How microphysical choices affect simulated infrared brightness temperatures

Infrared satellite observations are strongly affected by clouds, which complicates their effective use in data assimilation. While observation minus first-guess (FG departure) statistics for cloud-free data are close to a normal (Gaussian) distribution, the occurrence of clouds leads to strongly increased uncertainty, systematic differences between observations and model forecasts and subsequently a clear deviation of the FG departures from the Gaussianity that is usually assumed in data assimilation. This study aims to classify the cloud impact on Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared brightness temperature observations and model equivalents to mitigate the issues of non-Gaussian FG departure statistics for data assimilation. A threshold brightness temperature is introduced that allows us to quantify the cloud impact and to derive an error estimate for FG departures as a function of the cloud impact. The use of the dynamic error estimate leads to substantially more Gaussian FG departure statistics. Based on the dynamic error estimate, an observation error model is derived for the assimilation of infrared brightness temperature observations in an all-sky approach. The proposed method allows us to treat cloud-free and cloud-affected observations in a uniform way, without the need for cloud screening.

Section: Accounting For the Cloud Impact On Brightness Temperaturescontrasting

confidence: 99%

Error model for the assimilation of cloud‐affected infrared satellite observations in an ensemble data assimilation system

Harnisch¹,

Weißmann²,

Periáñez

2016

“…Parameterizations of COSMO were also extended. In later model versions, two‐moment microphysics is available that can influence the amount of high clouds (Eikenberg et al , ). In a region with frequent deep convection, this can certainly influence the net radiation and, with this, the whole diurnal cycle (Marsham et al , ).…”

Section: Discussionmentioning

confidence: 99%

Initiation of deep convection in the Sahel in a convection‐permitting climate simulation for northern Africa

Maurer

Bischoff‐Gauß

Kalthoff

et al. 2017

A convection-permitting climate simulation was performed for Northern Africa for the year of 2006 and a domain of 2561 × 1721 × 50 grid points. The general performance of the simulation was evaluated for the Sahel region for July and August, where the land-surface-atmosphere feedback has a strong influence on convection initiation. The simulation showed reasonable skill.A tracking algorithm was applied to identify the initiation locations of mesoscale convective systems (MCSs). The aim of the investigation was to distinguish the different processes of MCS initiation during the afternoon and particularly to assess the influence of spatial land-surface anomalies on convection initiation. On average, for all initiations considered, a clear spatial signal of the ratio of sensible to latent heat flux at the land surface (Bowen ratio), indicative of a land-surface influence, was not detected. Only when certain initiations were selected using objective as well as subjective criteria did a signal of the land surface become apparent. The largest number of afternoon initiations in the Sahel could be attributed to convergence caused by gust fronts of approaching systems (35%). Still, almost 16% of the initiations that were identified were preferentially downstream from a region with an elevated Bowen ratio. About 9% were regenerated systems and 19% of the MCSs were not assigned to one of these classes, including systems that formed by the merging of smaller cells developing at multiple locations in a region of large-scale favourable conditions, e.g., due to large-scale convergence.The results confirm the importance of local convection initiation downstream of spatial land-surface anomalies in the Sahel. They also highlight the mechanism of convection initiation by cold pools and related gust fronts.

“…It can be noted that biases related to the ice scheme have previously been investigated for COSMO (e.g. Böhme et al , ; Eikenberg et al , ), but remain difficult to attribute due to strong intercorrelations between emissions from the surface, ice clouds, liquid clouds, precipitations and, to a lesser extent, water vapour at 10.8 mu m. Therefore, despite the fact that Figure strongly suggests an issue with the representation of ice and mixed‐phase clouds in ICON, further dedicated analyses remain necessary to validate this conclusion.…”

Section: Cloudsmentioning

confidence: 93%

“…Infrared satellite data have been derived from the forward operator Synthetic Satellite imagery (SynSat: see e.g. Keil et al , ; Eikenberg et al , ; Senf and Deneke, 2016), which provides an interface to a radiative transfer model (RTTOV v11.2: Saunders et al , ). The forward operator needs 3D fields of thermodynamic and hydrometeor variables, as well as surface fields, and simulates synthetic cloud‐free and cloud‐affected infrared radiances as observable by the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI/MSG).…”

Section: Model Description Set‐up and Simulation Outputmentioning

confidence: 99%

Large‐eddy simulations over Germany using ICON: a comprehensive evaluation

Heinze

Dipankar

Henken

et al. 2017

213

287

Large-eddy simulations (LES) with the newThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. R. Heinze et al.at building confidence in the model's ability to simulate small-to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small-to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.