1

Estimation of Extreme Daily Precipitation

Thermodynamic Scaling Using Gridded

Satellite Precipitation Products Over

Tropical Land

5

Supplementary Material

Rémy Roca1

1Observatoire Midi-Pyrénées, LEGOS, Toulouse, France

Correspondence to: Dr. Rémy Roca (remy.roca@legos.obs-mip.fr) 10

Submitted to ERL

March 2019

Revised

July 2019 15

Corrected

July 2019

20

2

3

1 Data

1.1 A set of gridded precipitation products All the products are used over the tropical belt (30°s-30°n) at a 1°x1° daily resolution. A

common land sea mask at 1°x1° is used for all the products. Small islands and lakes are not 5

accounted for in the statistics. The characteristics of the data sets are summarized in Table

S1 as well as the short names used throughout the paper to refer to them. Datasets are

briefly presented below. Otherwise indicated, the data files originate from the FROGS

common database (Roca et al. 2019) and identified with the DOI: 10.14768/06337394-73A9-

407C-9997-0E380DAC5598. A first ensemble is built and is refers to as the entire ensemble 10

(ENS) in the paper. It consists of 10 products spanning various satellite-based products,

merged or not with rain gauges observations and a couple of rain gauges only gridded

products. For each satellite product, the flagship product of the family of existing products is

used.

1.1.1 Satellite only (no rain gauges) data products 15

TAPEER v1.5

The recently released TAPEER product is based on the universally adjusted GOES

precipitation index technique (Xu et al. 1999) that merges geostationary infrared imagery

with microwave instantaneous rain rates estimates to yield the daily precipitation

accumulation (Kidd et al. 2003). The current implementation relies on the BRAIN L2 dataset 20

(Viltard et al. 2006) for a suite of conical microwave imagers (TRMM/TMI, GCOMW/AMSR-2,

DSMP/SSMI/S F15, F16, F17 F18) and include the SAPHIR data from the Megha-Tropiques

mission (Roca et al. 2015) for rainfall detection and runs at 1°x1° (Roca et al. 2018) . Along

with the accumulation, an estimation of the sampling uncertainty of the daily accumulation

4

is provided (Roca et al., 2010; Chambon et al., 2012). The TAPEER product has been

extensively compared against various datasets over tropical Africa and showed good to

excellent performances there under various metrics (Guilloteau et al. 2016; Gosset et al.

2018) but lacks a systematic evaluation of the ability to document the tropical extremes.

Unlike many other operational satellite precipitation products, the TAPEER estimations do 5

not ingest nor are calibrated to any rain gauges datasets. As such, it provided one solution

independent from the rain gauges network and with an enhanced tropical sampling thanks

to the use of the SAPHIR data from the Megha-Tropiques mission. Note that due to a

limitation of the Level 2 retrieval, no accumulation is provided for the grid boxes with mean

altitude higher than 3000m. 10

1.1.2 Satellite and rain gauges multi source blended data products

MWSEP v2.2

MWSEP is a product which aims at providing stream flow adjusted multi source precipitation

estimates (Beck et al. 2017b). The various source of precipitation used in this product are: 15

satellite estimation, rain gauges measurements and reanalysis results. MWSEP correspond

to a pragmatic approach that average existing product to provide a best estimate over land

with a optimization based on hydrological modelling and observed stream-gauges data (Beck

et al. 2017b). Version 2.2 is used (Beck et al., 2018). MSWEP is thought of as a representative

product of the merged products that use reanalysis and indirect rain estimates, like soil 20

moisture or stream-gauges. MWSEP v2.2 data have been aquired directly from H. Beck.

5

3B42 v7.0

The 3B42 v7 product (Huffman et al. 2009; Huffman et al., 2011) is a reference product in

various previous studies of the tropical rainfall distribution. It also exemplifies what a dataset

that is highly geared towards microwave data can provide in terms of daily accumulation. It

also put in perspective the use of scattering based retrieval over land for the instantaneous 5

retrieval (Gopalan et al. 2010). It also relies on a sophisticated bias correction approach that

make use of GPCC (see below) and the use of the combined radar-imager product (Haddad

et al., 1997) as a reference for the other imagers. While this popular product has been

evaluated over a very large number of small regions and catchments as well as over the

whole tropics for certain metrics (Sun et al. 2017) its tropical scaling has not been explored 10

in details so far. As a consequence, it is included here if even NASA has announced the

discontinuation of its production post 2018.

GsMAPnrt v6

The Global Satellite Mapping of Precipitation product provides high resolution precipitation 15

estimation using satellite observations from multiple platforms (Kubota et al. 2007). This

product in mainly based on microwave estimation of rainfall for a suite of microwave

imagers. The microwave instantaneous rain rates estimates (Aonashi et al. 2009) are

propagated based on Cloud Motion Wind vectors originally derived from IR geostationary

imagery to yield to a gridded high resolution precipitation product (Ushio et al. 2009). Here 20

the latest reprocessing of the near real time product with gauges correction is used.

6

CMORPH v1.0 CRT

The CMORPH product (Joyce et al. 2004; Xie et al. 2017) belongs to the microwave based

morphing algorithms like GsMAP (Kubota et al. 2007). The microwave derived instantaneous

rain rates estimates are extrapolated using Cloud Motion Wind vectors originating from IR

geostationary imagery and a Kalman filter (Joyce and Xie 2011). Then gauges adjustment is 5

performed based on the CPC gauges analysis (Xie et al. 2003).

GPCP 1DD v1.3 CDR

The Global Precipitation Climatology Product Version 1.3 daily product is another reference

product used in various previous studies and is included here for this reason. It also 10

showcases the expectation of using one single microwave platform and a dedicated bias

adjustment scheme relying on the GPCC data set over land (Huffman et al. 2001).

PERSIANN CDR v1

PERSIANN-CDR v1 (Ashouri et al. 2015) is a IR-based product, trained over microwave data, 15

and normalized to GPCP monthly totals. It is thought of as an alternative daily downscaling

of the GPCP monthly data to that of GPCP 1DD. Despites sharing monthly totals, the two

products significantly differ in their estimation of the daily precipitation distribution (Sun et

al. 2017) and by consequence are used in the present comparison.

20

7

CHIRPS v2.0

The Climate Hazards Infrared Precipitation with Stations (CHIRPS) (Funk et al. 2015) is a

satellite based precipitation product (Funk et al. 2015). However, unlike previous products, it

relies solely on infrared observations (no microwave data is used) and is calibrated against

rain gauges observations. It is used here to illustrate the expectation from such an IR based 5

approach.

1.1.3 Rain gauges only products

The Global Precipitation Climatology daily product

The Global Precipitation Climatology Center realizes a suite of gridded precipitation product

based on rain gauges measurements, dedicated quality control and a krigging/filling 10

algorithm (Becker et al. 2013). A global analysis is available as well as a first guess analysis

product (Schamm et al. 2014) are available at the 1°x1° daily scale. Here two full data set are

used: the first guess product (Scham et al., 2015) and the full daily dataset version 2 (Ziese et

al., 2018). This “no satellite data” product is thought of as a representative of the available

various rain gauges based daily reanalysis like the Climate Prediction Center daily reanalysis 15

(Xi et al., 2010) and is used here to put the satellite data results into perspectives comparing

with conventional observations gridded products.

1.2 A subset of precipitation products A second ensemble is built using a subset of the above listed products and is refers to as the

sub-ensemble (sENS) in the paper. The sub-ensemble includes TMPA, GSMAP, CMORPH and 20

MSWEP. These four products rely one way or another on the use of satellite microwave

observations from multiple platforms a.k.a as constellation-based products as well as ground

based networks for bias correction. The use of multiple platforms enhances the sampling

and increases the accuracy of these constellation-based products (Roca et al. 2018;

8

Chambon et al. 2013). These datasets are further well clustered, both in terms of the full

distribution (Fig. 1 and S1) or the “extreme” extreme values (Figure S2) and corresponds to

an ensemble that is more robust than the entire ensemble.

1.3 ERA-interim near surface temperature over land The surface air temperature used here is obtained from the ERA interim analysis (Dee et al., 5

2011). The daily 1°x1°average 2m temperature statistics are built from the 0.75°x0.75° 4

times daily archive using simple bilinear interpolation in space and averaging in time. Scaling

analysis has been shown to show little to no sensitivity to the selection of ERA interim as the

temperature reference dataset (Liu et al., 2012) nor to the details of the local spatial

averaging (Wasko et al., 2016). 10

2 Method Percentiles as a metric of extreme precipitation

Various climate extremes indices have been promoted to help quantifying the extreme and

here we are using the high percentiles of the precipitation distribution as a quantitative

statistically well-defined index of extreme precipitation. This approach has been preferred to 15

other indices of extreme daily precipitation like the annual maximum daily rainfall for its

relevance to the computation of the physical scaling of the extreme with local surface

temperature (Schär et al. 2016). A 5-year period, corresponding to more than 39x106

individuals 1°x1° daily accumulated precipitation estimates over the 30°s-30°n is used to

explore the distribution of tropical precipitation. The daily precipitation estimates for the 20

wet days (> 1mm/d) are binned according to the 2m temperature with a 1K resolution and

the 99.9th, 99th and 90th percentiles of the distribution per bin is calculated. The 99th and

99.9th percentiles are simply computed from this large dataset and used to characterize the

“moderate” and “extreme” extremes, respectively. Note that the results for the 90th

9

percentile are also reported even if the magnitude of the associated rain accumulation is not

severe.

Estimating the thermodynamical scaling

The scaling coefficient is finally estimated through a simple log-linear fit over the

temperature range the percentiles for which the physical scaling can be invoked. The 5

distribution of the number of points per temperature bin is similar to that of the

contribution to total amount of rainfall (Figure 2). Over the considered surface temperature

range (293K-299K), the total population reaches around 3e6 points and the population over

each bin varies from 4%, corresponding to ~1.0e5 data points to 15% corresponding to

~4.5e5 data points. The binning method could yield to biased estimation of the scaling of 10

high percentile for small sample size (Wasko 2014) which is not the case here as we build our

statistics with a large number of data points.

10

3 Supplementary tables

Product

shortname

Product

name and

version

Use of

rain

gauges

data

Use of

IR

satellite

data

Use of

MW

satellite

data

References

TAPR TAPEER

v1.5

No Yes multiple

platforms

(Roca et al,

2018)

TMPA 3B42 v7.0 Yes Yes multiple

platforms

(Huffman et

al. 2009)

GSMArtg GSMAP-

NRT-

gauges v6.0

Yes yes multiple

platforms

(Kubota et

al., 2007)

PERS PERSIANN

CDR v1 r1

yes Yes No (Ashouri et

al., 2015)

CMORg CMORPH

V1.0, CRT

Yes Yes multiple

platforms

(Xie et al.,

2017)

GPCP GPCP 1DD

CDR v1.3

Yes Yes One

platform

(Huffman et

al. 2001)

MSWE MSWEP 2.2 Yes Yes Yes (Beck et al.,

2019)

CHIR CHIRPS

v2.0

Yes Yes No (Funk et al.

2015)

GPCC GPCC Full

Daily v2018

Yes No No (Schneider

et al., 2018)

GPCCfg GPCC Fisrt

Guess v1

Yes No No (Becker et

al. 2013)

Table S1: List of gridded products and their acronyms.

11

Product dp99.9/ dt2m

dp99/ dt2m

dp90/ dt2m

TAPR 0.79 2.96 4.48

TMPA 3.89 4.09 4.57

GSMArtg 6.24 6.17 5.45

CMORg 5.16 5.34 5.09

MSWE 5.42 4.25 3.53

GPCP 2.64 2.80 4.58

PERS 1.42 2.65 5.13

CHIRg 0.03 1.91 4.39

GPCC 0.54 1.39 2.44

GPCCfg 3.49 2.62 2.98

Mean ENS

2.96 3.42 4.26

STD ENS 2.22 1.51 0.98

Cvar

ENS

75.0 44.3 23.0

Mean sENS

5.18 4.96 4.66

STD sENS 0.98 0.98 0.84

Cvar sENS 18.9 19.7 17.9

Table S2: The value of the slope of the distribution of the percentiles as a function of the 2-meters temperature over the 293K-299K regime for the 99.9th, 99th and 90th percentiles in 5 %/K. Mean (%/K), standard deviation (%/K) and coefficient of variation (%) are also reported for the ensemble of all the products (ENS) and for the sub-set of the ensemble defined in the text (sENS).

10

12

4 Supplementary figures

Figure S1: (top) Whole range percentiles of the 1°x1° daily accumulated precipitation distribution over the tropical (30°s-30°n) land for the period 2012-2016. (bottom) Upper range of the percentiles distribution. The colors correspond to various precipitation datasets 5 listed on the right. (bottom) the normalized standard deviation of the above distribution across the ensemble of all the products (ENS) and for the sub-set of the ensemble defined in the text (sENS).

13

Figure S2: (top) The value of the 99.9th percentile of the 1°x1° daily accumulated precipitation over the tropical (30°s-30°n) land for the period 2012-2016 as a function of the 2-meters daily temperature. The colors correspond to various precipitation datasets listed on the right. (bottom) the normalized standard deviation of the above distribution across 5

14

the ensemble of all the products (ENS) and for the sub-set of the ensemble defined in the text (sENS).

15

Figure S3 : (top) The value of the 99th percentile of the 1°x1° daily accumulated precipitation over the tropical (30°s-30°n) land for the period 2012-2016 as a function of the 2-meters daily temperature. The colors correspond to various precipitation datasets listed on the right. (bottom) the normalized standard deviation of the above distribution across the 5

16

ensemble of all the products (ENS) and for the sub-set of the ensemble defined in the text (sENS).

17

Figure S4 : (top) The value of the 90th percentile of the 1°x1° daily accumulated precipitation over the tropical (30°s-30°n) land for the period 2012-2016 as a function of the 2-meters daily temperature. The colors correspond to various precipitation datasets listed on the 5 right. (bottom) the normalized standard deviation of the above distribution across the

18

ensemble of all the products (ENS) and for the sub-set of the ensemble defined in the text (sENS).

19

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