ALBERT

Description: Total column water vapour has been retrieved from TROPOMI measurements in the visible blue spectral range and compared to a variety of different reference data sets for clear-sky conditions during boreal summer and winter. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme and then the retrieved slant column densities are converted to vertical columns using an iterative scheme for the water vapour a priori profile shape, which is based on an empirical parameterization of the water vapour scale height. Moreover, a modified albedo map was used combining the OMI LER albedo and scaled MODIS albedo map. The use of the alternative albedo is especially important over regions with very low albedo and high probability of clouds like the Amazon region. The errors of the total column water vapour (TCWV) retrieval have been theoretically estimated considering the contribution of a variety of different uncertainty sources. For observations during clear-sky conditions, over ocean surface, and at low solar zenith angles the error typically is around values of 10 %–20 %, and during cloudy-sky conditions, over land surface, and at high solar zenith angles it reaches values around 20 %–50 %. In the framework of a validation study the retrieval demonstrates that it can well capture the global water vapour distribution: the retrieved H2O vertical column densities (VCDs) show very good agreement with the reference data sets over ocean for boreal summer and winter whereby the modified albedo map substantially improves the retrieval's consistency to the reference data sets, in particular over tropical land masses. However, over land the retrieval underestimates the VCD by about 10 %, particularly during summertime. Our investigations show that this underestimation is likely caused by uncertainties within the surface albedo and the cloud input data: low-level clouds cause an underestimation, but for mid- to high-level clouds good agreement is found. In addition, our investigations indicate that these biases can probably be further reduced by the use of improved cloud input data. For the general purpose we recommend only using VCDs with cloud fraction 0.1, which represents a good compromise between spatial coverage and retrieval accuracy. The TCWV retrieval can be easily applied to further satellite sensors (e.g. GOME-2 or OMI) for creating uniform, long-term measurement data sets, which is particularly interesting for climate and trend studies of water vapour.

Description: The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.

Description: This work presents the methane (CH4) and nitrous oxide (N2O) products as generated by the IASI (Infrared Atmospheric Sounding Interferometer) processor developed during the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The processor retrieves CH4 and N2O with different water vapour and water vapour isotopologues (as well as HNO3) and uses a single a priori data set for all the retrievals (no variation in space and time). Firstly, the characteristics and errors of the products are analytically described. Secondly, the products are comprehensively evaluated by comparisons to the following reference data measured by different techniques and from different platforms as follows: (1) aircraft CH4 and N2O profiles from the five HIAPER Pole-to-Pole Observation (HIPPO) missions; (2) continuous in situ CH4 and N2O observations performed between 2007 and 2017 at subtropical and mid-latitude high-mountain observatories (Izaña Atmospheric Observatory and Jungfraujoch, respectively) in the framework of the WMO–GAW (World Meteorological Organization–Global Atmosphere Watch) programme; (3) ground-based FTIR (Fourier-transform infrared spectrometer) measurements made between 2007 and 2017 in the framework of the NDACC (Network for the Detection of Atmospheric Composition Change) at the subtropical Izaña Atmospheric Observatory, the mid-latitude station of Karlsruhe and the Kiruna polar site.The theoretical estimations and the comparison studies suggest a precision for the N2O and CH4 retrieval products of about 1.5–3 % and systematic errors due to spectroscopic parameters of about 2 %. The MUSICA IASI CH4 data offer a better sensitivity than N2O data. While for the latter the sensitivity is mainly limited to the UTLS (upper troposphere–lower stratosphere) region, for CH4 we are able to prove that at low latitudes the MUSICA IASI processor can detect variations that take place in the free troposphere independently from the variations in the UTLS region. We demonstrate that the MUSICA IASI data qualitatively capture the CH4 gradients between low and high latitudes and between the Southern Hemisphere and Northern Hemisphere; however, we also find an inconsistency between low- and high-latitude CH4 data of up to 5 %. The N2O latitudinal gradients are very weak and cannot be detected. We make comparisons over a 10-year time period and analyse the agreement with the reference data on different timescales. The MUSICA IASI data can detect day-to-day signals (only in the UTLS), seasonal cycles and long-term evolution (in the UTLS and for CH4 also in the free troposphere) similar to the reference data; however, there are also inconsistencies in the long-term evolution connected to inconsistencies in the used atmospheric temperature a priori data.Moreover, we present a method for analytically describing the a posteriori-calculated logarithmic-scale difference of the CH4 and N2O retrieval estimates. By correcting errors that are common in the CH4 and N2O retrieval products, the a posteriori-calculated difference can be used for generating an a posteriori-corrected CH4 product with a theoretically better precision than the original CH4 retrieval products. We discuss and evaluate two different approaches for such a posteriori corrections. It is shown that the correction removes the inconsistencies between low and high latitudes and enables the detection of day-to-day signals also in the free troposphere. Furthermore, they reduce the impact of short-term atmospheric dynamics, which is an advantage, because respective signals are presumably hardly comparable to model data. The approach that affects the correction solely on the scales on which the errors dominate is identified as the most efficient, because it reduces the inconsistencies and errors without removing measurable real atmospheric signals. We give a brief outlook on a possible usage of this a posteriori-corrected MUSICA IASI CH4 product in combination with inverse modelling.

Description: Volume mixing ratio water vapour profiles have been retrieved from IASI (Infrared Atmospheric Sounding Interferometer) spectra using the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. The retrievals are done for IASI observations that coincide with Vaisala RS92 radiosonde measurements performed in the framework of the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) in three different climate zones: the tropics (Manus Island, 2° S), mid-latitudes (Lindenberg, 52° N), and polar regions (Sodankylä, 67° N). The retrievals show good sensitivity with respect to the vertical H2O distribution between 1 km above ground and the upper troposphere. Typical DOFS (degrees of freedom for signal) values are about 5.6 for the tropics, 5.1 for summertime mid-latitudes, 3.8 for wintertime mid-latitudes, and 4.4 for summertime polar regions. The errors of the MUSICA IASI water vapour profiles have been theoretically estimated considering the contribution of many different uncertainty sources. For all three climate regions, unrecognized cirrus clouds and uncertainties in atmospheric temperature have been identified as the most important error sources and they can reach about 25 %. The MUSICA IASI water vapour profiles have been compared to 100 individual coincident GRUAN water vapour profiles. The systematic difference between the data is within 11 % below 12 km altitude; however, at higher altitudes the MUSICA IASI data show a dry bias with respect to the GRUAN data of up to 21 %. The scatter is largest close to the surface (30 %), but never exceeds 21 % above 1 km altitude. The comparison study documents that the MUSICA IASI retrieval processor provides H2O profiles that capture the large variations in H2O volume mixing ratio profiles well from 1 km above ground up to altitudes close to the tropopause. Above 5 km the observed scatter with respect to GRUAN data is in reasonable agreement with the combined MUSICA IASI and GRUAN random errors. The increased scatter at lower altitudes might be explained by surface emissivity uncertainties at the summertime continental sites of Lindenberg and Sodankylä, and the upper tropospheric dry bias might suggest deficits in correctly modelling the spectroscopic line shapes of water vapour.

Description: This paper presents upper tropospheric methane (CH4) and nitrous oxide (N2O) concentrations retrieved from thermal infrared spectra as observed by the remote sensor IASI (Infrared Atmospheric Sounding Interferometer) on-board the EUMETSAT/MetOp meteorological satellites. The CH4 and N2O mixing ratios are retrieved as side products of the MetOp/IASI retrieval developed for the European Research Council project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The MUSICA/IASI CH4 and N2O retrieval strategy is described in detail as well as their characterisation in terms of the vertical resolution and expected errors. Theoretically, we document that MUSICA/IASI products can capture the upper tropospheric CH4 and N2O variability (at ≈ 300–350 hPa) with a precision better than 2 %. We compare the remote sensing data to coincident high precision aircraft vertical profiles taken within the HIAPER Pole-to-Pole Observations (HIPPO) project and empirically estimate a precision of 2.1 % (38.2 ppbv) for each individual IASI CH4 observation. The precision is improved to 1.7 % (32.1 ppbv) for IASI data that have been averaged within 2° × 2° boxes. For N2O the empirically estimated precision is 2.7 % (8.7 ppbv) for each individual observation and 2.1 % (6.9 ppbv) for the 2° × 2° averages. The empirical study works with data from the missions HIPPO1 and HIPPO5, which cover latitudes between 67º S and 80º N during typical winter and summer conditions in both hemispheres, thus being reasonably representative for global observation during different seasons. In addition, we present a product that combines the CH4 and N2O retrieval estimates. The combination is made a-posteriori and we theoretically and empirically show that the combined product has a much better precision than the individual CH4 and N2O products. For the combined product the theoretical precision is 0.8 % and the comparison with HIPPO data gives an empirical precision estimate of 1.5 % (26.3 ppbv) when considering all individual IASI observations and of 1.2 % (21.8 ppbv) for the 2° ×2° averages. In the case that the horizontal, vertical and temporal variation of N2O can be robustly modeled, we can easily reconstruct CH4 from the combined product and generate high quality IASI CH4 data.

Description: Volume mixing ratio water vapour profiles have been retrieved from IASI (Infrared Atmospheric Sounding Interferometer) spectra by using the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. The retrievals are made for IASI observations that coincide with Vaisala RS92 radiosonde measurements performed in the framework of the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) in three different climate zones: the tropics (Manus Island, 2° S), mid-latitudes (Lindenberg, 52° N) and polar regions (Sodankylä, 67° N). The retrievals show good sensitivity with respect to the vertical H2O distribution between 1–2 km above ground and the upper troposphere. Typical DOFS (degree of freedom for signal) values are about 5.5 for the tropics, 5.0 for summertime midlatitudes, 4.0 for wintertime mid-latitudes, and 4.5 for summertime polar regions. The errors of the IASI water vapour profiles have been theoretically estimated considering the contribution of a large number of uncertainty sources. For all three climate regions unrecognized cirrus clouds and uncertainties in atmospheric temperature have been identified as the most important error sources, whereby the total errors are estimated to be typically 25 %. The IASI water vapour profiles have been compared to 100 individual coinciding GRUAN water vapour profiles. The systematic difference between the IASI and GRUAN data is within 12 % at all altitudes. The scatter is largest close to the surface and close to the tropopause, but does never exceed 30 %. The study documents that the MUSICA MetOp/IASI retrieval processor provides H2O profiles with good accuracy and captures the variations in H2O volume mixing ratio profiles from 1–2 km above ground up to altitudes close to the tropopause with a precision that is in accordance to the theoretical error assessment.

Description: The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromises their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The exemplary model study clearly reveals the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modeled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.

Description: We used a global long-term (1995–2015) data set of total column water vapour (TCWV) derived from satellite observations to quantify to which extent the temporal patterns of various teleconnections can be identified in this data set. To our knowledge, such a comprehensive global TCWV data set was rarely used for teleconnection studies. One important property of the TCWV data set is that it is purely based on observational data. We developed a new empirical method to decide whether a teleconnection index is significantly detected in the global data set. We compared our new method to well-established hypothesis tests and found good agreement with the results of our approach. Based on our empirical method more than 40 teleconnection indices were significantly detected in the global TCWV data set derived from satellite observations. In addition to the satellite data we also applied our method to other global data sets derived from ERA-Interim. One important finding is that the spatial patterns obtained for the ERA TCWV data are very similar to the observational TCWV data set indicating a high consistency between the satellite and ERA data. Moreover, similar results are also found for two selections of ERA data (either all data or mainly clear-sky data). This finding indicates that the clear-sky bias of the satellite data set is negligible for the results of this study. However, for some indices, also systematic differences in the spatial patterns between the satellite and model data set were found probably indicating possible shortcomings in the model data. For most “traditional” teleconnection data sets (surface temperature, surface pressure, geopotential heights and meridional winds at different altitudes) a smaller number of significant teleconnection indices was found than for the TCWV data sets, while for zonal winds at different altitudes, the number of significant teleconnection indices (up to 〉 50) was higher. The strongest teleconnection signals were found in the data sets of tropospheric geopotential heights and surface pressure. In all global data sets, no “other indices” (solar variability, stratospheric AOD or hurricane frequency) were significantly detected. Since many teleconnection indices are strongly correlated, we also applied our method to a set of orthogonalised indices, which represent the dominant independent temporal teleconnection patterns. The number of significantly detected orthogonalised indices (20) was found to be much smaller than for the original indices (42). Based on the orthogonalised indices we derived the global spatial distribution of the cumulative effect of teleconnections. The strongest effect on the TCWV is found in the tropics and high latitudes.

Description: The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.