Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms

Abstract

Abstract. The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) – a crucial parameter to estimate the thermal cloud radiative forcing – can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare the ten SEVIRI cloud top pressure (CTP) datasets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas good agreement is found for the cores of the deep convective system having a high optical depth. Furthermore, a good agreement between the algorithms is observed for trade wind cumulus and marine stratocumulus clouds. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CHT data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted signal. Therefore some systematic diffrences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP datasets range between 0.77 and 0.90. The mean CTH differences between the SEVIRI algorithms and CPR observations are smaller than for CALIOP ranging from −0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations range between 0.82 and 0.89. To discuss the origin of the CTH deviation we elaborate the comparison for three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference datasets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP but similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to uncertainties in and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.

Eruption chronology of the December 2021 to January 2022 Hunga Tonga-Hunga Ha’apai eruption sequence

“…This histogram method for extraction of umbrella clouds is primarily based on the image segmentation technique 17 . After assessing the BT 11.2 μm values for a given umbrella cloud and verifying that the umbrella clouds are optically thick and in thermal equilibrium with their surroundings, we could retrieve the top heights of umbrella clouds using ERA5 temperature profiles [18][19][20][21] .…”

“…For evaluating umbrella heights, we matched the satellite's estimated BT Hist with the collocated and linearly interpolated ERA5 21 temperature profile in real-time. This conversion method of brightness temperature value to vertical height using the ERA5 temperature profile is called the "temperature method"1 8,19,20 . As stated above, this method is especially useful for optically thick umbrella clouds when they are in thermal equilibrium with the surrounding environments.…”

“…In contrast, averaging brightness temperature over a spatial fixed domain induces biases by excluding portions of large umbrella clouds and including clearsky and non-volcanic cloud pixels in evaluation of small umbrella clouds. To determine the umbrella cloud top heights from brightness temperature, we use the "temperature method" [18][19][20] , which assumes that the umbrella clouds are in thermal equilibrium with their surroundings (see Methods section). This temperature method primarily utilizes the real-time ECMWF Reanalysis version-5 (ERA5) 21 data.…”

Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

“…This precise algorithm combination is used with great success for the operational retrievals of cloud top properties for the MSG SEVIRI imager (Hamann et al, 2014). Data input is provided by the collected radiances I A in combination with the profiles of atmospheric temperature, moisture, ozone, and surface temperature.…”

Use of spectral cloud emissivity to infer ice cloud boundaries: Methodology and assessment using CALIPSO cloud products

“…With regard to geometric differences of IR cloud tops from the actual cloud tops, optically thin but geometrically thick clouds show the largest bias, since the level of which the integrated optical thickness reaches 1 is much lower than the height at which the first ice particles occur. In a review of different ten satellite retrieval methods for cloud top heights by IR measurements (Hamann et al, 2014), the heights inferred for optically thin clouds are generally below the cloud's mid-level height. When lower-level clouds are present below the cirrus in a vertical column, the inferred cloud height can be between the cloud layers, depending on the optical thickness of the uppermost layer.…”