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Study of Water Vapor Variations Associated with Meso-γ Scale Convection: Comparison between GNSS and Non-Hydrostatic Model Data

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Water vapor variations associated with a meso-γ scale convection were investigated using GNSS (Global Navigation Satellite System) derived PWV (precipitable water vapor) and high resolution numerical model data with a 250 m horizontal grid interval. A rapid increase of GNSS-derived PWV that occurred prior to the initiation of surface rainfall was well simulated by the numerical model. In the model, PWV values began to increase 16 min before the rainfall occurred at the surface. A local maximum of PWV was formed because of the generation of shallow free convection and surface water vapor flux convergence due to a lifting of an air parcel at approximately 1 km elevation by a preceding surface wind convergence. Due to the existence of a stable inversion layer between 2.2 and 3.5 km elevation, the shallow free convection took 11 min to rise above the inversion layer to form a deep convection. These results suggest that observation of local distributions of GNSS-derived PWV is useful for monitoring the generation of deep moist convection.
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27SOLA, 2015, Vol. 11, 27−30, doi:10.2151/sola.2015-007
Abstract
Water vapor variations associated with a meso-γ scale convec-
tion were investigated using GNSS (Global Navigation Satellite
System) derived PWV (precipitable water vapor) and high resolu-
tion numerical model data with a 250 m horizontal grid interval.
A rapid increase of GNSS-derived PWV that occurred prior to the
initiation of surface rainfall was well simulated by the numerical
model.
In the model, PWV values began to increase 16 min before
the rainfall occurred at the surface. A local maximum of PWV
was formed because of the generation of shallow free convection
and surface water vapor ux convergence due to a lifting of an air
parcel at approximately 1 km elevation by a preceding surface
wind convergence. Due to the existence of a stable inversion layer
between 2.2 and 3.5 km elevation, the shallow free convection
took 11 min to rise above the inversion layer to form a deep con-
vection. These results suggest that observation of local distribu-
tions of GNSS-derived PWV is useful for monitoring the genera-
tion of deep moist convection.
(Citation: Oigawa, M., E. Realini, and T. Tsuda, 2015: Study
of water vapor variations associated with meso-γ scale convec-
tion: Comparison between GNSS and non-hydrostatic model data.
SOLA, 11, 27−30, doi:10.2151/sola.2015-007.)
1. Introduction
Water vapor is a source of energy for moist convection and
has an inuence on its developments. Therefore, understanding
relationships between variations of water vapor and moist convec-
tive activities is important for the prediction of heavy rainfall. For
example, it is reported that initiations of localized heavy rainfall
are affected by inhomogeneous distributions of surface water
vapor in the meso-γ scale by reducing CIN (convective inhibition)
(Bodine et al. 2010).
In Japan, studies of moisture variations associated with moist
convection have been conducted by analyzing PWV (precipitable
water vapor) derived from a nationwide GNSS (Global Navigation
Satellite System) observation network known as GEONET (GNSS
Earth Observation Network System), with mean inter-station dis-
tances of approximately 20 km. GEONET studies have reported
that water vapor variations precede initiations of moist convec-
tions (Seko et al. 2007; Inoue and Inoue 2007). However, the
horizontal resolution of GEONET is not high enough to capture
the ne structure of a water vapor eld associated with a convec-
tion cell since the horizontal scale of isolated convection is on the
order of a couple of km.
A few observational studies have been conducted to inves-
tigate relationships between water vapor variations and meso-γ
scale convections by using regional GNSS networks with reso-
lutions lower than 10 km. Seko et al. (2004) employed 75 GPS
receivers within an area of 20 × 20 km2 around Tsukuba, Japan
and analyzed the thickening of the humid boundary layer before
the generation of a thunderstorm by using a tomography method
and GNSS derived slant path delay (SPD) data (Shoji et al. 2004).
Realini et al. (2012) and Sato et al. (2013) installed 17 GNSS
stations with a horizontal spacing of 1−2 km near Uji campus of
Kyoto University, which is located south of Kyoto prefecture,
Japan. They succeeded to improve the horizontal resolution of
the retrieved PWV maps by analyzing PWV from high elevation
SPDs. However, it is difcult to understand physical relationships
between water vapor variations and the occurrence of meso-γ
scale convections only from the information of observed PWV
and surface rainfall. Therefore, the objective of this study is to
clarify the cause for meso-γ scale water vapor uctuations prior
to moist convections by conducting a numerical downscaling
experiment of a mesoscale non-hydrostatic model and comparing
observed PWV data with output model data.
This manuscript is organized as follows: Section 2 describes
analytical results based on observational data, such as weather
radar, GNSS-derived PWV, and rain-gauge. Section 3 describes
the explanation about the analyzed model and its analysis result.
Relationships between simulated surface rainfall and simulated
PWV and the associated three-dimensional movement of water
vapor were the focus of this analysis. Discussion and summary are
presented in Section 4.
2. Observational results
2.1 Synoptic conditions and the rainfall system
This study focuses on a heavy rainfall event on August 13−14,
2012, around Uji, Kyoto. Ishihara et al. (2013) reported that MCSs
(meso-scale convective systems) produced 400 mm of accumu-
lated rainfall in 13 h due to unstable atmospheric conditions, i.e.
the CAPE (convective available potential energy) was 2340 J kg−1
at 21 LST on 13 August, 2012 at Shionomisaki, which is located
170 km south of Uji. Moist convections could occur anywhere
around Kyoto due to the humid air supply near the surface in
proximity to the stationary front, south of which the MCSs were
generated. GEONET-derived PWV values at 21 LST on 13
August, 2012 at Shionomisaki and Uji were more than 60 mm and
this fact suggests that CAPE value was also high in Uji. Figure
1 shows movement of a precipitation cell, which was part of a
back-building type MCS, south of Kyoto. A new precipitation cell,
which passed over the Uji campus within 30 min, was observed
south-west of the Uji campus at 2000 LST on 13 August, 2012.
2.2 GNSS-based observations of PWV
Temporal variations of GNSS-derived PWV and surface rain-
fall amounts derived from a rain gauge associated with the pas-
sage of the precipitation cell were observed at the Uji campus (Fig.
2). PWV data used here was derived from the GNSS receiver at
the Uji campus, which is one of the receivers of the hyper-dense
GNSS network used by the authors in earlier studies (Realini et al.
2012; Sato et al. 2013). Precipitation intensity at the GNSS station
rapidly increased from 2010 LST on 13 August, 2012, reached
a maximum of more than 60 mm h−1 within a few minutes, and
diminished by 2020 LST. The GNSS-derived PWV started to in-
crease approximately 10 min before the intensication of surface
precipitation. As a result, PWV value increased by approximately
4 mm in 10 min.
Study of Water Vapor Variations Associated with Meso-γ Scale Convection:
Comparison between GNSS and Non-Hydrostatic Model Data
Masanori Oigawa1, Eugenio Realini1, 2, and Toshitaka Tsuda1
1Research Institute for Sustainable Humanosphere (RISH), Kyoto University, Gokasho, Uji, Kyoto, Japan
2Geomatics Research & Development (GReD) srl, Via Cavour 2, Lomazzo, Como, Italy
Corresponding author: Masanori Oigawa, Research Institute for Sustain-
able Humanosphere (RISH), Kyoto University, Gokasho, Uji, Kyoto 611-
0011, Japan. E-mail: masanori_ohigawa@rish.kyoto-u.ac.jp. ©2015, the
Meteorological Society of Japan.
28 Oigawa et al., Study of Water Vapor Variations Associated with Meso-γ Scale Convection
increased from 40 m to 886 m as their height increased. The prog-
nostic variables were wind components, temperature, pressure and
all water-related quantities such as vapor, rain, clouds, snow, grau-
pel, and ice clouds (all three-dimensional).
Although experiments had been conducted by setting initial
times of 2 km-NHM and 250 m-NHM as 1800 LST and 1900 LST
on 13 August, 2012 respectively, the observed precipitation
cell was not reproduced. In contrast, if we set initial times as
0000 LST and 0100 LST on 14 August, 2012 for 2 km-NHM and
250 m-NHM, respectively, a back-building type MCS was sim-
ulated by 2 km-NHM and a precipitation cell which was part of
the MCS and initiated around Uji was simulated by 250 m-NHM.
Figure 3 shows the precipitation intensity maps simulated by
the 250 m-NHM. Surface precipitation around Uji started at
0118 LST. Subsequently, the cell continued to grow and reached
an intensity of more than 10 mm h−1 at 0128 LST.
Although the initiation place of the simulated cell was shift-
ed of about 5 km southward with respect to the observation, the
difference of the initiation place is small and its moving direction
agreed well with the observation result. In addition, the fact that
the observed and simulated precipitation cells were both part of
the same type of MCSs indicates that these cells were generated
under ambient conditions very similar to those of the observation
result. Figure 4 shows the temporal variations of PWV and the
precipitation intensity as reproduced by the 250 m-NHM simu-
lation. These values were calculated at a location approximately
3. Simulation results
3.1 Design of the simulation and validation of the model data
Analyzed numerical model data were derived from the Japan
Meteorological Agency Non-hydrostatic Model (JMA-NHM)
(Saito et al. 2007). First, numerical simulation using a horizontal
grid interval of 2 km (2 km-NHM) were performed to express
unstable background condition and the MCS south of Kyoto. The
2 km-NHM has 451 × 451 horizontal grid points and a horizontal
domain centered at 35°N, 135°E on a Lambert conformal projec-
tion. The initial and boundary conditions were set on the basis of
JMA mesoscale analysis data (NPD/JMA 2013). It is commonly
known that phenomena expressible by numerical models have
horizontal scales at least more than 5 times that of the models’
grid intervals. Since we focus on water vapor variations within
few kilo meters, we conducted downscaling to a horizontal grid
interval of 250 m (250 m-NHM). The 250 m-NHM has 161 × 161
horizontal grid points and a horizontal domain centered at
34.88°N, 135.77°E on a Lambert conformal projection. Three-ice
bulk microphysics was used in both experiments (Lin et al. 1983;
Murakami et al. 1990), with no cumulus parameterization. A
hybrid terrain following coordinate is adopted as the vertical coor-
dinate and the vertical resolution was 50 levels from the surface to
the model top (21801 m) in both models. The lowest atmospheric
level was 20 m above the surface, and the depth of the layers
Fig. 1. Maps of precipitation intensity observed by weather radar in 10
min intervals from 1950 LST to 2020 LST on August 13. The location of
the Uji campus is indicated by the “▲” symbol.
Fig. 2. Temporal variations of GNSS-PWV (red) and precipitation inten-
sity (green) observed at the Uji campus from 1950 LST to 2020 LST on
August 13.
Fig. 3. Time variation of precipitation intensity (mm h−1) simulated by the
250 m grid interval simulation. The location of the Uji campus is indicated
by the black “▲” symbol. Simulated PWV variation at the point indicated
by the red “×” symbol was compared with observations.
Fig. 4. Time variations of PWV (red) and precipitation intensity (green)
as reproduced by the 250 m-NHM simulation at the point indicated by the
red “×” symbol in Fig. 3.
29SOLA, 2015, Vol. 11, 27−30, doi:10.2151/sola.2015-007
5.5 km south-southeastward of the Uji campus, indicated in Fig.
3. A rapid PWV increase prior to the intensication of surface
precipitation was successfully simulated and this result suggests
the validity of the numerical modeling, particularly regarding the
relationship between water vapor variation and rainfall intensity.
For the above reasons, the data of 250 m-NHM is worth using for
analysis at least to investigate the physics of the observed precipi-
tation cell.
3.2 Simulated meso-γ scale PWV uctuations
Figure 5 shows maps of PWV and precipitation intensity as-
sociated with the precipitation cell and its evolution over time in
6 min intervals from the 250 m-NHM. The indicated horizontal
domain has an area of 12.8 km × 8.9 km. The PWV values in
the eastern side have relatively lower values, because mountains
cover this region. Surface precipitation started at 0118 LST. We
focused on the local PWV maximum indicated by a “▲” in the
gure at 0118 LST. This maximum formed before the start of sur-
face precipitation and can be recognized by its shape at 0106 LST,
12 min before the start of rainfall. The horizontal velocity of the
local PWV maximum was estimated by assuming constant move-
ment from 0106 LST to 0130 LST and the locations of the local
PWV maxima for each time are indicated by “▲” symbols in Fig.
5. Vertical proles of water vapor and wind at these points were
analyzed to investigate the mechanism for the PWV increase asso-
ciated with the cumulonimbus cloud.
Figure 6a shows temporal variations of the PWV anomaly and
VIL (vertically integrated liquid water) at the local PWV maxi-
mum. The PWV started to increase at 0102 LST, 16 min before
the start of the surface rainfall. Figure 6b shows the vertical distri-
bution of the vertical wind velocities and its temporal evolution.
Before 0118 LST, a downward wind region can be seen at an
elevation of approximately 2 km, with upward wind motion oc-
curring below this region. After 0118 LST, upward wind velocities
became strong and deep convection was generated. The down-
ward wind region prior to 0118 LST was likely an inversion layer.
Before 0118 LST, upward wind velocities were more than 4 m s−1
and we can deduce that a shallow free convection had already gen-
erated under the downward wind region. Figure 6b shows a strong
upward wind region at a height of 1 km that can be traced back to
0107 LST. In addition, the rate of the PWV increase became large
at 0107 LST. Therefore, the initiation of the free convection oc-
curred at approximately 0107 LST. Before 0107 LST, horizontal
surface wind convergence can be seen in Fig. 6c and this conver-
gence caused the lifting of an air parcel around 1 km elevation.
The maximum horizontal convergence formed at an elevation of
1.7 km at 0118 LST, following the breakthrough of the inversion
layer (Fig. 6c). The humid boundary layer thickened with time due
to shallow convection and the following horizontal convergence
of moisture near the surface from 0107 LST to 0118 LST (Fig.
6d), resulting in the increase of PWV.
Figure 7 (left) shows the vertical distributions of equivalent
potential temperature and the saturated equivalent potential tem-
perature at 0105 LST. An inversion layer can be seen between 2.2
and 3.5 km elevation and a strong convective instability is evident
between 1.00 and 1.28 km elevation. Air at elevations between
0.73 and 1.08 km was saturated with water vapor at 0105 LST and
it was conrmed that an air parcel in this layer was located at the
LFC (level of free convection) (Fig. 7 (left)). The height of the
convectively unstable layer increased with time after the shallow
convection was initiated at 0107 LST and the inversion layer can
no longer be seen at 0120 LST after its breakthrough at 0118 LST.
4. Summary and discussion
Variations of water vapor associated with a meso-γ scale
convection were investigated using a non-hydrostatic mesoscale
Fig. 5. Maps of PWV (shade) and precipitation intensity (contour: 1 mm
h−1) at time steps of 6 min reproduced by the 250 m grid interval sim-
ulation from 0100 LST to 0130 LST on August 14. Vertical proles of
physical values were analyzed by tracing the convection cell at the points
depicted by “▲” symbol. The “▲” symbol at 0124 LST corresponds to
the red “×” symbol in Fig. 3.
Fig. 6. (a) Time variations of PWV increase from the initial time of the
250 m grid interval simulation (line) and the Vertically Integrated Liquid
water (VIL) (boxes) within the convection. (b) Vertical distributions of
vertical wind velocity and its time evolution within the convection. (c)
Vertical distributions of horizontal wind divergence and its time evolution
within the convection. (d) Vertical distributions of water vapor mixing
ratio and its time evolution within the convection. Location of proles of
each times are indicated by the “▲” symbol in Fig. 5.
30 Oigawa et al., Study of Water Vapor Variations Associated with Meso-γ Scale Convection
model and observational data. Temporal variations of PWV and
surface precipitation intensity associated with the passage of a
convection cell that was part of a back-building type MCS, which
brought heavy rainfall to Uji on 13−14 August, 2012, were ob-
served using a GNSS receiver and rain gauge. Based on observa-
tions at the Uji campus, PWV values started to increase rapidly
approximately10 min before the intensication of surface precipi-
tation.
A downscaling simulation was performed to simulate water
vapor variations within a cumulonimbus cloud that passed over
the Uji campus using the JMA-NHM by setting a horizontal grid
interval to 250 m. Although there is a small gap in the formation
time between the simulated and observed rain bands, back-build-
ing type MCSs were simulated by a 2 km-NHM. Furthermore,
the validity of the downscaling results was conrmed because
the 250 m grid interval simulation successfully simulated the ob-
served rapid PWV increase prior to the surface rainfall.
In the model, PWV associated with moist convection began
to increase at 0102 LST, 16 min before the start of surface rainfall
(at 0118 LST) and local maximums of PWV were formed prior to
the rainfall. Analysis of output data revealed that free convection
was initiated at 0107 LST, 11 min before the surface rainfall, due
to the lifting of an air parcel at approximately 1 km elevation.
The air parcel became buoyant because the air column around
1 km elevation was highly convectively unstable, allowing the air
parcel to reach the LFC, and the horizontal wind had previously
converged near the surface before the initiation of the free con-
vection. It was also revealed that it took 11 min for the free con-
vection to rise above a stable inversion layer that existed between
2.2 and 3.5 km elevation to generate deep convection. Therefore,
the PWV increase from 0102 to 0107 LST was caused by surface
wind convergence and the PWV increase from 0107 to 0118 LST
was caused by shallow free convection under the inversion layer.
Then, rainfall at the surface started at 0118 LST.
During this heavy rainfall event, a large area was affected by
high PWV values of more than 60 mm. Under such atmospheric
conditions, the distribution of surface wind convergence may be
more important than local convergence of moisture to determine
where the convection is initiated. However, even under such
conditions, observation of the local distribution of PWV is useful
for monitoring convective activity because local convergence of
moisture is detectable from PWV maps and air parcels require
time to form deep convections with the presence of an inversion
layer.
Acknowledgements
The numerical model used in this study was provided by
the JMA and the initial and boundary data were provided by the
“Meteorological Research Consortium.” This work is partly sup-
ported by a Grant-in-Aid for the Japan Society for the Promotion
of Science (JSPS) Fellows. The authors appreciate the cooperation
offered by Dr. Kazutoshi Sato in setting up GNSS and rain gauge
observation instruments. The authors would like to express their
gratitude to Dr. Takuya Kawabata, Dr. Hiromu Seko, and Dr.
Yoshinori Shoji of Meteorological Research Institute for providing
useful comments and suggestions. We extend our thanks to Dr.
Atsuki Shinbori for his support to use IDL software and the other
members of the Tsuda Laboratory of RISH, Kyoto University, for
providing useful comments.
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Fig. 7. (Left) Vertical distributions of equivalent potential temperature
(black) and saturated equivalent potential temperature (blue broken line)
at 0105 LST. (Right) Vertical proles of equivalent potential temperature
within the convection cell at 0107 LST (black), 0114 LST (blue broken
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“▲” symbol in Fig. 5.
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The Global Navigation Satellite System (GNSS) receiver is one of the many sensors embedded in smartphones. The early versions of the Android operating system could only access limited information from the GNSS, allowing the related Application Program Interface (API) to obtain only the location. With the development of the Android 7.0 (Nougat) operating system in May 2016, raw measurements from the internal GNSS sensor installed in the smartphone could be accessed. This work aims to show an initial analysis regarding the feasibility of Zenith Total Delay (ZTD) estimation by GNSS measurements extracted from smartphones, evaluating the accuracy of estimation to open a new window on troposphere local monitoring. Two different test sites have been considered, and two different types of software for data processing have been used. ZTDs have been estimated from both a dual-frequency and a multi-constellation receiver embedded in the smartphone, and from a GNSS Continuously Operating Reference Station (CORS). The results have shown interesting performances in terms of ZTD estimation from the smartphone in respect of the estimations obtained with a geodetic receiver.
... [104] shows that the spatial inhomogeneities of the ZTD are correlated to intense rainfall, suggesting that they can be used to monitor thunder-storms. [74] show that deep moist convection can be monitored by GNSS derived PWV variation. Similar increasing and decreasing patterns during intense rain events are highlighted also by [89], [124], [8], and [98]. ...
... Una de las variables importantes a la hora de entender la convección es el vapor de agua precipitable (PWV), esta variable ha sido ampliamente usada para estudiar las características de eventos convectivos (e.g., Adams et al., 2013;Wang et al., 2015;Oigawa et al., 2015). Adams et al. (2013) realizaron mediciones durante 3.5 años del PWV, para evaluar el comportamiento de la convección. ...
Thesis
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Here we study the characteristics of deep convective events related to surface westerly winds in the Sabana de Bogotá. Using GOES-13 images and in-situ precipitation data, we identify 128 events between 2011 and 2017, out of which 61 have wind data, with 43 showing surface westerly winds. Deep convective events occur mainly between 14:00 and 16:00 local time during the region's two rainy seasons. Composites of these events show that precipitable water vapour, wind speed and surface temperature are the most important variables for the development of convective events. Precipitable water vapour is higher during convective days and shows a steep increase starting around 12:00 local time. Likewise, convective days have weaker than average winds until noon, when a rapid increase begins for about 2 hours, after which it decreases quickly. On the other hand, surface temperature reaches a higher than average peak value around 13:00 local time, decreasing quickly after that, consistent with the thermodynamic processes associated with continental convection. Although westerly winds are predominant during the afternoon in the Sabana de Bogotá, convective events only occur in less than 25% of the days. This suggests that although these westerly winds are important for the development of deep convection (likely due to the convergence they cause in the region) they are not the main cause of it, and therefore they are not a good predictor.
... The study demonstrated the possibility of detecting PWV variations within the Uji network area, by exploiting high-elevation slant delays to increase the horizontal resolution of the retrieved PWV field (Sato et al. 2013). The collaborative work on the Uji hyper-dense network continued in the following years, allowing for simulations and comparisons between the GNSS-derived and NWPderived PWV (Oigawa et al. 2014(Oigawa et al. , 2015. ...
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Recent advances in the field of atmospheric and ionospheric sensing by GNSS and SAR technologies were discussed during two workshops held in February 2016 and October 2016 in Italy, hosted by GEOlab of Politecnico di Milano under partial support of the JSPS Bilateral Open Partnership Joint Research Projects. Another symposium was held in March 2017 at the Research Institute for Sustainable Humanosphere of Kyoto University, to discuss (1) the water vapor and ionospheric maps retrieval from space-borne and airborne SAR, (2) ionosphere and troposphere monitoring by the ground-based GNSS network and radio occultation, (3) mesoscale numerical weather prediction models and data assimilation, and (4) ground-based remote-sensing techniques, such as a wind profiling radar. This special issue collects high-quality papers that describe the findings reported during these three meetings, not limited to GNSS and SAR, but also including ground-based atmospheric sensing systems and numerical weather prediction models.
... RMS of PWV SPD-H became strong after 0140 LT at a horizontal scale between 5 and 10 km, while RMS of PWV CON became larger after 0205 LT. In addition, RMS values of PWV CON and PWV SPD-H achieved maximum values, preceding the peak time of the surface rainfall at RISH. Oigawa et al. (2015) analyzed a 250-m mesh model data simulated by JMA-NHM that successfully simulated the observed rapid increase in PWV prior to surface rainfall during the Uji heavy rainfall event. It was found that in the model, the local PWV maximum began to form about 16 min before the surface rainfall due to wind convergence near the ground. ...
Article
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We studied the assimilation of high-resolution precipitable water vapor (PWV) data derived from a hyper-dense global navigation satellite system network around Uji city, Kyoto, Japan, which had a mean inter-station distance of about 1.7 km. We focused on a heavy rainfall event that occurred on August 13–14, 2012, around Uji city. We employed a local ensemble transform Kalman filter as the data assimilation method. The inhomogeneity of the observed PWV increased on a scale of less than 10 km in advance of the actual rainfall detected by the rain gauge. Zenith wet delay data observed by the Uji network showed that the characteristic length scale of water vapor distribution during the rainfall ranged from 1.9 to 3.5 km. It is suggested that the assimilation of PWV data with high horizontal resolution (a few km) improves the forecast accuracy. We conducted the assimilation experiment of high-resolution PWV data, using both small horizontal localization radii and a conventional horizontal localization radius. We repeated the sensitivity experiment, changing the mean horizontal spacing of the PWV data from 1.7 to 8.0 km. When the horizontal spacing of assimilated PWV data was decreased from 8.0 to 3.5 km, the accuracy of the simulated hourly rainfall amount worsened in the experiment that used the conventional localization radius for the assimilation of PWV. In contrast, the accuracy of hourly rainfall amounts improved when we applied small horizontal localization radii. In the experiment that used the small horizontal localization radii, the accuracy of the hourly rainfall amount was most improved when the horizontal resolution of the assimilated PWV data was 3.5 km. The optimum spatial resolution of PWV data was related to the characteristic length scale of water vapor variability.
... RMS of PWV SPD-H became strong after 0140 LT at a horizontal scale between 5 and 10 km, while RMS of PWV CON became larger after 0205 LT. In addition, RMS values of PWV CON and PWV SPD-H achieved maximum values, preceding the peak time of the surface rainfall at RISH. Oigawa et al. (2015) analyzed a 250-m mesh model data simulated by JMA-NHM that successfully simulated the observed rapid increase in PWV prior to surface rainfall during the Uji heavy rainfall event. It was found that in the model, the local PWV maximum began to form about 16 min before the surface rainfall due to wind convergence near the ground. ...
Article
Full-text available
We studied the assimilation of high-resolution precipitable water vapor (PWV) data derived from a hyper-dense global navigation satellite system network around Uji city, Kyoto, Japan, which had a mean inter-station distance of about 1.7 km. We focused on a heavy rainfall event that occurred on August 13–14, 2012, around Uji city. We employed a local ensemble transform Kalman filter as the data assimilation method. The inhomogeneity of the observed PWV increased on a scale of less than 10 km in advance of the actual rainfall detected by the rain gauge. Zenith wet delay data observed by the Uji network showed that the characteristic length scale of water vapor distribution during the rainfall ranged from 1.9 to 3.5 km. It is suggested that the assimilation of PWV data with high horizontal resolution (a few km) improves the forecast accuracy. We conducted the assimilation experiment of high-resolution PWV data, using both small horizontal localization radii and a conventional horizontal localization radius. We repeated the sensitivity experiment, changing the mean horizontal spacing of the PWV data from 1.7 to 8.0 km. When the horizontal spacing of assimilated PWV data was decreased from 8.0 to 3.5 km, the accuracy of the simulated hourly rainfall amount worsened in the experiment that used the conventional localization radius for the assimilation of PWV. In contrast, the accuracy of hourly rainfall amounts improved when we applied small horizontal localization radii. In the experiment that used the small horizontal localization radii, the accuracy of the hourly rainfall amount was most improved when the horizontal resolution of the assimilated PWV data was 3.5 km. The optimum spatial resolution of PWV data was related to the characteristic length scale of water vapor variability.[Figure not available: see fulltext.].
Chapter
The authors conceived the GNSS for Meteorology (G4M) procedure to remote-sense the Precipitable Water Vapor (PWV) content in atmosphere with the aim to detect severe meteorological phenomena. It can be applied over an orographically complex area, exploiting existing networks of Global Navigation Satellite System (GNSS) Permanent Stations (PSs) and spread meteorological sensors, not necessarily co-located. The results of a posteriori analysis of four significant meteorological events are here presented, also in comparison with rain gauge data, to show the effectiveness of the method. The potentiality of G4M to detect and locate in space and time intense rainfall events is highlighted. The upcoming application of G4M in near-real time could provide a valuable support to existing Decision Support System for meteorological alerts.
Conference Paper
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The objective of this work is to study and characterize the temporal and spatial variability of water vapor at a local scale, i.e. less than 10 km, by analyzing wet tropospheric delays estimated by a dense GNSS network. Experiments using high-rate (30 s and 5 s) observations are conducted in order to investigate also the short periodic refractivity fluctuations induced by turbulence, expected to be in the range of minutes to seconds. The effects induced by interpolation errors of satellite clocks with high-rate observations are investigated. The GPS-derived precipitable water vapor (PWV) is validated by comparison with the PWV measured by radiosondes and a radiometer, obtaining differences of about 2 mm in RMS. The distribution of PWV is studied by geostatistics (kriging) and turbulence analyses. Spatial and temporal structure functions are computed for both zenith wet delays and show power-law behaviors varying between 5/3 and 2/3, consistently with the Treuhaft-Lanyi model (i.e. a long baseline approximation for Kolmogorov turbulence theory). Power-law behaviors in temporal structure functions result both for long-term variations, with correlation lengths depending on the weather conditions, and for short-term fluctuations, until about 10 seconds; for shorter time lags the structure functions decorrelate into noise.
Article
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This work describes a system aimed at the near realtimemonitoring of precipitable water vapor (PWV) by means of a dense network of Global Navigation Satellite System (GNSS) receivers. These receivers are deployed with a horizontal spacing of 1-2 km around the Uji campus of Kyoto University, Japan. The PWV observed using a standard GPS meteorology technique, i.e., by using all satellites above a low elevation cutoff, is validated against radiosonde and radiometer measurements. The result is a RMS difference of about 2 mm. A more rigorous validation is done by selecting single GPS slant delays as they pass close to the radiosonde or the radiometer measuring directions, and higher accuracy is obtained. This method also makes it possible to preserve short-term fluctuations that are lost in the standard technique due to the averaging of several slant delays. Geostatistical analysis of the PWV observations shows that they are spatially correlated within the area of interest; this confirms that such a dense network can detect inhomogeneous distributions in water vapor. The PWV horizontal resolution is improved by using high-elevation satellites only, with the aim of exploiting at best the future Quasi-Zenith Satellite System (QZSS), which will continuously provide at least one satellite close to the zenith over Japan. Copyright Fuji Technology Press, Ltd.
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A two-dimensional, time-dependent cloud model has been used to simulate a moderate intensity thunderstorm for the High Plains region. Six forms of water substance (water vapor, cloud water, cloud ice, rain, snow and hail, i.e., graupel) are simulated. The model utilizes the `bulk water' microphysical parameterization technique to represent the precipitation fields which are all assumed to follow exponential size distribution functions. Autoconversion concepts are used to parameterize the collision-coalescence and collision-aggregation processes. Accretion processes involving the various forms of liquid and solid hydrometeors are simulated in this model. The transformation of cloud ice to snow through autoconversion (aggregation) and Bergeron process and subsequent accretional growth or aggregation to form hail are simulated. Hail is also produced by various contact mechanisms and via probabilistic freezing of raindrops. Evaporation (sublimation) is considered for all precipitation particles outside the cloud. The melting of hail and snow are included in the model. Wet and dry growth of hail and shedding of rain from hail are simulated.The simulations show that the inclusion of snow has improved the realism of the results compared to a model without snow. The formation of virga from cloud anvils is now modeled. Addition of the snow field has resulted in the inclusion of more diverse and physically sound mechanisms for initiating the hail field, yielding greater potential for distinguishing dominant embryo types characteristically different from warm- and cold-based clouds.
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A case study illustrating the impact of moisture variability on convection initiation in a synoptically active environment without strong moisture gradients is presented. The preconvective environment on 30 April 2007 nearly satisfied the three conditions for convection initiation: moisture, instability, and a low-level lifting mechanism. However, a sounding analysis showed that a low-level inversion layer and high LFC would prevent convection initiation because the convective updraft velocities required to overcome the convective inhibition (CIN) were much higher than updraft velocities typically observed in convergence zones. Radar refractivity retrievals from the Twin Lakes, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) showed a moisture pool contributing up to a 2 degrees C increase in dewpoint temperature where the initial storm-scale convergence was observed. The analysis of the storm-relative wind field revealed that the developing storm ingested the higher moisture associated with the moisture pool. Sounding analyses showed that the moisture pool reduced or nearly eliminated CIN, lowered the LFC by about 500 m, and increased CAPE by 2.5 times. Thus, these small-scale moisture changes increased the likelihood of convection initiation within the moisture pool by creating a more favorable thermodynamic environment. The results suggest that refractivity data could improve convection initiation forecasts by assessing moisture variability at finer scales than the current observation network.
Article
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In order to study small-scale water vapor variations over distances from a few km to 20 km, two campaign observations with a dense GPS network were carried out for 2.5 months in total at Tsukuba, Japan. For the observations 79 GPS antennas were installed at 75 sites within a 20 km by 20 km square area, at 1 to 3 km intervals. The PCV models provided by the US National Oceanic and Atmospheric Administration (NOAA) were applied to remove unmodeled phase center variation (PCV) specific to GPS antenna type. In addition, new PCV maps (MPS map) were constructed for all the antennas by stacking one-way postfit residuals over both campaign periods, to remove not only azimuth dependent PCV, but also the errors due to multipath effects. After MPS maps were introduced into the analysis, strong elevation dependence as well as azimuth dependence of postfit phase residuals, almost disappeared for all the antennas. In addition, the time variations in postfit residuals which were common to all the GPS sites, were subtracted to remove satellite orbits and/or clock errors. This led to the accurate estimate of slant path delay (SPD), which enabled the SPD to be applied to tomography analyses of water vapor (Seko et al. 2003). The horizontal scale of SPD was estimated using correlation distributions. As a result, the horizontal scale of the zenith total delay, the gradient component, and the postfit residual may be roughly considered as 644 +/- 120 km, 62 +/- 23 km, and 2-3 km, respectively. Improvement of the postfit residuals following the application of MPS maps also showed a positive impact on PWV estimation. Systematic biases of GPS derived PWV between different antenna types (Trimble and Ashtech) were reduced, resulting in a better agreement of GPS PWV, with RMS errors of 2.0 mm or less relative to PWV by rawinsonde or water vapor radiometer observations. The distribution of time-averaged PWV estimated at the 75 GPS sites showed a systematic pattern which has a negative correlation with the antenna height of each site.
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This paper reviews nonhydrostatic atmospheric models for research and NWP. Classification of non-hydrostatic atmospheric models and numerical methods to treat sound waves are described with their relative advantages. The current operational nonhydrostatic NWP models at various forecast centers and community nonhydrostatic models for research are reviewed. Brief history and development of the JMA nonhydrostatic model, a community mesoscale model for research and NWP in Japan, is introduced. Current status and near future plans of the operational non-hydrostatic mesoscale model at JMA are presented.
Article
A 3-dimensional, anelastic cloud model is applied to the simulation of the July 19, 1981 Cooperative Convective Precipitation Experiment (CCOPE) case study cloud. The model utilizes the bulk water microphysical parameterization technique where number concentrations of cloud ice and snow are taken into account in addition to the mixing ratios of six water species (water vapor, cloud water, cloud ice, rain, snow and graupel/hail). Cloud ice is initiated only by primary nucleation processes (deposition/sorption and heterogeneous and homogeneous freezing of cloud droplets) in the present model. The timing reference was established between the simulation and observations based on a remarkable change in the rise rates of both the observed and simulated cloud tops, and the model results are compared with the observations as a function of time and space. The general features of the cloud (such as cloud top height, cloud size, arrival time of precipitation at the cloud base, radar first echo, etc.) seem to have been well reproduced. Furthermore, the model cloud simulated quite well the location of hydrometeors with respect to the updraft/downdraft structure, the number concentration of precipitating ice particles, updraft velocity and cloud water content along the King Air's penetrating pass. The main features which are not accurately reproduced are the cloud base height, the rise rate of the cloud top and the radar echo near the ground. The cloud base height is too low, which is attributed to the lack of representativeness in the input data taken from the closest radiosonde sounding, while the too rapid rise rate of the cloud top seems to be attributable to the way in which convection is initiated. The rapid decrease in radar reflectivity of the simulated cloud seems to be attributable to inadequate parameterization for rain and graupel.
Article
Characteristics of the water-vapor field in relation to thunderstorms on summer days over the Kanto district in Japan were studied, using precipitable water-vapor (PWV) derived from GPS during 2001-2005. PWV averaged on the active thunderstorm days showed distinct diurnal variation. In general, hourly data showed that PWV maximum appeared 1∼2 hours prior to the maximum thunderstorm activity (precipitation or cloud to ground stroke). The increase of PWV in the mountainous region of northern Kanto coincided well with the increase of low-level wind toward the mountains, which was observed by Wind profiler Network and Data Acquisition System (WINDAS). Using the 5 minute data, we further examined the time lag between the PWV and cloud to ground (CG) stroke related to individual thunderstorms in detail. The PWV maxima preceded that of CG stroke by 15∼30 minutes, for about 40% of the thunderstorms. In many cases, both PWV and its increment in 30 minutes showed large values within one hour before the CG stroke occurrence. This suggested that GPS derived PWV appears to reflect well the local variations associated with a thunderstorm.
Article
The Tsukuba GPS Dense Net campaign that took place in the autumn of 2000 and in the summer of 2001 measured the meso-γ scale distribution of water vapor. As part of the campaign, 75 GPS receivers and 20 automatic meteorological observation systems (AMOS) recorded water vapor variations associated with a thunderstorm on 1 August 2001. The three-dimensional water vapor distribution in the area was estimated from slant water vapor (SWV) data derived from GPS receivers using tomographic methods. SWV is the total amount of water vapor per unit area between a GPS receiver on the ground and a GPS satellite. The SWV data used in this study were obtained with sufficient accuracy by carefully removing multi-path effects, phase center variations of the GPS antenna, and other error sources. SWV was converted to a value that was projected onto the vertical direction (VSWV), so that the influence of the elevation angle on the slant path was removed. VSWV values from adjacent receivers to individual satellites were strongly correlated with each other. Variations in VSWV depended on the GPS receiver positions relative to the developing or moving thunderstorm. Our results indicate that SWV data can provide useful information about the water vapor distribution in the vicinity of thunderstorms. Correlations between the variation of VSWV and the precipitable water vapor (PWV) distribution around the GPS receiver were also calculated. The directions of the large values of VSWV corresponded to regions of high PWV. The three-dimensional water vapor distribution, estimated tomographically, agreed well with Doppler radar-observed reflectivity. Regions of high water vapor near the surface occurred on the northern side of a region of intense reflectivity. The more humid regions above 3 km corresponded to regions where reflectivity increased. The water vapor distribution estimated from the GPS showed an increase of water vapor above a height of 1 km, which preceded the appearance of radar echoes by about 20 minutes during the thunderstorm formation.
Article
Data from the Automated Meteorological Data Acquisition System (AMeDAS), global positioning system-derived precipitable water vapor (GPS-PWV), conventional and Doppler radar observations, and results from a numerical simulation by the Japan Meteorological Agency Non-Hydrostatic Model (JMANHM) were used to investigate the evolution and structure of the convective systems that caused the Nerima heavy rainfall, which was a disastrous rainfall event in the Tokyo metropolitan area, with hourly precipitation amount reaching 110 mm.Two types of convective systems comprised the thunderstorms that caused the rainfall event: a thunderstorm developed in a warm and moist region where southerly inflow from Tokyo and Sagami bays and northeasterly flow over the northern Kanto Plain converged, and a convective band that organized to the west of the first system. The first convective system quickly decayed after the mature stage, because divergent flow from intense rainfall prevented the southerly inflow from reaching the updraft region. The middle-level airflow characterized by cold temperature invaded this system from the north after its mature stage. Because it entered the updraft region of the system, it did not enhance the convection through the intensification of the cold outflow that produces the convergence with the low-level inflow from the south. However, abundant water vapor in the region of convergence, resulted in heavy rainfall in spite of the short duration of the system. For the second convective system, water vapor of low-level southerly inflow directly fed into the band, and thus the band maintained its intensity after the first system decayed. Low-level northerly airflows that lifted up the southerly inflow were far more intense than that of the first system. These northerly airflows acted to organize the convective band and forced it southeastward. The middle-level cold airflow that also invaded the system after the mature stage entered the downdraft region, resulting in an enhancement of the convection. However, due to rapid propagation speed of the second band, the rainfall duration at a fixed point was relatively short, so that the band did not produce floods.For prediction of thunderstorms, monitoring of low-level convergence zones of moist air was found to be possible, using indexes of accumulation and convergence of water vapor, as well as the Doppler radar radial wind in the non-precipitation weak echoes.