# Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground-based GNSS and fifth-generation ECMWF reanalysis (ERA5) data

Edouard Lees, Olivier Bousquet, Jimmy Leclair de Bellevue

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## To cite this version:

Edouard Lees, Olivier Bousquet, Jimmy Leclair de Bellevue. Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground-based GNSS and fifth-generation ECMWF reanalysis (ERA5) data. Quarterly Journal of the Royal Meteorological Society, 2020, pp.1-20. 10.1002/qj.3915. hal-03047878

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DOI: 10.1002/qj.3915

RESEARCH ARTICLE

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# Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground-based GNSS and fifth-generation ECMWF reanalysis (ERA5) data

Edouard Lees<fnref n="1" /> ID |

Olivier Bousquet<fnref n="1" /> ID |

Denis Roy<fnref n="2" /> |

Jimmy Leclair de Bellevue<fnref n="1" />

## Correspondence

E. Lees, Laboratoire de l'Atmosphère et des Cyclones, UMR8105 Université de La Réunion, 15 av. René Cassin, Saint-Denis de La Réunion, 97490, France.
Email: edouard.lees@meteo.fr

## Funding information

Program Interreg-V Océan Indien
2014–2020, the European Union and
Réunion Regional Council, grant/award:
ReNovRisk Cyclones and Climate Change

## Abstract

The spatial and temporal distribution of tropospheric water vapour in the South Indian Ocean (SIO) basin is investigated using observations collected from twelve GNSS stations spanning the basin. The comparison of GNSS-derived integrated water vapour (IWV) content against radiosoundings and satellite-borne microwave radiometer data shows good agreement, with global uncertainties ranging from 0.76 to 1.17 kg·m⁻<fnref n="2" />, depending on GNSS station locations. GNSS-derived IWV contents show a strong seasonal cycle, characterized by higher water vapour content during the austral summer, when the InterTropical Convergence Zone (ITCZ) is located in the Southern Hemisphere. At the seasonal time-scale, the observed annual IWV amplitude varies from 10 to 15 kg·m⁻<fnref n="2" /> near the Equator to 20 to 30 kg·m⁻<fnref n="2" /> in the Subtropics. The GNSS IWV signature of the Madden–Julian Oscillation (MJO) is hardly noticeable during the Austral winter, but varies from 1–2 to 4 kg·m⁻<fnref n="2" /> between the active and suppressed phases of the MJO during austral summer. At diurnal time-scales, GNSS IWV shows larger diurnal amplitude over land (2–3 kg·m⁻<fnref n="2" />) than over open ocean (1–2 kg·m⁻<fnref n="2" />), with highest amplitudes (up to 7 kg·m⁻<fnref n="2" />) observed over large and mountainous islands. The phase analysis of the IWV diurnal cycle indicates that the diurnal maximum (minimum) is reached in the late afternoon/evening (morning) over land, at night (mid-day) over ocean and in the early morning (late afternoon) at coastal locations. A comparison of GNSS-derived IWV contents against fifth-generation European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis (ERA5) data shows that ERA5 generally correctly reproduces the IWV content at both seasonal, intra-seasonal and diurnal time-scales, although some discrepancies can be noticed over small islands characterised by steep orography. The signature of the MJO in ERA5 also shows good agreement with GNSS observations at most studied locations.

## KEYWORDS

Diurnal cycle, ERA5, GNSS meteorology, seasonal cycle, tropical Indian Ocean, tropospheric humidity

<footnote n="1">¹Laboratoire de l'Atmosphère et des Cyclones, UMR8105 (Météo-France, Réunion University, CNRS), Saint-Denis de La Réunion, France</footnote>

<footnote n="2">²Regional Specialized Meteorological Centre, Météo-France, Saint-Denis de La Réunion, France</footnote>
# 1 | INTRODUCTION

Ground-based Global Navigation Satellite System (GNSS) meteorological data, which are by-products of geodetic positioning, have been widely used for weather applications such as data assimilation in numerical weather prediction (NWP) systems (e.g., Mahfouf et al., 2015; Mile et al., 2019) or improvement of quantitative precipitation forecasts associated with heavy precipitating events (e.g., Yang et al., 2020). The primary weather observation that can be derived from GNSS sensors is the tropospheric humidity content, also known as Integrated Water Vapour (IWV: Bevis et al., 1992). This key parameter in both atmospheric and climate sciences, can be used to monitor the variability of atmospheric humidity at timescales extending from 30 min to several years (Bordi et al., 2014), as well as a proxy of large-scale atmospheric dynamics to identify areas of moisture convergence or large-scale subsidence (Stephens, 1990). For instance (Jin et al., 2008) used IWV data over continental China to identify variability modes ranging from annual to semi-diurnal time-scales as well as to produce a local climatology of IWV in this region. Former climatology studies conducted in the western tropical Pacific basin (Holloway and Neelin, 2009) as well as Amazon basin and bordering oceanic regions (Wu and Lee, 2019) also shown that IWV variability is strongly influenced by free troposphere water vapour content and closely linked with the occurrence of deep convective events. To the best of our knowledge, similar studies have not been conducted yet in the South Indian Ocean (SIO) basin.

The SIO (20–100°E, 0–30°S) is considered as one of the least instrumented areas in the world and has very scarce meteorological observation networks. Most areas located north of 12°S experience high sea surface temperature (SST), with SST 27–29°C annual average and often slightly higher in the eastern part of the basin (Black et al., 2003). Such SST range is close to the 28°C threshold defined as a necessary condition for deep convection to occur over tropical oceans (Graham and Barnett, 1987). The SIO is also characterised by large-amplitude diurnal SST variations, which significantly influence the air–sea heat and moisture fluxes (Vialard et al., 2009; Feng et al., 2020) and make atmospheric response very sensitive to SST variations. As most countries around the SIO are subject, and highly vulnerable, to severe weather hazards (drought, extreme precipitation and tropical cyclones), it is therefore crucial to achieve a better understanding of the water cycle to improve both short-term and seasonal weather forecasts and knowledge of general climatic features in this poorly studied area.

In this regard, high-resolution time series of GNSS data is an efficient tool to investigate the diurnal cycle of

tropical convection. The latter account for 30–40% of total variance over land (Yang and Slingo, 2001) and is believed to significantly influence the general circulation in the Tropics through heating and moistening of the troposphere, especially over large tropical islands (Cronin et al., 2015). GNSS time series can also be relied upon to study specific phenomena such as the Madden–Julian Oscillation (MJO), which is the dominant variance mode of tropical convection at intra-seasonal (15–90 days) time-scales (Madden and Julian, 1972; Pohl and Camberlin, 2006).

The primary aim of this study is to analyse the capabilities of ground-based GNSS sensors to monitor the variability of tropospheric humidity at various time-scales using International GNSS System (IGS) observations available throughout the SIO basin. With this regard, GNSS-derived integrated water vapour (IWV) columns are compared against IWV observations derived from radiosoundings (RS) and satellite-borne microwave radiometer (MWR) brightness temperature data, before being used to investigate the properties of the water vapour cycle at seasonal, intraseasonal and diurnal time-scales. GNSS IWV content is also compared against European Centre for Medium-range Weather Forecast (ECMWF) fifth-generation reanalysis (ERA5) data, which feature higher horizontal and temporal resolutions than previous reanalyses (e.g., ERA-Interim). As tropospheric GNSS observations are not assimilated into ERA5 <fnref n="1" />, GNSS-derived IWV data analysed in this study are also used as an independent source of observations to evaluate the capacity of ERA5 to capture the spatial and temporal distribution of humidity in this area as well as to complete, explain or consolidate GNSS IWV findings.

This paper is organised as follows. Section 2 describes the data and methodologies used to infer IWV columns from both GNSS measurement and ERA5 reanalysis data, which are later compared to radiosoundings and satellite observation data. The regional water vapour cycle is discussed in Section 3 with emphasis on seasonal, intra-seasonal and diurnal time-scales, while Section 4 discusses further research perspectives based on the use of GNSS data in the Southwest Indian Ocean (SWIO) basin.

# 2 | DATA, METHODOLOGY AND UNCERTAINTIES

## 2.1 | Datasets

This study is based on an ensemble of water vapour observations derived from GNSS, RS, satellite-borne MWR and reanalysis data, whose details are given below.

<footnote n="1">¹https://confluence.ecmwf.int/display/CKB/ERA5+data+documentation gives ERA5 specifications (accessed 4 October 2020)</footnote>
**FIGURE 1** Location of the twelve IGS ground-based GNSS stations (circles) and the five radiosonde stations (triangles) used in this study. Insert shows the locations of new GNSS research stations recently deployed in the southwest Indian Ocean basin in the framework of the Indian Ocean GNSS Applications for Meteorology (IOGA4MET) research program. Red, blue and green colours indicate the three tracts discussed in Section 3

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IWV is estimated from 5 min resolution operational GNSS data available from the IGS (Dow *et al.*, 2009)<fnref n="2" />, which is a voluntary federation of ~200 self-funding agencies, universities, and research institutions providing high-quality processed GNSS data, products and services in support of the terrestrial reference frame, Earth observation and research. Twelve GNSS stations, whose locations and main characteristics are given in Figure 1 and Table 1, are used in this study: ABPO (Madagascar), BAKO (Java Island), XMIS, COCO and KARR (Australia), VACS (Mauritius), ZAMB (Zambia), MAL2 (Kenya), SEYG (Seychelles), DGAR (Chagos archipelago) and REUN and MAYG (French islands of Réunion and Mayotte). Data availability over the period of analysis (2009–2019) ranges from 69 to 92% depending on GNSS stations. The impact of missing data on the results presented in this study is nevertheless limited as data gaps generally occur over continuous periods of time (example in Figure 2 for COCO and DGAR).

RS profiles used to evaluate GNSS-derived IWV, are extracted from version 2 of Integrated Global Radiosonde Archive (IGRA)<fnref n="3" />, which aggregates available operational sounding observations all around the world. IGRA data consist of temperature and dew point measurements on pressure levels. Water vapour pressure is computed from dew point measurements following the method of (Bolton, 1980). It is then used to estimate specific humidity (*q*), and IWV (by integrating *q* from surface level up to 300 hPa).

Satellite-borne microwave radiometer IWV observations are derived from brightness temperature measurements collected by the Special Sensor Microwave Imager Sounder (SSMIS) deployed onboard the DMSP (Defense Meteorological Satellite Program) F17 satellite<fnref n="4" />.

The latter is known to show good temporal stability over the 2009–2019 period compared to other satellite-borne microwave sensors (figure 2 in Mears *et al.*, 2015). Data consist of gridded IWV available twice daily at a resolution of 0.25° × 0.25°. Satellite-derived IWV estimated 25 km away from the coast are interpolated at GNSS stations to avoid land contamination due to change in emissivity.

Surface weather data (temperature, surface pressure, specific humidity) required to compute IWV data are extracted from ECMWF ERA5 Reanalysis<fnref n="5" />, which recently replaced the ERA-Interim reanalysis (the latter ceased production on 31 August 2019). ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables over the Earth on a 0.25×0.25° horizontal grid (~31 km) using 137 levels from the surface up to 80 km. At the time of writing, ERA5 covers only the period 1979–2019, but the complete reanalysis (starting from 1950) is due to be available in late 2020.

## 2.2 | Methodology

Methods used to compute IWV estimates from ZTD observations are described below together with associated uncertainty estimates. GNSS-derived IWV contents are then compared to RS, satellite and ERA5 IWV estimations at several sites, depending on verification data availability.

### 2.2.1 | GNSS IWV retrieval

IWV estimates are computed from Zenith Tropospheric Delay (ZTD) data provided by IGS following the approach of Bevis *et al.*, (1992), hereafter referred to as BBH92. Since colocated surface weather data required to compute

<footnote n="2">²ftp://cddis.nasa.gov/gnss/products/troposphere/zpd/</footnote>

<footnote n="3">³ftp://ftp.ncdc.noaa.gov/pub/data/igra/data/; both accessed 4 October 2020</footnote>

<footnote n="4">⁴http://www.remss.com</footnote>

<footnote n="5">⁵https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</footnote>
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<figure><img src="image_7.png" /><figcaption>FIGURE 2 10-year time series (2009–2019) of retrieved IWV differences from (a, b) GNSS and SSMI/S, (c, d) ERA5 and GNSS, and (e, f) ERA5 and SSMI/S for (a, c, e) DGAR and (b, d, f) COCO. The monthly averaged difference is indicated by the black line</figcaption></figure>

<table><caption>TABLE 1 Details of GNSS, ERA5 and RS data used in this study</caption><thead><tr><th>GNSS ID</th><th>Latitude</th><th>Longitude</th><th>z<sub>GNSS</sub></th><th>Period</th><th>d<sub>ERA5</sub></th><th>z<sub>ERA5</sub></th><th>RS ID</th><th>d<sub>RS</sub></th><th>z<sub>RS</sub></th></tr></thead><tbody><tr><td>MAL2</td><td>02°59′46″S</td><td>40°11′38″E</td><td>4 m</td><td>2009–2019</td><td>6.2 km</td><td>7 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>SEYG</td><td>04°40′43″S</td><td>55°31′50″E</td><td>4 m</td><td>2016–2019</td><td>8.6 km</td><td>14 m</td><td>63985</td><td>&lt; 1 km</td><td>4 m</td></tr><tr><td>BAKO</td><td>06°29′24″S</td><td>106°51′00″E</td><td>140 m</td><td>2009–2019</td><td>11.1 km</td><td>315 m</td><td>96749</td><td>46 km</td><td>8 m</td></tr><tr><td>DGAR</td><td>07°16′11″S</td><td>72°22′13″E</td><td>10 m</td><td>2009–2019</td><td>13.4 km</td><td>0 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>XMIS</td><td>10°27′00″S</td><td>105°41′19″E</td><td>265 m</td><td>2009–2019</td><td>8.8 km</td><td>12 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>COCO</td><td>12°11′18″S</td><td>96°50′02″E</td><td>4 m</td><td>2009–2019</td><td>11.4 km</td><td>0 m</td><td>96996</td><td>&lt;1 km</td><td>3 m</td></tr><tr><td>MAYG</td><td>12°46′55″S</td><td>45°15′29″E</td><td>4 m</td><td>2016–2019</td><td>3.7 km</td><td>19 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>ZAMB</td><td>15°25′32″S</td><td>28°18′40″E</td><td>1326 m</td><td>2012–2019</td><td>10.6 km</td><td>1131 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>ABPO</td><td>19°01′06″S</td><td>47°13′45″E</td><td>1551 m</td><td>2009–2019</td><td>3.0 km</td><td>1369 m</td><td>67083</td><td>36 km</td><td>1279 m</td></tr><tr><td>VACS</td><td>20°17′50″S</td><td>57°29′49″E</td><td>423 m</td><td>2009–2019</td><td>5.2 km</td><td>110 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>KARR</td><td>20°58′53″S</td><td>117°05′50″E</td><td>117 m</td><td>2009–2019</td><td>10.3 km</td><td>97 m</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>REUN</td><td>21°12′30″S</td><td>55°34′18″E</td><td>1556 m</td><td>2009–2019</td><td>8.8 km</td><td>717 m</td><td>61980</td><td>36 km</td><td>5 m</td></tr></tbody></table>

Notes: Columns 1–5: GNSS ID, latitude, longitude, elevation (above sea level) and period of data availability for the 12 GNSS ground stations used here.

Columns 6–7: horizontal and vertical distances between considered GNSS stations and closest ERA5 grid point.

Columns 8–10: World Meteorological Organization ID of RS stations, distance to GNSS stations and start altitude of RS (NA indicates that no RS is available within 50 km of the GNSS station).

Stations shown in bold are used for the instrumental intercomparisons presented in Section 2.

IWV at GNSS locations are not always available, we chose to mimic surface synoptic measurements by using ERA5 Reanalysis data at the closest grid points to GNSS stations. The local surface temperature ($T_s$) is estimated using a

6.5 K·km⁻¹ moist adiabatic vertical temperature gradient assumption, while atmospheric pressure is assessed from reanalysed surface pressure ($P_s$) and temperature data using the hypsometric model as in (Hagemann et al.,
<figure><img src="image_8.png" /></figure>

2003). Due to differences in time resolution between IGS and ERA5 data (5 min versus 1 hr), surface data are also linearly interpolated every 5 min. More details regarding ERA5 data processing, including altitude mismatches considerations, are discussed in Section 2.2.3.

Overall ZTD can be divided into two components known as Zenith Hydrostatic Delay (ZHD) and Zenith Wet Delay (ZWD) such that

$$ZTD = ZHD + ZWD. \quad (1)$$

ZHD can be estimated, assuming hydrostatic balance, from the Saastamoinen (1972) model:

$$ZHD = \frac{C \cdot P_s}{f(\lambda, H)} \quad (2)$$

where $P_s$ (hPa) and $\lambda$ (°) are the surface pressure and latitude at GNSS locations, $H$ (m) is the height of the GNSS antenna, $C$ is a constant equal to $2.767 \pm 0.0015 \text{ mm} \cdot \text{hPa}^{-1}$ and $f(\lambda, H)$ is given by:

$$f(\lambda, H) = 1 - 2.66 \times 10^{-3} \cos(2\lambda) - 2.8 \times 10^{-7} H. \quad (3)$$

Knowing ZTD and ZHD, it is thus possible to retrieve IWV from

$$IWV = \frac{ZWD}{Q} \quad (4)$$

and

$$Q = 10^{-6} \rho_l R_w \left( k'_2 + \frac{k_3}{T_m} \right), \quad (5)$$

where $\rho_l$ (kg·m⁻³) is the liquid water density, $R_w$ (J·kg⁻¹·K⁻¹) is the specific gas constant for water vapour and $k'_2$ (17±10 K·hPa⁻¹) and $k_3$ ((3.776 ± 0.004) × 10⁵ K²·hPa⁻¹) are optical constants derived from laboratory experiments (table 1 in BBH92). Thus, the Q factor is a proportionality factor between ZWD and IWV, derived from optical formulae (BBH92 give further details), and $T_m$ is the mean weighted temperature of the atmosphere (Davis et al., 1985), defined by:

$$\frac{1}{T_m} = \int_{0}^{\text{toa}} \frac{P_v}{T^2} dz / \int_{0}^{\text{toa}} \frac{P_v}{T} dz. \quad (6)$$

Since colocated vertical temperature and water vapour profile are not always available at GNSS locations, $T_m$ must generally be estimated by other means. Former studies (e.g., Wang et al., 2005) have shown that $T_m$ could be deduced from NWP models with a mean error of 1.3 K. In the current study we chose to estimate this parameter using the empirical formula of BBH92 (Equation 7 below) in order to follow a purely observational approach, as in (Van Malderen et al., 2014). In order to avoid potential

effect of diurnal cycle caused by large diurnal amplitude in surface temperature (Morland et al., 2009), a 24 hr running average is applied to surface temperature ($T_s$ in Equation 7) prior to $T_m$ computation. The average of surface temperatures assumes that $T_m$ diurnal variability can be neglected for IWV diurnal cycle analysis. The amplitude of $T_m$ diurnal cycle was estimated to be 1.5 K (0.5 K) over continents (oceans) by (Wang et al., 2005), leading to an error of 0.5% (0.3 kg·m⁻²) over continents and 0.16% (0.1 kg·m⁻²) over oceans for IWV content of 60 kg·m⁻², which is indeed negligible compared to actual IWV diurnal amplitude (as seen below).

$$T_m = 0.72 T_s + 70.2. \quad (7)$$

In order to assess uncertainties in GNSS IWV retrieval, we follow the approach proposed by Ning et al., (2016):

$$\sigma_{\text{IWV}} = \sqrt{\left(\frac{\sigma_{\text{ZTD}}}{Q}\right)^2 + \left(\frac{\sigma_{\text{ZHD}}}{Q}\right)^2 + \left(\frac{\text{IWV}\sigma_Q}{Q}\right)^2}. \quad (8)$$

The first component accounts for uncertainty originating from known error sources such as cut-off angle, satellite orbits, clock uncertainty, multiple paths, heavy precipitating events as well as the ZTD retrieval mapping function (Ning et al., 2016). This uncertainty, provided by IGS, is estimated to be 4 mm (Byun and Bar-Sever, 2009), leading to an uncertainty of 0.65 kg·m⁻² in IWV. This value agrees well with the study of Deblonde et al., (2005), who estimated the error on IWV due to ZTD uncertainty to be 0.6 kg·m⁻².

The second component accounts for uncertainty on ZHD retrieval (Equation 2), which is mostly due to errors in surface pressure estimates at GNSS locations:

$$\sigma_{\text{ZHD}} = \sqrt{\left(\frac{C\sigma_{P_s}}{f(\lambda, H)}\right)^2 + \left(\frac{\sigma_C P_s}{f(\lambda, H)}\right)^2} \quad (9)$$

In the present case, uncertainty in surface pressure is principally due to the lack of colocated surface measurements at GNSS locations. This limitation can be overcome by using ERA5 reanalysis data to retrieve surface pressure values at all locations. Note that the horizontal resolution of ERA5 data (~31 km) can generate significant horizontal pressure gradients in extreme conditions (e.g. tropical cyclones) which could potentially bias ZTD estimates. Such events are nevertheless too rare to significantly impact pressure estimates over the period of analysis considered in this study.

Uncertainty in ERA5 surface pressure can be estimated from the Integrated Surface Database (ISD; Smith
<table><caption>TABLE 2 Intercomparison between mean tropospheric temperature computed from radiosounding profiles and BBH92 formula</caption><thead><tr><th>RS/GNSS ID</th><th>Latitude<sub>RS</sub></th><th>Longitude<sub>RS</sub></th><th>z<sub>RS</sub> (m)</th><th>t (UTC)</th><th>t (LST)</th><th>No.</th><th>&lt; T<sub>mb</sub> - T<sub>m</sub><sup>RS</sup> &gt; (K)</th><th>RMSE (K)</th><th>&lt; T<sub>m</sub><sup>RS</sup> &gt; (K)</th></tr></thead><tbody><tr><td>61980/REUN</td><td>20°53′24″S</td><td>55°30′59″E</td><td>20</td><td>1115</td><td>1515</td><td>2,681</td><td>-2.89 ± 2.39</td><td>3.76</td><td>280.4</td></tr><tr><td>96996/COCO</td><td>12°11′18″S</td><td>96°50′02″E</td><td>3</td><td>2315</td><td>0545</td><td>3,281</td><td>-2.77 ± 1.88</td><td>3.35</td><td>288.6</td></tr><tr><td>63985/SEYG</td><td>04°40′28″S</td><td>55°31′19″E</td><td>10</td><td>1115</td><td>1515</td><td>798</td><td>-2.97 ± 2.01</td><td>3.59</td><td>288.9</td></tr><tr><td>67083/ABPO</td><td>18°47′49″S</td><td>47°28′44″E</td><td>1,280</td><td>2230</td><td>0130</td><td>2,493</td><td>-0.79 ± 2.60</td><td>2.72</td><td>280.2</td></tr><tr><td>67083/ABPO</td><td>—</td><td>—</td><td>—</td><td>1030</td><td>1330</td><td>2,134</td><td>-2.61 ± 2.55</td><td>3.65</td><td>282.0</td></tr><tr><td>96749/BAKO</td><td>06°07′32″S</td><td>106°39′21″E</td><td>10</td><td>2315</td><td>0615</td><td>3,582</td><td>-0.43 ± 1.62</td><td>1,67</td><td>286.6</td></tr><tr><td>96749/BAKO</td><td>—</td><td>—</td><td>—</td><td>1115</td><td>1815</td><td>3,560</td><td>-0.93 ± 1.69</td><td>1.93</td><td>287.1</td></tr></tbody></table>

Notes: Column 1-7: identifier, latitude, longitude, altitude, RS time (UTC and LST) and number of RS considered in the analysis.

Columns 8-10: mean bias ± standard deviation ( < T<sub>mb</sub> - T<sub>m</sub><sup>RS</sup> > ) and RMSE between BBH92- and RS-derived mean temperature and mean RS temperature.

All soundings are taken over the period 2009–2019 except for soundings 61980/REUN (2012–2019) and 63985/SEYG (2016–2019).

et al., 2011) data<fnref n="6" />. The comparison of ISD and ERA5 surface pressure data at COCO, DGAR, MAYG and SEYG shows a maximum root mean square error (RMSE) of 0.7 hPa, which is consistent with the results obtained recently over China by Zhang *et al.*, (2019). Taking these uncertainties into account in Equation 9 leads to a maximum uncertainty of 0.36 kg·m⁻² in retrieved IWV columns (for instance, IWV computed at COCO and SEYG from ISD and ERA5 pressure data shows differences of -0.04 ± 0.12 kg·m⁻² and 0.06 ± 0.13 kg·m⁻², respectively).

The third component represents the uncertainty on the Q factor estimate, which is given by:

$$ \sigma_Q = 10^{-6} \times \rho_l R_w \sqrt{\left(\frac{\sigma_{k_3}}{T_m}\right)^2 + \sigma_{k'_2}^2 + \left(\frac{k_3 \sigma_{T_m}}{T_m^2}\right)^2} \quad (10) $$

According to Ning *et al.*, (2016), this uncertainty is principally related to the $T_m$ estimate. Using radiosounding data over the continental United States, BBH92 found that uncertainties in $T_m$ average around 2 K, which translates to an error of ~1% in IWV. A comparison between $T_m$ retrieved from Equation 7 and $T_m$ computed from RS data (noted $T_m^{RS}$) for the five GNSS stations SEYG, COCO, REUN, ABPO and BAKO (Table 1) is shown in Table 2. Overall, $T_m$ obtained from Equation 7 shows a cold bias of 0–3 K with respect to $T_m^{RS}$, with RMS errors ranging from 1.8 to 3.8 K depending on sounding locations and times. This result is in good agreement with the former study of Wang *et al.*, (2005), which reported a cold bias ranging from 1 to 6 K in the Tropics. Taking this uncertainty into account in Equation 10, with $T_m = 285$ K and $\sigma_{T_m} = 3.8$ K, gives an uncertainty of ~1.5% for Q, which translates into an error of 0.15 (0.9) kg·m⁻² for IWV values of 10 (60) kg·m⁻².

In order to directly assess the influence of $T_m$ estimated from Equation 7 on IWV retrieval, $T_m^{RS}$ was also used to compute IWV at the GNSS stations of COCO and SEYG, which are both colocated with RS stations. At these two locations, we observed a mean difference of -0.39 ± 0.23 and -0.44 ± 0.24 kg·m⁻² from using $T_m$ and $T_m^{RS}$, respectively. Considering mean IWV values of ~45 kg·m⁻² at these two stations (Figure 3), this translates into an relative error of ~1 ± 0.5%.

Through combining the aforementioned uncertainties on all three components of Equation (8), the maximum uncertainty on GNSS-derived IWV content can be estimated to 0.76 (1.17) kg·m⁻² for IWV values of 10 (60) kg·m⁻², which is consistent with IWV uncertainty estimates that can be found in the literature (Deblonde *et al.*, 2005; Ning *et al.*, 2016).

### 2.2.2 | Intercomparison between GNSS- and RS-derived IWV columns

In this section we compare GNSS IWV with those derived from radiosounding data available at the five sites indicated in Table 1. RS- and GNSS-derived IWV are compared over the hour following the launch of the RS. Three of the five RS stations (ABPO, REUN, BAKO) are not colocated with GNSS stations, which can translate into significant difference of elevation (e.g. ~1,500 m for REUN). To account for these altitude differences, RS IWV is computed from a pressure level that matches surface pressure at GNSS station. Results of these intercomparisons are given in Table 3.

For the two RS stations co-located with GNSS stations (COCO and SEYG) a strong correlation is obtained (> 0.98), together with both low bias (<1 kg·m⁻²) and standard deviation (1.8 kg·m⁻²). These results are consistent with the former studies of Van Malderen *et al.*, (2014) and

<footnote n="6">⁶available at ftp://ftp.ncdc.noaa.gov/pub/data/noaa</footnote>
<figure><img src="image_9.png" /><figcaption>TABLE 3 Comparison between Integrated Water Vapour (IWV) values derived from GNSS and RS observations</figcaption></figure>

<table><thead><tr><th>RS ID</th><th>GNSS ID</th><th>t (UTC)</th><th>t (LST)</th><th>Sonde type</th><th>No<sub>RS</sub></th><th>&lt;IWV<sub>RS</sub>−IWV<sub>GNSS</sub>&gt;<br>(kg·m<sup>−2</sup>)</th><th>Corr</th></tr></thead><tbody><tr><td>61980</td><td>REUN</td><td>1115 (1200)</td><td>1515</td><td>Modem M10</td><td>1,917</td><td>−1.94 ± 3.30</td><td>0.927</td></tr><tr><td>96996</td><td>COCO</td><td>2315 (0000)</td><td>0545</td><td>Vaisala RS92/RS41</td><td>3,021</td><td>−0.65 ± 1.85</td><td>0.980</td></tr><tr><td>63985</td><td>SEYG</td><td>1115 (1200)</td><td>1515</td><td>Vaisala RS41</td><td>525</td><td>0.20 ± 1.84</td><td>0.982</td></tr><tr><td>67083</td><td>ABPO</td><td>2230 (0000)</td><td>0130</td><td>Vaisala RS92/RS41</td><td>1,670</td><td>−0.58 ± 2.41</td><td>0.965</td></tr><tr><td>67083</td><td>ABPO</td><td>1030 (1200)</td><td>1330</td><td>Vaisala RS92/RS41</td><td>1,401</td><td>−1.32 ± 2.62</td><td>0.954</td></tr><tr><td>96749</td><td>BAKO</td><td>2315 (0000)</td><td>0615</td><td>Meisei RS-01G/iMS100</td><td>3,204</td><td>−0.40 ± 2.98</td><td>0.938</td></tr><tr><td>96749</td><td>BAKO</td><td>1115 (1200)</td><td>1815</td><td>Meisei RS-01G/iMS100</td><td>3,183</td><td>−3.03 ± 3.66</td><td>0.895</td></tr></tbody></table>

Notes: RS and GNSS identifiers, RS launch times (actual and nominal) in UTC and local time (LST), radiosonde type, number of RS used in the comparison for each location/time, IWV bias ± standard deviation between RS and GNSS and correlation between RS- and GNSS-derived IWV. Note that Meisei sondes include a correction for both dry and wet biases and thus present error characteristics different from Vaisala sondes (Ingleby, 2017).

Miloshevich et al., (2009), which reported uncertainties on RS values of 2.25 kg·m⁻² (±5% for a mean IWV of 45 kg·m⁻²) for Vaisala sondes such as used at SEYG, COCO and ABPO (Table 3). The agreement between GNSS- and RS-derived IWV columns however decreases with GNSS/RS distance ($d_{RS}$).

Diurnal soundings such as REUN/61980 1200 UTC (ABPO/67083 1200 UTC) launched at 1515 (1330) local time (LT) show a dry bias, whereas the night-time sound- ing of Madagascar (ABPO/67083 0000 UTC/0130 LT) shows larger correlation, lower standard deviation as well as lower dry bias. This dry bias in daytime, already noticed in previous studies, is likely related to the radia- tive heating of the RS humidity sensor at high solar angle. (Miloshevich et al., 2009) found for instance a dry bias of up to 5% for high (more than 30° above the horizon) solar angle when comparing IWV content derived from RS92 radiosondes and ground-based MWR observations.

A dry bias can nevertheless be also noticed in BAKO/96749 soundings (0615 and 1815 LT), with a mean value of up to 3 kg·m⁻² for the evening one. These two soundings also show lower correlation and higher stan- dard deviation than others. As the solar angle is rather low at these times of the day, one can assume that radiative effects can be neglected. In this case, we suspect observed differences to be related to the different environmental conditions found at the GNSS and RS sites (BAKO sound- ing is located in the western part of Java island, about 4 km from the northern shore, while the BAKO GNSS station is located inland, 46 km to the south; Table 1).

### 2.2.3 | Intercomparison between GNSS- and ERA5-derived IWV columns

Although ERA5 assimilates RS data as well as cloudy satellite radiance under low rain conditions, IGS ground-based GNSS data are not assimilated into ERA5,

so GNSS-derived IWV contents can be used as indepen- dent datasets to evaluate ERA5 tropospheric water vapour analysis. In order to account for elevation differences between GNSS locations and ERA5 closest grid points, ERA5-derived IWV are computed starting from the height of each GNSS station as soon as the latter is at least 50 m above that of ERA5 grid point (ZAMB, XMIS, ABPO, REUN, VACS). Following the approach of (Fernández et al., 2019), ERA5-derived specific humidity<fnref n="7" />, $q$, is then linearly interpolated on the lowest nine pressure levels (1,000, 975, 950, 925, 900, 875, 850, 825 and 800 hPa) to estimate its value at the GNSS level. If the ERA5/GNSS surface pressure is greater than 1,000 hPa, $q$ is extrapolated using the same slope as that between 975 and 1,000 hPa. Conversely, when ERA5 grid point elevation is higher than that of the GNSS station (as for BAKO), or when the height difference is lower than 50 m (as for MAL2, SEYG, DGAR, MAYG, COCO and KARR) no correction is applied. The impact of height correction on ERA5 IWV retrieval, which could reach up to 12 kg·m⁻² at Réunion Island (REUN), can be seen in Figure 3.

The estimated ERA5 IWV (IWV*_{ERA5}) starting from the height of the GNSS station is given by

$$IWV_{ERA5}^* = IWV_{ERA5} - \frac{1}{g} \int_{P_{ERA5}}^{p_{GNSS}} q(p) dp, \quad (11)$$

where $q(p)$ is the specific humidity in kg·kg⁻¹, $p$ is the atmospheric pressure in Pa, $g$ is the gravitational constant (9.81 m·s⁻²), and $p_{ERA5}$ ($p_{GNSS}$) is the atmospheric pressure in Pa at ERA5 (GNSS) grid point elevation computed using the hypsometric relationship.

Results of this comparison are shown in Table 4. ERA5 tends to underestimate IWV at most GNSS locations by

<footnote n="7">⁷https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview</footnote>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>FIGURE 3 Monthly averages of Integrated Water Vapour columns (kg·m⁻²) derived from GNSS observation at the 12 stations shown in Figure 1 (black line and black y-axis to the left). Blue lines show ERA5 minus GNSS IWV difference (kg·m⁻², blue y-axis to the right) before (solid) and after (dashed) height correction (see text for details). Results are for the period 2009–2019 except for (a, f) 2016–2019 and (e) 2012–2019</figcaption></figure>

0.3 to 1.3 kg·m⁻² (~1.5 to 7.5% depending on location), except for BAKO which shows a larger absolute bias (~2.8 kg·m⁻²). This difference arises from the fact that ERA5 grid point is above the GNSS altitude, hampering IWV height correction from the method described earlier. This underestimation was already noticed with previous generation reanalysis such as ERA-Interim and MERRA ((Parracho et al., 2018)), which indicated a dry (moist) bias in the Tropics (Extratropics) of 0.5 to 1 kg·m⁻². The correlation factor between GNSS and ERA5 IWV is nevertheless always >0.95.

Standard deviations are between 2 and 3 kg·m⁻². These values can be compared with those obtained from previous studies making use of various instruments to estimate IWV contents (e.g., Van Malderen et al., 2014; Chen and Liu, 2016). For instance, through comparing GNSS- RS- and satellite-derived IWV with ERA-Interim and NCEP reanalyses, (Chen and Liu, 2016) found an average RMS error of 3.82 kg·m² in tropical regions for the GNSS-ERA-Interim comparison (their table 1 and figure 3) against ~2–3 kg·m² for the GNSS-ERA5 comparison, as shown in Table 4 (which suggests some improvement with respect to previous reanalyses).

### 2.2.4 | Intercomparison of GNSS and ERA5 IWV against SSMI/S f17 observations

In this subsection GNSS IWV contents are compared against IWV estimates derived from the analysis of SSMI/S

f17 satellite radiances at COCO, DGAR, SEYG and MAYG. These four sites being located at sea level, GNSS IWV data can be directly compared against SSMI/S f17 IWV. The latter are available once to twice daily (1.3 per day on average) giving a sample of 4667 (4545, 1263 and 1199) IWV comparison with space and time matches for COCO (DGAR, SEYG and MAYG). Results shown in Table 4 show that IWV values inferred from satellite data are usually slightly moister than GNSS IWV with a mean difference of 0.5–0.8 kg·m⁻² and standard deviation of 2.1–2.4 kg·m⁻² for mean IWV above 40 kg·m⁻². These results are in good agreement with those found in Mears et al. 2015 regarding GNSS and space-borne MWR IWV retrieval comparisons.

A comparison of IWV time series derived from GNSS, SSMIS and ERA5 data at COCO and DGAR is shown in Figure 2. At first, one can notice a strong seasonal fluctuation in both SSMIS/GNSS (Figure 2a,b) and SSMIS/ERA5 (Figure 2e,f) time series of IWV differences, which is not present in ERA5/GNSS comparisons (Figure 2c,d). As mentioned in Mears et al. 2015, this seasonality in MWR data can be attributed to wind-speed-dependent errors in IWV retrieval (i.e., overestimation of IWV for low wind speeds and underestimation for wind speed above 10 m·s⁻¹).

At the COCO station, discontinuities of up to ±2 kg·m⁻² can also be noticed in 2011 and 2016 in both GNSS/SSMIS (Figure 2b) and GNSS/ERA5 IWV (Figure 2d). Such discontinuities could not be noticed at the three other evaluated island sites. Since these
<figure><img src="image_12.png" /><figcaption>**TABLE 4** Comparison between integrated water vapour (IWV) values derived from GNSS, SSMI/S and ERA5 data at selected locations</figcaption></figure>

<table><thead><tr><th>GNSS ID</th><th>Comparison</th><th>Period</th><th>&lt;IWV<sub>GNSS</sub>&gt;<br>(kg·m<sup>-2</sup>)</th><th>Bias ± std<br>(kg·m<sup>-2</sup>)</th><th>Corr</th><th>RMS<br>(kg·m<sup>-2</sup>)</th></tr></thead><tbody><tr><td rowspan="3">SEYG</td><td>ERA5 vs GNSS</td><td>2016–2019</td><td>46.40</td><td>-0.30 ± 2.92</td><td>0.945</td><td>2.93</td></tr><tr><td>SSMIS f17 vs GNSS</td><td></td><td></td><td>0.77 ± 2.14</td><td>0.970</td><td>2.28</td></tr><tr><td>ERA5 vs SSMIS f17</td><td></td><td></td><td>-1.22 ± 2.66</td><td>0.948</td><td>2.92</td></tr><tr><td rowspan="3">COCO</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>44.68</td><td>-0.72 ± 2.37</td><td>0.976</td><td>2.48</td></tr><tr><td>SSMIS f17 vs GNSS</td><td></td><td></td><td>0.63 ± 2.28</td><td>0.977</td><td>2.36</td></tr><tr><td>ERA5 vs SSMIS f17</td><td></td><td></td><td>-1.64 ± 2.05</td><td>0.982</td><td>2.63</td></tr><tr><td rowspan="3">DGAR</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>49.86</td><td>-0.75 ± 2.71</td><td>0.953</td><td>2.81</td></tr><tr><td>SSMIS f17 vs GNSS</td><td></td><td></td><td>0.50 ± 2.33</td><td>0.967</td><td>2.39</td></tr><tr><td>ERA5 vs SSMIS f17</td><td></td><td></td><td>-1.57 ± 2.35</td><td>0.965</td><td>2.83</td></tr><tr><td rowspan="3">MAYG</td><td>ERA5 vs GNSS</td><td>2016–2019</td><td>41.89</td><td>-0.87 ± 2.81</td><td>0.968</td><td>2.94</td></tr><tr><td>SSMIS f17 vs GNSS</td><td></td><td></td><td>0.57 ± 2.13</td><td>0.982</td><td>2.20</td></tr><tr><td>ERA5 vs SSMIS f17</td><td></td><td></td><td>-1.84 ± 2.45</td><td>0.969</td><td>3.06</td></tr><tr><td>ZAMB*</td><td>ERA5 vs GNSS</td><td>2012–2019</td><td>21.17</td><td>0.33 ± 1.93</td><td>0.985</td><td>1.96</td></tr><tr><td>ABPO*</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>20.43</td><td>-0.64 ± 2.21</td><td>0.972</td><td>2.30</td></tr><tr><td>VACS*</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>28.57</td><td>-1.24 ± 2.55</td><td>0.971</td><td>2.84</td></tr><tr><td>KARR</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>25.50</td><td>-0.56 ± 2.21</td><td>0.986</td><td>2.27</td></tr><tr><td>REUN*</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>16.37</td><td>-1.33 ± 2.33</td><td>0.963</td><td>2.68</td></tr><tr><td>MAL2</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>42.16</td><td>-0.61 ± 2.44</td><td>0.946</td><td>2.51</td></tr><tr><td>XMIS*</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>40.40</td><td>-1.00 ± 2.49</td><td>0.975</td><td>2.68</td></tr><tr><td>BAKO</td><td>ERA5 vs GNSS</td><td>2009–2019</td><td>49.77</td><td>-2.82 ± 2.94</td><td>0.941</td><td>4.08</td></tr></tbody></table>

Notes: GNSS ID, sensor comparison type (xx vs. yy), comparison period, GNSS IWV average value over the comparison period (&lt;IWV<sub>gnss</sub>&gt;), average difference between GNSS, ERA5 and SSMIS IWV (bias ± std), correlation and RMS error. In column 1, * indicate GNSS stations located more than 50 m above the ERA5 grid point for which bias, standard deviation, correlation factor and RMS are assessed from height-corrected ERA5 IWV. No correction is applied for other stations (see text for more details)

breakpoints are not present in the associated ERA5/SSMIS comparison (Figure 2f), this suggests a potential issue with GNSS measurements. As mentioned in (Diedrich et al., 2009) and (Vey et al., 2009), such breakpoints are usually related to changes in the hardware (antenna, receiver or radome) or in the amount of information recorded. The examination of COCO IGS log files suggests that these discontinuities indeed correspond to a change of the GNSS receiver (from TRIMBLE NETR5 to TRIMBLE NETR8 in October 2011 and from TRIMBLE NETR8 to SEVEN POLARXS in June 2015). Although (Schröder et al., 2016) suggested that such discontinuities in IWV time series would require rigorous investigation to investigate intra-seasonal water vapour variability and trends, the breakpoints observed at COCO are nevertheless unlikely to affect the interpretation of the results at time-scales investigated in this study.

## 3 | REGIONAL CLIMATOLOGY OF IWV

In the following, GNSS-derived IWV columns are relied upon to investigate the spatial and temporal variability of water vapour at various time-scales and locations of the SIO basin in order to understand the regional water cycle as well as its relationship with the Madden-Julian Oscillation. In a second step, GNSS-derived IWV columns are compared against those derived from ERA5 data to investigate potential seasonal dependency and assess ERA5 capability to accurately reproduce IWV variability at these time-scales.

### 3.1 | Seasonal cycle

Figure 3 presents the seasonal cycle of IWV over different part of the SIO basin as deduced from the twelve
GNSS stations shown in Figure 1. These twelve stations are used to investigate the water vapour distribution along three transects distributed on both sides of 12°S. The first transect (MAL2, SEYG, DGAR, BAKO, red in Figure 1) highlights the water vapour distribution north of 8°S, from Kenya to Indonesia, while the second one (ZAMB, MAYG, COCO, XMAS, blue in Figure 1) covers the area 8–16°S, from central Africa to Christmas Island. The third transect (ABPO, REUN, VCS, KARR, green in Figure 1) is located along 20°S and extends from Madagascar to western Australia.

Along the first transect (Figure 3a–d), GNSS-derived measurements show a rather weak seasonal cycle (10 to 15 kg·m⁻² amplitude on average) with highest mean values ranging from 42.2 to 50 kg·m⁻² along Kenya’s coast (MAL2), in the centre of the basin (DGAR) and over Java Island (BAKO). All GNSS measurements exhibit a drier period during austral winter with significantly weaker seasonal cycle over the centre of the basin (DGAR). Measurements made in Kenya (MAL2) also show very distinctive features with two seasonal maxima in April/May and November/December, which reflect the two annual passes of the InterTropical Convergence Zone (ITCZ) over the region, and mark the transition from East African monsoon to West African monsoon and vice versa (e.g. Bock et al., 2007).

The next group of four GNSS stations spans from Lusaka (ZAMB), East Africa, to Christmas Island (XMIS) at the eastern edge of the SIO basin (just south of Java, Indonesia). The seasonal cycle tends to be stronger in East Africa and within the Mozambique channel (i.e., in the western part of the basin) with annual amplitudes reaching 25 kg·m⁻² and a short (1 month) transition between dry and wet seasons. The seasonal cycle is also less marked in the eastern part of the basin (around 15–20 kg·m⁻²) and is characterised by a smoother seasonal transition.

GNSS stations located along 20°S all show a large seasonal cycle, the strongest one being encountered in western Australia (KARR) with variations of up to 30 kg·m² between summer and winter. However, large height differences between GNSS stations make comparisons between sites difficult. As seen from Table 1, ABPO and REUN are located at the same elevation (1,550 m) and roughly 800 km apart with a 2° difference in latitude. REUN shows much lower IWV values than ABPO during austral summer (24 versus 32 kg·m⁻²). Such a difference is also noticed during winter months, although it is slightly less marked. It is likely due to the fact that Madagascar represents a much larger landmass than Réunion Island, resulting in larger continental heating which tends to produce more moisture during austral summer.

Over the 2009–2019 period ERA5 reproduces the phase and amplitude of the observed GNSS IWV seasonal cycle

with little discrepancy, although a small dry bias is seen in the Tropics, as already noticed in previous generation reanalyses (e.g., Chen and Liu, 2016). A larger absolute bias can also be noticed at stations having a height difference >50 m (e.g., BAKO, ZAMB, XMIS, ABPO, REUN, VACS; Table 1 gives further details on ERA5/GNSS altitude) between ERA5 and GNSS grid points, where no height correction is applied (solid blue curves in Figure 3). Overall, the absolute bias is larger during austral summer (November–April) with differences up to 12 kg·m⁻² for REUN (Figure 3j), located at ~ 800 m above the corresponding ERA5 grid point.

As already demonstrated by Van Malderen et al., (2018), who analysed the seasonal cycle and frequency distribution of IWV time series at 118 GNSS stations worldwide, further insights into the IWV seasonal cycle can be gained from analyzing IWV frequency distributions. In the following we qualitatively investigate the frequency distribution of IWV time series along the three aforementioned transects using the Foster et al., (2006) classification, which features three dominant modes: a simple lognormal mode, which is a common feature at most subtropical location, a reverse lognormal mode, where the abrupt upper bound of the distribution indicate saturation of the troposphere, and a bimodal mode, which indicates the influence of a distinct centre of action (e.g., the ITCZ during local summer and subtropical subsidence during local winter for monsoon climates). Along the 0–8°S transect (Figure 4a–d), the IWV distribution presents a shift from a log-normal distribution in the western part of the basin (MAL2) towards a reverse log-normal distribution in the eastern edge (BAKO), with a transition in between (DGAR and SEYG both have a reversed log-normal distribution although SEYG exhibits a “shouldered” distribution with more dry values). This indicates that the eastern part of the basin is moister and has a dominant moist mode condition, while the western part encounters more frequent dry conditions (with a dominant dry mode at MAL2). Such a result is not surprising as the eastern part of the basin experiences higher SST as being part of the tropical warm pool that encompasses the western Pacific and eastern Indian Oceans as well as the Maritime Continent (Webster and Lukas, 1992). Furthermore, GNSS stations located in the eastern (western) part of the basin, which lie within the ascending (subsiding) branch of the Walker circulation, experience large-scale moisture convergence (divergence), and thus higher (lower) IWV.

Stations located within the latitude band 8–16°S (Figure 4e–h), as well as ABPO (Figure 4i, Madagascar) show a bimodal distribution. This mode indicates the influence of a monsoonal climate (Foster et al., 2006), with the presence of large dry air masses originating from subtropical subsidence in austral winter, and moisture
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**FIGURE 4** IWV distribution derived from GNSS (black) and ERA5 (blue) data over 2009–2019 at the twelve locations shown in Figure 1. Red line in (j) and (d) shows ERA5-derived IWV values at Réunion Island (REUN) and Java Island (BAKO) before height correction (see text for details)</figcaption></figure>

convergence brought by ITCZ during austral summer. The ZAMB station shows a dominant dry mode, suggesting that the dry season lasts longer than the eastern African monsoon. The two modes are also well separated, which indicates a rather short transition time between dry and wet seasons. The COCO and XMIS stations show the less defined mode. At these locations, moist (dry) conditions are encountered even during the dry (wet) season and the transition season lasts longer than at other stations.

Except for ABPO, stations along the 20°S transect (Figure 4j–l) are associated with a log-normal distribution, suggesting that the dry season there also lasts longer than the wet season. The latter, in particular, does not show a permanent wet condition but rather infrequent wet conditions (associated with nearby transiting tropical lows for example). Overall Figures 3 and 4 indicate that ERA5 is able to properly reproduce the IWV seasonal cycle in the SIO basin both in terms of phase and amplitude with only little deviation (less than 1 kg·m⁻²) from GNSS IWV at GNSS station height. ERA5 also properly reproduces the GNSS IWV distribution with only a slight shift at BAKO (Figure 4d), due to the difficulties involved in correcting for height differences.

The observed good agreement between ERA5- and GNSS-derived IWV in the SIO basin can be used to relate the properties of the seasonal cycle to the spatial distribution of IWV at the regional scale (Figure 5). With this regard, a striking feature is the strong seasonal cycle of the IWV distribution, which is discussed in the following. During Austral summer (Figure 5a), IWV has

a maximum along 10°S, with a southern departure in the Mozambique Channel and northwestern Australia regions, (likely) related to the presence of thermal lows over large landmass that drag the monsoon trough further south than over the open ocean. However, in the Subtropics, the western part of the basin is moister than along the western Australian coast. This could be explained by the accumulation of warm surface water associated with trade wind transport to the west and coastal upwelling along western Australia to the east (Xu *et al.*, 2013), resulting in moister conditions in the western subtropical SIO basin.

During austral winter (Figure 5d), moister conditions are encountered over the Indian peninsula and Bay of Bengal with a second maximum near 5°S, in the equatorial eastern part of the Indian Ocean basin (Near-Equatorial Trough, NET). South of 10°S, IWV decreases rapidly with minimum values encountered over Eastern Africa and Western Australia. IWV contours are zonal over most of the basin between 10° and 30°S, but tend to curve southward (northward) along the southeast (northeast) coast of Madagascar. This is due to the effect of mountain ranges, which forces part of the moist low-level southeasterly flow to bypass the island. A shelter effect can also be noticed south and west of Madagascar, where lower IWV values can be noticed.

Between seasons (Figure 5b, c), maxima of IWV occur near the Equator within an area of strong zonal gradient due to the dominant westerlies and a east–west surface pressure gradient (Hastenrath and Polzin, 2004). These particular conditions lead to the enhancement of
<figure><img src="image_15.png" /><figcaption>**FIGURE 5** Integrated Water Vapour climatology derived from ERA5 reanalysis data over the 2009–2018 period for the area 30°N–30°S, 25°–125°E averaged over months (a) JFM, (b) AMJ, (c) JAS and (d) OND. The locations of GNSS stations are shown by circle symbols in green, red and yellow corresponding to bimodal, lognormal and reversed lognormal distributions, respectively</figcaption></figure>

convection in this area as a result of warm surface water accumulation and moisture convergence over the Maritime Continent.

DGAR and BAKO stations (yellow dots in Figure 5), which both lie within the ITCZ in summer months, are located near high IWV value areas during both austral winter (due to the NET presence along 5°S) and between the seasons (due to moisture convergence and higher SSTs brought by the dominant westerly winds during transition seasons). This results in a dominant wet mode with negatively skewed IWV distribution as seen in Figure 4c,d.

SEYG appears less influenced by the moist conditions prevailing over the central and eastern part of the basin and shows significantly drier period during winter, while conditions remain moist during the rest of the year. MAL2, on the western edge of the basin, encounters moister conditions between seasons, while summer and winter seasons are generally dry. The short period during which the ITCZ influences the MAL2 station makes it very sensitive to intra-seasonal variability.

GNSS stations characterized by a bimodal IWV distribution (green dots in Figure 5) either lie within (MAYG and ZAMB) or near (XMIS and COCO) the ITCZ during austral summer, and are directly influenced by dry air originated from subtropical subsidence during the austral winter. GNSS stations showing a dominant dry mode (red dots in Figure 5) such as REUN, VACS and KARR are also strongly influenced by dry air subsidence during austral winter. During summer months these stations are located within the strong meridian IWV gradient, located south of the ITCZ, which explains the observed dominant dry mode and the less frequent wet events that lead to positively skewed IWV distributions seen in Figure 4j–l.

## 3.2 | Intra-seasonal variability

At intra-seasonal time-scales (20–90 days), the MJO has a large influence on the occurrence and intensity of rainfall in Equatorial East Africa during the two wet seasons (Pohl
<figure><img src="image_16.png" /></figure>

and Camberlin, 2006) as well as over countries bordering the Indian Ocean (Seychelles, Chagos, northern part of Madagascar, northern Australia, Java island). This eastward moving tropical anomaly, which crosses the planet before returning to its initial starting point in 30 to 60 days, also modulates the tropical cyclone activity through its influence on tropical cyclogenesis in the central and eastern part of SIO basin (Duvel, 2015) and northern Australia (Hall et al., 2001). The MJO consists of two phases, referred to as active and inactive, which correspond to periods of either enhanced or suppressed convection over a given area. The location of the convective phases of the MJO are usually grouped into geographically based stages, numbered from 1–8 (Wheeler and Hendon, 2004), which indicate the progression of the MJO along the Equator.

The impact of the MJO on IWV and ozone total column during austral summer and winter has been previously studied by Fathurochman et al., (2017) using a 40-year sample (1973–2013) of ECMWF ERA-Interim reanalysis. This previous study shows that IWV tends to be a maximum during phase 2–3 of the MJO in the Indian Ocean and phase 3–4 west of Borneo and Java during austral summer. Outgoing long-wave radiation (OLR) anomalies are also found to be anti-correlated with IWV anomalies (meaning that tropical convection enhanced/suppressed according to the MJO phase is the main process that drives IWV anomalies).

In the following, the impact of the MJO on IWV columns during both wet and dry seasons is investigated within the 3–12°S latitude band from ERA5 and GNSS data at five locations (DGAR, COCO, XMIS, BAKO, MAL2) over the 2009–2019 period. For this purpose, a bandpass filter is applied to GNSS- and ERA5-derived IWV by subtracting the 90-day IWV running average from the 15 days IWV running average. The averaged IWV anomaly is then computed for each MJO phase over the 10-year period. The choice of wet and dry seasons is based upon the analysis of the IWV seasonal cycle made in the previous section. For DGAR, COCO, XMIS and BAKO, November to April is the wettest period and May to October is the driest one. At MAL2, the wettest periods are found between seasons (October to December and March to May), while dry seasons extend from January to February and June to September.

The averaged IWV anomaly for each MJO phase during the wet (November to April at all sites but October–December plus March to May at MAL2) and dry (May to October at all stations but January–February plus June to September at MAL2) seasons is shown in Figure 6. During the wet season (upper panel), ERA5- and GNSS-derived IWV are in good agreement both in terms of phase and amplitude associated with the MJO propagation across the SIO basin. Maximum IWV anomaly

is reached during phase 1 of the MJO in MAL2 (eastern part of the basin), phase 2 in DGAR (central part), phase 3 in COCO (eastern part) and phase 4 in BAKO and XMIS (Maritime Continent). Amplitudes of 1 to 5 kg·m⁻² and phase maxima observed during the wet season are in good agreement with previous studies conducted over the Indian Ocean and Maritime Continent (Fathurochman et al., 2017). Largest amplitudes are found in the eastern Indian Ocean (COCO) and adjacent to Java Island (XMIS) with values ranging from 4 to 5 kg·m⁻². For a given phase of the MJO, the difference between the 1st and 4th quantile (inter-quantile range) can nevertheless attain 7 kg·m⁻².

Over the Maritime Continent (BAKO), the variability is much reduced, with a median difference of only 1 kg·m⁻² between phases 7 and 4. For a given phase of the MJO, the IQR is also much lower (3 kg·m⁻²). Smaller amplitudes (2 to 2.5 kg·m⁻²) are also observed at DGAR between the suppressed and enhanced phases of convection. According to Schreck et al., (2013), areas associated with persistent deep convection show lower standard deviation in upper-tropospheric water content than regions bordering areas of deep convection, which are more sensitive to subsidence and dry air advection. Consequently, DGAR and BAKO present the highest IWV during the austral summer season. These two sites, located within the ITCZ, are less sensitive to fluctuations induced by the MJO than locations indirectly influenced by the ITCZ, such as COCO, XMIS and MAL2. At MAL2, the large intra-seasonal fluctuations associated with the MJO and the short length of the two wet seasons nevertheless suggests that rainfall amount received during the wet seasons is also very sensitive to the MJO in east equatorial Africa.

During the dry season (lower panel), the MJO has little influence on the IWV anomaly, except for BAKO, where a small oscillation can be observed on the median and quantile values of water vapour anomalies. The differences between rainy season and dry season are particularly striking in COCO and XMIS, where the amplitude reached 4–5 kg·m⁻² during the rainy season compared to less than 1 kg·m⁻² in the dry season.

From these results, it appears that the MJO signature in IWV is negligible during the dry season, but significant during the wet season, with median values variations of 1–2 to 4 kg·m⁻² between suppressed and active phases and IQR variations of 3–4 to 7 kg·m⁻² within each phase. This signature also shows larger amplitudes of both median values and IQR for stations bordering the ITCZ (COCO and XMIS) than those located inside (BAKO and DGAR). The phase propagation is also clearly visible with maximum IWV occurring during phase 1 in East Africa, phase 2 in the central part of the basin, phase 3 in its eastern part and phase 4 in Indonesia. Finally, ERA5 and GNSS IWV
<figure><img src="image_17.png" /><figcaption>**FIGURE 6** Filtered (15–90-day) IWV for MJO phases 1–8, as derived from GNSS (blue) and ERA5 (red) data averaged over the wet (top row) and dry (bottom row) seasons at (a1, a2) MAL2, (b1, b2) DGAR, (c1, c2) COCO, (d1, d2) XMIS and (e1, e2) BAKO. Wet (dry) seasons extend from November to April (May to October) at all stations except MAL2 where two wet seasons (March–May and October–December) and two dry seasons (January–February and June–September) occur</figcaption></figure>

show overall good agreement during both local dry and wet seasons.

## 3.3 | Diurnal cycle

In a previous study, Itterly and Taylor (2014) investigated the ability of MERRA and ERA-Interim reanalyses, to reproduce the convective diurnal cycle by looking at the top-of-tmosphere flux diurnal cycle. While general features such as land/sea contrast were correctly reproduced, both reanalyses tended to misrepresent the diurnal cycle over tropical oceans and to underestimate it over tropical landmasses. They also failed to properly reproduce regional features as well as convection propagation. In order to assess the capability of ERA5 to better capture the observed GNSS-derived IWV diurnal cycle, we picked six GNSS stations operated in distinct environments: DGAR (open ocean), REUN (a small island with a steep orography), ABPO (a large island with extended elevated terrain), ZAMB (continental station far from any water body), MAL2 (coastal area) and MAYG (small island directly influenced by the large land mass of Madagascar).

Both GNSS- and ERA5-derived IWV diurnal cycle are shown in Figure 7. ERA5 (lower panel) is generally able to reproduce the proper phase of the diurnal cycle at most locations, which is a major improvement compared to previous generation reanalyses. The amplitude of the diurnal cycle nevertheless tends to be systematically

underestimated by ~20% and some mis-matches can be noticed at particular locations.

Over the open ocean (DGAR; Figure 7a), the diurnal cycle is relatively weak and peaks at night with maximum values in the evening (18 to 24 local solar time or LST) and a minimum around noon (0900 to 1500 LST). Amplitudes also remain low and range between 1 and 2 kg·m⁻². In this particular situation ERA5 appears unable to capture the phase of the diurnal cycle (Figure 7g) and show two maxima at 0000 LST and 1200 LST. Amplitude is also much lower than for GNSS IWV.

Despite being unable to reproduce the GNSS IWV diurnal cycle over open ocean, ERA5 is nevertheless capable of reproducing its phase over small islands such as Réunion Island (Figure 7b,h), where IWV is maximum in the afternoon (1200–1800 LST) and minimum in the morning hours (0300–0900 LST). GNSS IWV diurnal amplitude can reach up to 5.2 kg·m⁻² in December, a value much larger than over the open ocean. Such large diurnal amplitude can be attributed to both daytime evapotranspiration and moisture convergence induced by thermally forced local circulation during daytime (Lesouëf et al., 2011). The latter likely have the greater contribution considering the rather small (~25 km) radius of the island (Cronin et al., 2015).

The seasonal variability of IWV diurnal amplitude is well captured by ERA5 (Figure 7h). The minimum is observed in austral winter and the maximum in late spring, consistently with the occurrence of maximum solar radiation and lower cloud cover.
<figure><img src="image_18.png" /><figcaption>**FIGURE 7** Monthly average of Integrated Water Vapour diurnal anomaly for (a–f) GNSS stations and (g–l) ERA5 at (a, g) DGAR, (b, h) REUN, (c, i) ABPO, (d, j) ZAMB, (e, k) MAL2 and (f, l) MAYG. (Figure 1 shows locations.)</figcaption></figure>

However ERA5 tends to underestimate diurnal amplitude at REUN by at least 20%. This results from the inability to resolve the complex geography of this island in the model. By examining the ERA5 land-sea mask and terrain data (at https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation; accessed 4 October 2020), we note that the size of Réunion Island is 40% larger in reality than in ERA5 (nine grid points, 3,562 versus 2,594 km²), and that its orography is also strongly smoothed (highest summit tops at 716 m amsl in ERA5 versus 3070 m in reality). The proper representation of IWV diurnal phase in ERA5 likely results from significant improvement in convection parametrization compared to previous reanalyses. Hence, Hersbach *et al.*, (2020) recently showed that the implementation of the convective available potential energy closure scheme of Bechtold *et al.*, (2014) in ERA5 acts to shift the diurnal maximum of convection from noon to late afternoon over land, which compares reasonably with observations (Yang and Slingo, 2001) as well as with our results.

In Madagascar the GNSS station is deployed in a high plateau region located in the centre of the island. While this large island (594,301 km²) is not wide enough to prevent sea breeze circulation from reaching the central part of the country, it nevertheless extends over 6° of longitude and is characterized by a significant proportion of elevated terrain. It thus represents a large surface heating source compared with the surrounding water, allowing for large diurnal circulation. Overall, the observed seasonal cycle of GNSS IWV diurnal anomaly (Figure 7c) follows the same pattern than in Réunion Island (Figure 7b),

with larger amplitudes from October–April which peak in November/December. The diurnal maximum occurs at the end of the afternoon/early evening (1600–2200 LST) and the minimum value is observed in the morning (0600–1200 LST), which is consistent with the properties of the precipitation diurnal cycle over large tropical islands (Kikuchi and Wang, 2008). The maximum diurnal amplitude reaches 7.6 kg·m⁻² in November for a mean IWV value of 25–30 kg·m⁻². However in Madagascar the phase is shifted 3–4 hr compared to Réunion Island. Using high-resolution idealized simulations of diurnal convection over circular islands of various sizes, Cronin *et al.*, (2015) show that precipitation over tropical islands, which usually coincides with daily IWV maximum (e.g., Wu *et al.*, 2003), peaks 3–4 hr later for large islands (radius > 100 km) than for small ones (radius < 20 km). For the latter, the maximum surface convergence predicted by the linear sea breeze theory presents almost no lag, with maximum surface heat fluxes occurring within an hour of the local solar maximum (1200 LST; Smith *et al.*, 1992), whereas a lag of 3–4 hr is seen for a large island. The observed 3–4 hr phase shift in IWV diurnal anomaly between ABPO and REUN seems consistent with these previous results and could be related to the difference in size of the two islands (500 × 1,500 versus 60 × 40 km², respectively). As in REUN, the ERA5-derived IWV diurnal cycle (Figure 7i) agrees well with GNSS data but nevertheless shows a slightly lower amplitude.

For a continental GNSS station located far away from the ocean such as ZAMB (Figure 7d), the IWV diurnal cycle presents a maximum in late afternoon
(1600–2000 LST), with weaker amplitudes than for the large island of Madagascar (2–3 versus 3–8 kg·m⁻²). The observed maximum in late afternoon coincides once again with the occurrence of the precipitation maximum over a continental tropical landmass (Kikuchi and Wang, 2008). While no significant seasonal phase variations are observed, the diurnal amplitude nevertheless tends to be slightly higher from March to May (e.g., at the end of or right after the rainy season) as higher soil moisture and wetter vegetation provide higher water vapour supply. ERA5 (Figure 7j) is again able to accurately capture the phase of the IWV diurnal cycle, but still tends to underestimate its amplitude, especially from December to March, and is also unable to capture the seasonal variation of the diurnal amplitude.

In Kenya (Figure 7e), the IWV diurnal anomaly shows a morning (0600–1200 LST) maximum and an afternoon (1200–1800 LST) minimum. This finding is consistent with the previous study of Bock et al., (2007), who noticed a similar diurnal cycle at this location. It is also in good agreement with precipitation diurnal cycle described by Camberlin et al., (2018) who observed an extended morning precipitation maximum associated with the onset of the sea breeze along the coast. The latter study also revealed the existence of an onshore phase propagation of precipitation with a maximum ranging from 1500 LST in coastal plains to 0000 LST further inland. Such behaviour explains the afternoon IWV minimum seen at at MAL2, which is due to compensatory subsidence from inland convection. At this location, ERA5 (Figure 7k) diurnal amplitude and phase are in good agreement with GNSS observations.

In MAYG (Figure 7f), GNSS-derived IWV shows a clear seasonal phase dependency. The maximum value occurs in early morning (0400 to 0800 LST) from May to October and during the afternoon (1400 to 1800 LST) from November to April, with a short transitional period (September to November) during which both behaviours coexist. In summer, the phase of the observed diurnal cycle is quite similar to that observed at Réunion Island (Figure 7b), suggesting that local breeze circulation is likely to occur during this period. An interesting feature lies in the presence of an early morning maximum in austral winter. Although Mayotte Island is strongly impacted by convection propagating offshore from Madagascar Kikuchi and Wang (2008), the atmosphere is however dry and stable during this period. The offshore propagation of deep convection away from Madagascar is thus unlikely to account for this observed morning maximum. A possible explanation is related to the existence of a nocturnal gap flow circulation, fed by the strong trade winds occurring at this time of year, along the western coast of Madagascar which could trigger lifting of lighter and moister air south of Mayotte. Such behaviour, already observed in midlatitudes

(Bousquet and Smull, 2003) could account for the observed early morning maximum immediately followed by a minimum corresponding to the propagation of the dry density current over Mayotte. The validity of such a hypothesis is currently being assessed from high-resolution numerical simulations. At Mayotte, ERA5 (Figure 7l) does not manage to reproduce the observed seasonal shift. It shows no clear diurnal cycle from January to March and a morning peak (0600 to 1000 LST) during the rest of the year. XXX

## 4 | CONCLUSIONS AND PERSPECTIVES

Many past studies have demonstrated the capabilities of ground-based GNSS to monitor the variability of atmospheric humidity at time-scales extending from 30 min to several years. In the present study the spatial and temporal distribution of water vapour in the South Indian Ocean (SIO) basin is investigated using observations from twelve IGS GNSS stations spanning the basin. IWV data derived from GNSS observations are also compared to RS- and satellite-derived data as well as to ERA5 reanalyses to evaluate its ability to capture the observed IWV cycle at seasonal, intraseasonal and diurnal time-scales.

Intercomparisons between RS- and GNSS-derived IWV show high correlation (0.98) and low RMS errors (2 kg·m²) at collocated RS/GNSS sites. The correlation (RMS) is nevertheless found to decrease (increase) for non-collocated sites such as Madagascar or Java Island where larger deviations and lower correlations are observed during daytime due to difference in IWV diurnal cycle amplitude (ABPO) or phase (BAKO) at RS and GNSS sites. Overall, ERA5- and GNSS-derived IWV values show lower RMS errors than for previous generation reanalyses, although a dry bias of 0.5 to 1 kg·m⁻² can still be noticed in ERA5 IWV. Correlation always reaches 0.95 or more, but is a minimum for near-equatorial stations likely due to lower IWV variability in these regions.

The comparison of satellite IWV, derived from the SSMI/S f17 MWR sensor, against GNSS IWV at four GNSS island stations shows a small dry bias (0.5–0.8 kg·m⁻²) in GNSS IWV, with standard deviation ranging from 2.1 to 2.4 kg·m⁻². GNSS-satellite comparisons at COCO also show breakpoints in time series of IWV due to changes of receiver in October 2011 and June 2015.

The amplitude of the water vapour cycle at the seasonal time-scale is larger in the southern part of the SIO basin. The analysis of associated IWV distribution shows, in particular, that predominantly wet conditions occur in the eastern and central parts of the basin, near the Equator. Stations located between 8° and 16°S as well as in Madagascar, which are all under the influence of the monsoonal
regime, show a bimodal IWV distribution, while subtrop-
ical stations (Réunion Island, Mauritius and western Aus-
tralia) show drier distributions with a dominant dry mode.
The IWV spatial distribution also shows striking seasonal
variability characterised by an ITCZ structure near 10°S
during austral summer, a maximum of IWV over the Mar-
itime Continent during the inter-season, and a maximum
of IWV over the Indian subcontinent and Southeast Asia
during austral winter.

At intra-seasonal scale, IWV anomalies linked with
suppressed/active phase of the MJO show larger ampli-
tude during the wet season with highest amplitude in
areas located south of the ITCZ in austral summer. Distinct
phase propagation of IWV anomalies can be observed,
with maximum positive anomalies during phase 1 in the
western part of the basin, phase 2 over central Indian
Ocean, phase 3 over the eastern part of the basin and
phase 4 over the Maritime Continent.

The water vapour diurnal cycle shows larger amplitude
over land (~2–3 kg·m⁻² for ZAMB) than over open ocean
(~1–2 kg·m⁻² for DGAR) with daily maximum (mini-
mum) occurring in late afternoon (early morning) over
land and at night (mid-day) over oceanic areas. Large and
mountainous island present the largest diurnal amplitudes
(up to ~5 kg·m⁻² for REUN and ~7.5 kg·m⁻² for ABPO),
with minimum values observed in the morning hours and
maximum values at the end of afternoon/early evening.
A 3–4 hr phase shift, attributed to a delayed surface con-
vergence predicted from linear sea breeze theory, is also
observed between Réunion and Madagascar. At MAL2, the
IWV diurnal cycle is in phase opposition with respect to
the diurnal cycle over land. Daily maximum (minimum)
is reached in the morning (afternoon) hours as a result of
a sea breeze onset (compensatory subsidence). At MAYG
the diurnal cycle has a rather atypical behaviour and shows
a seasonal modulation of IWV diurnal phase, with maxi-
mum IWV occurring in late afternoon during summer, but
in early morning during winter.

ERA5 was found to accurately describe the phase and
seasonal amplitude modulation of the IWV cycle at all
time-scales and for all meteorological environments con-
sidered in this study (continental areas as well as large and
small islands), but shows a underestimation of the IWV
cycle amplitude at the diurnal scale. It is also unable to
capture the night-time maximum of IWV over open ocean
(as did the former generation ERA-Interim reanalysis) as
well as the impact of a large landmass (such as Madagas-
car) on the IWV diurnal cycle observed in neighbouring
islands (Mayotte in this case).

The ability of the ERA5 reanalysis to reproduce the
seasonal and intra-seasonal water vapour cycle with lit-
tle discrepancies in the SIO basin opens up new per-
spectives in the field of meteorology and climate in this

region, which is largely under-equipped with meteorolog-
ical measurement equipment. Nevertheless, and although
some progress has been made with respect to previous
lower-resolution reanalysis datasets, ERA5 still has diffi-
culties to reproduce the water vapour cycle at the diurnal
scale under the specific conditions mentioned above. In
this perspective, the use of ground-based GNSS stations
remains essential to study and predict the spatial and
temporal evolution of the water vapour field at the local
scale.

In this regard, the ongoing deployment of a new net-
work of terrestrial GNSS stations as part of the Indian
Ocean GNSS Applications for Meteorology (IOGA4MET)
project should make it possible, in the short term, to
further strengthen water vapour observation capacities
in the southwestern part of the basin (Bousquet et al.,
2020b). At the present time, the IOGA4MET GNSS net-
work (insert in Figure 1) consists of nine ground-based
stations deployed by Réunion University between 2017
and 2019 at Seychelles (Aldabra, ALBR), Madagascar
(Diego-Suarez, DSUA; Tamatave, MATA; Fort-Dauphin,
MAFD; Sainte-Marie, MASM; and Nosy Be, MABE) and
Eparses Islands of Europa (EURP), Juan de Nova (JDVN)
and Tromelin (TROM). Data from all these stations except
Eparses are transmitted at hourly frequency and processed
in real time by the French Institut Geographique National
(IGN), before being deposited on several databases avail-
able to researchers. Additional data at the Eparses Island of
Glorieuses (GLOR) are also available in real time through
collaborations with Institut de Physique Globe de Paris
(IPGP), and seven new stations should be deployed in
Madagascar, Seychelles and Comoros in early 2021. As
well as further improving the description of the water
vapour cycle in this basin, the assimilation of these new
data will also help to improve the description of the
diurnal cycle and its role in the development of convec-
tion in high-resolution operational NWP systems such as
AROME Indian Ocean (AROME-IO). This is the NWP
system used by the regional tropical cyclone forecast cen-
tre to predict tropical cyclone activity at the basin scale
(Bousquet et al., 2020a). In the medium term, it should
also be noted that most of these new stations are likely to
be integrated into the IGS network, thus providing sub-
sequent additional publicly available data for climatology
and weather studies in this area.

ACKNOWLEDGEMENTS

This study was partly funded by the European Union, the
Réunion Regional Council and the French state under
INTERREG-V Indian Ocean project "ReNovRisk Cyclone
and Climate Change".

Data used in this study are available at the following
URLs (all accessed 4 October 2020):
* ZTD from IGS at a 5 min temporal resolution:

ftp://cddis.gsfc.nasa.gov/gnss/products/troposphere/zpd/

* ERA5 data (surface pressure, 2 m temperature, 2 m dewpoint temperature and IWV):

https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview and

https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview

* Radiosonde data from version 2 of IGRA (temperature and dewpoint depression) at pressure levels:

ftp://ftp.ncdc.noaa.gov/pub/data/igra/data/

* Satellite-borne microwave radiometer IWV from SSMIS F-17 satellite:

http://www.rems.com

ORCID

Edouard Lees ID https://orcid.org/0000-0002-0446-5927

Olivier Bousquet ID https://orcid.org/0000-0002-2567-2079

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**How to cite this article:** Lees E, Bousquet O, Roy D, Bellevue JL. Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground-based GNSS and fifth-generation ECMWF reanalysis (ERA5) data. *Q.J.R. Meteorol. Soc.* 2021;147:229–248. https://doi.org/10.1002/qj.3915