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This article is devoted to the presentation of the MAP-IO observation program. This program, launched in early 2021, has enabled the observation of nearly 700 d of measurements over the Indian and Southern Ocean with the equipment of 17 meteorological and oceanographic scientific instruments on board the ship Marion Dufresne. Several observational techniques have been developed to respond to the difficulties of observations on board the ship, in particular for passive remote sensing data, as well as for quasi-autonomous data acquisition and transfer. The first measurements made it possible to draw up unprecedented climatological data of the Southern Ocean regarding the size distribution and optical thickness of aerosols, the concentration of trace gases and greenhouse gases, UV, and integrated water vapor. High-resolution observations of phytoplankton in surface waters have also shown a great variability in latitude in terms of abundance and community structure (diversity). The operational success of this program and these unique scientific results together establish a proof of concept and underline the need to transform this program into a permanent observatory. The multi-year rotations over the Indian Ocean will enable us to assess the trends and seasonal variability of phytoplankton, greenhouse gases, ozone, and marine aerosols in a sensitive and poorly documented climatic region. Without being exhaustive, MAP-IO should make it possible to better understand and assess the biological carbon pump, to study the variability of gases and aerosols in a region that is remote in relation to the main anthropogenic sources, and to monitor the transport of stratospheric ozone by the Brewer–Dobson circulation. The meteorological MAP-IO data set is publicly available at https://www.aeris-data.fr/catalogue-map-io/ (last access: 26 August 2024) (atmospheric data) and at 10.17882/89505 (phytoplankton data). [ABSTRACT FROM AUTHOR]

This article is devoted to the presentation of the MAP-IO observation program. This program, launched in early 2021, has enabled the observation of nearly 700 days of measurements over the Indian and Southern Ocean thanks to the equipment of 17 meteorological and oceanographic scientific instruments on board the ship Marion Dufresne. Several observation techniques have been developed to respond to the difficulties of observations on board ships, in particular for passive remote sensing data, as well as quasi-autonomous data acquisition and transfer. The first measurements made it possible to draw up unprecedented climatological data of the Southern Ocean of the size distribution and optical thickness of aerosols, of the concentration of trace gases and greenhouse gases, of UV, and of integrated water vapor. High resolution observations of phytoplankton in surface waters have also shown a great variability in latitude, in terms of abundance and community structure (diversity). The operational success of this program and these unique scientific results all together establish a proof of concept and underline the need to transform this program into a permanent observatory. [ABSTRACT FROM AUTHOR]

The atmospheric water vapor is commonly monitored from ground Global Navigation Satellite System (GNSS) measurements, by retrieving the tropospheric delay under the Zenith Wet Delay (ZWD) component, linked to the water vapor content in the atmosphere. In recent years, the GNSS ZWD retrieval has been performed on shipborne antennas to gather more atmospheric data above the oceans for climatology and meteorology study purposes. However, when analyzing GNSS data acquired by a moving antenna, it is more complex to decorrelate the height of the antenna and the ZWD during the Precise Point Positioning (PPP) processing. Therefore, the observation modeling and processing parametrization must be tuned. This study addresses the impact of modeling on the estimation of height and ZWD from the simulation of shipborne GNSS measurements. The GNSS simulation is based on an authors-designed simulator presented in this article. We tested different processing models (elevation cut-off angle, elevation weighting function, and random walk of ZWD) and simulation configurations (the constellations used, the sampling of measurements, the location of the antenna, etc.). According to our results, we recommend processing shipborne GNSS measurements with 3° of cut-off angle, elevation weighting function square root of sine, and an average of 5 mm·h − 1 / 2 of random walk on ZWD, the latter being specifically adapted to mid-latitudes but which could be extended to other areas. This processing modeling will be applied in further studies to monitor the distribution of water vapor above the oceans from systematic analysis of shipborne GNSS measurements. [ABSTRACT FROM AUTHOR]

Integrated water vapour (IWV) measurements from similar or different techniques are often inter-compared for calibration and validation purposes. Results are usually assessed in terms of bias (difference of the means), standard deviation of the differences, and linear fit slope and offset (intercept) estimates. When the instruments are located at different elevations, a correction must be applied to account for the vertical displacement between the sites. Empirical formulations are traditionally used for this correction. In this paper we show that the widely used correction model based on a standard, exponential, profile for water vapour cannot properly correct the bias, slope, and offset parameters simultaneously. Correcting the bias with this model degrades the slope and offset estimates and vice versa. This paper proposes an improved correction method that overcomes these limitations. It implements a multiple linear regression method where the slope and offset parameters are provided from a radiosonde climatology. It is able to predict monthly mean IWVs with a bias smaller than 0.1 kgm-2 and a root-mean-square error smaller than 0.5 kgm-2 for height differences up to 500 m. The method is applied to the inter-comparison of GPS IWV data in a tropical mountainous area and to the inter-validation of GPS and satellite microwave radiometer data. This paper also emphasizes the need for using a slope and offset regression method that accounts for errors in both variables and for correctly specifying these errors. [ABSTRACT FROM AUTHOR]

The "Marion Dufresne Atmospheric Program-Indian Ocean" (MAP-IO) project is a research program that aims to collect long-term atmospheric observations in the under-instrumented Indian and Austral Oceans. As part of this project, a Global Navigation Satellite System (GNSS) antenna was installed on the research vessel (R/V) Marion Dufresne in October 2020. GNSS raw data is intended to be used to retrieve Integrated Water Vapour (IWV) content along the Marion Dufresne route, which cruises more than 300 days per year in the tropical and austral Indian Ocean. This paper presents a first assessment of this GNSS-based IWV retrieval, based on the analysis of 9 months of GNSS raw data acquired along the route of the R/V Marion Dufresne in the Indian Ocean. A first investigation of GNSS raw data collected during the first 5 months of operation has highlighted the bad positioning of the antenna on the R/V that makes it prone to interference. Changing the location of the antenna has been shown to improve the quality of the raw data. Then, ship-borne GNSS-IWV are compared with IWV estimates deduced using more conventional techniques such as European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), ground-launched radiosondes and permanent ground GNSS stations operating close to the route of the R/V Marion Dufresne. The rms difference of 2.79 kg m − 2 shows a good match with ERA5 and subsequently improved after the change in location of the GNSS antenna (2.49 kg m − 2 ). The match with ground-based permanent GNSS stations fluctuates between 1.30 and 3.63 kg m − 2 , which is also shown to be improved after the change in location of the GNSS antenna. However, differences with ground-launched radiosondes still exhibit large biases (larger than 2 kg m − 2 ). Finally, two operational daily routine analyses (at day+1 and day+3) are presented and assessed: the rms of the differences are shown to be quite low (1 kg m − 2 for the day+1 analyses, 0.7 kg m − 2 for the day+3 analysis), which confirms the quality of these routine analysis. These two routine analyses are intended to provide a continuous monitoring of water vapour above the Indian Ocean and deliver ship-borne IWV with a low latency for the entire scientific community. [ABSTRACT FROM AUTHOR]

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly 50 stations distributed over the Caribbean arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC 4 A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality integrated water vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the trade winds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD)-to-IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams International Airport (GAIA), Bridgetown, Barbados. A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (-2.9 kgm-2) and the GNSS results (-1.2 kgm-2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kgm-2), where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a collocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth-generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS-derived IWV peaks. Two successive peaks are observed on 22 January and 23–24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organization for five representative GNSS stations across the Caribbean arc using visible satellite images. A statistically significant link was found between the cloud patterns and the local IWV observations from the GNSS sites as well as the larger-scale IWV patterns from the ECMWF ERA5 reanalysis. The reprocessed ZTD and IWV data sets from 49 GNSS stations used in this study are available from the French data and service centre for atmosphere (AERIS) (10.25326/79;). [ABSTRACT FROM AUTHOR]

In the framework of the EUREC 4 A (Elucidating the role of clouds–circulation coupling in climate) campaign that took place in January and February 2020, integrated water vapour (IWV) contents were retrieved over the open tropical Atlantic Ocean using Global Navigation Satellite System (GNSS) data acquired from three research vessels (R/Vs): R/V Atalante , R/V Maria S. Merian and R/V Meteor. This paper describes the GNSS processing method and compares the GNSS IWV retrievals with IWV estimates from the European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared products and from terrestrial GNSS stations located along the tracks of the ships. The ship-borne GNSS IWV retrievals from R/V Atalante and R/V Meteor compare well with ERA5, with small biases (-1.62 kgm-2 for R/V Atalante and +0.65 kgm-2 for R/V Meteor) and a root mean square (rms) difference of about 2.3 kgm-2. The results for the R/V Maria S. Merian are found to be of poorer quality, with an rms difference of 6 kgm-2 , which is very likely due to the location of the GNSS antenna on this R/V prone to multipath effects. The comparisons with ground-based GNSS data confirm these results. The comparisons of all three R/V IWV retrievals with MODIS infrared products show large rms differences of 5–7 kgm-2 , reflecting the enhanced uncertainties in these satellite products in the tropics. These ship-borne IWV retrievals are intended to be used for the description and understanding of meteorological phenomena that occurred during the campaign, east of Barbados, Guyana and northern Brazil. Both the raw GNSS measurements and the IWV estimates are available through the AERIS data centre (https://en.aeris-data.fr/ , last access: 20 September 2020). The digital object identifiers (DOIs) for R/V Atalante IWV and raw datasets are 10.25326/71 and 10.25326/74 , respectively. The DOIs for the R/V Maria S. Merian IWV and raw datasets are 10.25326/72 and 10.25326/75 , respectively. The DOIs for the R/V Meteor IWV and raw datasets are 10.25326/73 and 10.25326/76 , respectively. [ABSTRACT FROM AUTHOR]

A ground-based network of more than 1200 Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) was analysed using GIPSY-OASIS II software package for the documentation of time and space variations of water vapor in atmosphere during the North Atlantic Waveguide and Downstream impact EXperiment (NAWDEX) during fall 2016. The network extends throughout the North Atlantic, from the Caribbeans to Morocco through Greenland. This paper presents the methodology used for GNSS data processing, screening, and conversion of Zenith Tropospheric Delay (ZTD) estimates to Integrated Water Vapor content (IWV) using surface parameters from reanalysis. The retrieved IWV are used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses ERAI and ERA5. ERA5 shows an overall improvement over ERAI in representing the spatial and temporal variability of IWV over the study area. The mean bias is decreased from 0.31±0.63 to 0.19±0.56 kg m -2 (mean ±1σ over all stations) and the standard deviation reduced from 2.17±0.67 to 1.64±0.53 kg m -2 combined with a slight improvement in correlation coefficient from 0.95 to 0.97. At regional scale, both reanalyses show a general wet bias at mid and northern latitudes but a dry bias in the Caribbeans. We hypothesize this results from the different nature of data being assimilated over the tropical oceans. This GNSS IWV data set is intended to be used for a better description of the high impact weather events that occurred during the NAWDEX experiment. [ABSTRACT FROM AUTHOR]

To study key processes of the water cycle, two special observation periods (SOPs) of the Hydrological cycle in the Mediterranean experiment (HyMeX) took place during autumn 2012 and winter 2013. The first SOP aimed to study high precipitation systems and flash flooding in the Mediterranean area. The AROME-WMED (western Mediterranean) model is a dedicated version of the mesoscale Numerical Weather Prediction (NWP) AROME-France model, which covers the western Mediterranean basin providing the HyMeX operational center with daily real-time analyses and forecasts. These products allowed for adequate decision-making for the field campaign observation deployment and the instrument operation. Shortly after the end of the campaign, a first reanalysis with more observations was performed with the first SOP operational software. An ensuing comprehensive second reanalysis of the first SOP, which included field research observations (not assimilated in real time) and some reprocessed observation datasets, was made with AROME-WMED. Moreover, a more recent version of the AROME model was used with updated background error statistics for the assimilation process. This paper depicts the main differences between the real-time version and the benefits brought by HyMeX reanalyses with AROME-WMED. The first reanalysis used 9 % additional data and the second one 24 % more compared to the real-time version. The second reanalysis is found to be closer to observations than the previous AROME-WMED analyses. The second reanalysis forecast errors of surface parameters are reduced up to the 18 and 24 h forecast range. In the middle and upper troposphere, fields are also improved up to the 48 h forecast range when compared to radiosondes. Integrated water vapor comparisons indicate a positive benefit for at least 24 h. Precipitation forecasts are found to be improved with the second reanalysis for a threshold up to 10 mm (24 h) -1. For higher thresholds, the frequency bias is degraded. Finally, improvement brought by the second reanalysis is illustrated with the Intensive Observation Period (IOP8) associated with heavy precipitation over eastern Spain and southern France. [ABSTRACT FROM AUTHOR]

We have investigated calibration variations in the Rameau water vapor Raman lidar. This lidar system was developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). It aims at calibrating Global Navigation Satellite System (GNSS) measurements for tropospheric wet delays and sounding the water vapor variability in the lower troposphere. The Rameau system demonstrated good capacity in retrieving water vapor mixing ratio (WVMR) profiles accurately in several campaigns. However, systematic shortterm and long-term variations in the lidar calibration factor pointed to persistent instabilities. A careful testing of each subsystem independently revealed that these instabilities are mainly induced by mode fluctuations in the optic fiber used to couple the telescope to the detection subsystem and by the spatial nonuniformity of the photomultiplier photocathodes. Laboratory tests that replicate and quantify these instability sources are presented. A redesign of the detection subsystem is presented, which, combined with careful alignment procedures, is shown to significantly reduce the instabilities. Outdoor measurements were performed over a period of 5 months to check the stability of the modified lidar system. The calibration changes in the detection subsystem were monitored with lidar profile measurements using a common nitrogen filter in both Raman channels. A short-term stability of 2-3% and a long-term drift of 2-3% per month are demonstrated. Compared to the earlier Development of Methodologies forWater Vapour Measurement (DEMEVAP) campaign, this is a 3-fold improvement in the long-term stability of the detection subsystem. The overall water vapor calibration factors were determined and monitored with capacitive humidity sensor measurements and with GPS zenith wet delay (ZWD) data. The changes in the water vapor calibration factors are shown to be fairly consistent with the changes in the nitrogen calibration factors. The nitrogen calibration results can be used to correct the overall calibration factors without the need for additional water vapor measurements to within 1% per month. [ABSTRACT FROM AUTHOR]