# Is Precipitation a Good Metric for Model Performance?

Francisco J. Tapiador, Rémy Roca, Anthony del Genio, Boris Dewitte, Walt Petersen, Fuqing Zhang

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Francisco J. Tapiador, Rémy Roca, Anthony del Genio, Boris Dewitte, Walt Petersen, et al. Is Precipitation a Good Metric for Model Performance?. Bulletin of the American Meteorological Society, 2019, 10.1175/BAMS-D-17-0218.1. [hal-04720623](https://hal.science/hal-04720623v1)

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<figure><img src="image_1.png" /></figure>
# IS PRECIPITATION A GOOD METRIC FOR MODEL PERFORMANCE?

FRANCISCO J. TAPIADOR, RÉMY ROCA, ANTHONY DEL GENIO, BORIS DEWITTE, WALT PETERSEN, AND FUQING ZHANG

The suitability of precipitation as a metric for model performance and as a tool for model improvement is explored.

Precipitation is essential for the existence of life and for human activity, but too much precipitation can lead to flooding, a major hazard whose accurate forecast is always in demand. Too little precipitation, on the other hand, will incur drought, leading to crop failures, death of livestock, and other potential hazards such as increased fire risk. For this reason, precipitation is one of the primary outputs of weather and climate models. Despite its significance, precipitation is an atmospheric variable that is notoriously difficult to predict in numerical weather models. It is not uncommon that models fail

to pinpoint the exact location and timing of precipitation at the surface, along with its intensity and total accumulation, as well as the phase of hydrometeors.

In the climate realm, the ability of models to simulate precipitation has been described as “dreadful” (Stephens et al. 2010). As Fig. 1 shows, the dispersion in the mean precipitation pattern among 31 models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) can be large, with discrepancies on the order of the magnitude of the signal. This is not surprising, as precipitation results from complex processes that are mostly parameterized in atmospheric

**AFFILIATIONS:** TAPIADOR—Earth and Space Sciences Group, Department of Environmental Sciences, Institute of Environmental Sciences, University of Castilla-La Mancha, Toledo, Spain; ROCA—Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, Observatoire Midi-Pyrénées, Toulouse, France; DEL GENIO—NASA Goddard Institute for Space Studies, New York, New York; DEWITTE—Laboratoire d'Etudes en Géophysique et Océanographie Spatiales, Observatoire Midi-Pyrénées, Toulouse, France, and Centro de Estudios Avanzado en Zonas Áridas, and Departamento de Biología, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile; PETERSEN—ST-11, Earth Science Office, NASA Marshall Space Flight Center, and National Space Science and Technology Center, Huntsville, Alabama; ZHANG—Department of

Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania

**CORRESPONDING AUTHOR:** Francisco J. Tapiador, francisco.tapiador@uclm.es

The abstract for this article can be found in this issue, following the table of contents.

DOI:10.1175/BAMS-D-17-0218.1

In final form 4 July 2018
©2019 American Meteorological Society

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
models, owing to their nonlinear nature and multi-scale aspects that are still not well known and far from being sufficiently resolved. There remain significant sensitivities of the models to the use of different mixing and cloud parameterizations independent of whether the numerical core can correctly simulate the dynamics of the atmosphere (Tan et al. 2016; Cesana et al. 2017).

As a “final” product of the modeling, precipitation suffers the multiplicative effect of errors in both thermodynamics and dynamics. To correctly simulate precipitation, one first has to be able to successfully model (up to some precision) longwave and shortwave radiation; the onset and strength of convection; humidity; and the microphysics of liquid, solid, and mixed phases. One also has to model well the dynamics of the atmosphere so the air density,

pressure, wind, and temperature are in the right place at the precise moment.

A key property of precipitation is that it can be spatially patchy, in contrast with variables such as temperature, water vapor content, and wind speed that are either more smoothly varying or fields with more clear-cut gradients such as the temperature gradient near the boundary between two different air masses. Considered as a scalar field, the spatial variability of rain can differ sharply from other meteorological fields (Fig. 2). For solid precipitation, discrepancies are even larger. A major difference in terms of the spatiotemporal structure of instantaneous precipitation is the likely presence of large areas with constant zero values with scattered greater-than-zero and exponentially growing values. Such a feature is uncommon in other meteorological fields, which

tend to be smoother and more consistent over time. This makes prediction extremely difficult, as minor mismatches in either time or space can yield drastically different scores, with errors larger than 100% not uncommon. For example, in a summer shower, one can easily transition from 50 to 0 mm h⁻¹ rain rates in a few tens of meters. A behavior having such a high level of nonlinearity is certainly difficult to model.

**CONS.** There are several reasons not to privilege precipitation as a metric. Errors in modeled precipitation come from uncertainties and model shortcomings in both clouds and convection, and error propagation is multiplicative. To be specific, an error of just 1°C in the sea surface temperature (SST) estimation around Palmén's 26°C threshold (Palmén 1948) can result in convection being or not being triggered, and that can dramatically impact the mean large circulation and potentially

<figure><img src="image_2.png" /></figure>

**FIG. 1.** Mean and trend in SST and precipitation: (a) ensemble mean of the mean precipitation and (b) dispersion (root-mean square) among the ensemble for the historical runs (1920–2005) of 31 CMIP5 models (mm day⁻¹). The thick red line in (a) indicates the 27°C isotherm for the ensemble mean. Ensemble mean of the long-term trend in (c) SST and (d) precipitation for the representative concentration pathway (RCP) 8.5 scenario simulations (2006–95) (°C decade⁻¹ for SST and mm day⁻¹ decade⁻¹ for precipitation). Dispersion of the trend in (e) SST and (f) precipitation among the ensemble for the RCP8.5 scenario simulations (2006–95).
<figure><img src="image_3.png" /><figcaption>FIG. 2. Fields of several meteorological variables from the University of Castilla-La Mancha WRF Model over South Korea. The spatial variability is measured using the semivariance (normalized so fields can be compared). As the lag distance varies, the variables become more and more decorrelated. Note the peculiar spatial decorrelation of precipitation. The grid size of the simulations is 300 m, and the fields are instantaneous estimates. Data from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) campaign.</figcaption></figure>
shift precipitation regimes. Simply put, the precipita-
tion field is a much more complicated field to inter-
pret (and correct), although simple models taking into
account this convective threshold effect show some
skill, for instance, in the tropical zone (cf. Jauregui
and Takahashi 2018).

The complexity of the processes behind rainfall
and snow is also a curse for model improvement (the
major drive of model validation), since it neither
eases the interpretation of the biases in models nor
identifies the specific sources of the bias. This is
because latent heating balances radiative cooling in
the atmosphere (or alternatively, because evaporation,
which balances precipitation, must balance radiative
and sensible heating at the surface) in the climatologi-
cal mean. Thus, by itself, precipitation biases cannot
guide model improvement.

In spite of all efforts and huge advances over the
last decades, precipitation is not well modeled yet,
and that is a valid point against promoting its use
in validation. The number of free parameters and
empirical choices in microphysics modules is large
and includes intricate details such as the efficiency
of drop coalescence, aerosol activation threshold,
fall velocities, cloud fraction parameters, assumed
droplet number concentration, and entrainment rate.
As an example, precipitation rate in single-moment
microphysics schemes [those most commonly used
in global circulation/climate models (GCMs), which
advect hydrometeor mass only] varies significantly
among schemes. Double-moment schemes (which
also advect hydrometeor number concentration)
fare better, but still show discrepancies between
the methods (Shipway and Hill 2012). Aerosols and
the chemistry of clouds and precipitation are key
to further advancing modeling, as recognized by
the recent Decadal Survey for Earth Science and
Applications from Space (National Academies of
Sciences, Engineering, and Medicine 2018), and the
same applies to convection. However, in spite of the
advances, there are still critical processes that are not
modeled in detail, notably the aqueous chemistry,
which is practically absent in today's GCMs.

There are many aspects of cloud physics where
the exact mechanisms that produce precipitation
are unknown. The same applies for the exact value
of empirical parameters embedded into various
parameterizations (Tapiador et al. 2018). For example,
in warm clouds, collision-coalescence theories sug-
gest that precipitation should take hours to form, yet
rain often is produced within time scales of minutes.
While there are many theories (e.g., specific aerosol
initiating precipitation, turbulence), this and other

microphysical problems remain an active area of
research where more understanding is required to
produce more accurate precipitation forecasts and
climate projections.

Furthermore, precipitation from convective clouds
depends on dynamics that is either unresolved at
the global model grid scale (as for isolated cells) or
is comparable to or larger than the grid size and
thus in the “gray zone,” where processes are partly
resolved and partly parameterized (as in organized
mesoscale systems). Therefore, it can be argued that
modeled precipitation is still fairly incomplete and
too dependent on empirical values obtained in a few
field campaigns carried out over small regions of the
planet.

Another fact that would favor alternatives to pre-
cipitation such as humidity, geopotential, or cloud
properties as a metric is that the large sensitivity of
atmospheric models to cloud and mixing parameter-
izations precludes validating aspects of the dynamical
response to SSTs from precipitation observations.
It should be noted that the best estimates of global
precipitation continue to be inconsistent with the best
estimates of Earth's surface and atmospheric energy
balance (Stephens et al. 2012). Until these are recon-
ciled, models cannot be overly influenced by mean-
state biases relative to these estimates. It is important
to note here that mean-state biases in global models
are vastly overrated as a basis for deciding which
models have the best predictive power, because of the
variety of tuning approaches and metrics chosen for
analysis (Schmidt et al. 2017).

Regarding the potential role of precipitation in
trend detection, compensating effects among differ-
ent possible processes associated with climate change
and precipitation (wet-get-wetter versus dry-get-drier
mechanism; that is, areas with large precipitation
amounts are expected to get even more in models
and the inverse for arid zones) make the detection of
trends difficult. The trends in tropical precipitation
associated with anthropogenic forcing are less sig-
nificant than those in SST (Cai et al. 2014), as Fig. 1
illustrates. The figure shows that the dispersion of the
trend in precipitation is larger than for SST relative
to the ensemble mean value, illustrating the different
pattern of mean trend and dispersion of the mean
trend for precipitation compared to SST. Note that
the models seem to agree in the amplification of the
southern branch of the double intertropical conver-
gence zone (ITCZ), since the dispersion among the
models is weaker there. It has also been shown that
changes in the precipitation cycles in the historical
period are minute (Tapiador et al. 2016). In fact, it is
even doubtful that models
can simulate precipitation
cycles with the required
accuracy and precision.

Another well-known
issue in the validation of
precipitation estimates is
the large uncertainties in
the reference data (IPCC
2013). Gauge-only, gauge-
plus-satellite, and satellite-
only datasets usually dis-
agree in the location and
quantity of precipitation
(Adler et al. 2017). Gauges
have known issues, such
as in-splash, out-splash,
or difficulties measuring
in windy conditions. They
suffer increased uncer-
tainties and errors when
the precipitation is solid
rather than liquid, and

performing very localized measurements might not
be representative of the surrounding area. Gauges
also have an extremely low spatial coverage (cf. Kidd
et al. 2017) and usually undersample the range of
amounts that occurred in any precipitation event.
Ground-based radars, which are also used to evaluate
models, present large uncertainties such as the use
of standardized power-law reflectivity-to-rain-rate
(Z-R) relationships, which are often inaccurate for
some regimes. In addition, radar often misses light
precipitation (due to reflectivity being proportional to
the sixth power of hydrometeor diameter and drizzle
drops being small). Furthermore, while the spatial
coverage of radar is quite good in developed countries
such as the United States, it is often very poor in the
tropics and in developing countries.

The need for consistency in reference data has
prompted initiatives such as the European Global
Precipitation Climate Record, which aims to build
a dataset suitable for climate model validation,
including the best-available data and an objective
estimate of the uncertainties (Roca et al. 2014). In the
near future, measurements from the Global Precipita-
tion Measurement (GPM) mission will certainly help
thanks to the Dual-Frequency Precipitation Radar
(DPR) and GPM Microwave Imager (GMI) capabili-
ties resident on the GPM *Core Observatory* satellite
(Skofronick-Jackson et al. 2014). However, the GPM
satellite datasets have not been collected for a long-
enough period (the satellite was launched in 2014) to

derive the more-than-20-yr-long series required for
validating climate models, though it is vital to vali-
date hypotheses on tropical storms and hurricanes
(Fig. 3) and to verify the solid precipitation estimates
of weather models (Bytheway and Kummerow 2018).
Moreover, there are also inherent limitations and
uncertainties in the GPM-derived precipitation es-
estimates as well.

In addition to those observational issues, not all
models automatically conserve moisture, which is
essential for precipitation. This is especially true for
semi-Lagrangian advection approaches [which are
computationally less expensive than the Eulerian
advection used by some models such as the Weather
Research and Forecasting (WRF) Model]. In such
cases, mass-conservation methods have to be applied
in order to correct the issue (Zerroukat and Shipway
2017), which represents a serious issue for validating
the physics.

Precipitation is also one of the more computation-
ally expensive parameterizations of any weather and
climate model (around 10% of the total cost). Other
precipitation-related processes (e.g., aerosol-cloud
interactions) can also be quite expensive. Therefore,
even if we get everything else correct in the model,
our ability to accurately forecast precipitation will
be a complex trade-off between how much compu-
tational power can be afforded to run the models
quickly enough to produce operational weather
forecasts and how much improvement can be gained

<figure><img src="image_4.png" /><figcaption>FIG. 3. GPM Core Observatory dissection of Hurricane Maria on 18 Sep 2017. The figure illustrates the ability of the GPM Core Observatory satellite to map combined GMI radiometer-estimated precipitation rates in a broad 2D swath with a coincident narrower swath of 3D storm structure and hydrometeor phase profiled using GPM DPR. Here warm colors indicate liquid precipitation rates and cool colors indicate precipitation rates in the ice phase. Credit: Science Visualization Studio, NASA Goddard Space Flight Center.</figcaption></figure>
from increasing the microphysical complexity of the model.

Such a state of affairs might suggest that precipitation is not a good metric to gauge model performance, that is, to decide if a model is suited to the purpose for which it was built. In the case of weather forecasting, one primary use of a forecast model is for determining when, where, and how much rain will fall, but given the chaotic nature of the moist atmosphere, predictability of precipitative processes will intrinsically have decreasingly smaller predictive lead times at finer scales (Zhang et al. 2003, 2007), which means that it is next to impossible for a forecast model to precisely pinpoint precipitation in both space and time (right time, right place) given strong spectral power and variabilities of precipitation at smaller scales (Guilloteau et al. 2017; Bei and Zhang 2014).

In the case of climate, models are intended to check whether embedded hypotheses yield a climate consistent with observations, the consistency of which is often measured in terms of biases and correlations against instrumental records of temperature and precipitation; a recent study of Zhang et al. (2016) showed very limited skill for either the CMIP3 or CMIP5 ensemble of models in their predictive capability for simulating regional precipitation at scales below 2,000 km.

**PROS.** There are, however, good arguments to favor precipitation as a good metric of model ability and thus favor its use for model improvement. The other side of the “too stringent test” argument is that it has been so difficult to get it right that precipitation should actually be considered as the ultimate test for model performance. It is hard to conceive that it would be possible to get instantaneous precipitation right for the wrong reasons at a spatial resolution of kilometers. Even if the temporal aggregation smooths the field when climatologies are built, deficiencies in models quickly reveal themselves in the precipitation field, with the double ITCZ rainbands being a classic example (Li and Xie 2014; Popp and Lutsko 2017).

Disparities among reference precipitation data can also be a strength rather than a weakness in terms of achieving a faithful representation of nature in climate models. When different satellite estimates of rain rate disagree, important information is revealed that can help to fine-tune models (Hourdin et al. 2017). For example, the considerable discrepancy between passive microwave and radar estimates of rain rate in the eastern Pacific ITCZ (Liu and Zipser 2013) revealed that assumptions about the depth or microphysical properties of rain-producing clouds

that work well in some regions are not universally valid. While the issue has been known for a long time, the specific details, and crucially the mechanical description, are better expressed in terms of precipitation.

Precipitation estimates have already proven their usefulness for model improvement. Almost half (48%) of modelers consider the use of global precipitation as a metric to be important or very important, and almost two-thirds (65%) consider the use of regional patterns of precipitation important or very important (Hourdin et al. 2017). Examples of success include its use to better constrain model simulations of aerosol direct and indirect forcing (Chung and Soden 2017); the phase, amplitude, and propagation of diurnal precipitation cycles (Dai et al. 1999); determination of the sensitivity of extreme precipitation to changes in temperature (Allan et al. 2010); and critical insight into the dry-get-drier and wet-get-wetter mechanism of global warming (Allan et al. 2010).

The usefulness of precipitation is also apparent when it is compared with its alternatives. For example, precipitation was instrumental in documenting the existence and propagation of the Madden-Julian oscillation (MJO) anomalies (Madden and Julian 1994; del Genio et al. 2015; Wang et al. 2015). Here the advantage of precipitation over the more commonly used outgoing longwave radiation (OLR) is that OLR anomalies over the Maritime Continent can be affected by the fairly ubiquitous high cloud cover. Instead, the rain anomalies are proved to be very helpful in isolating the onset phase of the MJO, when shallow and congestus rain dominate as the biggest source of error in GCM cumulus parameterizations and in preventing the development of a robust MJO. This particular case illustrates that it is precisely because of its complexity that precipitation can be superior to other variables: OLR-based indices of convection greatly overestimate surface rain over Africa, because they sense only the high cold clouds and cannot tell that rain is evaporating more strongly into the relatively dry lower troposphere there and not reaching the ground to the extent that it does in humid regions such as the Amazon (Liu and Zipser 2005, Ling and Zhang 2011).

The diurnal cycle is another good example of the relevance of precipitation as a metric. The phase of the diurnal cycle of precipitation over land is thought to be incorrect in most GCMs (e.g., Dai et al. 1999; Yin and Porporato 2017). However, there are some differences in the phase of the diurnal cycle depending on the dataset used. For example, rain climatologies that rely on infrared (IR) measurements [e.g., Tropical
Rainfall Measuring Mission (TRMM) 3B42] tend to peak approximately 3 h earlier in the afternoon than climatologies that are based on radar data (e.g., TRMM 3B68; Kikuchi and Wang 2008), telling us that the former is likely biased by high clouds that are not producing rain or not producing heavy rain.

There are many other examples to favor precipitation. In tropical cyclone (TC) research, the magnitude of precipitation by itself is a key measure of the severity of the hazard (while on the other hand, the evolution, structure, and intensity of severe convective storms and TCs can be critically dependent on the type and amount of precipitation). Here, better estimates and better observations of precipitation physics offered by GPM (Fig. 3) and other microwave satellite sensors permit the testing of assumptions with unprecedented capabilities (e.g., Sieron et al. 2017, 2018), providing new analytical capabilities to investigate emerging phenomena such as TCs landing in Europe (cf. Tapiador et al. 2007). Among other findings, it appears that for TCs the amount of surface precipitation is dominantly controlled by dynamics (water lifting), while the role of microphysical processes is secondary (but still important).

**FUNDAMENTAL REASONS TO FAVOR PRECIPITATION.** There are also fundamental physical reasons to favor precipitation as a metric to elucidate processes still poorly represented in models. One is the connection between precipitation and the atmospheric energy budget (L'Ecuyer et al. 2015). Changes in global mean precipitation are determined by changes in radiative cooling of the atmosphere (Stephens and Ellis 2008), so it is extremely important to be as precise as possible in determining such changes if the model is intended to understand changes in the radiative forcing, either by natural or anthropogenic causes. In the tropics, mean precipitation and the extreme of the distribution is largely dominated by organized mesoscale convective systems (Roca et al. 2014; Rossow et al. 2013), and the trends in precipitation are also related to the fate of organized convection (Tan et al. 2015). Representation of organized mesoscale systems in GCMs is still in its infancy (del Genio 2012), while grand-domain cloud-resolving model (CRM) simulations become more and more available. Both contribute to making precipitation in the tropics important for gauging new-generation model performances, and therein comparison with observations is critical.

The partitioning of rain into convective and stratiform components is crucial to the latent heating profile of convective systems, because the former

peaks in the lower/midtroposphere while the latter peaks in the upper troposphere. This affects the tropical general circulation (Schumacher et al. 2004). GCMs have so far been able to capture the major features of the climate without representing organized mesoscale systems, which show a transition from bottom-heavy to top-heavy heating over the life cycle (by underestimating convective entrainment and overproducing deep penetrative convection that penetrates too deeply, and thus capturing some of the upper-level heating as an artifact of this error). Getting the right answer for the wrong reason in a climatological mean field in this way is one example of the limitations of using mean fields as metrics. The model parameterization errors become obvious only when higher-order variability metrics such as the MJO or the continental diurnal cycle, which depend on the timing of the transition from bottom-heavy to top-heavy latent heating profiles, are used for evaluation. The latent heating algorithms that have been developed for satellite rain data diagnose this partitioning from characteristics of the rain and reflectivity fields to produce realistic heating profiles and thus to improve representation of this heating in GCMs (Tao and Li 2016). The same arguments can also be applied to high-resolution limited-area models, which are commonly used for weather forecasting but in the last few years have been extended to climate predictions as well (Kendon et al. 2017).

Processes of SST-wind-precipitation interaction are also likely to be involved in long-term trends and variability in the surface circulation in the tropics. For instance, while in the subtropical eastern boundary upwelling regions, an increase of the equatorward winds is expected (and observed in some regions) owing to the poleward displacement and intensification of the anticyclone/Hadley cells; in the tropical Pacific region, the trends in upwelling-favorable winds are more ambiguous and are sensitive to concurrent changes in SST and rainfall, as observed off Peru from coupled model experiments (Belmadani et al. 2014). Therefore, processes associated with moist convection and subsidence in the far eastern Pacific are likely important to understand trends in upwelling systems, and their investigation will benefit from precipitation observations and will require model evaluations based on those.

Another fundamental reason for using precipitation as a model-comparison metric is that precipitation is often considered as a proxy for inferring change statistics in extreme events. To name but one example, the precipitation response to SST during strong El Niño events encapsulates the process associated with
the nonlinear amplification of the Bjerknes feedback (Takahashi and Dewitte 2016) and therein can be considered a better metric of El Niño–Southern Oscillation (ENSO) extremes than SST anomalies alone. Thus, the relationship between precipitation in the eastern equatorial Pacific (Niño-3 region) and the SST gradient near the equatorial region during El Niño exhibits a marked nonlinear pattern that enhances or eases the detection of extreme events. In fact, a precipitation-based definition of an extreme El Niño event (those events for which the Niño-3 rainfall index is above 5 mm day⁻¹) has been proposed recently that is based on the precipitation anomalies averaged over the Niño-3 (5°S–5°N, 150°–90°W) region (Cai et al. 2014, 2017). Based on this precipitation-based index, Cai et al. (2014) analyzed CMIP3 and CMIP5 models and found a doubling in the occurrence of extreme El Niño events in the future in response to greenhouse warming, while no significant change in statistics in extreme El Niño events is found based on the “classical” Niño-3.4 SST index. Power et al. (2013) also shows that ENSO-driven precipitation exhibits a clearer longer-term change than SST anomalies. Thus, precipitation may be seen as a “better” field to reveal, diagnose, and quantify the nonlinear relationship between the variability in the climate system and changes in mean state.

**SUMMARY.** This essay discusses the usefulness of precipitation for model verification and validation

and the crucial role of highly precise and reliable satellite estimates, such as those from the GPM Core Observatory, to test model hypotheses and assumptions. It is widely acknowledged that good climate models are those capable of correctly simulating the MJO, ENSO, or the mean ITCZ, but it should be noted that those processes are also precisely identified as a fingerprint in the precipitation field (Fig. 4), a fact that reinforces use of precipitation for model verification.

However, there are several other compelling reasons to favor precipitation as a metric of model performance, not the least of which is assuring a tough test of model performance. At the end, it can be said that the ultimate test of a fully fledged coupled model is to get precipitation right, a demand that is also spurred by the societal demand for more reliable forecasts of extreme rainfall events, and that includes weather and climate models. As noted, models still have a limited ability to simulate precipitation at adequate temporal and spatial resolution. Such shortcomings demonstrate not only the need to continue devoting resources to improving models, but also suggest that precipitation can be used as a stringent quantitative criterion to evaluate model advances.

Concomitantly, the evaluations of models based on precipitation reinforce the need to continually improve the precipitation estimates themselves. Developments in the observation network should follow the path imposed by progresses in modeling

<figure><img src="image_5.png" /><figcaption>**FIG. 4.** Sensitivity of precipitation within the ITCZ in the eastern tropical Pacific to cumulus (CU) and planetary boundary layer (PBL) parameterizations in WRF (horizontal resolution = 30 km): (a) mean precipitation for Mar 2007 from TRMM, (b) ensemble mean, and (c) dispersion (i.e., standard deviation) for precipitation in 25 simulations of Mar 2007 using different combinations of 5 CU and 5 PBL parameterizations. (d) Characteristics of the ITCZ over the two regions (0°–15°N, 130°–100°W and 0°–15°S, 130°–100°W) in observations (gray bars) and the 25 simulations (color bars). Bars indicate the latitudinal extension of the branches of the ITCZ. The thick black line indicates the latitude of the relative maximum precipitation during this particular month. The number near each bar provides the value of total precipitation, and the bar thickness is proportional to this value.</figcaption></figure>
that continue to reveal the importance of scale inter-
actions in convective activity and its upscaling effect
on climate. As we get to higher-resolution and more
complex models, it becomes more pressing to validate
aspects of the circulation that had been disregarded
or poorly modeled so far, and this includes the initial
precipitation physics.

Finally, it is worth remembering that some of the
processes ultimately producing precipitation occur at
planetary scales, but that some others develop at very
small scales (micrometers). We are unlikely to ever
be able to resolve the smallest scales in a weather or
climate model. Precipitation will continue to require
parameterizations, and therefore the resulting pre-
cipitation will be highly dependent on the empiri-
cal choices and assumptions embedded into these.
Therein lies the likely continuing suitability of this
crucial element for life to gauge model performance.

**ACKNOWLEDGMENTS.** F. J. Tapiador acknowledges Projects CGL2013-48367-P and CGL2016-80609, R. B. Dewitte acknowledges supports from FONDECYT (Project 1171861). We thank the three anonymous reviewers for their constructive comments. Katerina Goubanova (CEAZA) is thanked for providing WRF Model simulations performed using HPC resources from CALMIP (Grant 2014-1044).

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