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Elcin Tan
Department of Meteorological Engineering, Istanbul Technical University, Istanbul, 34469, Turkey 5
Correspondence to: Elcin Tan ([email protected])
Abstract. Providing high accuracy in quantitative extreme precipitation forecasting (QEPF) is still a
challenge. California is vulnerable to extreme precipitation, which occurs due to atmospheric rivers and
might be more intense with climate change. Accordingly, this study is an attempt to evaluate the
extreme precipitation forecasting performance of a QPF model, the Weather Research and Forecast 10
Model, version 3.1.7, for the extreme precipitation event that caused the 1997 New Year’s flood in
California. Sensitivities of 19 microphysics schemes are tested by utilizing 18 various Goodness of Fit
(GoF) tests for hourly and point-wise comparisons between 3-km horizontal domain resolution
simulations of the WRF Model and observations. The results indicate that the coefficient of persistence
(cp) is the first metric that needs to be evaluated because it determines whether simulation versus 15
observation values are reasonable. Comparisons of 3 out of 8 stations in the American River Watershed
passed this test. The results also show that Normalized Root Mean Square Errors (NRMSE) and Percent
Bias (PBIAS) metrics are more representative than others due to their ability to discriminate model
performances. Further, microphysics (MP) schemes are also significantly sensitive to location.
Although 3 of the stations that passed the cp test are quite near to each other spatially, different MP 20
schemes become prominent for different observation locations. For instance, for the ALP station, MP3,
MP8, MP17, and MP28 indicate better performances, whereas the errors of MP3, MP8, MP9, and
MP17 are less than other MPs for the BTA station. However, MP11 has the only reasonable results,
according to cp values for the CAP station. The MPs are also evaluated for 72-hr and basin-averaged
precipitation estimations of the WRF Model by means of true percent relative errors. The results show 25
that the accuracy of the WRF Model is much higher for the 72-hr total basin-averaged evaluations than
Microphysics parameterization sensitivity of the WRF Model version 3.1.7 to extreme precipitation: evaluation of the 1997 New Year’s flood of California
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
2
for the hourly and point-wise comparisons. Thus, the Thompson Scheme (MP8) indicates more
trustworthy results than others, with a 3.1% true percent relative error. Although WRF simulations
overestimate the 72-hr basin-averaged precipitation for most of the MP schemes, this may not be
pronounced for moderate, heavy, and extreme precipitation when hourly and point-wise evaluations are
performed but is valid for light precipitation. 5
1 Introduction
The IPCC noted that the magnitude, frequency, and duration of extreme events may increase as a result
of climate change (2012). This impact could be significant for regions such as California where drought
intensities also tend to increase and excessive amounts of precipitation should therefore be saved for
future periods of drought. When drought causes these regions to take one step back, extreme 10
precipitation events can take them two steps forward. Hence, smart and new water management
strategies should be developed to advance climate change adaptation. Some of the solutions might be
related to improving the time and space problem of quantitative extreme precipitation forecasting
(QEPF). The Weather Research and Forecasting Model (WRF) (Skamarock et al., 2008) has been
widely used to increase resilience to extreme precipitation events in California (Jankov et al., 2007; 15
Jankov et al., 2009; Tan, 2010; Eiserloh and Chiao, 2014), which are closely correlated with
atmospheric river events (Tan, 2010). Atmospheric rivers are also expected to increase in terms of both
frequency and intensity with climate change (Dettinger, 2011). Generally, for precipitation simulations,
a combination of the microphysical, cumulus parameterization and the planetary boundary layer
parameterization schemes is mainly evaluated using the WRF Model, although all of the 20
parameterization schemes and processes have effects on precipitation occurrence. Moreover,
microphysical schemes play a significant role if the horizontal resolution of the interested domain is
finer than 9 km. Recent studies have been conducted for the evaluation of the microphysics schemes of
the WRF Model with respect to winter precipitation in California, with a focus on atmospheric river
phenomena (Jankov et al., 2007; Jankov et al., 2009; Tan, 2010; Jankov et al., 2011; Han et al., 2013), 25
which is the reason for the extreme precipitation that occurs along the West coast of the US (Zhu and
Newell, 1998; Ralph et al., 2004; Tan, 2010; Houze, 2012). Jankov et al. (2007) evaluated the Lin,
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
3
Ferrier, WSM6, and Thompson schemes of version 2 of the WRF-ARW for the February 27, 2006,
atmospheric river event by using a 3-km horizontal grid resolution. Jankov et al. (2009) also
investigated the Lin, WRF Single-Moment 6-Class (WSM6), Thompson, and Schultz microphysics
schemes for a 3-km horizontal resolution by simulating December 30–31, 2005, January 1, 2006,
February 1, 2006, February 27, 2006, and March 5, 2006 events. Jankov et al. (2011) included the 5
double-moment Morrison microphysics scheme in the four MP physics they used in their previous
research (Jankov et al., 2009) and changed the finest horizontal grid resolution to 4 km to evaluate only
the December 30–31, 2005 event, using version 3.0 of the WRF Model. Han et al. (2013) evaluated the
Goddard scheme, WRF single-moment 6-class scheme, Thompson scheme and Morrison double-
moment scheme of version 3.1 of the WRF Model by simulating the December 30–31, 2005 10
atmospheric river (AR) event that occurred in northern California and Nevada, with a 1.3-km horizontal
resolution. Tan (2010) calibrated the WRF Model version 3.1.1 by using the Kessler, Lin, WSM 3-
class, WSM 5-class, Ferrier, WSM 6-class, Goddard GCE, Thompson, Morrison, WDM 5-class, WDM
6-class, and Old Thompson schemes for the 1997 New Year’s event at the American River Watershed,
with a 3-km horizontal resolution and 72-hr basin-averaged consideration for the purpose of probable 15
maximum precipitation (PMP) estimation. She also validated the WRF Model using the Thompson
microphysics scheme for 42 historical atmospheric river events that occurred between 1951 and 2002.
Hereupon, the main goal of this study is to evaluate hourly quantitative extreme precipitation
forecasting (QEPF) performance of the WRF Model for the most extreme atmospheric river event in the
history of California. The 1997 New Year’s flood, which occurred between December 26, 1996, and 20
January 3, 1997, has been ranked as the fifth of California’s top 15 weather events in the 1900s (URL-
1) and is still the major impactful atmospheric river event of the state. Thus, the 1997 New Year’s
atmospheric river event is simulated for the evaluation of version 3.7.1 of the WRF Model. In this way,
new MP schemes that were not evaluated by Tan (2010) are also included in this study. As a result,
hourly QPF of the total 19 bulk parameterization schemes is evaluated by using 18 different Goodness 25
of Fit (GoF) tests at a 3-km horizontal grid resolution by comparing hourly ground-based rain gauge
observations. Additionally, 72-hr basin-averaged values are also discussed for comparison with the
previous studies.
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
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This study is organized as follows: Section 2 presents data used for the simulations and comparisons.
The model design is detailed in Section 3, including MP schemes and evaluation methods. Section 4
discusses results in terms of both in-situ observations and 72-hr basin-averaged values. Finally, the
study is concluded in Section 5.
5
2 Data
2.1 Reanalysis Data Set
WRF simulations are initialized by using a 6-hourly NCEP/NCAR Global Reanalysis Model (Kalnay et
al., 1996). The resolution of this model is T62 (209 km) with 28 vertical sigma levels. The dataset
(2.5°x2.5°) is continuously available online, starting from 1948, at the Research Data Archive (RDA) of 10
the University Corporation for Atmospheric Research (UCAR) (NCEP et al., 1994).
2.2 Observational Data
Hourly rain-gauge data are obtained from the California Data Exchange Center (CDEC) (URL-2) for
the American River Watershed where Pacific Standard Time (PST) is utilized as the local time. Because
the WRF Model uses Coordinated Universal Time (UTC), all the comparisons are scaled accordingly. 15
That is, the WRF Model comparisons are presented in this study based on UTC and 72-hr basin-
averaged comparisons are kept with local time comparisons in order to be consistent with the literature.
Hourly rainfall rates of the WRF model are evaluated by considering 8 rain gauge stations: Alpha
(ALP- 2316 m), Beta (BTA- 2316 m), Owens Camp (OWC- 1371 m), Caples Lake (CAP- 2438 m),
Hell Hole (HLH- 1396 m), Pilot Hill (PIH- 366 m), Lincoln (LCN- 61 m), and Hurley (HUR- 11 m) 20
(Figure 1). The stations’ IDs and their elevations are given in the parentheses.
Precipitation rates are classified as follows: Light: Trace-2.5 mm/hr, Moderate: 2.5mm/hr-7.5mm/hr,
Heavy: 7.5mm/hr-10mm/hr, and Extreme: >10mm/hr.
The hourly time series of each station shows that the ALP and BTA stations observed extreme
precipitation during the New Year’s flood of 1997 (Figure 2). 25
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
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Figure 1: The map of the American River Watershed and the precipitation stations
Figure 2: Precipitation rate hourly time series of 8 rain gauge stations. 5
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
6
These rain gauges are not included in the estimation of the 72-hr basin averaged precipitation values.
The 72-hr basin-averaged precipitation data obtained by USACE (2005) is used to compare with the
precipitation output of the WRF Model. The 72-hr basin-averaged output of the WRF Model is
calculated by using the shape file masking method of the NCAR Command Language (NCL, 2016). 5
3. Model design
Version 3.7.1 of the Weather Research and Forecast Model (Skamarock et al., 2008) is utilized in this
study, and the 72-hr basin-averaged results are compared to the results of Tan (2010), who used
versions 3.1 and 3.1.1 of the same model. The model design exactly follows that of Tan (2010) so that 10
microphysics parameterization sensitivities can be discussed. The domain configuration is presented in
Figure 3, where the outer-most domain (D1) has a 27-km resolution and its two-way nested domains are
configured with a ratio of 3 so that the horizontal resolution of the finest domain (D3) is 3 km.
15
20
25
Figure 3: The WRF-ARW model domain configuration
d01
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
7
The American River Watershed is centered on the finest resolution (3 km) domain. D1 consists of 43 x
44 x 28 grid points, the middle domain (D2) has 64 x 67 x 28 grid points, and D3 has 82 x 76 x 28 grid
points for the meridional, zonal, and vertical directions, respectively. Lambert conformal is used for
map projection. The cumulus parameterization scheme, 3D Grell, is only utilized for 27- and 9-km 5
domains, while cloud microphysics schemes are switched on only for 9- and 3-km domains, as
suggested by Tan (2010). Because several studies indicate that the YSU planetary boundary layer (PBL)
scheme has less bias than other PBL schemes, all microphysics sensitivities are tested using this scheme
(Hu et al., 2010; Tan, 2010; Gomez-Navarro et al., 2015). Long wave radiation parameterization is
selected as the rrtm scheme, whereas for shortwave radiation the Dudhia scheme is used. The Unified 10
Noah model is utilized as the land-surface parameterization without using any urban canopy model. The
revised MM5 Monin-Obukhov scheme is applied as a surface layer option. The simulations began at 6
am, December 29, 1996 and stopped at 6 am, January 3, 1997.
3.2 MP schemes
WRF-ARW Model version 3.7.1 has 23 microphysics (MP) options. Four of these options are not 15
included in this study for the following reasons. One of those four options is option zero
(mp_physics=0), which means that no microphysics is used. Another one is the NSSL 2-moment 4-ice
scheme (Mansell et al, 2010) with predicted CCN (mp_physics=18), which is suggested for use with
idealized cases rather than real ones. The remaining two are spectral bin microphysics, i.e., fast (Khain
et al, 2010), and full (Khain et al, 2004) versions of HUJI (Hebrew University of Jerusalem, Israel), i.e., 20
mp_physics=30 and mp_physics=32, respectively, which are beyond the scope of this study. Therefore,
19 bulk microphysical parameterization schemes are evaluated with respect to rainfall amount. A brief
summary of these 19 schemes is presented in Table 1, where the first column is the Scheme ID number
that the WRF name list uses. The second column of the table provides the names of the schemes and the
third one indicates the number of the variables predicted by these schemes, such as water vapor, rain, 25
snow, cloud ice, cloud water, graupel, hail, and aerosol. The fourth and the fifth columns indicate
whether ice-phase and mixed phase processes are utilized, respectively.
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
8
Table 1. General properties of the tested microphysics schemes.
Scheme ID Scheme Name Number of Variables Ice-Phase Processes Mixed Phase Processes
MP1 Kessler 3 No No
MP2 Lin et al. 6 Yes Yes
MP3 WSM 3-class simple ice 3 Yes No
MP4 WSM 5-class 5 Yes Yes
MP5 Ferrier (New Eta) 4 Yes Yes
MP6 WSM 6-class 6 Yes Yes
MP7 Goddard GCE 6 Yes Yes
MP8 Thompson 5 Yes Yes
MP9 Milbrandt-Yau 7 Yes Yes
MP10 Morrison 6 Yes Yes
MP11 CAM 5.1 4 Yes No
MP13 SBU_YLIN 5 Yes Yes
MP14 WDM 5-class 5 Yes Yes
MP16 WDM 6-class 6 Yes Yes
MP17 NSSL 2-moment 6 Yes Yes
MP19 NSSL 1-moment 7 Yes Yes
MP21 NSSL 1-moment 6 Yes Yes
MP28 Aerosol-aware Thompson 8 Yes Yes
MP95 Ferrier (Old Eta) 2 No No
Although each scheme has been constructed by using a couple of previous parameterization methods,
they might be classified into 4 groups in terms of their main origins:
1. Kessler type- MP1: Kessler Scheme (Kessler, 1995); 5
2. Rutledge and Hobbs (1984; RH84) type- MP2: Lin et al. Scheme (1983), MP3: WRF Single–moment
3–class Scheme (Hong et al., 2004), MP4: WRF Single–moment 5–class Scheme (Hong et al, 2004),
MP6: WRF Single–moment 6–class Scheme (Hong and Lim, 2006), MP9: Milbrandt–Yau Double
Moment Scheme (Milbrandt and Yau, 2005a; Milbrandt and Yau, 2005b), MP10: Morrison 2–moment
Scheme (Morrison et al, 2009), MP11: CAM V5.1 2–moment 5–class Scheme (Eaton, 2011), MP13: 10
Stony–Brook University Scheme (Lin and Colle, 2011), MP14: WRF Double Moment 5–class Scheme
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
9
(Lim and Hong, 2010), MP16: WRF Double Moment 6–class Schemes (Lim and Hong, 2010), and
MP95: Ferrier (Old Eta) (Ferrier et al., 1995);
3. Reisner et al. (1998; RRB98) type- MP5: Ferrier (New Eta) Scheme (NOAA, 2001), MP7: Goddard
Scheme (Tao et al., 1989), MP8: Thompson Scheme (Thompson et al, 2008), and MP28: Aerosol-
Aware Thompson Scheme (Gilmore et al., 2014); 5
4. Ziegler (1985) type- MP17: NSSL 2–moment Scheme (Mansell et al., 2010), MP19: NSSL 1–
moment 7–class Scheme, and MP21: NSSL 1–moment 6–class Scheme (Gilmore et al., 2004).
3.3 Goodness of fit (GoF) tests
The 1997 New Year’s flood event is simulated to compare the sensitivities of the WRF Model to the 10
extreme precipitation that occurred due to atmospheric rivers. Because the 1997 flood is the highest
ranked atmospheric river event, considering the loss of life and property, this event is selected as the
case. Precipitation simulations of 19 microphysics schemes of the WRF Model version 3.7.1 are
compared to the hourly rain gauge observations. First, time series of the observed and modeled values
are plotted for the 8 rain-gauge locations. Precipitation rates of the model at these locations are 15
estimated by using the bilinear interpolation method following Tan (2010) rather than using the nearest
grid point value.
Chai and Draxler (2014) suggested that a combination of metrics should be used to assess model
performance rather than utilizing only Root Mean Square Error (RMSE) and Mean Absolute Error
(MAE). Therefore, 18 Goodness of Fit (GoF) tests of the hydroGOF-package in R (Mauricio, 2014) are 20
calculated to compare simulated and observed rainfall rate time series. The GoF tests, utilized in this
study, are Mean Error (ME), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean
Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Percent Bias (PBIAS), Ratio of
RMSE to the standard deviation of the observations (RSR), Ratio of Standard Deviations (rSD), Nash-
Sutcliffe Efficiency (NSE), Modified Nash-Sutcliffe Efficiency (mNSE), Index of Agreement (d), 25
Modified Index of Agreement (md), Coefficient of Persistence (cp), Pearson product-moment
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
10
correlation coefficient (r), Coefficient of determination (R2), Coefficient of determination multiplied by
the slope of the regression line (bR2), Kling-Gupta efficiency (KGE), and Volumetric Efficiency (VE).
In addition to the hourly precipitation assessment of the model parameterizations, 72-hour basin-
averaged precipitation values of the American River Watershed of California are estimated by using the
shape file masking method of NCL (NCL, 2016). The total precipitation values that are masked out by 5
this method are then compared to the basin-averaged observation value obtained by USACE (2005).
The 72-hr basin-averaged results of each microphysics option are also evaluated by estimating the true
percent relative error.
4. Results
4.1 Hourly and rain gauge-based comparisons 10
As mentioned in Section 3.3, 18 GoF methods are utilized to evaluate the performance of the
microphysics schemes based on hourly precipitation simulations at selected rain gauge stations. One of
those tests, i.e., coefficient of persistence (cp), indicates that the model performance can be acceptable if
its value is either greater than or equal to zero. The cp values of model and observation comparisons at
5 out of the 8 rain-gauge station locations, i.e., HLH, HUR, LCN, OWC, and PIH (Figure 1), are less 15
than zero for all of the MP options. Thus, the model performance is not acceptable for them in terms of
hourly precipitation comparisons. For this reason, time series graphics and GoF test results for these
unacceptable station locations are not given here. This might show us that a significant source of error is
still added to the model by means of MP schemes depending on the location with the consideration of
hourly rainfall assessments. However, the results of some MP schemes are found acceptable for the 20
observations of the ALP, BTA, and CAP stations, whose graphical comparisons are presented in Figure
4, Figure 5, and Figure 6, respectively, including their GoF test results (Figure 7, Figure 8, and Figure
9). After accepting the performances of MP schemes based on cp values, the best MP scheme is
determined by considering all of the GoF metrics studied. In general, the results show that the metrics
capability in discriminating model performances is of great importance, as Chai and Draxler (2014) 25
mentioned. According to the hourly extreme precipitation simulations, the PBIAS metrics can be
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
11
evaluated as the best GoF test in terms of discriminating the errors (Figure 7b, Figure 8b, and Figure
9b). However, MSE is also a good metric because it exaggerates the worst performances. Although
Chai and Draxler (2014) indicate that RMSE is better than MAE, for extreme precipitation evaluations,
they both have the similar error patterns for all MPs. Moreover, ME and NRMSE indicate a similarity
between MAE and RMSE, as expected. The rest of the GoF tests, i.e., d, md, r, R2, bR2, RSR, rSD, 5
NSE, mNSE, KGE, and VE, may not be used to evaluate the best options alone. For this reason, a
combination of NRMSE and PBIAS metrics is utilized to determine the best MP option, depending on
the station location.
For extreme precipitation evaluation, time series graphics indicate that none of the MP schemes is able
to estimate the maximum precipitation amount of 31.75 mm recorded on January 2, 1997 at 22:00 UTC 10
at the ALP station (Figure 4). Moreover, neither spatial nor temporal shift for this maximum amount is
detected in the model output. Although MP11 tends to estimate the maximum, it predicts so much
rainfall for the remaining hours that the cp value of this scheme is less than zero, which means that the
results of MP11 are unreasonable for ALP station comparisons. The MP14, MP19, and MP21 options
also cannot be included in the comparisons due to a similar reason. The rainfall amounts of the 15 other 15
MP schemes for the ALP station are acceptable (Figure 4 and Figure 7). MP3 and MP8 have minimum
PBIAS values, i.e., 4.6% and 2.9%, respectively, whereas MP17 and MP28 are the best options based
on NRMSE values, i.e., 101.5% and 105.6%, respectively (Figure 7b).
The extreme rainfall amount observed at the BTA station is 18.3 mm, which is also not exactly
simulated by any of the MPs, although some of them estimated this amount with a time shift. The GoF 20
test results show different patterns for the BTA station than those of the ALP station. Nine MPs, MP1,
MP2, MP4, MP6, MP11, MP14, MP16, MP19, and MP21, could not pass the threshold value of 0 for
the cp test (Figure 5 and Figure 8a). It seems that these MPs cannot capture abrupt precipitation changes
very well (Figure 5). For BTA station comparisons, MP3 and MP8 have minimum PBIAS values, i.e., -
1.3% and -4.7%, respectively. MP9 and MP17 have better test results than the others in terms of 25
NRMSE metrics, i.e., 97.2% and 98.3%, respectively (Figure 8b).
Although the extreme rainfall observed (10.2 mm) at the CAP station is less than that at the ALP and
BTA stations, precipitation observations indicate an unusual pattern at the CAP station: the rainfall
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
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continues at a moderate rate between 3.5 mm/hr and 7 mm/hr approximately 36 hours. Simulation
results of this stationary rainfall pattern show that only MP11 passes the cp threshold, although it could
not pass this level for the ALP and BTA stations. PBIAS and NRMSE values for MP11 are -8.9% and
104.9%, respectively (Figure 9b).
5
10 Figure 4. Observation versus model comparisons at the ALPHA (ALP) station for each microphysics option in terms of hourly precipitation (a. MP1, b. MP2, c. MP3, d. MP4, e. MP5, f. MP6, g. MP7, h. MP8, i. MP9, j. MP10, k. MP11, l. MP13, m. MP14, n. MP16, o. MP17, p. MP19, q. MP21, r. MP28, s. MP95)
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ALP− Observation vs Simulation MP1
Time
Prec
ipitat
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ate, [m
m/h]
05
1015
2025
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● MP1Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.63MAE = 2.98RMSE = 4.42NRMSE = 117.1PBIAS = 28.7RSR = 1.17rSD = 0.85NSE = −0.38mNSE = −0.31d = 0.49md = 0.36r = 0.22R2 = 0.05bR2 = 0.02KGE = 0.15VE = −0.36cp = 0.12MSE = 19.57
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ALP− Observation vs Simulation MP2
Time
Prec
ipitat
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ate, [m
m/h]
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1015
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● MP2Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1MAE = 3.05RMSE = 4.62NRMSE = 122.2PBIAS = 45.8RSR = 1.22rSD = 0.88NSE = −0.5mNSE = −0.34d = 0.45md = 0.33r = 0.19R2 = 0.04bR2 = 0.02KGE = 0.07VE = −0.39cp = 0.05MSE = 21.3
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ate, [m
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1015
2025
30
● MP3Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.1MAE = 2.66RMSE = 4.12NRMSE = 109.1PBIAS = 4.6RSR = 1.09rSD = 0.64NSE = −0.2mNSE = −0.17d = 0.4md = 0.35r = 0.16R2 = 0.03bR2 = 0.01KGE = 0.09VE = −0.22cp = 0.24MSE = 16.98
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ALP− Observation vs Simulation MP4
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP4Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.87MAE = 3.06RMSE = 4.62NRMSE = 122.4PBIAS = 39.9RSR = 1.22rSD = 0.78NSE = −0.51mNSE = −0.34d = 0.38md = 0.31r = 0.09R2 = 0.01bR2 = 0KGE = −0.01VE = −0.4cp = 0.04MSE = 21.39
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ALP− Observation vs Simulation MP5
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP5Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.49MAE = 2.54RMSE = 4.14NRMSE = 109.5PBIAS = −22.5RSR = 1.09rSD = 0.57NSE = −0.21mNSE = −0.12d = 0.39md = 0.38r = 0.12R2 = 0.01bR2 = 0KGE = −0.01VE = −0.16cp = 0.23MSE = 17.11
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ALP− Observation vs Simulation MP6
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP6Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.78MAE = 3RMSE = 4.55NRMSE = 120.4PBIAS = 35.5RSR = 1.2rSD = 0.81NSE = −0.46mNSE = −0.32d = 0.4md = 0.31r = 0.14R2 = 0.02bR2 = 0.01KGE = 0.05VE = −0.37cp = 0.07MSE = 20.68
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ALP− Observation vs Simulation MP7
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP7Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.12MAE = 3.09RMSE = 4.53NRMSE = 119.8PBIAS = 51.3RSR = 1.2rSD = 0.68NSE = −0.45mNSE = −0.36d = 0.36md = 0.3r = 0.08R2 = 0.01bR2 = 0KGE = −0.11VE = −0.41cp = 0.08MSE = 20.48
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ALP− Observation vs Simulation MP8
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP8Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.06MAE = 2.46RMSE = 3.99NRMSE = 105.7PBIAS = 2.9RSR = 1.06rSD = 0.61NSE = −0.13mNSE = −0.08d = 0.43md = 0.39r = 0.2R2 = 0.04bR2 = 0.01KGE = 0.11VE = −0.12cp = 0.28MSE = 15.96
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ALP− Observation vs Simulation MP9
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP9Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.39MAE = 2.61RMSE = 4.06NRMSE = 107.6PBIAS = 63.2RSR = 1.08rSD = 0.58NSE = −0.17mNSE = −0.15d = 0.47md = 0.4r = 0.27R2 = 0.07bR2 = 0.04KGE = −0.05VE = −0.19cp = 0.26MSE = 16.52
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ALP− Observation vs Simulation MP10
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP10Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.89MAE = 3.03RMSE = 4.42NRMSE = 117PBIAS = 86.1RSR = 1.17rSD = 0.65NSE = −0.38mNSE = −0.33d = 0.47md = 0.38r = 0.23R2 = 0.05bR2 = 0.03KGE = −0.21VE = −0.38cp = 0.12MSE = 19.53
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ALP− Observation vs Simulation MP11
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP11Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 4.4MAE = 5.44RMSE = 7.74NRMSE = 204.8PBIAS = 200.8RSR = 2.05rSD = 1.5NSE = −3.23mNSE = −1.39d = 0.34md = 0.27r = 0.13R2 = 0.02bR2 = 0.02KGE = −1.24VE = −1.48cp = −1.69MSE = 59.86
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ALP− Observation vs Simulation MP13
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP13Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.47MAE = 2.89RMSE = 4.34NRMSE = 114.8PBIAS = 21.6RSR = 1.15rSD = 0.65NSE = −0.33mNSE = −0.27d = 0.36md = 0.29r = 0.08R2 = 0.01bR2 = 0KGE = 0VE = −0.32cp = 0.15MSE = 18.83
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ALP− Observation vs Simulation MP14
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP14Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.2MAE = 3.38RMSE = 4.96NRMSE = 131.2PBIAS = 100.3RSR = 1.31rSD = 0.82NSE = −0.74mNSE = −0.48d = 0.42md = 0.36r = 0.17R2 = 0.03bR2 = 0.02KGE = −0.32VE = −0.54cp = −0.11MSE = 24.59
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ALP− Observation vs Simulation MP16
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP16Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.23MAE = 3.06RMSE = 4.63NRMSE = 122.6PBIAS = 56RSR = 1.23rSD = 0.83NSE = −0.52mNSE = −0.34d = 0.42md = 0.33r = 0.17R2 = 0.03bR2 = 0.01KGE = −0.01VE = −0.4cp = 0.03MSE = 21.47
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ALP− Observation vs Simulation MP17
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP17Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.39MAE = 2.38RMSE = 3.83NRMSE = 101.5PBIAS = 17.7RSR = 1.01rSD = 0.38NSE = −0.04mNSE = −0.05d = 0.31md = 0.31r = 0.15R2 = 0.02bR2 = 0.01KGE = −0.07VE = −0.09cp = 0.34MSE = 14.7
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ALP− Observation vs Simulation MP19
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP19Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.23MAE = 3.41RMSE = 5.11NRMSE = 135.3PBIAS = 56.1RSR = 1.35rSD = 0.93NSE = −0.85mNSE = −0.5d = 0.35md = 0.3r = 0.06R2 = 0bR2 = 0KGE = −0.09VE = −0.56cp = −0.18MSE = 26.13
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ALP− Observation vs Simulation MP21
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP21Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.05MAE = 3.22RMSE = 4.93NRMSE = 130.5PBIAS = 47.8RSR = 1.31rSD = 0.92NSE = −0.72mNSE = −0.41d = 0.37md = 0.33r = 0.11R2 = 0.01bR2 = 0.01KGE = −0.01VE = −0.47cp = −0.1MSE = 24.32
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ALP− Observation vs Simulation MP28
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP28Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.79MAE = 2.47RMSE = 3.99NRMSE = 105.6PBIAS = 36.1RSR = 1.06rSD = 0.58NSE = −0.12mNSE = −0.08d = 0.43md = 0.4r = 0.22R2 = 0.05bR2 = 0.02KGE = 0.04VE = −0.13cp = 0.28MSE = 15.91
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ALP− Observation vs Simulation MP95
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP95Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.49MAE = 2.54RMSE = 4.14NRMSE = 109.5PBIAS = −22.5RSR = 1.09rSD = 0.57NSE = −0.21mNSE = −0.12d = 0.39md = 0.38r = 0.12R2 = 0.01bR2 = 0KGE = −0.01VE = −0.16cp = 0.23MSE = 17.11
a b c d e
f g h i j
k l m n o
p q r s
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
13
5
Figure 5. Observation versus model comparisons at the BETA (BTA) station for each microphysics option in terms of hourly precipitation (a. MP1, b. MP2, c. MP3, d. MP4, e. MP5, f. MP6, g. MP7, h. MP8, i. MP9, j. MP10, k. MP11, l. MP13, m. MP14, n. MP16, o. MP17, p. MP19, q. MP21, r. MP28, s. MP95) 10
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BTA− Observation vs Simulation MP1
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP1Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.5MAE = 3.2RMSE = 4.42NRMSE = 131.3PBIAS = 20.9RSR = 1.31rSD = 0.95NSE = −0.74mNSE = −0.31d = 0.39md = 0.34r = 0.1R2 = 0.01bR2 = 0KGE = 0.08VE = −0.35cp = −0.19MSE = 19.53
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BTA− Observation vs Simulation MP2
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP2Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.83MAE = 3.17RMSE = 4.34NRMSE = 128.8PBIAS = 35RSR = 1.29rSD = 0.98NSE = −0.67mNSE = −0.3d = 0.43md = 0.34r = 0.18R2 = 0.03bR2 = 0.02KGE = 0.1VE = −0.34cp = −0.13MSE = 18.79
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TimePr
ecipi
tation
Rate
, [mm/
h]
05
1015
● MP3Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.03MAE = 2.81RMSE = 3.86NRMSE = 114.6PBIAS = −1.3RSR = 1.15rSD = 0.74NSE = −0.32mNSE = −0.16d = 0.4md = 0.34r = 0.15R2 = 0.02bR2 = 0.01KGE = 0.11VE = −0.19cp = 0.09MSE = 14.89
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP4Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.65MAE = 3.16RMSE = 4.33NRMSE = 128.6PBIAS = 27.6RSR = 1.29rSD = 0.86NSE = −0.67mNSE = −0.3d = 0.35md = 0.31r = 0.07R2 = 0bR2 = 0KGE = 0.02VE = −0.33cp = −0.15MSE = 18.74
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP5Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.47MAE = 2.7RMSE = 3.97NRMSE = 118PBIAS = −19.9RSR = 1.18rSD = 0.66NSE = −0.4mNSE = −0.11d = 0.35md = 0.37r = 0.04R2 = 0bR2 = 0KGE = −0.04VE = −0.14cp = 0.04MSE = 15.77
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP6Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.59MAE = 3.12RMSE = 4.26NRMSE = 126.5PBIAS = 25RSR = 1.27rSD = 0.93NSE = −0.61mNSE = −0.28d = 0.4md = 0.31r = 0.15R2 = 0.02bR2 = 0.01KGE = 0.11VE = −0.32cp = −0.11MSE = 18.14
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP7Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.92MAE = 3.11RMSE = 4.05NRMSE = 120.3PBIAS = 38.8RSR = 1.2rSD = 0.75NSE = −0.46mNSE = −0.28d = 0.42md = 0.31r = 0.12R2 = 0.01bR2 = 0.01KGE = 0.01VE = −0.31cp = 0MSE = 16.4
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP8Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.11MAE = 2.68RMSE = 3.77NRMSE = 112.1PBIAS = −4.7RSR = 1.12rSD = 0.7NSE = −0.27mNSE = −0.1d = 0.41md = 0.37r = 0.16R2 = 0.02bR2 = 0.01KGE = 0.1VE = −0.13cp = 0.13MSE = 14.24
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TimePr
ecipi
tation
Rate
, [mm/
h]
05
1015
● MP9Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.15MAE = 2.54RMSE = 3.27NRMSE = 97.2PBIAS = 48.3RSR = 0.97rSD = 0.63NSE = 0.05mNSE = −0.04d = 0.64md = 0.42r = 0.45R2 = 0.2bR2 = 0.14KGE = 0.18VE = −0.07cp = 0.34MSE = 10.72
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP10Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.69MAE = 3.02RMSE = 3.94NRMSE = 116.9PBIAS = 71.5RSR = 1.17rSD = 0.73NSE = −0.38mNSE = −0.24d = 0.54md = 0.39r = 0.28R2 = 0.08bR2 = 0.05KGE = −0.05VE = −0.27cp = 0.05MSE = 15.49
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
30
● MP11Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.64MAE = 4.72RMSE = 6.59NRMSE = 195.8PBIAS = 153.6RSR = 1.96rSD = 1.56NSE = −2.87mNSE = −0.94d = 0.43md = 0.32r = 0.24R2 = 0.06bR2 = 0.05KGE = −0.8VE = −0.99cp = −1.65MSE = 43.47
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP13Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.32MAE = 2.91RMSE = 3.97NRMSE = 118PBIAS = 13.7RSR = 1.18rSD = 0.73NSE = −0.4mNSE = −0.2d = 0.39md = 0.32r = 0.09R2 = 0.01bR2 = 0KGE = 0.05VE = −0.23cp = 0.04MSE = 15.77
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BTA− Observation vs Simulation MP14
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP14Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.87MAE = 3.35RMSE = 4.43NRMSE = 131.5PBIAS = 78.7RSR = 1.31rSD = 0.88NSE = −0.74mNSE = −0.38d = 0.49md = 0.36r = 0.19R2 = 0.04bR2 = 0.03KGE = −0.13VE = −0.41cp = −0.2MSE = 19.59
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP16Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.96MAE = 3.13RMSE = 4.26NRMSE = 126.5PBIAS = 40.6RSR = 1.27rSD = 0.94NSE = −0.61mNSE = −0.29d = 0.42md = 0.32r = 0.18R2 = 0.03bR2 = 0.02KGE = 0.09VE = −0.32cp = −0.11MSE = 18.15
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BTA− Observation vs Simulation MP17
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP17Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.23MAE = 2.44RMSE = 3.31NRMSE = 98.3PBIAS = 9.9RSR = 0.98rSD = 0.42NSE = 0.03mNSE = 0d = 0.44md = 0.31r = 0.25R2 = 0.06bR2 = 0.03KGE = 0.05VE = −0.03cp = 0.33MSE = 10.94
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP19Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.06MAE = 3.52RMSE = 4.7NRMSE = 139.5PBIAS = 44.5RSR = 1.39rSD = 1.03NSE = −0.96mNSE = −0.45d = 0.4md = 0.3r = 0.1R2 = 0.01bR2 = 0.01KGE = −0.01VE = −0.49cp = −0.35MSE = 22.06
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP21Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.91MAE = 3.3RMSE = 4.52NRMSE = 134.2PBIAS = 38.2RSR = 1.34rSD = 1.05NSE = −0.81mNSE = −0.36d = 0.45md = 0.34r = 0.17R2 = 0.03bR2 = 0.02KGE = 0.09VE = −0.39cp = −0.25MSE = 20.4
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BTA− Observation vs Simulation MP28
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP28Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 0.53MAE = 2.53RMSE = 3.39NRMSE = 100.8PBIAS = 22.2RSR = 1.01rSD = 0.63NSE = −0.02mNSE = −0.04d = 0.56md = 0.39r = 0.31R2 = 0.1bR2 = 0.05KGE = 0.19VE = −0.07cp = 0.3MSE = 11.52
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BTA− Observation vs Simulation MP95
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP95Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.47MAE = 2.7RMSE = 3.97NRMSE = 118PBIAS = −19.9RSR = 1.18rSD = 0.66NSE = −0.4mNSE = −0.11d = 0.35md = 0.37r = 0.04R2 = 0bR2 = 0KGE = −0.04VE = −0.14cp = 0.04MSE = 15.77
a b c d e
j i h g f
k l m n o
s r q p
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
14
5
Figure 6. Observation versus model comparisons at the CAPLES LAKE (CAP) station for each microphysics option in terms of hourly precipitation (a. MP1, b. MP2, c. MP3, d. MP4, e. MP5, f. MP6, g. MP7, h. MP8, i. MP9, j. MP10, k. MP11, l. MP13, m. MP14, n. MP16, o. MP17, p. MP19, q. MP21, r. MP28, s. MP95)
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CAP− Observation vs Simulation MP1
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025 ● MP1
Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.05MAE = 4.11RMSE = 5.88NRMSE = 218PBIAS = 130.4RSR = 2.18rSD = 1.86NSE = −3.79mNSE = −0.72d = 0.44md = 0.36r = 0.26R2 = 0.07bR2 = 0.05KGE = −0.73VE = −0.75cp = −3.32MSE = 34.55
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CAP− Observation vs Simulation MP2
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
● MP2Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.7MAE = 3.76RMSE = 5.41NRMSE = 200.7PBIAS = 115RSR = 2.01rSD = 1.75NSE = −3.06mNSE = −0.58d = 0.45md = 0.36r = 0.29R2 = 0.09bR2 = 0.07KGE = −0.55VE = −0.61cp = −2.66MSE = 29.27
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CAP− Observation vs Simulation MP3
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP3Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.39MAE = 3.46RMSE = 4.34NRMSE = 160.8PBIAS = 102.2RSR = 1.61rSD = 1.18NSE = −1.61mNSE = −0.45d = 0.5md = 0.35r = 0.25R2 = 0.06bR2 = 0.06KGE = −0.28VE = −0.48cp = −1.35MSE = 18.8
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP4Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.44MAE = 3.49RMSE = 4.48NRMSE = 166PBIAS = 104.3RSR = 1.66rSD = 1.27NSE = −1.78mNSE = −0.47d = 0.5md = 0.36r = 0.26R2 = 0.07bR2 = 0.06KGE = −0.31VE = −0.49cp = −1.51MSE = 20.04
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
20
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Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.2MAE = 3.96RMSE = 5.09NRMSE = 188.9PBIAS = 136.6RSR = 1.89rSD = 1.39NSE = −2.6mNSE = −0.66d = 0.47md = 0.33r = 0.27R2 = 0.07bR2 = 0.06KGE = −0.6VE = −0.69cp = −2.24MSE = 25.95
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
● MP6Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.02MAE = 3.94RMSE = 5.29NRMSE = 196.3PBIAS = 129RSR = 1.96rSD = 1.57NSE = −2.89mNSE = −0.65d = 0.46md = 0.34r = 0.27R2 = 0.07bR2 = 0.06KGE = −0.59VE = −0.68cp = −2.5MSE = 28.02
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CAP− Observation vs Simulation MP7
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
20
● MP7Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.64MAE = 2.85RMSE = 3.62NRMSE = 134.1PBIAS = 69.9RSR = 1.34rSD = 1.05NSE = −0.81mNSE = −0.2d = 0.56md = 0.4r = 0.32R2 = 0.1bR2 = 0.09KGE = 0.02VE = −0.22cp = −0.64MSE = 13.07
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CAP− Observation vs Simulation MP8
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP8Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.68MAE = 3.04RMSE = 3.88NRMSE = 143.9PBIAS = 71.7RSR = 1.44rSD = 1.14NSE = −1.09mNSE = −0.28d = 0.53md = 0.39r = 0.27R2 = 0.07bR2 = 0.07KGE = −0.04VE = −0.3cp = −0.88MSE = 15.06
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CAP− Observation vs Simulation MP9
Time
Prec
ipitat
ion R
ate, [m
m/h]
02
46
810
1214
● MP9Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.17MAE = 3.64RMSE = 4.49NRMSE = 166.7PBIAS = 135.4RSR = 1.67rSD = 1.14NSE = −1.8mNSE = −0.53d = 0.54md = 0.39r = 0.39R2 = 0.16bR2 = 0.12KGE = −0.49VE = −0.55cp = −1.53MSE = 20.2
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CAP− Observation vs Simulation MP10
Time
Prec
ipitat
ion R
ate, [m
m/h]
02
46
810
● MP10Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.89MAE = 3.56RMSE = 4.32NRMSE = 160.4PBIAS = 123.2RSR = 1.6rSD = 0.96NSE = −1.59mNSE = −0.49d = 0.51md = 0.38r = 0.25R2 = 0.06bR2 = 0.06KGE = −0.44VE = −0.52cp = −1.34MSE = 18.7
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CAP− Observation vs Simulation MP11
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
● MP11Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = −0.21MAE = 2.08RMSE = 2.83NRMSE = 104.9PBIAS = −8.9RSR = 1.05rSD = 0.78NSE = −0.11mNSE = 0.13d = 0.55md = 0.44r = 0.32R2 = 0.1bR2 = 0.06KGE = 0.28VE = 0.11cp = 0MSE = 8
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CAP− Observation vs Simulation MP13
Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
20
● MP13Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.84MAE = 3.2RMSE = 4.13NRMSE = 153.1PBIAS = 78.6RSR = 1.53rSD = 1.24NSE = −1.36mNSE = −0.34d = 0.5md = 0.35r = 0.26R2 = 0.07bR2 = 0.06KGE = −0.11VE = −0.37cp = −1.13MSE = 17.04
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Time
Prec
ipitat
ion R
ate, [m
m/h]
02
46
810
12
● MP14Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.46MAE = 3.97RMSE = 4.84NRMSE = 179.5PBIAS = 147.6RSR = 1.79rSD = 1.03NSE = −2.25mNSE = −0.66d = 0.48md = 0.36r = 0.23R2 = 0.05bR2 = 0.04KGE = −0.67VE = −0.69cp = −1.93MSE = 23.42
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
2025
● MP16Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.75MAE = 3.63RMSE = 5.16NRMSE = 191.4PBIAS = 117.3RSR = 1.91rSD = 1.56NSE = −2.69mNSE = −0.52d = 0.46md = 0.38r = 0.25R2 = 0.07bR2 = 0.06KGE = −0.5VE = −0.55cp = −2.33MSE = 26.63
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Time
Prec
ipitat
ion R
ate, [m
m/h]
02
46
810
● MP17Obs
Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.78MAE = 2.8RMSE = 3.42NRMSE = 126.9PBIAS = 76.1RSR = 1.27rSD = 0.92NSE = −0.62mNSE = −0.18d = 0.58md = 0.41r = 0.36R2 = 0.13bR2 = 0.12KGE = 0VE = −0.2cp = −0.46MSE = 11.71
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
20
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Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 1.87MAE = 3.18RMSE = 4.12NRMSE = 152.7PBIAS = 79.9RSR = 1.53rSD = 1.31NSE = −1.35mNSE = −0.34d = 0.55md = 0.4r = 0.32R2 = 0.1bR2 = 0.1KGE = −0.09VE = −0.36cp = −1.12MSE = 16.95
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
20
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Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.43MAE = 3.67RMSE = 4.99NRMSE = 184.9PBIAS = 103.8RSR = 1.85rSD = 1.6NSE = −2.45mNSE = −0.54d = 0.49md = 0.38r = 0.29R2 = 0.08bR2 = 0.07KGE = −0.39VE = −0.56cp = −2.11MSE = 24.85
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Time
Prec
ipitat
ion R
ate, [m
m/h]
02
46
810
12
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Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 2.49MAE = 3.46RMSE = 4.16NRMSE = 154.4PBIAS = 106.3RSR = 1.54rSD = 0.96NSE = −1.4mNSE = −0.45d = 0.48md = 0.34r = 0.2R2 = 0.04bR2 = 0.04KGE = −0.33VE = −0.48cp = −1.17MSE = 17.33
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Time
Prec
ipitat
ion R
ate, [m
m/h]
05
1015
20
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Dec 2907:00
Dec 3000:00
Dec 3100:00
Jan 0100:00
Jan 0200:00
Jan 0300:00
GoF's:
ME = 3.2MAE = 3.96RMSE = 5.09NRMSE = 188.9PBIAS = 136.6RSR = 1.89rSD = 1.39NSE = −2.6mNSE = −0.66d = 0.47md = 0.33r = 0.27R2 = 0.07bR2 = 0.06KGE = −0.6VE = −0.69cp = −2.24MSE = 25.95
a b c d e
j i h g f
k l m n o
s r q p
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
15
Figure 7. Microphysics Schemes versus errors at the ALPHA (ALP) station for each goodness of fit test class (a. MAE, RMSE, cp, MSE, b. NRMSE, PBIAS, c. ME, RSR, rSD, NSE, mNSE, KGE, VE, d. d, md, r, R2, bR2)
5
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
16
Figure 8. Microphysics Schemes versus errors at the BETA (BTA) station for each goodnees of fit test class (a. MAE, RMSE, cp, MSE, b. NRMSE, PBIAS, c. ME, RSR, rSD, NSE, mNSE, KGE, VE, d. d, md, r, R2, bR2)
5
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
17
Figure 9. Microphysics Schemes versus errors at the CAPLES LAKE (CAP) station for each goodnees of fit test class (a. MAE, RMSE, cp, MSE, b. NRMSE, PBIAS, c. ME, RSR, rSD, NSE, mNSE, KGE, VE, d. d, md, r, R2, bR2) 5
Because PBIAS values indicate both negative and positive values depending on the station, it may not
be concluded that the WRF overestimates precipitation during hourly and point-wise evaluation,
although Figures 4, 5, and 6 indicate this bias. However, an overestimation of light precipitation has
been detected in all of the MP scheme results, which is consistent with the findings of Gao and Sui 10
(2013).
15
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
18
4.2 72-hr total and basin-averaged comparisons
The 72-hr basin averaged total precipitation of the WRF model is calculated by using the shape file of
the American River Watershed borders, as presented in Figure 1 with green, following Tan (2010). Grid
point rainfall values that are close to the ARW borderline are interpolated to the points of the border
shape file. Grid points that are located inside of the watershed are also included in the estimate of the 5
hourly basin averaged values. The 72-hr total of these hourly values is compared to the adopted value of
USACE (2005), which is 285 mm (shown in Figure X with a black line). In Figure 10, the 72-hr basin-
averaged precipitation amount of each microphysics scheme is compared to the adopted value for the
72-hr time frame. Blue bars indicate that the basin averaged 72-hr total between December 31, 1996 and
January 3, 1997 00 PST (Pacific Standard Time) is consistent with the time frames of USACE (2005) 10
and Tan (2010). MP1, MP2, MP3, MP4, and MP8 reach their 72-hr total maximum precipitation in this
time interval. Green, yellow, and purple markers show that the 72-hr total maximum precipitation is
simulated with a 9-hr, 8-hr, and 6-hr time shift, respectively, according to the actual time frame. That is,
MP9, MP10, and MP11 achieve maximum precipitation between December 30, 1996 15:00 PST and
January 2, 1997 15:00 PST (green markers in Figure 10), while the MP5, MP6, MP7, MP13, MP14, 15
MP16, MP17, MP28, and MP95 schemes achieve total maximum precipitation between December 30,
1996 16:00 PST and January 2, 1997 16:00 PST (yellow markers in Figure 10). Similarly, MP19 and
MP21 reach their maxima between December 30, 1996 18:00 PST and January 2, 1997 18:00 PST
(purple markers in Figure 10). As also shown in Figure 10, the conclusion time of these intervals is
presented in the legends. True percent relative errors of the MP schemes (secondary ordinate in Figure 20
10) indicate that errors of 3 MPs are less than 5%, i.e., MP7, MP8, and MP13. Moreover, MP8 might be
counted as the best MP for the 72-hr basin averaged consideration because it is the only MP scheme that
simulates the maximum precipitation at an accurate time interval among these 3 MPs. This result is
consistent with the findings of Tan (2010), although she simulated the event using WRF V3.1.1, which
basically had 12 MP schemes at that time. Her findings are also presented in Figure 10 (red markers) for 25
comparison purposes. MP1, MP2, MP3, MP4, MP5, MP6, MP7, MP8, MP10, MP14, and MP16 can be
compared with those of the current version (3.7.1). The red marker on MP28 indicates the result of the
oldest Thompson scheme because the aerosol-aware Thompson scheme was not included in V3.1.1, as
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
19
in the case of schemes MP9, MP11, MP13, MP17, MP19, MP21, and MP95. Therefore, there is no red
marker for them in the graph. Comparisons of the same microphysics schemes in different versions
show that the precipitation amounts do also vary depending on the version of the model, although the
same domains and same initial and boundary conditions are used. The general tendency is that V3.7.1
produces more precipitation than that of V3.1.1 for 9 MP schemes out of 11, where the 2 exceptions are 5
MP2 and MP7.
Figure 10. 72-hr basin-averaged total precipitation comparisons of microphysics schemes in terms of both precipitation amount and time.
10
Comparisons of Thompson schemes, which represent the best option among all, show that MP8 of the
Thompson scheme in V3.1.1 (with 2.4% relative error (RE)) is slightly better than MP8 of the
Thompson scheme in V3.7.1 (with 3.1% RE). The oldest Thompson Scheme, MP98 in V3.1.1, has
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
20
12.2% RE, whereas the newest Aerosol-Aware Thompson Scheme, MP28 in Version 3.7.1, has 25.3%
RE. This might show us that the Aerosol-Aware Thompson scheme should be tested further to obtain
better results for the maximum precipitation simulation of the American River Watershed.
V3.7.1 produces more precipitation than V3.1.1. Although domain setups are similar in these two
experiments, the only significant difference is the vertical level number. In this study, 41 vertical levels 5
are utilized, whereas the study of Tan (2010) is based on 28 vertical levels. This might indicate that
increasing vertical resolution adds spurious rainfall amount due to more frequent grid cell saturation.
This result is consistent with the findings of Aligo et al. (2010), who also suggested that low vertical
resolution in the initial and boundary conditions could be the reason for this counterintuitive finding.
10
5. Summary and Conclusions
In this paper, sensitivity experiments of 19 Microphysics (MP) schemes currently used in the WRF
Model version 3.7.1, are performed to identify a model setup that minimizes the bias in hindcast
simulations of the most impactful atmospheric river event, i.e., the 1997 New Year’s storm. The
NCEP/NCAR Reanalysis data set is used for the initial and boundary conditions of the WRF Model. 15
The hindcast simulation is constructed using 3 nested two-way domains, whose horizontal resolutions
range from 27 km to 3 km with a ratio of 3, and the vertical resolution is determined using 41 sigma
levels. 5-day simulations are conducted from December 29, 1996 at 06:00 UTC to January 3, 1997 at
06:00 UTC. The results of the innermost domain (3-km resolution) are presented, and their comparisons
with observations are discussed. 20
The 18 Goodness of Fit (GoF) tests are utilized to evaluate the role of MP parameterization in terms of
hourly and point-wise rainfall. One of the GoFs, i.e., cp, initially helps to decide which MP scheme’s
result are not comparable with observations. Applying the cp threshold, which is zero, several MP
options are eliminated from further discussion. Out of 8 rain-gauge stations, only 3 of them (ALP, BTA,
and CAP) have comparable MP results. This might show that a significant source of error is still added 25
to the model by means of MP schemes, depending on the location with consideration of hourly rainfall
assessments.
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
21
Out of 18 GoFs, a combination of NRMSE and PBIAS metrics is utilized to determine the best MP
option depending on the station location. According to these 2 metrics, MP3, MP8, MP17, and MP28
are the best options for the ALP station comparisons; MP3, MP8, MP9, and MP17 are the best for the
BTA station; and MP11, as the only parameterization that passed, is the best for the CAP station
comparisons. The results do not indicate any systematic error for hourly and point-wise evaluations, and 5
the errors of MP options vary depending on location and the amount of rainfall. However,
overestimation of light precipitation has been detected in all of the MP scheme results, which is
consistent with the findings of Gao and Sui (2013).
The 72-hr total and basin-averaged precipitation results of MPs indicate different behavior than that of
the hourly and point-wise comparisons. The results show that the 72-hr basin-averaged values are 10
estimated much better than the hourly and point-wise calculations, as expected. However, the WRF’s
over-prediction bias is much more obvious than in the hourly comparisons. The most significant
problem is to match the maximum precipitation in the observed time interval. Thus, the amount and the
time interval are only estimated accurately by using the Thompson Scheme (MP8). This result is
consistent with the findings of Tan (2010), but the errors of her study are less than the findings of the 15
current one because 41 vertical levels are used in this study, where as she used only 28. It is assumed
that this unexpected result is obtained as a result of either the low vertical resolution of the initial and
boundary conditions or more frequent grid cell saturation calculation as the levels are increased (Aligo
et al., 2010).
Another counterintuitive result is that the errors of MP8 are less than those of MP28. Because MP28 is 20
an improved version of MP8 by means of adding the aerosol effect and Ault et al. (2011) mentioned
that Asian dust may have an impact on atmospheric rivers, MP28 was expected to simulate more
accurate precipitation values, at least for the 72-hr basin-averaged evaluations. Because MP28 works in
two aerosol input modes, such as using constant values and using input from WPS, and the constant
value option is selected in this study, it is highly possible that the latter option may yield better results, 25
which needs to be evaluated for the next studies with the actual aerosol distribution of the 1997 New
Year’s storm.
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22
Regarding the ongoing debate, this study also shows that the WRF model results are better than these of
the preceding one, i.e., MM5. For instance, Ohara et al. (2010) and Ishida et al. (2014) used MM5 to
maximize precipitation to estimate Probable Maximum Precipitation (PMP) over the American River
Watershed; and their constructed model results for the 1997 New Year’s storm, with the 72-hr basin-
averaged perspective, were 330 mm and 325.54 mm, which correspond to a 15.8% and 14.2% true 5
percent relative error, respectively, based on the comparisons of this study. According to Tan (2010),
who also simulated the 72-hr basin averaged precipitation using the WRF Model for the American
River Watershed, this error is 2.4%, whereas this study finds the relative error to be 3.1%. Thus, this
also shows us the importance of selecting the best MP options: if the calibrated models have larger
errors, then the simulations may also reserve them and indicate inaccurate results. 10
In conclusion, the results show us that the precipitation results of the WRF model can be used for
design, water management and planning, and hindcasting purposes, with a high accuracy (less than 5%
RE) when considering 72-hr basin-averaged values, whereas for the quantitative precipitation
forecasting (QPF) purposes, which include hourly and point-wise evaluations, this accuracy varies
depending on the location and magnitude of the storm in addition to the choice of MP scheme. Because 15
this study aims to evaluate mainly extreme precipitation, the BIAS test indicates a less than 10%
accuracy range, whereas NRMSE shows that errors are greater than 90% for the best MP options. The
results infer that precipitation simulations are highly sensitive to the MP choice, especially for extreme
precipitation forecasting, as expected.
Model uncertainties in extreme precipitation simulations can also depend on the following factors, 20
which are not discussed in this study:
§ Horizontal resolution
§ Domain selection
§ Representative grid cell of the location
§ Selection of other physical schemes rather than microphysics (MP) 25
§ Initial and Boundary Condition data set
§ Initial time setup of the event of interest
§ Variety of events
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23
§ Evaluation of vertically integrated water vapor, i.e., Precipitable Water, simulations
§ Detailed evaluation of physical processes in MP schemes.
In the future, these factors must be evaluated in detail to quantify the uncertainty of each component
with respect to the intensity, duration, and the location of the extreme precipitation events.
Code and data availability 5
The Weather Research and Forecast Model (WRF) Advanced Research WRF (ARW) version 3.7.1 is
evaluated in this paper. The code is freely available after a quick and free registration
(http://www2.mmm.ucar.edu/wrf/users/download/get_source.html). The WRF Model is initialized by
using the 6-hour NCEP/NCAR Global Reanalysis Model (Kalnay et al., 1996), which can also be
downloaded after registration at the Research Data Archive (RDA) of the University Corporation for 10
Atmospheric Research (UCAR) (NCEP et al., 1994) (http://rda.ucar.edu/datasets/ds090.0/). Hourly
rain-gauge data are obtained from the California Data Exchange Center (CDEC)
(http://cdec.water.ca.gov). Goodness-of-fit tests are performed by using R version 3.2.2 (2015-08-14) --
"Fire Safety" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-apple-
darwin13.4.0 (64-bit) and the package hydroGOF is utilized accordingly (Mauricio, Z.-B.: hydroGOF: 15
Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R
package version 0.3-8. http://CRAN.R-project.org/package=hydroGOF, 2014). The 72-hr total basin
averaged precipitation value for the American River Basin is used, following the adopted value of the
US Army Corps of Engineers (USACE, Stochastic modelling of extreme floods on the American River
at Folsom Dam, Appendix C 72-hour precipitation-frequency relationship and uncertainty analysis for 20
the 1860-mi2
American River Watershed, RD-48C, US Army Corps of Engineers, Hydrological
Engineering Center, 27pp, 2005)
(http://www.hec.usace.army.mil/publications/ResearchDocuments/RD-48C.pdf). The 72-hr basin-
averaged values of the WRF are extracted by using the shape file masking method of the NCAR
Command Language NCL (The NCAR Command Language (Version 6.3.0) [Software], Boulder, 25
Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-94, 2016Manuscript under review for journal Geosci. Model Dev.Published: 21 June 2016c© Author(s) 2016. CC-BY 3.0 License.
24
Colorado: UCAR/NCAR/CISL/TDD, doi: 10.5065/D6WD3XH5, 2016).
(https://www.ncl.ucar.edu/Applications/shapefiles.shtml).
Finally, the PhD dissertation of Dr. Tan is available at
(https://www.researchgate.net/publication/252755552_Development_of_a_methodology_for_probable_
maximum_precipitation_estimation_over_the_American_River_watershed_using_the_WRF_model) 5
DOI: 10.13140/RG.2.1.4069.4167.
Acknowledgements. I would like to thank Prof. M. Levent Kavvas for his mentorship during my PhD at
UC Davis. Although the main focus of this manuscript is different from the subject of my PhD
dissertation, I believe that the perception I gained from him helped me complete this work. Mr. Ali 10
Hasan Tan for his valuable discussions regarding the methods and Ms. Burcu Yircali, for her help to
edit the manuscript are kindly acknowledged.
I would also like to acknowledge the manuscript editing services provided by Nature Language Editing.
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