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General definitions
In the scope of the Copernicus Climate Change Service (C3S), a Climate Data Record (CDR) has a fixed end point, whereas an Interim Climate Data Record (ICDR) is extended continuously. The TCWV_MERIS_SSMI_TCDR product is a CDR which consists of two satellite-based estimates of the Total Columnar Water Vapour (TCWV) of the atmosphere. TCWV is a measure of the total water vapour content of the atmosphere over a certain area [kg/m2] and is also referred to as Precipitable Water Vapour (PWV) or Integrated Water Vapour (IWV). Hereafter and throughout C3S this variable will always be called TCWV.
The two estimates used in this CDR are derived by two different instruments over two types of surface. Over land, data from the Medium Resolution Imaging Spectrometer (MERIS) are used. Over water, the Special Sensor Microwave/Imager (SSM/I) is used. All documents related to the TCWV_MERIS_SSMI_TCDR product are mostly limited to processing of MERIS data by the Freie Universität Berlin (FUB). SSM/I data have been provided by the German Weather Service (DWD) and were not part of the processing at FUB and are thus not discussed in this or any of the other supplementary documents.
Scope of the document
This document is the Algorithm Theoretical Basis Document (ATBD) for the TCWV_MERIS_SSMI_TCDR product (level 2 and level 3), version 1.0b, produced by FUB from the Medium Resolution Imaging Spectrometer (MERIS) onboard the ESA Environmental Satellite (ENVISAT). It describes the algorithms used to generate the TCWV product, including the scientific justification for the algorithms selected to derive the product, an outline of the proposed approach and a listing of the assumptions and limitations of the algorithm. Furthermore, the merging of the data and flag definitions for the level 3 daily composites and monthly means will be described.
Information about the quality of the data and user guidance can be found in the related document: the Product Quality Assurance Document (PQAD) [D1].
Executive summary
The TCDR of Total Column Water Vapour (TCWV) from the Freie Universität Berlin (FUB) is an in-house product delivered to the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S).
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The MERIS TCWV and SSMI TCWV datasets are combined by the FUB team and comprises of 10 years (2002-2012) L3 monthly mean time series. The CDR provided here includes monthly means of TCWV on a regular global latitude-longitude grid, merged from MERIS (over land) and SSM/I (over ocean).
1. Instruments
The algorithm used to process the TCWV_MERIS_SSMI_TCDR was originally developed for use on the Ocean and Land Colour Instrument (OLCI) flying onboard the Sentinel 3a and 3b platform. In principle, however, OLCI is the successor of the Medium Resolution Imaging Spectrometer (MERIS) and thus OLCI was built in such a way that it incorporates very similar band and camera configurations as found in MERIS. Furthermore, the CAWA algorithm itself is based on the Global Water Vapour (GVAP) 1D-Var retrieval algorithm which originally was developed for MERIS in the first place [Lindstrot et al., 2012].
The measurements of the satellite sensor MERIS will be described briefly in the following. For further information on the Special Sensor Microwave/Imager (SSM/I) which is also part of the TCWV_MERIS_SSMI_TCDR we refer to the Algorithm Theoretical Basis Document (ATBD) of HOAPS-S [Graw et al., 2017]. In actual fact, the instrument SSM/I flew only on the first satellites and was later replaced by the Special Sensor Microwave Imager Sounder (SSMIS). Despite difference, the TCWV retrieval on both instruments works the same. We hereafter refer to both instruments with SSM/I for simplicity reasons.
1.1 Medium Resolution Imaging Spectrometer (MERIS)
1.1.1 EnvironmentalSatellite (ENVISAT)
MERIS is one of several instruments onboard the polar orbiter ENVISAT. The satellite was launched on the 1st of March 2002. It is a polar orbiter and flies in a sun-synchronous orbit with an equator-crossing time of 10:00 AM, descending node and 98.5° inclination. The satellite is still in orbit today, however contact was lost on the 8th of April 2012, marking the mission end of ENVISAT.
1.1.2 Scope of the mission for MERIS
MERIS was built primarily for the measurement of upwelling radiances just above the sea surface for use in bio-optical models and the retrieval of bio-geophysical parameters. Since the signal from water-bodies is only a small percentage of the integrated radiance picked up by a sensor in space, a high Signal-to-Noise Ratio (SNR) exists. For bio-geophysical applications atmospheric correction of influence factors such as scattering by aerosols or absorption by water vapour is needed.
This directly leads to the secondary objective of MERIS: the retrieval of atmospheric properties which include but are not limited to cloud detection, aerosol detection and water vapour content retrieval.
1.1.3 Technical Configuration of MERIS
MERIS is built up of 5 identical push-broom imaging spectrometers operating in the solar spectral range (390 to 1040 nm). The spectrometers are arranged in a fan shape configuration which covers a total field of view of 68.5° and spans a swath width of around 1150km (Figure 1).
The spectral dispersion is achieved by mapping the entrance slit of a grating spectrometer onto a CCD array. The integration time, instrument optics and CCD array resolution are adjusted such that MERIS has a maximum spatial resolution of 0.3 km × 0.3 km and a spectral sampling of 1.25 nm.
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A known issue with the instrument is the so called “smile effect” or “spectral smile”. The central wavelengths of the spectral channels as listed in Table 1 vary slightly across the field of view of MERIS. This is caused by curvature of the image of the slit formed in the focal plane array, resulting in viewing angle-dependent central wavelengths of the spectral MERIS channels.
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MERIS Band ID | Central Wavelength (nm) | Bandwidth (nm) | Application |
1 | 412.5 | 10 | Yellow substance, turbidity |
2 | 442.5 | 10 | Chlorophyll |
3 | 490 | 10 | Chlorophyll, pigment |
4 | 510 | 10 | Suspended matter, turbidity |
5 | 560 | 10 | Chlorophyll, suspended matter |
6 | 620 | 10 | Suspended matter |
7 | 665 | 10 | Chlorophyll |
8 | 681.25 | 7.5 | Chlorophyll |
9 | 708.25 | 10 | Atmospheric correction, “red edge” |
10 | 753.75 | 7.5 | Cloud optical thickness, cloud-top pressure reference |
11 | 761.875 | 3.75 | Cloud-top pressure |
12 | 778 | 10 | Aerosol, vegetation |
13 | 865 | 20 | Aerosol, atmospheric correction |
14 | 885 | 10 | Water vapour reference |
15 | 900 | 10 | Water vapour absorption |
Anchor section2 section2
2. Input and auxiliary data
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2.1 MERIS Input Data
We use MERIS Level 1b data of the 3rd revision of the data set.
2.1.1 Radiances
The algorithm uses normalized radiances, which means that the top of atmosphere (TOA) radiances are normalized with the solar constant at the respective center wavelength of each band. Due to the technical configuration there is a smile-effect that shifts the central wavelength of each band along the pixels of the scanline. This effect is accounted for.
For the water vapour retrieval the MERIS channels 13 and 14 are used as reference (window) bands and channel 15 is used as the absorption band.
2.1.2 Viewing Geometry
Further input to the algorithm is the viewing geometry at each pixel which is defined by the viewing zenith angle (VZA), the Sun zenith angle (SZA) and the azimuth difference angle (ADA). All the data are given in the Level 1b files of MERIS (see MERIS Product Handbook).
2.2 Auxiliary Data
The algorithm needs additional information in order to reliably estimate the water vapour. The current state of the atmosphere (temperature, pressure) is approximated by using reanalysis fields.
2.2.1 ECMWF ERA Interim TCWV, 2m-Temperature and Mean Sea Level Pressure (MSLP)
As input for MSLP, 2m temperature (T2m) and TCWV we use ERA Interim reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) [Dee et al., 2011]. Data are provided on a 0.5° regular grid and interpolated to the irregular satellite grid. MSLP is adapted to actual surface height, yielding the Surface Pressure (SP). ERA Interim TCWV is used as a priori estimate of TCWV in the 1D-Var algorithm, while SP and T2mdescribe the current state. See Table 2a and 2b.
Table 2a: Satellite measurements taken from Level 1b instrument data files.Anchor table2a table2a
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Quantity | Unit | Valid range | Source | Comment |
Surface Pressure (SP) | hPa | 200-1050 | MSLP from ERA Interim | adapted to actual surface height |
Surface Temperature (T2m) | K | 260-330 | ERA Interim |
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Wind Speed | m/s | 2-15 | ERA Interim | only over water |
3. Algorithms
This section describes the algorithms used, including the uncertainty characterization and pre-processing steps needed for a successful retrieval. In our case, only the algorithm for MERIS TCWV is described in more detail, for the SSM/I retrieval of TCWV we refer to the HOAPS version 4.0 ATBD [Graw et al., 2017].
3.1 MERIS-TCWV Algorithm Overview
The algorithm described in the following retrieves water vapour content over cloud-free land and water surfaces. It is based on a differential absorption approach in the so called
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The algorithm employs a Look-Up-Table (LUT) with radiance values for pre-calculated conditions that the satellite measurements are compared to. In the optimal estimation the best solution between the two is found in an iterative process.
3.2 MERIS-TCWV Algorithm Description
Measured radiances from satellites in a water vapour absorption band carry information about the TCWV. The backbone of the retrieval is the forward operator that simulates TOA radiances. A 1D-Var scheme optimizes the difference between simulated and measured radiances by iteratively varying the TCWV value. Thereby, information from each absorption band is used, following the scheme after Rodgers (2000) (see Sect. 3.3.1).
3.2.1 Theoretical Basis
The basis of this TCWV algorithm is the nature of the water molecules: due to a combination of three fundamental vibrational modes of the water molecule it has various strong absorption features in the solar and terrestrial spectrum. In theory, from measuring reflected sunlight in one of the absorption bands we can estimate the water vapour along the path given that:
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The optical depth of a certain medium describes its efficiency in absorbing photons along a given path and at a specific wavelength. This efficiency depends on the position on the gas absorption line. The depth and width of an individual water vapour absorption line changes due to pressure- and temperature dependent broadening processes. Hence, the knowledge of the actual temperature profile and the surface pressure is necessary in order to simulate the correct atmospheric transmittance. We use reanalysis data for the surface pressure and surface temperature (see Section 2 Input and Auxiliary Data).
3.2.2 The Look-Up-Table
The Look-Up-Table (LUT) builds the core of the 1D-Var TCWV retrieval. Stored in the LUT are the values of TOA radiances (
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All simulations were performed with the Matrix Operator Model (MOMO), [Fell and Fischer, 2001], [Hollstein and Fischer, 2012], [Doppler et al., 2014].
3.2.3 The Universal Forward Operator
The Forward Operator (FO) is a function that uses the LUT described in the previous chapter. The Forward Operator assigns varying atmospheric and surface conditions to normalized TOA radiances (
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Figure 2: The schematic of the MERIS TCWV processing at FUB. First Level 1b data provided by ESA are filtered pixel-wise with corresponding Level 2 flags provided by ESA. Then, the filtered Level 1b radiances are fed together with auxiliary data from ECMWF’s ERA Interim reanalysis into the algorithm.
3.2.4 Inversion and Retrieval
The final step and the subsequent retrieval of TCWV is performed with inverse modelling. In an iterative procedure, the deviation between the simulated and measured radiances in the window and absorption bands are reduced by changing the water vapour content. After the last step of this procedure the uncertainty (error) of the retrieval is derived.
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where K is the Jacobian matrix that contains the partial derivatives of the radiance to the TCWV value in each band. Se is the measurement error covariance matrix which contains the measured radiance scaled with the signal to noise ratio (SNR) for each band.
3.2.5 Uncertainty/Error estimate
After the final iteration of the procedure the retrieval uncertainty is calculated, taking into account these sources of uncertainty:
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The processor is mostly written in Python, while the iterative inversion procedure is programmed in Fortran, in order to speed up processing. Algorithm output is on the irregular satellite projection grid. In the next step, this output needs to be regridded on a regular longitude-latitude grid and merged to produce Level 3 products.
4. Level 3 Merging
In this section we will describe the procedure of binning the data together into Level 3 daily composites and the final product, monthly means. With the combination of both datasets we gain a highly accurate product with global coverage which combines the advantages of two formidable instruments for the retrieval of TCWV.
Daily Composite and Monthly Mean merging has been done in the same way as for the ESA DUE GlobVapour SSM/I & MERIS Dataset which only went from 2003-2008 [Lindstrot et al., 2014].
4.1 Daily Composites
The daily composites of TCWV combine the MERIS measurements above land with the daytime measurements of SSM/I orbits over the open ocean.
MERIS has an equator crossing time (ECT) of 10:00 AM local time and measures only on the descending node, due to the necessity of sun light. The satellites carrying the SSM/I (DMSP-F13 to F19), however, vary from satellite to satellite in their ECTs (see Figure 3) and measure both on the ascending and descending node. Most DMSP satellites have an ECT in the early morning for the descending node. Thus, in order to keep the merged data between the MERIS and SSM/I comparable, we only chose DMSP satellites with a descending ECT in morning hours (5:00 – 9:00 AM) and only merged the descending nodes in the daily composite. We also decided to only include the satellites F13, F14, F17 and F18.
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As another means to assess the representative nature of one grid box, we also included the number of satellite observations per grid box. For MERIS TCWV grid boxes we also calculated two additional fractions: the valid fraction of observations per total number of pixels in a grid box and the cloud fraction, the number of cloudy pixels per total number of pixels in a grid box. The fractions are only available for MERIS observations.
Figure 4 shows an example of a global SSM/I–MERIS daily composite of TCWV (a) and its associated error (b) for the 25th of August 2011.
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Figure 4: Global daily composite of MERIS and SSMI TCWV (a) in high resolution (HR) and its corresponding error (b)
Anchor section4.2 section4.2
4.2 Monthly Means
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The monthly means of TCWV in each grid box are calculated from the average of all available daily values. For MERIS grid boxes this results in a cloud-free, forenoon monthly average of TCWV. Analogous to the error estimate in daily composites, for monthly means it is derived from the average of all available errors. Again, the standard deviation among the daily values of TCWV is provided to describe the temporal variability of TCWV during the course of a month. The number of observations (n_obs) is also included and is calculated from the sum of valid observations per grid box per month. In version 1.0 of the dataset this does not represent the number of days per grid box. We oriented ourselves on the number of observations calculated for the GVAP dataset where the number of observations is the number of all valid pixels which contributed to one grid box over the course of the whole month. Furthermore we calculated the average of the valid fraction per grid box. This gives an estimate on how many pixels per grid box were valid as a measure for the representativeness of the TCWV value for the grid box.
For SSM/I grid boxes this gives the forenoon monthly average of TCWV and its assigned average error. A standard deviation is provided as well. For MERIS grid boxes this gives the forenoon cloud-free average of TCWV and its assigned average error.
As an example, Figure 5 shows the global distribution of monthly mean TCWV from MERIS and SSM/I for June 2011 (5a), as well as the corresponding error (5b).
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The monthly means are a formidable way to get full global coverage from satellite platforms that are limited in swath size or number of simultaneous instruments in orbit. In order to get full coverage at a sub-monthly level, so-called 3-day-composites or 3-day rolling window averages might be helpful where three daily composites are combined into one product. This product – depending on the cloud cover during these three days – can yield full global coverage.
5. Output data
The algorithm outputs a multitude of parameters per pixel. We only take the TCWV value, its corresponding error estimate and standard deviation. The background of the 1D-Var retrievals (both for MERIS and SSM/I), the TCWV from the ERA Interim reanalysis is also given.
Furthermore we provide the absolute (n_obs) and relative (vf) number of valid observations per grid box, as well as the fraction of cloud pixels (see Section 4.2).
Finally, a flag is also included in the output data. It contains information about the surface type, the products and, limited to MERIS only, the quality. Flags are bitwise and can be used to only look at snow-covered areas or at ocean separately. The flags can also be used to separate MERIS (over land) and SSM/I (over ocean) TCWV respectively.
The available processing Levels are the Level 3 monthly means at high resolution (= 0.05°) and low resolution (= 0.5°) from May 2002 to March 2012 with global coverage. Please use the Product User Guide [D2] for further details and data description.
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Variable | Long name | Unit | Valid range | Comment |
tcwv | atmospheric water vapour content | kg/m2 | 0-75 | Total Column Water Vapor (also called integrated Water Vapor (IWV) or Precipitable Water Vapor (PWV)) is the integrated amount of gaseous water in the total column of the atmosphere over an area of 1 m2. |
tcwv_err | atmospheric water vapour content | kg/m2 | 0-60 | Error of total column water vapor from 1D-Var retrieval |
tcwv_stdev | atmospheric water vapour content | kg/m2 | 0-50 | Standard deviation of total column water vapor |
tcwv_back | atmospheric water vapour content | kg/m2 | 0-75 | Background of total column water vapor |
n_obs | number of observations | 1 | 0-580 | sum of observations over a whole month |
valid_fraction | valid pixels/all pixels | 1 | 0-1 | only for MERIS grid boxes |
cloud_fraction | cloud pixels/ all pixels | 1 | 0-1 | only for MERIS grid boxes |
l3_flags | surface & product flags | 1 | 0-265 | Level 3 Flags are bits that contain masks for surface type, quality level and instrument in bits. Flag Codings are: 1 WATER, 2 LAND, 4 SNOW, 8 CLOUD, 16 MERIS_TCWV, 32 MERIS_ALGORITHM_FAIL, 64 MERIS_NOT_PROCESSED, 128 SSMI_TCWV, 256 INVALID_OR_NAN |
References
Bennartz, R. and J. Fischer, 2000: A modified k-distribution approach applied to narrow band water vapour and oxygen absorption estimates in the near infrared. Journal of Quantitative Spectroscopy & Radiative Transfer, 66:539–553.
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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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