RMS is
heavily weighted by the mean error.]
[Kuo: Again, I think we only need to show the verification in terms of refractivity, and no
need to show excess phase (Fig. 14).]
c. 24h forecast impact experiments
In this atmospheric river event, an intense storm ultimately made heavy landfall across
the Pacific Northwest of the U.S., mainly in the Washington and Oregon states, on 6~8
November 2006. To further evaluate the GPS RO observation impact on this heavy
rainfall event, we performed a 24-hr forecast with 36-km higher resolution over the
second domain in Fig.2 starting at 1200 UTC 6, as shown in Fig.5. The 24-h
accumulative precipitation was shown in Fig.15, and panel (a) shows the 1-Day gauge
observation validating at 1200 UTC 7. Three big heavy rainfall zones distribute in both
Washington and Oregon states. CTRL run without GPS RO observation produces the
similar precipitation to gauge including location and distribution, but intensity is still a
little weaker than gauge observation. Comparing with the forecast from CTRL (Fig.15 b),
LOC (Fig. 15c) and NON-LOC (Fig.15d) , we find that assimilating GPS RO information
produce more precipitation and make it much closer the gauge observation (Fig15 a),
although the positive impact is not obvious.
15
5. Summary and Discussion
In this study, the impact of GPS RO profiles on an intense atmospheric river case has
been evaluated with both local refractivity and non-local excess phase operators in the
GSI/WRF system. In the cycling data assimilation experiment, three runs (CTRL, LOC
and NON-LOC) are conducted to examine the performance with the GPS RO profiles
data assimilation on this storm from 3 to 9 November 2006. Since this intense AR event
mainly took place over the data-sparse eastern North Pacific Ocean, GPS RO soundings,
especially the near-real-time COSMIC data that provide the important new dataset,
significantly increased the observational information. The results in the cycling and 24-h
forecast experiment present GPS RO data have a good potential to improve the
simulation abilities of AR case as a new type satellite observation.
The feasibility of GPS RO soundings data assimilation in the NCEP GSI system is tested
with the advanced non-local excess phase operator in this atmospheric river case.
Through comparing with simple local refractivity operator, non-local excess phase show
more potential to represent the advantage of GPS RO data, which produce more accuracy
in the strong horizontal moisture/temperature gradient areas without more significant
computational cost. It also gives the further proof in the real case study to support the
conclusions drawn from OSSE (Ma et al. 2008).
The numerical simulation experiments have been conducted in order to examine the
impact of assimilated GPS RO data on 24-h precipitation forecasts after AR landfalling.
Results show significant differences among experiments with initial conditions derived
from analysis with/without assimilation of GPS RO profiles data. More moisture can be
obtained with assimilation of GPS RO data, especially with non-local excess phase
operator in the GSI system. The 24-h forecast can produce more precipitation with nonlocal operator initialization than local operator.
16
This study has demonstrated the potential benefits of GPS RO data on numerical
simulations of an intense atmospheric river with the GSI system. Research on the impact
assessment for the GPS non-local excess phase operator in the NCEP GSI system with a
real case is an quite important step towards its operational application. It is recognized
that a preferred way to evaluate the impact of GPS RO profiles data on strong weather
phenomena like AR would be to consider carefully.
Acknowledgments:
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20
Table 1: Locations, dates, times, and minimum occultation altitudes of GPS RO
soundings used to construct composite thermodynamic soundings on the north side of the
atmospheric river of 6-8 November 2006.
Sounding #
Latitude
(°N)
Longitude
(°W)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
43.853
45.729
37.404
25.960
54.134
43.416
21.805
53.281
22.506
51.880
23.460
52.998
52.758
22.254
48.127
31.800
41.887
50.264
52.962
33.819
48.744
46.915
29.637
36.673
119.125
153.705
116.654
124.612
109.756
111.590
123.969
132.938
131.918
140.375
138.557
108.337
101.932
100.800
114.775
Date
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
6-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
21
0816
0827
0937
1855
0354
0423
0446
0455
0456
0548
Minimum
occultation altitude
(m)
424
837
1048
125
327
475
99
26
120
672
0732
1950
2216
2357
0302
0330
0358
0408
0424
0435
0524
0524
295
68
120
613
341
1150
142
346
336
257
598
20
Time
(UTC)
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
29.417
48.015
47.530
32.164
53.890
49.722
50.087
23.628
21.835
26.574
23.612
32.068
33.110
44.728
42.255
55.033
26.612
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
7-Nov-2006
22
0534
0600
0608
0633
0705
0845
2
443
302
330
1
446