# The Joint IPWG/GEWEX Precipitation Assessment

Rémy Roca, Z.S. Haddad, F.F. Akimoto, L. Alexander, A. Behrangi, G. Huffman, S. Kato, C. Kidd, P.E. Kirstetter, T. Kubota, et al.

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## To cite this version:

Rémy Roca, Z.S. Haddad, F.F. Akimoto, L. Alexander, A. Behrangi, et al. The Joint IPWG/GEWEX Precipitation Assessment. 2021. hal-03401324

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# The Joint IPWG/GEWEX Precipitation Assessment

## June 2021

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**Bibliographic information**

This report should be cited as:

Roca, R., Z.S. Haddad, F.F. Akimoto, L. Alexander, A. Behrangi, G. Huffman, S. Kato, C. Kidd, P.E. Kirstetter, T. Kubota, C. Kummerow, T.S. L'Ecuyer, V. Levizzani, V. Maggioni, C. Massari, H. Masunaga, M. Schröder, F.J. Tapiador, F.J. Turk and N. Utsumi, 2021: The Joint IPWG/GEWEX Precipitation Assessment. WCRP Report 2/2021, World Climate Research Programme (WCRP): Geneva, Switzerland, 125 pp.

**Contact information**

All enquiries regarding this report should be directed to wcrp@wmo.int or:

World Climate Research Programme
c/o World Meteorological Organization
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**Cover image credit**

“Rain-day” by Flickr user Anatol C

**Copyright notice**

This report is published by the World Climate Research Programme (WCRP) under a Creative Commons Attribution 3.0 IGO License (CC BY 3.0 IGO, www.creativecommons.org/licenses/by/3.0/igo) and thereunder made available for reuse for any purpose, subject to the license's terms, including proper attribution.

**Authorship and publisher's notice**

This report was authored by members of the International Precipitation Working Group (IPWG) and the Global Energy and Water Exchanges (GEWEX) Data and Analysis Panel (GDAP).

*List of authors*

Ziad S. Haddad, Francis J. Turk, Nobuyuki Utsumi, Pierre-Emmanuel Kirstetter, Chris Kidd, Viviana Maggioni, Rémy Roca, Seiji Kato, Tristan L'Ecuyer, Francisco J. Tapiador, Vincenzo Levizzani, Hirohiko Masunaga, Fumie F. Akimoto, Takuji Kubota, Chris Kummerow, Marc Schröder, Hirohiko Masunaga, Lisa Alexander, George Huffman, Christian Massari and Ali Behrangi

The IPWG focuses the scientific community on operational and research satellite-based quantitative precipitation measurement issues and challenges. It provides a forum for operational and research users of satellite precipitation measurements to exchange information on methods for measuring precipitation and the impact of space borne precipitation measurements in numerical weather and hydrometeorological prediction and climate studies.

GDAP was organized to bring together theoretical and experimental insights into the radiative interactions and climate feedbacks associated with cloud processes. The central question that governs the GDAP mission is: “how sensitive is the Earth's climate to changes in radiative and other forcings?”
WCRP is co-sponsored by the World Meteorological Organization (WMO), the Intergovernmental Oceanographic Commission (IOC) of UNESCO and the International Science Council (ISC), see www.wmo.int, www.ioc-unesco.org and council.science.
**Disclaimer**

The designations employed in WCRP publications and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of neither the World Climate Research Programme (WCRP) nor its Sponsor Organizations – the World Meteorological Organization (WMO), the Intergovernmental Oceanographic Commission (IOC) of UNESCO and the International Science Council (ISC) – concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

The findings, interpretations and conclusions expressed in WCRP publications with named authors are those of the authors alone and do not necessarily reflect those of WCRP, of its Sponsor Organizations – the World Meteorological Organization (WMO), the Intergovernmental Oceanographic Commission (IOC) of UNESCO and the International Science Council (ISC) – or of their Members.

Recommendations of WCRP working groups and panels shall have no status within WCRP and its Sponsor Organizations until they have been approved by the Joint Scientific Committee (JSC) of WCRP. The recommendations must be concurred with by the Chair of the JSC before being submitted to the designated constituent body or bodies.

This document is not an official publication of the World Meteorological Organization (WMO) and has been issued without formal editing. The views expressed herein do not necessarily have the endorsement of WMO or its Members.

Any potential mention of specific companies or products does not imply that they are endorsed or recommended by WMO in preference to others of a similar nature which are not mentioned or advertised.

While we have taken every practicable step to ensure that this work if free of factual error, typographic mistakes, and copyright infringing materials, George Mason University and GEWEX disclaim any and all warranties with regard to this publication including all implied warranties of merchantability, fitness and accuracy.
# Foreword

This report is a result of an interesting journey that originated in discussions between Rémy Roca, chair of Global Energy and Water Exchanges (GEWEX) Data and Analysis Panel (GDAP), and Ziad Haddad, chair of International Precipitation Working Group (IPWG), during visits to Toulouse and to Pasadena as well as during many of the various workshops and conferences dedicated to precipitation. It culminated in the present report built from contributions by more than 20 scientists from all over the world. The first written draft was completed immediately following an open meeting for interested contributors that took place on a Saturday morning in a public library in the heights of San Diego (Haddad and Roca, 21 October 2017). From this initial plan, many objectives were successfully achieved without modification, while a few required maturation before we were able to include them in the completed assessment, all of this in spite of the ongoing pandemic. The current report completes the GEWEX assessment portfolio and follows the previous IPWG assessment by 10+ years (Gruber and Levizzani, 2008). Hopefully it is the first of a more frequent and regular series of precipitation assessments.

Rémy Roca, Toulouse, 7 October 2020; Ziad S. Haddad, Pasadena, 7 October 2020
# Contents

<table><tr><td><strong>1. Assessment of the Sub-Daily Global Satellite Precipitation Products</strong></td><td style="text-align: right;"><strong>1</strong></td></tr><tr><td style="padding-left: 2em;">1.1. Intrinsic uncertainty in the sub-daily satellite products at their native resolutions</td><td style="text-align: right;">1</td></tr><tr><td style="padding-left: 2em;">1.2. Validating the intrinsic uncertainty: Implications for hydrologic applications</td><td style="text-align: right;">23</td></tr><tr><td style="padding-left: 2em;">1.3. Monitoring of satellite precipitation estimates through the IPWG validation studies</td><td style="text-align: right;">36</td></tr><tr><td><strong>2. Climate Applications</strong></td><td style="text-align: right;"><strong>42</strong></td></tr><tr><td style="padding-left: 2em;">2.1 Energy and water closure</td><td style="text-align: right;">42</td></tr><tr><td style="padding-left: 2em;">2.2. Climate variability and trends</td><td style="text-align: right;">52</td></tr><tr><td style="padding-left: 2em;">2.3. Climate model validation</td><td style="text-align: right;">58</td></tr><tr><td style="padding-left: 2em;">2.4. Intercomparison of products for climate applications</td><td style="text-align: right;">74</td></tr><tr><td style="padding-left: 2em;">2.5. Extreme and intense precipitation</td><td style="text-align: right;">81</td></tr><tr><td><strong>3. Emerging directions</strong></td><td style="text-align: right;"><strong>94</strong></td></tr><tr><td style="padding-left: 2em;">3.1. Toward the new generation of products</td><td style="text-align: right;">94</td></tr><tr><td style="padding-left: 2em;">3.2. Directions in error modeling</td><td style="text-align: right;">102</td></tr><tr><td style="padding-left: 2em;">3.3. Emerging techniques for precipitation assessment and consistency studies</td><td style="text-align: right;">108</td></tr><tr><td style="padding-left: 2em;">3.4. Requirements for a constellation of precipitation sensors</td><td style="text-align: right;">116</td></tr><tr><td><strong>Annex 1 - Acronyms</strong></td><td style="text-align: right;"><strong>121</strong></td></tr></table>
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# 1. Assessment of the Sub-Daily Global Satellite Precipitation Products

## 1.1. Intrinsic uncertainty in the sub-daily satellite products at their native resolutions

Ziad S. Haddad<fnref n="1" />, Francis J. Turk<fnref n="1" />, Nobuyuki Utsumi<fnref n="2" /> and Pierre-Emmanuel Kirstetter<fnref n="3" />

### 1.1.1. Background

For many hydrological and weather forecasting applications, an important quantity is the amount of precipitation that falls on the Earth's surface over a given time interval, i.e., the surface precipitation rate. However, no satellite instrument is unambiguously sensitive to the instantaneous precipitation rate at the Earth's surface. A vertically profiling radar such as the Dual-Frequency Precipitation Radar (DPR) onboard the joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) mission satellite (Hou et al., 2014) is directly sensitive to rain in the near-surface layers of the atmosphere unaffected by surface ground clutter, but its measurements are affected by attenuation due to the condensed water in the higher layers of the cloud. Furthermore, the DPR instrument scan swath is typically too narrow (240 km) to provide substantial global coverage at sub-weekly scales. Passive microwave (MW) radiometers do have more substantial coverage owing to their wider swath (between 800 and 2500 km). These measurements are less directly sensitive to surface rain, with more direct sensitivity to the condensed water in the cloud. The height of the "peak" sensitivity increases as the radiometer channel wavelength decreases, up to infrared (IR) radiometers, which are directly sensitive only to the condensed water at the very top of the cloud (Haddad et al., 2017). IR radiometers are now carried by all meteorological geostationary satellites, providing global coverage with very frequent temporal sampling (at least every 30 minutes or better).

Therefore, the algorithms that generate today's global satellite precipitation products attempt to capitalize on the different strengths of these three types of instrument (radar, passive MW radiometers and frequent-refresh IR radiometers). These algorithms rely upon the reasonable sensitivity of the microwave radiometers, sharpened by reference to the precipitation radars, and enlist the help of IR to interpolate the microwave estimates to the often-long intervals of time between consecutive passive MW observations. These "revisit gaps" can be 5 hours in the tropical latitudes (Kidd et al., 2018a). As a result, one expects three broad sources of uncertainty in the products: a detection-related uncertainty that results from the possible confusion in the interpretation of the passive MW or IR observations between surface rain and cloud-column condensation; an estimation uncertainty that results from the quantitative conversion of the passive MW or IR observations into surface-rain amounts; and an interpolation issue stemming from the ambiguous use of frequent-but-indirectly-sensitive IR information to fill the revisit gaps.

To illustrate this general approach, Figure 1.1.1 depicts an *idealized* set of observations collected from four passive MW satellite overpasses over a given location during a 12-hour period. The data from each passive MW satellite measurement provide an estimate of the

<footnote n="1">¹Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA</footnote>

<footnote n="2">²Nagamori Institute of Actuators, Kyoto University of Advanced Science, Kyoto, Japan</footnote>

<footnote n="3">³University of Oklahoma, Norman, OK, USA</footnote>
surface precipitation (blue arrows). In this example, the satellites are equally spaced in their observing times, leaving 3-hour revisit gaps. By combining and merging these data with higher time resolution geostationary satellite data, the desired high spatial and temporal resolution precipitation products are produced (denoted by the red arrows). The errors resulting from the combination of all of the processing steps mentioned above are aggregated into the final high-resolution products that are provided to data users. Most often, the error analysis is carried out on the products that are produced at the end of this process, which represents an accumulated error. The purpose of this section is to provide insight to product users on the nature of the error introduced by the various steps. The remainder of this section is devoted to the discussion of these error components in more detail.

<figure><img src="image_6.png" /><figcaption>**Figure 1.1.1.** Idealized depiction showing how intermittent passive MW satellite-based precipitation data (blue arrows) are generated from a limited set of satellite overpasses, and used to produce a high spatial and temporal resolution (red arrows) satellite precipitation product</figcaption></figure>

### 1.1.2. Satellite precipitation measurements

For the past two decades, the centerpieces of satellite precipitation measurements from space are the GPM core spacecraft (2014–present) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) (1997–2014) (Kummerow et al., 2000). Only these two spacecraft have a scanning precipitation radar instrument. TRMM and GPM’s passive MW radiometers observe precipitation across scan swaths that are much wider (800 km) than the respective radar swath, and indeed the radar measurements are used as benchmarks, available over the common narrow swath, to guide the estimation of surface rain from the passive MW radiometers alone outside the common swath. Yet the swath of each passive MW radiometer is still not wide enough to allow global coverage on sub-daily time scales (i.e. revisit periods of less than 24 hours, let alone three or less as in the example of Figure 1.1.1). That is why the estimates at sub-daily scales require the aggregation of estimates from all available passive MW radiometers. Table 1 of Kidd et al. (2018a) lists the characteristics of the current GPM passive MW radiometers [GPM microwave imager (GMI), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer 2 (AMSR-2), Microwave Humidity Sounder (MHS) and Advanced Technology Microwave Sounder (ATMS)].
Many of these passive MW radiometers are carried by operational weather satellites, which orbit in sun-synchronous patterns and produce observations near fixed local solar times (so their observations can be efficiently assimilated into numerical weather prediction systems). Others, like GPM, TRMM and the Megha-Tropiques (M-T) satellite built by the Indian Space Research Organization (ISRO) and the French Centre National d'Etudes Spatiales (CNES), orbit in non-sun-synchronous orbits whose local observing times change from day to day, in 30-60-day cycles (depending upon latitude) (Roca et al., 2015; Negri et al., 2002). In contrast to the idealized depiction of Figure 1.1.1, Figure 1.1.2 shows a more realistic set of passive MW observations from the current (2020) GPM-era constellation, whose revisits, unlike Figure 1.1.1, are not evenly spaced in time. Moreover, these consist of different types of radiometers with different channels, resolutions and sensitivities. Therefore, the detection and estimation errors shown in Figure 1.1.1 are different for each sensor.

<figure><img src="image_7.png" /><figcaption>**Figure 1.1.2.** Depiction of the different passive MW satellites and their capabilities, observing times and revisit intervals during a typical 12-hour period, from the current (2020) GPM constellation era (contrast to idealized Figure 1.1.1)</figcaption></figure>

The sensitivity of the passive MW radiometers is different for different radiometers and for different cloud types. These radiometers measure the net upwelling radiation from the Earth's surface through the atmosphere, in different frequency bands. The upwelling radiation comes primarily from the surface, and is modulated by the constituents of the atmosphere: the gases absorb (and therefore emit), and the hydrometeors (rain drops and the varieties of ice, snow, graupel, etc.) absorb/emit and also scatter the upwelling radiation, mostly out of the beam. This explains why the passive MW radiometers are not specifically sensitive to the precipitation in any single height layer of the cloud, let alone the surface layer. The radars, on the other hand, do not have this limitation, and do enable the estimation of vertical precipitation profiles. That is the justification for the multi-channel passive MW retrieval techniques which use a reference set of nearly-coincident nearly-simultaneous radar measurements (compiled offline, pairing passive MW measurements with the underlying precipitation rates as retrieved from the coincident simultaneous radar measurements), to produce instantaneous surface-rain estimates. These data can indeed be used to spread or “transfer” the DPR/Precipitation Radar (PR) radar structure information to each of the constellation radiometers (e.g., Turk et al., 2018; Kidd et al. 2018a; Petty and Li, 2013), so that each “Level-1” radiometer pixel can be assigned a “Level-2” estimate of the surface precipitation rate. The coincidence reference dataset can be used to retrieve any quantity that can be retrieved from the precipitation radar's measurements, with varying amounts of uncertainty that can be quantified from the reference data themselves, if the reference data are sufficiently extensive to be representative of global precipitation. Fortunately, both GPM and TRMM have asynchronous orbits so that, throughout the course of
a year, they do provide a very large number of near-coincident observations with each of the constellation radiometer satellites.

Lastly, the collection of Level-2 passive MW precipitation products is incorporated into global products covering a time interval, posted at a fixed grid resolution that can be near or finer than the scale of some of the Level-2 products that were used. These are termed “Level-3” products, which provide accumulated precipitation estimates at scales as fine as 30-minute refresh cycles, and 0.1° gridded resolution. For the remainder of this section, the focus is on the Level-3 products that incorporate the Level-2 passive MW precipitation products and high-resolution, fast-refresh cycle geostationary-satellite infrared (IR) observations to cover what would otherwise be lengthy revisit gaps from the Level-2 passive MW precipitation alone.

The next subsection attempts to quantify the order of magnitude of the uncertainties in the Level-3 products separately for each of the three sources of uncertainty. Examples of current widely-used global Level-3 precipitation products of this type include the Integrated Multi-Satellite Retrievals for GPM (IMERG) (Huffman et al., 2018); the Global Satellite Mapping of Precipitation (GSMaP) (Ushio et al., 2009), the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Morphing Technique (CMORPH) (Joyce and Xie, 2011), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) (Hsu et al., 1997), and the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) (Kuligowski et al., 2013). A description and an intercomparison of many current global precipitation datasets from stations and satellites can be found in Sun et al. (2018). Because they are published at such relatively high spatial and temporal resolution, it is important to give an objective assessment of the uncertainty in these values at their reported resolution.

### 1.1.3. Intrinsic uncertainty of the Level-3 merged products

Early evaluation initiatives of a number of research Level-3 products were fostered by the International Precipitation Working Group (IPWG) (Ebert et al., 2007; Turk et al., 2008 and references within; see also 1.3 below). More recently in the post-GPM era, more detailed Level-3 evaluation efforts have been reported. A detailed list of these evaluation efforts would be lengthy (and likely incomplete); for reference, we refer to several recently-published studies (Maranan et al., 2020; Le Coz et al., 2020; Chen et al., 2019; Tan et al., 2016; Maggioni et al., 2014) and references within each of these. These studies generally proceed by comparing the values that are reported by a given product with other precipitation estimates. In contrast, in this subsection we review the uncertainties due to the sources of error in the successive steps in the estimation process. Indeed, the Level-3 uncertainty is the result of the accumulated uncertainty in the different steps of the estimation process, which for the purposes here are assumed to start with the Level-2 precipitation products themselves.

There are three main sources of uncertainty: 1) the uncertainty introduced in the precipitation detection by the Level-2 passive MW algorithms (either by omission, if no precipitation is detected so that the retrieval algorithm is not run and the precipitation is assumed to be zero when in fact precipitation was present, or by processing the passive measurements through the retrieval process when in reality there was no rain at the surface); 2) for the detected pixels, the uncertainty introduced by the instantaneous retrieval algorithms; and 3) the uncertainty introduced by the revisit-gap mitigation, i.e. the propagation in time from one Level-2 passive MW precipitation estimate to the next (i.e. from the Level-2 passive MW precipitation at time-1, to the passive MW precipitation at time-2).
In the following subsections, the Level-3 propagation-based methods common to operationally-produced precipitation products – IMERG, CMORPH, and GSMaP – are used as examples in the discussion of the third error component (use of IR data to fill passive MW revisit gaps). Products other than these three use different approaches to bring passive MW information to bear on IR data, including self-calibration (as in the case of SCaMPR) or artificial neural networks (as in the case of PERSIANN). The fundamental source of uncertainty in these mostly IR-based products is discussed in 1.1.3.5.

#### 1.1.3.1. Detection errors

<figure><img src="image_8.png" /><figcaption>**Figure 1.1.3.** Side-by-side comparison of the precipitation estimate produced by the GPM combined DPR+GMI (CORRA) product (left panel), and the corresponding GMI-only (GPROF) estimate (right panel).</figcaption></figure>

Establishing the minimum detectable precipitation rate for the Level-2 passive MW algorithms is not a straightforward proposition. The issue is illustrated in Figure 1.1.3, showing a typical pair of instantaneous retrievals, one made by the GMI radiometer algorithm (Goddard Profiling Algorithm, GPROF) (Kummerow et al., 2015) and one by the GPM combined sensor (DPR+GMI) radar radiometer algorithm (Combined Ku Radar-Radiometer Algorithm, CORRA) (Grecu et al., 2016). The substantial areas where the GMI algorithm detects surface precipitation > 0 mm/hr while the CORRA algorithm reports 0 mm/hr surface rain could indicate a greater (if indirect) sensitivity of GMI to lower precipitation rates than the radar, or they could be false alarms where passive MW retrievals were made over a precipitation-free column. In these discrepancies, the radiometer-retrieved rates are never greater than a few mm/hr, yet they do cover a significant area. The reverse can happen too (in the case illustrated, most notably in the area around 29N 96.5W), though this “failure to detect” by the instantaneous passive MW radiometer retrieval algorithms is less common.

Using a research passive MW precipitation profiling method (emissivity principal components, EPC) (Turk et al., 2018) based on the microwave surface emissivity, Utsumi et al. (2020) showed that the probability of detection (POD) over ocean surfaces exceeded 0.7 and was fairly consistent across the five algorithms tested and the choice of precipitation threshold. Over land, POD can drop below 0.6 for vegetated surfaces or coasts (Turk et al., 2018). Similar POD
scores were noted across passive MW sensors (You et al., 2020). Chen et al. (2019) performed a similar experiment and concluded that the conically-scanning imagers generally outperformed the cross-track scanning sounders.

### 1.1.3.2. Errors inherent to the instantaneous passive MW retrievals

Compared to the Level-3 products, relatively few independent validation studies of Level-2 passive MW constellation products have been done. A comparison of the areas with larger surface rain rates in Figure 1.1.3 illustrates the intrinsic uncertainty in the Level-2 instantaneous retrievals from passive MW measurements. Note how the substantial areas with rain rates higher than 30 mm/hr in the benchmark combined radar+radiometer retrievals are not matched by comparable estimates in the passive-MW-only retrievals. This subsection discusses these uncertainties and their order of magnitude.

Early (SSMI era) passive MW precipitation approaches were based upon mostly statistical methods (Grody et al., 1991) that matched brightness temperature (TB) combinations to observed near-surface reference precipitation estimates made by rain gauges or by rain-gauge-trained ground radars. As such, these methods were tuned to the characteristics of the limited-domain input radar data used. The spatial and temporal variability in the precipitation microphysics and the nature of the weather conditions and surface conditions were not accounted for. The retrievals also failed to provide any physical estimate of the error in the precipitation rate. More recent passive MW Level-2 precipitation data products are more physically-based and account for the error inherent to the passive MW precipitation scheme itself. These include, but are not limited to, GPROF (Kummerow et al., 2015), surface emissivity-based methods (Turk et al., 2018), PRPS (Kidd et al., 2018b), k-nearest neighbor (Takbiri et al., 2019), TB "pseudo channel" schemes (Petty and Li, 2013), MIRS (Boukabara et al., 2011), the JAXA GSMaP scheme (Aonashi et al., 2009) and 1DVAR snowfall (Meng et al., 2017). Often, these methods apply data reduction to the input TB data to isolate the precipitation signal from the naturally-occurring variability in the input TB signal. Provided the collection of *a-priori* data used (in one way or another) is sufficiently representative of the conditions being sampled by the passive MW-based satellites, Bayesian-based methods provide a means to determine the error associated with the mean precipitation estimate.

In the merging algorithms that incorporate the Level-2 passive MW precipitation products and high-resolution, fast-refresh cycle geostationary satellite-based infrared (IR) observations, currently only one of these passive MW algorithms is used. For example, the IMERG Level-3 dataset relies upon passive MW precipitation produced by the GPROF algorithm (Kummerow et al., 2015), and the GSMaP product uses the JAXA passive MW algorithm (Aonashi et al., 2009). Therefore, only the error associated with this single passive MW precipitation technique affects the downstream Level-3 processing.

In reality, each of the passive MW precipitation methods makes different assumptions on many variable processes, such as the form of the particle size distribution, its spatial uniformity or inhomogeneity, etc. For GPM, the core satellite DPR-based precipitation algorithm has four main precipitation products, a DPR-only and a combined DPR+GMI algorithm (CORRA) (Grecu et al., 2016), each with a Normal Scan (NS, or Ku-band only) and a Matched Scan (MS, or Ku+Ka-band) variant, for a total of four reported instantaneous estimates. Each of these retrieval algorithms make various simplifying assumptions to invert the measured radar reflectivity profiles into hydrometeor precipitation profiles. For example, the drop size distribution models used in the DPR algorithms are largely based on measurements from the tropics (Seto et al., 2013), which may not be applicable across the variety of weather systems encountered in the GPM coverage area. The drop size distribution models are selected based
on predefined precipitation types that are imperfectly identified from features in the radar reflectivity profiles. Another source of variability arises from the assumptions used to handle the variable surface radar surface cross section and (for CORRA) the surface emissivity variability (Munchak et al., 2016; Munchak et al., 2020). The multiple sources of uncertainty carry over into the passive MW-only precipitation profile estimates from each constellation MW radiometer.

<figure><img src="image_9.png" /><figcaption>**Figure 1.1.4.** Comparison of five different instantaneous microwave retrievals versus the benchmark ground gauge-radar based Ground Validation Multi-Radar/Multi-Sensor estimates (horizontal axis; see section 1.2). From left, the EPC algorithm based on the GPM combined radar+radiometer algorithm (CORRA) Normal Scan (NS; Ku-band only) precipitation product, the EPC algorithm based on the GPM radar-only algorithm (DPR) Ku-band only precipitation product, the EPC algorithm based on the CORRA Matched Scan (MS; Ku+Ka-band) precipitation product, the EPC algorithm based on the GPM radar-only algorithm (DPR) Ku+Ka-band) precipitation product, and the Version 5 GPROF algorithm. The rows indicate data is from ocean, vegetated and coastal surface classes. Note the log-log scaling of each panel.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 1.1.5.** Maps showing the two locations of the case studies summarized in 1.1.3.2 and 1.1.3.3.</figcaption></figure>

Currently, there is no consistent way to account for the range of uncertainty introduced in these instantaneous estimates, as they are used to make the passive MW constellation estimates,
and ultimately the Level-3 products including IMERG, GSMaP, CMORPH and others. However, one can estimate the magnitude of this uncertainty by comparing instantaneous retrievals to a common reference. Figure 1.1.4 shows a comparison of five different instantaneous GMI microwave retrievals (5 columns) relative to pixel-matched precipitation products derived from the benchmark U.S. ground radar-gauge Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) (Kirstetter et al., 2012, 2018b) products (horizontal axis; see Section 1.2), illustrating the uncertainty in any individual estimate to be above 100%. Since passive MW measurements are sensitive to the emission from the surface, the validation is separated by the underlying surface classification (ocean, vegetation, coastal), corresponding to each row. A common reference also makes it possible to diagnose and propagate the uncertainty to the Level-3 products (see Figure 1.1.12). Kidd et al. (2018a) used multi-radar multi-sensor (MRMS) to validate the GPROF algorithm across sensors.

<figure><img src="image_11.png" /><figcaption>**Figure 1.1.6.** Time series of the five instantaneous passive microwave retrievals in Figure 1.1.4 for a specific 0.1-degree resolution element near Lafayette, LA (see Figure 1.1.4) on the ground, for a 42-hour period (0 UTC on 6 December 2017 through 18 UTC on December 7). The spread of the estimates is shown (up to 200% at times) as well as the discrepancy between the detection according to the satellite instrument and the lack of rain detected by the gauges at several times. This is most notable at 20 and 22 UTC on the 6th, at 12 and 13 UTC on the 7th.</figcaption></figure>

While Figure 1.1.4 shows overall “bulk” comparisons of many individual per-pixel retrievals, the differences between each of the passive MW products at a specific pixel or “point” are more variable and can vary from location to location. To highlight an individual point location, Figure 1.1.6 shows the range of the five passive MW precipitation products from Figure 1.1.4 for a 42-hour period. The time series is from a specific 0.1-degree grid box point (the posted resolution of many Level-3 precipitation products) shown in Figure 1.1.5. In this example, the spread of the estimates is shown (up to 200% at times) as well as the discrepancy between the detection according to the satellite instrument and the lack of rain detected by the gauges at several times.
#### 1.1.3.3. Errors introduced by revisit-gap mitigation

During the revisit gap between any two successive passive MW precipitation datasets, the precipitation is evolving. In Level-3 products such as CMORPH, GSMaP and IMERG, the precipitation evolution between successive passive MW precipitation datasets is accounted for by filtering/"morphing" methods. This process requires the use of ancillary data that is sampled frequently during the revisit gaps, to infer sufficient information to evolve the precipitation at one end of the temporal gap time-1 to the other time-2 (time-1 and time-2 being the observation times of the two passive MW satellites at the two ends of the revisit gap). This is done variously by tracking radiometrically cold IR cloud top patterns between successive 30-min imagery, or by tracking the motion of the precipitable water vapor patterns from global model reanalysis fields (Tan et al., 2019), or by tracking the motion of the IR-derived surface-precipitation fields, or using an empirically-derived filter based on these combined data. In effect, a several-degree box size is used to track the frequently-sampled data features from one time to the next, and to estimate the evolution of surface precipitation along these tracks.

<figure><img src="image_12.png" /><figcaption>**Figure 1.1.7.** The top row shows the IMERG estimates of surface rain on 24 July 2014 at 0130 Z and 0200Z over a convective storm in West Africa, with marked differences between the rain fields at the two consecutive times despite the striking similarity of the geostationary IR observations at these times shown in the bottom row, along with the passive MW brightness temperatures measured by the MHS radiometer at 0150Z (Haddad et al., 2017).</figcaption></figure>

Figure 1.1.7 illustrates the result in the case of a mesoscale convective system that developed over West Africa early on the morning of 24 July 2014. The surface rain estimated by the finest-resolution (30-min) IMERG product at 0130 Z is quite different from that at 0200 Z, in spite of the fact that the geostationary IR temperatures are quite similar at both times (lower panels). At 0130 UTC, the HQ precipitation source was from an MHS sounder. At 0200 UTC, the most-recent passive MW source was from the SSMIS (left side of the orange line) but only to the left of the orange line, with no passive MW data available in this 30-minute time on the right side of the line (where the storm was located). The main difference in the precipitation estimates is the large amount of low precipitation rates that appear at 0200 all around the storm as it was delineated at 0130, almost entirely due to the fact that between 0130 to 0200 there was a passive MW observation, by MHS at 0150Z, but between 0200 and 0230 the only MW observation was by SSMIS and missed this storm entirely, so that the estimates at 0200 are
strongly affected by revisit-gap mitigation. In this case, the precipitation at 0200 covers an area
that is almost double that at 0130, the excess consisting entirely of rain rates below 2 mm/hr.

<figure><img src="image_13.png" /><figcaption>**Figure 1.1.8.** Accumulated precipitation for the same morphed products shown in Figure 1.1.6, for the Lafayette location (left) and the Bay of Bengal location (right). The comparison shows the result of using different passive MW precipitation products in the generation of the Level-3 merged satellite product, showing spreads that can grow to a factor of three at the end of the 42-hour period.</figcaption></figure>

A more systematic evaluation of the cumulative effect of the interpolation was performed using IMERG as the evaluation medium. To that end, the IMERG technique was adapted into a “test mode” (IMERG-T) (Utsumi et al., 2018), where instead of feeding the IMERG process with the passive MW precipitation data from the GPROF algorithm, it can be fed with other, different passive MW precipitation retrievals. Figure 1.1.8 shows the same case as Figure 1.1.6, but in this example the accumulated precipitation is shown. The accumulated precipitation from the Bay of Bengal area in Figure 1.1.5 is also shown, as a side comparison. IMERG-T was run separately for each of the four passive MW algorithms in Figure 1.1.4. This was done with (blue lines) and without (green lines) the calibration step that is used in IMERG to assure that the histograms of the GPROF-GMI passive MW precipitation match the histograms of each of the other constellation sensors. The “spread” amongst the ten products (four EPC and GPROF, each with a calibrated and uncalibrated version) is shown by the red vertical line “Retrievals”. Figure 1.1.8 shows the net effect at a “pointwise” scale, which is the scale at which many Level-3 product users actually use and interpret these data for their analyses. While it shows only two locations, it does highlight the expected variability at the 0.1-degree scale. Other Level-3 product users may spatially average these native 0.1-degree, 30-minute data further, for example, into common 1-degree daily grid boxes (discussed in section 2 below) or 5-day pentads.
<figure><img src="image_14.png" /></figure>

**Figure 1.1.9.** (Top right) Final IMERG precipitation product for the 30-minute interval ending at 05 UTC on 6 December 2017, showing an area in the Indian Ocean. (Top left) Corresponding image, except using the EPC CORRA-NS retrieval for each constellation radiometer (except MHS), to carry out the morphing procedure in IMERG-T. (Lower left) The ensemble spread (range of maximum-minus-minimum, in mm hr⁻¹) that results when each of the four EPC-based retrievals are fed through the IMERG-T morphing. (Bottom right) Final precipitation estimates along the cross-section line shown in the lower right panel for each of the four EPC-based retrievals. IMERG, which morphs using only GPROF precipitation, is shown in the black line.

In Figure 1.1.9, the official IMERG precipitation data valid at 0500 UTC on 6 December 2017 is shown in the upper right panel. This represents the average rain rate in the 30 minutes between 0430-0500 UTC. The collection of passive MW precipitation data products that feed into this IMERG estimate come from different satellite overpasses. Depending upon the orbit characteristics, some pixels in this area may have had a more recent “refresh” from a passive MW satellite than others. In this example, the passive MW precipitation products from the EPC products are based on the DPR+GMI combined Ku-band only product (Grecu et al., 2016). While the overall pattern of the precipitation is similar in the top two panels, there are small scale differences at the 0.1-degree level reported for the IMERG product.

This same process was repeated with the other three radar algorithms, each of whose passive MW precipitation data products were fed into the IMERG processing. The resultant ensemble spread expresses the overall range of variability in the final products considering that all of these factors are processed through IMERG. The lower left panel shows the range (maximum minus minimum) of values produced by the members of this ensemble.
To highlight the difference, the bottom right panel of Figure 1.1.9 shows the final precipitation estimates along the cross-section line shown in the lower right panel (IMERG, which morphs using only GPROF precipitation, is shown for comparison, in the black line), for each of the four EPC-based retrievals. The individual colors each represent the output when each of the four EPC-based estimates are used in the IMERG-T morphing procedure. Note the difference in some of the heaviest precipitation locations can be as much as 40% higher, but only slightly lower, than the IMERG product. This ensemble analysis expresses the range of precipitation, considering each of the DPR-based precipitation estimates processed through each constellation radiometer, and processed through IMERG.

<figure><img src="image_15.png" /></figure>

**Figure 1.1.10.** Analysis of the 36-hour period from 0 UTC on 6 December 2017 to 12 UTC on 7 December 2017, where the domain is a 5° x 5° box in the Bay of Bengal. (**Left**) Domain-average IMERG precipitation product at each 30-minute time step. (**Right**) Accumulated precipitation. The domain-integrated accumulation at the end of the 36-hour period varies between 160 and 188 mm, a span of about 16% of the ensemble mean.

While Figure 1.1.9 highlights differences at the instantaneous (single overpass) scale, Figure 1.1.10 illustrates the cumulative effect of these same ambiguities when averaged over a 5° x 5° domain and over the 36-hour duration of the storm. As expected, spatial and temporal averaging of the native fine-scale data reduces the instantaneous pointwise errors illustrated in Figure 1.1.9, but the uncertainty does not shrink to zero. Using a one-minute updating rain gauge network over Korea, Turk et al. (2009) examined the performance of the NRL-Blend fast-update precipitation product across telescoping space-time averaging scales. The space-time root mean square (RMS) error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time (this is necessary since the satellite measurement responds to the precipitation before it has fallen to the ground, where the gauges measure). For ±10 minute rain gauge time windows (Figure 1.1.11), a correlation of 0.6 was achieved at 0.1-degree spatial scale by averaging over 3 days; coarsening the spatial scale to 1.8 degrees produced the same correlation by averaging over one hour. Finer than approximately 24-hours and 1-degree time and space scales, respectively, a rapid decay of the error statistics was obtained by trading off either spatial or time resolution.
<figure><img src="image_16.png" /></figure>

**Figure 1.1.11.** Space-time contour plots of the correlation coefficient, root mean square error and mean bias for the rain gauge network time window average of ±10 minutes, centered about the time of the GMS satellite observation of Korea. The abscissa and ordinate of each contour plot denotes the spatial and temporal scales, respectively, used to average the rain gauge data and the NRL-blended satellite technique estimated rain (figure adapted from Turk et al., 2009).

#### 1.1.3.4. Sensitivity to IR observations: Uncertainty in quantitative estimation and in interpolation during revisit gaps

<figure><img src="image_17.png" /></figure>

**Figure 1.1.12.** Relative bias in percentage between the PERSIANN-CCS precipitation estimates relative to the reference GV-MRMS as a function of the IR brightness temperature. This intrinsic bias results from the algorithm assumption that deeper clouds, represented by colder brightness temperatures, produce more surface rain. The conditional bias is a decreasing function of the brightness temperature following the redistribution assigning higher precipitation rates to colder brightness temperatures. The -100% bias above 242 K relates to missed precipitation that also results from the assumption (adapted from Kirstetter et al., 2018a).

Starting in the 1980s, when passive MW data was unavailable or scarce (routine SSMI data began in 1987), but geostationary IR measurements were routinely available hourly, the idea to use instantaneous IR radiances to estimate instantaneous surface rain was proposed and implemented. This was justified by the fact that, in convective storms, deeper clouds tend to produce more surface rain than shallower clouds (Arkin and Meissner, 1987; Huffman et al., 1987).
1997). However, a cold IR temperature indicates a high cloud, which is not the same as a deep cloud. Even statistically, TRMM has established that the deepest clouds are not the ones that produce the highest surface rain rates (Hamada and Takayabu, 2016). The ambiguities are even greater away from the tropics, where the rain amounts produced by storms do not have a monotonic relation with the height of the cloud. IR radiances provide indirect information on the occurrence and magnitude of precipitation at the surface. Depending on the cloud type and life cycle, a given IR brightness temperature can be associated with various rain rates, since not all clouds produce precipitation or produce it at the same rate. IMERG combines IR-based Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS: Hong et al., 2004) and retrievals from passive MW estimates. As discussed earlier for passive MW estimates, the uncertainty associated with each PERSIANN-CCS retrieval is critically lacking for optimally merging PERSIANN-CCS outputs with passive MW precipitation and quantifying the propagation of this uncertainty into the final IMERG estimates. Kirstetter et al. (2018b) investigate this question with GV-MRMS over the U.S. over two summers of 2014–2015. They estimate that the PERSIANN-CCS intrinsic uncertainty, arising from assuming that deeper clouds produce more surface rain, can be described as a conditional bias typically ranging from –100% to +600% with the observed brightness temperatures (Fig. 4 in Kirstetter et al., 2018a, reproduced here in Figure 1.1.12). The volume of precipitation that is missed or erroneously detected is substantial (more than 50%), and the quantitative variability of correctly-detected precipitation is not well reproduced.

That is why the idea of using the frequently-available IR measurements to make instantaneous estimates of the underlying surface rain evolved to give rise to the less problematic concept of using them to guide the revisit-gap evolution estimation instead (Joyce et al., 2004, and references within). In this IR-based filtering, the features that are tracked represent cloud top patterns (IR cloud temperatures), rather than the actual evolution in time of the near-surface precipitation itself. The actual precipitation may be evolving faster or slower in space and intensity, in ways that are not easily approximated by locally linear tracking. For example, the precipitation at the surface may be moving slower or faster than the motion inferred from the upper cloud regions, or in extreme cases (where upper/lower vertical shear exists), moving in a different direction. To mitigate, certain algorithms such as CMORPH locally adjust the morphing tracking speed, based on comparisons with precipitation tracked from ground radars (Joyce and Xie, 2011). These discrepancies are a source of uncertainty in the Level-3 precipitation processing.

Using the IMERG-T analysis described above, Figure 1.1.13 illustrates the magnitude of the revisit-gap evolution speed uncertainty. The specific analysis quantifies the effect of artificially slowing down (or speeding up) the motion by 20, 30 or 50%. The difference in the final precipitation field is shown relative to the original precipitation. In this example, the largest differences are observed for the cases where the motion field was slowed down relative to what the IR-based tracking alone provides.

The “spread” at a given point due to the propagation vector variability was already illustrated in Figure 1.1.8 (highlighted by the vertical red arrow labeled “Prop. vector”) for each of the two 0.1-degree gridbox locations of Figure 1.1.5. In this example, the most noticeable effect occurs when the motion vectors were slowed down.
2017/12/06 08:00UTC

<figure><img src="image_18.png" /></figure>

**Figure 1.1.13.** Left four panels: Net effect when the motion vector is slowed down by 20, 30 and 50% of its original value (upper left panel). Right four panels: Same, in the case when the motion vector is sped up by 20, 30 and 50% of its original value.

### 1.1.4. Summary of the assessment of intrinsic uncertainty

The Level-3 "sub-daily global merged satellite precipitation products" are typically reported on a fixed rectangular latitude-longitude grid at high spatial and temporal resolution (respectively 0.1° and ~0.5 hour). This subsection specifically concerned the uncertainties in these products at their reported resolution. These include the satellite precipitation products listed in Table 1.1.1, and are the building blocks for further coarser-resolution products included in later subsections.

The discussion above summarized the uncertainties that are inherent in the retrieval and processing steps that are used to produce the Level-3 estimates. These include the detection error, the passive MW and IR estimation errors, and the error incurred when using frequent IR information to fill long revisit gaps between passive MW estimates.

By themselves, passive MW observations alone are not capable of perfect detection. There is always an ambiguity between the passive MW TB and the particular atmospheric state that gives rise to these same TBs. For example, very light rainfall over ocean has a similar TB structure as a large amount of non-precipitating cloud water. For lack of a perfect benchmark reference, the exact sensitivity of a passive MW radiometer is challenging to determine.

The uncertainties in the passive MW estimates (second error above) originate from the limited sensitivity of the set of radiometer channels to the desired near-surface precipitation. As the signal that is measured by a passive MW or IR observation originates predominantly from the tops of the clouds, none of the instantaneous-level observations produced from these same sensors are directly and unambiguously sensitive to the underlying near-surface precipitation. Establishment of an absolute error is challenging since a common reference dataset is not globally available. Ground radars are an appropriate source of independent validation, but only cover specific continental land areas—and carry uncertainties of their own.
The majority of passive MW precipitation data is tied to specific local observing times by virtue of the operational nature of the host satellite platform. Only the GPM (and M-T) satellites currently orbit in a non-sun-synchronous orbit pattern capable of sampling (within its latitude coverage). This leads to lengthy gaps in satellite revisit, which are filled in with precipitation estimates created from geostationary-based IR observations. Currently this process is done via weighting of a priori prescribed uncertainties from the individual passive MW observations, with no regard for error in the speed or direction of the motion vectors used to transport the precipitation structure from one time period to the next. Few studies have been done in this regard, which warrant further study.

This section has quantified the order of magnitude of the uncertainty that *has to be expected* in today's merged satellite precipitation products at their native relatively-high (spatial and temporal) resolution, and which therefore exists as the current mutual ambiguity to be expected between the different Level-3 products. The following sections will describe, in more detail, independent validation of the uncertainty attributed to several of the precipitation datasets identified in this section, which includes the use of gauges and ground radars as an independent source.

<table><caption>Table 1.1.1. Global satellite precipitation products evaluated</caption><thead><tr><td>Product</td><td>Resolutions</td><td>Advantages</td><td>Disadvantages (in addition to possible local bias)</td></tr></thead><tbody><tr><td>Level-3 MiRS</td><td>When aggregated by user: no finer than single-instrument resolution (20–40 km)<br/>When pre-aggregated: daily</td><td>- Reasonably good sensitivity<br/>- Instantaneous estimate for every MW-constellation obs<br/>- Same approach over land or water</td><td>Snapshots every ~80 minutes on average<br/>Revisit time could stretch to 3 hours</td></tr><tr><td>Level-3 GPROF</td><td>When aggregated by user: no finer than single-instrument resolution (20–40 km)<br/>When pre-aggregated: 0.5°/hourly (“3G68” product)</td><td>- Reasonably good sensitivity<br/>- Instantaneous estimate for every MW-constellation obs<br/>- Estimates calibrated by the reference GPM radiometer</td><td>Snapshots every ~80 minutes on average<br/>Revisit time could stretch to 3 hours</td></tr><tr><td>Level-3 GPM radar/combined</td><td>When aggregated by user: 5 km<br/>When pre-aggregated: 0.25°/daily</td><td>- High direct sensitivity to surface rain<br/>- High spatial resolution (~5 km)</td><td>Revisit time calculated in days</td></tr></tbody></table>
<table><tr><td>HOAPS</td><td>When aggregated by user: no finer than single-instrument resolution<br/>When pre-aggregated: 0.5°/6-hourly</td><td>Conically-scanning MW radiometers only</td><td>Ocean only</td></tr><tr><td>CMORPH</td><td>8 km/30 minutes (and various other pre-aggregated versions)</td><td>- Frequent temporal reporting (~30 mins)<br/>- Good spatial resolution (~25 km)<br/>- Uses MiRS for instantaneous MW estimates<br/>- Uses advection scheme from IR in between MW overpasses</td><td>Persistence of any misdetection/amplification of estimation uncertainty</td></tr><tr><td>GSMaP</td><td>0.1°/hourly (and various other pre-aggregated versions)</td><td>- Frequent temporal reporting (~30 mins)<br/>- Good spatial resolution (0.1°)<br/>- Uses GSMaP_mwLUT for instantaneous MW estimates<br/>- Uses change in IR to adjust MW-estimated rain</td><td>Persistence of any misdetection/amplification of estimation uncertainty</td></tr><tr><td>IMERG rt</td><td>0.1°/half-hourly</td><td>- Frequent temporal reporting (~30 mins)<br/>- Good spatial resolution<br/>- Uses GPROF for instantaneous MW estimates<br/>- Uses advection scheme from IR in between MW passes<br/>- Available within about 5 hours of obs</td><td>Persistence of any misdetection/amplification of estimation uncertainty</td></tr><tr><td>IMERG late</td><td>0.1°/half-hourly</td><td>- Frequent temporal reporting (~30 mins)<br/>- Good spatial resolution<br/>- Uses GPROF for instantaneous MW estimates<br/>- Available within a few weeks of obs</td><td>Persistence of any misdetection/amplification of estimation uncertainty</td></tr><tr><td>IMERG science</td><td>0.1°/half-hourly</td><td>- Frequent temporal reporting (~30 mins)<br/>- Good spatial resolution<br/>- Uses GPROF for instantaneous MW estimates</td><td>Persistence of any misdetection/amplification of estimation uncertainty</td></tr></table>
<table><caption>**Table 1.1.1.** Listing of global satellite precipitation products with spatial resolution finer than 0.5 degrees and temporal resolution finer than 6-hourly, that have been evaluated by the IPWG validation subgroup</caption><tr><td></td><td></td><td>- Available 3 months after obs</td><td></td></tr><tr><td>PERSIANN</td><td>0.04°/ half-hourly (PERSIANN-CCS)</td><td>- Long record back to ~1979<br/>- NN approach accounts for regional variability</td><td>Mainly IR based – highly Indirect and non-monotone sensitivity to surface rain</td></tr></table>

### 1.1.5. Recommendations

**Recommendation 1.1.1:** Encourage the a priori quantification of the uncertainty that should be expected in a given product, given the errors in the input and the uncertainties introduced by the product generation.

In the absence of an undisputed reference truth, the physical validation of an estimation method is very useful in putting bounds on the uncertainty that can be expected, given the author's knowledge of the simplifying assumptions that were made to produce the estimates.

**Recommendation 1.1.2:** Encourage precipitation product providers to provide uncertainty estimates for each space/time scale of the final precipitation product.

Current global fine-resolution (< 0.1° horizontal, < daily temporal) satellite precipitation products are not mere aggregates of instantaneous satellite estimates. They rely on complex detection, estimation and filtering steps to produce a regularly-gridded product whose individual estimates carry quite a bit of uncertainty. These uncertainties vary from product to product, and consist of a mixture of misdetections and estimation errors that are compounded by revisit-gap-filling procedures that introduce additional uncertainty. Nevertheless, if these uncertainties are considered by the user, the passive MW-based products carry far less uncertainty than those based on geostationary IR alone or on rain gauges.

### 1.1.6. References

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## 1.2. Validating the intrinsic uncertainty: Implications for hydrologic applications

Pierre-Emmanuel Kirstetter¹

¹University of Oklahoma, Norman, OK, USA

Reliable quantitative information on the spatial distribution of precipitation is essential for hydrologic and climatic applications that range from real-time hydrologic hazards forecasting (e.g. floods, droughts, landslides), to water resources and urban drainage management and agriculture, to diagnosing hydroclimate patterns and trends, to evaluating regional and global atmospheric model simulations. Physical processes associated with these applications cover multiple scales, from minutes to decades and from metres to the synoptic scale. The critical importance of accurate water flux estimates for applications explains the large body of verification analyses focusing on precipitation estimates, in terms of occurrence, average and extremes. An abundance of independent validation has been carried out directly on Level-3 products using gauges and sometimes ground radar data from various over-land locations. Very few are implemented at the relevant scales to address the intrinsic uncertainty of precipitation products. Without relevant information on key uncertainty features, applications making use of satellite Level-3 precipitation products are impacted both in terms of outcomes and physical realism.

### 1.2.1. Benchmarks for satellite precipitation: Sensors

Accurately measuring rainfall has been a challenge for the research community predominantly because of its high variability in space and time. There are primarily three major types of techniques of precipitation measurement: (1) surface-based rain gauge, (2) weather radar and (3) space-based meteorological satellites.

Among precipitation sensors, only the rain gauge directly measures precipitation rates or time accumulations. Rain gauges collect rainfall directly in a small orifice and measure the water depth, weight or volume. Rain gauges provide quite reliable point measurements of precipitation and records frequently span more than 100 years. These are therefore the best source for long-term studies of precipitation extremes and trends. The global distribution of gauges is heterogeneous, with higher densities in more populated regions and lower densities in rural and remote areas. Critically, the number of gauges available also depends on their temporal sampling resolution, with stations sampling at finer scales being rarer. Gauges are routinely used to represent areas of 100 to 3000 m² from measurements taken over a few square centimeters. However, their measurements are affected by uncertainties (for example, wind undercatch, evaporation, snow) and lack areal representation, which becomes particularly problematic for intense rainfall with high spatial variability (for example, Zawadzki, 1975). The spatial representativeness of each gauge measurement depends on the autocorrelation distance of precipitation (for example, Delahaye et al., 2015). While the autocorrelation increases with time integration, it varies greatly with precipitation regime and is typically short for extreme events (for example, Lebel et al., 1987). Interpolation of rain gauge observations is mandatory to obtain spatial information. When it comes to comparing gauges with other area-averaged precipitation estimates such as from radars or satellites, the spatial variability of rainfall at small scales and the large resolution difference (as much as nine orders of magnitude in area) may cause large differences in the statistical sampling properties of the extremely variable rainfall process (for example, Habib et al., 2004). The added statistical noise when comparing the two measurements is especially significant for short accumulation periods (1 hour or less; Ciach and Krajewski, 1999).
Precipitation is associated with specific generating processes, such as convection, orographic enhancement in complex terrain or warm rain processes. Measuring variations in the drop size distribution and the vertical structure of events is essential for understanding precipitation processes but cannot be captured by rain gauges. Remote sensing is the only way to explicitly observe the spatial distribution of precipitation. However, complex interactions between the spatiotemporal variability of precipitation processes, sensor resolution, sensitivity, calibration and the indirect nature of precipitation retrievals introduce complications (Section 1.1). In the last decades, weather radar systems have become a valuable tool to fill multiple observational gaps in time, surface 2D and 3D. As active sensors, ground-based radars provide range-resolved information on precipitation that is not available from most satellite sensors. Radar systems reveal precipitation characteristics, including intermittency, types (for example, stratiform, convective, snow and hail) and rates, with better resolution and accuracy than gauges and satellites, respectively. Through real-time and high-resolution volume scanning, weather radars offer more comprehensive information on the horizontal and vertical structure of rainfall. Radar networks upgraded with dual-polarization technology give additional insights into precipitation microphysics specifically on the size, shape, orientation and phase of hydrometeors. Ground-based weather radar data are now widely used by national weather services for quantitative precipitation estimation (QPE) at fine scales (for example, 1 km/5 min). Radar QPE is subject to specific uncertainties (that is, sensor calibration, attenuation depending on the radar frequency, ground clutter and beam blocking, variation of reflectivity with height, conversion from radar moments to precipitation rate, etc.; for example, Delrieu et al., 2009; Villarini and Krajewski, 2010; Berne and Krajewski, 2013). The characterization of these uncertainties has motivated studies for several decades. Radar-rain gauge merging approaches combining the fine spatio-temporal resolution of radar and the local accuracy of gauges have been proposed for QPE (for example, Delrieu et al., 2014) and are applied operationally (for example, Zhang et al., 2016), while novel approaches are being developed to integrate uncertainty as part of the quantitative estimation process (for example, Kirstetter et al., 2015; Neuper and Ehret, 2019).

The last decade has witnessed the growing use of satellite-based observations for seamless observation of precipitation over land and oceans, with quasi-global coverage that is not available with radar or gauge networks (for example, Skofronick-Jackson et al., 2017). As shown in Section 1.1, most instantaneous-level spaceborne precipitation observations are performed with passive MW sensors, providing more indirect observations of surface rainfall amounts than radars. Many multi-sensor precipitation retrievals combine IR and passive MW data to produce near-real time estimates at high spatial and temporal resolution (for example, 30 min, 0.1°). A description and an intercomparison of current global precipitation datasets from stations and satellites can be found in Sun et al. (2017).

Sensor limitations discussed in this section are listed in Section 2.3.1. In order to overcome these limitations, it is crucial to recognize that no single sensor combines accuracy, resolution and representativeness over relevant spatial and temporal scales, which are essential characteristics for applications making use of precipitation inputs. Achieving these characteristics requires the expert combination of observations that maximize each sensor's advantages while minimizing its weaknesses. Ultimately, such a combination does not produce perfect estimates with no uncertainty, but estimates with uncertainties that are deemed sufficiently low.
### 1.2.2. Benchmarks for satellite precipitation: Requirements for quantifying intrinsic uncertainty

Because precipitation displays variability at all scales, the satellite's intrinsic uncertainty can only be assessed at the primary scale of the precipitation retrievals. Preserving the product's intrinsic characteristics precludes any scale alteration such as interpolation, averaging, smoothing or oversampling, which affects key characteristics such as the retrieved rainfall amount, the rainy area and the distribution of precipitation rates. The true precipitation averaged over the spatial domains and time intervals corresponding to the primary scale of satellite precipitation retrievals is unknown. A reference precipitation used as a proxy for the true precipitation and as a benchmark should spatially and temporally match the satellite retrieval domain and display acceptable levels of accuracy.

Ground sensors constitute a natural choice to create a benchmark because their measurements are more directly sensitive to surface precipitation than satellite sensors. A trustworthy surface reference rainfall dataset should combine the complementary qualities of ground-based sensors, specifically the local accuracy of gauges and the spatial and temporal resolution provided by radars. An example of a satellite precipitation benchmark is the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS; Kirstetter et al., 2018b) that is derived from the MRMS system (Zhang et al., 2016). MRMS incorporates observations from all polarimetric Weather Surveillance Radar, 1988, Doppler (WSR-88D) radars and from gauge networks in the conterminous U.S. and creates a seamless 3D radar mosaic. Automatic quality controls and corrections procedures mitigate radar uncertainties (section 1.2.1) and generate high-resolution mosaicked radar-based surface precipitation products at a 0.01° horizontal resolution and 2 minute update cycles. Dual-polarization improves the radar data quality and enables the identification of hydrometeors where the ground-radar estimates are the most reliable. The radar-based data are integrated with atmospheric environmental data and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. A surface reference precipitation framework is derived from MRMS to support the GPM mission for ground validation and intercompare satellite sensors (Kirstetter et al., 2014; Petersen et al., 2020) and to validate Level-3 precipitation products (Gebregiorgis et al., 2017; Tan et al., 2017). It applies conservative adjustments, quality controls and quantity controls on MRMS products to refine the most trustworthy radar-gauge precipitation estimates towards specific satellite purposes and needs. This processing is designed to maximize accuracy, minimize uncertainties and standardize the GV-MRMS precipitation reference products across the Continental United States (CONUS).

Thanks to their resolution, which is higher than any satellite precipitation product, GV-MRMS data are designed to be pixel-matched in both time and space, and to build statistics for comparing reference precipitation intensities to Level-2 and Level-3 satellite-based estimates. Note that no reference perfectly matches the true precipitation; however, eliminating systematic error sources and non-robust reference values is necessary to improve confidence in the reference precipitation. The reference data covers a broad range of land surface types (mountains, coasts, plains) and precipitation regimes and captures a variety of situations to document representative features of satellite intrinsic uncertainty.

An extended characterization of the reference precipitation should include additional key precipitation properties such as typology. The typology of rainfall can be assessed within the satellite sensor's field of view (FOV) (for Level-2 products) or pixel grid (for Level-3 products) through precipitation properties such as the Convective Percent Index (CPI). CPI quantifies the volume contribution of convective rainfall to the reference precipitation (Kirstetter et al., 2020).
The CPI is expressed in percent between 0% (purely stratiform rainfall within the FOV or pixel) to 100% (purely convective rainfall). CPI values between 0% and 100% indicate mixed precipitation types (Figure 1.2.1).

<figure><img src="image_19.png" /><figcaption>**Figure 1.2.1.** (a) Map of CONUS area with GV-MRMS instantaneous rain rates at 0725 UTC on 11 April 2011. The red area shows the good quality radar coverage; (b) the convective percent index (CPI).</figcaption></figure>

### 1.2.3. Non-homogeneity of gridded satellite precipitation products: Implication for their hydrologic assessment

It is essential to recognize that gridded Level-3 satellite precipitation products are not homogeneous because of the dynamical interplay between a variety of error sources described in previous sections. Precipitation characteristics such as convection are a challenge for satellite retrievals, although convective precipitation is a strong driver of extremes. They condition systematic biases at all levels (for example, see Figure 1.2.2). The estimation error varies also depending on which sensor is weighted more in the retrieval. For example, estimates originating from IR display different error patterns from passive MW (for example, see Figure 1.2.2d). Consistency and homogeneity are properties that any Level-3 satellite precipitation product is designed to achieve, but these properties are often overlooked in assessment exercises. It follows that a gap remains with our ability to consistently merge precipitation estimates into gridded products and assess the procedure.

An endemic limitation in the extensive body of literature on satellite precipitation validation and error modeling is that the satellite product is implicitly assumed to be consistent and homogeneous over the spatial and temporal domain of comparison. This is rarely the case because comparison samples gather a variety of precipitation characteristics (for example, intermittency, typology, rates) for which the satellite algorithm (or combination of algorithms for Level-3 merged products) is likely to behave differently. More generally the comparison is always performed with precipitation estimates ambiguously derived from the satellite sensor observation through the retrieval algorithm and associated assumptions. Individual passive MW/IR retrievals are underconstrained by nature and sensitive to unobserved atmospheric parameters (Stephens and Kummerow, 2007). The combined products inherit the varying passive MW/IR performances and create additional uncertainties with temporal/spatial resampling.
<figure><img src="image_20.png" /></figure>

**Figure 1.2.2.** Performances of space-based QPE as functions of Convective Percent Index with respect to GV-MRMS: (a) space-based radars' relative bias for TRMM-PR (grey), DPR/Ku (black), DPR/Ka (blue) and DPR/Ka-Ku (red); (b) GPROF-GMI systematic error; (c) GPROF-GMI relative bias as a function of CPI difference with GV-MRMS; and (d) IMERG systematic error for the passive MW (red) and IR (blue) components. Convective and stratiform situations correspond to CPI=100% and CPI=0%, respectively. Comparison data include 2M+ matched ground-satellite pairs from June 2014 to September 2016 (from Kirstetter et al., 2020).

Common assessment typically uses bulk comparison metrics (for example, probability of detection, correlation, bias) that depict averaged space/time properties while the errors tend to be non-stationary and sensitive to parameters not accounted for in the assessment formulation. These metrics are sometimes applied without necessarily checking their relevance (for example, the linear correlation is generally insufficient to describe the non-linear and heteroscedastic dependence structure between a satellite precipitation estimate and the precipitation reference). Hence validation practice generally provides limited insight in the complex error characteristics of satellite precipitation estimates.

In addition, the representativeness of any overall satellite QPE assessment or error model is confined to the time and space domain over which it is performed. It tends to be specific to the satellite instrument (for example, resolution), the retrieval algorithm, the space-time-scale and the accuracy of the reference, and has limited applicability for other precipitation regimes, regions, products, etc. The actual benefit of these analyses to satellite precipitation users and
developers is limited. There is a need to formulate the goals of validation and error modeling, and to design appropriate comparison practices.

Integrated assessment of the intrinsic uncertainty across multiple sensors and products is necessary to track the origin of errors and their propagation through various Level-2 active to passive MW to Level-3 merged satellite precipitation estimates. Targeting the most significant factors driving the state of the satellite estimation error (for example, precipitation types) is essential to characterize uncertainties in satellite QPE and lead to a generalization of their assessment (Kirstetter et al., 2020; Shige et al., 2013; Taniguchi et al., 2013; Yamamoto et al., 2017). Figure 1.2.2 illustrates the propagation of uncertainty that arises from precipitation types in the form of systematic biases from spaceborne radars (for example, GPM DPR) through MW precipitation estimates (GPROF-GMI) to the IMERG Level-3 merged product.

### 1.2.4. Impact of satellite precipitation intrinsic uncertainty on hydrologic applications

Hydrologic applications of satellite data include agriculture, freshwater availability and natural disasters monitoring (for example, floods, droughts, landslides; Serrat-Capdevila et al., 2014). Each application is characterized by specific spatial and temporal requirements that can vary significantly. For example, global hydrological modeling to assess the occurrence of flood events is uniquely enabled with the coverage provided by Level-3 precipitation products (such as the Global Flood Monitoring System, http://flood.umd.edu/; Wu et al., 2014). Anticipating flood events enables the assessment of associated risks and optimized decision making, specifically in developing countries (Kirschbaum et al., 2017). The detection of floods and inundations is critical for hazard response by agencies such as the United Nations World Food Program and the International Federation of Red Cross and Red Crescent Societies (Gray, 2015). On the other side of the precipitation spectrum, precipitation deficits are monitored with satellites as drivers of drought and food and water security (for example, the Famine Early Warning Systems Network, FEWS NET; www.fews.net). Water resources applications are reservoir operations that use precipitation products at monthly time scales (for example, Yang et al., 2017). However, applications of Level-3 products have not been demonstrated yet in contexts involving hydrologic processes over short scales (for example, a few kilometres, hourly), such as flash-flood monitoring, because the Level-3 resolution or latency are limiting factors.

Hydrologic applications often require an understanding of the error structure in the satellite precipitation products. Errors in hydrologic simulations result from a complex interaction between the forcing uncertainty (that is, precipitation), the model structure and approximations, the estimation of model parameters, and observations (for example, gauged streamflow). Hence precipitation errors and uncertainty sources have the potential to affect hydrologic applications (Maggioni and Massari, 2018). For example, simulations using Level-3 products for predicting streamflow and runoff are greatly impacted by their performance in terms of precipitation detection and quantification. Common issues involve systematic bias in the precipitation estimates, since hydrologic simulation highly depends on basin-scale water budget assumptions that directly impact the streamflow simulation (for example, Thiemig et al., 2012). Systematic biases arising at the satellite Level-2 and propagating to the Level-3 products are conditioned on a number of factors, some of which are independent of surface hydrology, such as precipitation physics, climatologies, sensors and algorithms (see Figure 1.2.1).
<figure><img src="image_21.png" /><figcaption>**Figure 1.2.3.** An example of propagation of resolution-induced rainfall error to streamflow simulations, using a distributed hydrologic model on the Tarboro watershed (North Carolina, U.S.) over the period 2002–2009. Metrics of hydrologic model performance as functions of basin area and streamflow threshold are presented: (a) ratio of streamflow (Q) relative bias to rainfall (R) relative bias, (b) ratio of streamflow (Q) relative RMSE to rainfall (R) relative RMSE and (c) Nash–Sutcliffe coefficient of efficiency (NSCE) of streamflow simulations. The values between parentheses indicate the probability of occurrence of the corresponding threshold. From Vergara et al., 2014</figcaption></figure>

One strategy dealing with the uncertainty is to mitigate it by (1) debiasing for reconciliation with higher-resolution hydrologic models, (2) averaging/filtering/smoothing to obtain coarser-resolution products (typically 1-day 1-degree; see Chapter 2). Applications running at the monthly or seasonal time scales are less affected by biases because Level-3 satellite products increasingly benefit from gauge-based adjustments at coarser scales. However, many applications require spatial resolutions finer than 25 km and temporal resolutions less than 3 hours (Kirschbaum et al., 2017). In many cases, the uncertainty is transferred into the applications. Bias correction techniques can reduce streamflow errors (for example, Serrat-Capdevila et al., 2014); for example, by applying climate-scale bias corrections (for example, Beck et al., 2017). However, the multi-factor and nonstationary nature of satellite-based precipitation biases (that are not well understood yet; see Chapter 1.1) hinders the effectiveness of correction techniques. Another option is to compensate the forcing biases with hydrologic model calibration (for example, Xue et al., 2013; Nikolopoulos et al., 2013). It is made possible because the observed hydrologic response (discharge at the basin outlet) results from unobserved and integrated contributions of surface and subsurface processes. This endemic lack of observational hydrologic constraints leaves a considerable range of options to adjust hydrologic model parameters to reproduce the observed behavior (Beven, 2001), sometimes at the expense of physical realism. Model recalibration has been applied across watersheds with various geomorphologies and climatologies around the world to cope with satellite precipitation biases and improve streamflow prediction. This transfer of uncertainties from the satellite precipitation estimates to modeled hydrologic processes estimates hinders the broad application of hydrologic modeling, especially at sub-basin scales.
Uncertainty also arise due to the resolution of current satellite-based rainfall products and impacts applications of hydrologic modeling and forecasting systems. Resolution modifies the spatial structure of rainfall fields, and its interplay with basin area conditions the propagation of biases in distributed hydrologic models (for example, Vergara et al., 2014; Figure 1.2.3). The effects of precipitation resolution can be accounted for during the calibration of hydrologic models. The systematic analysis of the complex and combined effects arising from satellite uncertainties and resolution, basin geomorphologic characteristics and hydrologic modeling approaches remains a great challenge for the hydrologic application of satellite precipitation estimates. For more reliable flood simulations, additional physical constraints can be brought by observations, such as using soil moisture as a fingerprint of past rain occurrence (for example, Crow et al., 2011; Ciabatta et al., 2015).

<figure><img src="image_22.png" /><figcaption>**Figure 1.2.4.** Precipitation rate distributions conditioned on the IR brightness temperature. The thick black line represents the median (50% quantile), the blue curves represent the 25 and 75% quantiles, the thin black lines represent the 10 and 90% quantiles. The red curve represents the expected value. The intrinsic bias in Figure 1.1.12 is mitigated by the probabilistic approach, while the intrinsic uncertainty is represented by the spread of the conditional precipitation rate distribution (adapted from Kirstetter et al., 2018a).</figcaption></figure>

Another strategy dealing with satellite-based precipitation is to explicitly integrate uncertainty into the precipitation estimation process (see Chapter 3.1). Most precipitation products are deterministic and represent a single "best guess" realization of precipitation but are blind to their intrinsic uncertainty. Recent probabilistic precipitation estimates are developed to explicitly represent uncertainty (Kirstetter et al., 2015, 2018a; Wright et al., 2017), as shown in Figure 1.2.4.

### 1.2.5. Summary on intrinsic uncertainty and implications for hydrologic applications

Understanding hydrometeorological processes and applications requires more than just one deterministic "best estimate" to adequately cope with the intermittent, highly-skewed distribution that characterizes precipitation. The intrinsic uncertainty structure of satellite-based quantitative precipitation estimates is still largely unknown at the spatiotemporal scales near the sensor measurement scale. Advancing the use of uncertainty as an integral part of QPE in the relationship between sensor measurements and the corresponding "true" precipitation has the potential to provide a framework for diagnosing intrinsic uncertainty when instruments sample raining scenes or processes challenging QPE algorithms' assumptions. It provides the basis for multisensor merging and precipitation assimilation, hydrometeorological hazard mitigation, decision making and hydrological modeling. Hydrologic applications are not generally
configured to directly ingest probabilistic estimates of precipitation, but current research
explores this avenue (for example, Hartke et al., 2020).

Numerous validation studies have been performed on satellite precipitation products. Chapter 1.3 summarizes what has been done to date by the IPWG validation subgroup. Limited progress has been made on quantifying the intrinsic uncertainty and its impact on hydrologic applications. This is because few studies are carried out at the primary precipitation retrieval scale. This endeavor requires the expert use of other precipitation sensors such as radar-gauge combinations.

1.2.6. Recommendations

**Recommendation 1.2.1:** Encourage more satellite precipitation comparisons at the actual satellite retrieval scale to study the intrinsic uncertainty.

The homogeneity of satellite precipitation is often overlooked in the evaluations while it remains an endemic challenge in the generation of such products and their applications. The dynamic interplay between precipitation characteristics, sensors and satellite algorithms is critical to study in order to make progress. There is a need to formulate the goals of validation and error modeling and to design appropriate comparison practices.

**Recommendation 1.2.2:** The non-homogeneity of satellite estimates at the retrieval scale needs more attention in order to improve products and their applications.

Currently hydrological applications are greatly impacted by the satellite precipitation intrinsic uncertainty. A better understanding of this uncertainty is critical to make progress and mitigate precipitation forcing errors and to avoid error propagation in other modeling components of the water cycle. Explicitly accounting for uncertainty in precipitation products is a promising way to explore coordination with hydrologic applications.

**Recommendation 1.2.3:** Further means to explicitly represent uncertainty in precipitation products and their hydrologic applications (beyond root mean squared additive error) should be explored.

1.2.7. References

Beck, H.E., A.I.J.M. van Dijk, V. Levizzani, J. Schellekens, D.G. Miralles, B. Martens and A. de Roo, 2017: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. *Hydrology and Earth System Sciences*, 21, 589–615, https://doi.org/10.5194/hess-21-589-2017.

Berne, A., and W.F. Krajewski, 2013: Radar for hydrology: Unfulfilled promise or unrecognized potential? *Advances in Water Resources*, 51, 357–366, https://doi.org/10.1016/j.advwatres.2012.05.005.

Beven, K., 2001: How far can we go in distributed hydrological modelling? *Hydrology and Earth System Sciences*, 5, 1–12, https://doi.org/10.5194/hess-5-1-2001.

Ciabatta, L., L. Brocca, C. Massari, T. Moramarco, S. Puca, A. Rinollo, S. Gabellani and W. Wagner, 2015: Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory. *Journal of Hydrometeorology*, 26, 1341–1355, https://doi.org/10.1175/JHM-D-14-0108.1.
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Crow, W.T., M.J. van den Berg, G.J. Huffman and T. Pellarin, 2011: Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). *Water Resources Research*, 47, W08521, doi:10.1029/2011WR010576.

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## 1.3. Monitoring of satellite precipitation estimates through the IPWG validation studies

Chris Kidd¹ and Viviana Maggioni²

¹Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA and NASA/Goddard Space Flight Center, Greenbelt, USA

²Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA

### 1.3.1. Status

The International Precipitation Working Group (IPWG) builds upon the expertise of scientists to provide a focus for the precipitation community to develop and improve precipitation measurements and their utilization, to improve scientific understanding of precipitation, and to further develop international partnerships (Turk and Bauer, 2006; Kidd et al., 2010; Levizzani et al., 2018). A major activity of the IPWG is the verification, validation and intercomparison of precipitation products to enable product developers and users to continually monitor and assess the performance of the available products. This activity has developed an ongoing validation program, comparing surface reference datasets and satellite precipitation products to better inform product developers and the user community.

Several key precipitation intercomparison projects have been organized to assess satellite-based products against surface data. These have included regional and global assessments of the Global Precipitation Climatology Project (GPCP) Algorithm Intercomparison Programme (AIP) series (see Arkin and Xie, 1994; Barrett and Bellerby, 1992; Allam et al., 1993; Ebert, 1996; Ebert et al., 1996), and of the NASA WetNet Precipitation Intercomparison Projects (PIP) series (see Barrett et al., 1994; Smith et al., 1998; Adler et al., 2001). Since 2002, a number of validation sites have been organized by IPWG members, based primarily upon the availability of their regional surface reference datasets (see Kidd et al., 2020). Comparisons of the satellite/model precipitation products against surface data are typically analyzed at the 0.25°x0.25°, daily scale in near real time, although intercomparisons at the full, instantaneous resolution of the products have also be developed (see Kidd et al., 2018.)

The validation work of the IPWG should be seen as complementary to the targeted ground validation (GV) campaigns of mission-specific programs (Skofronick-Jackson et al., 2015; Petersen et al., 2016; Petersen et al., 2020). Key differences relate to the end goal of the validation: the IPWG validation aims to improve satellite precipitation products, focusing upon statistical analysis over regions with existing reference data at moderate temporal/spatial resolutions. Mission-specific validation tends to relate more to the microphysical scale, aimed at improving our fundamental understanding of precipitation-observation capabilities using a multi-tier (satellite, airborne, surface) approach at fine, instantaneous resolutions.

Figure 1.3.1 shows the global distribution of the IPWG validation regions together with the source of their validation data. Note that these are largely operated on a best-effort basis with only a few regions receiving funding, and consequently not all regions operate continuously (also due to cyber-security issues). The development of a validation region over the Indian subcontinent through collaboration with the Indian Meteorological Department is ongoing.
<figure><img src="image_23.png" /><figcaption>**Figure 1.3.1.** Distribution of past and current IPWG validation regions and their surface reference datasets.</figcaption></figure>

The surface reference data for the IPWG sites encompasses surface radar and/or gauge datasets. Radar data are in many ways most useful since they provide frequent, regular spatial measures of instantaneous precipitation, which can be easily matched to the satellite data. Aggregating these data into daily totals to match the satellite/model precipitation products is relatively easy. However, care is needed to address artefacts in such data (for example, missing low-level precipitation beam blockage, anomalous propagation errors, etc). Gauge data, at least for these studies, are usually already gridded and reported at (local time) daily scales: as such, the gauge data may not be co-temporal with the satellite products, which usually accumulate midnight-midnight UTC. Some regional variations between the sites is therefore inevitable to ensure that the most is made of the available surface datasets.

Each of the IPWG sites is hosted by local institutions, although all provide similar information on the assessment of the precipitation products and surface data at a common scale (see http://www.isac.cnr.it/~ipwg/calval.html). These assessments follow a strategy of keeping any analysis clear and simple to ensure that they are understandable and pertinent to the user community. An example of a daily validation display for South Korea is shown in Figure 1.3.2. The information contained generally includes images of co-registered satellite/model and surface products for visual analysis and placing the statistics with the context of particular meteorological events. Satellite/model-surface scatterplots, cumulative distribution plots and bar plots provide further visual information on the product performance. Statistical information is provided through categorical statistics of probability of detection (POD), false alarm ratio (FAR) and Heidke Skill Scores (HSS), descriptive statistics for both the estimates and observed precipitation, and statistical scores, that is, bias, ratio (product/validation), RMSE, correlation coefficient and number of samples. Together, this information can be used by both the algorithm/product developer and the user to assess the performance of different algorithms, over different regions, for different meteorological situations.
<figure><img src="image_24.png" /><figcaption>**Figure 1.3.2.** Example of IPWG validation over South Korea for the IMERG product versus the Korea Meteorological Administration radar analysis on 15 September 2020.</figcaption></figure>

In addition to the near real time “monitoring” assessments, more detailed studies have been
done to evaluate the precipitation products over a longer record and with a greater degree of
accuracy. For example, the IPWG validation site over Japan, started in 2003, uses radar data
from the Automated Meteorological Data Acquisition System (AMeDAS) network (Makihara et
al., 1996; Makihara, 2007). Studies have included the assessment of precipitation products
(Kubota et al., 2009) together with the evaluation of their Global Satellite Mapping of
Precipitation (GSMaP) product over mountainous regions (Shige et al., 2013, 2014; Taniguchi
et al., 2013; Yamamoto and Shige 2015; Yamamoto et al., 2017). They note, however, that
validation of heavy, but shallow snowfall (see Murakami et al., 1994) is beyond the scope of
this study (and many others). Studies over South America using daily rain gauge data have
shown varying biases in satellite estimates on seasonal scales (see Kidd et al., 2020). Other
IPWG-related studies have investigated smaller temporal/spatial scale comparisons, such as
Kidd et al. (2012) together with the representation of the diurnal cycle at the global scale (Kidd
et al., 2013). Maggioni et al. (2016) reviewed satellite precipitation validation efforts during the
TRMM era, identifying mountains and semiarid areas as problematic geographic regions,
seasonal problems associated with winter, and problems associated with light rainfall, snowfall
and mixed-phase precipitation.

**1.3.2. Recommendations**

Key areas that have been identified for further development within the IPWG validation
program are:

i. inclusion errors and uncertainties in the validation process,

ii. validation of snowfall,

iii. validation of precipitation, where possible, over open-ocean regions, and

iv. large scale validation against existing precipitation climatologies [such as from the Global Precipitation Climatology Centre (GPCC)].
Crucial to the validation activities of the IPWG is the need for practical funding since most of this work continues unfunded at present.

### 1.3.3. References

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Allam, R.E., G. Holpin, P. Jackson and G.-L. Liberti, 1993: Second Algorithm Intercomparison Project (AIP-2), U.K. and Northwest Europe, February–April 1991, Pre-Workshop Report. 439pp [available from Satellite Image Applications Group, U.K. Meteorological Office, Bracknell, Berkshire, RG12 2SZ, United Kingdom.]

Arkin, P., and P. Xie, 1994: The Global Precipitation Climatology Project: First Algorithm Intercomparison Project. *Bulletin of the American Meteorological Society*, 75, 401–419.

Barrett, E.C., and T.J. Bellerby, 1992: The application of satellite infrared and passive microwave rainfall estimation techniques to Japan – results from the 1st GPCP Algorithm Intercomparison Project. *Meteorological Magazine*, 121, 34–46.

Barrett, E.C., J. Dodge, M. Goodman, J. Janowiak, C. Kidd and E.A. Smith, 1994: The First WetNet Precipitation Intercomparison Project. *Remote Sensing Reviews*, 11, 49–60.

Ebert, E.E., 1996: *Results of the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP)*. Revision 1. Melbourne, Bureau of Meteorology Research Centre.

Ebert, E.E., M.J. Manton, P.A. Arkin, R.J. Allam, G.E. Holpin and A. Gruber, 1996: Results from the GPCP Algorithm Intercomparison Programme. *Bulletin of the American Meteorological Society*, 77, 2875–2887.

Kidd, C., R.R. Ferraro and V. Levizzani, 2010: The Fourth International Precipitation Working Group. *Bulletin of the American Meteorological Society*, 8, 1095–1099, DOI: 10.1175/2009BAMS2871.1.

Kidd, C., P. Bauer, F.J. Turk, G.J. Huffman, R. Joyce, K.-L. Hsu and D. Braithwaite, 2012: Inter-comparison of high-resolution precipitation products over Northwest Europe. *Journal of Hydrometeorology*, 13, 67–83, DOI:10.1175/JHM-D-11-042.

Kidd, C., E. Dawkins and G. Huffman, 2013: Comparison of Precipitation Derived from the ECMWF Operational Forecast Model and Satellite Precipitation Datasets. *Journal of Hydrometeorology*, 14, 1463–1482.

Kidd, C., J. Tan, P.-E. Kirstetter and W.A. Petersen, 2018: Validation of the Version 05 Level 2 precipitation products from the GPM Core Observatory and constellation satellite sensors. *Quarterly Journal of the Royal Meteorological Society*, DOI:10.1002/qj.3175.

Kidd, C., S. Shige, D. Vila, E. Tarnavsky, M.K. Yamamoto, V. Maggioni and B. Maseko, 2020: The IPWG satellite precipitation validation effort. In: *Satellite Precipitation Measurement* (V. Levizzani, C. Kidd., D.B. Kirschbaum, C.D. Kummerow, K. Nakamura, F.J. Turk, eds.).
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Levizzani, V., C. Kidd, K. Aonashi, R. Bennartz, R.R. Ferraro, G.J. Huffman, R. Roca, F.J. Turk and N.-Y. Wang, 2018: The activities of the International Precipitation Working Group. *Quarterly Journal of the Royal Meteorological Society*, doi:10.1002/qj.3214.

Makihara, Y., 2007: Steps towards decreasing heavy rain disasters by short-range precipitation and land-slide forecast using weather radar accompanied by improvement of meteorological operational activities (in Japanese). *Tenki*, 54, 21–33.

Makihara, Y., N. Uekiyo, A. Tabat, and Y. Abe, 1996: Accuracy of Radar-AMeDAS precipitation. *IEICE Transactions on Communications*, 79, 751–762.

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Shige, S., S. Kida, H. Ashiwake, T. Kubota and K. Aonashi, 2013: Improvement of TMI rain retrievals in mountainous areas. *Journal of Applied Meteorology and Climatology*, 52, 242–254.

Shige, S., M.K. Yamamoto and A. Taniguchi, 2014: Improvement of TMI rain retrieval over the Indian subcontinent. In *Remote Sensing of the Terrestrial Water Cycle* (V. Lakshmi, D. Alsdorf, M. Anderson, S. Biancamaria, M. Cosh, J. Entin, G. Huffman, W. Kustas, P. van Oevelen, T. Painter, J. Parajka, M. Rodell and C. Rüdiger, eds.). Hoboken, New Jersey, Wiley, https://doi.org/10.1002/9781118872086.ch2.

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## 2. Climate Applications

### 2.1 Energy and water closure

Rémy Roca<fnref n="1" />, Seiji Kato<fnref n="2" /> and Tristan L'Ecuyer<fnref n="3" />

#### 2.1.1 Introduction

The water cycle of the Earth distributes water mass through the various water storage reservoirs of the planet. The water cycle is tightly related to the energy cycle of the Earth through diabatic heating in the atmosphere when water changes its phase (Stephens et al., 2012). The energy cycle of the Earth is represented by the flux of solar and terrestrial radiation, turbulent fluxes and moist static energy divergence within the Earth climate system. One notorious example of the coupling between the water and energy cycle in the climate system is manifested in the water vapor feedback process (Ramanathan, 1981). When radiative forcing is imposed by increasing the concentration of CO₂ in the atmosphere, the direct radiative effect is to warm the surface and the lower troposphere and cool the stratosphere. As surface temperature increases, evaporation also increases, allowing further increase in water vapor concentration in the warmer atmosphere. The increase of water vapor concentration is roughly 7% for each 1 K increase in temperature, following the Clausius–Clapeyron equation (Held and Soden, 2006). Water vapor itself is a strong greenhouse gas. It reinforces the initial warming, which induces a positive feedback on the climate system. Increased evaporation from the surface and a larger water vapor concentration in the atmosphere also imply an enhanced precipitation rate that also contributes to the warming of the atmosphere. More precisely, the increase in global precipitation rate in response to global warming is driven by the atmospheric radiative cooling rate, and is currently estimated to be around 2–3% for each 1 K increase in temperature (Stephens and Ellis, 2008). The slower increase in the precipitation rate than the increase in water vapor concentration is clear evidence of a tight relationship between the energy and water cycles. This coupling is an important aspect that has profound ramifications up to the climate sensitivity estimate because it is strongly related to hydrological sensitivity (Mauritsen and Stevens, 2015; Watanabe et al., 2018).

This prompted earlier investigators to explore the consistency among water and energy cycle elements and assess the closure of the water-energy budget observational capabilities. Budget assessments from observations of the water cycle only (Sheffield et al., 2009) or of both water and energy cycles (Stephens et al., 2012) further identified some significant deficits of closure in the observational portfolio. These studies lead to the conclusion that there is a need to adjust some fluxes to tend towards closure (Meyssignac et al., 2019). Significant progress in Earth observations of the water cycle prompt further assessment of the state of the art in our observational capabilities (Stephens et al., 2020).

Recently, optimal techniques that perform the adjustments objectively have been brought forward (L'Ecuyer et al., 2015; Rodell et al., 2015). These optimization techniques rely on enforcing global conservation laws playing with the uncertainty information that comes along with the data products. Modifications to the original datasets when closure is enforced are performed assuming changes lay within the stated uncertainty of each data product. This

<footnote n="1">¹Laboratoire d'Études en Géophysique et Océanographie Spatiales (Université de Toulouse III, CNRS, CNES, IRD), Toulouse, France</footnote>

<footnote n="2">²NASA Langley Research Center, Hampton, Virginia</footnote>

<footnote n="3">³University of Wisconsin–Madison, Madison, Wisconsin</footnote>
approach allows the assessment of whether the various fluxes are consistent (or not) among each other. In this approach, the closure of the water and energy cycles is enforced objectively and the consistency of the different fluxes is assessed. The paradigm of assessing the closure is shifted to the new paradigm: "enforcing the closure to assess consistency".

We assess here both aspects; that is, how global and regional precipitation observations are closing budgets, and when closure is enforced, how consistent the precipitation estimates are. It is interesting to note that water and energy closure studies are a good complement to more classic evaluations of the gridded products using ground reference observations, particularly in data-scarce regions.

## 2.1.2. Water-only budget

At continental scales, the terrestrial water balance equation links precipitation (P) with river runoff (R), evapotranspiration (E) and water storage (S) as

$$P-E-R=dS/dt$$

Closure estimates rely to some extent on precipitation, but also on the other terms and their consistency, providing an integrated way to assess the performance of the precipitation. Note that bias in P and E can compensate easily in the water closure. The usually less-accurate evapotranspiration and runoff products may also not provide a strong constraint on precipitation.

### 2.1.2.1. Global land and globally-distributed basins studies

Munier and Aires (2018) explore the water budget closure framework over global land areas and perform optimization for 1°x1°grid boxes at a monthly scale. They use four satellite precipitation products (3B42v7, GPCP v2.2, CMORPH v1 uncorrected and PERSIANN-CDR v1).

The optimization using the closure only improves the original product's scores for 60% of the stations (Figure 2.1.1). There, the improvements in RMS remains moderate, around 19%. This suggests that the original multi-product average is already close to reference ground-based observations and that other elements of the budget can only slightly improve the situation overall in this framework. Most of the evaluation is performed over the U.S. and Europe, which prevents drawing conclusions over the tropical regions.

<figure><img src="image_25.png" /></figure>

**Figure 2.1.1.** Comparison of corrected precipitation using CCM+CIC with FLUXNET observations. Left: scatter plot of RMSE of original satellite datasets and CCM corrected dataset (N is the percentage of stations where CCM improves P, I is the average relative improvement). Right: location of stations where the CCM improves (blue) or degrades (red) P. Adapted from Munier and Aires, 2018
Focusing on 96 globally-distributed catchments of various size and under various climates, Lorenz et al. (2014) explore the water closure of different datasets, including for ground-based precipitation products [GPCC data, National Weather Service Climate Prediction Center retrospective analysis (CPC), The University of East Anglia's Climatic Research Unit's global climate dataset (CRU) and data from Willmott, Matsuura and collaborators at the University of Delaware (UDEL)] and one satellite product (GPCP). Water budget closure is reasonably achieved only in a few cases with a given combination of datasets. In most catchments, the major characteristic is a significant imbalance. Precipitation strongly influences the budget in the tropics; the GPCC and GPCP products show best scores. In the Arctic, GPCP provides the best results, probably owing to the undercatch correction. The study emphasizes that performance over an individual catchment does not hold for the other regions.

### 2.1.2.2. Regional land studies

While a systematic exploration of all the ongoing regional studies about precipitation and closure is out of the scope of the present chapter, we have selected a few references that convey the main messages.

#### 2.1.2.2.1. High Mountain Asia

Yoon et al. (2019) explore the regional water closure using a water balance model and ten gridded precipitation datasets over 17 years. It includes in situ, reanalysis and satellite-based products. The products that incorporate rain gauges are shown to reach higher accuracy in the surface balance estimates. Satellite products exhibit systematic underestimation and low correlations over the Tibetan Plateau and high elevation areas. The spread in the precipitation estimates at the regional scale is significantly large than those from global studies. Generally, the in situ-based products outperform the other datasets.

#### 2.1.2.2.2. Mediterranean Area

Pellet et al. (2019) estimate the closure of the water budget over Mediterranean catchments using a few observational precipitation products (the same as Munier and Aires, 2018) and various other fluxes estimates. The optimization method brings only a marginal improvement on the original multi product simple average with a 10–15% improvement on the RMS and no change on the correlation with the ENSEMBLES Observation EOBS reference dataset. This is indicative of the relative proper accuracy of the gauge corrected satellite products over this area at this scale.

#### 2.1.2.2.3. Mississippi Basin

Munier et al. (2014) focus on this well-gauged basin to assess their methodology at the regional scale. The study is limited to a few sets of precipitation products [3B43 V7 CMORPH, V1.0, the NRL blended technique, and the Global Precipitation Climatology Project (GPCP, V2.2)]. It reveals a strong discrepancy between NRL and CMORPH and the gauge adjusted products. Yet enforcing the water budget closure at the catchment scale permits optimization of the products reaching very high R2 scores (>0.85) for each of the 4 products at the monthly scales. The corrected product seems to be fit for future hydrological analysis.

### 2.1.3. Regional atmospheric budget over ocean

The vertically-integrated atmospheric water budget links precipitation (P), evaporation (E) and the convergence of water vapor in the atmosphere (▽Q) after neglecting the storage term

$$E-P=\nabla Q$$
$\nabla Q$ is nominally obtained from atmospheric reanalysis and as with the previous hydrologic budget equation, it allows the assessment of the consistency of the precipitation products with the other data sources, but compensating errors will not be revealed.

Brown and Kummerow (2014) perform such budget calculations over various tropical oceanic basins using the precipitation from GPCP. Figure 2.1.2 indicates a remarkably good ability to close the budget at these scales over this 10-year period.

<figure><img src="image_26.png" /><figcaption>**Figure 2.1.2.** Monthly average time series of SeaFlux evaporation (SF E) and GPCP precipitation over the Tropical Indian Ocean region. Observation-based freshwater flux (E-P), European Centre for Medium-Range Weather Forecasts Re-Analysis dataset (ERA-Interim) atmospheric moisture divergence ($\nabla Q$) and sea surface temperature (SST) are also shown.</figcaption></figure>

The accuracy of the GPCP estimate hence revealed is of similar magnitude over the other basin with some slight changes in the Pacific Ocean along the time yet to be fully understood. Since a number of satellite products eventually adjust onto GPCP monthly over the ocean (CMORPH, PERSIANN-CDR), this good behavior is likely to hold for these products as well. The E-P ocean freshwater budget is linked to the salinity of the ocean. Recent measurements of surface salinity could contribute to further constrain E-P estimates over the world's oceans. The relationship between E-P and salinity is governed by upper ocean dynamics, ice sheets melting, and other phenomenon, making it somewhat difficult to infer from salinity observations. Salinity could nevertheless bring additional consistency constraints that could eventually help to assess E-P over the ocean (Yu et al., 2020).

### 2.1.4. Water and energy budget

The water and energy cycles follow water mass and energy conservation laws respectively. The conservation of water mass and energy at the surface are coupled through evaporation (see also Kato et al., 2016). The water mass balance for a regional land surface is

$$dS/dt = P-E-R$$

where S is the land water storage, P and E are precipitation and evaporation rate, and R is runoff. At an annual global scale, the evaporation rate at the surface balances with the precipitation rate. The energy balance at the surface is

$$NET = DLW+DSW-ULW-USW-SH-L_eE$$
NET is the surface net energy, DLW (DSW) the downward longwave (shortwave) radiation, ULW (USW) is the upward longwave (shortwave) surface radiation, SH is the sensible heat flux, and LeE is evaporation rate multiplied by the enthalpy of vaporization. Note that bias in P and E can compensate easily in the water closure and less so in the water and energy closure owing to the radiation constraint. The usually more-accurate radiation estimates can also provide a stronger constraint on the precipitation.

### 2.1.4.1. Global

Previous studies have demonstrated that, in the current climate, variability in atmospheric energy balance, ΔNETATM, is primarily governed by changes in longwave radiation (ULW - DLW) and precipitation (P) (Allen and Ingram, 2002; Held and Soden, 2006). As a result, atmospheric longwave cooling exerts a robust control on global precipitation in the equilibrium climate as demonstrated by Stephens and Ellis (2008). The implications of this link between the energy and water cycle are readily evident in recent reconstructions of Earth's energy budget. When energy and water cycle fluxes from state-of-the-art satellite observations or reanalysis are combined to reconstruct the global atmospheric and oceanic energy budgets, large residuals emerge that exceed in situ estimates of atmospheric and ocean heat uptake by an order of magnitude. One or more fluxes must be adjusted to resolve these imbalances. Two approaches emerged for reconciling the implied energy imbalances with in situ observations. Trenberth et al. (2009) chose to reduce the downwelling radiation (primarily DLW) into the surface while Stephens et al. (2012) argued that global precipitation estimates should be increased, sparking intense debate as to which flux datasets were more accurate. While subjective arguments could be made for adjusting either precipitation or DLW, the discrepancies in the resulting global, annual-mean precipitation estimates exceeded 10%.

The debate fueled by these competing energy budget reconstructions led a large group of investigators in NASA's Energy and Water cycle Study (NEWS) to develop an objective approach to imposing energy and water cycle closure constraints. By adjusting fluxes using a 1D-VAR framework that explicitly accounted for uncertainties in component fluxes, L'Ecuyer et al. (2015) and Rodell et al. (2015) generated closed energy and water budgets on global and continental scales. This work suggests that current satellite-based estimates of global precipitation need to be increased by 4%, an adjustment that falls within existing error bars, to properly balance global evaporation and close the atmospheric and surface energy budgets (Rodell et al., 2015).

#### 2.1.4.1.1. *Towards assessing multiple precipitation products water and energy closure*

As a preliminary step towards assessing the various precipitation products' consistency within the optimized framework, a first comparison of the global diabatic heating variability is needed. Indeed, on the global and annual scale, net atmospheric irradiance divergence must be balanced by surface sensible heating and diabatic heating rate by precipitation (Stephens and Ellis, 2008). The net atmospheric radiation divergence is derived from Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) edition 4 and the global latent heat value is derived from a subset of global precipitation products from Frequent Rainfall Observations on GridS (FROGS: Roca et al., 2019). The quasi-global satellite products have been completed poleward using the GPCP "truly" global product data, forming a larger ensemble of products to assess. In complement to satellite-based estimates, a handful of reanalysis products is also included in the study.

The variability of monthly anomalies is +-1 Wm-2 (Figure 2.1.3). The results indicate a strong lack of consistency of the precipitation products with the exception the GPCP estimates and
that of European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), especially after 2007. The PERSIANN and the CMORPH products also seem to track the net atmospheric irradiance well, which is not surprising since the products are scaled on GPCP at monthly scale (only over ocean for CMORPH). Most of the precipitation products overestimate the diabatic heating variability and some show substantial trends with no equivalent in the radiation-derived budget.

<figure><img src="image_27.png" /><figcaption>**Figure 2.1.3.** Time series of the deseasonalized monthly anomaly of global diabatic heating for various filled satellite (top left) and reanalysis (top right) and truly global satellite (bottom left) products. In both panels, anomalies of net atmospheric irradiance are shown by the red line.</figcaption></figure>

### 2.1.4.1.2. Multiple precipitation products using water budget only closure

Hobeichi et al. (2020a) investigate five global products, two from satellite (IMERG, GPCP), two ground-based [GPCC, Rainfall Estimates on a Gridded Network (REGEN)] and one reanalysis, the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). The assimilation-based method of Hobeichi et al. (2020b) is used. It is implemented globally at the resolution of half a degree and at monthly time scales and performs a simultaneous enforcement of the closure of the surface water and energy budgets. Using various metrics, the analysis shows that GPCC best closes the budget of the high latitudes while GPCP leads in the tropics. The REGEN data test seems to best perform over semi-arid regions of northern Africa and the Middle East and in the moist Southeast Asia. IMERG outperforms the other products only over Australia.

Figure 2.1.4 indicates that despite having a significantly lesser performance, the MERRA-2 uncertainty characterization is relevant as the adjustments due to the closure remain bounded by the uncertainty. Unlike MERRA-2, the satellite and ground-based products' uncertainty appears not to be adequate in most of the regions, suggesting a deeper elaboration on uncertainty for these products.
<figure><img src="image_28.png" /><figcaption>**Figure 2.1.4.** Regions where a P dataset had to undergo adjustments beyond its uncertainty bounds, indicating that it was originally underestimated or overestimated. Green grid cells are locations where P was found to be underestimated in at least one calendar month, but never overestimated. Orange grid cells refer to locations where P was found overestimated in at least one calendar month but never underestimated. Magenta grid cells show regions where P exhibited different behaviors (underestimated/overestimated) in difference calendar months. Beige grid cells are regions where changes applied to P do not exceed its uncertainty. From Hobeichi et al., 2020a</figcaption></figure>

### 2.1.5. Summary

Optimization techniques have provided a useful way to assess the capability of the existing observations to close the water budget or the water and energy budget as well as the consistency of the estimated fluxes, once the closure is enforced. This leap forward enables the assessment of the new generation of products at the global scale as well as regionally, including the oceans, for which precipitation products' performances is usually poorly known. The emergence of new observational constraints on the surface freshwater budget via surface salinity measurements can further help with the consistency analysis over the ocean.

Water balance studies at a regional scale emphasize the better accuracy of the rain gauge-based products compared to the reanalysis and satellite datasets. Water budget only
optimization yields only moderate changes and improvements of a few precipitation products, suggesting a relatively good consistency with evaporation and runoff. This status is of no use for global investigations and embodies the difficulty of benefiting from numerous regional land investigations from a global climate perspective.

Water and energy budget simultaneous closure optimization at global, multi-year scales shows that current global precipitation estimations need an adjustment that falls within existing error bars. Preliminary time series comparisons between energy and precipitation at the global scale, on the other hand, show large spread from the various precipitation products and significant unrealistic variability.

Systematic evaluation of the breadth of products available remains challenging, as most of the studies explore one or two products, making it difficult so far to reach a community-centric overview. Preliminary efforts using a dozen *global* datasets nevertheless reveal the large inconsistency between the precipitation and radiation budget, except for GPCP and ERA5.

## 2.1.6. Recommendations

Based on this first and partial attempt to assess the capability of precipitation products to contribute to water and energy cycle closure as well as their consistency with other fluxes, we are in a position to formulate some recommendations for the agencies and the community.

**General recommendations:**

* Validation/intercomparison/assessment studies should embrace the large breadth of existing products and not be restricted to one or two products

* Consolidate present findings; elaborate and refine the current set of diagnostics

* Improve the products, as the assessment has identified some non-robust features that deserve further attention. Provide feedback to the dataset providers on the details, perhaps with specific workshops.

* Fill the gaps in the assessment.

* Communicate the robust features of the datasets to support further research using the datasets.

### 2.1.6.1.1. *Specific recommendations*

* Better convey the optimization results at regional scales with ongoing field programs (for example, the GEWEX Hydroclimatology Panel)

* The documentation of the precipitation products' uncertainty should be advanced to fully benefit the optimization framework. This includes auto-correlation and structural error characterization at the monthly scale.

* Support the systematic use of the various precipitation products instead of the single product approach to help better identify the strengths and weaknesses of the products.

## 2.1.7. Acknowledgments

We thank the following individuals for their contribution to our better depiction of the status of observational capabilities: Benoit Meyssignac, Durgesh Nanand Piyush, Tim Boyer, Maria Hakuba, Graeme Stephens and the GDAP panel members.

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## 2.2. Climate variability and trends

Francisco J. Tapiador

University of Castilla-La Mancha (UCLM), Spain

"Climate variability" is defined as the temporal variations of the atmosphere-ocean system around a mean state. The term "natural climate variability" is further used to identify climate variations that are not attributable to or influenced by any activity related to humans (American Meteorological Society, 2021). Regarding "climatic trends", those are defined as a climate change characterized by a reasonably smooth, monotonic increase or decrease of the average value of one or more climatic elements during the period of record (American Meteorological Society, 2021).

The variability of precipitation has two dimensions in climate. The first one is how precipitation departs from the average over a number of years, say the 1990-2020 mean over the 1960-1990 mean, at each location on the globe. The second one is how to gauge climate variability as a whole looking at precipitation. The latest is the province of studies analyzing the dominant modes of variability and includes research on the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Indian Ocean Dipole (IOD) and the El Niño Southern Oscillation (ENSO). Good climate models correctly simulate the Madden-Julian Oscillation (MJO), ENSO and the mean Intertropical Convergence Zone (ITCZ). Those processes are also precisely identified as a fingerprint in the precipitation field. Thus, satellite precipitation estimates are fundamental to achieve a proper representation of such climate variability and to validate models (cf. Chapter 2.3 below).

Precision and accuracy of the precipitation estimates are both important, as is the global scope. It is known that the 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 to understand changes in the radiative forcing, either by natural or anthropogenic causes. Regional estimates are also a must. 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 related to the fate of organized convection (Tan et al., 2015). Latent heating algorithms that have been developed for satellite rain data diagnose the convective/stratiform partitioning from characteristics of the rain and reflectivity fields to produce realistic heating profiles and thus to improve the modeling of the climate variability. Indeed, model parameterization errors become obvious only when higher-order variability metrics such as PDO, AMO, IOD, MJO and ENSO are used. The continental diurnal cycle, which depends on the timing of the transition from bottom-heavy to top-heavy latent heating profiles, is also relevant for climate variability and trends analyses. In fact, precipitation was instrumental in documenting the existence and propagation of 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 have proved to be very helpful in isolating the onset phase of the MJO, when shallow and congestus rain dominate as the biggest sources 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).

Regarding the ENSO, 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. It 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 Coupled Model Intercomparison Project (CMIP) phase 3 (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. There is more evidence on the central role of precipitation: 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.

There are many available precipitation datasets suitable for climate validation (Tapiador et al., 2017). The existence of different approaches and merging methods is a plus in climate variability and trends studies. When satellite estimates differ, important information is revealed. Identifying trends and breakpoints in precipitation series is not trivial, but has proved useful in the context of validating climate models (Figure 2.2.1, Tapiador et al., 2018; Tan et al., 2015). 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 are valid. While the issue has been known for a long time, the specific details, and crucially the mechanistic description, are better

<figure><img src="image_29.png" /><figcaption>**Figure 2.2.1.** Time series of tropical (30S–30N) precipitation (mm/day) over land for 1980–2000. From Tapiador et al., 2018</figcaption></figure>
expressed in terms of precipitation. Therein, it is important to keep and maintain a host of
precipitation datasets from different sources and methodologies. Single, one-instrument and
multisource datasets are both valuable for analyzing different aspects of the climate variability
and the trends. Section 2.4 below delves more deeply into the nature of 11 comprehensive
datasets and acknowledges that none of them can be considered as the “true” representation
of global precipitation. A first-order metric such as the global (60N-60S) mean precipitation over
land can vary from 1.81 mm/day (GSMap) to 2.33 mm/day [PERSIANN-Climate Data Record
(PERSIANN-CDR)]. Differences in the polar areas are larger. Such discrepancies raise several
challenges on the appropriate approach to follow in the validation of climate models for present
climate simulations (cf. Chapter 2.3 below). Careful consideration of the algorithmic choices
and the sampling errors (cf. Chapter 1.1 above) is also required when these datasets are used
for analyzing climate variability and trends. Large uncertainties in extremes in both reanalysis
and observations (Chapter 2.5 below) also raise issues on their fitness-for-purpose on this
realm. Error modeling (Chapter 3.2 below) is fundamental for the use of satellite precipitation
datasets for these applications.

There are more examples of the need for satellite precipitation data in climate variability research. Processes of SST-wind-precipitation interaction are also likely involved in long-term trends and variability in the surface circulation in the tropics (Tapiador et al., 2019). 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.

Regarding climate variability in precipitation, the fingerprints have been observed following
different methods and approaches (Hidalgo et al., 2017; Kenyon and Hegerl, 2010). The impact
of anthropic activity in climate variability is a major driver (Vera et al., 2019). Multidimensional
analyses involving other environmental sciences also require detailed precipitation data
(Trauernicht, 2019; Suarez and Kitzberger, 2010). The use of precipitation data for analyzing
extremes in the climate variability realm is also valuable (van Pelt et al., 2015; Shawul and
Chakma, 2020; Liu and Allan, 2012; Ummenhofer and England, 2007; Teegavarapu, 2016).
Precipitation estimates over the poles are also of interest: Antarctica is significantly colder and
more prone to climate variability than the Arctic, although both regions are strongly responsive to
large-scale variability including the northern and southern annular modes (Screen et al., 2018).

To conclude this section, it is worth noting that climate variability and trends are relevant for a
number of applications. Climate services are mostly targeted at informing adaptation to them,
widely recognized as an important challenge for sustainable development. The role of satellite
precipitation datasets is central in this realm. Better identification of the modes of climate
variability, the definition of new precipitation-based metrics and novel methods to gauge trends
and changes, all depend on the continuous availability of long, continuous and global
measurements of liquid and solid precipitation (cf. Chapter 3.1 below). The need to continually
improve the precipitation estimates from satellite and new developments in the observation
network should follow the path imposed by progresses in modeling.
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Belmadani, A., V. Echevin, F. Codron, K. Takahashi and C. Junquas, 2014: What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile? *Climate Dynamics*, 43, 1893-1914, doi:10.1007/s00382-013-2015-2.

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Cai, W., G. Wang, A. Santoso, X. Lin and L. Wu, 2017: Definition of Extreme El Niño and Its Impact on Projected Increase in Extreme El Niño Frequency. *Geophysical Research Letters*, https://doi.org/10.1002/2017GL075635.

Del Genio, A.D., J. Wu, A.B. Wolf, Y. Chen, M. Yao and D. Kim, 2015: Constraints on Cumulus Parameterization from Simulations of Observed MJO Events. *Journal of Climate*, 28, 6419–6442, https://doi.org/10.1175/JCLI-D-14-00832.1.

Hidalgo, H.G., E.J. Alfaro and B. Quesada-Montano, 2017: Observed (1970–1999) climate variability in Central America using a high-resolution meteorological dataset with implication to climate change studies. *Climatic Change* 141, 13–28, https://doi:10.1007/s10584-016-1786-y.

Kenyon, J., and G.C. Hegerl, 2010: Influence of modes of climate variability on global precipitation extremes. *Journal of Climate*, 23(23), 6248–6262, https://doi.org/10.1175/2010JCLI3617.1.

Ling, J., and C. Zhang, 2011: Structural evolution in heating profiles of the MJO in global reanalyses and TRMM retrievals. *Journal of Climate*, 24, 825–842, doi:10.1175/2010JCLI3826.1.

Liu, C., and R.P. Allan, 2012: Multisatellite observed responses of precipitation and its extremes to interannual climate variability. *Journal of Geophysical Research Atmospheres*, vol. 117, D3, https://doi.org/10.1029/2011JD016568.

Liu, C., and E.J. Zipser, 2005: Global distribution of convection penetrating the tropical tropopause, *Journal of Geophysical Research*, 110, D23104, doi:10.1029/2005JD006063.

Liu C., and E. Zipser, 2013: Regional variation of morphology of the organized convection in the tropics and subtropics, Part I: regional variation, *Journal of Geophysical Research*, 118, 453–466, doi:10.1029/2012JD018409.

Madden, R.E., and P.R. Julian, 1994: Observations of the 40-50 day tropical oscillation-A review. *Monthly Weather Review*, 122: 814–837.

Power, S., F. Delage, C. Chung, G. Kociuba and K. Keay, 2013: Robust twenty-first-century projections of El Niño and related precipitation variability. *Nature*, 502, 541–545.
Roca, R., J. Aublanc, P. Chambon, T. Fiolleau and N. Viltard, 2014: Robust observational quantification of the contribution of mesoscale convective systems to rainfall in the tropics. *Journal of Climate*, 27, 4952–4958, doi:10.1175/JCLI-D-13-00628.1.

Rossow, W.B., A. Mekonnen, C. Pearl and W. Goncalves, 2013: Tropical precipitation extremes. *Journal of Climate*, 26, 1457–1466, doi:10.1175/JCLI-D-11-00725.1.

Screen, J.A., C. Deser, D.M. Smith, X. Zhang, R. Blackport, P.J. Kushner, T. Oudar, K.E. McCusker and L. Sun, 2018: Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. *Nature Geoscience*, 11, 3, 155–163, DOI:10.1038/s41561-018-0059-y.

Shawul, A.A., and S. Chakma, 2020: Trend of extreme precipitation indices and analysis of long-term climate variability in the Upper Awash basin, Ethiopia. *Theoretical and Applied Climatology*, 140, 635–652, https://doi.org/10.1007/s00704-020-03112-8.

Suarez, M.L., and T. Kitzberger, 2010: Differential effects of climate variability on forest dynamics along a precipitation gradient in northern Patagonia. *Journal of Ecology*, https://doi.org/10.1111/j.1365-2745.2010.01698.x.

Takahashi, K., and B. Dewitte, 2016: Strong and Moderate nonlinear El Niño regimes. *Climate Dynamics*, doi: 10.1007/s00382-015-2665-3.

Tan, J., C. Jakob, W. B. Rossow and G. Tselioudis, 2015: Increases in tropical rainfall driven by changes in frequency of organized deep convection. *Nature*, 519, 451–454, doi:10.1038/nature14339, http://dx.doi.org/10.1038/nature14339.

Tapiador, F.J., A. Navarro, A. Jiménez, R. Moreno and E. García-Ortega, E. 2018: Discrepancies with satellite observations in the spatial structure of global precipitation as derived from global climate models. *Quarterly Journal of the Royal Meteorological Society*, 144, 419-435, https://doi.org/10.1002/qj.3289.

Tapiador, F.J., A. Navarro, V. Levizzani, E. García-Ortega, G.J. Huffman, C. Kidd, P.A. Kucera, C.D. Kummerow, H. Masunaga, W.A. Petersen, R. Roca, J.-L. Sánchez, W.-K. Tao and F.J. Turk, 2017: Global precipitation measurements for validating climate models. *Atmospheric Research*, 197, 1–20, https://doi.org/10.1016/j.atmosres.2017.06.021.

Tapiador, F.J., R. Roca, A. Del Genio, B. Dewitt, W. Petersen and F. Zhang, 2019: Is precipitation a good metric for model performance? *Bulletin of the American Meteorological Society*, 100(2), 223–233, https://doi.org/10.1175/BAMS-D-17-0218.1.

Teegavarapu, R.S.V., 2016: Climate variability and changes in precipitation extremes and characteristics. In: *Sustainable Water Resources Planning and Management Under Climate Change* (E. Kolokytha, S. Oishi, R. Teegavarapu, eds.). Springer, Singapore, https://doi.org/10.1007/978-981-10-2051-3_1.

Trauernicht, C., 2019: Vegetation—Rainfall interactions reveal how climate variability and climate change alter spatial patterns of wildland fire probability on Big Island, Hawaii. *Science of the Total Environment*, 650, 459–469, https://doi.org/10.1016/j.scitotenv.2018.08.347.

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Wang, S., A.H. Sobel, F. Zhang, Y. Qiang Sun, Y. Yue and L. Zhou, 2015: Regional Simulation of the October and November MJO Events Observed during the CINDY/DYNAMO Field Campaign at Gray Zone Resolution. *Journal of Climate*, 28, 2097–2119.
## 2.3. Climate model validation

Francisco J. Tapiador<fnref n="1" /> and Vincenzo Levizzani<fnref n="2" />

As of 2020, climate models include traditional Global Circulation/Climate Models (GCMs), Regional Climate Models (RCMs) and, more recently, Earth System Models (ESMs). All these models are different from Numerical Weather Prediction (NWP) models in that they are intended to provide estimates of the climate rather than meteorological forecasts: they produce “climates”, that is, mean values and other statistical moments of the distribution, but not realistic sequences of the actual weather. The World Meteorological Organization (WMO)’s standard length for constructing climatologies is 30 years, with the standard period being the 1960–1990 interval (the “historical climate”). More recently, the 1980–2010 period is used, which is more into the “satellite-era” of meteorological observations.

Climate model outputs are generically named “simulations”. They are deemed “projections” when they aim to gauge the climates of the future based on a set of assumptions of anthropogenic forcings and therefore ultimately of social behavior (scenarios). Working in hindsight or to derive estimates of past geological eras, climate models produce “present-climate climatologies” and “paleoclimates” respectively. By going back in time, they can benefit from ancillary data and historical information to fine-tune the simulations, but in the case of experiments on future climate change, such advantage is naturally absent, so different challenges appear.

Validation of climate models is mostly circumscribed to simulations of the present-day climatologies (say 1960–1990) and comparison with meteorological observations. While paleoclimates can also be validated, the absence of an instrumental record obliges us to rely on proxies, which are indirect. Assuming the “rosy” and ceteris paribus assumptions (Smith, 2002), simulations of future climates are useful to understand our changing climate. Nonetheless, the first step to trust such simulations is ensuring models provide a faithful picture of current climate.

Model developers routinely compare their partial results in the developing phase with observations and adjust their models accordingly (Voosen, 2016). While this is unavoidable, it can hardly be considered as scientific validation. First, validation requires independence both in data not used to develop the model, and in the group of people who do the validation. However important self-validation by the developers is for model consistency, it is not a substitute for proper scientific scrutiny by independent, unrelated teams. Secondly, there is a set of protocols and standards that make validation a field in itself. Quality Control (QC) standards have been proposed for this field to address the increasingly pressing requirement as climate becomes more and more interwoven with activities of mitigation and adaptation to global warming. The public and the decision makers demand that the science behind policies is traceable, transparent and auditable, and that includes the validation of climate model outputs.

A major issue in the field of validating present climates is that in the development stage, climate models are tuned to current conditions. The empirical values and assumptions implied in the procedure may vary in the future due to ongoing human emissions and land use changes, and therefore must be fully documented.

<footnote n="1">¹University of Castilla-La Mancha (UCLM), Spain</footnote>

<footnote n="2">²National Research Council of Italy-Institute of Atmospheric Sciences and Climate (CNR-ISAC)</footnote>
Quality Control is now an integral part of climate model validation, as it has long been in the remote sensing field where International Organization for Standardization (ISO) requirements are strictly followed. Failure to do so may result in catastrophic mission failures. International standardization techniques are slowly permeating the procedures of climate validation. It needs to be noted that providing confidence that quality requirements will be fulfilled (e.g., ISO 9000) implies not only that the product is suited for the specific purpose it was conceived in the first place (a dataset or a climate model in our case), but also that the product has been created following a well-defined set of rules and methods that builds confidence in the whole production process. Quality Assurance (QA) procedures are designed to minimize errors and mistakes, setting double-blind evaluations and sanity checks and providing a traceable flow of several stages of the process of generating the product.

To achieve a QA-standard, each step of the production process has to be clearly defined and subject to auditing. This is not a problem for most merged precipitation datasets, since these are carefully-designed products whose science can be traced back to an Algorithm Theoretical Basis Document (ATBD). The ATBDs are the cornerstone of the confidence in merged precipitation datasets, in the same way that metadata and technical notes perform for pure observational datasets. They provide the rationale of the many decisions taken over the process of developing the product, and allow users to trace back each step, also permitting duplication of the product by another party. Reputable climate models also have the equivalent to the ATBD in the form of model documentation describing the physics of the dynamical core, the numerical methods employed, the parameterizations and the empirical choices used to fine-tune the model.

Validating climate model outputs of prognostic variables such as temperature is a difficult exercise per se and becomes even more problematic when dealing with diagnostic variables such as precipitation. Precipitation has been considered "the ultimate test" for validating models for a number of reasons (Tapiador et al., 2019). However, it is difficult to find homogeneous precipitation datasets over reasonably long time periods that cover the whole globe. These are hard to find among ground-based observing systems, which are obviously limited to land areas. Indeed, until 2010 validations of climate models were done by the same teams that develop the software and for grid-point, rain gauge measurements only. Moving to independent, satellite and gauge-satellite combined data, Tapiador (2010) (see Figure 2.3.1) relied on increased availability of robust and public datasets such as those described in Tapiador et al. (2017). The latest published Intergovernmental Panel on Climate Change (IPCC) report, the Fifth Assessment Report (AR5) (IPCC, 2015), acknowledged that the detection and attribution of regional precipitation changes had generally focused on continental areas using in situ data because observational coverage over oceans was limited to a few island stations, although model-data comparisons over continents also illustrated large observational uncertainties. The report also noted that available satellite datasets that could supplement oceanic studies are short and their long-term homogeneity is still unclear, and that accordingly they have not yet been used for detection and attribution of changes. The IPCC concluded in 2014 that "continuing uncertainties in climate model simulations of precipitation make quantitative model/data comparisons difficult (e.g., Stephens et al., 2010), which also limits confidence in detection and attribution."
<figure><img src="image_30.png" /><figcaption>**Figure 2.3.1.** An example of the first validation of regional climate outputs using several precipitation datasets instead of gauge-only data. From Tapiador, 2010</figcaption></figure>

Tapiador et al. (2017) first conducted a thorough analysis of the existing satellite-based precipitation datasets in view of their application for climate model validation. The authors provide guidance on the use of precipitation datasets for climate research, including model validation and verification for improving physical parameterizations. Strengths and limitations of the datasets for climate modeling applications are presented, underlining that not all datasets are suitable for this purpose. The checklist of items that must be considered in the field of validation of precipitation outputs from climate models includes several crucial points that we will now try to separate between facts and recommendations.

### 2.3.1. Facts

1. Rain gauges provide pointwise measurements that may not be fully representative of the area, especially for large areas with few observations (for example, the Amazon basin).

2. Rain gauges have known technical limitations and biases and the spatial distribution/length record of the instruments is highly variable.

3. Ground radars are characterized by many sources of uncertainty that have diverse natures (beam blockage, attenuation, anomalous propagation, etc.).

4. Precipitation (solid, liquid and mixed phase) has a large spatial and temporal variability.

5. Satellite estimates are indirect and have limited temporal sampling.

6. Satellite estimates over land, coast and ocean are derived using different methods and assumptions.
7. Merged precipitation databases are not intended for trend analyses as sensor drifts are present over limited time spans.

8. Many of the techniques used in Level-2 products are built upon Bayesian estimates (that is, they require an a priori estimate).

9. The quality of Level-3 precipitation products is driven by microwave observations and therefore is dependent on their availability and quality.

10. There are significant latitudinal differences in the satellite and ground-based estimates in terms of known biases and uncertainties.

11. The error characteristics resulting from the merging of disparate datasets are not well known.

12. There are known uncertainties in the estimation of diabatic heating fields that affect how models represent some precipitation processes.

13. Model outputs that have been bias-corrected or that are the results of model output statistic techniques cannot be validated.

14. Series derived from global circulation model (GCM)-driven regional climate models (RCMs) cannot be directly compared with time series of observations.

15. High-resolution global cloud-resolving models (G-CRM) are becoming better suited than RCMs to inform policies and advance our knowledge of the physics of precipitation.

16. End-to-end characteristics of the satellite-based retrieval process are not yet fully understood.

17. There is less agreement among satellite products in trends and variability at global scale than in regional variability.

### 2.3.2. Recommendations

i. Uncertainty figures from ground radars should be considered when assimilating data.

ii. Challenges posed to the validation of precipitation by its spatiotemporal variability need to be considered, paying attention when using precipitation datasets in model validation.

iii. The indirect character of satellite estimates and their limited temporal sampling should be considered in the comparisons.

iv. The tuning of the models with specific datasets must be considered for ensuring a truly independent validation.

v. Parameterizations must be validated with data not used in their development and tuning.

vi. Global measurements of microphysics are important to avoid overfitting models to empirical parameters.

vii. Ground validation campaigns are essential for improving the representation of precipitation in models.
viii. "Scope principle": a model cannot claim performances at better resolutions that those at which it has been validated.

ix. Blending methods in deriving global precipitation products involves subtleties than must be considered in any validation process.

x. Parameters and techniques used in the estimation process using satellites and rain gauges may not be universally applicable, both in space and time.

xi. The precise measurement of shallow and very light precipitation still represents a scientific challenge and more research is needed in this direction.

xii. While precipitation is a key variable to validate models, there is no agreement on the reference to be compared with. More research and targeted observations are required to fill this gap.

xiii. Public auditing of model code and precipitation database algorithms is required if models are used for policy-making and societal applications other than pure research.

xiv. Every aspect of model and database development should be subject to QC methods and be fully traceable, transparent and auditable.

xv. Models must be independently validated by scientists not involved in their development or belonging to the same research network.

xvi. Users should be made fully aware of the confidence level that can be attributed to model outputs and observational databases.

When considering the need for validation campaigns that produce new insights for a correct representation of precipitation processes in the models, under-represented areas and processes come first. This is particularly true for tropical forests. In this latter case, the problem is still to make sure that the correct microphysics is understood for its inclusion into the precipitation estimation algorithms prior to conducting any meaningful validation exercise. Recently the project Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA), held in Brazil, has substantially contributed to improving our level of understanding in this direction (Machado et al., 2014). Another key example is the need for characterizing the relation of mid-latitude frontal precipitation mechanisms and their modification by terrain to rainfall estimation uncertainties. The Olympic Mountain Experiment (OLYMPEX) (Houze et al., 2017) assessed satellite measurements made by the GPM along the northeastern Pacific coastline. At the same time, warm rain processes are still not completely understood, and the Integrated Precipitation and Hydrology Experiment (IPHEX) sought to characterize warm season orographic precipitation regimes and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain (Erlingis et al., 2018).

There are a number of considerations that need to be made before approaching satellite precipitation datasets for model validation. Validation can be performed on the precipitation model means, which are those most used for applications, or on other first-order statistics. These include the modeling of the ENSO phenomenon (Neale et al., 2008), the representation of the diurnal cycle of rainfall (Betts and Jakob, 2002) and the frequency of occurrence of high- and low-intensity rainfall events (Sun et al., 2006). Validation can also be performed on more
physical quantities such as latent heat (LH) and in the intricacies of the microphysics of precipitation in models.

1. **Latent heat release.** It is a consequence of phase changes between the vapor, liquid and frozen states of water, which cannot be measured or detected using present observational instruments. The vertical distribution of LH has, however, a strong influence on the atmosphere, controlling large-scale tropical circulations, exciting and modulating tropical waves, maintaining the intensities of tropical cyclones, and even providing the energetics of midlatitude cyclones and other midlatitude weather systems (Li et al., 2017). The launch of the Tropical Rainfall Measuring Mission (TRMM) satellite in November 1997 provided a much-needed and accurate measurement of rainfall as well as the ability to estimate the four-dimensional (4D) structure of LH over the global tropics (Simpson et al., 1988, 1996). The success of TRMM made it possible to have another major precipitation measuring mission from the National Aeronautics and Space Administration (NASA), the Global Precipitation Measurement (GPM) mission. GPM is considered by NASA to be the centerpiece mission of its Global Water & Energy Cycle research program. On the modeling side, Cloud resolving models (CRMs) have been identified as being a valuable tool for algorithm developers and are considered a key component for one of the major GPM ground validation (GV) sites. In addition, CRMs are one of the most important tools used to establish quantitative relationships between diabatic heating and rainfall. Thus, simulated data from the Goddard Cumulus Ensemble (GCE) model have been used extensively in TRMM for the development of both rainfall and heating retrieval algorithms (Simpson et al., 1996; Tao et al., 2006). Five different TRMM LH algorithms designed for application with satellite-estimated surface rain rate and precipitation profile inputs have been developed, compared, validated and applied for over two decades (Tao et al., 2001, 2006, 2016b). They are the: (1) Goddard Convective-Stratiform Heating (CSH) algorithm, (2) Spectral Latent Heating (SLH) algorithm, (3) Goddard Trained Radiometer (TRAIN) algorithm, (4) Hydrometeor Heating (HH) algorithm, and (5) Precipitation Radar Heating (PRH) algorithm. The strengths and weaknesses of each algorithm are discussed in Tao et al. (2006). Ling and Zhang (2011) compared the heating profiles between TRMM retrieved (CSH, SLH and TRAIN) and global reanalyses [(ERA-I, Japanese 25-year ReAnalysis (JRA-25) and Climate Forecast System Reanalysis (CFSR))]. All heating data exhibit three longitudinal maxima but with different amplitudes; for example, heating over South America and Africa is much stronger in three models (CSH, SLH, and CFSR) than in the others. Heating is weaker over the Maritime Continent than over the eastern Indian Ocean and western Pacific in some data [for example, apparent heat source Q₁ (Q₁), TRAIN LH, ERA-I Q₁, and JRA25 Q₁], but not so in others. Among all, TRAIN has the largest low-level heating over the east Pacific, which might be an overestimate owing to shallow convection (Grecu et al., 2009). Low-level heating over the eastern Pacific is also present with smaller amplitudes in Q₁ from ERA-I and LH from CFSR. The distribution of boundary heating of the LH from CFSR is almost the same, and it may also be related to precipitating marine stratus clouds over the ocean (vanZanten and Stevens, 2005). It is reasonable to say that the upper peak is related to precipitation by cold (ice or mixed phase) clouds and the lower one to precipitation by warm (liquid phase) clouds. LH in TRAIN and CFSR and Q₁ in JRA25 do not have any obvious double-peak structure. Ling and Zhang (2011) also pointed out that the discrepancies among the heating datasets are not merely between the TRMM and reanalysis datasets or between LH and Q₁. Differences within the TRMM
and reanalysis products, respectively, and within various products of LH or Q₁ are no less than those between the TRMM and reanalysis data and between LH and Q₁. These differences reflect our current level of estimating diabatic heating fields: we may get some basic properties of the heating field (for example, longitudinal locations of maxima) correct, but there are many details with large uncertainties. These uncertainties should by no means stop us from cautiously using the currently available heating products to provide as much information as they may credibly provide.

2. **Microphysics of precipitation.** This is the framework through which to understand the links between interactive water vapor, aerosol, cloud and precipitation processes. Global measurements of microphysics are important to avoid overfitting the models to specific places when tuning the empirical parameters, which is the standard procedure to adjust models to observations (Voosen, 2016). CRMs with advanced microphysical schemes have been used to study the interactions between aerosol, cloud and precipitation processes at high resolution. These processes play a critical role in the global water and energy cycle. Validation of CRMs with observational databases is important both to ascertain the fidelity of the outputs and to improve the models. The interest in this topic lies in the many uncertainties associated with various microphysics schemes. In part, this reflects the fact that microphysical processes cannot always be measured (or observed) directly. Herein cloud properties, which can be estimated, have been used to validate model results. The spectral bin microphysical (SBM) schemes represent the most sophisticated representations of microphysical processes. They generally perform better in simulating realistic cloud properties and surface precipitation compared with bulk microphysical schemes (Li et al., 2010). SBM schemes have helped to improve the bulk scheme [Lang et al., 2014; Tao et al., 2016a also used the microphysics bin scheme from the Regional Atmospheric Modeling System (RAMS) to parameterize their cloud activation]. However useful, SBM schemes are not perfect, though they are more direct (and realistic) than the bulk MP parameterizations used in GCMs. Uncertainties in these can be expected to be larger.

There are also additional considerations to be made to validate climate models with precipitation datasets drawing on recent results. Apart from time span, spatial resolution and calibration quality of the data, there are very important subjects that need attention:

1. First, we need to consider the way the retrieval of precipitation was conducted. As stated by Stephens and Kummerow (2007), precipitation retrievals from space are highly sensitive to the specific radiative and microphysical model used in the retrieval process. Identifying a cloud as precipitating is not a trivial exercise and can result in large errors that make the dataset almost useless. This is the reason why most recent datasets including observations from passive and active sensors are necessary for improving cloud and precipitation retrievals (Levizzani et al., 2020a, 2020b).

2. The second crucial aspect concerns the precipitation phase. When validating climate models, precipitation type should be known, but this is a kind of knowledge that is far from being totally achieved. For the time being, only data from the MODerate resolution Imaging Spectroradiometer (MODIS) were used to verify climate model outputs such as in the case of Matiu et al. (2020) within the European Coordinated Regional Downscaling Experiment (EURO-CORDEX).
3. However, failure to accurately predict the location, magnitude and frequency of precipitation, including snowfall, heavily impacts climate modeling (for example, Field and Heymsfield, 2015). This is why a number of studies have recently started using observations and climate models to identify deficiencies in the actual modeling and validation approaches. Fowler et al. (2020), for example, show results that underscore the importance of evaluating clouds, their optical properties, and the top-of-the-atmosphere radiation budget in addition to precipitation when performing mesh refinement global simulations. Heymsfield et al. (2020) have been the first to produce a global view of the precipitation process partitioning, using a combination of satellite and global climate modeling data (Figure 2.3.2). They showed that significant differences between satellite- and model-based results are found and the reasons require investigations far more complex than the simple traditional surface precipitation differences. Note also that increasing temperatures may also imply increasing melting level height, which obviously impacts surface precipitation phase and intensity and thus model verification (Prein and Heymsfield, 2020). At the same time, care must be taken in improving frozen hydrometeors representation in the climate models (especially at the regional scale) since large discrepancies (up to 5 times) are found between modeled and observed brightness temperatures in the microwave that may undermine the value of intercomparison results (for example, Rysman et al., 2018).

4. Precipitation intensity at the ground is thus not sufficient to characterize the changing climate. The mean and the other statistical moments are just a first step in validation. Trenberth et al. (2003) argued that advancing understanding and the ability to model and predict the character of precipitation is vital and requires new approaches to examining data and models. The timing, duration and intensity of precipitation can be explored via the diurnal cycle (Betts and Jakob, 2002), whose correct simulation in models remains an unsolved challenge of vital importance in global climate change. This can only be done with truly global datasets such as those derived from satellite observations. Here, reanalyses are expected to play a dominant role in the near future.

5. Observational uncertainty quantification is essential for climate studies, climate model evaluation and statistical post-processing. Recently, Tang et al. (2020) have shown variable performance of the IMERG product with respect to other precipitation datasets and reanalyses. Prein and Gobiet (2017) have in turn demonstrated that differences between global precipitation datasets have the same magnitude as precipitation errors found in regional climate models.

6. Finally, precipitation is the most important process for a deeper understanding of climatic changes, but it is linked to several other processes within the water cycle and thus a combined use of precipitation datasets with other datasets (for example, soil moisture, sea surface temperature, evapotranspiration, wind fields, etc.) is unavoidable when assessing climate model outputs (Levizzani and Cattani, 2019).

After considering all possible caveats in using satellite precipitation datasets for climate model validation, nonetheless we register their increasing use in this field.