INTERPRETATION OF STALL PRECURSOR SIGNATURES

A specific phenomenon that has been observed in many experimental studies on turbomachinery compressors and fans is discussed under the term ‘rotating instabilities’. It is associated to a local aerodynamic phenomenon, typically occurring in the tip region at highly loaded near stall conditions and often linked to blade vibrations. Even though the effect has been discussed over more than two decades, a very ambiguous interpretation still prevails. A particular problem is that certain signatures in measurement data are often considered to characterize the phenomenon despite possible misinterpretations. The present paper illustrates that a specific image of a pulsating disturbance that has been established in the 1990s needs to be reconsidered. At the example of a recent investigation on a composite fan the difficulties concerning sensor placement and post-processing techniques is discussed with a focus on spectral averaging, isolation of non-synchronous phenomena and multi-sensor cross-correlation methods.

Cloud optical property benchmarking study

<p>A benchmarking study to compare various cloud optical property schemes is proposed. The transmittance, reflectance and absorptance of clouds of cloud optical property schemes that are widely used in weather and climate have in recent years been shown to give different results. Not much attention has been paid to this, probably due to the fact that errors in cloud cover have larger impacts on the shortwave and longwave radiative fluxes than the cloud optical properties. Here the optical properties are the mass extinction coefficient, the single scattering albedo and the asymmetry factor. Cloud optical property schemes are typically based on detailed theoretical calculations, which are then parametrized. Furthermore, spectral band averaging is performed to get optical properties that match the spectral bands of the radiative transfer scheme. Here the 14 shortwave and 16 longwave spectral bands of the RRTM scheme are mostly used presently. Both the equations used in the parametrizations and the spectral averaging matter. For the spectral averaging it must be considered that both the shortwave (solar) and longwave (thermal) spectra change following the circumstances in the atmospheric environment. Thus, a dry atmosphere has different spectral characteristics from a moist atmosphere. In the ultraviolet part of the spectrum ozone is also important to account for, as is the impact of carbon dioxide, methane, nitrous oxide and lesser greenhose gasses in the longwave part of the spectrum. This also affects the spectral distribution of the available radiative flux within individual spectral bands. Thus, the impact of the atmospheric environment must also be accounted for in the benchmarking study. The setup and an online framework for sharing benchmarking results will be presented.</p>

Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

Broadband albedo of Arctic sea ice from MERIS optical data

Arctic summer sea ice experiences rapid changes in its sea-ice concentration, surface albedo, and the melt pond fraction. This affects the energy balance of the region and demands an accurate knowledge of those surface characteristics in climate models. In this paper, the broadband albedo (300–3000 nm) of Arctic sea ice is derived from MEdium Resolution Imaging Spectrometer (MERIS) optical swath data by transforming the spectral albedo as an output from the Melt Pond Detector (MPD) algorithm with a newly developed spectral-to-broadband conversion (STBC). The new STBC replaces the previously applied spectral averaging method to provide a more accurate broadband albedo product, which approaches the accuracy of 0.02–0.05 required in climate simulations and allows a direct comparison to broadband albedo values from climate models. The STBC is derived empirically from spectral and broadband albedo measurements over landfast ice. It is validated on a variety of simultaneous spectral and broadband field measurements over Arctic sea ice, is compared to existing conversion techniques, and performs better than the currently published algorithms. The root-mean-square deviation (RMSD) between broadband albedo that was measured and converted by the STBC is 0.02. Other conversion techniques, the spectral averaging method and the linear combination of albedo values from four MERIS channels, result in higher RMSDs of 0.09 and 0.05, respectively. The improved MERIS-derived broadband albedo values are validated with airborne measurements. Results show a smaller RMSD of 0.04 for landfast ice than the RMSD of 0.07 for drifting ice. The MERIS-derived broadband albedo is compared to broadband albedo from ERA5 reanalysis to examine the albedo parameterization used in ERA5. Both albedo products agree over large scales and in temporal patterns. However, consistency in point-to-point comparison is rather poor, with differences up to 0.20, correlations between 0.69 and 0.79, and RMSDs in excess of 0.10. Differences in sea-ice concentration and cloud-masking uncertainties play a role, but most discrepancies can be attributed to climatological sea-ice albedo values used in ERA5. They are not adequate and need revising, in order to better simulate surface heat fluxes in the Arctic. The advantage of the resulting broadband albedo data set from MERIS over other published data sets is the accompanied data set of available melt pond fraction. Melt ponds are the main reason for the sea-ice albedo change in summer but are currently not represented in climate models and atmospheric reanalysis. Additional information about melt evolution, together with accurate albedo retrievals, can aid the challenging representation of sea-ice optical properties in those models in summer.

Broadband albedo of Arctic sea ice from MERIS optical data

Summer in the Arctic is the season when the sea ice covered ocean experiences rapid changes in its sea ice concentration, the surface albedo, and the melt pond fraction. These processes drastically affect the energy balance of the region and it is a challenge for climate models to represent those correctly. In this paper, the broadband albedo (300–3000 nm) of Arctic sea ice is derived from Medium Resolution Imaging Spectrometer (MERIS) optical swath data by transforming the spectral albedo as an output from the Melt Pond Detector (MPD) algorithm by a newly developed spectral-to-broadband conversion (STBC). The new STBC replaces the previously applied spectral averaging method to provide a more accurate broadband albedo product which approaches the accuracy of 0.02–0.05 required in climate simulations and allows a direct comparison to broadband albedo values from climate models. The STBC is derived empirically from spectral and broadband albedo measurements over landfast ice. It is validated on a variety of simultaneous spectral and broadband field measurements over Arctic sea ice, is compared to existing conversion techniques and shows a better performance than the currently published algorithms. The root mean square deviation (RMSD) between measured and broadband albedo converted by the STBC is 0.02. Other conversion techniques, the spectral averaging method and the linear combination of albedo values from four MERIS channels, achieve higher RMSDs of 0.09 and 0.05. The improved MERIS derived broadband albedo values are validated with airborne measurements. Results show a smaller RMSD of 0.04 for landfast ice than the RMSD of 0.07 for drifting ice. The MERIS derived broadband albedo is compared to broadband albedo from ERA5 reanalysis to examine the albedo parameterization used in ERA5. Both albedo products agree in the large-scale pattern. However, consistency in point-to-point comparison is rather poor, with correlations between 0.71 and 0.76 and RMSD in excess of 0.12. This suggests that the climatological sea ice albedo values used in ERA5 are not adequate and need revising, in order to better simulate surface heat fluxes in the Arctic. The advantage of the resulting broadband albedo data set from MERIS against other published data sets is the additional data set of melt pond fraction available from the same sensor. Melt ponds are the main reason for the sea ice albedo change in summer but currently are not represented in climate models. Additional information on melt evolution together with the accurate albedo product can aid the challenging representation of sea ice optical properties in summer in climate models.