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The summertime atmospheric hydrologic cycle over the southwestern US
Bruce T. AndersonDepartment of Geography, Boston University
675 Commonwealth Ave.Boston, MA 02215-1401
brucea@bu.edu
Hideki Kanamaru and John, O. RoadsScripps Institution of Oceanography
UCSD-0224La Jolla, CA 92093-0224
2
ABSTRACT
In this paper we examine the large-scale summertime hydrologic cycle associated with the
northwestern branch of the North American monsoon, centered on the southwestern United
States, using a suite of surface and upper-air based observations, reanalysis products, and
regional model simulations. In general, it is found that on an area-averaged basis, seasonal
precipitation is balanced predominantly by evaporation; in addition, this evaporation also
supports a net vertically integrated moisture flux divergence from the region of the same
magnitude as the precipitation itself. This vertically-integrated large-scale moisture flux
divergence is the result of an offsetting balance between convergence of low-level moisture and
divergence of moisture aloft (<750mb). Over the western portion of the domain, most of this
low-level moisture convergence is related to advection from the Gulf of California and eastern
Pacific; over the eastern portion of the domain, low-level moisture convergence is related to
advection from the Gulf of Mexico. The low-level moisture, supplied both by evaporation and
advection, is carried aloft primarily by convection (as opposed to large-scale vertical velocities),
which then feeds both the precipitation and large-scale divergence fields. The large-scale
divergence augments the anti-cyclonic circulation of moisture aloft, resulting in enhanced exiting
fluxes over the Great Plains. A new metric for measuring recycling of moisture in convective
semi-arid areas is introduced; this metric is designed to better capture the importance of
evaporative processes for supporting regional precipitation in these types of environments.
Using this metric, it is shown that about 70-90% of the area-averaged precipitation is the result
of evaporative processes, while the remaining 10-30% is related to low level convergence of
moisture.
3
1. Introduction
Much effort has been dedicated to investigating the climatological, interannual, and
intraseasonal characteristics of the northwestern branch of the North American monsoon system,
situated over the southwestern United States and northwestern portion of Mexico. In general, the
region shows strong interactions between the land-surface, ocean, and atmosphere, resulting in
complex coupling of the momentum and moisture fields. Included among these coupling
processes are: the maintenance and modulation of low-level moisture flux advection from the
Gulf of California, the Gulf of Mexico, and the eastern Pacific (Brenner, 1974; Hales, 1974;
Douglas et al., 1993; Stensrud et al., 1995; Schmitz and Mullen, 1996; Berbery and Fox-
Rabinovitz, 2003); the lofting and divergence of moisture associated with deep convection over
the Sierra Madres and Sierra Nevadas (Berbery, 2001; Fawcett et al., 2002); instabilities related
to the passage of mid-latitude, mid-troposphere weather systems (Carleton, 1986; Stensrud et al.,
1995; Maddox et al., 1995; Kiladis, 1999; Mo, 2000; Anderson and Roads, 2002); the passage of
subtropical low-pressure systems over Mexico (Farfan and Zehnder, 1994; Anderson et al., 2000;
Englehart and Douglas, 2001; Douglas and Leal, 2003); and the establishment of large-scale
upper-air teleconnection patterns (Carleton et al., 1990; Comrie and Glenn, 1998; Higgins et al.,
1998; Higgins and Shi, 2001; Cavazos et al., 2002). An excellent summary of many of these
influences can be found in Douglas et al. (1993), and Higgins et al. (2003).
Despite these efforts, however, it does not appear that there is a description of the explicit
large-scale hydrodynamic balances that support the summertime precipitation field in this region.
For instance, is the climatological precipitation field the result of a net, vertically-integrated
convergence of moisture? If so, does this convergence of moisture occur aloft, indicating the
4
possible influence from remote source regions, or at lower levels, suggesting more local dynamic
forcing? And how do the dynamic-based convergence fields compare with atmospheric moisture
convergence related to local evaporation? Roads et al. (1994), Roads and Betts (2000) and Roads
and Chen (2000) have looked at these balances for the United States as a whole using Reanalysis
data as well as fine-scale regional modeling data. Schmitz and Mullen (1996) looked at these
balances for the southwestern United States but were hampered by the coarse-scale resolution of
the atmospheric analysis under consideration. Berbery (2001) considered these balances over the
core monsoon region in western Mexico but the analysis only extended through the northern
portion of the Gulf of California. Here we attempt to address similar issues by explicitly
quantifying the terms that comprise the atmospheric moisture tendency equation, using as many
data sources as possible. Using the results, we then hope to provide a better description of the
important processes influencing the climatological hydrologic cycle for the summertime
southwestern US. Section 2 describes these datasets. Section 3 then investigates the relevant
hydrodynamic terms found in each. Section 4 summarizes the findings and discusses their
implications for moisture recycling within the region.
2. Datasets
To perform this analysis, we use a global to regional spectral modeling system developed at
the National Centers for Environmental Prediction (NCEP) to produce regional simulations of
the summertime atmospheric hydrologic cycle over the southwestern US (Juang and Kanamitsu,
1994; Juang et al., 1997). The modeling system nests the regional spectral model (RSM) within
daily forecasts from NCEP's global spectral model (GSM); these GSM forecasts are initialized
using NCEP's operational analysis data. The resolution of the GSM is triangular-62
5
(approximately 200 km); the resolution for the RSM is 25 km (Figure 1). Vertically, the
prognostic equations are solved on 18 levels represented in sigma coordinates (where s=p/ps).
The RSM domain extends from northern California to central Mexico and includes the Sierra
Nevada and Rocky Mountain ranges in the United States, the Baja and Sierra Madre mountain
ranges in Mexico, along with the Gulf of California. Also shown in Figure 1 are selected
radiosonde observing sites (see below) used for evaluation of the simulated hydrodynamic fields.
In total, five 92-day continuous GSM/RSM forecast simulations of the summertime (July-
September) southwestern US atmosphere have been performed for the period 1998-2002. In
order to produce the continuous simulations of the RSM (as opposed to a set of ninety 24-hour
weather forecasts), the coarse-scale GSM and fine-scale RSM short-term forecast fields at 2400
UTC (and not the reinitialized 0000 UTC Reanalysis field) were used as the initial conditions for
the next RSM nesting period. Numerically, this technique allows the RSM forecast, which
inherently contains corrections for anomalies associated with enhanced orography and
resolution, to carry these corrections forward from one nesting period to the next, thereby
reducing spin-up effects. Additional details about the full modeling system are found in the
Appendix and in Anderson and Roads (2002), which also includes a detailed evaluation of the
model’s simulation of precipitation for the summer of 1999.
For this research, we will principally use the RSM’s explicitly-calculated sigma-level time-
integrated diagnostic budget terms for the water vapor tendency equation (Anderson, 2002):
†
∂q∂t
+ v ⋅ —q + ˙ s ∂q∂s
Ê
Ë Á
ˆ
¯ ˜ + P - g∂E
∂s
Ê
Ë Á
ˆ
¯ ˜ = F
tendency + (large - scale divergence) + (precipitation - vertical diffusion) = Residual(1)
6
†
q - Moisture content (kg)v - Horizontal velocity vector— - Two - dimensional divergence operators - Vertical (sigma) coordinate (equal to p ps)E -Vertical eddy diffusionP -PrecipitationF - Residual Forcings
Although these terms are output at 6-hour intervals, they are calculated at each model time-
step (approximately 90 seconds) before being averaged over the 6-hour period; hence they
capture interactions associated with both the mean and fluctuating fields. Here, the residual
forcings include horizontal diffusion, semi-implicit model adjustment and boundary forcing. In
general the horizontal diffusion term serves to smooth fine-scale noise seen in the horizontal
convergence term over regions of complex topography. The boundary forcing term can be large
at the edges of the domain, however this term is damped to zero within approximately ten grid
points of the edge. It should be noted that for this paper, moisture divergence associated with the
explicitly-resolved vertical and horizontal advective fluxes of moisture will be referred to
alternatively as “large-scale divergence”, “advective divergence”, or simply “divergence”; the
moisture divergence associated with vertical diffusion processes, related to the time-rate of
change of moisture due to parameterized sub-grid scale vertical motions in the model, will be
referred to either explicitly as “vertical diffusion” or alternatively as “convection”. In addition,
because of fine-scale variations related to the spectral method, all diagnostic budget terms from
the RSM are smoothed using a Lanczos smoothing filter of order 1.0, which is consistent with
the smoothing performed on the terrain data within the model simulation itself.
In addition to the RSM data, we also use diagnostic terms related to the atmospheric
hydrologic cycle derived from both reanalysis and observational data products. From the
7
reanalysis, we archive the 4-times daily pressure level data for winds, temperature and humidity.
At the surface, we archive the 4-times daily data for surface pressure, precipitation, and
evaporation. Extensive documentation of the reanalysis product can be found in Kalnay et al.
(1996). Evaluation of the reanalysis hydrologic fields during the summertime monsoon season
can be found in Higgins et al. (1997); Roads et al., (1999); and Roads and Chen (2000).
Observationally, we have archived the 1x1-degree GPCC monthly precipitation dataset
available through NASA’s Distributed Active Archive Center, which spans the period from
1986-2002. This dataset uses rain-gauge measurements archived by the World Meteorological
Organization, as well as other meteorological and hydrological services/institutions; a subset of
about 6700 meteorological stations are selected and data from these stations are analyzed using a
spatial objective analysis method (Rudolf, 1996; see Acknowledgements for data availability). In
addition, we use the Climate Prediction Center’s (CPC) Unified precipitation data set, which is a
daily precipitation data set spanning 55 years (1948-2002) for the continental United States
(Higgins et al., 1996). This data set takes quality-controlled data from daily co-op stations, first-
order stations, and hourly observing sites and grids it to 0.25-degree resolution (approximately
25km). We also archived the 2-times daily radiosonde profiles, taken from NOAA’s FSL
Radiosonde Database (see Acknowledgments for data availability). The radiosonde profiles are
used to provide estimates of large-scale moisture flux divergence values for the region
(radiosonde locations shown in Figure 1). Finally, we archived the Land Data Assimilation
System (LDAS) evaporation estimates to compare with the vertically-integrated convective
terms taken from the RSM. This dataset is produced by forcing an offline land surface model
(Mosaic - Koster and Suarez, 1994) with estimates of atmospheric (precipitation and radiation)
and surface (soil, vegetation, elevation) parameters derived from observations and regional
8
model assimilations (Mitchell, 2003; Luo et al., 2003). The output from this land data
assimilation scheme has been evaluated by Robock et al., (2003), although only for the southern
Great Plains during the warm season. In general it was found that the latent heat flux (associated
with evaporation) was too large, however, this product represents one of the few alternative
evaporation estimates available; hence, the data are used here simply as a qualitative check on
the RSM and reanalysis output.
3. Results
To begin to evaluate the climatological characteristics of the analyzed and simulated
precipitation fields, Figure 2 shows the summertime (July-September) mean precipitation taken
from the observed (CMAP and Unified) datasets, along with the Reanalysis and regional model
estimates for the 5-year period of overlap (1998-2002). These results indicate that the RSM
summertime climatological precipitation for this region is in good agreement with that seen in
the analyzed observations. In particular, the RSM captures the increased precipitation over the
eastern portion of the domain; in addition it captures the fine-scale, orographic precipitation
found over northern Arizona, along the Mogollon rim, and through central Utah. The RSM,
however, fails to capture the local maximums found along the Arizona-Mexico border. In
general, though, the large-scale area-average for the RSM and observations are very similar
(1.3mm/day for both products; see Table 1 for all area-average values calculated in this paper).
In contrast, the coarse-scale NCEP Reanalysis precipitation data show an eastward shift relative
to the GPCC estimates and are generally weaker throughout the domain.
To see how representative of the long-term climatology this 5-year period is, Figure 3 shows
the difference between the 5-year climatology (1998-2002) and the full climatology for the two
9
observed data products. Overall, the 5-year period under consideration has negative anomalies
over the southeastern corner of the domain, with some regions showing a decrease in
precipitation of up to 40%. However, over the rest of the domain, precipitation during this
period is similar to that seen in the longer-term averages, with an area average percent change of
only 16% for the CPC Unified dataset (18% for the GPCC data). As such, these results suggest
that studies of the hydrologic cycle during this period may be representative of the overall
climatology.
To further evaluate the reanalysis and model-based results, and to help determine whether the
domain-wide precipitation may be related to large-scale moisture convergence advected from
outside the domain region, we next calculate large-scale estimates of horizontal moisture
divergence using wind and moisture profiles from the selected radiosonde stations (see Figure 1
for station locations). The station locations are selected based upon consistency of record and
incorporation of twice-daily soundings. Unfortunately, this prevents the use of stations in
Mexico, which would have given a better representation of moisture divergence profiles
associated with the intense monsoon precipitation found over the Sierra Madre Occidental;
however, these locations do provide excellent coverage for the southwestern portion of the US.
The moisture-divergence estimates are next derived using a new objective line-integral technique
developed by Kanamaru and Salvucci (2003) designed to correct for the spurious mass
divergence from the observed wind fields, which subsequently causes errors in the estimated
moisture flux divergence. The method adjusts the spatial weights applied to each radiosonde
station in the calculation of divergence. The adjustment is done in a least squares sense to
minimize the modification to the weights under the constraint of mass conservation. In this
10
paper, we chose zero mass divergence over the 5-year mean at each observation time (0000 UTC
and 1200 UTC) as the constraint.
Similar large-scale moisture divergence estimates are also calculated using simulated 2-times
daily profiles (interpolated to the observed station locations) taken from the RSM and Reanalysis
output to better compare with observations, as well as 4-times daily profiles to determine if there
are systematic biases associated with sampling frequency; in addition the RSM estimates are
calculated using simulated profiles only at the standard reporting levels, as well as at higher-
vertical resolution (every 50mb through the bottom 500mb; every 100mb above 500mb).
In order to handle the varying surface pressures and the practice of reporting at fixed pressure
levels from radiosondes, the observed and simulated values of moisture flux, mass flux, and
specific humidity (needed to account for mass balance adjustment) are vertically integrated
through the lowest few pressure levels (below 925mb for the observations and below 850mb for
the simulations and the Reanalysis) at each sounding and then averaged over the climatological
mean. For the purpose of illustration, the moisture divergence value is plotted as if it were
uniformly distributed over the first few pressure levels (which may not be the case), however the
overall computation represents the correct estimate for the integrated flux divergence near the
surface.
Vertical profiles of the 2xdaily moisture divergence estimates are shown in Figure 4. All
three standard reporting level estimates indicate large-scale moisture divergence above 750mb,
in agreement with previous work (Anderson and Roads, 2001). In addition, all three estimates
indicate moisture convergence from the surface to approximately 850mb. The apparent
difference in magnitude between the low-level RSM and Reanalysis profiles compared with the
observed profile is due to the difference in the level to which the low-level convergence is
11
integrated (see above); the integrated moisture convergence over this low-level region (surface to
850mb) for the RSM, reanalysis and observed profiles is actually very similar (0.4-0.5mm/day –
see below). The full, vertically-integrated moisture divergence estimates from the surface to the
top of the atmosphere (TOA) are found in Table 1. Profiles from the radiosonde data indicate net
moisture divergence over the region of approximately 1.9mm/day. For the coarse-scale RSM
estimate, the net divergence is also 1.9mm/day while for the Reanalysis the net divergence is
0.8mm/day, principally due to the significant underestimation of divergence between 800-
600mb. This discrepancy seems to indicate the need for fine-scale horizontal resolution in
simulations of the moisture and momentum fields, even when calculating large-scale averages of
these non-linear quantities. In addition, it can be seen that the higher vertical resolution profiles
taken from the RSM differ significantly from the coarse-resolution profiles, principally due to
the maximum moisture divergence values found at 650mb and 600mb. When the full vertical
integral is calculated using the higher-resolution profiles, the net divergence increases to
2.3mm/day, or about 20%. This result suggests an additional sensitivity to the vertical resolution
of the moisture and momentum fields, even at the upper levels where the absolute fields may be
smaller than at the surface. Although not shown, the RSM and reanalysis profiles are also
calculated using 4xdaily values. For the reanalysis estimates, the net divergence is essentially
the same as that found using the 2xdaily profiles (0.9mm/day). In addition, the RSM profiles
also closely match their 2xdaily counterparts (net divergence is 2.1mm/day and 2.4mm/day for
the coarse- and high-resolution vertical profiles respectively).
Previous research identified the large-scale, upper-air (<700mb) moisture divergence seen in
the three profiles and suggested that the presence of this moisture divergence discounted the
hypothesis that climatological summertime precipitation is supported by moisture convergence
12
from remote source regions (Anderson and Roads, 2001). In addition to this upper-air
divergence, the profile-based estimates presented above also suggest that there is a net area-
average moisture divergence through the full atmosphere. To better characterize the geographic
structure of this moisture divergence, and compare it with the other terms in the moisture
tendency equation, we look at the vertically-integrated RSM moisture budget terms, calculated
from the surface to the top of the atmosphere (Figure 5). These fields indicate that summertime
precipitation (panel c) for this region, which is shown to be in good agreement with observed
values (Figure 2), is balanced solely by vertical diffusion of moisture supplied by evaporation
(panel b). The largest evaporative flux is found over the Gulf of California, however there is
also significant local evaporation throughout the domain. The grid-point evaporation over the
southwestern US in fact exceeds precipitation, resulting in net, vertically-integrated large-scale
divergence of moisture over most of the region (panel a). The area-averaged value of grid-point
moisture divergence over the southwestern US is approximately 1.3mm/day, which is lower than
the large-scale estimates derived from the observed and simulated radiosonde profiles (see Table
1). Possible reasons for this discrepancy will be discussed in a bit. Still, this divergence
estimate, along with the radiosonde-based estimates, are larger than previous model estimates for
this region (see Figure 6c from Berbery and Fox-Rabinovitz, 2003), which suggest fluctuating
moisture divergence and convergence through the months of July and August, but no apparent
net seasonal mean. Although this discrepancy may arise due to the fact that we are looking at
seasonal mean values whereas the previous study looked at the onset of the monsoon from June
through August, monthly mean divergence estimates for July and August, taken from the RSM
as well as the radiosonde profiles, continue to indicate net divergence for each month. Instead,
the discrepancy may arise due to the fact that the year under consideration in the previous study
13
(1993) had a particularly late onset date, followed by significant rainfall through August
(Berbery and Fox-Rabinovitz, 2003), which may have resulted in a modification of the
climatological values.
To further evaluate the grid-point RSM tendency terms, we next compare the vertically-
integrated vertical diffusion term (which is equivalent to surface evaporation) with evaporation
estimates taken from LDAS (Figure 6). Overall, the two fields show good geographic
agreement, with a slight gradient running from west to east, along with maximum values over the
Sierra Madre Occidental in Mexico and a local maximum over the elevated regions of Colorado
and the Arizona/New Mexico border. However, the estimates from the RSM are larger than the
LDAS estimates by about 1mm/day throughout the domain (the area-average difference is
1.3mm/day – see Table 1). Although this result suggests that the regional model simulation
over-estimates surface evaporation, it is important to note that the grid-point precipitation field
from the RSM is very similar to that derived from observations; at the same time, the vertically-
integrated grid-point moisture divergence is actually less than the large-scale estimate derived
from the radiosonde profiles, suggesting that the grid-point evaporation field from the RSM may
be underestimating the actual values. To see whether the LDAS estimate of evaporation is
consistent with these same observational fields, the LDAS data is smoothed to 25km resolution
and is then subtracted from the analyzed observed precipitation (Figure 7). Results indicate that
there is near-zero difference throughout the southwestern US (the area average difference for the
region of interest is approximately 0.05mm/day). This result contrasts with the significant
moisture divergence estimates derived from the large-scale radiosonde network, suggesting that
in fact the LDAS estimates for this region may be significantly less than the actual evaporation
rates.
14
At this point, it is difficult to determine the actual evaporation rates; grid-point estimates
from the RSM suggest it is about 2.6mm/day while the observed estimates, derived from the
difference between the area-averaged analyzed precipitation (taken from the Unified dataset) and
the radiosonde-derived divergence fields, suggest it is closer to 3.2mm/day, resulting in a
difference between the two estimates of about 0.6mm/day (see Table 1). To investigate where
the discrepancy may lay, we subdivide the net vertically-integrated flux divergence into its low-
level convergent component and its upper-level divergent component. As mentioned, the large-
scale vertically-integrated moisture divergence is dominated by moisture divergence above
850mb; below this level there is weaker moisture convergence. If the vertically-integrated net
divergence is calculated from the surface to 850mb, both the simulated and observed radiosonde-
based estimates suggest convergence of 0.4 mm/day (Table 1). In addition, if the grid-point
based estimates of the horizontal divergence, integrated from the surface to 850mb, is computed
using the RSM tendency terms, the area-average also shows a convergence of 0.4mm/day (Table
1); results are quantitatively similar if the grid-point values are averaged along sigma levels as
opposed to pressure levels in order to account for strong variations in the fine-scale orography.
Hence, it appears the low-level dynamic component of the moisture divergence term is properly
captured by the grid-point tendency estimates. However, when the horizontal moisture
divergence term is integrated from 850mb to the top of the atmosphere, the simulated and
observed radiosonde-based estimates both indicate a net divergence of 2.3mm/day while the
grid-point based estimates derived from the RSM indicate a net divergence of 1.6mm/day (again,
results are the same if the area-average is done along sigma levels as opposed to pressure levels);
the resulting difference between these two estimates is 0.7mm/day, very similar to the difference
in the surface evaporation estimates described above, which suggests that the difference in area-
15
average evaporation is translating into a difference in the area-average large-scale divergence
aloft.
One way to test whether the difference in the upper-air divergence estimates arises from an
error in the large-scale estimates derived from the radiosonde profiles (both observed and
simulated) or from the RSM grid-point simulations is to extend the averaging domain for the
RSM grid-point values to the northern and eastern edge of the model domain (i.e. extend the top
boundary of the averaging region to the top of the RSM domain and the eastern boundary to the
right-hand side of the domain); in these regions, there are large boundary-forcing and offsetting
moisture divergence terms that bring the RSM moisture and momentum fields into agreement
with the larger-scale fields provided by the GSM. When these fields are included, the area-
average moisture divergence integrated from 850mb to the TOA is 2.2mm/day, which is in better
agreement with the values derived from the radiosonde-based profiles (see Table 1). This result
suggests that the model is underestimating surface evaporation over the center of the domain;
this under-estimation translates into an under-estimation of upper-level divergence over the
interior of the domain, which is then compensated by a numerically-prescribed enhancement of
divergence near the boundaries in order to preserve mass and moisture continuity with the larger-
scale fields. Despite these apparent discrepancies between the RSM grid-point estimates and the
area-averaged estimates of evaporation and upper-air divergence, both the explicit grid-point
fields and the derived large-scale fields indicate a balance in which low-level moisture
convergence and evaporation support seasonal-mean precipitation and a net divergence of
moisture aloft.
One issue that has yet to be dealt with is the process whereby the low-level moisture is re-
distributed to the upper-levels. Using the RSM grid-point tendency terms, it appears that the
16
vertical diffusion of moisture, integrated from 850mb-TOA, results in a net convergence of
moisture of 3.0mm/day, which is larger than the full vertical integral of 2.6mm/day; the extra
0.4mm/day is of course due to the additional moisture supplied by low-level convergence, which
is subsequently lofted via convection. Hence, at approximately 850mb, the large-scale vertical
circulation does not significantly contribute to the vertical redistribution of moisture within the
column. As one moves higher in the atmosphere, however, this contribution increases such that
at approximately 650mb, the large-scale vertical convergence from the lower atmosphere to the
upper-atmosphere is about 0.7mm/day and reaches a maximum of approximately 1mm/day at
500mb. In comparison, the vertical diffusion term indicates convergence of approximately
1.7mm/day and 0.1mm/day above these levels. Unfortunately, these values are extremely
difficult to evaluate using observational products, however they do suggest that convective
lofting is the primary mechanism responsible for vertically redistributing low-level moisture to
the mid-troposphere, at which point the large-scale vertical circulation becomes important.
Finally, to see how the moisture flux fields are related to the large-scale upper-level
divergence and low-level convergence of moisture described above, the vertically-integrated
moisture flux vectors are calculated using the 4xdaily RSM grid-point moisture and momentum
fields, along with the 2xdaily observed profiles; the vertical integral is taken from the surface to
800mb and separately from 800mb-TOA in order to capture the fluxes associated with the
vertical heterogeneity in the divergence profiles. Both the simulated and observed moisture flux
fields agree well with results in Berbery (2001) (see Figure 12) and therefore are not shown here.
However, we will summarize two important results. First, seasonal mean horizontal moisture
fluxes calculated from 4-times (2-times) daily data taken from the RSM (radiosondes) and
integrated from the surface to 800mb indicate that, for the western portion of the domain, large-
17
scale low-level fluxes into the southwestern US originate from both the eastern Pacific and the
Gulf of California, in agreement with the 800 mbar moisture fluxes shown in Berbery (2001). In
addition, for the upper-level moisture fluxes, integrated from 800mb to the top of the
atmosphere, there is a large, anti-cyclonic circulation of moisture over the entire domain. It is
important to note, however, that along most of the trajectory, these fluxes are being augmented
by the large-scale divergence of water vapor at these levels; this feature is captured by increases
in the magnitude of the moisture flux vectors as seen in Figure 12d from Berbery (2001),
indicating an enhancement of the moisture fluxes exiting over the Great Plains.
4. Summary and Discussion
a. Summary
In this paper, we investigated the large-scale summertime hydrologic cycle associated with
the southwestern branch of the North American monsoon. Using analyzed, simulated and
observed estimates of precipitation, as well as estimates of the large-scale diagnostic budget
terms related to the moisture tendency equation, we attempted to characterize and quantify the
various dynamic and hydrodynamic processes important for producing summertime rainfall in
this region, shown schematically in Figure 8 and quantified in Table 1. In general, low-level
moisture convergence (~0.4 mm/day), associated with advection of moisture from the Gulf of
California and the Pacific Ocean to the western portion of the domain, and with the advection of
moisture from the Gulf of Mexico to the eastern portion of the domain (Berberry, 2001),
augments the local convergence due to evaporation (~2.9 mm/day, representing the average
value between the RSM grid-point estimate of 2.6mm/day and the observationally-based
estimate of 3.2mm/day). Both of these processes supply low-level moisture to the region, which
18
is then carried aloft by convection (~3.0mm/day) and large-scale vertical advection (~0.3
mm/day, representing the average value between the surface and 700mb); this lofted moisture
feeds both the precipitation (~1.3 mm/day) and upper-air divergence fields (~2.0mm/day, again
representing the average value between the RSM grid-point estimate of 1.6mm/day and the
observationally-based estimate of 2.3mm/day). The upper-air divergence subsequently results in
enhanced exiting moisture fluxes over the Great Plains (Schmitz and Mullen, 1996; Berberry,
2001). In addition, the upper-air divergence exceeds the low-level convergence, resulting in a
net, vertically-integrated divergence of moisture from the region, making the southwestern US an
effective seasonal source of moisture to the atmosphere.
It should be emphasized that these values represent the climatological cycling of moisture in
the atmosphere and that both intraseasonal and interannual variability in this cycling may have
very different balances. For instance, the hydrologic balance associated with “synoptic”-type
events in this region has been described previously (Anderson, 2002). Results indicate that
precipitation events themselves are predicated upon a weakening of the upper-level advection of
water vapor from the precipitating region, such that the quasi-stationary vertical diffusion of
moisture into the atmosphere balances rainfall as opposed to large-scale moisture divergence. In
contrast, controlled model simulations have shown that interannual variability in precipitation in
this region is the result of enhanced low level moisture convergence associated with changes in
the low-level temperature, pressure and surface circulation patterns produced by anomalous
evaporation, indicating a very subtle interaction between the momentum, moisture, and energy
fields (Kanamitsu and Mo., 2003). Further characterization and description of the dominant
large-scale hydrologic budget terms associated with this interannual variability in precipitation
over the southwestern United States is being investigated presently.
19
b. Discussion
Based upon the balances found in this convectively-active semi-arid region, it appears that
the precipitation rate is strongly dependent upon the rate of evaporation of local moisture. One
way to quantify this relationship is by estimating the “recycling” of precipitation, which is
defined as the ratio of locally-derived precipitation (Pl) to total precipitation (P). Using a
traditional method for estimating the recycling rate, in which advective fluxes of moisture (Fin)
along a parcel trajectory length (L) are compared with the evaporative fluxes (E), gives
(Trenberth, 1999):
†
Pl
P=
ELEL + 2Fin
(2)
Given this estimation technique, we find that for a typical trajectory of 1000km, evaporative
fluxes of approximately 2.9mm/day (representing the average value between the RSM- and
observationally-based estimates) and advective fluxes of approximately 50kg/ms (which is the
average orthogonal component along the southern and western boundaries) the recycling rate is
about 0.25 (the recycling rate is about 0.14 if a 500km path trajectory is assumed). The low
numbers in this region are due principally to the low evaporative rate compared with advective
fluxes passing through the region. However, this recycling rate masks an important feature for
the hydrologic cycle described here, namely that although the evaporative fluxes are small
compared with the advective fluxes, they are relatively large compared with the large-scale
moisture convergence itself.
To better estimate the importance of evaporative fluxes to the rate of precipitation in this
region, we decide to utilize knowledge about the cycling of moisture found here. To start, we
look at the control volume delineated by the radiosonde network. Within this region we compute
the area-average evaporation through the bottom of the atmospheric column. We then compute
20
the area-average low-level horizontal moisture convergence. We then assume that both the
evaporative moisture and the convergent moisture are convected aloft by turbulent processes.
Some of this moisture precipitates out as rainfall while some is advected away, resulting in
upper-air divergence from the region. The main assumption here is that the low-level moisture,
supplied both by evaporation and large-scale dynamic convergence, is well mixed during the
convective process. This allows us to assume that the proportion of locally-generated rainfall
(Pl) and advectively-generated rainfall (Pa) are in the same ratio as the area-averaged evaporative
convergence and the low-level moisture convergence. Making the assumptions above allows us
to write:
†
P = Pl + Pa =E
E + -r—r u q low( )
P +-r—
r u q low
E + -r—r u q low( )
P (3)
The recycling ratio, Pl/P, then simply becomes:
†
Pl
P=
EE + -r—
r u q low( )(4)
For the region under consideration, we estimated the evaporative convergence to be
2.9mm/day, which is approximately the mean of the values provided by the radiosonde profiles
and the grid-point RSM tendency term. The low-level convergence is more difficult to estimate
because it is reliant on a definition of “low level”. One option is to integrate from the surface to
the level of precipitation. Another is to integrate from the surface to some fixed pressure height.
A final option is to integrate from the surface to the sigma level at which the moisture
convergence goes to zero. For the region under consideration, both the radiosonde-based area-
average estimates and the RSM grid-point estimates of the low-level moisture divergence,
integrated from the surface to 850mb, indicate a net moisture convergence of 0.4mm/day; in
addition, the RSM sigma-level grid-point moisture divergence values, integrated from the
21
surface to the level of zero divergence, also indicate a convergence of 0.4mm/day. Therefore,
for this region, we will use an estimate of 0.4mm/day, although for other regions the two
methods may not necessarily produce the same estimate. Using these values for the area-
averaged evaporation and low-level moisture convergence gives a recycling ratio of 0.88. In
other words about 85-90% of the area-average precipitation is due to recycling of moisture
within the column while about 10-15% is due to introduction of moisture via advective
convergence. Calculating similar recycling ratios for individual grid-points suggests that values
range from 0.7-0.9 (not shown). Overall, these numbers suggest a much greater importance of
evaporative fluxes in generating regional precipitation than does the traditional estimate.
It should be emphasized, however, that the two estimates are based upon different
assumptions. The first ratio assumes that all moisture entering the control volume (either through
evaporation or advection) is well-mixed within the control volume; the second assumes that only
convergent sources of moisture (i.e. evaporation and low-level convergence) are well-mixed and
that these in turn supply divergent sinks of moisture (i.e. precipitation and upper-air divergence).
Which estimate one chooses to emphasize depends in large part upon what question one wishes
to answer, i.e. the question of how often moisture is cycled within the system before it is
advected out of the control volume (which is fairly quickly given the relatively large moisture
flux aloft) or the question of whether the overall precipitation rate is related to the rate of cycling
of moisture within the control volume or the rate of large-scale moisture convergence.
It is also important to note that the method, as constructed here, is only applicable for
convectively-active regions in which there is large-scale low-level moisture convergence and
upper-level moisture divergence. As an example, in a region where moisture convergence
occurs aloft (at the precipitating level itself for instance), with little convergence at lower levels,
22
this method would show a large evaporative component to the precipitation when in fact much of
the precipitation may be related to larger-scale dynamic processes. Here we introduce the metric
as a better means of estimating recycling for this particular region given the hydrodynamic
balances identified in the research. For the index to be more generally applicable it would need
to be modified in order to incorporate a broader range of balances (for instance ones in which
there is large-scale divergence of moisture at low levels or convergence aloft). In addition, a
better index should also account for the correlations between the anomalous dynamic
convergence of moisture (either on interannual or intraseasonal time-scales) and the anomalous
evaporation and precipitation on these time-scales. However, since most recycling estimates are
based upon monthly mean values (Brubaker et al., 1993; Eltahir and Bras, 1996; Trenberth
1999), we decided to construct one which could be estimated from similar fields. Further
evaluation of this index as a diagnostic metric, as well as alternative indices based upon
intraseasonal variations in the various hydrodynamic terms, will be introduced in a subsequent
manuscript.
23
Acknowledgements. The authors wish to thank the Distributed Active Archive Center (Code
902), and Brian Cosgrove in particular, at the Goddard Space Flight Center, Greenbelt, MD,
20771 for distributing the evaporation data; and the science investigator, Dr. Bruno Rudolf at the
GPCC, Deutscher Wetterdienst, Germany, for producing the GPCC precipitation data products.
Goddard's DAAC is sponsored by NASA's Mission to Planet Earth program. Overviews of the
Goddard DAAC can be found at:
http://eosdata.gsfc.nasa.gov/CAMPAIGN_DOCS/FTP_SITE/INT_DIS/readmes/gpcc.html#400.
In addition, FSL Radiosonde data can be found at: http://raob.fsl.noaa.gov/
This research was funded by a cooperative agreement from NOAA-NA17RJ1231, NOAA-
NA16GP1622 and the NSF SAHARA project. The views expressed herein are those of the
authors and do not necessarily reflect the views of NOAA or NSF.
24
APPENDIX
Regional Spectral Modeling System
The NCEP global to regional modeling system used in this study contains two components
- a low-resolution global spectral model (GSM) and a regional spectral model with a single nest
(RSM). The nesting method is a one-way, non-interactive procedure that is designed to calculate
regional responses (or adjustments) of the RSM to the large-scale background fields provided by
the coarser-resolution GSM. This nesting procedure is performed through the entire domain, not
only at the lateral boundary zones and is therefore referred to as a perturbation method (see
Juang and Kanamitsu, 1994; Juang et al., 1997 for details).
The GSM and RSM use the same primitive hydrostatic sigma-coordinate equations
expressed in slightly different forms (Juang and Kanamitsu, 1994; hereafter JK). The GSM
equations consist of the vorticity, divergence, virtual temperature, and conservation equation for
water vapor on 18 sigma-layer coordinates and a mass continuity equation for surface pressure
(Kanamitsu, 1989). The regional model differs slightly in that it utilizes the momentum
equations instead of the vorticity/divergence equations (JK). The model physics were described
in Kalnay et al. (1996). Below are some of the important parameterizations:
Deep convection - Deep convection is modeled using a simplified Arakawa-Schubert
parameterization (Kalnay et al., 1996). This scheme assumes a quasi-equilibrium available
buoyant energy; changes in buoyant energy associated with convective activity are provided by
large-scale changes associated with the environmental stability of the atmosphere. The
instability is removed by relaxing the temperature and moisture profiles towards equilibrium
values using a prescribed time interval (Grell, 1993). In the simplified version, cloud size is
effectively prescribed as opposed to being determined from a parameterization-dependent
25
spectrum. In addition Hong and Pan (1996) detail modifications to this scheme. In particular, in
the subcloud layers, the level of maximum moist static energy represents the level of origination
for updraft-air; however, if the depth between this level and the level of free convection is
greater than a certain threshold, convection is suppressed. Convection can also be produced in
regions with large convective available potential energy (Hong and Pan, 1996). Importantly, the
scheme accounts for downdrafts associated with convection; in previous modeling attempts over
the southwest US it was shown that the parameterization scheme needs to consider evaporation
of precipitation below the cloud base in order to create outflow and increased convection in the
southwestern United States (Dunn and Horel, 1994b).
Large-scale Precipitation - A vertical iteration starting from the top sigma level checks for
supersaturation at each level. Supersaturated layers are set to a near-saturated state using an
approximate wet-bulb process; all excess liquid water goes into the form of precipitation. As the
precipitation moves downward through the column, it is either augmented (by passage through
additional supersaturated layers) or reduced (by passage through unsaturated layers). If it is
reduced due to passage through unsaturated layers, evaporation in that layer occurs where the
rate of evaporation is dependent upon the drop-size distribution (NMC, 1988).
Boundary Layer Processes - Boundary layer processes (Hong and Pan, 1996) are based upon a
nonlocal boundary layer vertical diffusion in which the surface layer and boundary layer are
coupled using a prescribed profile with similarity-based scale parameters. Included in this
scheme are also countergradient diffusion processes, which are shown to be important for mixing
low-level moisture upward more efficiently (Hong and Pan, 1996). Importantly, the scheme
strongly couples the boundary layer physics and the convective processes described earlier,
improving the precipitation forecasts for heavy rain events (Hong and Pan, 1996). Boundary
26
layer height is computed iteratively by first computing the boundary height without accounting
for virtual temperature instability near the surface; from this estimate, calculations of vertical
velocities can be obtained, which then allow estimation of the virtual temperature instability and
subsequently modified PBL heights (Hong and Pan, 1996). The diffusion parameters for
momentum and mass can then be determined from the boundary layer height using the
prescribed profile shape. Above the boundary layer, vertical eddy transfer uses a more-
traditional Richardson number-dependent diffusion process.
Soil Model Processes - The soil model in the RSM is based upon the Oregon State University
scheme as described in Chen et al. (1996). The scheme includes an explicit vegetation canopy,
soil hydrology and soil thermodynamics. For the soil scheme, it uses a 2-level (0.1 and 1.9m
thick) prognostic soil model for both moisture and temperature, with parameterized diffusion
between the two levels and a spatial distribution of soil type and soil properties as described in
Betts et al. (1997). Also included in the prognostic scheme are equations for moisture contained
in vegetation and snow (Chen et al., 1996). Unlike the atmospheric fields, the prognostic soil
moisture and temperature fields are not updated from the GSM/Reanalysis output but are instead
continuously evolving fields based solely upon the RSM physics. Evaluation and analysis of the
soil model performance has been done both within the NCEP Eta (Chen et al., 1996; Betts et al.,
1997) and RSM (Roads and Chen, 2000) modeling systems. To initialize the soil model, soil
moisture and temperature values are derived from the Reanalysis data at the start of the summer
season (July 1). As described by Roads et al. (1999, 2000, 2003), Kanamitsu et al. (2003), and
Chen and Roads (2004), long term simulations with the GSM/RSM can result in unrealistic soil
moisture, due to coupled land-atmosphere feedbacks that can sometimes result in excessive
drying. A number of solutions have been developed to counter this feedback for not only global
27
and regional simulations but also for analysis models (e.g. Kalnay et al. 1996, Kanamitsu et al.
2003). For this study, we decided to initialize the soil moisture from NCEP's Global Data
Assimilation System output at the beginning of each summer run in order that at least the upper
layers of the soil moisture would come into quasi- equilibrium with the RSM atmosphere and at
the same time the lower layers would not adversely affect the simulations. This re-initialization
once every 3 months did appear to produce more realistic RSM simulations than a preliminary
run that ran continuously for several years as well as another run that was initialized every day
from the global analysis. Again, a number of methodologies, including precipitation assimilation,
are still underway to further improve these kinds of regional simulation studies dependent upon
land-atmosphere feedbacks.
Sea Surface Temperatures - Sea-surface temperatures in the RSM are re-initialized daily using
the coarse-scale Reanalysis data. The one exception is in the Gulf of California, which is absent
from the global data. For the Gulf, SSTs are assigned a constant value equal to the temperature
at the mouth of the Gulf (approximately 29° C). This assignment is based upon results of
previous modeling studies indicating the importance of proper SST fields in the Gulf (Dunn and
Horel, 1994b); sensitivity studies indicate that finer-scale SST resolution does not quantitatively
affect the dynamic fields in the region (Anderson, 1998).
28
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33
TABLE LEGENDS
Table 1 Atmospheric water vapor tendency equation estimates and their data sources. Signconvention such that positive values equivalent to atmospheric moisture divergence
FIGURE LEGENDS
Fig. 1 RSM (• - 25 km resolution) and GSM (+ - T62) grid points and RSM orography contours.Orography contour interval is 250 meters; shading interval is 750 meters with maximumshaded elevation of 3000 meters. Location of important geographic and selected upper-airobserving stations (O) also shown (NKX - Miramar AFB, San Diego; TUS - Tucson; EPZ -Santa Teresa; MID - Midland; AMA - Amarillo; DDC - Dodge City; LBF - North Platte;SLC - Salt Lake City; DRA - Desert Rock). Box indicates region over which area-averagevalues are calculated - see text. Latitude-longitude lines every 5-degrees.
Fig. 2 a) Mean daily summertime (JAS) precipitation from the CPC UNIFIED dataset. Shadinginterval is 0.5 mm day-1; minimum shading is 0.5 mm day-1. Average taken over the time-period 1998-2002 b) Same as a) except for the GPCC data. c) same as a) except for theRSM data. d) same as a) except for the NCEP Reanalysis data.
Fig.3 a) Daily summertime (JAS) precipitation anomaly for the period 1998-2002, as seen in theClimate Prediction Center’s (CPC) UNIFIED dataset. Shading interval is 0.25 mm day-1;minimum shading is +/-0.25 mm day-1. Positive values are shaded; negative values aredashed. Anomaly taken against the time-period 1986-2002. b) same as (a) except for theGPCC data-set.
Fig. 4 Area-averaged seasonal-mean moisture divergence profiles calculated using wind andhumidity data taken from selected 2xdaily radiosonde locations (see Figure 1). Divergenceprofiles calculated for observed data (solid line), simulated data interpolated to the standardreporting levels (closed squares), simulated data interpolated to high resolution pressurelevels (every 50mbar below 500mbar; every 100mbar above 500mbar; open squares), andReanalysis profiles (triangles).
Fig. 5 Mean daily summertime (JAS) vertically-integrated RSM diagnostic moisture tendencyterms. Shading interval is 1 mm day-1; minimum shading is +/-1 mm day-1. Positive valuesare shaded; negative values are dashed. (a) large-scale total moisture divergence; (b) verticaldiffusion moisture divergence (equivalent to evaporation but of opposite sign); (c) totalprecipitation (same as Figure 2c but with different scaling); (d) overall moisture tendency.
Fig. 6 (a) Mean daily summertime (JAS) evaporation estimates, taken from the MOSAIC/LDASanalysis. Values averaged over the period 1998-2002. Shading interval is 1 mm day-1;minimum shading is +/-1 mm day-1. (b) same as (a) except for evaporation estimates takenfrom the RSM data product
Fig. 7 Mean daily summertime (JAS) difference between precipitation and evaporation.Precipitation estimates taken from the CPC Unified dataset; evaporation estimates taken
34
from the MOSAIC/LDAS analysis and extrapolated to the CPC 25km gridpoints. Valuesaveraged over the period 1998-2002. Shading interval is 0.5 mm day-1; minimum contour is+/- 0.5 mm day-1. Positive values are shaded; negative values are dashed.
Fig. 8 Schematic diagram showing 3-dimensional moisture cycling over the southwestern UnitedStates. Small blue arrows indicate low-level moisture fluxes; Large arrow indicates upper-level (<700mb) moisture fluxes. Black ovals represent convective activity. Shown in colorare the RSM daily precipitation estimates overlying the model orography, shown on the z-axis.
35
Table 1 Atmospheric water vapor tendency equation estimates and their data sources. Signconvention such that positive values equivalent to atmospheric moisture divergence
Precipitation Evaporation Vertically-integratedDivergence
Upper-levelDivergence850mb-TOA)
Low-levelDivergence(Sfc-850mb)
RSMgrid-point
1.3mm/d -2.6mm/d 1.3mm/d 1.6mm/d -0.4mm/d
ObservedRadiosonde
— — 1.9mm/d 2.3mm/d -0.4mm/d
Reanalysis-estimatedRadiosonde
— — 0.8mm/d — —
RSM-estimatedRadiosonde:Standard levels
— — 1.9mm/d 2.3mm/d -0.4mm/d
RSM-estimatedRadiosonde: Full
— — 2.3mm/d — —
CPC Precipitation 1.3mm/d — — — —
LDAS — -1.3mm/d — — —
Net Obs. Div. +Precipitation
— -3.2mm/d — — —
Fig. 1 RSM (
dots
- 25 km resolution) and GSM (crosses - T62) grid points and RSM orographycontours. Orography contour interval is 250 meters; shading interval is 750 meters with maxi-mum shaded elevation of 3000 meters. Location of important geographic and selected upper-airobserving stations (O) also shown (NKX - Miramar AFB, San Diego; TUS - Tucson; EPZ - SantaTeresa; MID - Midland; AMA - Amarillo; DDC - Dodge City; LBF - North Platte; SLC - SaltLake City; DRA - Desert Rock). Box indicates region over which area-average values are calcu-lated - see text. Latitude-longitude lines every 5-degrees.
CPC UNIFIED Dataset: 1998-2002
Fig. 2 a) Mean daily summertime (JAS) precipitation from the CPC UNIFIED dataset. Shading inter-
val is 0.5 mm day
-1
; minimum shading is 0.5 mm day
-1
. Average taken over the time-period 1998-2002 b) Same as a) except for the GPCC data. c) same as a) except for the RSM data. d) same as a)except for the NCEP Reanalysis data.
Daily Mean Summertime PrecipitationFrom Observational and Simulated Data Products
NCEP Reanalysis Dataset: 1998-2002RSM Dataset: 1998-2002
GPCC Dataset: 1998-2002
(c)
(b)
(d)
(a)
Fig.3 a) Daily summertime (JAS) precipitation anomaly for the period 1998-2002, as seen in the Cli-
mate Prediction Center s (CPC) UNIFIED dataset. Shading interval is 0.25 mm day
-1
; minimum
shading is +/-0.25 mm day
-1
. Positive values are shaded; negative values are dashed. Anomaly takenagainst the time-period 1986-2002 b) same as (a) except for the GPCC data-set.
GPCC Dataset: Anomaly: 1998-2002(b)CPC UNIFIED Anomaly: 1998-2002(a)
Daily Mean Observed Summertime Precipitation Anomaly
−0.015 −0.01 −0.005 0 0.005 0.01 0.015
0
200
400
600
800
1000
Moisture Divergence (10−6 kg kg−1 s−1)
Pre
ssur
e (m
b)RadiosondeRSM − Standard levelsRSM − every 50mbReanalysis
Fig. 4 Area-averaged seasonal-mean moisturedivergence profiles calculated using wind andhumidity data taken from selected 2xdaily radio-sonde locations (see Figure 1). Divergence profilescalculated for observed data (solid line), simulateddata interpolated to the standard reporting levels(closed squares), simulated data interpolated tohigh resolution pressure levels (every 50mbarbelow 500mbar; every 100mbar above 500mbar;open squares), and Reanalysis profiles (triangles).
Precipitation
Vertical DiffusionLarge-scale Divergence
Moisture Tendency(c) (d)
(b)(a)
Fig. 5 Mean daily summertime (JAS) vertically-integrated RSM diagnostic moisture tendency terms. Shading
interval is 1 mm day
-1
; minimum shading is +/-1mm day
-1
. Positive values are shaded; negative values aredashed. (a) large-scale total moisture divergence; (b) vertical diffusion moisture divergence (equivalent toevaporation, but of opposite sign); (c) total precipitation (same as Figure 2c but with different scaling); (d)overall moisture tendency.
Evaporation Estimate from LDAS: 1998-2002 Evaporation Estimate from RSM: 1998-2002(a) (b)
Fig. 6 (a) Mean daily summertime (JAS) evaporation estimates, taken from the MOSAIC/LDAS analysis.
Values averaged over the period 1998-2002. Shading interval is 1 mm day
-1
; minimum shading is +/-1mm
day
-1
. (b) same as (a) except for evaporation estimates taken from the RSM data product
CPC Precip - LDAS Evap.
Fig. 7 Mean daily summertime (JAS) differencebetween precipitation and evaporation. Precipita-tion estimates taken from the CPC Unified dataset;evaporation estimates taken from the MOSAIC/LDAS analysis and extrapolated to the CPC 25kmgridpoints. Values averaged over the period 1998-
2002. Shading interval is 0.5 mm day
-1
; minimum
contour is +/- 0.5 mm day
-1
. Positive values areshaded; negative values are dashed.
Fig. 8 Schematic diagram showing 3-dimensional moisture cycling over the southwesternUnited States. Small blue arrows indicate low-level moisture fluxes; Large arrowindicates upper-level (<700mb) moisture fluxes. Black ovals represent convectiveactivity. Shown in color are the RSM daily precipitation estimates overlying the modelorography, shown on the z-axis.
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