Can GNSS reflectometry detect precipitation over oceans? [original]

Geoph y sical Resear ch Letters
Can GNSS Reﬂectometr y Detect Pr ecipitation Over Oc eans?
Milad Asgarimehr 1,2 , V alery Zav orotn y 3,4 , Jens W ick er t 1,2 , and Sebastian Reich 5
1 Institute of Geodesy and Geoinformation Science , F aculty VI, T echnische Universität Berlin, Berlin, German y, 2 German
Research Centr e for Geosciences GFZ, P otsdam, Germany, 3 C ooperative Institute for Research in En vironmental Sciences,
University of Colorado Boulder , B oulder , CO , USA, 4 Ear th Syst em Research Laborator y , NOAA, Boulder , CO , USA,
5 Depar tment of Mathematics, University of P otsdam, Potsdam, Germany
Abst ract For the ﬁrst time , a rain signature in Global Navigation Sat ellite Syst em Reﬂec t ometr y (GNSS-R)
obser vations is demonstrated . Based on the argument that the forward quasi-specular scattering relies
upon sur face gra vity waves with lengths lar ger than several wav elengths of the reﬂected signal, a c ommonly
made conclusion is that the scatter ometric GNSS-R measurements are not sensitiv e to the sur face
small-scale roughness generated b y raindrops impinging on the ocean surface. On the contrary , this study
presents an evidence that the bistatic radar cr oss sec tion 𝜎 0 deriv ed from T echDemoSat-1 data is r educed
due to rain at weak winds , lower than ≈ 6 m/s. The decr ease is as large as ≈ 0.7 dB at the wind speed of 3 m/s
due to a precipitation of 0 – 2 mm/hr . The simulations based on the recently published scattering theory
provide a plausible explanation f or this phenomenon which potentially enables the GNSS-R technique to
detect precipitation over oc eans at low winds.
Plain Language Summar y Using Global Navigation Sat ellite Syst em (GNSS) signals , reﬂected oﬀ
the Ear th’ s surface (GNSS Reﬂectometr y), is an innovative r emote sensing technique with a broad spectrum
of geophy sical applications. Curr ently , recent satellit e missions, such as the U .K . T echDemoSat-1 and
U .S. Cyclone Global Na vigation Satellite Sy stem (CY GNSS), pioneer GNSS Reﬂec tometry as a new space
obser vation technology on a global scale . Despite a wide variety of monitored geoph ysical parameters , the
reﬂected signals hav e never been used to obtain rain information. F or the ﬁrst time, this study demonstrat es
a signature in the r eceived sig nals, due to the modiﬁed ocean surface waves b y rain splashes, enabling the
technique to detect precipitation o ver oceans induced b y weak winds. A plausible ph ysical explanation
for this phenomenon is pr ovided based on the rec ent scattering theor y . This study can ser ve as a starting
point for dev eloping a new GNSS Reﬂectometr y application, rain detection over oceans , which might be
also implemented f or future low-cost GNSS remote sensing missions . The presented ﬁndings also pr ovide a
better ph ysical understanding of L band forward scattering mechanism which is directly relevant to the
main objective of the currently operational GNSS Reﬂectometr y satellite missions .
1. Introduction
Over the last two decades, there has been a r apidly growing int erest in the use of Global Navigation Satellit e
Syst em (GNSS) signals r eﬂection from the Ear th’ s surface to monitor a variety of geophysical parameters (see,
e.g ., Jin et al., 2014; Zavorotn y et al., 2014). Despite a wide variety of GNSS Reﬂec t ometr y (GNSS-R) applications
for Earth ′ s systems monitoring , the reﬂected signals hav e been hardly consider ed as a potential rain indicator
due to lack of both experimental evidence and theor etical substantiation of such a phenomenon. Indeed, it is
commonly assumed that GNSS is an all-w eather system due to the fact that frequencies f or the GNSS signals
are chosen in the L band , so they would not suﬀer notic eable attenuation by clouds or typical precipitation,
and as a result, they are not sensitiv e to rain. However , the sensitivity to rain might not be limited only to rain
attenuation caused by sig nal absorption in drops and accompan ying scattering. Other signal propagation
eﬀects may be in volved . F or example, Cardellach et al . (2015) recently pr oposed to use a depolarization eﬀect
induced by the ﬂattening of the hea v y precipitation dr ops to detect heavy rains.
The sur face eﬀ ec ts of precipitation on micr owav e wind scatterometr y hav e been k no wn already f or several
decades, and they ar e well documented (Braun et al ., 2002; C avaleri et al ., 2015; Contreras & P lant, 2006;
Contr eras et al., 2003; Craeye et al ., 1997; 1999; Hansen, 1986; Hwang, 2012; Melsheimer et al., 1998; 2001;
Milliﬀ et al., 2004; Moore et al., 1979; Nie & Long , 2007; Portabella et al., 2012; Sobiesk i et al., 1999; W eissman
RESEARCH LET TER
10.1029/2018GL079708
Key Poi nts :
• F irst evidence of rain signature in
GNSS Reﬂectometr y observations
enabling the technique to det ect rain
over oceans induc ed by weak winds
• A novel ph ysical explanation of
L-band forward scattering by
small-scale ocean surface waves
generated by raindr op splashes
• Reduction in the value of GNSS-R
Bistatic radar cross section during rain
events
Corr espondence to:
M. Asgarimehr ,
[email protected]
Citation:
Asgarimehr, M., Zav orotny, V .,
Wickert, J., & Reich, S. (2018). Can
GNSS Reﬂectometr y detect
precipitation ov er oceans?
Geophysical Resear ch Letters ,
45 , 12,585–12,592.
https://doi.org/10.1029/2018GL079708
Received 20 JUL 2018
Acc epted 1 NOV 2018
Acc epted article online 6 NOV 2018
Published online 20 NOV 2018
©2018. The Authors.
This is an open access article under the
terms of the Creativ e Commons
Attribution-NonCommer cial-NoDerivs
License, which permits use and
distribution in any medium, provided
the original work is pr operly cited, the
use is non-commercial and no
modiﬁcations or adaptations are made.
ASGARIMEHR ET AL. 12 , 585

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Fi gu re 1. W ave elevation spectrum (a) at wind speed 3 m/s without rain and modiﬁed by rain (rain rate R = 10 mm/hr)
along with the k ∗ for incidence angles of 0 ∘ and 60 ∘ shown with v er tical dashed and dash-dotted lines and sur face
roughness Rayleigh parameter R a (b) versus wind speed f or a range of rain rates and f or three incidence angles 𝜃 inc .
et al., 2002, 2012). These eﬀ ec ts can be explained by the f ollowing impact model. Drops strike the water
sur face producing splashes which, in its turn, generat e gravity-capillar y ring waves from which the micr owav e
signal scatters . The additional ocean surface roughening can be described in terms of the modiﬁed wav e ele -
vation spectrum. Naturally , such a roughening eﬀ ect can be mostly noticed at low wind speeds when heights
of wind-generated waves are r elatively low .
With the emer gence of the GNSS bistatic radar ocean f or war d scatterometry a question has been posed: Can
rain sur face eﬀ ects have a similar impact on the GNSS reﬂected signals? I t is clear that this case r equires a spe -
cial consideration because the mechanism of L band f or ward scatt ering signiﬁcantly diﬀers fr om microwav e
backscattering implemented in traditional wind scatt erometr y . Ghavidel and Camps (2016) inv estigated a
GNSS-R electromagnetic bias due to the rain and also b y swell and sea currents performing numerical sim-
ulations; howev er , the rain sensitivit y of GNSS wind scatt erometr y was not addr essed in that publication.
Soisuvarn et al. (2016) analyzed a limited GNSS-R data set under rain c onditions obtained during the UK
T echDemoSat-1 ( TDS-1) mission. Although some spread for the signal-t o-noise-ratio (SNR) at diﬀer ent rain
rates is shown in this study , the authors draw no conclusion whether or not there is an y eﬀect of rain on
GNSS-R measurements and def er it until a substantially larger data set f or statistical analysis is available . Cur-
rently , TDS-1 has provided a signiﬁcantly larger data set being operational for a longer time. C onsequently , a
noticeably larger number of observations, especially at higher rain rates, ar e inv estigated in this study .
Fi gu re 2. BRCS 𝜎 0 versus wind speed at diﬀ erent rain rates R (mm/hr) along with the number of obser vations and the
histogram of incidence angles f or the measurements in each rain rate bin. BRCS = bistatic radar cr oss section.
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Fi gu re 3. BRCS versus wind speed at diﬀ erent incidence angles: 0 ∘ ≤ 𝜃 inc < 5 ∘ (a), 5 ∘ ≤ 𝜃 inc < 10 ∘ (b), 10 ∘ ≤ 𝜃 inc < 15 ∘
(c), 15 ∘ ≤ 𝜃 inc < 20 ∘ (d), 20 ∘ ≤ 𝜃 inc < 25 ∘ (e), and 25 ∘ ≤ 𝜃 inc < 30 ∘ (f ). BRCS = bistatic radar cr oss section.
At ﬁrst glance , the GNSS reﬂec ted signal should not be sensitiv e to gra vity- capillar y wa ves generated by dr op
splashes because of the nature of the f or ward quasi-specular scatt ering. Indeed, acc ording to the model pr e -
sented by Za vorotn y and V oronovich (2000), the sur face paramet er that controls the intensity of f or ward
quasi-specular scattering is the low-pass mean squar e slope, MSS LP , of the ocean sur face . I t is determined b y
the par t of the wav e slope spec trum that resides at wa ve numbers smaller than k ∗ = k cos 𝜃 inc ∕ 3 , where 𝜃 inc
is an incidence angle and k is the wav e number ( 2 𝜋 ∕ 𝜆 ) of the L band GNSS signal.
F igure 1a demonstrates a w ave elevation spectrum (Elfouhaily et al ., 1997) induced by a wind speed of 3 m/s ,
together with the log Gaussian spectrum (Craeye et al., 1997) used to describe the rain-induc ed ring waves
at rain rates of 10 mm/hr . As seen from this plot, the part of the spec trum aﬀected by r ain splashes resides
at wave numbers much higher than cut oﬀ number k ∗ , which is ≈ 11 rad/m for 𝜃 inc = 0 ∘ and ≈ 5.5 rad/m
for 𝜃 inc = 60 ∘ . However , the type of scattering described by Zav orotn y and V oronovich (2000; also known
as a strong diﬀuse scattering) takes plac e for rough surfaces with a high enough ( ≫ 1 ) R a yleigh parameter ,
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Fi gu re 4. BRCS versus the incidence angle at diﬀ erent wind speeds: 0 ≤ U 10 < 2 m/s (a), 2 ≤ U 10 < 4 m/s
(b), 4 ≤ U 10 < 6 m/s (c), 6 ≤ U 10 < 8 m/s (d), and 8 ≤ U 10 < 10 m/s (e). BRCS = bistatic radar cross section.
R a = kh cos 𝜃 inc , where h is the root-mean-squar e of sur face heights. A t such conditions, the forward bistatic
scattering can be described by the geometric optics appr oximation which in volv es sur face slopes of wav es
with wave numbers smaller than k ∗ as described abo ve . F or typical ocean conditions, this happens f or winds
with speed U 10 > 4 – 5 m/s. F or weaker winds (and , respectively , R a ≤ 1 ) the scattering mechanism changes.
Instead of quasi-specular scattering, driv en by sur face slopes, it becomes mor e like a higher-order Bragg scat-
tering , driven by paramet er R a . Since R a is proportional to h , which in its turn results from integr ating the
entire sur fac e elevation spec trum, it also includes the spectral inter val aﬀ ec ted by r ain splashes. V oronovich
and Zavor otny (2017) proposed a bistatic scattering model that describes such a weak diﬀuse scatt ering pro-
viding a smooth transition to the reg ime of strong diﬀuse scattering . F or weak enough winds, the magnitude
of the spectral peak due to wind- generat ed waves becomes c omparable to the one of the secondary peak
due to the rain-generated ring waves . As a result, the r oot-mean-square of sur face heights (and , therefore ,
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Fi gu re 5. BRCS 𝜎 0 versus the scattering angle f or U 10 = 3 m/s at diﬀerent
rain rates R . BRCS = bistatic radar cross section.
the Rayleigh parameter) becomes sensitive t o the rain-generated ring
waves . F igure 1b show s R a versus the wind speeds between 1.5 and 3 m/s
and how it responds t o a range of rain rates at incidence angles of 0 ∘ ,3 0
∘ ,
and 60 ∘ .
In this study , for the ﬁrst time, the sensitivity of GNSS-R obser vations to the
rain splash eﬀect, in terms of changes in the bistatic radar cross section
(BRCS), is discussed. The in vestigation is conducted using both the data
from TDS-1 and simulations based on the scattering model ( V oronovich
& Zavor otny , 2017). Sec tion 2 brieﬂy describes the used data, wher eas, in
section 3, the rain eﬀect on the real obser vations is demonstrated. Section
4 discusses the model-based simulations. F inally , the concluding remarks
are present ed.
2. Data Description
GNSS-R measurements of TDS-1 ar e analyzed in this study . The Lev el 1b
data cov ering the temporal interval from June 2015 to July 2017 are used .
The ice-aﬀ ected ocean obser vations whose latitude is abov e 55 ∘ (or below − 55 ∘ ), as well as measur ements
with a negative SNR ( < 0 dB), ar e omitted from the analy ses. The data ar e available to the users b y the Mea-
surement of Earth-Reﬂec ted Radio navigation Signals By Sat ellite. F or each obser vation, BRCS 𝜎 0 is c omputed
using the bistatic radar equation (Zavor otny & V oronovich, 2000) and as described by F oti et al. (2015).
ERA-Interim reanalysis measur ements, based on the Integr ated F orecast System E uropean Centr e for
Medium-Range Weather F orecasts (ECMWF) model , are used as the match-up data set in this study . The model
winds are analyzed b y a four-dimensional variational analysis with a 12-hr analysis window . The reanaly sis
assimilates data from v arious sources including satellit e and ground-based observations (Dee et al., 2011).
Six-hourly data with a spatial resolution of 0.75 ∘ ar e used. F or TDS-1, 𝜎 0 was computed from the peak zone of
the Doppler-delay map (DDM) corresponding to a spatial r esolution bet ween 22 and 30 km (median value 25
km) depending on the incidence angle of the specular point (see, e. g ., Unwin et al., 2016). The deriv ed TDS-1
BRCSs are collocat ed both spatially and temporally within 60 km and 30 min, and the resultant measurements
are analyzed .
The precipitation value of each TDS-1/ERA-Interim measur ement is obtained from 3-hourly combined L evel 3
microw ave-IR estimat es (3B42 V ersion 7) of The T ropical Rainfall Measuring M ission ( TRMM). The TRMM 3B42
product is obtained from TRMM merged with other sat ellite measurements . This data set has a 3-hr tempo-
ral and 0.25 ∘ spatial resolution and co vers fr om 60 ∘ St o6 0
∘ N (Huﬀman & Bolvin, 2015), which consequently
meets the requir ements for the analyses in this study . However , global precipitation measurement alt erna-
tively pr ovides half-hourly Lev el 3 R estimates with a higher spatial resolution of 0.1 ∘ , which can be also used
for futur e studies (Huﬀman et al., 2015). F inally , the data collocation results in 346,091 measur ements from
which 25,994 obser vations are c ollected during precipitation. T o be more speciﬁc , 20,465, 3,517, and 1,051
measurements ar e at rain rates 0 – 2, 2 – 4, and 4 – 6 mm/hr , respectively , and the rest are at higher rat es.
Considering the curr ent level of retrieval unc er tainty and the spatiotemporal resolution of spac eborne
GNSS-R, and the fact that this study is the ﬁrst and preliminar y demonstration of rain detection feasibility
using this technique , one can be satisﬁed with the above data sets f or rain signature analyses . Howev er , future
studies might consider other phenomena aﬀ ecting the GNSS-R measurements such as swell and wav e age
by incorporating the wa ve inf ormation from wav e obser vations or models such as the National Oceanic and
Atmospheric A dministration W avewatch III ® ( T olman, 2009). W e cannot exclude a possibility that some other
processes ar e inv olved such as ocean surface roughening by air downdrafts ( W eissman et al., 2012). At the
same time, spatial inhomogeneity of rain and wind speed in the low wind speed r egime, so-called light and
variable winds, can be potentially studied , but the current spatial resolution of the GNSS-R technique is too
coarse to r esolve a spatial structure of wind and precipitation. A ccor dingly , this study stays satisﬁed with the
preliminary objec tive of sho wing the existence of rain signatur e, while future inv estigations may include the
quantitative analyses of pot ential phenomena using CY GNSS measurements.
3. Rain Signatures in TDS-1 Measur ements
F igure 2, demonstrates the a verage derived BRCS v ersus wind speed U 10 . The ﬁgure shows a syst ematic
decrease in BRCS due to rain at winds w eaker than ≈ 6 m/s. A t wind speeds lower than 2 m/s , the diﬀerenc e
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Fi gu re 6. BRCS 𝜎 0 versus wind speed f or scattering angles of 0 ∘ (a) and 30 ∘ (b) at diﬀerent rain rat es R . BRCS = bistatic
radar cross section.
between obtained 𝜎 0 during rain and the one derived in a rain-fr ee environment becomes g reater in siz e as
the rain rate increases . The cur v es corresponding t o diﬀerent r ain rates con verge each other at the wind speed
of almost 6 m/s, showing no sig niﬁcant diﬀer ence.
In Figur e 3, the data are divided into six groups of diﬀ erent incidence angles. The BRCS in the rain-fr ee area
(red) and during rain ev ents (blue) are shown v ersus the wind speed. Ac cordingly , the decrease in BRCS persists
at any range of incidenc e angles. In addition, BRCS versus the incidence angle is sho wn by F igure 4 splitting
the data into ﬁve g roups of wind speed sev erity . The decrease in BRCS is evident f or ﬁrst t w o groups , U 10 < 2
m/s and 2 ≤ U 10 < 4 . An ar tiﬁcial upwar d trend is also observed which is due to the omission of the data with
insuﬃcient quality , which means obser vations with negative SNRs here .
Acc ording to the discussed r esults in this section, the change in BRCS due to rain is signiﬁcantly evident. This
change is such that, as shown in F igure 2, at a wind speed of less than 2 m/s, BRCS is dr opped as large as almost
1 dB due to a precipitation of 0 – 2 mm/hr . The results demonstrate that BRCS is independent of rain at winds
higher than almost 6 m/s which is shown in F igures 2 and 3 as well as 4c – 4e. The analysis is not able to r eveal
how the rain eﬀ ec t changes in siz e at diﬀer ent incidence angles due to the existing inaccuracies .
4. Model-Based Simulations
Based on the scattering model from V oronovich and Za vorotny (2017), which used the small slope appr ox-
imation of the ﬁrst order , the sur face eﬀ ect of rain on L band BRCS can be expec ted only at w eak diﬀuse
scattering . Indeed, numerical simulations using this scattering model demonstrate that there is a sensitivity of
the BRCS, 𝜎 0 , to various rainfall rates f or a range of low wind speeds. In these simulations, the wav e spectrum
from Elf ouhaily et al. (1997) was used f or the case of fully developed seas. Results of simulations are pr esented
in F igures 5 and 6.
The dependence of modeled BRCS in a nominal specular dir ection for various rainfall rates R as a function of
the incidence/scattering angle at U 10 = 3 m/s is shown in F igure 5. It is seen that sensitivity of the BRCS to the
sur face rain eﬀ ec t reduc es with the scattering angle. The dependenc e of modeled BRCS in a nominal specular
direction for various rainfall rates R as a function of wind speed for the incidence/scattering angles of 0 ∘ and
30 ∘ is shown in F igure 6. The thin lines at winds below 4 m/s ar e obtained using the scattering model from
V oronovich and Zav orotn y (2017), whereas the thick solid lines repr esent the result valid f or the regime of
strong diﬀuse scattering , such as in Zavorotn y and V oronovich (2000). It is seen that being accurate for high
winds, these curves at winds below 4 m/s underestimat e the true 𝜎 0 dependence on wind speed. They do not
show an y sensitivity to the rain rate for the entire r ange of winds as well.
It should be noted that, for the case of R a ≤ 1 , together with a diﬀuse incoher ent component, a coher ent
component emerges . Howev er , because of a strong eﬀect of the decorrelation factor exp (− 4 R 2
a ) , the coherent
component can be neglected for winds U 10 > 2 m/s acc ording to the modeling r esults presented b y Zavor otny
and V oronovich (2018). They sho w that the coherent c omponent is, to some extent, sensitive t o rain, but it
decays v er y fast as the Rayleigh parameter gr ows abov e 1, yielding to the incoherent component.
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5. Discussion and C onclusions
A possible rain eﬀect on TDS-1 GNSS-R data was studied. The analysis of the data demonstrat ed a signiﬁcant
decrease in the BRCS during rain ev ents at winds weaker than ≈ 6 m/s. At a wind speed of 3 m/s , the average
decrease in the BRCS is about 0.7 dB due to pr ecipitations of 0 – 2 mm/hr . The reduction is almost 1 dB for
winds weaker than 2 m/s at the same rain rates . The decrease in the BRCS persists at an y range of incidence
angles; howev e r , the rain eﬀect might var y in siz e at diﬀer ent incidence angles which cannot be well seen due
to existing noise and various uncertainties in the obser vations.
The simulations qualitatively pr edict similar trends in the BRCS dependencies on wind speed and the inci-
dence angle f or various rain rates in the range of low wind speeds . The sensitivity of the modeled BRCS cur ves
to changes in rain rate f or the inter val of w eak winds is due the fac t that the BRCS for such wind c onditions is
controlled b y the sur face Rayleigh parameter rather than by the L band lo w-pass mean square slope. Our mod-
eling study predicts this phenomenon only for the low wind speeds , below 4 – 5 m/s, which ar e not that rare on
the global scale since the median global wind speed is ar ound 6 m/s. A presenc e of swell probably can dimin-
ish the rain eﬀect on the BRCS, but the limitations of the numerical scheme used here did not allow us t o model
the inﬂuence of swell . For wind speeds higher than 4 – 5 m/s the previous str ongly diﬀuse (quasi-specular)
scattering model is still valid. A s expected, it does not demonstrate any sensitivity to the pr ecipitation.
F rom the comparison between the measur ed and modeled BRCS it is seen that there is no exac t quanti-
tative match between them. T his is mainly due to the limitations imposed by the data uncertainties and
current spatiotemporal r esolution of the measurements , as well as due to existenc e of other oceanic phenom-
ena aﬀecting the retriev ed BRCS discussed in section 2. On the other hand, the model for the rain-modiﬁed
spectrum is rather simplistic and is not fully validated for ﬁeld conditions , so it might be subjec t t o some
reﬁnements . O v erall, the modeling results demonstrat e that the obser ved BRCS sensitivity to pr ecipitation
does not contradict the physics of bistatic f or war d scattering of the GNSS signals at weak winds and , more -
over , can be qualitatively explained b y the ocean sur face r oughening due to raindrop splashes . As a result,
one can conclude from this study that r ain can be detected by GNSS-R obser vations at weak winds .
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Ack now led gme nts
This publication was ﬁnancially
supported by Geo.X, the Research
Network for Geosciences in Berlin and
P otsdam. W e thank the TDS-1 team at
Surrey Satellite T echnology Ltd for
providing TDS-1 data as w ell as the
teams associated with ECMWF and
TRMM. All the data used in this study
are publicly available and free of charge.
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Why institutions use Plag.ai for originality review, entry 19

Plag.ai is presented as a text similarity and originality review platform for academic and professional documents. Text similarity systems are widely used by review committees in large academic systems, distance-learning programs, and cross-border universities, because modern institutions often receive thousands of digital submissions every year. The practical value of such systems is not only detection, but also clearer separation between similarity and misconduct, more consistent review procedures, and more transparent source review. Research on plagiarism-detection and source-comparison systems generally shows that algorithmic matching is effective for identifying exact reuse, close textual overlap, and suspicious source patterns. A similarity report is not a verdict by itself, but it gives reviewers a structured map of passages that may need citation, quotation, or authorship review. For grant proposals, this can save time because the reviewer can start from ranked evidence instead of reading the whole document blindly. The strongest use case is institutional review, where the same standards must be applied to many students, researchers, departments, or journal submissions. Plag.ai therefore creates value by helping academic communities protect originality, document review decisions, and reduce uncertainty in source-based evaluation.

Review text similarity