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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 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.
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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.
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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.
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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).
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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).
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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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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).
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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 2‑12.1 shows the actual values used for the limit percentiles in the binomial test.
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Table 2‑12.1: Water Vapour TPH ICDR target requirementsAnchor table2_1 table2_1
KPI Title | Percentile | Performance target |
Accuracy Tropospheric Humidity Profile ICDR wrt reference dataset | 97.5 | 0-4 km: -0.93% 4-8 km: +0.82% 8-12 km: +2.13% |
Accuracy Tropospheric Humidity Profile ICDR wrt reference dataset | 2.5 | 0-4 km: -2.00% 4-8 km: -1.04% 8-12 km: -0.66% |
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 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:
Table 2.2: Water Vapour UTH TCDR target requirementsAnchor table2_2 table2_2
KPI # | KPI Title | Performance Target | Frequency |
KPI.D10.1 | Accuracy Upper Tropospheric Humidity TCDR | Mean absolute error: 5% | At release |
KPI.D10.2 | Stability Upper Tropospheric Humidity TCDR | 1% / dec | At release |
Table 2.3: Water Vapour UTH ICDR target requirementsAnchor table2_3 table2_3
KPI Title | Percentile | Performance target |
Accuracy Upper Tropospheric Humidity ICDR wrt reference dataset | 97.5 | 0.55% (until 31/12/2018) |
Accuracy Upper Tropospheric Humidity ICDR wrt reference dataset | 2.5 | -0.47% (until 31/12/2018) |
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.
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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 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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Threshold | 3 kg/m2 | 5 kg/m2 | 0.4 kg/m2/dec |
Target | 1.4 kg/m2 | 2 kg/m2 | 0.2 kg/m2/dec |
Optimal | 0.6 kg/m2 | 1 kg/m2 | 0.08 kg/m2/dec |
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.
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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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2.5.1 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 1st 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.
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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.
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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.
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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.
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here 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):
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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].
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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.
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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.
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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.
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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.
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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.
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With the inclusion of other platforms, especially future geostationary satellites, the TCWV from NIR will reach a new and higher level (see section 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 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.
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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 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 Section 2.9.5, 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.
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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 21st 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.
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