<figure><img src="image_31.png" /><figcaption>**Figure 2.3.2.** Mean surface precipitation rate retrieved from CloudSat, Global Precipitation Measurement (GPM) mission, Goddard Profiling (GPROF) algorithm, and Tropical Rainfall Measurement Mission (TRMM), derived from output from the Community Atmosphere Model (CAM) and the Met Office models, and from the Global Precipitation Climatology Project (GPCP) product. These are for land and ocean areas. [from Heymsfield et al. 2020; courtesy American Meteorological Society]. Note that model results are not independent from the satellite observations.</figcaption></figure>

In the RCMs realm, satellite precipitation datasets were widely used during the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Coupled Model Intercomparison Project (CMIP) both sponsored by the World Climate Research Programme (WCRP) of the WMO. Target areas have been mostly Africa and Asia with special attention to the Tropics using a range of RCMs. Before that, data from the Ensemble-based Predictions of Climate Changes and their Impacts (ENSEMBLES) and Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects (PRUDENCE) projects were used to compare satellite datasets with RCM outputs. Biases of the single model depending on the African region and season were identified during CORDEX while simulating the West African summer monsoon (Akinsanola et al., 2015). In a previous study during CORDEX-Africa, Nikulin et al. (2012) showed that a multimodel average generally outperforms any individual simulation, showing biases of similar magnitude to differences across a number of observational datasets. At the same time, the authors confirmed that a common problem in the majority of the RCMs is that precipitation is triggered too early during the diurnal cycle with differences among the models as first suggested by Dai (2006). More recently, Wu et al. (2020) results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation-related improvements are strongly model dependent.

The precipitation datasets have proved instrumental also in identifying deficiencies in the simulation of the CMIP5 models. Figure 2.3.3 shows the climatologies of 40 CMIP5 models and several precipitation reference data. Over the Tropics, model output and satellite precipitation dataset intercomparisons represent a substantial added value in identifying strengths and
<figure><img src="image_32.png" /><figcaption>**Figure 2.3.3.** An example of the use of satellite-derived precipitation datasets in order to validate 40 Global Climate Models (GCMs) and Earth System Models (ESMs). The data represents the 1980–2000 climatology of the mean precipitation. From Tapiador et al. (2018)</figcaption></figure>

weaknesses of the approaches of the various models to predict water cycle changes. For example, when considering one of the key convective areas of the planet, the Congo basin in Central Africa, there appears to be little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons
(Washington et al., 2013). Higher-resolution satellite data can surely help in disentangling persisting uncertainties in the area. Over the ocean, the satellite precipitation datasets have contributed to find that precipitation in CMIP5 models is overestimated in most areas (Yang et al., 2018). This is consistent with the previous results of Hirota and Takayabu (2013), who concluded that a proper representation of the sensitivity of deep convection to humidity and higher resolution of the ocean models with better equatorial trades are important for reducing the double ITCZ and the cold tongue biases.

Another key aspect of climate model studies is the need for verifying improvements among different versions of the models and of their ensembles. An example of the application of satellite precipitation datasets is provided by Kumar et al. (2014), whose results show little change in the central tendency, variability, uncertainty of historical skills or consensus across CMIP3 and CMIP5. At the same time, there are regions and seasons where significant changes, performance improvements and even degradation in skills are suggested. This fact clearly demonstrates the potential of using satellite-derived datasets at the global as well as the regional scales. In fact, Pathak et al. (2019) have found that over the south Asian region, some of the convective and large-scale precipitation biases are common across CMIP5 model groups, emphasizing that although on a global scale the bias patterns may be sufficiently different to cluster the models into different groups, regionally, it may not be true.

Substantial work is still needed in examining the ability of both satellite observations and models in capturing extremes, droughts and floods. While observations need to be done at very high resolution and for long time periods, posing problems for using the available global datasets, models need to better represent convection. Kendon et al. (2019) argue that with a more accurate representation of convection in the models, projected changes in both wet and dry extremes over Africa may be more severe than they are actually predicted. Such conclusions need, however, to be linked to the way we conduct intercomparisons and numerical experiments. Recently, Yosef et al. (2020) have concluded that the use of percentile-based indices, such as those of the Expert Team on Climate Change Detection and Indices (ETCCDI), gives very different results when choosing a base period that included records from the last two decades (e.g., 1981-2010, 1988-2017) over Israel. At the same time, Alexander et al. (2019) argue that to advance the use of satellite precipitation data in the applications on extremes differences between data products, limitations in satellite-based estimation processes, and the inherent challenges of scale need to be better understood. Several efforts are on their way in recent times to come to the production of global datasets able to account for extremes (for example, Beck et al., 2020). We will most surely witness an intense activity in this field because it is crucial for climate change studies and to make sure that global precipitation datasets contain the necessary information for all types of events, especially the most severe ones that should be linked to changed climatic conditions.

Alongside research for clarifying the number and intensity of extremes per se, one more subject has come up in recent times. The understanding of short-duration rainfall extremes is also crucial, but data are often subject to errors and inhomogeneities and these events are poorly quantified in projections of future climate change. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales as argued by Blenkinsop et al. (2018) who launched the project INTENSE to overcome this lack of knowledge via sub-daily gauge dataset collection. Satellite datasets should help in this direction as they acquire higher temporal resolution.
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## 2.4. Intercomparison of products for climate applications

Hirohiko Masunaga<fnref n="1" />, Fumie F. Akimoto<fnref n="1" />, Takuji Kubota<fnref n="2" />, Chris Kummerow<fnref n="3" /> and Marc Schröder<fnref n="4" />

### 2.4.1. Introduction

Observational datasets of global precipitation are widely used for a range of climate applications, including atmospheric water and energy budget analyses (section 2.1) and climate model assessment (section 2.3), as well as meteorological studies on regional scales (for example, extreme events, see section 2.5) and synoptic scales (for example, tropical disturbances such as MJO). The precipitation products, however, are not strictly a “true” representation of nature but have their own uncertainties related to issues such as sampling errors and algorithmic assumptions. Extensive efforts have been made to document the biases in existing precipitation products. Such studies include the systematic assessment of numerous products on a global scale (for example, Gruber and Levizzani, 2008; Gehne et al., 2016; Beck et al., 2017; Sun et al., 2018) as well as a large body of literature on regional (typically continental-scale) intercomparisons (see review by Maggioni et al., 2016).

This assessment report is a concise update to existing efforts on the assessment of global precipitation products. Particular attention is paid to the potential bias characteristics in geographical pattern, ocean-land contrasts and mean versus extreme precipitation.

### 2.4.2. Data

In this sub-chapter, we analyze 11 global products consisting of the Climate Hazards Group InfraRed Precipitation with Station version 2 (CHIRPS v2.0, Funk et al., 2015), CMORPH v1.0 (Joyce et al., 2004; Xie et al., 2017), CPC v1.0 (Xie et al., 2007), GPCC Full Data Daily v2018 (Becker et al., 2013; Ziese et al., 2018), GPCP v1.3 daily (Huffman et al., 2001), GSMaP v6 (Kubota et al., 2007, 2020), Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) v4.0 (Andersson et al., 2010), IMERG v5 (Huffman et al., 2015, 2020), PERSIANN-CDR v1r1 (Ashouri et al., 2015), the Tropical Amount of Rainfall with Estimation of ERors (TAPEER) v1.5 (Roca et al., 2018), and TRMM Precipitation L3 1 day 0.25 degree x 0.25 degree V7 (TRMM 3B42 V7; Huffman et al., 2007). Note that HOAPS is an over-ocean product and the CHIRPS, CPC and GPCC data are available only over land. All estimates are adjusted to a daily 1°x1° grid in accordance with the FROGS data format (Roca et al., 2019). The analysis shown here is largely based on the results recently published by Masunaga et al. (2019).

Among these products, CMORPH, GPCP, GSMaP, IMERG, TAPEER and TRMM 3B42 all rely on the Low Earth Orbit (LEO) microwave radiometry and/or sounding data for baseline estimates of precipitation, with the GEO infrared measurements incorporated to fill in spatial and temporal gaps (see Section 1.1 for extended discussion on the methodology and error characterizations). In GPCP, GSMaP, IMERG and 3B42, a further adjustment is made with in situ measurements from gauge networks over land. See the individual documents cited above for product-specific details in the algorithmic strategy.

<footnote n="1">¹ Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan</footnote>

<footnote n="2">² Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan</footnote>

<footnote n="3">³ Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA</footnote>

<footnote n="4">⁴ Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany</footnote>
### 2.4.3. Results

Figure 2.4.1 shows the global (60°S-60°N) mean precipitation for different products with the daily data aggregated over 20 years from 1998 to 2017. The oceanic mean precipitation ranges from 2.39 mm/d to 3.4 mm/d, and the land mean precipitation varies between 1.81 mm/d and 2.28 mm/d. Note that the two gauge-only products (CPC and GPCC) largely disagree against each other, suggesting that uncertainties specific to gauge measurements such as sampling errors and the wind-induced undercatch may be as much responsible for the inter-product discrepancies as retrieval uncertainties in satellite algorithms (see section 1.2 for additional discussion on the utility and limitations of gauge measurements). This discrepancy partially accounts for the spread over land in the merged products as well, since IMERG is adjusted to GPCC over land and the daily GPCP is calibrated with GPCC through the monthly GPCP, while GSMaP and CMORPH adopt CPC for the gauge correction.

<figure><img src="image_33.png" /><figcaption>**Figure 2.4.1.** Global mean precipitation (mm/d) for each product (60°S-60°N, 1998–2017) over ocean (blue), land (orange) and all surfaces (gray). TAPEER and TRMM 3B42 are not included because these datasets do not cover the whole 60°S-60°N band.</figcaption></figure>

Figure 2.4.2 presents the zonal-mean precipitation of different products for the year of 2015. All products qualitatively agree in the meridional structure of precipitation. A systematic bias, however, is evident in a quantitative sense, with the peak rainfall over ocean, representing the ITCZ, varying from 7 mm/d to 9 mm/d. The zonal-mean precipitation agrees better in the subtropics, but the spread expands over ocean for latitudes higher than 40°, where lighter precipitation that is difficult for radiometers to separate from cloud water, as well as some solid precipitation, makes the retrieval technically challenging (see also section 3.1). GSMaP marks the lowest while IMERG hits the highest at high latitudes as found in the global mean precipitation (Figure 2.4.1), while this order is reversed in the tropics. The uncertainty at high latitudes is a primary driver of the inter-product spread in the global-mean precipitation. Precipitation over land in the northern high latitudes reasonably agrees among different products, presumably owing to the dense gauge networks there to which satellite estimates are adjusted.
<figure><img src="image_34.png" /><figcaption>**Figure 2.4.2.** Zonal mean precipitation (mm/d) over ocean (left) and over land (right) for the year of 2015. Adopted from Masunaga et al. (2019)</figcaption></figure>

The global distribution of all the products averaged together and the anomalous geographical pattern for selected products from the ensemble mean is depicted in Figure 2.4.3. The GSMaP annual-mean precipitation is higher in the Pacific ITCZ and lower elsewhere than the ensemble mean, while the IMERG precipitation is just opposite in geographical pattern to GSMaP. This striking contrast is somewhat surprising, given that GSMaP and IMERG share aspects of the fundamental product design such as the native grid resolution (0.5°), temporal sampling (hourly for GSMaP and half-hourly for IMERG), and the overall algorithmic flow (LEO microwave → GEO infrared morphing → gauge adjustment). When compared in extreme (ninety-ninth percentile) precipitation, GSMaP and IMERG, however, have fundamentally different anomaly patterns relative to their annual means. Both the two products stay lower than the ensemble mean across global oceans, while the anomaly is opposite in sign over land. This particular case offers an illustrative example that the bias characteristics in the climatological precipitation are generally a poor predictor of the extreme rain biases. See Masunaga et al. (2019) for the global maps of the other products included in the assessment.

<figure><img src="image_35.png" /><figcaption>**Figure 2.4.3.** Global map of annual-mean precipitation (top) and ninety-ninth percentile extreme precipitation or R99p (bottom) for the year of 2015. The ensemble mean of all products (left) and the anomaly from the ensemble mean for selected products: GSMaP (middle) and IMERG (right). Adopted from Masunaga et al. (2019)</figcaption></figure>
Finally, the deseasonalized time series of different products are shown in Figure 2.4.4. Different curves are found to be clustered into a few groups rather than spread widely. The monthly-mean precipitation agrees relatively well over oceans with the exception of GSMaP, staying somewhat lower. Some products exhibit more pronounced interannual variability than others: HOAPS has a striking peak associated with the 1997 El Niño, and PERSIANN shows a sharp minimum of unknown origin in 2017. Over land, the monthly-mean precipitation appears to be divided into two groups anchored to the two-gauge products (GPCC and CPC). This may be partly due to the gauge adjustment procedure carried out in each product. The ninety-ninth percentile extremes are spread more widely than the monthly mean. Oceanic extremes in GPCP and PERSIANN, the latter of which is adjusted to the former in monthly mean, are modest in intensity relative to other datasets. As such, the bias characteristics specific to each product are essentially different between the mean and extreme precipitation as noted above.

<figure><img src="image_36.png" /><figcaption>**Figure 2.4.4.** Time series of monthly mean precipitation with the annual cycle being removed: global ocean (left column) and global land within 50°N/S (right column), mean over all defined values (top row), and ninety-ninth percentile (bottom row). Adopted from Masunaga et al. (2019)</figcaption></figure>

### 2.4.4. Fitness of gridded daily observations for different applications

Spatial and temporal resolutions and the period of data record depend largely on the products. Those with fine grid intervals include CHIRPS (0.05°) and GSMaP and IMERG (0.1°). The temporal sampling is as dense as half-hourly for IMERG and hourly for GSMaP. The data record dates back to the late nineteenth century for a monthly version of GPCC, but is otherwise limited to more recent decades. Most satellite-based products are available only after late 1990s with a few exceptions, including the monthly GPCP, which is available for 1979 onwards. The products with fine resolutions may be of great utility for regional hazard monitoring, while those with decades of data record would be optimal for climate studies focused on long-term changes in the water cycle. It is noted that a grid size as small as 0.1°
does not necessarily guarantee that the information content is as fine as 10 km, since low-frequency microwave FOVs are significantly larger than 10 km.

Among the critical requirements for operational applications is data latency. Many of the products (CMORPH, GSMaP, IMERG, and PERSIANN, for instance) offer a near-real-time option in which the data are distributed to the users as quickly as possible at the expense of accuracy (for example, Kubota et al., 2020). The same products often provide a better calibrated version at a later time for general users who prioritize reliability over latency.

This assessment is not intended to show which product is "better" than another because the absolute reference does not exist. One of the primary goals of the assessment is to document the characteristics of structural bias in hopes to help the dataset providers further refine the algorithm. The intercomparison results shown above would change as the participating products are upgraded to future versions. The assessment will need to be regularly updated as well to accommodate the continuous evolution of the products.

### 2.4.5. Summary

We presented in this sub-chapter an intercomparison of 11 global precipitation datasets. Major conclusions are:

i. While the overall geographical pattern of precipitation is coherent among products, the magnitude varies from one dataset to the other. The agreement is poor particularly at high latitudes, since light and/or solid precipitation typical of high latitudes is difficult to estimate accurately from satellite microwave radiometry.

ii. A systematic bias is present between gridded gauge products (GPCC and CPC), which is presumably partially responsible for the spread in merged multi-satellite datasets adjusted to the gauge products.

iii. The bias characteristics in the annual/monthly mean precipitation are a poor predictor of those in extreme precipitation.

### 2.4.6. Recommendations

Specific recommendations to this chapter are:

i. An accurate estimation of the tropical precipitation is important as an observational constraint on the tropical dynamics (Hadley cells, MJO, etc.) and the Earth energy budget. The current uncertainty in the ITCZ rainfall (7 mm/d–9 mm/d) could be problematic and further effort is urged to reconcile this discrepancy.

ii. The inter-product spread is large at latitudes higher than 40°S/N. This high-latitude uncertainty is among the major factors responsible for the disagreement in the global-mean precipitation. To mitigate this issue, further improvement is critically important on the estimation of cold-season precipitation. The difficulty in separating cloud water from light rainfall is likely another source of uncertainty in the microwave retrieval of precipitation and requires better modeling in the algorithm.

iii. Many products apply a gauge adjustment to satellite-based precipitation estimates over land. The present intercomparison reveals that the uncertainties intrinsic of gridded gauge datasets (GPCC and CPC) can be a bottleneck for all the products internally using these data as a reference. Better consistency between different gauge products is critically desired.
iv. Uncertainties in accumulations have not been reduced significantly from many regional validation studies. More emphasis on physically derived uncertainties for weather and climate applications will be critical to gain confidence in these products going forward.

### 2.4.7. References

Andersson, A., K. Fennig, C. Klepp, S. Bakan, H. Grassl and J. Schulz, 2010: The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data - HOAPS-3. *Earth System Science Data*, **2**, 215–234.

Ashouri, H., K.-L. Hsu, S. Sorooshian, D.K. Braithwaite, K.R. Knapp, L.D. Cecil, B.R. Nelson and O.P. Prat, 2015: PERSIANN-CDR: Daily precipitation climate data record from multi-satellite observations for hydrological and climate studies. *Bulletin of the American Meteorological Society*, **96**, 69–83.

Beck, H.E., N. Vergopolan, M. Pan, V. Levizzani, A.I.J.M. Van Dijk, G.P. Weedon, L. Brocca, F. Pappenberger, G.J. Huffman and E.F. Wood, 2017: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. *Hydrology and Earth System Sciences*, 21(12), 6201–6217, doi:10.5194/hess-21-6201-2017.

Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. *Earth System Science Data*, **5**, 71–99.

Funk, C., P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, S. Shukla, G. Husak, J. Rowland, L. Harrison, A. Hoell and J. Michaelsen, 2015: The climate hazards infrared precipitation with stations–A new environmental record for monitoring extremes. *Scientific Data*, **2**, 1–21.

Gehne, M., T.M. Hamill, G.N. Kiladis and K.E. Trenberth, 2016: Comparison of global precipitation estimates across a range of temporal and spatial scales. *Journal of Climate*, **29**, 7773–7795.

Gruber, A., and V. Levizzani (eds.), 2008: *Assessment of global precipitation products*. WCRP Series Report No. 128 and WMO TD-no. 1430, Geneva.

Huffman, G.J., R.F. Adler, M.M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B. McGavock and J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations, *Journal of Hydrometeorology*, **2**, 36–50.

Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker and D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. *Journal of Hydrometeorology*, **8**, 38–55.

Huffman, G.J., D.T. Bolvin and E.J. Nelkin, 2015: *Day 1 IMERG Final Run Release Notes*. https://gpm.nasa.gov/sites/default/files/document_files/IMERG_FinalRun_Day1_release_notes.pdf.

Huffman, G.J., D.T. Bolvin, D. Braithwaite, K.-L. Hsu, R.J. Joyce, C. Kidd, E. J. Nelkin, S. Sorooshian, E.F. Stocker, J. Tan, D.B. Wolff and P. Xie, 2020: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG). In: *Satellite Precipitation Measurement* (V. Levizzani, C. Kidd., D.B. Kirschbaum, C.D. Kummerow, K.
Nakamura, F.J. Turk, eds.). Springer Nature, Cham, Advances in Global Change Research, 67, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19.

Joyce, R.J., J.E. Janowiak, P.A. Arkin and P. Xie, 2004: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. *Journal of Hydrometeorology*, **5**, 487–503.

Kubota, T., S. Shige, H. Hashizume, A. Aonashi, N. Takahashi, S. Seto, M. Hirose, Y.N. Takayabu, T. Ushio, K. Nakagawa, K. Iwanami, M. Kachi and K. Okamoto, 2007: Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation. *IEEE Transactions on Geoscience and Remote Sensing*, **45**, 2259–2275.

Kubota, T., K. Aonashi, T. Ushio, S. Shige, Y.N. Takayabu, M. Kachi, Y. Arai, T. Tashima, T. Masaki, N. Kawamoto, T. Mega, M.K. Yamamoto, A. Hamada, M. Yamaji, G. Liu and R. Oki, 2020: Global Satellite Mapping of Precipitation (GSMaP) products in the GPM era. In: *Satellite Precipitation Measurement* (V. Levizzani, C. Kidd., D. B. Kirschbaum, C. D. Kummerow, K. Nakamura, F.J. Turk, eds.), Springer Nature, Cham, Advances in Global Change Research, 67, 355–373, https://doi.org/10.1007/978-3-030-24568-9_20.

Maggioni, V., P. Meyers and M. Robinson, 2016: A review of merged high resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM)-Era. *Journal of Hydrometeorology*, **17**, 1101–1117.

Masunaga, H., M. Schröder, F.A. Furuzawa, C. Kummerow, E. Rustemeier and U. Schneider, 2019: Inter-product biases in global precipitation extremes. *Environmental Research Letters*, **14**, 125016, doi:10.1088/1748-9326/ab5da9.

Roca, R., N. Taburet, E. Lorant, P. Chambon, M. Alcoba, H. Brogniez, S. Cloché, C. Dufour, M. Gosset and C. Guilloteau, 2018: Quantifying the contribution of the Megha-Tropiques mission to the estimation of daily accumulated rainfall in the Tropics. *Quarterly Journal of the Royal Meteorological Society*, **144** (Suppl. 1), 49–63.

Roca, R., L.V. Alexander, G. Potter, M. Bador, R. Jucá, S. Contractor, M.G. Bosilovich and S. Cloché, 2019: FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products. *Earth System Science Data*, **11**, 1017–1035, https://doi.org/10.5194/essd-11-1017-2019.

Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian and K.L. Hsu, 2018: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. *Reviews of Geophysics*, **56**(1), 79–107, doi:10.1002/2017RG000574.

Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu and S. Yang, 2007: A gauge-based analysis of daily precipitation over East Asia. *Journal of Hydrometeorology*, **8**, 607–626.

Xie, P., R. Joyce, S. Wu, S. Yoo, Y. Yarosh, F. Sun and R. Lin, 2017: Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998. *Journal of Hydrometeorology*, **18**, 1617–1641.

Ziese, M., A. Rauthe-Schöch, A. Becker, P. Finger, A. Meyer-Christoffer and U. Schneider, 2018: GPCC Full Data Daily Version 2018 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. DOI: 10.5676/DWD GPCC/FD_D_V2018_100.
## 2.5. Extreme and intense precipitation

Rémy Roca<fnref n="1" />, Hirohiko Masunaga<fnref n="2" /> and Lisa Alexander<fnref n="3" />,<fnref n="4" />

### 2.5.1. Introduction

Extreme and intense precipitation is at the core of many scientific and societal concerns, both from a meteorological and a climate change perspective. In recognition of this central role and the need to enhance research efforts, WCRP established a Grand Challenge on Weather and Climate Extremes, a core research focus of which was on heavy precipitation (Alexander et al., 2016). The maturity of new precipitation observational datasets has triggered interest in their ability to help document extreme precipitation. In this chapter, we summarize our current assessment and we focus on intense precipitation, the wet end of extreme precipitation, leaving the dry end of the spectrum (droughts) for a later time.

The recent emergence of multiple satellite-based datasets, reanalyses from multiple centers and a new set of ground-based gridded datasets indeed prompts the need to assess how extreme intense situations are described by these renewed observational-based capabilities (Roca et al., 2019). This is even more urgent, as none of these efforts have been purposely constructed for very intense rainfall conditions. We note that the following condensed review is not a guidance document and we do not address the technicalities here (order of operation, selection of an extreme index; the requirement for length of the record, etc). Rather, we focus on what our current ability is to document extreme precipitation.

Intense precipitation can arise from either a short burst of very precipitating deep convection and/or a long spell of moderately raining systems, or both. The definition of “extreme” precipitation therefore remains scale-dependent, and here we put the emphasis on daily precipitation at ~100 km scale.

The chapter is organized by first exploring global land and ocean. Then a limited regional investigation is proposed along with a process-oriented assessment of the products' capability. A third section is dedicated to showcasing a few subjectively-selected studies where the data are actually used for a scientific application. Finally, a list of recommendations is offered.

### 2.5.2. Global land

Owing to the conventional networks of rain gauges, the documentation of precipitation over global land benefits from a large number of datasets including in situ gridded, satellite-based on atmospheric reanalysis. Alexander et al. (2020) used many extreme indices based on those recommended by ETCCDI (Zhang et al., 2011). Figure 2.5.1 shows the large discrepancies across observationally-based products characterized by a factor of 2 in magnitude. The figure also shows how the various sub-ensemble of products (in situ, satellite uncorrected, satellite gauge corrected and reanalyses) contribute to the overall spread. This intercomparison generally emphasizes that global space-based precipitation products show the potential for

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

<footnote n="2">² Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan</footnote>

<footnote n="3">³ Climate Change Research Centre, University of New South Wales, Sydney, Australia</footnote>

<footnote n="4">⁴ ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia</footnote>
<figure><img src="image_37.png" /><figcaption>**Figure 2.5.1.** The time series of quasi-global land (50°S–50°N) averaged wettest day (Rx1day) for various gridded datasets. The spread of the various sub-ensemble of products is also shown on the right panel using a boxplot including all products (black boxplot). Adapted from Alexander et al., 2020</figcaption></figure>

climate scale analyses of extremes as a complementary source to in situ gridded data while
reanalysis should be used with caution.

Bador et al. (2020a) further indicates that better agreement on the space/time location of extremes is found among the products rather than on the actual magnitude of the extreme metrics. Donat et al. (2019) looked at the trends in extreme precipitation to reveal that precipitation totals and extremes have increased in humid regions since the mid-twentieth century. Conversely, despite showing tendencies to increase, no robust changes can be detected in the drier regions. Masunaga et al. (2019) performed a large number of product intercomparison and suggested that, for many of the satellite-based products, the uncertainty of
the climatology is shown to be a poor predictor of the uncertainty in the extreme of the distribution. Origins of the systematic bias depend qualitatively on precipitation regimes (climatology versus extremes, for instance) and may be traced back to uncertainties at fundamental levels of the satellite microwave algorithms (Sekaranom and Masunaga 2017, 2019). This underscores the need to focus on extreme and intense precipitation in the assessment and the intrinsic difficulty in exploring both totals and extreme precipitation. Focusing on a subset of products [PERSIANN-CDR, ERA-I, Water and global Change (WATCH) Forcing Data methodology applied to ERA-Interim data (WFDEI), National Centers for Environmental Prediction-Department of Energy Reanalysis 2 (NCEP2), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP)] over the 1979–2017 period and GPCC as a reference, Chen et al. (2020) confirms the large discrepancies in the absolute magnitude of extremes intensity. The study further shows nuanced performances from the reanalysis and PERSIANN-CDR, highlighting a regional and seasonal variability in their capability to represent extremes compared to the reference. It is furthermore shown that performance can vary along the temporal record, adding uncertainty to trend analyses.

The previous generation of products were not well nor systematically assessed, but for the satellite-based products, studies revealed large spread among products (Herold et al. 2017; Aghakouchak et al. 2011, Sun et al., 2018). The current assessment suggests that the situation might have improved somewhat.

### 2.5.3. Global ocean

Conventional in situ data from networks of buoys (Wu and Wang, 2019), rain gauges over atolls in the tropics (Greene et al., 2008), radar measurements from islands (Henderson et al., 2017) or ship-based disdrometer observations (Klepp et al., 2018) are tentatively used to evaluate and characterize reanalyses and the satellite-based products. However, due to the scarcity of in situ precipitation observations (Serra, 2018), the assessment of the capabilities is usually very weak. It is even worse in the case of the extreme precipitation.

Figure 2.5.2 shows the probability of exceedance over the tropical ocean for various satellite-based products (De Meyer and Roca, 2021), exhibiting the large spread for threshold above ~75 mm/day. The analysis further reveals the disparity between the microwave constellation-based products and the IR or single microwave platform products (PERSIANN and GPCP). It also shows that two clusters of products emerge, one with a larger occurrence of “extreme” extremes (IMERG, GSMaP and HOAPS) than the other [CMORPH, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA)]. MSWEP, as a simple combination of the two later products, is close to this cluster as well.

Burdanowitz et al. (2019) explore the scaling of ERA-5 extreme instantaneous precipitation at 30 km with SST over the global ocean and show significant departures from the OceanRAIN dataset. Masunaga et al. (2019) generally find a larger spread among the extremes of various products over the ocean compared to land (Masunaga et al., 2019).
<figure><img src="image_38.png" /><figcaption>**Figure 2.5.2.** Probability of exceedance of daily 1°x1° accumulated precipitation over the tropical ocean (30°S–30°N) for the period 2017–2017 except for the TAPEER product where it is restricted to the 2012–2016 period.</figcaption></figure>

### 2.5.4. Regional and process-oriented investigations

While a systematic exploration of all ongoing regional studies about precipitation extreme is out of the scope of the present chapter, we have selected only a few references that convey the main messages. These references can serve at a starting point for the interested reader.

#### 2.5.4.1. Regional

##### 2.5.4.1.1. Asia

Over the Tibetan Plateau, Wu et al. (2019) show that TRMM and CHIRPS overestimate extreme high precipitation. He et al. (2019) intercompared in situ, satellite and reanalysis gridded products over East mainland China (105°–140°E, 15°–35°N). Figure 2.5.3 shows the daily precipitation distribution for boreal summer together with the references rain-gauge network data.

The intercomparison confirms global results with reanalysis strongly underestimating the “extreme” extreme cases. The in situ gridded observations also seem to suffer from the same issue. PERSIANN, as already pointed out, is truncated and does not exhibit values above 120 mm/d. Only the 3B42 (TRMM) product seems to approach the reference datasets. In this region, extreme precipitation is distributed around two maximum centers, over the lower-middle reach of the Yangtze River basin and in South China. ERA-Interim, MERRA, and CFSR do not represent these regional features. The moist-season extreme precipitation in the Korean peninsula and Japan is often brought about by warm rain processes and hence may be difficult to properly capture in the satellite-based products relying partly on the microwave scattering by ice particles (Sohn et al., 2013). The same difficulty may be encountered for extremes in other regions beyond east Asia as well (Hamada et al., 2015).
<figure><img src="image_39.png" /></figure>

**Figure 2.5.3.** Frequency of occurrence of daily precipitation in mm/d for boreal summer. From Wu et al (2019). CN05.1 is based on the interpolation of data from 2400 observational stations in China (Wu and Gao, 2013). The Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) dataset is based on rain gauge observation records over Asia.

### 2.5.4.1.2. Western and central Europe

Using the European daily high-resolution Observational Gridded Dataset (E-OBS) as a reference, seven gridded products (one gauge-based, three satellite-based and two reanalysis-based products) were analyzed using temporally and spatially matched pairs of precipitation estimates with a focus on the detection of extreme events (Lockhoff et al., 2019). The occurrence of wet-day intensities is generally well reproduced by all products and deficiencies are noted in coastal regions and dry areas in the region. The performances show substantial scale dependence with skills showing up 3-day and 1.25° and above scale. This is confirming earlier results for GPCP daily that exhibits better performances at 3°/5 days (Lockhoff et al., 2014).

### 2.5.4.1.3. Over the continuous U.S.

In an effort to identify robustness among various products, five in situ-based gridded products, three satellite-based datasets, two regional reanalyses and one regional climate model simulation have been intercompared in pairs using a Generalized Extreme Value (GEV) framework (Timmermans et al., 2019). The products are used at the 25 km, 5-day resolution. The results are in line with the European-based investigations with a strong scale dependence revealed by this consistency exploration. The inconsistency appears stronger over complex terrain in all products, and satellite-based products are characterized by seasonally varying performances.

### 2.5.4.1.4. Africa

Harrison et al. (2019) investigate changing precipitation in Sub-Saharan Africa using rain gauge and satellite products. They show that satellite products struggle to correlate with the REGEN reference for the R1xday (wettest day) and Rx5day (consecutive 5-day maxima) extreme precipitation indices over the 1983–2013 period. They further compare a set of 12 satellite products and various rain gauge-based gridded products over a limited time span with a focus on the wet season (Figure 2.5.4). Their analysis concludes that sparse data indicates a positive trend in African rainfall extremes and that the satellite products were found useful to fill some space/time gaps in the conventional observational record.
<figure><img src="image_40.png" /></figure>

**Figure 2.5.4.** Taylor diagram for wet season indices, 1998–2013. Number of rain days (R1 mm). Diagrams show how gauge and satellite products compare to REGEN 1998–2013 data in terms of Pearson’s correlation (azimuth angle), ratio of standard deviations (distance from black curve) and mean square error (distance from black circle on x-axis). Adapted from Harrison et al., 2019

While not directly an intercomparison or an assessment, the recent work of Le Coz and Van De Giesen (2020) offers some guidance for end-users about what satellite products to use for floods and extreme applications among others tailored for Africa, and is worth being mentioned as a useful entry point for this region.

#### 2.5.4.1.5. Australia

Australia, like some other regions, has a very good ground-based network and associated gridded products [for example, the Australian Water Availability Project (AWAP, Jones et al., 2009) daily gridded 5 km x 5 km resolution dataset]. However, even then, in sparsely populated regions like the central and western parts of the country, very few in situ gauges exist and estimating extreme rainfall and/or trends in these regions is problematic (King et al., 2013). Contractor et al. (2015) intercompared daily precipitation values from AWAP with GPCP 1DD version 1.2 and TRMM 3B42 V7 over the period 1998–2013 and found that correlations were reasonably good, although were not better than 0.6 for Australia as a whole. The satellite products generally underestimated the most “extreme” extremes across the range of cities that Contractor et al. (2015) considered. It is worth noting, though, that an in-depth intercomparison of extreme precipitation in all products across whole the region has yet to be performed.

### 2.5.4.2. Process-oriented

#### 2.5.4.2.1. Atmospheric rivers

Atmospheric rivers are associated with extreme precipitation. Focusing on the winter of 2017 on the Californian coast, Wen et al. (2018) showed that six major satellite estimates were able to report heavy precipitation during the atmospheric rivers. Yet while the ensemble mean was close to the rain gauge references, there was considerable spread characterized in the individual products. None of the products or the operational radar network accurately documented the peak extreme rain rate during the sequence of the events. Ramos et al. (2020) compared two cases of atmospheric rivers over California and Portugal in winter 2016 and evaluated more than 20 satellite, in situ and reanalysis gridded precipitation products. Similar
results were obtained for the satellite products. But unlike at the global scale, the reanalysis datasets were shown to outperform the satellite and gridded in situ products slightly with respect to this region and processes when compared with reference rain gauges (Wen et al., 2018).

#### 2.5.4.2.2. Scaling of extreme precipitation with surface temperature

The sensitivity of extreme precipitation to surface conditions (water vapor and temperature) has received considerable interest, and over the tropical land, earlier analysis raised questions about an otherwise physically-based theory based on thermodynamics that stipulates a ~6–7% K increase of the extreme with each surface degree of warming. By pooling data over land using a 10+ suite of satellite products, Roca et al. (2019) showed that the most recent, constellation-based, gridded daily products support the theory and do indeed exhibit a robust (low spread) Clausius-Clapeyron sensitivity. Over tropical oceans, the sensitivity of extremes for this generation of products is also in line with the theory and characterized by a small spread (De Meyer and Roca, 2021). The investigation further reveals that while the constellation-based products show robust scaling behavior, considerable uncertainty remains on the absolute magnitude of extreme precipitation as shown in Figure 2.5.5. It is not possible to identify which clusters might be the closest to truth.

In summary, the community has produced an extensive evaluation effort over various regions. The results are generally in line with the global studies. Yet we ought to take better advantage

<figure><img src="image_41.png" /><figcaption>**Figure 2.5.5.** The value of the 99.9th percentile of the 1°x1° daily accumulated precipitation as a function of the SST lagged by 2 days. Each color corresponds to a precipitation product. Solid line for Operational SST and Sea Ice Analysis (OSTIA), dashed line for Optimally Interpolated Sea Surface Temperature (OISST) and dash-dotted lines for Optimally Interpolated Remote Sensing Systems Sea Surface Temperature (OIRSS). For the period 2007–2017. Regimes are separated by vertical dashed lines. The grey shaded areas indicate the non-robust cold regime between precipitation products (left) and the non-robust warm regime between SST products (right). Black dash-dotted lines correspond to the Clausius-Clapeyron 6%/K rate. From De Meyer and Roca, 2021</figcaption></figure>
of these valuable studies in the assessment through new mechanisms to be proposed. The
emergence of process-oriented multi-product assessments is encouraging to help end-users to
navigate products.

2.5.5. Example applications

While assessment exercises tend to highlight the non-robust features of the assessed data, as it fuels dataset improvements, we have also highlighted some robust features that permit further scientific analysis. Indeed, the balance of evidence suggests we are now in an interesting position where some datasets are fit for some scientific applications while clearly not all the datasets are fit for all scientific investigations. While a guidance document would help navigate the situation, in the following paragraphs we showcase a sub-selection of studies that actually make use of the datasets to further advance our understanding of extremes within the water cycle.

**2.5.5.1. Atmospheric physics**

Using constellation-based satellite precipitation estimates from FROGS and the recent mesoscale convective system database Tracking Of Organized Convection Algorithm through a 3-DsegmentatioN (TOOCAN), based on homogeneous infrared observations from geostationary satellites (Fiolleau et al., 2020), the role of organized convection on the occurrence of extreme precipitation in the tropics has been investigated. The study shows that the long-lived systems are disproportionally responsible for extreme precipitation relative to their occurrence (Roca and Fiolleau, 2020).

**2.5.5.2. Climate model evaluation of extremes**

The sensitivity of climate model extremes to model resolution was investigated and assessed against the spread in the observational record (Bador et al., 2020b). It was shown that an increase in resolution, while improving the model's representation of intense precipitation, is unlikely to be enough to improve model performance significantly. This indicates that, in tandem to higher resolution, improvements in model physics and/or tuning is required to better improve model scores at simulating extremes. The use of the observational record for model evaluation of extremes is further considered in the framework of the emerging Department of Defense (DoD)-based and precipitation-centric model evaluation project (Pendergrass et al., 2020).

**2.5.6. Summary**

The observational estimation of extreme precipitation has benefited from the recent emergence of many new datasets, from in situ, reanalysis and satellite data. The current assessment effort falls short of addressing all of the concerns related to extreme precipitation. Among the identified gaps are extreme snowfall events over land and ocean and orographic intense precipitation. Nevertheless, this first assessment effort points towards a few sound conclusions.

Generally speaking, the new generation of satellite products can be used in support of scientific investigations along with the in situ-based gridded datasets. The current reanalysis datasets appear to suffer from larger uncertainties, making their fitness-for-purpose in extreme precipitation analysis more arguable.

More specifically, in the case of the satellite-based observations over land, the situation appears to have improved from the last decade and a number of salient features have
emerged. The new generation of satellite-based global datasets now appear to have the potential for climate scale analyses of extremes. For these products, uncertainties in climatology have been shown to be a poor predictor of the uncertainty in the extremes of the distribution. A very large number of regional investigations have been performed over various land areas, generally in line with the global assessment results, although such a number of studies is difficult to integrate.

Over the oceans, while a number of features are robust among the last generation of satellite constellation-based products, the large spread in the absolute magnitude of the extreme value remains to be addressed. Process-oriented evaluation has started to emerge in the literature and shows good promise to formulate further assessments.

As a consequence, a number of scientific investigations related to extreme precipitation have been supported by the datasets (scaling with SST, climate model evaluation, and so on), which is encouraging. Yet the various datasets may not all meet the requirements for some of the scientific investigations needed, prompting recommendations from agencies and product developers.

## 2.5.7. Recommendations

Based on this first and partial attempt to assess the ability of observational gridded precipitation products to document extreme precipitation, we are in a position to formulate some recommendations for the agencies and the community.

### 2.5.7.1. General considerations

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

ii. Improve the products, as the assessment has identified some non-robust features that deserve further attention. Feedback to dataset providers with specific details could be formulated in targeted workshops.

iii. Fill the gaps in the assessment

iv. Communicate the robust dataset features to support further research using the datasets

### 2.5.7.2. Specific considerations

i. Explore further the process-oriented assessment

ii. A community effort is needed to clarify the magnitude of extreme precipitation over the ocean.

iii. Better benefit from the scattered myriad of local and regional evaluation studies, possibly through an extensive regionally-oriented assessment with a common set of scores and metrics. GEWEX Regional Hydroclimate Projects and WCRP/Regional offices could play a role.

iv. The fact that some products are better fit for purpose than others requires a guidance document to help navigate the large suite of available datasets.

v. The products need to be assessed for the dry precipitation extremes (droughts) too.

## 2.5.8. Acknowledgments

We thank the following individuals for their contribution to our better depiction of the status of the observational capability: Victorien De Meyer, Romulo Juca, Mike Bosilovitch, Margot Bador.
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# 3. Emerging directions

## 3.1. Toward the new generation of products

George Huffman<fnref n="1" /> and Pierre-Emmanuel Kirstetter<fnref n="2" />

The fine time interval provided in modern precipitation products is only possible by combining estimates from many individual high-quality satellite sensors, and even then, additional approximations are needed to fill numerous gaps in the mosaic of short-interval segments from the various sensors. As such, the future directions of global observationally-based precipitation products involve improvements in the individual retrievals, improvements and operationalization of additional sensor estimates and innovations in assembling the merged products, including the intercalibration and homogenization of the data record. One major challenge is to both provide consistent estimates in any particular epoch of the constellation and to provide consistent estimates across generations of sensors with differing capabilities.

### 3.1.1. Passive microwave retrievals

#### 3.1.1.1. Outstanding problems

Passive microwave (PMW) retrievals form the mainstay of modern global precipitation estimates. Improvements over 3+ decades of development have expanded their utility, but challenges remain, including orographic precipitation, snowfall and the performance of the estimates in specific weather regimes as discussed in Chapter 1. These topics critically impact detection and rate estimation (see Chapter 1), and are best addressed at the sensor level, as opposed to the merger process, because that allows detailed sensor information to be applied to the issues, in combination with ancillary data, such as atmospheric temperature and moisture profiles, generally drawn from numerical reanalyses/forecasts. The diversity of channels, resolutions and scanning patterns that sensors "see" considerably complicates efforts to make uniform retrievals as well.

#### 3.1.1.2. Machine learning

Recent work on precipitation retrievals has focused on applying current concepts in machine learning algorithms. For example, recent work has shown encouraging results in applying machine learning approaches to PMW data (Adhikari et al., 2020). Importantly, machine learning techniques require quality training data. Uncertainties in satellite precipitation estimation often transfer from the calibration data set, for example, from PMW to Geostationary Earth Orbit Infrared (GEO-IR) estimates (Upadhyaya et al., 2020). A training precipitation set with properties improperly matching the capabilities of the satellite sensor and its observed information content can negatively impact precipitation detection and quantification retrievals and propagate systematic and random errors (Chapter 1.2).

#### 3.1.1.3. Probabilistic QPE

The uncertainty structure of satellite-based quantitative precipitation estimation (QPE) is largely unknown at fine spatio-temporal scales and requires more than just one deterministic "best precipitation estimate" to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. Because satellite retrievals are underdetermined, uncertainty

<footnote n="1">¹ NASA Goddard Space Flight Center</footnote>

<footnote n="2">² University of Oklahoma, Norman, OK</footnote>
should be an integral part of QPE (Kirstetter et al., 2018; Chapter 1.2). Precipitation probability mapping has been shown to outperform deterministic estimates by mitigating systematic biases in the deterministic retrievals, quantifying uncertainty and advancing the monitoring of precipitation extremes.

### 3.1.1.4. Surface-based calibrators

While satellite retrievals have almost exclusively been calibrated by precipitation gauges, the installed base of surface radars in the U.S., Europe, Australia, Japan and elsewhere seems to invite use if questions of quality control, access and archive record can be overcome (Chapter 1.2). The proliferation of communications microwave link-base precipitation estimates holds similar promise as well (Leijnse et al., 2007; Messer et al., 2006), with the same caveats.

## 3.1.2. Other estimates

Besides PMW retrievals, a number of other satellite sensor families provide precipitation estimates. In the grand tradition of precipitation estimation, none of these sensors provides totally new data band, but (except for GEO-IR), little or no use has been made of them.

### 3.1.2.1. GEO

GEO-IR estimates pre-date the start of PMW data, and present the interesting dilemma that they are plentiful, but tend to be of lower quality due to reliance on relating cloud-top structure to surface precipitation. As such, these data are used as backup information in multi-satellite products that depend on PMW data. Nonetheless, this use is a key and ongoing need, meaning continued advancement is important. Following on the discussion of machine learning approaches above, researchers are specifically applying machine learning to GEO-IR data (including Tao et al., 2018). Research has already demonstrated that using multi-channel GEO data can provide improved precipitation estimates, including Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Multispectral Analysis (PERSIANN-MSA; Behrangi et al., 2009). Machine learning techniques should be beneficial here as well, but a primary barrier to operational use of multiple channels is the need to access the global collection of the requisite channels, and in some cases handling the differences among similar channels on different sensors. At present, only the GEO-IR is available in an archived and current source of uniformly formatted global datasets, including the CPC Global 4-km Merged IR dataset (CPC, 2020) and the Gridded Satellite (GridSat) collection (Knapp et al., 2011). The long-term (and current) record of GEO precipitation estimates would be significantly improved by creating global datasets for the GEO-Visible and GEO-Water Vapor channels as companions to the GEO-IR.

Succeeding generations of GEO satellites carry progressively more-capable sensors, exemplified by NOAA's Advanced Baseline Imager (ABI) sensor on board the latest Geostationary Operational Environmental Satellites (GOES-R Series). ABI provides three times more spectral channels, four times the resolution and five times faster scanning when compared to its predecessor imager on board previous-generation GOES (Schmit et al., 2017). The GOES-R series also carries a lightning sensor that potentially provides additional input for estimating precipitation. This new generation of GEO sensors opens new opportunities in quantifying precipitation rates, and makes it imperative to provide easy, analysis-ready access to the multiple channels provided across the various satellites, including developing analysis schemes that account for the different channel frequencies. One clear requirement is that the precipitation research community needs to demonstrate the skill of multi-channel GEO versus single-channel GEO-IR versus PMW, all using modern algorithms, to determine the cost-
benefit analysis of the three-channel (and even-more-channel) GEO retrievals against the
development effort and expense for the requisite input datasets.

3.1.2.2. Advanced Very High Resolution Radiometer (AVHRR)

One limitation to GEO observations is that the footprints are sufficiently distorted above the 60°
latitude circle in both hemispheres that the data are not used at higher latitudes, as exemplified
by the CPC Global 4-km Merged IR dataset covering the latitude band 60°N-S. In addition,
current GEO-IR schemes tend to confuse surface ice and snow with cloudiness, leading to low
skill in polar regions. Recently, Xie et al. (2019) introduced the use of AVHRR IR data for
precipitation estimates. These sensors have a history back to 1979 on the NOAA-series polar
orbiters, are uniformly processed and include cloudiness estimates that allow the (approximate)
separation of surface snow and ice from clouds. Ehsani et al. (2020) provide another example
of this concept. Despite flying on only a few polar orbiters, the convergence of orbital swaths
near the poles allows relatively frequent observations of any given location. Going forward, the
Visible Infrared Imaging Radiometer Suite (VIIRS) sensor will provide equivalent data.

3.1.2.3. Cloud volume

The series of the old Television-Infrared Operational Sounder (TIROS) Operational Vertical
Sounder (TOVS), current Advanced Infrared Sounder (AIRS) and relatively new Cross-track
Infrared Sounder (CrIS) instruments have been used to provide precipitation estimates based
on cloud volume parameters [and the same could be done with the Infrared Atmospheric
Sounding Interferometer (IASI)]. Even though the original (Susskind and Pfaendtner, 1989;
Susskind et al., 1997) estimates are fairly approximate, they have proved useful as a basis for
providing high-latitude estimates for most of the GPCP Versions 1, 2, and 3 products (Huffman
et al., 2001; Adler et al., 2018; Huffman et al., 2020a). Improvements with the Smith and Barnet
(2019) Version 7 products are expected to refine the AIRS and CrIS estimates, and the advent
of an equivalently-long timeseries of AVHRR/CrIS implies that the two approaches should be
evaluated for use together at the high latitudes for both GPCP and other multi-satellite
products. The MODIS instruments on Terra and Aqua have 16 IR "cloud" channels that might
be used in a similar way, but are not presently.

3.1.2.4. Assimilation/forecast

An additional data source that warrants consideration is numerical assimilation/model
estimates of precipitation. While not "observational", many end-users are less concerned about
the origin of the estimates and more about having the "best" estimates. Most numerical
schemes have known problems in "convective" weather regimes, which typifies much of the
tropics and sub-tropics. However, it has long been the case that numerical products prove
better than observations in "stratiform" weather regimes that typify higher latitudes (Ebert et al.,
2007). These considerations suggest that the merged products should have data fields that
incorporate such numerical assimilation/forecast estimates in locations/times for which they are
competitive with the observational estimates. Such a combination can naturally incorporate
improvements in both retrievals and assimilations as enhanced versions are released. This
discussion raises the point that the relative performance of each is a worthy research topic on
an ongoing basis, and of considerable interest to end-users.

3.1.2.5. Soil moisture

Another approach to estimating precipitation is to work backwards from satellite estimates of
soil moisture. In summary, given local soil, vegetation and previous rainfall conditions, changes
in soil moisture sensed by satellites can be approximately related to current rainfall. The Soil
Moisture Analysis Rainfall Tool (SMART; Crow et al., 2011) and Soil Moisture to Rain (SM2Rain; Brocca et al., 2014) are examples, with the former focusing on creating adjusted satellite datasets to obtain the timeseries of rainfall that is most consistent with the record of soil moisture changes. The relatively infrequent soil moisture observations make operational use a challenge that is a matter of current research.

### 3.1.3. Merged products

#### 3.1.3.1. Outstanding problems

Most users focus on merged precipitation data products, as noted previously, and Chapter 1 has discussed known issues intrinsic to the merger schemes. First, it is helpful to recall that some merged datasets prioritize homogeneity in the data record, usually by severely down-selecting the choice of input data. These are said to follow Climate Data Record (CDR) standards, and include GPCP (Adler et al., 2018) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). High-Resolution Precipitation Products (HRPP), on the other hand, try to enforce homogeneity, but include “all possible” data. These include CMORPH (Joyce et al., 2004), GSMaP (Kubota et al., 2007), and IMERG (Huffman et al., 2020b), among others. All of these datasets choose some intercalibration, with CDRs usually picking a PMW standard, and HRPPs tending to use a calibrator that incorporates spaceborne radar. Homogeneity for the calibrator across sensors is key, and one main reason that continued access to a reasonably wide-swath spaceborne radar in future missions is considered a high priority. A second outstanding issue is how best to fill the numerous gaps that exist when PMW data are segmented to a short-interval time grid (typically 30–60 minutes), discussed in Chapter 1 as “revist-gap mitigation.” As a first approximation, the “morphing” concept pioneered in Joyce et al. (2004) is used in CMORPH, GSMaP and IMERG. The best choice for how to compute the propagation vectors is a matter of current research, and the IMERG team plans to introduce the Scheme for Histogram Adjustment of Ranked Precipitation Estimates in the Neighborhood (SHARPEN; Tan et al., 2020) to counteract some of the averaging effects implicit in morphing. The grand challenge is to develop and operationalize a better “storm development” algorithm that estimates the lifecycle stages of precipitation systems. For example, Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates (REFAME; Behrangi et al., 2010) uses time series of local GEO-IR brightness temperatures to better track the evolution of the precipitation between PMW overpasses.

#### 3.1.3.2. Short-interval combinations with surface data

One modification to satellite-only merged products that seems attractive is to use submonthly precipitation gauge data to adjust multi-satellite estimates, since gauges are generally considered the gold standard for actual amounts. It would be nice to do this at the finest-possible time interval, but even daily gauges tend to be representative of relatively small regions (i.e., short correlation distances), meaning rather dense gauge networks are required. Clearly, oceans and many land areas lack the necessary station coverage. Xie and Xiong (2011) developed a scheme that used probability density functions (PDFs) of dense gauge data to both bias-correct CMORPH and create a combination with the de-biased CMORPH. The University of California Santa Barbara/Climate Hazards Center generates gauge-enhanced 0.05°x0.5° precipitation estimates by blending Global Telecommunication Systems station observations with bias-corrected cold cloud duration imagery in GEO-IR (Funk et al., 2015). The time interval is chosen to provide gauge estimates that are representative of larger regions than the typical daily gauge reports. The same research group is considering applying this approach to IMERG.
Other candidate surface-based precipitation datasets for possible merger with or routine calibration of merged datasets includes surface radar networks, microwave links for telecommunications, lightning-detection networks and citizen science data, such as from the Global Learning and Observations to Benefit the Environment (GLOBE) program (https://www.globe.gov/). All of these sources present challenges in terms of availability in real or post-real time, accessible archives of both the Level 1 (that is, sensor) and Level 2 (product) data and quality control. Note well that the potential for using surface data should not obscure the fact that many regions lack adequate surface data for routine use in merged products, or even for confident validation.

#### 3.1.3.3. Weighted merger

As quasi-global precipitation products have proliferated, Beck et al. (2017) took advantage of the diversity of estimates to develop regionally-varying weights for each dataset to build a “best” average global dataset, MSWEP. This approach should score better than any individual estimate, but it depends critically on what the standard is for choosing the weights, and paradoxically only maintains a strong, stable advantage as long as the individual datasets continue to be produced and included. Additionally, the weighting should be recomputed any time any of the inputs is upgraded to a new version, although it could be a matter of judgment whether any particular change requires the effort of computing new weights.

### 3.1.4. Uncertainty estimates

A final unmet need is for precipitation datasets to provide estimates of uncertainty, starting with the individual sensors, and carrying through to the merged products (Chapter 1). While Chapter 1 contains a discussion of sources of uncertainty, here, the future development work is to turn these concepts into gridbox-by-gridbox estimates. The focus has to be on using globally available inputs for this error computation, which excludes detailed use of surface data, except where those data are being merged into the product.

One potential approach is for the individual sensor estimates to provide probabilistic QPE, as described in Chapters 1.1.4 and 3.1.1, and then carry that entire set of information through the merged datasets. It is an open question whether specifying a set of quantiles or giving coefficients of a fitted function is best. There is also a critical need for methods to aggregate the gridbox-level uncertainties to larger space/time data averaging, such as daily and/or 1°x1°. At these scales, the work of the Megha-Tropiques team has paved the way for a more systematic evaluation of the uncertainty associated with sampling (Roca et al., 2018; Chambon et al., 2012). Whatever form these “expert” error estimates take, it is important that the merged datasets provide a “simple” error statement for non-expert users. For example, the IMERG team has prototyped a “Quality Index” that maps from a quantitative estimate of correlation to a simple “stoplight” chart (Huffman et al., 2020b), but much research remains. Regardless of the approach, it is a best practice that the simple error index should be traceable to a quantitative error statement. Chapter 3.2 addresses this topic in more detail.

### 3.1.5. Recommendations

i. Address orographic and snowfall regimes at the single-sensor level.

ii. Match training datasets for machine learning to the satellite sensor capabilities.

iii. Pursue scientific, technical and administrative issues to unlock the promise that surface-based datasets such as radar and communication microwave links hold as calibration and validation information.
iv. Assemble analysis-ready multi-channel GEO datasets; IR, visible and water vapor cover the long record, while more channels, as well as lightning data, are available in recent years.

v. Pursue retrievals from AVHRR, VIIRS, TOVS, AIRS, CrIS and IASI to provide useful estimates at high latitudes.

vi. Upgrade merged products to provide data fields that incorporate numerical assimilation/forecast estimates where they are useful, and provide routine reports on the relative skill of retrievals and assimilations/forecasts.

vii. Pursue the use of soil moisture observations in satellite precipitation estimates.

viii. Address the grand challenge of making detailed observational estimates of storm lifecycle development for use in merged products.

ix. Pursue incorporating short-interval surface data, including gauges, radar, telecommunications microwave links, lightning-detection networks and citizen science, recognizing that deficiencies in data coverage are severe limitations on global application.

x. Continue to develop skill-weighted mergers of datasets.

xi. Prioritize advancing uncertainty estimates at the gridbox level, and developing a methodology for aggregating these estimates to larger time/space scales.

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## 3.2. Directions in error modeling

Viviana Maggioni¹ and Christian Massari²

¹ Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA. E-mail: vmaggion@gmu.edu

² Research Institute for Geo-Hydrological Protection, National Research Council (CNR)

### 3.2.1. Introduction

Past validation studies highlighted a myriad of factors, such as retrieval techniques, sensor types, topography, spatial and temporal sampling and algorithms, that contribute to uncertainties associated with satellite precipitation products. All these factors result in errors in precipitation products, whose estimation is fundamental for hydrological modeling, land data assimilation systems, water resources management and climate studies. The ability to model errors in precipitation is paramount for not only obtaining information about satellite products' accuracy and precision in regions and during periods in which in situ measurements are not available, but also to perturb input precipitation to force land surface and hydrologic models. Despite the importance of error models, there is a clear imbalance between research efforts dedicated to the assessment of the performance of satellite precipitation products (as highlighted in Chapter 1.2) and those related to the development of precipitation error modeling.

Nevertheless, in the recent past, several models have been developed to estimate errors and uncertainties in satellite precipitation datasets. Some rely on a reference, some focus on the uncertainty component alone and do not require a reference, some are based on an additive approach and others use a multiplicative error assumption. Error models highly depend on the product temporal and spatial resolution, on precipitation rates and products, and on a priori error model structure. Thus, error models are unlikely to be universal. This section reviews the most common techniques, discusses their limitations and provides recommendations for the agencies and the scientific community.

### 3.2.2. Results

Two main types of error models are commonly used to assess errors and uncertainties in precipitation data: additive and multiplicative. The multiplicative error model was found superior to the additive approach thanks to its ability to separate the systematic and random components of the error; its applicability to the large range of variability in daily precipitation; and its predictive skills (Tian et al., 2013). Most methods require a benchmark dataset to estimate such errors and uncertainties. However, Adler et al. (2009) proposed a framework, later expanded by Tian and Peters-Lidard (2010), to assess uncertainties in global satellite precipitation datasets using the spread of coincidental and co-located estimates from an ensemble of six different products. Nevertheless, this approach only provides a relative analysis and some of the products in the ensemble are not completely independent.

One of the first error models was proposed by Huffman et al. (1997) and applied to the GPCP analysis (Adler et al., 2003; Huffman et al., 2009) and the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA; Huffman et al., 2007). This model provides root mean square random error estimates for each grid box and at each time step (i.e., monthly and daily). Once again, the limitation of this approach is that the error estimates depend on the samples being functionally independent, which may not be the case when considering finer temporal resolution.
Several studies focused on the estimation of the sampling error and modeled its standard
deviation using a power law (e.g., Bell et al., 1990; Gebremichael and Krajewski, 2004; Steiner
et al., 2003; Hong et al., 2006). For instance, Gebregiorgis and Hossain (2013, 2014) proposed
estimating the variance of daily satellite precipitation error using information such as rain rate
and geophysical features. However, these techniques assume that: i) precipitation errors can
be modeled with a lognormal distribution, which, at high rain rates, can be unrealistic
(Gebremichael and Krajewski, 2005); and ii) the variance is an appropriate estimator of the
satellite precipitation error. The Gebregiorgis and Hossain (2013, 2014) approach was shown
to perform better when the false alarm and the hit components of the error are dominant, but
the performance degraded when the missed precipitation represents a large component of the
total error.

Solutions have been proposed to characterize uncertainties and errors associated with global
high-resolution satellite precipitation datasets. For instance, Hossain and Anagnostou (2006)
introduced the two-dimensional satellite rainfall error model (SREM2D), a stochastic model that
estimates the joint probability of successful delineation of rainy and non-rainy areas. SREM2D
has been successfully applied in hydrologic modeling for streamflow simulations and debris
flow predictions (Maggioni et al., 2013; Falck et al., 2015; Nikolopoulos et al., 2017). An
example of the SREM2D performance in adjusting a satellite precipitation product (Hydro
Estimator, HYDRO-E), used as input to a hydrological model in the Tocantins-Araguaia basin in
Brazil, is presented in Figure 3.2.1.

<figure><img src="image_42.png" /><figcaption>**Figure 3.2.1.** Streamflow simulations in a sub-basin of Tocantins-Araguaia watershed in Brazil during October 15, 2010–October 14, 2011 using input precipitation from different sources: i) the satellite-based HYDRO-E product, ii) rain gauges and iii) HYDRO-E perturbed using the SREM2D error model (ensemble). This figure shows how SREM2D was able to push the satellite product closer to both ground obversions of streamflow (black dashed line) and the simulation that uses reference rain gauges as input (black solid line).</figcaption></figure>

Bellerby and Sun (2005) designed a methodology to quantify the uncertainty of high-resolution
satellite precipitation products by generating probabilistic and ensemble representations of the
measured precipitation field. Teo and Grimes (2007) presented a model for uncertainty
estimation of satellite rainfall values based on a stochastic ensemble generation of rainfall.
Many of these methods use a Monte Carlo approach to generate spatially correlated random
fields and ensembles of precipitation error. However, describing the spatial dependence of
precipitation fields is not straightforward, as different rainfall intensities are characterized by
different correlation structures (that is, when observed values are low, they tend to be scattered
and intermittent with poor spatial dependence, while when the rainfall intensity is high, it tends to be more temporally spatially dependent; Bárdossy and Pegram, 2009).

Gebremichael et al. (2011) proposed a non-parametric method to estimate errors and uncertainties at fine resolutions (3-hourly/25 km), based on conditional density functions of satellite precipitation at each grid box, calibrated using a ground reference. Maggioni et al. (2014) introduced the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework, which models errors as a combination of a random and a systematic component and considers missed precipitation cases, false alarms and hit biases. PUSH was later modified by Oliveira et al. (2018) to account for factors like seasonality and surface type and has been proven to have potential when estimating satellite precipitation errors on a global scale (Khan and Maggioni, 2020). On a similar note, Wright et al. (2017) proposed a simpler approach, based on a shifted gamma distribution, to characterize precipitation and produce a "best guess" distribution of the true precipitation by also considering hits, misses and false alarms. They were the first to explore the potential benefit of incorporating atmospheric variables such as humidity and precipitation from numerical weather models (specifically, atmospheric reanalysis) in a satellite precipitation error model.

As noted above, the complexity in error model formulation varies quite largely, from methods that estimate the variance of a precipitation product to others that also evaluate false alarms and missed cases. Very simple or more complex bias correction methods can significantly reduce errors in streamflow simulated by a hydrological model (Serrat-Capdevila et al., 2014). For instance, simple long-term bias correction was shown by Beck et al. (2017) to yield reasonable streamflow performance over tropical regions. However, such correction may not be appropriate anywhere and anytime, given the non-stationary nature of precipitation biases, or for any kind of application (Ciabatta et al., 2016; Bitew and Gebremichael, 2011).

### 3.2.3. Summary

Although in the recent past numerous attempts have been made to develop error models of satellite precipitation products, several issues limit their use in applications. First off, the majority of these approaches is based on assumptions regarding the distribution of precipitation (and/or associated errors). Second, simple error models may be preferable for some applications, but more complex solutions may be more appropriate for others. For instance, hydrological models used to simulate floods should be particularly sensitive to extreme precipitation events and the ability of detecting such events. Thus, an error model that accounts for missed precipitation cases and false alarms would be preferable. Third, precipitation errors and uncertainties depend on the product's temporal and spatial resolution, seasonality, rain rate and geophysical features. Thus, the same error model would unlikely perform similarly everywhere in the world (for example, oceans versus land, complex topography versus plains, tropics versus high latitudes), at any time (for example, winters versus summers), for any precipitation event (for example, solid versus liquid precipitation, convective versus stratiform systems) and for any application (drought versus flood monitoring).

### 3.2.4. Recommendations

Based on this first and partial attempt to assess the current status of satellite precipitation error modeling, here are some recommendations for the agencies and the community:

* Encourage the use of satellite precipitation error models in applications;

* Elaborate on the limitations and capabilities of current error models;
* Investigate possible reference datasets for calibrating error model parameters (such as distribution parameters, missed precipitation fraction and false alarm rates), especially in those regions of the world where no dense ground observation networks exist;

* Communicate such limitations to support further research using these models;

* Incorporate ancillary information (for example, topography, land surface characteristics, climate variables) within model precipitation errors;

* Consolidate current modeling approaches targeting different applications (different applications may have different needs, and the "one fits all" model may not be the appropriate solution); and

* Assess the performance of error models of different complexity across regions characterized by a variety of land uses, topography and climatologies.

### 3.2.5. Acknowledgments

The authors would like to thank Dr. Sana Khan for her contribution to this assessment and a better depiction of the status of satellite precipitation error modeling.

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## 3.3. Emerging techniques for precipitation assessment and consistency studies

Ali Behrangi

University of Arizona

### 3.3.1. Introduction

Accurate precipitation estimation is critical to study the Earth and advance science and applications. Towards this goal, observations from satellite, ground radar and in situ instruments are used, either individually or in combination, to better capture spatiotemporal sampling and accuracy of precipitation. Despite their overall merits, ground radars and in situ observations provide limited coverage for global precipitation estimation. In certain conditions, in situ data also face major uncertainties (for example, to measure snowfall due to gauge undercatch problems) (Yang et al., 2005; Behrangi et al., 2018). Recognizing their spatiotemporal coverage, various space-borne sensors and missions have been utilized for precipitation retrieval. Some of them have precipitation studies and estimation as their main objective (for example, the TRMM and GPM missions) and several others are used because they are capable of providing the information needed for precipitation retrieval. Besides adding to precipitation sampling, the new sensors often contribute by filling remaining gaps in one or more areas. For example, with TRMM, major advancement in estimating near-surface and profile of moderate and intense precipitation was obtained over the tropics (~35°S/N); CloudSat enabled detection and estimation of snowfall and light rain with unprecedented sensitivity (~-28 dBZ using the 94 GHz Cloud Profiling Radar observations) within ~81°S/N; and GPM extended TRMM capabilities and offered potentials for snowfall retrievals within ~65°S/N using its dual-polarization radar and passive microwave measurements at higher frequencies. Combination of such complementary datasets has made it possible to make new estimates for precipitation amount and distribution that can be used to guide and assess other precipitation products (for examples, see Behrangi et al., 2014; Olson et al., 2018). Besides those sensors that provide valuable information with direct application in precipitation retrieval, new opportunities have also become available through other instruments developed to better monitor and study the Earth system. If independent from typical methods used for precipitation retrieval, they may provide unique information for a consistency check or independent assessment. An example is the use of the Gravity Recovery and Climate Experiment (GRACE) mission (Tapley et al., 2004) to add insights on snowfall accumulation by monitoring mass variations. Here we discuss a few of such opportunities.

### 3.3.2. Mass change observations

In high latitudes and cold regions, satellite and ground measurements face large uncertainties. Unknown or variable surface emissivity over snow and ice surfaces and the dominance of light rain and snowfall in high latitudes and cold regions have made it difficult for both microwave and infrared techniques to retrieve snowfall. Rain gauges also face large snowfall undercatch that might exceed 100%, making it difficult to estimate snowfall and quantify its accumulation. It has been shown that using a completely independent observational technique (that is, gravimetry versus radiometry), it is possible to estimate snowfall accumulation by monitoring mass variations over sufficiently large areas. Using ten years of GRACE observations and through the mass balance method, where and when no surface melting occurs for at least a month, Behrangi et al. (2018) calculated monthly and seasonal snowfall accumulation and utilized them to assess other precipitation products (that is, GPCC and GPCP) as well as two common gauge-undercatch correction factors (CFs). In their study, evapotranspiration and
sublimation were quantified from reanalysis, but it was found that they only slightly contribute to the mass balance equation. The study was limited to grids with near surface temperature below 1°C for at least a month, so runoff could be negligible within the mass balance equation. GRACE and evapotranspiration observations were also used in large endorheic basins in High Mountain Asia to estimate monthly accumulated precipitation (Behrangi et al., 2017). An endorheic system is a closed drainage system that retains water and allows no outflow to other external bodies of water; therefore there is no need for streamflow observations to close the mass budget equation. The results were compared with satellite and in situ-based precipitation products, and it was found that most of the products agree well with each other and GRACE analysis in summer, but capture about or less than 50% of the total precipitation estimated using GRACE in winter. Similarly, GRACE observations were used to study the recent increase in lake volumes of the Tibetan Plateau's endorheic basins, and it was found that increased net precipitation contributes the majority of water supply for the lake volume increase (Zhang et al., 2017).

### 3.3.3. Mass change and streamflow observations

With streamflow observations at basins outlets, there is no need to limit the mass balance analysis to endorheic basins or grids that are cold enough to not generate runoff from snowmelt. Studies using streamflow and GRACE observations have shown great applications for precipitation assessment over the arctic basins. In a recent study, observations of streamflow and storage (mass) change from GRACE, together with estimates of evapotranspiration and sublimation, were used to close the mass budget equation over six arctic basins and investigate monthly time series and multiyear precipitation rates over the studied basins (Behrangi et al., 2019). These analyses were then used to assess two popular CFs: the Legates climatology (CF-L) utilized in GPCP and the Fuchs dynamic correction model (CF-F) used in GPCC monitoring product. The results over the study basins suggested that, based on GRACE and streamflow observations, the CF-F is preferred. GPCP uses CF-L to correct GPCC before merging with satellite data. This study suggests that more efforts are needed to assess which CF method (or combination of methods) should be used for global implementation in GPCP. In lack of other in situ observations, the use of mass change observations seems to provide valuable insights, even at coarse spatial resolution.

<figure><img src="image_43.png" /><figcaption>**Figure 3.3.1.** Times series of monthly mean precipitation rates from GPCC, GPCP, GPCC-L, GPCC-F, and GRACE over the Volga basin. Figures from Behrangi et al. (2019), with modifications. The red dots in panel (a) show the location of the outlet of the basins.</figcaption></figure>

### 3.3.4. Mass change and ice discharge observations

Precipitation estimation over ice sheets (for example, Antarctica) is challenging because in addition to difficulties that snow and ice surface bring to precipitation estimation from radiometers, estimating snowfall accumulation over Antarctic ice sheets is complicated by ice
divergence, the continual export of mass from the interior to the oceans via ice flow (Rignot et al., 2011). In other words, it is not possible to perform mass balance analysis (for example, using GRACE) over Antarctic grids or basins without accounting for ice discharge that can be as much as 2000 Gt per year (Gardner et al., 2018). However, efforts have made it possible to reconstruct ice discharge and changes in ice discharge over the Antarctic ice sheet by merging a comprehensive record of changes in Antarctic-wide ice flow, calculated by feature tracking of hundreds of thousands of Landsat image and mapping of surface velocity (Rignot et al., 2011; Gardner et al., 2018). Using mass change observations from GRACE and observational-based estimates of ice discharge values and their uncertainties over several Arctic basins, Behrangi et al. (2020) calculated annual snowfall accumulation over seven large Antarctic basins and compared the outcomes with several satellite and reanalysis products (Fig. 3.2.2). Their mass balance estimated snowfall accumulation using ice discharge and storage change observations were bounded by CloudSat snowfall estimates, presumably the most viable satellite-based snowfall product, with and without adjustment for the unmeasured near surface. Similar to Grazioli et al. (2017), the adjustment factor was calculated by dividing the cumulative precipitation at near-surface by precipitation accumulation at 1.2 km above the surface using the ECMWF Integrated Forecast System (ECMWF IFS). Such analyses provide an independent assessment of current satellite-based snowfall estimates over Antarctic and similar ice sheets (for example, part of Greenland where rainfall is not frequent), especially as GRACE-Follow On (GRACE-FO) continues to provide mass change observations.

<figure><img src="image_44.png" /><figcaption>**Figure 3.3.2.** Annual snowfall rate estimated from various products for each of the seven basins [shown in panel (f)]. The errors shown for mass budget estimates represent combined errors from GRACE, ice discharge and basal melt estimates with details discussed in Gardner et al. (2018). The figure is based on Behrangi et al. (2020) with some modifications.</figcaption></figure>

### 3.3.5. Snow depth observations

Observation of snow depth can provide an estimate of the net precipitation accumulation after accounting for snow density, sublimation, redistribution due to wind and melt losses between the two survey dates. By calculating snow water equivalent (SWE) from snow depth observations, it is possible to directly compare snowfall accumulation with changes in SWE.
Through the selection of a major snowfall event with a large accumulation signal, one can also maximize the snowfall accumulation signal and minimize the losses during the short period. Using spatially-complete SWE products from the Airborne Snow Observatory (ASO, Painter et al., 2016), Behrangi et al. (2018) used changes in SWE observations to quantify snow accumulation in cold mountain environments in the western U.S. and compared the outcomes to six satellite-based precipitation products, a ground-based radar, and three in situ snow pillows. They also assessed the bias-scaling relationship (that is, point versus areal estimates) that is often not considered when point measurements are used in evaluating gridded products. By focusing on snowfall over snow accumulation period in CONUS, Panahi and Behrangi (2020) used changes in SWE based on a gridded in situ observation product (University of Arizona snow water estimate, UA-SWE) together with mass change observations from GRACE to assess snowfall estimates of several precipitation products as well as to investigate the gauge undercatch correction methods. While expanding such analysis to other regions using satellite-based SWE observation is possible (for example, Tian et al., 2014), the quality of SWE products determines the extent of these assessment methods.

Snow depth observations are also available over sea ice and can be used for an independent assessment of snowfall accumulation. Almost no reference precipitation data sets exists over sea ice and the current precipitation products are highly uncertain. Studies have shown that using an ultra-wideband radar system on NASA's Operation IceBridge (OIB) airplane, snow depth on sea ice can be measured and used to determine uncertainties in other satellite-derived snow depth products (Brucker and Markus, 2013). By using a proper snow density value, snow depth measurements on sea ice have been used to assess snowfall accumulation from satellite (Song et al., 2020) or reanalysis (Boisvert et al., 2018; Blanchard-Wrigglesworth et al., 2018) products over sea ice. Efforts are underway to use satellite altimetry to produce spatially and temporally more-complete observations of snow depth on sea ice than that offered by OIB. Launched in September 2018, the Ice, Cloud and land Elevation Satellite (ICESat-2) together with CryoSat-2 should enable extending snow depth estimations over the entire arctic sea ice using differences in freeboard heights observed by the two instruments (e.g., Kwok et al., 2020). Upon retrieving quality snow depth estimates, the outcomes can potentially provide unique opportunities for the assessment of precipitation products over sea ice.

### 3.3.6. Complementary radar observations

Over ocean where in situ observations are generally lacking and precipitation products have large spread (Adler et al. 2012), using complementary observations from the best-known satellite products could provide a reference to assess other satellite products. Recognizing the complementary observations from CloudSat (for example, for drizzle, light precipitation and snowfall estimation) and TRMM (for moderate and intense precipitation), Behrangi et al. (2014; 2012) developed a Merged CloudSat, TRMM and Advanced Microwave Scanning Radiometer (AMSR) (MCTA) product and used that to determine the zonal distribution of precipitation over the ocean. AMSR coincides with CloudSat, and is used where and when CloudSat faces signal saturation problems under intense precipitation events. A comparison of MCTA with GPCP V2.2 showed that GPCP may underestimate oceanic precipitation by about 5%. This number agreed well with what Rodell et al. (2015) found through water budget analysis. The launch and operation of GPM enables us to extend the estimate of moderate and intense precipitation from the tropics to the extratropics (that is, from 35°S/N to 65°S/N). Accordingly, a new product was developed by Behrangi and Song (2020) that provides seasonal maps of the Merged CloudSat, TRMM, and GPM (MCTG) precipitation rates. MCTG provides additional insights on zonal and regional magnitude and distribution of precipitation rate over the ocean, and it was recently used to assess and revise GPCP over the oceans together with the Tropical Composite Climatology (TCC) (Adler et al., 2009; Wang et al., 2014). The revised GPCP product (V3.1)
(Huffman et al., 2020) shows major differences compared to its previous version (V2.3), especially over the Southern Oceans (Fig. 3.3.3), and suggests an increase of about 6% in global oceanic precipitation. This is another example of how recent sensors can be used to guide precipitation assessments where and when accurate or sufficient in situ data are generally lacking.

<figure><img src="image_45.png" /><figcaption>**Figure 3.3.3.** (Top row) Comparison of the zonal distribution of precipitation from GPCP V2.3 and GPCP V3.1. Annual maps of mean precipitation from of GPCP V2.3, GPCP V3.1, and their difference (GPCP V3.1 minus GPCP V2.3) are shown in the bottom left panel, the bottom middle panel and the bottom right panel, respectively. The plots are constructed based on average precipitation calculated from 36 years (1983–2018) of data.</figcaption></figure>

### 3.3.7. Concluding remarks

Here we provided examples of a few opportunities that can add insights to precipitation assessment, especially over regions and periods where current precipitation products face large uncertainties. These were mainly based on observation of some properties of precipitation (that is, mass change from GRACE and GRACE-FO, SWE from ASO, snow depth from OIB, etc.) together with other types of observations such as streamflow and ice discharge. The value of complementary observations from radars over the ocean was also discussed.

The use of complementary or independent observations to assess precipitation products seems valuable and is likely not limited to those presented here. With the emergence of new generation of sensors to better study the Earth system, it is important to keep an eye on their potential for guiding current precipitation products and consistency checks across the variables of the water cycle. Nonetheless, the extent that such observations can be useful depends on how they might contribute to filling existing gaps, their quality and associated uncertainties.

### 3.3.8. References

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Behrangi, A., A. Gardner, J.T. Reager, J.B. Fisher, D. Yang, G.J. Huffman and R.F. Adler, 2018: Using GRACE to Estimate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes. *Journal of Climate*, 31(21), 8689-8704, doi: 10.1175/jcli-d-18-0163.1.

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Behrangi, A., A.S. Gardner and D.N. Wiese, 2020: Comparative analysis of snowfall accumulation over Antarctica in light of ice discharge and gravity observations from space. *Environmental Research Letters*, https://doi.org/10.1088/1748-9326/ab9926.

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Boisvert, L.N., M.A. Webster, A.A. Petty, T. Markus, D.H. Bromwich and R.I. Cullather, 2018: Intercomparison of Precipitation Estimates over the Arctic Ocean and Its Peripheral Seas from Reanalyses. *Journal of Climate*, 31(20), 8441–8462, doi: 10.1175/jcli-d-18-0125.1.

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**3.4. Requirements for a constellation of precipitation sensors**

Chris Kidd

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

A fundamental requirement for the accurate measurement and representation of any parameter is that the observations made must be commensurate with, or finer than, the variable being measured. This is challenging for satellite-based observations due to the physical and engineering constraints imposed upon the characteristics of the observations being made. Measuring precipitation from spaceborne sensors is no exception.

**3.4.1. A constellation for precipitation**

For the measurement of precipitation, several key factors must be considered:

**Precipitation characteristics:** precipitation is temporally very variable with changes occurring over timescales of a few seconds, but which also impact the longer-term accumulations. Furthermore, precipitation varies greatly spatially, from a few tens of metres in intense storms, to over tens of kilometres in synoptic systems. Scale is a key driver of the characteristics of precipitation: at the instantaneous scale, precipitation intensity is heavily skewed towards the normal – zero, although coarser spatial and temporal scales results in more normally-distributed values.

**Observing capabilities:** Given the variability of precipitation, frequent and regular observations are key to providing representative measurements. However, frequent and regular observations (with reasonable resolution) are only available from GEO VIS/IR sensors, which do not observe precipitation per se but infer it from the cloud tops. Despite many schemes to derive precipitation from multi-spectral VIS/IR observations (some simple, some complex), the fact is that these still rely upon the cloud top properties. Such schemes will not improve upon schemes utilizing PMW data at the time of the PMW observation: however, VIS/IR observations have an essential role in providing information on precipitation when no PMW data is available.

**Engineering and physical aspects:** The use of PMW observations is key to providing good precipitation estimates. However, there are physical limitations to the engineering achievable and the range of channels that can be usefully employed. In particular, the resolution of PMW sensors is limited by the size of the antenna and the frequencies used. Although it may be possible to utilize higher frequencies to provide finer spatial resolutions, such higher frequencies are less direct to surface precipitation.

**User requirements:** Ultimately, precipitation is measured for the benefit of the user community, which has a vast range of requirements: spatially from metres through kilometres, temporally from seconds to annual and latency from minutes through seasonal (GEO 2010). Matching the fundamental characteristics of precipitation with the observational and engineering limitations effectively sets the boundaries within which the user community must operate.

Observations from PMW radiometers are therefore seen as *the* sensor for global precipitation measurements from satellites. These sensors, initially developed in the 1970s, have evolved into the suite of sensors that are available to the community today: these now form a constellation of about 10–12 precipitation-capable sensors available at any particular time. A broad range of science and user communities are now dependent upon the precipitation products provided by these sensors for a range of applications, from climate monitoring to
disease early warning. The current precipitation constellation consists of both conically scanning and cross-track multi-channel instruments, many of which are beyond their operational and design lifetime, but continue to operate through the cooperation of the responsible agencies. The Group on Earth Observations (GEO) and subsequent discussions by the Coordinating Group for Meteorological Satellites (CGMS) have raised the issue of how a robust future precipitation constellation should be constructed. The key factors to be considered can be summarized as:

i. sufficiently fine spatial resolutions necessary to capture precipitation-scale systems and reduce the non-linearity ("beam-filling effect") of the observations;

ii. wide channel diversity for each sensor necessary to cover the range of precipitation types, characteristics and intensities that are observed across the globe;

iii. an observation interval that provides temporal sampling commensurate with the variability of precipitation; and

iv. precipitation radars within the constellation to provide a consistent calibration source across the globe, as demonstrated by the impact of the first two spaceborne radars on the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) Core Observatory (CO).

These issues are critical in determining the direction of future constellation requirements, while preserving the continuity of the existing constellation necessary for long-term climate-scale studies.

The current precipitation constellation, as epitomized by the GPM mission, includes 10 or more precipitation-capable missions from several international agencies, including CNES, ESA, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), ISRO, JAXA, NASA, NOAA and the U.S. DoD. Additional operational precipitation-capable PMW missions exist, but data access/usage arrangements limit their widespread exploitation. The multi-agency aspect affects the coordination of each mission's orbital crossing times. To ensure consistent overpass times, the operational EUMETSAT Meteorological Operational satellite (MetOp)-B and -C, and NOAA Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 orbits are rigorously maintained, but in doing so, each pair essentially gathers observations at the same time. The overpass times of other missions drift over the course of 14–15 years between the extremes of ca.13:30 to 22:30 (ascending) while the precessing orbits of GPM and Megha-Tropiques provide overpasses across all times over the period of a few months (albeit with highly intermittent sampling). Due to the irregular nature of the PMW observations, the frequent and regular observations made by the GEO satellite sensors are crucial in providing continuous precipitation estimated through the use of merged precipitation retrieval schemes.

Despite the case for the utilization of satellite-based PMW sensors, satellites within the current precipitation constellation are old, with many missions beyond their designed operational lifetime: at present, their average age is just under 10 years old. It is therefore crucial that there is a concerted program of new satellites/sensors to ensure continuity in satellite-based precipitation measurements that meets the needs of the user community. The majority of the sensors in the current precipitation constellation are cross-track scanning sounding instruments, not designed for precipitation retrievals, and this is also reflected in the precipitation-capable sensors proposed in the near future (see below). The gain or loss of these sensors to/from the constellation directly impacts the temporal sampling of the observations together with the ability to accurately retrieve the precipitation. The oft-cited "3-hour" repeat observation time quoted for the GPM mission should be seen as an idealized (statistical) mean revisit time: in reality, there are significant regional temporal gaps of 4 hours or more in global sampling.
Several precipitation-capable missions are currently being planned for launch over the next decade. These include:

### 3.4.1.1. EUMETSAT: European Polar-orbiting System, Second Generation (EPS SG)

These satellites and sensors will provide continuity to the current MetOp series of satellites, with similar orbital characteristics to current missions. The Second Generation A (SG-A) satellites will carry the cross-track MicroWave Sounder (MWS), while the SG-B satellites will carry the conically-scanning MicroWave Imager (MWI) and the Ice Cloud Imager (ICI).

### 3.4.1.2. NOAA: Joint Polar System Satellite

The first in the JPSS series (JPSS-1, a.k.a. NOAA-20) is operational and will be joined by JPSS-2/-3/-4, with each satellite carrying an ATMS sounding instrument.

### 3.4.1.3. U.S. Department of Defense: Weather Satellite Follow-on–Microwave (WSF-M)

The U.S. DoD has two SSMIS/GMI-like sensors as part of their WSF-M with a contractual launch date of October 2023.

### 3.4.1.4. JAXA: Advanced Microwave Scanning Radiometer-3

JAXA is currently building the third generation AMSR sensor to be flown on their Global Observations SATellite for Greenhouse gases and Water Cycle satellite 3 (GOSAT-3). The sensor will build upon the heritage of the AMSR sensors with addition of high-frequency channels.

### 3.4.1.5. NASA: Time-Resolved Observation of Precipitation structure and storm Intensity with a Constellation of small Satellites (TROPICS)

The TROPICS mission will provide a total of seven cubesats, one pathfinder to be launched June 2021 in a polar orbit, to be followed by six in a low-inclination orbit to look at the evolution of weather systems across the Tropics.

### 3.4.1.6. NASA: Aerosols, Clouds, Convection and Precipitation (ACCP) mission

NASA is currently finalizing the ACCP mission architecture with a goal of observing precipitation processes.

### 3.4.1.7. China Meteorological Administration (CMA)/National Remote Sensing Center of China (NRSCC): Rain mapping missions

The Chinese rain mapping missions are scheduled to be flown from 2023 onwards as FY-3G and FY-3J. Both missions would include PMW and AMW sensors with similar characteristics to the current GPM mission.

The collection of sufficient observations for generating precipitation estimates with reasonable confidence is very precarious, even with the current constellation. The precipitation community has been very adept at exploiting data from a range of satellite missions and sensors not necessarily design for the retrieval of precipitation. A number of strategies needs to be
considered to ensure a continuation of adequate precipitation measurements from satellite systems:

i. New missions: These are the largest driver for maintaining the capabilities of the precipitation constellation. However, this requires long-term planning since missions (even cube-/small-sats) take a decade (or more) from formulation to operation. Crucially, there are few dedicated precipitation-specific missions planned, particularly in terms of mapping capabilities, channel selection, orbital characteristics and data latency. Coordination between the satellite agencies is crucial to ensure an optimal sampling strategy.

ii. Redundancy: larger satellite systems tend to provide a better degree of redundancy, allowing multi-decadal records of observations to be collected. The long-term reliability of (precipitation-capable) cubesats has yet to be fully evaluated, but their orbital characteristics are likely to be a main driver of their mission lifetime. While the MetOp and NOAA polar-orbiting missions typically have on-orbit backup satellites, use of their backup missions in the precipitation constellation is limited, since their observations generally cover the same space/time domains of the primary missions.

iii. Extended mission lifetimes: many missions operate beyond their designed operational lifetime, yet within the end-of-life disposal requirements. The utilization of such missions is essential to maintain the number of available satellite sensors. To date, missions such as TRMM and Megha-Tropiques have had extended mission lives, together with post-operation missions such as MetOp-A. The extension of mission lifetimes has been possible for medium to large satellite systems that often carry additional fuel: cubesats do not have this same capability.

iv. Retrieval scheme resilience: retrieval schemes rely upon a set of sensor-specific channels to generate a precipitation estimate. Unfortunately, most schemes will not provide an estimate if one channel is not usable, despite valid data from the other channels. In reality, a single channel loss on diverse-channel sensors (that is, SSMIS/AMSR-type) only degrades the retrieved precipitation very marginally. The flexibility of the retrieval schemes is therefore vital to deal with data loss from one or more input channels or sources. Further, new techniques should be investigated and developed that merge observational data before the retrieval stage, rather than merge precipitation estimates post-retrieval: it is possible to envisage a scenario where two satellites in very similar orbits, both experiencing channel degradation, could between them provide the capabilities of a single sensor.

### 3.4.1.8. ESA Copernicus Imaging Microwave Radiometer (CIMR)

The Copernicus Imaging Microwave Radiometer (CIMR) is currently being developed by ESA to provide fine resolution observations over frequencies from 1.4 to 37 GHz using a deployable 7m antenna. The anticipated launch date is post-2028.

### 3.4.1.9. ESA Arctic Weather Satellite (AWS)

The Arctic Weather Satellite (AWS) mission currently under development will provide frequent coverage of Earth for improved nowcasting and numerical weather prediction, carrying a cross-track microwave sounder.

## 3.4.2. Recommendations

i. Reaffirmation of a commitment and support for current and planned precipitation-capable missions
ii. Development a long-term strategy for a viable constellation of precipitation-capable sensors that meet the necessary scientific and user requirements. Specifically,

a) PMW sensors with diverse channels covering the primary precipitation-sensitive frequencies with good spatial resolution, exemplified by the AMSR/GMI class of sensors, and

b) operational AMW capabilities in a non-Sun-synchronous orbit for cross-calibration of all PMW (and IR) precipitation estimates, exemplified by the PR/DPR sensors.

iii) Support for the continuation of precipitation-capable missions beyond their nominal mission lifetime, subject to the limitations imposed by deorbiting/sensor degradation considerations

iv) Exploit new technologies, such as cubesats, where these meet the necessary scientific and user requirements

v) Implement mitigation strategies within the precipitation retrieval schemes to maximize the utilization of sub-optimal observations to help ensure continuity in adequate sampling.
Annex 1 - Acronyms

<table><tr><td>1D-VAR</td><td>One-dimensional variational</td></tr><tr><td>4D</td><td>Four-dimensional</td></tr><tr><td>ABI</td><td>Advanced Baseline Imager sensor</td></tr><tr><td>ACCP</td><td>Aerosols, Clouds, Convection and Precipitation mission</td></tr><tr><td>AIP</td><td>Algorithm Intercomparison Programme</td></tr><tr><td>AIRS</td><td>Advanced Infrared Sounder</td></tr><tr><td>AMeDAS</td><td>Automated Meteorological Data Acquisition System</td></tr><tr><td>AMO</td><td>Atlantic Multidecadal Oscillation</td></tr><tr><td>AMSR</td><td>Advanced Microwave Scanning Radiometer</td></tr><tr><td>AMSR-2</td><td>Advanced Microwave Scanning Radiometer version 2</td></tr><tr><td>APHRODITE</td><td>Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation of Water Resources dataset</td></tr><tr><td>AR5</td><td>Intergovernmental Panel on Climate Change Fifth Assessment Report</td></tr><tr><td>ASO</td><td>Airborne Snow Observatory</td></tr><tr><td>ATBD</td><td>Algorithm Theoretical Basis Document</td></tr><tr><td>ATMS</td><td>Advanced Technology Microwave Sounder</td></tr><tr><td>AVHRR</td><td>Advanced Very High Resolution Radiometer</td></tr><tr><td>AWAP</td><td>Australian Water Availability Project</td></tr><tr><td>AWS</td><td>Arctic Weather Satellite</td></tr><tr><td>CDR</td><td>Climate Data Record</td></tr><tr><td>CERES EBAF</td><td>Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)</td></tr><tr><td>CF</td><td>Correction factor</td></tr><tr><td>CF-F</td><td>Fuchs dynamic correction model</td></tr><tr><td>CF-L</td><td>Legates climatology correction factor</td></tr><tr><td>CFSR</td><td>Climate Forecast System Reanalysis</td></tr><tr><td>CHIRPS v2.0</td><td>Climate Hazards Group InfraRed Precipitation with Station version 2 data archive</td></tr><tr><td>CHUVA</td><td>Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (GPM)</td></tr><tr><td>CIMR</td><td>Copernicus Imaging Microwave Radiometer</td></tr><tr><td>CMA</td><td>China Meteorological Administration</td></tr><tr><td>CMIP</td><td>Coupled Model Intercomparison Project (phases 1-6)</td></tr><tr><td>CMORPH</td><td>NOAA Climate Prediction Center Morphing Technique</td></tr><tr><td>CNES</td><td>French Centre National d'Etudes Spatiales</td></tr><tr><td>CONUS</td><td>Continental United States</td></tr><tr><td>CORDEX</td><td>Coordinated Regional Climate Downscaling Experiment</td></tr><tr><td>CORRA</td><td>Combined Ku Radar-Radiometer Algorithm</td></tr><tr><td>CPC</td><td>National Weather Service Climate Prediction Center retrospective analysis</td></tr><tr><td>CPC 2020</td><td>Climate Prediction Center (CPC) Global 4-km Merged Infrared dataset</td></tr><tr><td>CPI</td><td>Convective Percent Index</td></tr><tr><td>CrIS</td><td>Cross-track Infrared Sounder</td></tr><tr><td>CRM</td><td>Cloud resolving models</td></tr><tr><td>CRU</td><td>The University of East Anglia's Climatic Research Unit's global climate dataset</td></tr><tr><td>CSH</td><td>Goddard Convective-Stratiform Heating algorithm</td></tr><tr><td>DoD</td><td>U.S. Department of Defense</td></tr><tr><td>DPR</td><td>Dual-Frequency Precipitation Radar</td></tr></table>
<table>
  <tr>
    <td>ECMWF</td>
    <td>European Centre for Medium-Range Weather Forecasts</td>
  </tr>
  <tr>
    <td>ECMWF IFS</td>
    <td>European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System</td>
  </tr>
  <tr>
    <td>ENSEMBLES</td>
    <td>Ensemble-based Predictions of Climate Changes and their Impacts project</td>
  </tr>
  <tr>
    <td>ENSO</td>
    <td>El Niño Southern Oscillation</td>
  </tr>
  <tr>
    <td>E-OBS</td>
    <td>European daily high-resolution Observational Gridded Dataset</td>
  </tr>
  <tr>
    <td>EOBS</td>
    <td>Ensemble-based Predictions of Climate Changes and their Impacts (ENSEMBLES) Observation dataset</td>
  </tr>
  <tr>
    <td>EPC</td>
    <td>Emissivity principal components</td>
  </tr>
  <tr>
    <td>EPS SG</td>
    <td>European Polar-orbiting System, Second Generation</td>
  </tr>
  <tr>
    <td>ERA5</td>
    <td>European Centre for Medium-Range Weather Forecasts Reanalysis version 5 dataset</td>
  </tr>
  <tr>
    <td>ERA-Interim</td>
    <td>European Centre for Medium-Range Weather Forecasts Re-Analysis dataset</td>
  </tr>
  <tr>
    <td>ETCCDI</td>
    <td>Expert Team on Climate Change Detection and Indices</td>
  </tr>
  <tr>
    <td>EUMETSAT</td>
    <td>European Organisation for the Exploitation of Meteorological Satellites</td>
  </tr>
  <tr>
    <td>EURO-CORDEX</td>
    <td>European Coordinated Regional Downscaling Experiment</td>
  </tr>
  <tr>
    <td>FAR</td>
    <td>False alarm ratio</td>
  </tr>
  <tr>
    <td>FEWS NET</td>
    <td>Famine Early Warning Systems Network</td>
  </tr>
  <tr>
    <td>FOV</td>
    <td>Field of view</td>
  </tr>
  <tr>
    <td>FROGS</td>
    <td>Frequent Rainfall Observations on GridS</td>
  </tr>
  <tr>
    <td>GCE</td>
    <td>Goddard Cumulus Ensemble model</td>
  </tr>
  <tr>
    <td>GCM</td>
    <td>General circulation model</td>
  </tr>
  <tr>
    <td>GCMS</td>
    <td>Coordinating Group for Meteorological Satellites</td>
  </tr>
  <tr>
    <td>G-CRM</td>
    <td>Global cloud-resolving model</td>
  </tr>
  <tr>
    <td>GEO</td>
    <td>Group on Earth Observations</td>
  </tr>
  <tr>
    <td>GEO-IR</td>
    <td>Geostationary Earth Orbit Infrared</td>
  </tr>
  <tr>
    <td>GEV</td>
    <td>Generalized Extreme Value framework</td>
  </tr>
  <tr>
    <td>GEWEX</td>
    <td>Global Energy and Water Exchanges project</td>
  </tr>
  <tr>
    <td>GLOBE</td>
    <td>Global Learning and Observations to Benefit the Environment program</td>
  </tr>
  <tr>
    <td>GMI</td>
    <td>Global Precipitation Measurement (GPM) Microwave Imager</td>
  </tr>
  <tr>
    <td>GOES-R</td>
    <td>Geostationary Operational Environmental Satellites</td>
  </tr>
  <tr>
    <td>GOSAT-3</td>
    <td>Global Observations SATellite for Greenhouse gasses and Water Cycle satellite 3</td>
  </tr>
  <tr>
    <td>GPCC</td>
    <td>Global Precipitation Climatology Centre</td>
  </tr>
  <tr>
    <td>GPCP</td>
    <td>Global Precipitation Climatology Project</td>
  </tr>
  <tr>
    <td>GPM</td>
    <td>Global Precipitation Measurement</td>
  </tr>
  <tr>
    <td>GPM CO</td>
    <td>Global Precipitation Measurement Core Observatory</td>
  </tr>
  <tr>
    <td>GPROF</td>
    <td>Goddard profiling algorithm</td>
  </tr>
  <tr>
    <td>GRACE</td>
    <td>Gravity Recovery and Climate Experiment</td>
  </tr>
  <tr>
    <td>GRACE-FO</td>
    <td>Gravity Recovery and Climate Experiment (GRACE)-Follow On</td>
  </tr>
  <tr>
    <td>GridSat</td>
    <td>Gridded Satellite</td>
  </tr>
  <tr>
    <td>GSMaP</td>
    <td>Global Satellite Mapping of Precipitation</td>
  </tr>
  <tr>
    <td>GV</td>
    <td>Ground validation</td>
  </tr>
  <tr>
    <td>GV-MRMS</td>
    <td>Ground Validation Multi-Radar/Multi-Sensor</td>
  </tr>
  <tr>
    <td>HH</td>
    <td>Hydrometeor Heating algorithm</td>
  </tr>
  <tr>
    <td>HOAPS</td>
    <td>Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data</td>
  </tr>
  <tr>
    <td>HRPP</td>
    <td>High-Resolution Precipitation Products</td>
  </tr>
  <tr>
    <td>HSS</td>
    <td>Heidke Skill Scores</td>
  </tr>
  <tr>
    <td>IASI</td>
    <td>Infrared Atmospheric Sounding Interferometer</td>
  </tr>
  <tr>
    <td>ICESat-2</td>
    <td>Ice, cloud and land elevation satellite</td>
  </tr>
</table>
<table>
  <tr>
    <td>ICI</td>
    <td>Ice Cloud Imager</td>
  </tr>
  <tr>
    <td>IMERG</td>
    <td>Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM)</td>
  </tr>
  <tr>
    <td>IMERG-T</td>
    <td>Test mode for the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM)</td>
  </tr>
  <tr>
    <td>IOD</td>
    <td>Indian Ocean Dipole</td>
  </tr>
  <tr>
    <td>IPCC</td>
    <td>Intergovernmental Panel on Climate Change</td>
  </tr>
  <tr>
    <td>IPHEX</td>
    <td>Integrated Precipitation and Hydrology Experiment</td>
  </tr>
  <tr>
    <td>IPWG</td>
    <td>International Precipitation Working Group</td>
  </tr>
  <tr>
    <td>IR</td>
    <td>Infrared</td>
  </tr>
  <tr>
    <td>ISO</td>
    <td>International Organization for Standardization</td>
  </tr>
  <tr>
    <td>ISRO</td>
    <td>Indian Space Research Organization</td>
  </tr>
  <tr>
    <td>ITCZ</td>
    <td>Intertropical Convergence Zone</td>
  </tr>
  <tr>
    <td>JAXA</td>
    <td>Japanese Aerospace Exploration Agency</td>
  </tr>
  <tr>
    <td>JRA25</td>
    <td>Japanese 25-year ReAnalysis</td>
  </tr>
  <tr>
    <td>LEO</td>
    <td>Low Earth Orbit</td>
  </tr>
  <tr>
    <td>LH</td>
    <td>Latent heat</td>
  </tr>
  <tr>
    <td>MCTA</td>
    <td>Merged precipitation estimate from the CloudSat, Tropical Rainfall Measuring Mission (TRMM) and Aqua platforms</td>
  </tr>
  <tr>
    <td>MCTG</td>
    <td>Merged precipitation estimate from the CloudSat, Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms</td>
  </tr>
  <tr>
    <td>MERRA-2</td>
    <td>Second Modern-Era Retrospective analysis for Research and Applications</td>
  </tr>
  <tr>
    <td>MetOp</td>
    <td>Meteorological Operational satellites (MetOp-A, MetOp-B and MetOp-C)</td>
  </tr>
  <tr>
    <td>MHS</td>
    <td>Microwave Humidity Sounder</td>
  </tr>
  <tr>
    <td>MJO</td>
    <td>Madden-Julian Oscillation</td>
  </tr>
  <tr>
    <td>MODIS</td>
    <td>Moderate resolution Imaging Spectroradiometer</td>
  </tr>
  <tr>
    <td>MRMS</td>
    <td>Multi-radar multi-sensor</td>
  </tr>
  <tr>
    <td>MS</td>
    <td>Matched Scan</td>
  </tr>
  <tr>
    <td>MSWEP</td>
    <td>Multi-Source Weighted-Ensemble Precipitation dataset</td>
  </tr>
  <tr>
    <td>M-T</td>
    <td>Megha-Tropiques</td>
  </tr>
  <tr>
    <td>MW</td>
    <td>Microwave</td>
  </tr>
  <tr>
    <td>MWI</td>
    <td>MicroWave Imager</td>
  </tr>
  <tr>
    <td>MWS</td>
    <td>MicroWave Sounder</td>
  </tr>
  <tr>
    <td>NASA</td>
    <td>National Aeronautics and Space Administration</td>
  </tr>
  <tr>
    <td>NCEP2</td>
    <td>National Centers for Environmental Prediction-Department of Energy Reanalysis 2</td>
  </tr>
  <tr>
    <td>NEWS</td>
    <td>National Aeronautics and Space Administration's Energy and Water cycle Study</td>
  </tr>
  <tr>
    <td>NOAA</td>
    <td>National Oceanic and Atmospheric Administration</td>
  </tr>
  <tr>
    <td>NOAA-20</td>
    <td>Satellite of the Joint Polar Satellite System constellation</td>
  </tr>
  <tr>
    <td>NRL</td>
    <td>Naval Research Laboratory</td>
  </tr>
  <tr>
    <td>NRL-Blend</td>
    <td>Naval Research Laboratory (NRL) blended satellite</td>
  </tr>
  <tr>
    <td>NRSCC</td>
    <td>National Remote Sensing Center of China</td>
  </tr>
  <tr>
    <td>NS</td>
    <td>Normal Scan</td>
  </tr>
  <tr>
    <td>NSCE</td>
    <td>Nash-Sutcliffe coefficient of efficiency</td>
  </tr>
  <tr>
    <td>OIB</td>
    <td>Operation IceBridge</td>
  </tr>
  <tr>
    <td>OISST</td>
    <td>Optimum Interpolation Sea Surface Temperature</td>
  </tr>
  <tr>
    <td>OLR</td>
    <td>Outgoing longwave radiation</td>
  </tr>
  <tr>
    <td>OLYMPEX</td>
    <td>Olympic Mountain Experiment</td>
  </tr>
  <tr>
    <td>OSTIA</td>
    <td>Operational Sea Surface Temperature (SST) and Sea Ice Analysis</td>
  </tr>
  <tr>
    <td>PERSIANN</td>
    <td>Precipitation Estimation from Remotely Sensed Information using Artificial</td>
  </tr>
</table>
<table><tr><td></td><td>Neural Networks</td></tr><tr><td>PERSIANN-CCS</td><td>Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System</td></tr><tr><td>PERSIANN-CDR</td><td>Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record</td></tr><tr><td>PERSIANN-MSA</td><td>Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Multispectral Analysis</td></tr><tr><td>PDF</td><td>Probability density function</td></tr><tr><td>PDO</td><td>Pacific Decadal Oscillation</td></tr><tr><td>PIP</td><td>Precipitation Intercomparison Projects</td></tr><tr><td>PMW</td><td>Passive microwave</td></tr><tr><td>POD</td><td>Probability of detection</td></tr><tr><td>PR</td><td>Precipitation Radar</td></tr><tr><td>PRH</td><td>Precipitation Radar Heating algorithm</td></tr><tr><td>PRPS</td><td>Precipitation Retrieval and Processing Scheme</td></tr><tr><td>PRUDENCE</td><td>Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects project</td></tr><tr><td>PUSH</td><td>Precipitation Uncertainties for Satellite Hydrology</td></tr><tr><td>Q<sub>1</sub></td><td>Apparent heat source Q<sub>1</sub></td></tr><tr><td>QA</td><td>Quality Assurance</td></tr><tr><td>QC</td><td>Quality Control</td></tr><tr><td>QPE</td><td>Quantitative precipitation estimation</td></tr><tr><td>RAMS</td><td>Regional Atmospheric Modeling System</td></tr><tr><td>RCM</td><td>Regional climate model</td></tr><tr><td>REFAME</td><td>Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates</td></tr><tr><td>REGEN</td><td>Rainfall Estimates on a Gridded Network dataset</td></tr><tr><td>RMS</td><td>Root mean square</td></tr><tr><td>SBM</td><td>Spectral bin microphysics</td></tr><tr><td>SCAMPR</td><td>Self-Calibrating Multivariate Precipitation Retrieval</td></tr><tr><td>SF</td><td>SeaFlux</td></tr><tr><td>SG-A</td><td>MetOp Second Generation group A satellites</td></tr><tr><td>SHARPEN</td><td>Scheme for Histogram Adjustment of Ranked Precipitation Estimates in the Neighborhood</td></tr><tr><td>SLH</td><td>Spectral Latent Heating algorithm</td></tr><tr><td>SMART</td><td>Soil Moisture Analysis Rainfall Tool</td></tr><tr><td>SNPP</td><td>Suomi National Polar-orbiting Partnership</td></tr><tr><td>SREM2D</td><td>Two-Dimensional Satellite Rainfall Error Model</td></tr><tr><td>SSMIS</td><td>Special Sensor Microwave Imager/Sounder</td></tr><tr><td>SST</td><td>Sea surface temperature</td></tr><tr><td>SWE</td><td>Snow water equivalent</td></tr><tr><td>TAPEER</td><td>Tropical Amount of Rainfall with Estimation of Errors algorithm</td></tr><tr><td>TB</td><td>Brightness temperature</td></tr><tr><td>TIROS</td><td>Television-Infrared Operational Sounder</td></tr><tr><td>TMPA</td><td>Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis</td></tr><tr><td>TOOCAN</td><td>Tracking Of Organized Convection Algorithm through a 3-D segmentation</td></tr><tr><td>TOVS</td><td>Television-Infrared Operational Sounder (TIROS) Operational Vertical Sounder</td></tr><tr><td>TRAIN</td><td>Goddard Trained Radiometer algorithm</td></tr><tr><td>TRMM</td><td>Tropical Rainfall Measuring Mission</td></tr></table>
<table><tr><td>TRMM 3B42 V7</td><td>Tropical Rainfall Measuring Mission (TRMM) Precipitation L3 1 day<br>0.25 degree x 0.25 degree V7</td></tr><tr><td>TROPICS</td><td>Time-Resolved Observation of Precipitation structure and storm Intensity<br>with a Constellation of small Satellites</td></tr><tr><td>UA-SWE</td><td>University of Arizona snow water estimate</td></tr><tr><td>UDEL</td><td>Data from Willmott, Matsuura and collaborators at the University of<br>Delaware</td></tr><tr><td>VIIRS</td><td>Visible Infrared Imaging Radiometer Suite</td></tr><tr><td>VIS</td><td>Visible</td></tr><tr><td>WATCH</td><td>Water and global Change</td></tr><tr><td>WCRP</td><td>World Climate Research Programme</td></tr><tr><td>WFDEI</td><td>Water and global Change (WATCH) Forcing Data methodology applied to<br>ERA-Interim data</td></tr><tr><td>WSF-M</td><td>Weather Satellite Follow-on–Microwave</td></tr><tr><td>WSR-88D</td><td>Weather Surveillance Radar, 1988, Doppler</td></tr></table>
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(WCRP)
facilitates analysis and
prediction of Earth system change
for use in a range of practical
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<figure><img src="image_46.png" /></figure>

World Climate Research Programme