Evaluating Ice Microphysics in NWP Models with Satellite Observations

Ice clouds are an important part of the Earth’s atmospheric water cycle and have a large impact on the global radiation budget. Yet ice clouds are still poorly understood and their correct representation remains a major challenge for state-of-the-art atmospheric models. Also, the evaluation of the models’ performance with respect to ice clouds is not straightforward; remote sensing instruments, for example, measure other quantities than the models predict. Therefore, two basic evaluation approaches exist: observation-to-model (commonly termed retrieval) and model-to-observation (commonly termed forward operator). Both approaches introduce errors into the comparison of models and observations because of the necessary intrinsic assumptions. The common practice in model evaluation of choosing either the one or the other of these approaches might give an incomplete picture. The present study evaluates the ice microphysics of two numerical weather prediction (NWP) models currently operational at the German weather service (Deutscher Wetterdienst, DWD): the global model GME and the regional model COSMO-DE (an application of the Consortium for Small-scale Modelling, COSMO). In doing so, this study contributes significantly to ongoing model development at DWD. Both case studies and long-term evaluations are carried out. Cloud Satellite (CloudSat) Cloud Profiling Radar (CPR) observations are heavily relied on; the CPR is the first and — up to date — only cloud radar in space and is able to vertically resolve even optically thick clouds. This study focuses on one specific question raised for each of the respective models and while doing so applies both approaches; the standard CloudSat radar reflectivity factor–ice water content (IWC) retrieval for the observation-to-model approach and the forward operator QuickBeam for the model-to-observation approach. This enables for one, to profit from the full informational content, and for the other, to compare both approaches directly to each other and evaluate them. For the global model GME, two precipitation schemes, a diagnostic and a prognostic one, are compared and evaluated. The focus is on the question whether the new prognostic scheme is capable of capturing ice clouds more realistically than the old diagnostic scheme. The prognostic scheme is shown to exhibit improved performance in comparison to the diagnostic scheme in terms of IWC magnitude. In both models snow is found to dominate over cloud ice in total IWC, emphasizing the need for including snow in the model’s radiation budget in the future. Furthermore, one reason for the remaining difference between the prognostic scheme and the observations — the unrealistic fall speed of snow — is identified. As a consequence, the new prognostic scheme with an adapted parameterization for snow fall speed was successfully introduced into operational service at DWD. In the regional NWP model COSMO-DE, a long-known bias between brightness temperatures simulated from COSMO-DE forecasts and those observed by Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) is investigated. The pivotal question is whether a novel two-moment cloud ice scheme exhibits improved performance with respect to this bias and, if that is so, why. It is shown that the novel two-moment cloud ice scheme does indeed reduce this bias and can therefore be considered an improvement in comparison to two standard schemes, the two-category ice scheme and the currently operational three-category ice scheme. The improvement in simulated brightness temperatures is due to a vertical redistribution of cloud ice to lower model levels. Furthermore, sensitivity studies identify two of the four changes introduced, which are responsible for most of the improved performance: the change to a different heterogeneous nucleation scheme and the inclusion of cloud ice sedimentation. Enhanced vertical level number and modifications in aerosol number concentrations reveal comparatively little effect. As a consequence, cloud ice sedimention will be included per se in DWD’s future NWP model, the Icosahedral non-hydrostatic (ICON) model, currently still under development. Concerning the two evaluation approaches conducted, the present study finds the general features in the two evaluations to be captured by both approaches. Some details are captured merely by the one or the other approach, in which case both approaches together give the more complete picture. However, the model-to-observation approach appears to be easier to interpret; its uncertainties are easier to assess than those of the observation-to-model approach and it ensures a better control over the comparison

Evaluation of Ice and Snow Content in the Global Numerical Weather Prediction Model GME with CloudSat

The present study evaluates the global numerical weather prediction model GME with respect to the grid-scale parameterization of frozen particles, both ice and snow, focusing on the performance of a diagnostic versus a prognostic precipitation scheme. As a reference, CloudSat Cloud Profiling Radar observations are utilized – the so far only near-globally available data set which vertically resolves clouds. Both the observation-to-model and the model-to-observation approach are applied and compared to each other. For the latter, the radar simulator QuickBeam is utilized. Criteria are applied to further improve the comparability between model and observations. The two model versions are statistically evaluated for a four-month period. <br><br> The comparison reveals that the prognostic scheme reproduces the shape of the CloudSat frequency distributions for both ice water content (IWC) and reflectivity factor well, while the diagnostic scheme produces no large IWCs or reflectivity factors because snow falls out instantaneously. However, the prognostic scheme overestimates the occurrence of high ice water paths (IWP), especially in the mid-latitudes. Sensitivity tests show that an increased fall speed of snow successfully reduces IWP. Both evaluation approaches capture the general features, but for details, the two together deliver the largest informational content. In case of limited resources, the model-to-observation approach is recommended. Finally, the results indicate that the lack of IWC in most global circulation models might be attributed to the use of diagnostic precipitation schemes, i.e., the lack of snow aloft. <br><br> Based on its good performance the prognostic scheme went into operational mode in February 2010. The adjusted snow fall speed went operational in December 2010. However, continual improvements of the ice microphysics are necessary, which can be assessed by the proposed evaluation technique

Roquin promotes constitutive mRNA decay via a conserved class of stem-loop recognition motifs.

Tumor necrosis factor-α (TNF-α) is the most potent proinflammatory cytokine in mammals. The degradation of TNF-α mRNA is critical for restricting TNF-α synthesis and involves a constitutive decay element (CDE) in the 3' UTR of the mRNA. Here, we demonstrate that the CDE folds into an RNA stem-loop motif that is specifically recognized by Roquin and Roquin2. Binding of Roquin initiates degradation of TNF-α mRNA and limits TNF-α production in macrophages. Roquin proteins promote mRNA degradation by recruiting the Ccr4-Caf1-Not deadenylase complex. CDE sequences are highly conserved and are found in more than 50 vertebrate mRNAs, many of which encode regulators of development and inflammation. In macrophages, CDE-containing mRNAs were identified as the primary targets of Roquin on a transcriptome-wide scale. Thus, Roquin proteins act broadly as mediators of mRNA deadenylation by recognizing a conserved class of stem-loop RNA degradation motifs

Roquin promotes constitutive mRNA decay via a conserved class of stem-loop recognition motifs.

Tumor necrosis factor-α (TNF-α) is the most potent proinflammatory cytokine in mammals. The degradation of TNF-α mRNA is critical for restricting TNF-α synthesis and involves a constitutive decay element (CDE) in the 3' UTR of the mRNA. Here, we demonstrate that the CDE folds into an RNA stem-loop motif that is specifically recognized by Roquin and Roquin2. Binding of Roquin initiates degradation of TNF-α mRNA and limits TNF-α production in macrophages. Roquin proteins promote mRNA degradation by recruiting the Ccr4-Caf1-Not deadenylase complex. CDE sequences are highly conserved and are found in more than 50 vertebrate mRNAs, many of which encode regulators of development and inflammation. In macrophages, CDE-containing mRNAs were identified as the primary targets of Roquin on a transcriptome-wide scale. Thus, Roquin proteins act broadly as mediators of mRNA deadenylation by recognizing a conserved class of stem-loop RNA degradation motifs

Translational Regulation of Specific mRNAs Controls Feedback Inhibition and Survival during Macrophage Activation

<div><p>For a rapid induction and efficient resolution of the inflammatory response, gene expression in cells of the immune system is tightly regulated at the transcriptional and post-transcriptional level. The control of mRNA translation has emerged as an important determinant of protein levels, yet its role in macrophage activation is not well understood. We systematically analyzed the contribution of translational regulation to the early phase of the macrophage response by polysome fractionation from mouse macrophages stimulated with lipopolysaccharide (LPS). Individual mRNAs whose translation is specifically regulated during macrophage activation were identified by microarray analysis. Stimulation with LPS for 1 h caused translational activation of many feedback inhibitors of the inflammatory response including NF-κB inhibitors (<i>Nfkbid</i>, <i>Nfkbiz</i>, <i>Nr4a1</i>, <i>Ier3</i>), a p38 MAPK antagonist (<i>Dusp1</i>) and post-transcriptional suppressors of cytokine expression (<i>Zfp36</i> and <i>Zc3h12a</i>). Our analysis showed that their translation is repressed in resting and de-repressed in activated macrophages. Quantification of mRNA levels at a high temporal resolution by RNASeq allowed us to define groups with different expression patterns. Thereby, we were able to distinguish mRNAs whose translation is actively regulated from mRNAs whose polysomal shifts are due to changes in mRNA levels. Active up-regulation of translation was associated with a higher content in AU-rich elements (AREs). For one example, <i>Ier3</i> mRNA, we show that repression in resting cells as well as de-repression after stimulation depends on the ARE. Bone-marrow derived macrophages from <i>Ier3</i> knockout mice showed reduced survival upon activation, indicating that IER3 induction protects macrophages from LPS-induced cell death. Taken together, our analysis reveals that translational control during macrophage activation is important for cellular survival as well as the expression of anti-inflammatory feedback inhibitors that promote the resolution of inflammation.</p></div

ARE scores and different patterns of regulation.

<p>(A) ARE scores were determined using the ARE<i>Score</i> algorithm and represented as boxplot for the groups g0–g4 (as defined in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004368#pgen-1004368-g004" target="_blank">Figure 4</a>); p-values were determined by two-sided Wilcoxon rank sum test. (B) Boxplot of ARE scores for groups of mRNAs with active and passive changes in ribosome load as defined in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004368#pgen-1004368-g004" target="_blank">Figure 4A</a>; p-values were determined by two-sided Wilcoxon rank sum test.</p

Work flow for the combined analysis of translation efficiency and mRNA expression patterns.

<p>Polysome association of mRNAs was measured by sucrose density gradient fractionation and microarray analysis of four pools (F: free, S: 40S-associated, L: light polysomes, H: heavy polysomes). For each mRNA, the distribution across the pools was calculated. The ratio H/L was used as a measure for ribosome load. mRNA levels were quantified by RNASeq at a high temporal resolution and grouped into five distinct patterns (g0–g4). By combining both data sets, mRNAs whose translation is actively regulated can be distinguished from mRNAs with passive changes in translation.</p

mRNA levels and translation of cytokines and feedback inhibitors.

<p>(A) Absolute mRNA expression levels were measured by RNASeq (n = 1) and depicted as rpkm before and 1 h after LPS treatment of RAW264.7 macrophages for all genes with at least one read in one of the conditions (box plot), and the subgroups of cytokines and feedback inhibitors (dot plots). Among the cytokines, 18 genes had an rpkm value of 0 before stimulation, and 4 after stimulation. (B) Ribosome load (H/L) as determined in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004368#pgen-1004368-g003" target="_blank">Figure 3</a> for all mRNAs detectable before or 1 h after LPS treatment of RAW264.7 macrophages, separately for all genes (box plot), cytokines and feedback inhibitors (dot blots). Translationally de-repressed cytokines and feedback inhibitors are labeled.</p