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Contributors: Hans Gleisner (DMI), Rory Gray (UKMO), Jan El Kassar (FUB), Nabiz Rahpoe (DWD)

Issued by: SMHI/Karl-Göran Karlsson
Date: 31/12/2020
Ref: C3S_D1.6.1-2020_202012_TRGAD_WV_v1
Official reference number service contract: 2018/C3S_312b_Lot1_DWD/SC1

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History of modifications

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Version Date Description of modification Chapters / Sections V1 05.02.2021 First version All

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Related documents

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Reference ID Document Anchor D1 D1 D1 ROM SAF Algorithm Theoretical Baseline Document: Level 3 Gridded Data, version 4.1, Ref: SAF/ROM/DMI/ALG/GRD/001 http://www.romsaf.org/product_documents/romsaf_atbd_grd.pdf Anchor D2 D2 D2 ROM SAF Product User Manual: Level 3 Gridded Data, version 2.6, Ref: SAF/ROM/DMI/UG/GRD/001 http://www.romsaf.org/product_documents/romsaf_pum_grd.pdf Anchor D3 D3 D3 Algorithm Theoretical Basis Document, UTH Edition 1.0, Upper Tropospheric Humidity TCDR, CM-14711 (SAF/CM/UKMO/ATBD/UTH) https://www.cmsaf.eu/SharedDocs/Literatur/document/2019/saf_cm_ukmo_atbd_uth_1_3_pdf.html Anchor D4 D4 D4 Report on Updated KPIs. Key Performance Indicators (KPIs) Anchor D5 D5 D5 ROM SAF : Algorithm Theoretical Baseline Document: Level 2A refractivity profiles, version 1.6, Ref: SAF/ROM/DMI/ALG/REF/001 http://www.romsaf.org/product_documents/romsaf_atbd_ref.pdf Anchor D6 D6 D6 ROM SAF Algorithm Theoretical Baseline Document: Level 2B and 2C 1D-Var Products, version 3.1, Ref: SAF/ROM/DMI/ALG/1DVAR/002 http://www.romsaf.org/product_documents/romsaf_atbd_1dvar.pdf Anchor D7 D7 D7 Product Quality Assurance Document, Microwave Upper Tropospheric Humidity (UTH) ICDR based on the UTH TCDR brokered from CM SAF, Ref: C3S_D2.3.10_201910_PQAD_v1.1.docx Upper tropospheric humidity gridded data from 1999 to present derived from satellite observations: Product Quality Assessment Report (PQAR) Anchor D8 D8 D8 Algorithm Theoretical Basis Document for TCWV_MERIS_SSMI Deliverable D1.3.2 latest version (see CDS documentation tab) Anchor D9 D9 D9 ValRep: Validation Report SSM/I and SSMIS products - HOAPS version 4.0 DOI: 10.5676/EUM_SAF_CM/HOAPS/V002 ValRep_SSM/I_SSMIS_HOAPS_4.0 Anchor D10 D10 D10 CM SAF ATBD: Algorithm Theoretical Baseline Document - HOAPS version 4.0 DOI: 10.5676/EUM_SAF_CM/HOAPS/V002 ATBD_CMSAF_HOAPS_4.0

Acronyms

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Acronym Definition AMSU Advanced Microwave Sounding Unit AMR Advanced Microwave Radiometer on Jason-2 AMSR Advanced Microwave Scanning Radiometer ATMS Advanced Technology Microwave Sounder ATBD Algorithm Theoretical Basis Document AVHRR Advanced Very High Resolution Radiometer CAWA Cloud Aerosol and Water Vapor algorithm CDR Climate Data Record CLASS Comprehensive Large Array-Data Stewardship System CM SAF Satellite Application Facility on Climate Monitoring COSMIC Constellation Observing System for Meteorology, Ionosphere and Climate cRMSD Centered (or Bias-Corrected) RMSD C3S Copernicus Climate Change Service DMSP Defense Meteorological Satellite Programme (USA) DWD Deutscher Wetterdienst (Germany's National Meteorological Service) ECMWF European Centre for Medium-range Weather Forecasts ECT Equator Crossing Time ECV Essential Climate Variable ENVISAT Environmental Satellite (ESA) EPS EUMETSAT Polar System EPS-SG EPS Second Generation ERA-Interim a global atmospheric reanalysis produced by the European Centre for Medium‐Range Weather Forecasts ESA European Space Agency EUMETSAT European Organization for the Exploitation of Meteorological Satellites FCI Flexible Combined Imager FIDUCEO Fidelity and uncertainty in climate data records from Earth Observations FTH Free Tropospheric Humidity FUB Freie Universitet Berlin FY Feng-Yun satellites (China) GCOS Global Climate Observing System GCOM-W Global Change Observation Mission for Water GMI GPM Microwave Imager GNSS Global Navigation Satellite System GPM Global Precipitation Measurement GPS Global Positioning System GRAS GNNS Receiver for Atmospheric Sounding GRM Geopotential Research Mission HOAPS The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data record IASI (-NG) Infrared Atmospheric Sounding Interferometer (Next Generation) ICDR Interim Climate Data Record IR Infrared (spectrum) Jason Joint Altimetry Satellite Oceanography Network JAXA Japan Aerospace Exploration Agency JPSS Joint Polar Satellite System KPI Key Performance Indicator MERIS Medium Resolution Imaging Spectrometer MetOp-SG Metop satellite – second generation MHS Microwave Humidity Sounder MODIS Moderate Resolution Imaging Spectrometer MTG Meteosat Third Generation MW Microwave MWI Microwave Imager on EPS-SG MWHS Microwave Humidity Sounder MWR Microwave Radiometer (Sentinel-3) MWS Microwave Sounder NASA National Aeronautics & Space Administration netCDF Network Common Data Format NIR Near-infrared (spectrum) NOAA National Oceanic and Atmospheric Administration (United States) NRL Naval Research Laboratory NWP SAF Numercial Weather Prediction Satellite Application Facility OISST Optimum Interpolation Sea Surface Temperatures OLCI Ocean and Land Colour Instrument OSCAR Observing Systems Capability Analysis and Review Tool RO Radio Occultation ROM SAF EUMETSAT Satellite Application Facility for Radio Occultation Meteorology RMSD Root-mean-squared deviation RTTOV Radiative Transfer for TOVS SNPP Suomi National Polar-orbiting Partnership SNR Signal to Noise Ratio SRAL Synthetic Aperture Radar Altimeter (Sentinel-3) SSM/I Special Sensor Microwave/Imager SSMIS Special Sensor Microwave Imager / Sounder SSM/T Special Sensor Microwave/Temperature TCDR Thematic Climate Data Record TCWV Total Column Water Vapor (also Integrated or Precipitable Water Vapor) TCWV_GV TCWV dataset produced by the ESA GlobVapour project. THP Tropospheric Humidity Product TIROS Television Infrared Observation Satellite TOVS TIROS Operational Vertical Sounder TRGAD Target Requirements and Gap Analysis Document QZSS Quasi-Zenith Satellite System UTH Upper Tropospheric Humidity WMO World Meteorological Organization

General definitions

The meaning of the terms uncertainty, accuracy and error is often difficult to interpret and may be treated differently in various referred documents. In this document we adopt the following interpretation:
The accuracy, uncertainty or error of an estimated ECV is described by three differently contributing components:

1. The systematic error
2. The random error
3. The time-dependent error

The systematic error is commonly the mean error or the Bias. For non-Gaussian distributions of the error the median or the mean absolute error can be a more useful quantity.

The random error is commonly the root-mean-squared deviation RMSD. Sometimes the Bias is subtracted yielding the centered root-mean-squared deviation cRMSD. Notice that if the Bias is zero the two mentioned quantities are equal and may be interpreted as the standard deviation of the error (often denoted standard error).

The time-dependent error is commonly the change in Bias over time (for ECVs over decades). We call this parameter stability.

More details on the estimation of these parameters are given in the Report on Updated KPIs (D4, e.g. section 2.1).

Scope of the document

This document provides relevant information on requirements and gaps for retrievals of atmospheric water vapour contents based on data from four different sources:

1. Radio occultation measurements
2. Microwave humidity sounders
3. Microwave imagers
4. Combined near infrared and microwave imagers.

The document is divided into three parts. Part 1 describes the products the present document refers to. Part 2 provides the target requirements for the products. Part 3 provides a past, present, and future gap analysis for the products and covers both gaps in the data availability and scientific gaps that could be addressed by further research activities (outside C3S).

Executive summary

Water Vapour products derived from radio occultation measurements – tropospheric humidity profiles (THP)
The Water Vapour THP TCDR product (v1.0, covering the period December 2006 – December 2016) and the associated ICDR product (starting in January 2017 with regular updates thereafter) are described together with target requirements and validation method descriptions. The target requirement for the TCDR product consists of a 3 % mean error requirement on the monthly mean values. No stability requirement has been defined yet, because measurements based on radio occultation only cover a relatively short period at present. The associated ICDR product is required to be consistent with the TCDR, which is formally checked through a binomial test.

An extensive description of past, current and future availability of radio occultation data and radio occultation measurements from low earth orbit (LEO) satellites is given. These measurements will continue well into the mid-2030s and even longer. Currently achieved retrievals of humidity profiles are associated with a small negative bias in the lower troposphere. Some positive biases are seen for higher elevations which mainly are explained by the use of background temperatures from ERA-Interim near the tropopause. Atmospheric sounding based on GNSS-RO measurements is still a field of active research. Further improvements are partly linked to the availability of improved background data from new reanalysis efforts. Also, development of methods for better bias corrections is needed.

Water Vapour products derived from microwave humidity sounders – upper tropospheric humidity (UTH)
The Water Vapour UTH_MW TCDR v1.0, covering the period 1999-2015, is described together with target requirements. The UTH mean absolute difference requirement is set to 5% with a corresponding stability requirement of 1% per decade.

A description of past, current and future availability of the upper tropospheric humidity channel near 183 GHz is given. The continuation of these measurements is well covered by several satellites and instruments and no data gaps are foreseen. The UTH product has some limitations in areas where the atmosphere is dry and cold, e.g. over mountains and poleward of 60 degrees latitude. Some improvements and developments are necessary for reducing these uncertainties.

The Water Vapour UTH ICDR product continues the time series of the TCDR dataset. The latest ICDR dataset delivery covers the period from 01/01/2016 to 31/08/2020. Key performance indicator (KPI) targets for the ICDR were set by determining the limits of the 2.5 and 97.5 percentiles of the differences between the UTH TCDR and the equivalent reference dataset derived from ECMWF's ERA Interim global atmospheric reanalysis (to monitor performance for the period up to 31/12/2018), and from the differences between the UTH ICDR during 2018 and the equivalent reference dataset derived from ECMWF's ERA-5 global atmospheric reanalysis (for performance monitoring from 01/01/2019 onwards)[D4, section 3].

Water Vapour products derived from combined NIR and microwave imager measurements (total column water vapour (TCWV_GV)
The total column water vapour TCWV_GV v1.0 is presented together with target requirements. The target goals are set at a cRMSD of 2 kg/m², a Bias of 1.4 kg/m² and bias trend of 0.2 kg/m²/decade, relative to reference dataset.

We show the past, present and future availability and capabilities of both the microwave imager SSM/I SSMIS and the water vapor sensitive NIR imager MERIS and successor OLCI. The TCDR is running from 2002 to 2012. OLCI is in orbit since 2016. The gap between 2012 and 2016 could be bridged with MODIS onboard the Aqua satellite. Furthermore, the start of the TCDR could be extended to 1999.

Dark surfaces, especially water surfaces, pose some minor difficulties for TCWV retrieval in the NIR. Also, daily or higher temporally resolved TCWV cannot be obtained with current polar orbiting satellites. A future outlook for the use of Sentinels (apart from OLCI onboard Sentinel 3) as well as systems such as IASI and MTG and their benefits to a future TCWV dataset, is outlined.

Water Vapour products derived from microwave imager measurements – total column water vapour (TCWV SSM/I & SSMIS)
The total column water vapour TCWV based on the HOAPS 4.0 algorithm is presented. The target requirements are the same as for the TCWV_GV.

The TCDR covers the period from 1988 to 2014 over ice-free ocean surfaces. The product is composed of monthly means and 6-hourly composites. The SSM/I and SSMIS sensors have been on different platforms, in the Defense Meteorological Satellite Program (DMSP) (F8-F19) from 1987 onwards and are still operational. Hence a long-term and homogeneous dataset based on same sensor configuration with a length of 33 years could be available. Unfortunately, the DMSP program has been stopped and the previously planned F20 satellite will not be launched. A possible gap might occur between the end of the lifetime of the current operating and utilised satellites (F16, F17 and F18) of microwave imagers and the launch of upcoming microwave imagers.

1. Product description

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1.1 Water Vapour THP TCDR v1.0

The Water Vapour Tropospheric Humidity Profile (THP) TCDR product consists of monthly mean values of tropospheric specific humidity covering the time period December 2006 to December 2016. The data product is provided on a monthly mean latitude-altitude grid (5˚ by 200 meters), with a global coverage and extending from the surface up to 12 km.

The monthly-mean gridded data are generated by the EUMETSAT ROM SAF from measurements made by the GRAS Radio Occultation (RO) instruments onboard the Metop polar-orbiting satellites. From observed atmospheric refractivity profiles, and background information from the ERA-Interim reanalysis, near-vertical tropospheric humidity profiles are retrieved through a 1D-Var approach. The retrieved humidity profiles are averaged into monthly means at the global latitude-altitude grid. Compared to other satellite observational techniques, RO data have high vertical resolution and the observations are not affected by clouds or the underlying surface (e.g., no land-sea differences).

1.2 Water Vapour THP ICDR v1.x

The Water Vapour Tropospheric Humidity Profile (THP) ICDR product consists of monthly mean values of tropospheric specific humidity. The data record starts in January 2017 and is regularly updated. It has the same characteristics (resolution, coverage, etc.) as the corresponding TCDR product described in Section 1.1.

The monthly-mean gridded ICDR product is generated by the EUMETSAT ROM SAF. It is an interim data product, generated from the same instruments as the corresponding TCDR product, using the same algorithms applied to data available at the time of the regular ICDR processing. The ICDR time series is commonly used to extend the TCDR time series. The ICDR processing initially used background data from ERA-Interim, i.e., similar to the TCDR. However, a switch of background data to ERA5 was done from August 2019 and onwards due to the termination of ERA-Interim processing at ECMWF.

1.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

The upper tropospheric humidity (UTH) TCDR product is produced by the Met Office from the microwave humidity sounding instruments AMSU-B and MHS. The TCDR covers the period 1999-2015. UTH is Jacobian weighted relative humidity in the upper troposphere. Measurements of equivalent blackbody brightness temperature in a water vapour channel at 183.31±1.00 GHz which probes the upper troposphere are approximately linearly dependent on the natural logarithm of the UTH. The coefficients used in the relationship between the logarithm of UTH and the brightness temperature of upper tropospheric water vapour emissions are determined by linear regression, using a training data set of atmospheric temperature and humidity profiles. The retrieval method is described in detail in reference document [D3] and it is based on previous works by Soden and Bretherton (1993), Buehler and John (2005) and Buehler et al. (2008).

The main advantage of microwave based UTH is the availability of almost all-sky data, whereas infrared data sample only clear-sky areas.

Threshold masks are applied in order to identify and remove measurements contaminated by thick clouds, for example due to scattering by large ice particles in the presence of deep convection or precipitation, and measurements contaminated by the surface. A correction is applied to account for the limb darkening effect.

The UTH product is provided in NetCDF-4 files on a global, daily, 1^o x 1^o latitude-longitude grid. Both ascending and descending UTH fields and the daily mean UTH, found by averaging the two daily overpasses, are provided for each grid cell, as well as the median, standard deviation, and information such as the number of measurements used and discarded due to cloud or surface contamination.

The UTH ICDR is a continuation of the TCDR product. The latest ICDR dataset delivery covers the period from 01/01/2016 to 30/08/2020. Regular updates will follow.

1.4 Water Vapour TCWV_GV TCDR v1.0

The Total Column Water Vapor (TCWV) GV product is a monthly mean product on a regular grid, available at low resolution (0.5° x 0.5°) and high resolution (0.05° x 0.05°). The integrated water vapor is retrieved with two different sensors. Over open ocean, the Special Sensor Microwave Imager (SSM/I) is used. Over land, coastal and inland waters, the Medium Resolution Imaging Spectrometer (MERIS) is used. MERIS is an instrument on a single satellite (ENVISAT) that flew for 10 years. SSM/I (and its successor SSMIS) flew on several different platforms in the Defense Meteorological Satellite Program (DMSP).

SSM/I TCWV data has been processed with the Hamburg Ocean and Atmosphere Parameters and Fluxes from Satellite (HOAPS) algorithm version 4.0 and was provided to FUB by DWD. SSM/I cannot retrieve water vapor over land surfaces and over areas with strong precipitation. SSM/I TCWV products have a resolution of approximately 25 km.

MERIS TCWV products were processed with the latest edition of the Cloud Aerosol and Water Vapor algorithm (CAWA - https://earth.esa.int/web/sppa/activities/multi-sensors-timeseries/cawa). MERIS TCWV is retrieved with the differential absorption technique at the water vapour absorption peak between 890 nm and 1000 nm with its center at 940 nm. TCWV is estimated from the ratio between the radiance at a band inside the absorption peak (absorption channel 15,  and the radiance at a band more outside of the peak (window channel)). The retrieval process is described in more detail in reference document [D8]. MERIS cannot retrieve TCWV over clouds or areas with an uncertain surface type. MERIS TCWV has a resolution of ca. 1 km.

MERIS only retrieves TCWV on the descending node with an equator crossing time (ECT) at 10:00 AM. Thus, SSM/I data were filtered for the descending nodes and with ECTs in the morning (5:00 to 9:00).

The monthly TCWV_GV products can be defined as the monthly average of fore-noon TCWV. In the case of MERIS grid boxes this is further defined as the monthly average of fore-noon clear sky TCWV. TCWV fields are achieved by calculating the error-weighted average over all daily composites of SSM/I and MERIS. Since the resolution of SSM/I is not fine enough for the high resolution, SSM/I grid boxes at 0.5° were upsampled 10 times with the nearest neighbor method to achieve a resolution of 0.05°.

1.5 Water Vapour TCWV SSM/I & SSMIS TCDR v1.0

The Total Column Water Vapor (TCWV) product provides monthly mean and daily values of four 6-hourly composites (00:00-06:00, 06:12:00, 12:00-18:00, and 18:00-23:59) on a regular grid, available at a resolution of 0.5° x 0.5° at Level 3. The integrated water vapor is a parameter from the Hamburg Ocean and Atmosphere Parameters and Fluxes from Satellite (HOAPS 4.0) algorithm version 4.0 and relies on measurements from SSM/I and its successor SSMIS (from 2005 onwards) flying on several DMSP platforms (see Table 1.1).

SSM/I cannot retrieve water vapor over land and ice surfaces and over areas with strong precipitation and hence it is limited over ice-free and open ocean surfaces. SSM/I and SSMIS TCWV products have a resolution of approximately 37 km x 28 km (footprint size of 37 GHz channel window) at Level 2.

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Table 1.1: Different sensors and the lifetime of SSM/I on different platforms on DMSP satellites (F8-F19).

2. User Requirements

2.1 Water Vapour THP TCDR v1.0

2.1.1 Summary of target requirements (KPIs)

The Water Vapour THP TCDR product consists of monthly mean values of tropospheric specific humidity. The data product is provided on a monthly mean latitude-altitude grid (5˚ by 200 meters), with a global coverage and extending from the surface up to 12 km.

The target requirements, as specified by the KPIs, consist of a 3% mean error requirement on the monthly mean values within the fundamental latitude-altitude bins (5˚ by 200 m). The requirement is tested by requiring that at least 60% of the observed bin values deviate by less than 3% from ERA-Interim. For each month, this test is applied to three broad latitude zones: tropics (30˚S–30˚N), mid-latitudes (30˚N–60˚N and 30˚S–60˚S), and polar latitudes (60˚N–90˚N and 60˚S–90˚S), and two tropospheric altitude intervals (0-8 km and 8-12 km), altogether six latitude-height regions (Figure 2.1).

2.1.2 Discussion of requirements with respect to GCOS and other requirements

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The GCOS 2016 Implementation Plan states a 5% measurement uncertainty (mean error) requirement on "tropospheric profiles of water vapour". Assuming that this requirement applies to monthly mean values, the accuracy requirement used for the C3S monthly-mean gridded humidity profiles is well in line with the GCOS requirements.

2.1.3 Data format and content issues

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This dataset provides monthly mean values of specific humidity in the troposphere, below an altitude of 12 km. The data are provided as monthly netCDF files containing the specific humidity on a global latitude-altitude grid as well as the monthly variability, and associated variables such as data numbers, sampling errors, and an estimate of the fraction of a priori (background model) information in the humidity data.

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Figure 2.1: The accuracy requirements applicable to the Water Vapour THP products are checked for each month and within six broad latitude-altitude regions here referred to as regions I to VI.

2.2 Water Vapour THP ICDR v1.x

2.2.1 Summary of target requirements (KPIs)

The consistency between the Water Vapour THP ICDR product, described in Section 1.2, and the corresponding TCDR product is checked by a test designed to detect certain type of differences between the ICDR and the TCDR [D4, section 3]. The relative differences between the monthly mean observed data and a reference data set are computed on a global latitude-height grid, for both the ICDR and the TCDR. These relative differences are globally averaged (properly area weighted) and vertically averaged (in 0-4 km, 4-8 km, and 8-12 km layers).

For each vertical layer, we find the 2.5% and 97.5% percentiles of the TCDR differences. These percentiles are used in a binomial test to check whether the corresponding ICDR differences are consistent with the TCDR differences. Table 2.1 shows the actual values used for the limit percentiles in the binomial test.

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Table 2.1: Water Vapour TPH ICDR target requirements

The ICDR used ERA-Interim as reference data set initially but from August 2019 and onward ERA5 is the reference.

Discussion of requirements with respect to GCOS and other requirements see Section 2.1.2.

2.2.2 Data format and content issues

See Section 2.1.3. However, unlike the TCDR, which is generated in a reprocessing activity, the ICDR is regularly updated, currently on a quarterly basis.

2.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

2.3.1 Summary of target requirements (KPIs)

The following two tables summarize target requirements for the TCDR and the ICDR:

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Table 2.2: Water Vapour UTH TCDR target requirements

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Table 2.3: Water Vapour UTH ICDR target requirements

2.3.2 Discussion of requirements with respect to GCOS and other requirements

The target requirements applicable for the UTH TCDR were mainly derived from the GCOS Implementation Plan (IP). According to the GCOS IP, the measurement uncertainty for UTH (expressed as the mean error) should be 5% and the stability per decade should be 0.3%. These values are very close to the optimal accuracy values for Free Tropospheric Humidity (FTH) retrieved from the water vapour channel near 6.3 µm on Meteosat geostationary imagers with values 2% for the bias and 0.26% for decadal stability.

The GCOS IP also states that the frequency of UTH observations should be 1 h, with a spatial resolution of 25 km, but these values are not achievable from polar orbiting MW humidity sounders. Instead, the product is given as daily gridded observations of 1 degree for ascending and descending orbits separately, together with their combined average, for each satellite.

For the UTH ICDR, a reference dataset of the equivalent UTH was derived using the NWP SAF radiance simulator, which constitutes an interface to the radiative transfer model of the NWP SAF RTTOV. RTTOV estimates the brightness temperature based on ERA-Interim input, which is then converted to UTH as is done for the microwave sounder observations, as described in reference document [D3]. KPIs were defined in terms of deviations of the TCDR UTH from the ERA-Interim reanalysis reference dataset, by the methods described in reference document [D4, section 3 and 4.2.5]. Reference performance targets were set by determining the UTH limits of the 2.5 and 97.5 percentiles of the differences between the UTH TCDR and the ERA-Interim derived reference data set. For any of the satellites, no more than 2.5% of the TCDR data was found to have a UTH value more than 0.47% below the reference UTH, and no more than 2.5% of the TCDR UTH data was more than 0.55% higher than the reference UTH. These limits were taken as the KPI values (see Table 2.2).

In 2019, when the ERA_Interim reanalysis dataset became unavailable, new KPIs were redefined for the MetOp-A and MetOp-B satellites by determining the UTH limits of the 2.5 and 97.5 percentiles of the differences between the UTH ICDR and the ERA-5 derived reference data set during the year 2018. This was done in the same way as had been done previously with the TCDR UTH and ERA-Interim reanalysis differences, as described and specified in document [D4, section 4.2.5]. For either of the satellites MetOp-A and MetOp-B, no more than 2.5% of the ICDR data was found to have a UTH value less than 0.40% above the reference UTH, and no more than 2.5% of the ICDR UTH data was more than 1.57% higher than the reference UTH. These limits were taken as the updated KPI values for the MetOp satellites against which to measure performance for data from 01/01/2019 onwards.

Comparison of the ICDR dataset against the reference dataset using the defined KPIs confirms that, for MetOp-A,-B and NOAA-18, until 31/12/2018 the ICDR reference dataset differs from the reference dataset in the same way that the TCDR dataset does, and for MetOp-A and MetOp-B, from 01/01/2019 onwards, the ICDR dataset differs from the reference dataset in the same way that it had done throughout the year 2018.

2.3.3 Data format and content issues

Orbital drift can impact the ascending arc only or descending arc only derived UTH. The local equator crossing time for NOAA satellites drifts with time. In order to minimize the complications from the diurnal cycle of UTH, daily means of UTH are created. The averaging is performed in UTH space. Ascending and descending 1 degree grid boxes are combined to create daily averages. Only grid boxes with a non-zero number of UTH retrievals for both ascending and descending grid boxes are considered.

The relevant MHS channel on NOAA-19 (channel 3) has shown erratic behaviour since July 2009, and should not be used.

2.4 Water Vapour TCWV_GV TCDR v1.0

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2.4.1 Summary of target requirements (KPI)

The KPIs for the TCWV_GV TCDR product are compared against values which have been set by the CM SAF project in the requirements review for HOAPS-4 TCWV. They can be found in Table 2.4 and are divided into three categories: threshold (minimum usefulness), target (significant improvement compared to threshold) and optimal (no improvement necessary). The KPIs have been put together from requirements established in GCOS, WMO OSCAR and other literature.

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Table 2.4: KPIs for the Water Vapour TCWV_GV TCDR as defined by CMSAF.

GCOS also states that the frequency of TCWV observations should be 4 h, with a spatial resolution of 25 km. While this resolution is barely achievable with a microwave imager such as SSM/I (footprint approx. 50 km with 25km spot spacing), MERIS (and other NIR imagers with similar band configurations) have resolutions of approx. 1 km or higher. Thus, the required resolution is achievable.

However, the temporal resolution of 4 h is not achievable in the current setup of polar-orbiting satellites. Even so the daily composites of MERIS consist of single measurements (at the poles multiple measurements) per day. SSM/I and SSMIS could provide a higher number of values per day. For consistency within the TCWV_GV dataset we chose to limit this to fore-noon observations.

This yields daily composites with varying coverage gaps. MERIS TCWV gaps are larger due to just having one satellite, compared to two to three DMSP satellites flying at the same time.

Reasonable goals would be three-day averages which could provide near-global coverage over land and ocean. Right now, we can provide monthly means which despite gaps in the daily composites provide a good representation of the monthly TCWV.

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2.5 Water Vapour TCWV SSM/I & SSMIS TCDR v1.0

2.5.1 Summary of target requirements (KPIs)

For the TCWV SSM/I & SSMIS, the target requirements are the same as for TCWV_GV (Section 2.4.1).

2.5.2 Discussion of requirements with respect to GCOS and other requirements

SSM/I and SSMIS provide a higher number of observations and overpasses per day of the 6-hourly composites which fulfills the GCOS observation frequency requirement of 4 h covered by different DMSP satellites, otherwise no global coverage would be possible for single satellite overpass. An example of the footprint (swaths) of the satellite measurements of one 6-hourly composite is shown in Figure 2.2.

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Figure 2.2: Swaths of different DMSP satellite overflights of one 6-hourly composite for 1^st of December 2007.

On the other hand, the spatial resolution of 50 km x 50 km at the equator, does not fulfill the spatial resolution requirement set by GCOS, which is the only exception.

2.5.3 Data format and content issues

The dataset is delivered in netCDF format with different variables of the total column water vapour, standard deviation, number of observations, number of the daily observations and satellite bit mask to discern between different satellite measurements.

3. Gap Analysis

3.1 Description of past, current and future satellite coverage

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3.1.1 Water Vapour THP TCDR v1.0

The Water Vapour THP TCDR is based on soundings of the Earth's atmosphere by RO instruments onboard satellites in low-Earth orbit. The RO technique exploits measurements of the amplitude and carrier phase of signals from Global Navigation Satellite System (GNSS) satellites, as they set or rise at the Earth's limb. Most RO sounding instruments up to date have been based on signals from the Global Positioning System (GPS), but there are also instruments that can utilize signals from the European Galileo system, the Chinese Beidou system, and the Japanese Quasi-Zenith Satellite System (QZSS). RO measurements made by scientific satellite missions have been available since 2001, while operational or quasi-operational RO measurements started in 2006.

The most important of the operational missions are:

• COSMIC-1: Six-satellite constellation launched in 2006. It is presently (2010) near its end of life, with only a single satellite remaining operational. Precessing satellite orbits ensure full coverage of local time over a time period of several months.

• EPS: The EUMETSAT Metop satellites were launched in 2006, 2012, and 2018. The satellite orbits are Sun-synchronous with ascending node 09:30.

• FY-3C: Chinese satellite launched in 2013. The first in a planned series of operational RO missions. Sun-synchronous satellite orbit with ascending node 22:00.

• COSMIC-2: Six-satellite constellation launched in 2019. Precessing low-inclination satellite orbits, with coverage of low- and mid-latitudes only.

• EPS-SG: The EUMETSAT Metop-SG satellites are planned for a first launch in 2023 (two satellites). Follow-on launches will ensure continuity into the early 2040s.

• Sentinel-6: The Sentinel-6 mission (also referred to as Jason-CS) consists of two satellites, one launched in 2020 and the other scheduled for launch in 2026. Precessing medium-inclination satellite orbits. RO instrument with heritage from the COSMIC-2 mission.

These operational missions, run by governmental or inter-governmental agencies, ensure the availability of RO data well into the mid-2030s and even longer. We can also foresee a number of RO research missions and possibly also commercial missions that will provide data in addition to the operational missions. The data numbers will vary considerably during the next decade, being temporarily boosted by the COSMIC-2 mission. The future of the commercial missions, and of the technical and commercial developments of nano-satellite systems, is currently uncertain. However, long-lasting data gaps are very unlikely during the next 20 years.

Despite the availability of RO data, there may be limitations related to coverage of local solar time and, hence, the diurnal cycle. Operational, meteorological satellites are preferentially placed in Sun-synchronous orbits, with limited local-time coverage. The operational satellite missions are important as "backbone", but missions with other type of orbits provide valuable information on the diurnal cycle, which is important when generating RO-based climatologies.

3.1.2 Water Vapour THP ICDR v1.x

The Water Vapour THP ICDR products are based on data from the same instruments and satellites as the corresponding TCDR. See Section 3.1.1.

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3.1.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

At microwave frequencies, the upper tropospheric humidity channel is located at 183.31±1.00 GHz for the SSM/T-2, AMSU-B, and MHS instruments. These instruments have been in orbit since 1992 and are planned to continue for decades through instruments such as ATMS on JPSS/SNPP/NOAA-20 and subsequent NOAA satellites, MWHS on the FY series, and MWS on MetOp-SG. No data gaps are foreseen.

3.1.4 Water Vapour TCWV_GV TCDR v1.0

The microwave imagers (SSM/I and since 2005 the successor SSMIS) onboard the DMSP satellites are providing TCWV over open ocean since 1987. The latest addition to the satellite family DMSP F19 stopped providing useful data in 2016. EUMETSAT is providing continuity of the SSMIS time series with the envisioned launch of MWI in 2023.

The MERIS instrument only flew on the ENVISAT satellite from 2002 until the mission end in 2012. Consequently, this mission period is limiting the TCWV_GV TCDR time series.

The successor of MERIS, the Ocean and Land Colour Instrument (OLCI), is in orbit since 2016 and 2018 onboard Sentinel 3-A and –B respectively. These imagers could be used to extend the time series. They have the same ECT and band configuration like MERIS. The gap between 2012 and 2016 could be bridged by using the Moderate Resolution Imaging Spectrometer (MODIS) onboard the Terra satellite. MODIS Terra has a similar band configuration and ECT of 10:30 AM UTC compared to MERIS. Diedrich et al. (2014) have shown that the approach used for MERIS TCWV is working with MODIS as well.

The continuity of the OLCI instrument is assured by COPERNICUS with launches of Sentinel 3-C and –D in 2022 and 2023.

The next generation of EUMETSATs geostationary satellites, Meteosat Third Generation (MTG) and the successor of the Advanced Very High Resolution Radiometer (AVHRR) series – METImage – will be operational from 2020/2021/2022. Both provide a single band in the H2O absorption band between 890 nm and 1000 nm. Thus, TCWV_GV processing is directly applicable to these instruments as well.

3.1.5 Water Vapour TCWV SSM/I & SSMIS TCDR v1.0

The microwave imagers (SSM/I and since 2005 the successor SSMIS) onboard the DMSP satellites are providing TCWV over open ocean since 1987. The latest addition to the satellite family DMSP F19 stopped providing useful data in 2016. EUMETSAT is providing continuity of the SSMIS time series with the envisioned launch of AMSR3 on GOSAT-GW in 2023 and MWI on EPS-SG in 2023.
There are several additional passive microwave radiometers (imagers and sounders), which are currently operational and are listed as follows (not mentioned: Russian and Chinese microwave sensors):

Imagers (Conical scan):

• WindSat – Navy NRL Coriolis (37-GHz only)
• AMSR-2 – GCOM-W1-Japan (JAXA)
• GMI-JAXA/NASA

Sounder (Cross-track scan):

• AMSU-A/B – 6 satellites (Aqua, NOAA 18/19) and European MetOP-A/B/C
• AMR-JASON 2
• AMR-C-Sentinel 6

These microwave imagers/sounders can potentially be used to fill the possible gap between the last measurement of SSMIS sensors, before the outage, and the launch of AMSR3 in 2023 and MWI in 2024.

3.2 Development of processing algorithms

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3.2.1 Water Vapour THP TCDR v1.0

Atmospheric sounding using GNSS-RO measurements is a field of active research. The measurement technique itself, and the processing methods employed, are still in a state of development. The tropospheric humidity retrieval starts with the phase and amplitude of GNSS radio signals as measured by a GNSS receiver onboard a satellite. The retrieval of humidity profiles includes two steps: a) the bending (refraction) angles are computed using a wave optics method, followed by retrieval of the atmospheric refractivity through an Abel integral [D5], and b) vertical profiles of specific humidity are calculated by combining the observed refractivity with information from a priori (background) data taken from an atmospheric model [D6]. The combination of observations and background is done by means of a 1D-Variational (1D-Var) optimization algorithm. The retrieval of humidity profiles is followed by averaging into sampling-error corrected gridded monthly mean data [D1].

The observed refractivity profiles often tend to be negatively biased in the moist lower troposphere. This leads to biases in the humidity climatologies. Technically, the problem is related to challenges in tracking the signals from the GNSS satellites under turbulent conditions in the lower troposphere, as well as limitations in the retrieval algorithms under such conditions. A related phenomenon known to introduce errors into the observed refractivity profiles, leading to errors in the humidity retrievals, is ducting (also known as super-refraction) associated with the planetary boundary layer. There are ongoing development activities aimed at improved identification of ducting situations, and better methods for reducing the impacts of negative refractivity bias within the planetary boundary layer.

As mentioned above, the humidity retrievals require a priori (background) data. The retrievals rely on the availability of background data with accurate error characteristics and a vertical resolution that match the RO observations. The background data is most commonly taken from an atmospheric model, e.g., a numerical weather prediction model or a reanalysis.  In the current ROM SAF processing for humidity TCDRs and ICDRs, ECMWF reanalyses are used as background. TCDR v1.0 and ICDR v1.0 use ERA-Interim, while ERA5 is used for ICDR v1.1 starting in August 2019. The next versions of TCDR and ICDR will only use ERA-5.

The RO retrieval methods are still evolving. For the generation of TCDRs, where temporal stability has high priority, filtering out of bias shifts and long-term variability in the background data is being investigated. Also, other types of development activities aimed at improving the temporal stability are foreseen.

3.2.2 Water Vapour THP ICDR v1.x

The Water Vapour THP ICDR data products are based on the same processing algorithms as the corresponding TCDR, and any development activities apply equally to both. See Section 3.2.1.

3.2.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

Although the UTH product is provided globally, there is some unreliability polewards of 60 degrees latitude. The atmosphere there is usually very dry, which means that the 183.31±1.00 GHz channel does not truly sense the upper troposphere, but mostly the lower levels of the troposphere. Furthermore, the training dataset used in the generation of coefficients for the transformation from brightness temperature to UTH was limited in Polar Regions.

Another limitation is the linear approximation for the correction of the limb darkening effect, which is also based on a training dataset.

Since July 2019, the ERA-Interim reanalysis used in the validation of the UTH TCDR and ICDR products has been replaced by ERA-5. This has necessitated a change in the validation dataset since that date. Validations have been performed using ERA-5 as the reference dataset against which to measure performance using new KPIs that were derived in the same manner as had been done using ERA-Interim, as checks over a period of overlap during 2018, during which both ERA-Interim and ERA-5 data are available, showed significant differences between using the different reanalysis datasets during that time. Since 01/01/2019, performance monitoring has been carried out against the KPIs derived with respect to the ERA-5 equivalent UTH.

3.2.4 Water Vapour TCWV_GV TCDR v1.0

Currently there are no plans to change the core of the algorithm. Further validation of the algorithm with ground truth will be used to insert some correction of possible systematic offsets. The only part of the algorithm that is further refined is the retrieval of TCWV over water since it is the most difficult surface to retrieve TCWV with the differential absorption technique. Better aerosol retrievals will improve the TCWV product over water substantially and decrease uncertainty.

3.2.5 Water Vapour TCWV SSM/I & SSMIS TCDR v1.0

The HOAPS 4.0 algorithm is based on a 1D-Var retrieval (Chap. 3 [D10]). Currently, there are developments on using neuronal network methods for evaluation of the TCWV and replacing the 1D-Var in future.  

3.3 Methods for estimating uncertainties

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3.3.1 Water Vapour THP TCDR v1.0

The Water Vapour THP TCDR product consists of monthly means of tropospheric specific humidity profiles, generated by a relatively straight-forward binning-and-average technique. The error of the monthly mean is assumed to be caused by two effects. First, each measurement has a random measurement error associated with it. This error is described in terms of a statistical uncertainty. Secondly, the finite number of measurements is not able to fully account for all variability within the latitude bin and time interval, resulting in a sampling error. Unlike the measurement errors, it is possible to estimate the actually realized sampling errors in the monthly means. This allows us to make a correction of the observed means, leaving a residual sampling error. The residual sampling error is assumed to be random, and is described in terms of a statistical uncertainty.

The measurement uncertainty of the individual humidity profiles is obtained from the formal errors resulting from the 1D-Var retrievals [D6]. The measurement uncertainty of the mean is then obtained under the assumption that the humidity profiles in a latitude-month bin have uncorrelated errors, and also taking the weighting applied to the profiles into account. This is described in detail in [D1].

The sampling errors are estimated by sub-sampling an atmospheric model (currently, the ERA-Interim reanalysis) at the observed times and locations [D1]. Based on these estimates, we do a sampling-error correction by subtracting the estimated sampling errors from the observed means, leaving only residual sampling errors. The uncertainties related to the residual sampling errors are estimated as a certain fraction of the original estimated sampling errors.

The measurement uncertainties and the uncertainties due to the residual sampling errors are finally combined to a total uncertainty for the monthly mean [D1]. The two components of uncertainties in the monthly means are provided together with the gridded monthly-mean data products. In principle, there is also a structural uncertainty due to algorithmic choices and underlying processing assumptions, but these are currently not provided together with the data. Neither is there any information on error covariances (error correlations) in the gridded data, which may be requested by users in the future. However, it should be noted that very few ECVs are currently provided with that type of detailed error (uncertainty) descriptions.

3.3.2 Water Vapour THP ICDR v1.x

The same methods for estimating the uncertainties are used for the Water Vapour THP ICDRs as for the corresponding TCDRs. See Section 3.3.1.

3.3.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

Uncertainty estimates, e.g. of instrument noise, are not explicitly considered at present. The standard deviation of the product in a grid box provides an initial basic estimation in terms of the UTH uncertainty, including the natural variability of the UTH field within the grid box.

3.3.4 Water Vapour TCWV_GV TCDR v1.0

TCWV_GV is based on a 1D-Var retrieval, both in the case of the SSM/I (and SSMIS) TCWV from the HOAPS-4 algorithm and MERIS TCWV algorithm. 1D-Var retrievals are characterized by a linear error propagation which takes into account instrument uncertainties and characteristics (i.e. SNR) as well as uncertainties in auxiliary data (i.e. a priori knowledge) and atmospheric and surface conditions (i.e. aerosol load, surface brightness). The error propagation yields reliable error estimates for each retrieved value.

3.3.5 Water Vapour TCWV_SSMI/SSMS TCDR v1.0

The TCWV HOAPS 4.0, by using SSM/I (and SSMIS) measurements, is based on a 1D-Var retrieval. The 1D-Var retrievals are characterized by a linear error propagation which takes into account model errors, instrument uncertainties and characteristics (i.e. SNR). Uncertainties in auxiliary data (i.e. a priori knowledge) and atmospheric state are also taken into account. For the latter, Look-Up-Tables of wind speed, near-surface humidity, sea surface temperature, and integrated water vapour were defined. Here,the systematic uncertainty component can be selected along with the random uncertainty component for describing the associated spread (Chap. 3.4 [D10]). For water vapour in HOAPS 4.0, currently the standard deviation is assigned as an uncertainty estimate for the derived total column values.

3.4 Opportunities to improve quality and fitness-for-purpose of the CDRs

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3.4.1 Water Vapour THP TCDR v1.0

Section 3.2.1 indicated some opportunities to improve the processing of RO measurements into profiles of tropospheric humidity. The aim of these improvements would be to reduce lower-tropospheric biases, but also to gain a better long-term temporal stability of the humidity time series. Any improvements of the humidity profile data lead to corresponding improvements of the monthly-mean humidity climatologies.

Reprocessing activities planned for by the EUMETSAT ROM SAF will utilize background data from new, modern reanalyses that are currently made available. The new background data will include improved error characterization. These developments are expected to lead to smaller systematic biases, better temporal stability, and more accurate description of the tropopause in future CDRs. In addition, filtering out of longer-term variability in the background data could be considered as a means to further improve the temporal stability.

Another line of development that can be foreseen as RO data numbers increase is the use of methods for global mapping of the RO data onto a latitude-longitude grid, instead of the currently available zonal latitude grid. Such methods exist but are currently not widely used.

3.4.2 Water Vapour THP ICDR v1.x

The opportunities to improve the CDRs apply equally to both the TCDRs and the ICDRs. See Section 3.4.1.

3.4.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

Since December 2019, MetOp-C data has been included in the NOAA Comprehensive Large Array-Data Stewardship System (CLASS) from which the data to generate the UTH products are downloaded and has been included and delivered as part of the UTH product since that time.

Concerning ATMS (on SNPP and NOAA-20 satellites), near real time data are being downloaded, and could be supplied in future UTH products under new contracts. Also, MWHS from the FY series could be included.

Other R&D in this area is being carried out at EUMETSAT, by EUMETSAT's CM SAF and by the EU FIDUCEO project, which are looking at Level 1B microwave humidity sounding data. CM SAF will also improve the UTH retrieval steps.

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3.4.4 Water Vapour TCWV_GV TCDR v1.0

TCWV from NIR imagers is an established product. However, there are many ways to improve the quality and applicability of this data set.

A major point to further research is the inter-satellite consistency. With inter-satellite consistency, i.e. between MERIS, MODIS and OLCI, data gaps could be bridged. The creation of a climate dataset ranging from 1999 until the present and near future would be possible with a single algorithm for different instruments.

At the same time, we still observe stratification of systematic errors due to specific environmental conditions: e.g. high, lifted aerosols above dark surfaces. The quantitative validation of the uncertainties with ground truth would certainly be beneficial to the final product's quality.

Additionally, further investigations are needed to quantify place and time dependent clear sky biases of NIR imagers.

With the inclusion of other platforms, especially future geostationary satellites, the TCWV from NIR will reach a new and higher level (see Section 3.6.4).

3.4.5 Water Vapour TCWV SSM/I SSMIS TCDR v1.0

The TCWV product is a long-term homogenized product based on passive microwave imagery with similar sensor characteristics. One obstacle of this product is that it is limited to open-ocean ice free surfaces up to 50 km from the coastline. The algorithm depends on a priori information (a predefined database of atmospheric background profiles) and sea surface temperature (OISST) observations. Hence, high-quality background information, as well as stable and high-quality sea surface temperature measurements, will improve data quality.

The inclusion of additional microwave instrument measurements would improve results and guarantee a homogenized product beyond the lifetime of SSMIS instruments.
Error propagation yields reliable error estimates for each retrieved value from 1D-Var retrieval. This technique is currently under investigation to be implemented for future data production .

3.5 Scientific Research needs

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3.5.1 Water Vapour THP TCDR v1.0

The tropospheric humidity retrievals depend on the availability of background data that are free from systematic biases. The uncertainties of the background data are assumed to be purely statistical with zero mean. In practice, a state-of-the-art reanalysis model is used as background in the 1D-Var retrievals. However, even the newest reanalysis models are not totally free of biases. One way to deal with this would be to develop and apply bias correction to the model data, something that would require detailed investigations into best practices, and how to ensure that artificial trends are not introduced in the retrieved data products.

Characterization of the uncertainties of humidity profiles as well as of gridded monthly-mean humidity data is essential for many applications. To obtain a reliable and accurate description of the uncertainties requires cross-comparisons with other observational data types, as well as a theoretical understanding of the error sources involved.

3.5.2 Water Vapour THP ICDR v1.x

See Section 3.5.1.

3.5.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

The dataset is global, therefore there should be inclusion over high orography and polar regions. Currently a mask filters out measurements directly affected by the surface. Research into possible improvements could be made in the algorithm for instances where the surface affects the atmospheric profiles, especially over high ground and the poles, such as into surface emissivity estimates used in radiative transfer calculations.

The training dataset is several years old and could be updated. A different training dataset was used to calculate the coefficients in the UTH formula than to calculate the limb darkening correction. Greater consistency could be achieved by using the same, up-to-date, training dataset for both.

Investigations could be made into improvement of the cloud mask that attempts to remove observations contaminated by convective and precipitating clouds.

3.5.4 Water Vapour TCWV_GV TCDR v1.0

Scientific research needs overlap with points made in the previous Section 3.4.4. However, one big aspect is discontinuities between bright land and dark water surfaces. These could be tackled by combining NIR and thermal IR retrievals.

A much more fundamental aspect would be the investigation of smaller scale water vapor structures present at the onset of cloud formation processes. We can observe these to a limited extent but particularly Meteosat Third Generation (MTG) will deliver highly valuable data and insights.

3.5.5 Water Vapour TCWV_SSMI/SSMS TCDR v1.0

Scientific research needs to address the improvements required in Section 2.5, more precisely improving the background profile database and reviewing the sea surface temperature dataset as input as well as including additional microwave instruments to improve the observation frequency.

Another step would be to improve the background error covariance matrix depending on atmospheric state in order to enhance the correlation structures between the parameters and the error propagation scheme and the uncertainty estimate of the TCWV product.

3.6 Opportunities from exploiting the Sentinels and any other relevant satellite

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3.6.1 Water Vapour THP TCDR v1.0

The Sentinel-6 mission (also referred to as Jason-CS) consists of two satellites, the first launched in 2020 and the second planned for launch 2026. Although primarily an altimetry mission, with orbital characteristics fit to that purpose, the Sentinel-6 satellites will also carry GNSS-RO instruments with heritage from the RO instruments onboard the COSMIC-2 satellites. The Sentinel-6 orbits are not Sun-synchronous, have a medium inclination, and the altitudes are higher than for the other RO missions (about 1300 km instead of the typical 600-800 km). The value of the Sentinel-6 RO data from a climate science perspective is primarily in providing data covering the full diurnal cycle, and in boosting the overall data numbers.

3.6.2 Water Vapour THP ICDR v1.x

The Water Vapour THP ICDR products are based on data from the same instruments and satellites as the corresponding TCDR. See Section 3.6.1.

3.6.3 Water Vapour UTH TCDR v1.0 + ICDR v1.x

The microwave sensors of the Sentinel satellites do not provide brightness temperature measurements at the 183.31±1.00GHz frequency required to calculate the UTH. Other future missions are as stated in Section 3.1.3.

3.6.4 Water Vapour TCWV_GV TCDR v1.0

The heritage of the MERIS instrument is currently flying in the form of OLCI on the Sentinel 3 satellites. The opportunities do not end there: the microwave radiometer (MWR) onboard Sentinel 3 is primarily used to support the Synthetic Aperture Radar Altimeter (SRAL). The MWR provides cloud liquid water and water vapor and is thus an additional source of TCWV data over the ocean and is available for, e.g., cross validation.

Furthermore, the current Sentinel 5-P and the future Sentinel 4 and Sentinel 5 missions as well as the high resolution IR sounder (IASI) and IASI-NG (next generation) all have band configurations that provide information about the vertical profile and column, too. Most of them could be used in a similar way to how the TCWV_GV algorithm is used for MERIS. Primarily the H₂O absorption peaks would lie in different spectral ranges.

A real "game changer" for TCWV retrieval in the NIR will be MTG. MTG will carry the Flexible Combined Imager (FCI) which features band configurations in the NIR in an absorption peak of gaseous H₂O. This would make it a formidable platform to observe water vapour at hourly or even sub-hourly frequencies at a reasonable resolution.

3.6.5 Water Vapour TCWV_SSMI/SSMS TCDR v1.

Currently there is only one instrument (AMR-C) designed as a microwave radiometer flown on board Sentinel 6A, launched on 21^st of November 2020. Although the AMR's main task is water vapour correction for the SRAL radar altimeter on Sentinel-3, it still can give the opportunity for exploitation of Sentinel 6 measurements. With its twin successor satellite, Sentinel 6B, which is planned to launch in 2025, it will guarantee a long-term total column water vapour product from the microwave radiation and can be integrated in the HOAPS retrieval. In addition, current state-of-the art measurements can be exploited from sensors as AMSR2 (on GCOM-W), which is still operational.

References

Buehler, S. A. and John, V. O.: A simple method to relate microwave radiances to upper tropospheric humidity, J. Geoph. Res., 110, D2, https://doi.org/10.1029/2004JD005111 , 2005.

Buehler, S. A., Kuvatov, M., John, V. O., Milz, M., Soden, B. J., Jackson, D. L., and Notholt, J.: An upper tropospheric humidity data set from operational satellite microwave data, J. Geoph. Res., 113, D14110, doi:10.1029/2007JD009314, 2008.

Diedrich, H., Preusker, R., Lindstrot, R., and Fischer, J.: Retrieval of daytime total columnar water vapour from MODIS measurements over land surfaces, Atm. Meas. Tech., 8, 823-836, doi:10.5194/amt-8-823-2015 , 2015.

Soden, B. J., & Bretherton, F. P.: Upper tropospheric relative humidity from the GOES 6.7 μm channel: method and climatology for July 1987. J. Geoph. Res., 98(D9), 1993.

Info

This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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