Vincent Herbreteau
email: vincent.herbreteau@ird.fr

Jean-Paul Gonzalez

Wasana Khaungaew

Gérard Salem

Jean- Louis Janeau

AGRICULTURAL CHANGES, WATER QUALITY AND HEALTH: INVESTIGATING THE HEALTH STATUS OF POPULATIONS LIVING IN AN AGRICULTURAL IRRIGATED AREA, USING SPATIAL ANALYSIS, IN PHRAE PROVINCE, THAILAND

Keywords: GIS, human health, water quality, environmental changes, North Thailand

Drastic environmental changes have occurred in Thailand during the twentieth century, along with the population growth and consecutive agricultural development consequently threatening the ecosystems. Infrastructures, and especially water reservoirs, were built to increase and improve the agricultural production. These changes have modified irreversibly the biotopes and, above all, the hydrographical network and water quality. In an effort to assess and monitor their consequences on human health, a health Geographic Information System was developed in Phrae province, North Thailand, focusing on the Mae Thang watershed, its reservoir and irrigated area downstream. In partnership with Phrae provincial Public Health Office, all patients' cases from 78 different pathologies, reported in Phrae rural areas from 1997 to 2004, were integrated and incidences were calculated among all the 693 villages, where patients and families live. Health infrastructures were also localized to assess the health offer, access, and recourse. Overall higher prevalence was recorded in Mae Thang study area as compared to the whole province, especially for infectious diseases that could be linked to water quality, as well as for upstream villages compared to those downstream. Marked geographical heterogeneity in disease prevalence between villages showed the importance of the choice of the study scale and the need for a public health survey at the village level to answer the hypotheses raised by the present observations.

INTRODUCTION

Understanding and measuring the consequences of environmental changes on human health is a tough task, taking up the activities of many researchers in different fields, comprising ecology, epidemiology or health geography. It benefits polluting activities whose environmental and health impact cannot really be assessed. This paper does not aim to enlighten clear effects of the environmental changes on human health but to illustrate an ongoing research on evaluating the environmental changes that could have an effect on human health and defining ways to find signs in populations. Health is determined by many interacting factors from the environment of people, global or local, and their activities conditioning the exposure and from their own condition, immunity, use of health services, determinants of vulnerability [START_REF] Epstein | Climate, ecology, and human health[END_REF]. Epidemiological records are usually the only source of information to get an historical track of the health status but their analysis through the numerous biases inherent to the records is highly perilous.

In a fast evolving context, driven by a rapid economic growth, Thailand has been subjected to radical demographical, social and economic changes, echoing on the global health status. From 8.2 millions inhabitants, at the time of the first census, in 1909, to 60.6 millions in 2000, the population was nearly multiplied by ten in a century. The demand for agricultural products increased consequently, leading to clearing forests and draining swampy areas. This agricultural expansion was realized more by increasing lands than increasing productivity, which has remained low, due to production constraints, primarily scarce lands, and a lack of investment. Forest fires are still occurring with an important rate, generally caused by people encroaching forested areas for cultivation, even if Thailand has now large protected areas covering about 13.4 millions of hectares (Thai Royal Forestry Department, 2003).

The study site was identified in Phrae province as exemplary for the problematic: a watershed, suffering from massive deforestation and intensification of agriculture; eroded and polluted soils; a dam at the outlet with poor quality water, used for irrigating a large agricultural plain. This project was initiated in the frame of the program "Usages des Sols et Sédiments : Impacts Sanitaires" supported by the Development Research French Institute (IRD) and realized in collaboration with the Ministry of Public Health (MOPH) and the National Park, Wildlife and Plant Conservation Department at Phrae.

MATERIALS AND METHODS

Study site

Located in the northern region of Thailand (17.5°N -18.3°N; 99.5°E -100.2°E), Phrae province stretches along the large flood plain of the Mae Yom River, bordered by two mountainous North-South alignments. With an overall area of 6,538.6 square kilometers, Phrae scarcely preserves 69.3% covered by forests, mostly on the highlands (Royal Forest Department, 2003). In spite of its remote location, 551 kilometers from Bangkok, Phrae has never appeared isolated, benefiting from the richness of its teakwood forests to develop economically. However, deforestation, forest fires, encroachment, agricultural expansion have been threatening forested areas. The Mae Thang study area, located in Rong Kwang district, one of the eight administrative district of Phrae province, was chosen as a representative example of the environmental changes occurring in northern Thailand. In 1995, a dam was constructed at the outlet of the 120 sq. km. Mae Thang watershed, to develop agriculture downstream by irrigating lands. The study area follows the course of water from the highlands to villages within the irrigated area (from 1,800 to 250 meters above sea level). Two villages are identified in the watershed, with scattered houses and a total population of 1,428 inhabitants. In the irrigated area, population is grouped together in ten villages with a total of 4,882 inhabitants, most of them farming rice fields around or raising cattle. Many farmers from these villages commute everyday to the watershed where they have lands and increasingly produce corn.

Since the Eighties, the Thai Ministry of Public Health has progressively provided every district with a hospital and every sub-district with a health center to reach nowadays a full coverage of the territory. Then, there is one health center in the watershed, two in the irrigated area and two others nearby. Ten kilometers from the study area is located Rong Kwang hospital, where acute patients are sent after consultation in any of these health centers.

Cases geo-referencing

Every clinical case from 78 different pathologies, recorded in the health centers or hospitals, are reported in a few days to the provincial office of the MOPH at Phrae. Since 1997, records keep track of the location of the patient's village, the place of consultation and date of the onset of the symptoms.

Using SavGIS ® , a GIS freeware developed by the IRD, a GIS was set up with Phrae provincial Public Health Office to provide a dynamic mapping of the epidemiological records. The location of each health infrastructure in the province (10 hospitals and 119 health centers) was recorded with a Global Positioning System. The location of villages was obtained from the National Statistic Office (NSO), responsible of the National Census. Codes for villages from MOPH and NSO differ significantly and were linked together according to their Thai names. As the extent of villages is not delimited, villages are geo-referenced with an approximate center point. Then every reported case could be geo-referenced either at the village of residence or the place of consultation.

RESULTS

Measurements of environmental changes

Since the seventies, agricultural lands are expanding in the Mae Thang watershed, starting with slash-and-burn cultivation of soya bean. In the eighties, agriculture was intensified with a second cultivation of mungbeans, contributing to massive soil erosion [START_REF] Janeau | Soil crusting and infiltration on steep slopes in northern Thailand[END_REF]. Since the end of the nineties, international companies have been increasingly leading agricultural activities, promoting corn cultivation and providing farmers with seeds, fertilizers and pesticides. Downstream of the dam, rice cultivation is still the main activity, boosted in production by irrigation. Important streaming has been measured in the Mae Thang watershed: from 20 to 30% for annual regular rainfall, with 10-15 g.l -1 of soil losses, directed to the dam. Nevertheless, no contamination by heavy metals was found in the reservoir (Cd, Pb). During the dry season, Fe and Mn appear under the thermocline, water is impoverished in O 2 and H 2 S is produced. Cyanobacteria, from unclarified origin, (Cylindrospermosis sp., Limnothrix, Lyngbia, Pseudanabaena, Raphidiopsis) are the majority of the plankton biomass. These bacteria are not harmful for humans but could cause minor gastric problems. High mortality observed in fisheries should be one of the consequences of these phenomena. Methane is produced in the deepest water stratum, where is extracted water for irrigation. From the reservoir, water is mostly used to cultivate rice. Direct contact with water may be occasional for farmers at risk of water-borne diseases infection. Use of water for bathing or cooking is not yet evaluated.

The increasing use of chemical fertilizers and pesticides (mostly insecticides and weed killers) in agriculture has some consequences on health, which are difficult to estimate in term of gravity and incidence due to limited report and unknown diagnostic. However some cases of pesticide poisoning are also increasingly recorded over the country by the MOPH, showing a significant correlation with the quantity of pesticides used (Figure 3).

Health status of the Mae Thang area, compared to Phrae province

Compared to Phrae province, all pathologies taken together present higher incidences every year, up to 1.8 times higher in 2000 (Figure 4). Three hypotheses can be formulated: a higher number of cases, a higher recourse to health care, or an easier access to health services. Most of the reported infectious diseases are diarrheas that can be from various origins but possibly linked to water quality. Two epidemics of conjunctivitis were recorded in 1998 and 2002 in both province and study area (figure 5). During the other years similar or lower records were reported from the Mae Thang area, showing that recourse and access to health services are not the real factor explaining the higher incidences. Food poisonings differ also from one year to another (figure 6). In 2002 high incidence of food poisoning remains unexplained but could have an environmental origin. High incidences of leptospirosis, transmitted from rats to humans through water, were also recorded in the Mae Thang area, during the epidemics starting in 1998. It illustrates the exposure of villagers to the infection by water-borne diseases.

Spatial approach of the health status

Incidences were calculated for 1,000 inhabitants by village and maps realized for each pathology. Few cases were reported from the two villages located in the watershed, probably due to a lower record of real incidences. Houses are isolated and access to health services more difficult. Populations are also living with lower incomes and may have a limited recourse to health care. Within the irrigated area, villages upstream show higher incidences than those 2 kilometers downstream for diarrhea (up to 2 times higher), conjunctivitis and food poisoning. Exposure and vulnerability should be higher in these villages, but cannot be assessed with the present data. Leptospirosis incidences vary from one village to another every year. Higher incidences were also recorded in the villages surrounded by rice fields. Further public health investigations are needed and planned to understand what can be the differences between villages in consumption habits, use of water or daily trips to rice fields.

DISCUSSION

Dealing with epidemiological data necessarily reflects the quality of the health system, practices and recourse to health services. An increasing incidence of an emerging disease, such as leptospirosis in Phrae, does not necessarily implies it did not exist before. The overall incidence of records (all pathologies together) is on average 2,692 for 100,000 inhabitants in Phrae, while it is 4,889 in Loei, a comparable province, 200 kilometers to the South-East of Phrae (489,590 inhabitants in Phrae and 631,408 in Loei). In both provinces, the percentage of diarrhea in total records is stable with an average of 56% for Phrae and 50% for Loei. It seems that reports of diseases are low in Phrae with many possible causes implicating the health system, service, quality and access but also the health behavior. More investigations are finally needed to assess the use of health services and estimate, from epidemiological reports, the theoretical incidences.

CONCLUSION

This work constitutes a first attempt of epidemiological mapping at a village scale in Thailand, raising the difficulties to deal with different coding systems. Administrative entities are not fixed but evolving, increasing in number consequently to the economic development. Among them, villages are not clearly defined, without borders, their number or even names depending on ministries or governmental agencies. There is a real need to agree on a common definition and classification of villages and also subdistricts in Thailand, and a need to provide administrative maps at each level (provinces to villages), matching with the different step of the evolution of the administrative divisions, and made on a national common agreement.

Understanding the consequences of environmental and especially agricultural changes on health needs long term investigations to observe and measure the dynamics. However, some signs can already be interpreted. Spatial analysis enabling different scales of study has proved to be a real support of analysis showing unknown radical differences in incidence a few kilometers apart, which cannot be observed with aggregated geo-referenced data at other scales.

Figure 1 .

 1 Figure 1. Location and land cover of Phrae province in Thailand (from Landsat V TM images in 2000).

Figure 2 .

 2 Figure 2. Phai Yo health center, a typical 2-floor building built in 1999, with 3 health officers

Figure 3 .

 3 Figure 3. Comparison between records of pesticide poisoning and imported pesticides in Thailand (Sources: MOPH, 1993; Department of Agriculture, 1998) The correlation observed at a country scale can be used to estimate in the study area a theoretical number of poisonings.

Figure 4 .

 4 Figure 4. Incidence of all reported diseases in Mae Thang area and Phrae province, Thailand (Sources: MOPH, NSO)

Figure 5 .

 5 Figure 5. Incidence of conjunctivitis in Mae Thang area and Phrae province, Thailand (Sources: MOPH, NSO)

Figure 6 .

 6 Figure 6. Incidence of food poisoning in Mae Thang area and Phrae province, Thailand (Sources: MOPH, NSO)

ACKNOWLEDGEMENTS

We thank Narissara Chatwatcharakul (IWMI-SEA, Bangkok), Arthorn Boonsaner (National Park, Wildlife and Plant Conservation Department, Thailand, Bangkok), and people from the Ministry of Public Health at Phrae provincial office for their precious assistance.


O Coopmann
email: olivier.coopmann@umr-cnrm.fr

N Fourrié

V Guidard

Analysis and Channel selection of MTG-IRS for NWP models 1 Analysis of MTG-IRS observations and general channel selection for Numerical Weather Prediction models

Keywords: MTG-IRS, Channel Selection, Numerical Weather Prediction, Radiative Transfer Model, Data Assimilation

IRS (infra-red Sounder) is an infra-red Fourier transform spectrometer that will be on board the Meteosat Third Generation series of the future EUMETSAT geostationary satellites and will have a unique 4D look at the atmosphere. After its planned launch in 2024, it will be able to measure the radiance emitted by the Earth at the top of the atmosphere using 1960 channels in two spectral bands between 680 -1210 cm -1 (long-wave infra-red) and 1600 -2250 cm -1 (mid-wave infra-red) at a spectral sampling of 0.6 cm -1 . It will perform measurements over the full Earth disk with high spatial and temporal resolution of 4 km at nadir and 30 minutes over Europe. Thus, the huge amount of data from IRS will present challenges, particularly in data transmission, data storage and assimilation into NWP models.

To reduce the volume of data, various methods are available including spatial sampling, principal component analysis and channel selection. The latter technique will be discussed in this paper by proposing general channel selection to provide Numerical Weather Prediction models. The objective of this selection is to improve essential variables for NWP such as temperature, humidity, skin temperature and ozone. This work has required the development of a large observation database and takes into account the main developments in assimilation techniques, including the use of full observation-error covariance matrices or the assimilation of ozone in global models for example. This study performs a specific analysis of the sensitivity of IRS observations and proposes a first general selection of 300 channels for NWP models. This selection allows us to reduce the analysis error in the troposphere by 48 % in temperature, 65 % in humidity and 17 % in ozone.

Introduction

The benefits of infra-red hyperspectral sounders in improving Numerical Weather Prediction and monitoring of atmospheric composition are well established. Nowadays, the IASI instrument, on board the Metop-B and C satellites, is the most performing instrument. They allow a large coverage of the globe, but they can only observe the same place twice a day due to the nature of the polar satellites on which they are flown. There are currently two hyperspectral sounders in geostationary orbit: the Geostationary Interferometric Infrared Sounder (GIIRS-1 and 2), already aboard the FY-4A geostationary satellite over East Asia [START_REF] Yin | The evaluation of fy4a's geostationary interferometric infrared sounder (giirs) long-wave temperature sounding channels using the grapes global 4d-var[END_REF].

The rapid evolution of instrumental and spectral performance has enabled EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) to propose its future geostationary satellite program, the Meteosat Third Generation (MTG) series. These platforms, whose first launch is planned for 2022, will host on board two types of instruments, an imaging platform (MTG-I) and a sounding platform (MTG-S). The latter, will have on board the Infra-Red Sounder (IRS) a imaging Fourier Transform Spectrometer with a spectral sampling of about 0.6 cm -1 in two spectral bands named Long-Wave Infra-red (LWIR or band 1) extending from 680 -1210 cm -1 and Mid-Wave Infra-red (MWIR or band 2) extending from 1600 -2250 cm -1 . After its launch, it will perform measurements over the full Earth disk with particular focus on Europe (revisited every 30 min), with a spatial resolution of 4 km at nadir. Thus, IRS will have higher spatial and temporal sampling than its precursor GIIRS. In addition, it should also be the first to satisfy operational requirements, GIIRS-1 and 2 being experimental. IRS is expected to have a significant impact on NWP and nowcasting. In addition to the future Copernicus Sentinel-4 instrument which will be on the same platform, the duo will provide a set of useful observations for atmospheric composition and air quality monitoring applications. The main challenges are:

• The rapidity of observations, where requirements on level 1 and 2 products are to be respectively made available within 15 and 30 minutes from sensing for any given dwell. • The spatial and temporal resolution of the observations will be very important and will induce huge amount of data, which will require information compression. The spectra will be distributed as principal components using a so-called hybrid method. This method will make it possible to conserve the essential part of the atmospheric variability. • The information content of IRS should be assessed and a selection of information can be made to reduce the amount and redundancy of information. The spectral sampling has been optimised from 0.625 to 0.6 cm -1 . An assessment of the cloud fraction and scene heterogeneity within each pixel will be available. • Preparing users for the coming of IRS is essential in order to be ready when the first data is being acquired. Test data are provided by EUMETSAT to familiarise them with these future observation format.

The main objectives of this study are the analysis of the different sensitivities of IRS observations and a selection of information to provide a first list of IRS channels able to achieve the expected improvements for NWP. To carry out this work, we have developed a large database of synthetic IRS observations, background and "realistic" atmospheric profiles. In order to create this database, we place this work within a one-dimensional framework with synthetic observations. Finally, we have selected IRS channels, retaining the most informative ones using a method derived from Rodgers' information theory [START_REF] Rodgers | Information content and optimization of high-spectral-resolution measurements[END_REF] and used for the selection of AIRS [START_REF] Fourrié | Evaluation of the AIRS near-real-time channel selection for application to numerical weather prediction[END_REF] or IASI [START_REF] Collard | Selection of IASI channels for use in numerical weather prediction[END_REF][START_REF] Coopmann | Update of infrared atmospheric sounding interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)[END_REF]) channels for example.

Section 2 describes in more details the instrumental and spectral characteristics of IRS and Section 3 presents a sensitivity analysis of IRS channels to thermodynamics and atmospheric chemistry, as well as their weighting functions and Jacobians. Then, Section 4 presents the different steps that led to the setting up of IRS observations and a synthetic observation database that can be used for the channel selection carried out in Section 5. Finally, Section 6 will deal with the conclusions and propose perspectives for future studies.

IRS caracteristics

The IRS instrument will simultaneously acquire a large number of spectral soundings using a bi-directional detector array in two spectral bands (Table 1). Thus the Earth's disc will be measured by a sequence of square sub-images called Dwell. The coverage of the Dwells will be stepped in an east/west direction to form a sounding line, before moving northward to form a new one. The complete disc formed by these Dwells will be separated into 4 Local Area Coverage zones (LAC) as shown in Figure 1. These 4 LAC will be defined and scanned sequentially. Each Dwell will be measured in 10 s with a coverage of 640 x 640 km 2 at nadir. Each Dwell will contain 160 x 160 pixels with a spatial sampling distance of 4 x 4 km at nadir and approximately 4 x 7 km over Europe. Europe

Radiative Transfer Model

In order to evaluate the IRS sensitivity, calculate the Jacobians and simulate IRS radiances, we used the Radiative Transfer Model (RTM) RTTOV which is developed and maintained by the Satellite Application Facility (SAF) of EUMETSAT for NWP. In the RTTOV algorithm, the input atmospheric profiles (temperature and humidity) are often variable and provided by the users, the other constituents such as O 3 , CO 2 , CH 4 , CO, N 2 O, etc. can also be provided or are assumed to be constant profiles in time and space (depending on the version of the coefficients). For our sensitivity study, all thermodynamic and chemical profiles will be variable.

Sensitivity Analysis

A spectral sensitivity study on the bands of the IRS spectrum was conducted using the RTTOV version 12 [START_REF] Saunders | An update on the RTTOV fast radiative transfer model (currently at version 12)[END_REF].

We used the method developed by [START_REF] Gambacorta | Methodology and information content of the NOAA NESDIS operational channel selection for the cross-track infrared sounder (CrIS)[END_REF] which consists in evaluating the brightness temperature response to a perturbation for each atmospheric constituent separately: temperature (T), skin temperature (T skin ), water vapour (q), ozone (O 3 ), carbon dioxide (CO 2 ), methane (CH 4 ), carbon monoxide (CO), nitrous oxide (N 2 O) and sulfur dioxide (SO 2 ). The brightness temperature response (∆BT) is calculated by the difference between simulations with perturbed profiles and simulations with unperturbed profiles. We used 6 atmospheric profiles spread over the globe from the [START_REF] Matricardi | The generation of RTTOV regression coefficients for IASI and AIRS using a new profile training set and a new line-by-line database[END_REF]) database which we collocated with the space-time coordinates and instrumental characteristics of IRS. The vertical profiles of each variable have been modified by a constant perturbation typical of atmospheric variability whose values are:

• T: 1 K • T skin : 1 K • q: 10 % • O 3 : 10 % • CO 2 : 1 % • CH 4 : 2 % • CO: 1 % • N 2 O: 1 % • SO 2 : 1 %
Figure 2 shows the sensitivity analysis of the IRS channels (1960) by averaging the ∆BT over the 6 atmospheric profiles. It can be seen that the IRS instrument is potentially sensitive to all the variables evaluated here. However, the use of IRS to observe methane seems to be complex since the spectrum of band 1 stops at the beginning of the CH 4 absorption band. We considered that channels with a ∆BT greater than 0.01 K are sensitive to the studied species. Thus, IRS is a suitable candidate for the observation of atmospheric temperature and water vapour, surface properties and also for the observation and monitoring of the atmospheric composition of species such as O 3 , CO 2 , CO, N 2 O and SO 2 .

Weighting function and Jacobians

In order to evaluate the sensitivity of these variables according to the vertical in the atmosphere, we calculated the Weighting Functions and Jacobians using the RTTOV RTM with the 6 previous profiles. Figure 3 shows the average for these 6 profiles of the normalized Weighting Functions, the normalized Jacobians of water vapour, ozone, carbon dioxide, methane, carbon monoxide, nitrogen oxide, sulphur dioxide and the Jacobians of temperature as a function of pressure and skin temperature for the 1960 IRS channels. The Jacobian shows to which levels in the atmosphere the brightness temperature at given wavenumber is sensitive, with respect to temperature, humidity or concentrations of the different gases present in our case. The Jacobian matrix is denoted H in the following.

It can be seen in Figure 3, that IRS channels are mainly sensitive in the troposphere for all atmospheric variables except for temperature and carbon dioxide which has early band 1 channels sensitive in the stratosphere and UTLS (Upper Troposphere Lower Stratosphere). Ozone band has also sensitive channels in these same parts of the atmosphere. It can be noted that Jacobians of temperature and water vapour are mainly sensitive in the lower troposphere for channels located in the first atmospheric window between 770 and 970 cm -1 . (a)

Database

Realistic atmospheric data

In order to carry out this study, we have set up a large database of atmospheric profiles spread over the IRS observation disc (max zenithal angle of 65 • ). To do this, we randomly selected coordinates in the 4 LACs for 7458 cases for the dates of 15 July and November 2016, day and night, on land and sea. Longitudes, latitudes, zenith and azimuth angles for the IRS instrument were provided by EUMETSAT and the solar zenith and azimuth angles have been calculated for these study cases. Finally, we extracted atmospheric profiles (temperature, humidity, ozone, cloud fraction, cloud liquid water, cloud solid water) and surface parameters (surface pressure, skin temperature, temperature at 2 metres, humidity at 2 metres, zonal and meridional wind at 10 metres) for the 7458 case studies. The profiles are extracted from a research configuration of the ARPEGE NWP global model at Météo-France (Coopmann et al. 2020a). This database will be used for the construction of the background profiles used as a priori and the synthetic observations. It will be considered as the reality (x t ) in the rest of the study. Since the ARPEGE profiles are on 105 levels, we have interpolated onto 101 levels in order to have the same pressure levels as the IRS coefficients provided for the RTTOV RTM.

Background atmospheric data

In order to produce the background profiles and IRS observations, we have chosen the method used in an OSSE (Observing System Simulation Experiment) context. In a first step, the background profiles were generated from the database of "true" profiles perturbed with the addition of background-errors such as:

x b = x t + B 1/2 .η b (1)
where x b is the perturbed background profile, x t is considered as the "true" profile, η b is a random vector drawn from a Gaussian distribution with zero mean and unit standard deviation and B 1/2 is the square root of the background-error standard deviations determined from a multi-variate background-error covariance matrix B. The latter is derived from the Coopmann et al. ( 2020b) study and calculated using the NMC (The National Meteorological Center) method [START_REF] Parrish | The national meteorological center's spectral statistical-interpolation analysis system[END_REF]) over one year of data for temperature, humidity, ozone and skin temperature.

Observation data

A major difficulty of this study is that no IRS observations are already available. To compensate for this lack of data, a framework is proposed for generating synthetic observations that are close to reality. As for background profiles, IRS observations are calculated from perturbed simulations with the addition of observation-errors such as:

y = H(x t ) + R 1/2 NE∆T .η o (2)
where y is the perturbed IRS observations, H(x t ) is the simulated observations from "true" profiles, η o is a random vector drawn from a Gaussian distribution with zero mean and unit standard deviation and R 1/2 NE∆T is the square root of the observation-error covariance matrix (R).

As a first approximation, the diagonal R matrix was constructed using the instrumental noise values provided by EUMETSAT. The IRS noise is given in terms of the equivalent noise of temperature difference at 280 K NE∆T(T ref ). For the 7458 case studies, R matrices were constructed independently from the converted NE∆T(T) for the corresponding scene temperature T with the formula:

NE∆T(T) = NE∆T(T ref ). ∂B/∂T(T ref ) ∂B/∂T(T) (3)
where (T ref ) denotes values at 280 K, (T) denotes values at the scene temperature and (B) is the Planck function. This conversion was applied for each channel and each study case.

To simulate IRS observations, we chose to use the RTTOV RTM version 12. Atmospheric profiles and surface parameters from x t were used as input to the RTM. Moreover, having cases on land and sea and in order to increase the realism of the simulations, we used the land emissivity values from the Combined ASTER MODIS Emissivity over Land (CAMEL) [START_REF] Borbas | The combined ASTER MODIS emissivity over land (CAMEL) part 1: Methodology and high spectral resolution application[END_REF] and the emissivity values over sea from a surface emissivity model (IREMIS) [START_REF] Saunders | RTTOV v12 science and validation report[END_REF]) available in RTTOV. Finally the simulations were performed in clear and cloudy skies. To take clouds into account, we used the microphysical properties assembled in the Optical Properties of Aerosols and Clouds (OPAC) software package [START_REF] Hess | Optical properties of aerosols and clouds: The software package OPAC[END_REF] for liquid water considering maritime and continental stratus and the optical properties of ice clouds provided by Baran 2018 for solid water [START_REF] Vidot | Comparison of the RTTOV-12 ice cloud models for hyperspectral IR instruments using the A-Train[END_REF]. Here, the simulations are performed without apodisation.

Thus, we simulated IRS observations for the 7458 case studies. Figure 4 shows the mean (a) and standard deviation (b) of the simulated spectra for the 1960 channels in clear (blue) and cloudy (grey) sky. As expected, it can be seen that the means are lower in cloudy sky for the atmospheric window channels and near-surface channels. Inversely, these same channels have higher standard deviations than in clear sky indicating a much greater variability induced by the presence of clouds.

Cloud detection

One of the main complexities in the assimilation of infra-red observations is the consideration of clouds. Indeed, a large part of the infra-red spectrum is sensitive to clouds. This can be problematic because, as [START_REF] Fourrié | Cloud characteristics and channel selection for IASI radiances in meteorologically sensitive areas[END_REF][START_REF] Mcnally | A note on the occurrence of cloud in meteorologically sensitive areas and the implications for advanced infrared sounders[END_REF] show, 90 % of the pixels of the IASI instrument are totally or partially contaminated by clouds. In order to avoid this under-use of observation, the major NWP centres have developed methods to assimilate clear channels above clouds. Among them, the (McNally and Watts 2003) Cloud Detection Scheme allows the detection of channel affected by the presence of a cloud.

Within the framework of this study, it is important to assess the sensitivity of IRS observations to clouds as well as the capacity of the scheme to detect them. To achieve this, we used the ECMWF Aerosol and Cloud Detection Software which uses the method given by ( McNally it from other contributions, the cloud detection algorithm works by taking the background departures (i.e., the difference between the observed and simulated clear brightness temperature (BT)) and looking for the signature of opacity that is not included in the clear-sky calculation (i.e. cloud). To do this, the channels are first ranked in the vertical, according to their height assignments [START_REF] Eresmaa | Ecmwf aerosol and cloud detection software user guide[END_REF].

In this section, we have two main objectives;

• Step 1: to evaluate the ability of the algorithm to correctly classify the clear/cloudy channels based only on the ideal IRS simulations • Step 2: to estimate the impact of the instrumental noise on the cloud detection when creating the synthetic IRS observation

For this first step we provide the cloud detection software with the ideal IRS simulations in clear (H clear (x t )), cloudy (H cloud (x t )) and the associated height assignments calculated in the previous section for the 1960 channels and 7458 pixels. Thus, the algorithm provides a clear/cloudy identification for all channels and the full data set based on the difference:

δ step1 = H cloud (x t ) -H clear (x t ) (4)
The figure 5 shows the frequency of IRS channels flagged clear for the 1960 channels and 7458 pixels. The behaviour is consistent with the results obtained by [START_REF] Mcnally | A cloud detection algorithm for high-spectral-resolution infrared sounders[END_REF] which show that channels located in the strongly absorbing parts of the spectrum (CO 2 absorption band from 680 to 740) are generally clear and are correctly evaluated. Also that channels in the atmospheric window are rarely clear and larger increases in absorption due to water vapour result in a marked increase in the occurrence and detection of clear channels. Nevertheless, we notice that our simulated observations appear clearer overall. This can be explained by the ideal framework used in this study, as explained by [START_REF] Masutani | Observing system simulation experiments[END_REF], by a degree of cloud contamination of the measurements that may be underestimated. In the same step, we can evaluate the clear/cloudy flagged channels as a function of the geophysical parameters used to simulate the all-sky observations in order to identify a consistency between the sensitivity level of the flagged channel and the cloud height. Thus, Figure 6 provides an example of the cloud fraction for three different cases; high (a), medium (b) and low (c) clouds associated with the normalized weighting functions of the identified clear (green) and cloudy (grey) channels. It can be seen that the cloud detection algorithm seems to work well by identifying as clear only those channels with a sensitivity peak above the cloud. In case (a) 42 channels are identified clear, 206 in case (b) and 588 in (c). Despite an overestimation of clearly identified channels, the algorithm is able to consistently classify IRS channels. For the second step, we proceed in the same way with the difference that the cloud simulation is perturbed by the IRS instrumental noise (Equation 2). This allows us to evaluate the effect of the noise on the ability of the software to correctly classify the clear/cloudy channels:

δ step2 = y cloud -H clear (x t ) (5)
Figure 7 shows the agreement rate between step 1 and step 2 according to the 1960 channels for the 7458 pixels considered. It can be seen that the calculation of the synthetic observations used in step 2, slightly introduce cloudy signal in the channels by 5 % over the whole spectrum (in yellow) and clears (in orange) the channels between 5 and 10 % in the CO 2 and water vapour absorption bands. Thus, the calculation of synthetic observations changes very slightly the nature of the observation in terms of cloud detection. These results will not have any impact in our study since we have chosen to work only in clear sky conditions. However, these results show that in a more operational assimilation framework, a misclassification of clear1-cloudy2 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 IRS channel number (in yellow) would prevent the assimilation of some valuable information, while cloudy1-cloudy2 (in orange) would introduce wrong information in the system. The cloud detection algorithm allows to highlight 1700 pixels of which all channels are identified as clear.

Observation-errors

The impact of the observation-errors in the data assimilation process is very important. This is why it is essential to estimate accurately the observation-error covariances R in this context which uses synthetic observations [START_REF] Vittorioso | An infrared atmospheric sounding interferometer-new generation (iasi-ng) channel selection for numerical weather prediction[END_REF].

For several years, innovative techniques have been used to achieve this by deriving estimates of the real observation-error from the departure statistics from assimilation systems (e.g. [START_REF] Hollingsworth | The statistical structure of short-range forecast errors as determined from radiosonde data. part i: The wind field[END_REF][START_REF] Desroziers | Diagnosis of observation, background and analysis-error statistics in observation space[END_REF]). These methods make it possible to diagnose both the variances and covariances of observation-error, including cross-channel error correlations allowing an accurate estimate of the total errors that characterise the observation (instrumental noise, error of spatial representativeness, error in the calculation of radiative transfer, etc.).

This work is exploratory since we try to estimate real observation-errors for synthetic observations. This is a difficult task but achievable within a precise synthetic observation framework. Indeed, [START_REF] Privé | The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment[END_REF]) have shown that an accurate representation of observation-errors is also important in a synthetic observation context. Nevertheless, the estimated error in this case will be smaller than the error of the real observations. As a first step, we estimated the potential bias of these synthetic observations. We calculated the innovations of each IRS channel for the 1700 profiles by computing the difference between the synthetic observations (y) and the simulations of observations using the background profiles within the RTTOV. The average of the first-guess departures was used to make a bias correction applied to the observations (y). This bias correction is nevertheless slight since the mean of (y -H(x b )) values are between 0.2 and -0.2 K.

In order to accurately estimate the variances and covariances of IRS observation-error, as well as inter-channel correlations, we have chosen to use the method developed in [START_REF] Coopmann | Update of infrared atmospheric sounding interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)[END_REF]. It consists in using the values of the standard deviations of the first-guess departures previously calculated as a first estimate for standard deviations of observation-error. This diagonal R matrix determined is the starting point of the computation. Then, we have used the NWP SAF One Dimensional Variational (1D-Var) data assimilation algorithm [START_REF] Havemann | NWPSAF 1D-Var User Manual[END_REF] to apply the Desroziers method to our database and thus to precisely estimate the IRS observation error-covariance matrix R.

Thus the 1D-Var experiment was carried out on the 1700 cases previously considered as clear (see above). We used the background profiles (x b on 101 levels) calculated from Equation 1, associated with the multi-variate B matrix mentioned previously. The synthetic IRS observations (y on 1960 channels) are calculated from Equation 2, associated with the diagonal R matrix estimated by the standard deviations of the First-Guess departure. 1D-Var data assimilation experiment minimized on temperature between 1000 and 0.1 hPa, humidity between 1000 and 100 hPa, ozone between 1000 and 0.1 hPa and skin temperature.

To diagnose the observation error covariance matrices R, the Desroziers method is commonly used in NWP centres [START_REF] Weston | Accounting for correlated error in the assimilation of high-resolution sounder data[END_REF][START_REF] Stewart | Estimating interchannel observation-error correlations for iasi radiance data in the met office system[END_REF][START_REF] Bormann | Enhancing the impact of iasi observations through an updated observation-error covariance matrix[END_REF][START_REF] Coopmann | Update of infrared atmospheric sounding interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)[END_REF]). Since the real state of the atmosphere is not known, the method shows that it is possible to estimate in the space of the observations, the R matrix with the deviations of the observations from the background and the analysis. Given several assumptions, the previous 1700 analyses allow to statistically diagnose the observation-error covariance matrix such as: to 700 cm -1 in the CO 2 absorption band. They are then always higher than instrumental noise and are between 0.5 and 1.7 K in atmospheric windows (700 -1000 cm -1 and 1060 -1250 cm -1 ), between 0.5 and 1.4 K in the ozone absorption band (1000 -1060 cm -1 ) and between 0.5 and 2.0 K in the water vapour absorption band (1600 -2250 cm -1 ).

R = E[d o a (d o b ) T ] (6
It is noticeable that the First-Guess departures standard deviation for atmospheric window channels are high values. This indicates a presence of a incorrect signal in some pixels of the 1700 clear cases considered and is probably due to the perturbation of skin temperatures used to obtain the background value as indicated in the cloud detection study previously performed. However, the diagnostic of observation-errors allows this effect to be smoothed out and consistent values between instrumental noise and observation-errors of FG departures standard deviation can be found. These diagnosed observation-errors are close to the errors generally obtained for infra-red sounders. Nevertheless, these errors are probably lower than those we would have with real observations. Moreover, the estimation of these errors takes into account this instrumental noise and radiative transfer errors but does not take into account errors related to spatial representativeness for example. In this context, the diagnostic of the R matrix allows a first estimation of the observation-errors for the IRS channels. The observation errors diagnosed for the channels of the ozone absorption band are lower than those of IASI (between 0.5 -1.5 K) for example. This is probably due to the limitation of the ozone variability within the IRS disk which does not provide measures over the poles.

The Figure 9 (b) shows higher error correlation values (0.2 -0.4) for the channels and inter-channels used for temperature sounding in the lower atmosphere between 700 and 750 cm -1 , as well as for the first atmospheric window between 750 and 950 cm -1 . The error correlation values for the channels in the ozone absorption band are about 0.2 and then 0.25 for the second atmospheric window between 1060 et 1200 cm -1 . Finally, the error correlation values are low for the channels of the water vapour band. These error correlation values are lower than expected in the same way that the errors are generally lower for these synthetic observations compared to real observations [START_REF] Privé | The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment[END_REF]. This diagnosed R matrix will be used in the following to perform IRS channel selection.

Channel selection

Methodology

The observations measured by the IRS instrument will represent an impressive amount of data. This high volume of information constitutes a challenge for data storage and transmission. The computational cost of assimilating all IRS channels would be too high and inefficient due to redundant information. Typically, EUMETSAT disseminate a smaller quantity of channels especially selected to improve the NWP models of weather centres. The aim is to make a precise selection of all IRS channels to provide the optimal information for the NWP models. Currently, the channels that can provide information on atmospheric temperature, water vapour and surface temperature represent the most useful observations for improving weather forecasts. In addition, several NWP centres have included ozone as a variable in the models because it improves the assimilation of specific channels in the infra-red, which are sensitive to ozone, temperature and water vapour, and it allows better accounting of radiative feedback, which improves weather forecasts [START_REF] Derber | The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system[END_REF][START_REF] Lahoz | Data assimilation of stratospheric constituents: a review[END_REF][START_REF] John | The impact of ozone lines on AMSU-B radiances[END_REF][START_REF] Ivanova | Impact of ozone radiative feedbacks on global weather forecasting[END_REF][START_REF] Coopmann | Assimilation of iasi ozone-sensitive channels in preparation for an enhanced coupling between numerical weather prediction and chemistry transport models[END_REF][START_REF] Dragani | Atmospheric composition priority developments for numerical weather prediction[END_REF]Coopmann et al. 2020a). Thus, we have selected the most informative channels in temperature, water vapour, skin temperature and ozone.

To carry out this selection, we applied a method that is widely used in the scientific community. This is the Degrees of Freedom for Signal (DFS), which allows to select a set of optimal channels having the largest information content for each atmospheric profile as described by [START_REF] Rodgers | Information content and optimization of high-spectral-resolution measurements[END_REF][START_REF] Rodgers | Inverse methods for atmospheric sounding: theory and practice[END_REF]. The DFS is based on the information theory and provides a measure of the gain in information gathered by the observations according to the formula:

DFS = Tr(I -AB -1 ) (7)
where Tr denotes the trace, I the identity matrix, B ∈ R nxn (n parameters to be retrieved) is the background-error covariance matrix and A ∈ R nxn is the analysis-error covariance matrix

A = (B -1 + H T R -1 H) -1 = (I -BH T R -1 H) -1 B (8)
where R ∈ R mxm (m channels considered) is the observation-error covariance matrix and H ∈ R mxn (the derivatives of each channel with respect to each parameter) represent Jacobians matrix for all IRS channels.

As we have seen previously, some IRS channels, like others in the infra-red, are sensitive to several variables (multi-sensitive channels). As we have a diagnosed observation-error covariance matrix including inter-channel errors, we have carried out this selection by using as a figure of merit the total DFS taking into account the DFS of temperature, water vapour, skin temperature and ozone, in order to select the most sensitive or poly-sensitive channels that provide the maximum amount of information, such as:

DFS TOT = DFS T + DFSq + DFS O3 + DFS T skin (9)
Finally, we chose 60 profiles among all the 1700 clear cases considered and distributed over the full IRS disk (15 profiles per LAC) in order to carry out the channel selection and to take into account different scenarios of atmospheric variability (see Figure 9). The use of cross-channel error correlations allows us to consider all channels during selection as well as adjacent channels. We used the diagnosed observation-error covariance matrix R, the multi-variate background-error covariance matrix B and the Jacobian matrix H containing for each profile the Jacobians of the 1960 IRS channels. For channel selection, the DFS calculation considers for each profile, 93 levels for temperature and ozone from 1000 to 0.1 hPa and 53 levels for humidity from 1000 to 100 hPa.

The first selection step consists in selecting for each of the 60 atmospheric profiles the most informative channel with the highest total DFS among all IRS channels using a R matrix R 1x1 . For each profile, the first selected channel is fixed and a new combination of this with a second channel from the (1959 -1) channels is searched using a R matrix R 2x2 . The combination giving the highest total DFS allows to select and fix the second channel. This operation is repeated as many times as necessary. We have chosen to stop this process after the selection of 500 IRS channels for each of the 60 profiles since EUMETSAT is limited in the dissemination of data by the Global Telecommunications System (GTS). Figure 10 shows that the information content increases very quickly with only 50 channels which represents a mean total DFS of 11.20 or 66.7 % of the mean total DFS available with 500 channels (total DFS of 16.79). It is noticeable that the mean DFS of humidity is slightly higher than the mean DFS of temperature. This is consistent with the previous sensitivity study which showed that a very large number of IRS channels are sensitive to water vapour even those located in the atmospheric window bands. Two other hypotheses can explain this behaviour. Contrary to other hyperspectral sounders, IRS observations start further out in the spectrum at around 680 cm -1 unlike IASI and CrIS which start at 645 and 650 cm -1 respectively. This deprives IRS of many strongly absorbing and temperature sensitive channels. In addition, IASI and CrIS observe in other parts of the spectrum that are highly sensitive to temperature between 1200 -1600 cm -1 and 2250 -2400 cm -1 which IRS does not possess. Finally, these results can also be explained by the background errors in humidity. Indeed, the models are more wrong in humidity than in temperature, so the humidity errors are proportionally stronger than the temperature errors. Finally, we observe that the average ozone DFS increases very rapidly to reach a plateau, while the mean skin temperature DFS reaches with only 3 channels more than 90 % of the mean skin temperature DFS available with 500 channels.

First step of selection

In order to analyse more specifically the selection process, we have plotted in Figure 11 a histogram of the percentage of the number of channels selected on the 60 considered atmospheric profiles. This means that if a channel is selected for all profiles, it achieves 100 % selection. Conversely, a channel never selected among 60 profiles reaches 0 % selection. These percentages are characterised by spectral band and main applications for the NWP (temperature sounding in the CO 2 absorption band, humidity sounding in the water vapour absorption band, ozone absorption band and atmospheric windows) and for all channels. This histogram shows that approximately 434 channels are always selected between 50 and 100 % of the time (for 30 to 60 profiles) and that 1526 channels are only selected between 0 and 50 % of the time (for 0 to 30 profiles). This shows an information redundancy of a large number of channels. Thus, a selection from the 434 channels should be sufficient to extract the maximum amount of useful information for numerical weather prediction. Percentage of the number of channels selected (up to 500 channels) on the subsets of 60 atmospheric profiles separeted by spectral group and main application to temperature sounding (in red), humidity sounding (in blue), ozone (in violet), atmospheric window (in green) and total (in black) with respect to number of related profiles.

Among the 1960 considered IRS channels, 151 channels are always selected from the 60 profiles (100 %), among them, 73 for temperature sounding, 48 in the ozone absorption band, 20 in atmospheric windows and 10 for humidity sounding (in green in Figure 12). Overall, there is a decrease in the number of channels selected up to 60 to 69% selection percentage and then an increase up to 0 % selection percentage. The number of channels always selected in the ozone absorption band and in the atmospheric windows is significant and shows the multi-sensitive properties of these channels, which can provide a large amount of information. Finally, 542 channels are never selected, mainly in the water vapour absorption band and in the atmospheric windows (in red in Figure 12).

Results

Once this first step was completed, we sorted these 500 pre-selected channels using two different methods to complete the final channel selection. The first method consists of selecting increasingly the most often selected channels while the second method selects the first rank selected channels. For both methods, we have separated the selections by packages of 75, 150, 300 and 500 channels. For each of these selections we have calculated the mean total DFS over 60 profiles. We notice that the first 75 channels selected have a higher mean total DFS than the 75 most selected. Then the selections with 150 channels have an equivalent mean total DFS. Finally, the selections with 300 and 500 most selected channels have higher mean total DFS than the first selected channels as shown in Figure 13. To ensure the robustness of these selections, the latter were used to calculate the mean total DFS for the 1700 clear profiles. The results are similar to the values shown in Figure 13. We have therefore chosen to evaluate the most often selected channel selections in the following. Table 2 summarizes the values of mean total DFS for temperature, humidity, ozone and skin temperature over 60 profiles for the 75, 150, 300, 500 most selected channels and all channels. It can be seen that the percentage of mean total DFS available increases rapidly between 75 and 150 channels (9.2 %), then 6.2 % between 150 and 300 channels and 4.4 % between 300 and 500 channels. A large part of the mean total DFS value is provided by the mean DFS of temperature and humidity, followed by the ozone and finally skin temperature components.

To assess the impact of these channel selections on weather analyses, we performed 1D-Var data assimilation experiments on the 1700 profiles considered clear. We used the diagnosed R matrix and the multi-variate B matrix. The data assimilation process allows to minimize on temperature, humidity, ozone and skin temperature. These experiments allowed us to calculate analysis-error covariance matrices averaged over the 1700 study cases. We expect these selections minimize the analysis-error mainly in temperature and humidity for optimal use in numerical weather prediction.

We have represented on Figure 14 the vertical profiles of background-error standard deviation (σ b ) and mean analysis-error standard deviation (σ a ) for temperature (a), humidity (b) and ozone (c) and the vertical profiles of relative difference between σ a and σ b for temperature (d), humidity (e) and ozone (f) with respect to pressure. The mean of the analysis-error standard deviations is calculated from the analysis-error covariance matrices A determined by the 1D-Var experiments over the 1700 cases considered. Data assimilation experiments were performed for selections of 75, 150, 300, 500 and all channels.

In Figure 14 (a,b,c), the reduction in analysis error is the greatest in the troposphere and upper stratosphere for temperature and over the entire troposphere for humidity. The vertical profiles of analysis error reduction are close between selections of 75 -150 channels and 300 -500 channels. Thus, a good compromise between the quality of the analysis error and the quantity of used channels would be to select the 300 IRS channel selection. In Figure 14 (d,e,f) are represented the relative analysis error reductions compared to the background errors. By selecting 300 channels, the analysis error compared to the background error can be reduced by up to 48 % for temperature in the troposphere, up to 65 % for humidity in the Mid-troposphere and up to 17 % for ozone in the UTLS. These results show the potential for improvement in weather forecasts by selecting 300 IRS channels for the NWP. This selection of 300 channels is shown in Figure 15 on a typical IRS spectrum per main application for NWP. There are 99 channels mainly selected in the CO 2 absorption band and used to provide information on atmospheric temperature, 55 in the water vapour absorption band for information on atmospheric humidity, 70 in the atmospheric windows to provide information on surfaces but also on temperature and humidity in the lower troposphere and 76 channels in the ozone absorption band to provide information on ozone but also on temperature in the troposphere and stratosphere and humidity in the troposphere. As seen previously, the ozone and surface channels have multiple sensitivities making them usable for several applications. This use is conditioned by the use of an observation error covariance matrix taking into account cross-channel error correlations. Thus, in order to extract the maximum amount of information from the selected channels, a precise estimation of the R matrix is necessary, which we have tried to do in this study.

Conclusions and perspectives

In order to make a first general selection of IRS channels, we were inspired by the synthetic observation method to build a database for this work. Our realistic atmospheric state is provided by the global model ARPEGE (research experiment). A database of more than 7000 atmospheric profiles was extracted from this model in order to simulate the satellite observations of IRS using the RTTOV (version 12) radiative transfer model. The simulations were carried out under clear and cloudy skies, allowing us to evaluate the cloudy detection capacity of the IRS channels. A database of more than 7000 synthetic IRS observations has been built up and collocated with atmospheric profiles. The latter were perturbed to obtain background profiles for temperature, humidity, ozone and skin temperature. This method makes it possible to obtain an initial database (observations and background) which can then be used for various studies such as the estimation of observation errors or the selection of the information content of IRS.

In this study we used a multi-variate background error covariance matrix in temperature, humidity, ozone and skin temperature, calculated in a previous study using the NMC method over one year of data. In order to extract the maximum of information from the IRS channels, it is essential to take into account the cross-channel error correlation. Thus, we calculated the full observation error covariance matrix taking into account the inter-channel errors using the Desroziers diagnostic. The estimation of this R-matrix was performed in a 1D-var data assimilation experiment using the clear-sky cases. The estimation of the observation errors shows results consistent with what is expected for infra-red instruments. The diagnosed observation error value is between the instrumental noise and the standard deviations of the first-guess departures (synthetic observationssimulated observations).

The results obtained for the observation error correlation matrix show similar behaviour to the other hyperspectral sounders, but with lower values compared to the correlations obtained for IASI or CrIS. The largest observations error correlations (0.25 to 0.75 value) were found for CO 2 channels sensitive in the lower troposphere (720-740 cm -1), the sensitive channels in the atmospheric windows (742-1010 cm -1 and 1070-1210 cm -1), in the ozone absorption band (1010-1060 cm -1). Another group of channels with large observation correlation errors but in a lesser extent is located in the beginning of the water vapour sensitive band (1600-1900 cm -1). This estimation of R provides interesting results but it is important to take into account the underestimation of errors induced by the use of synthetic observations. From this database, we have made a general selection of IRS channels that can be used for NWP models. The objective is to provide a first list of the most informative channels before the real arrival of IRS observations. This selection will also be useful in the OSSE framework for the regional AROME model, giving us a channel selection to be simulated and assimilated in the 3D-Var data assimilation system. The selection of channels was carried out over the full IRS spectrum using 60 profiles representative of the atmospheric variability in the IRS disk measurement area. A selection algorithm was implemented to select the most informative channels from the DFS (Degree of Freedom for Signal) method. This selection is carried out iteratively by selecting the most informative channel in temperature, humidity, ozone and skin temperature, then the combination of the first with the second most informative, and so on. We stopped the selection at 500 channels, which is generally the broadcast limit on the GTS at EUMETSAT. The mean evolution of the DFS shows that the IRS channels are slightly more informative in humidity than in temperature. This is due to a broad band of sensitivity to water vapour in the IRS spectrum and also to a high sensitivity of some channels to water vapour in the atmospheric windows. Two other hypotheses may explain this behaviour; the non-observation of parts of the infrared spectrum that are highly sensitive to temperature compared to other hyperspectral sounders and the higher humidity background errors than temperature. The information content reaches more than 66 % of the mean total DFS available over 500 channels (T, q, O 3 and T skin ) with only 50 channels. Over the 60 profiles used for selection, 151 channels are always selected and 542 are never selected. The always selected channels consist of 73 channels that are mainly sensitive to temperature, 48 to ozone, 20 in atmospheric windows and 10 to humidity. The sensitivity analysis carried out at the beginning of the study shows that the channels in the atmospheric windows are also sensitive to temperature and humidity in the troposphere and some channels in the ozone absorption band are also sensitive to temperature and humidity in the troposphere and to temperature in the stratosphere. The use of an R-matrix taking into account inter-channel error correlations allows the extraction of all the information that these channels can provide.

We evaluated the information-providing capacity of several channel selections (75, 150, 300, 500, all channels) from 1D-Var data assimilation experiments on 1700 clear cases. The channel selections were evaluated by their ability to reduce the analysis error relative to the background error. The results show that channel selections mainly reduce the analysis error in the troposphere in temperature and humidity and more slightly in the upper stratosphere. It can be seen that the (75 and 150) and (300 and 500) channel selections provide close results in analysis error reduction to each other.

Our objective is to make the best compromise between quantity and quality of information. We have thus chosen the selection of 300 IRS channels which allows to reduce on mean the analysis error in temperature in the troposphere by 48 % compared to the background error, by 65 % for humidity in the mid-troposphere and by 17 % for ozone at the UTLS. This selection consists of 99 channels located in the CO 2 absorption band primarily used for atmospheric temperature sounding, 55 in the vapour absorption band primarily for atmospheric humidity sounding, 76 in the ozone absorption band that can provide information for temperature and humidity and 70 in the atmospheric windows for skin temperature, surfaces, temperature and humidity in the lower troposphere.

This first selection of channels has been made within a general framework for global NWP. We have taken into account general atmospheric information over the full IRS disk at surface up to 0.1 hPa. In order to be able to use this selection of 300 IRS channels for the AROME model, it is important to identify the sensitive channels in the upper stratosphere since these will not be usable in AROME having a model top at 10 hPa. An additional channel sensitivity study from RTTOV shows that about 280 IRS channels are sensitive above 10 hPa and 58 are present in our channel selection (red circles in Figure 16). These are channels located in the CO 2 and ozone absorption band as shown in Figure 26. Thus, 242 channels of our selection will be potentially usable in our 3D-Var AROME data assimilation system (black circles in Figure 16). The IRS observations are expected to be distributed in the format of principal components (PCs). As we do not yet have the necessary information, we have carried out this study based on brightness temperatures (raw radiances). Data compression from PCA-based approaches has the advantage of drastically reducing data volume and reduces noise. However, the variations of atmosphere signals are more correlated across hyperspectral channels. This implies higher observation error correlations for reconstructed radiances. Hence the need for a specific study of the observation error diagnostics from these reconstructed radiances to take this behaviour into account. A study will be conducted on this subject.

Figure 1 .

 1 Figure 1. MTG Infra-Red Sounder dwell coverage. Credit: EUMETSAT

Figure 3 .

 3 Figure 3. Mean of Normalized Weighting Function (a), Temperature Jacobians (b), Normalized Water Vapour (c), Ozone (d), Carbon Dioxide (e), Methane (f), Carbon Monoxide (g), Nitrous Oxide (h), Sulfur Dioxide (i) Jacobians with respect to pressure and Skin Temperature Jacobians (j) for IRS channels.

Figure 4 .

 4 Figure 4. Means (a) and standard deviations (b) of IRS spectra simulated in brightness temperature from RTTOV in clear (blue) and cloudy (grey) sky for the 1960 channels.

Figure 5 .

 5 Figure 5. Frequency of IRS channels flagged clear.

Figure 6 .

 6 Figure 6. Normalized Weighing Functions of the IRS channels (identified clear in green and cloudy in grey) of band 1 (left) and cloud fraction (right) as a function of pressure for cases of high (a), medium (b) and low (c) clouds.

Figure 7 .

 7 Figure7. Agreement rate between step 1 and step 2 as a function of the IRS channel numbers for the 7458 study cases.

Figure 8 .

 8 Figure 8. Observation-error standard deviation from FG departures standard deviation in red line, diagnosed observation-errors from Desroziers method using 1D-Var data assimilation system in blue line and instrumental noise at 280 K in grey with respect to 1960 IRS channel number and wave-number [cm -1 ] (bands 1 & 2) over the set of 1700 clear atmospheric profiles (a). Diagnostic of IRS observation-error correlation with respect to the same channels as before (b).

Figure 9 .

 9 Figure 9. Mean ± standard deviation and min-max values of temperature (a), ozone (b) and humidity (c) profile with respect to pressure over the subset of 60 atmospheric database. Note that humidity statistics are shown between 1000 and 100 hPa

Figure 10 .

 10 Figure10. Evolution of mean DFS for temperature (red), humidity (blue), ozone (violet), skin temperature (green) and total (black) during the channel selection over the subset of 60 atmospheric profiles.

  Figure11. Percentage of the number of channels selected (up to 500 channels) on the subsets of 60 atmospheric profiles separeted by spectral group and main application to temperature sounding (in red), humidity sounding (in blue), ozone (in violet), atmospheric window (in green) and total (in black) with respect to number of related profiles.

Figure 12 .

 12 Figure12. Location on a typical IRS spectrum in brightness temperature of the always (green) and never (red) selected channels.

Figure 13 .

 13 Figure13. Evolution of the mean total DFS over 60 profiles as a function of the number of selected channels for the 75, 150, 300 and 500 first selected channels (in black) and the most selected channels (in red).

Figure 14 .

 14 Figure14. Vertical profiles of background-error standard deviation (σ b ) and mean analysis-error standard deviation (σ a ) for temperature (a), humidity (b) and ozone (c) and vertical profiles of relative différence between σ a and σ b for temperature (d), humidity (e) and ozone (f) with respect to pressure. This results are derived from 1D-Var data assimilation experiments over a set of 1700 atmospheric profiles with different channel selections (75, 150, 300, 500, all channels).

Figure 15 .

 15 Figure15. Location of the 300 selected IRS channels on a typical spectrum by main sensitivity.

Figure 16 .

 16 Figure16. Representation of the sensitive IRS channels above 10 hPa (in red) among the 300 channels selected on a typical spectrum.

Table 1 .

 1 IRS spectral specifications. Credit: EUMETSAT

		Band 1	Band 2
	Number of channels	881	1079
	Wave number [cm -1 ]	679.7034 -	1599.7688 -
		1210.4391	2250.5427
	Wave length [µm]	8.3 -14.7	4.4 -6.3
	Max Optical Path Difference [cm]	0.8290	0.8282
	Unapodized sampling rate [cm -1 ]	0.6031	0.6037

Skin Temperature Jacobians w.r.t IRS channels

	680	920	1162 Wavenumber [cm 1 ] 1792	2034	0.000 0.200 0.400 0.600 0.800 1.000	T P	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.08 0.06 0.04	BT T [K. K 1 ]
	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08	BT q * (0.1 * q)[K]	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08	BT O3 * (0.1 * O 3 )[K]
	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06	BT CO2 * (0.1 * CO 2 )[K]	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.0010 -0.0008 -0.0006 -0.0004 -0.0002 0.0000	BT CH4 * (0.1 * CH 4 )[K]
	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.0010 -0.0008 -0.0006 -0.0004 -0.0002 0.0000	BT CO * (0.1 * CO)[K]	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.0010 -0.0008 -0.0006 -0.0004 -0.0002 0.0000	BT N2O * (0.1 * N 2 O)[K]
	680	920	1162 Wavenumber [cm 1 ] 1792	2034	-0.0010 -0.0008 -0.0006 -0.0004 -0.0002 0.0000	BT SO2 * (0.1 * SO 2 )[K]	680	920	1162 Wavenumber [cm 1 ] 1792	2034	0.0 0.2 0.4 0.6 0.8 1.0	BT Tskin [K. K 1 ]

Table 2 .

 2 Mean of Total, temperature, humidity, ozone and skin temperature DFS over 60 profiles and percentage of available mean DFS for the 75, 150, 300, 500 most selected channels and all channels.

		Temperature DFS	Humidity DFS	Ozone DFS	Tskin DFS	Total DFS
	75 most selected channels	3.44 (27.7 %)	3.50 (25.0 %)	1.55 (22.2 %)	0.98 (98 %)	9.47 (26.4 %)
	150 most selected channels	4.98 (35.8 %)	5.01 (35.8 %)	1.78 (25.5 %)	0.99 (99 %)	12.76 (35.6 %)
	300 most selected channels	5.71 (41.0 %)	6.12 (43.7 %)	2.16 (30.9 %)	1.00 (100 %)	14.99 (41.8 %)
	500 most selected channels	6.29 (45.2 %)	6.79 (48.5 %)	2.48 (35.5 %)	1.00 (100 %)	16.56 (46.2 %)
	All channels	13.91	13.99	6.98	1.00	35.88

  A. List of the selection of the 300 new IRS channels

	Channel number	Wave-number	Main sensitivity	Channel number	Wave-number	Main sensitivity
	0116	749.06	Temperature	0245	826.86	Window
	0121	752.08	Temperature	0267	840.13	Window
	0122	752.68	Temperature	0268	840.73	Window
	0124	753.89	Temperature	0281	848.57	Window
	0125	754.49	Temperature	0284	850.38	Window
	0126	755.09	Temperature	0286	851.59	Window
	0127	755.70	Temperature	0287	852.19	Window
	0129	756.90	Temperature	0288	852.80	Window
	0131	758.11	Temperature	0289	853.40	Window
	0141	764.14	Temperature	0320	872.10	Window
	0147	767.76	Temperature	0331	878.73	Window
	0149	768.96	Temperature	0345	887.17	Window
	0152	770.77	Temperature	0377	906.47	Window
	0160	775.60	Window	0388	913.11	Window
	0161	776.20	Window	0439	943.87	Window
	0162	776.80	Window	0514	989.10	Window
	0166	779.22	Window	0529	998.14	Window
	0169	781.03	Window	0531	999.35	Window
	0173	783.44	Window	0533	1000.56	Ozone
	0174	784.04	Window	0536	1002.37	Ozone
	0175	784.64	Window	0538	1003.57	Ozone
	0176	785.25	Window	0540	1004.78	Ozone
	0187	791.88	Window	0542	1005.99	Ozone
	0188	792.49	Window	0543	1006.59	Ozone
	0189	793.09	Window	0544	1007.19	Ozone
	0190	793.69	Window	0548	1009.60	Ozone
	0192	794.90	Window	0549	1010.21	Ozone
	0194	796.10	Window	0550	1010.81	Ozone
	0195	796.71	Window	0556	1014.43	Ozone
	0197	797.91	Window	0561	1017.44	Ozone
	0198	798.52	Window	0562	1018.05	Ozone
	0199	799.12	Window	0568	1021.67	Ozone
	0200	799.72	Window	0569	1022.27	Ozone
	0202	800.93	Window	0570	1022.87	Ozone
	0204	802.13	Window	0572	1024.08	Ozone
	0206	803.34	Window	0573	1024.68	Ozone
	0207	803.94	Window	0574	1025.28	Ozone
	0208	804.55	Window	0576	1026.49	Ozone
	0210	805.75	Window	0577	1027.09	Ozone
	0214	808.17	Window	0578	1027.70	Ozone
	0222	812.99	Window	0579	1028.30	Ozone
	0224	814.20	Window	0580	1028.90	Ozone
	0242	825.05	Window	0581	1029.51	Ozone

Acknowledgements Olivier Coopmann is funded by the EUMETSAT Research Fellowship programme. The authors thank Bertrand Théodore of EUMETSAT for providing me with the geometric specification of the IRS observations and Jérôme Vidot of CEMS (Centre d' Études en Météorologie Satellitaire) for the rapid availability of a new set of IRS coefficients on the new spectral resolution.