Energy and moisture exchange processes over heterogeneous land-surfaces in a weather prediction model

Land-surfaces exhibit significant variablity at very small scales - in contrast to the atmosphere, where horizontal diffusion reduces small scale fluctuations effectively. It is a challenging task for numerical weather prediction (NWP) to account for these different characteristics while calculating exchange fluxes between these two systems: Surface processes need to be considered with higher spatial resolution than atmospheric effects and high resolution initial conditions and parameters of the surface are required. This study evaluates methods to solve these surface heterogeneity problems on the basis of integrations of the non-hydrostatic weather prediction model Lokal-Modell (LM) both in a NWP configuration (grid spacing of 7 km) and in a regional climate model set up (grid spacing of 21 km). The runs are performed for the 30-day periode of the LITFASS-2003 experiment. Two heterogeneity parameterisation schemes, the mosaic and tile approach, have been implemented into LM. Both methods decompose the surface within one atmospheric grid box into several patches to resolve subgrid scale variability. The mosaic approach utilises an explicit, geographical sub-grid, whereas the tile approach subdivides the surface according to a certain criteria, e.g. land-use. In general, the tile method requires less computational time since fewer patches are used. However, the mosaic technique is more flexible since it takes multivariate heterogeneity into account. Two major model enhancements are needed to simulate the observed exchange fluxes during LITFASS-2003 successfully: land-use dependent stomatal resistance parameters and vegetation albedo, and the use of accurate soil moisture data for initialisation. The latter is obtained by multi-year assimilation runs of the soil module of LM driven exclusively by observations. This technique ensures a balanced model state and allows to capture heterogeneity effects due to soil moisture variations induced by inhomogeneous rainfall. The flux predictions of all integrations using these enhancements agree well with the observations within the range of measurement uncertainty independently from the representation of heterogeneity. The impact of improved surface fluxes on forecasts of atmospheric state variables is beneficial. Using high resolution integrations (e.g. grid spacing of 1 km) as reference, a clear ranking of parameterisation schemes can be established: The mosaic approach leads to very accurate flux predictions, followed by the tile approach, and the operational homogeneous approach. The deviations in forecasted surface fluxes of all methods decay significantly, if averages over larger scales are considered. The ranking of the methods can be explained by analysing the small scale variance of high resolution runs: The variance of surface quantities is by far larger than those of corresponding atmospheric quantities. This supports the assumption inherent to the mosaic and tile approach to refine the surface only. During LITFASS-2003, a considerable fraction of flux variability is explained by soil moisture variations which are not correlated with land-use. These subgrid scale heterogeneities can only be resolved by the mosaic approach and not by a tile scheme.Energie- und Feuchteaustausch über heterogenen Landoberflächen in einem Wettervorhersagemodell Landoberflächen zeichnen sich durch eine hohe Variabilität auf kleinen Skalen aus - im Gegensatz zur Atmosphäre, in der horizontale Diffusion kleinskalige Fluktuationen effektiv reduziert. Diese unterschiedlichen Eigenschaften bei der Berechnung der Austauschflüsse von Energie und Feuchte zwischen beiden Systemen zu berücksichtigen, ist eine schwierige Aufgabe für die numerische Wettervorhersage: Oberflächenprozesse erfordern eine höhere räumliche Auflösung als atmosphärische Effekte und entsprechend werden hochaufgelöste Anfangsbedingungen und Parameter der Oberfläche benötigt. Diese Studie erprobt Methoden zur Lösung dieser Heterogenitätsprobleme auf der Basis von Rechnungen mit dem nicht-hydrostatischen Wettervorhersagemodell Lokal-Modell (LM) sowohl in einer Konfiguration zur Wettervorhersage (Gitterweite 7 km) als auch in einer Einstellung, die einem regionalen Klimamodell entspricht (Gitterweite 21 km). Diese Simulationen werden für den 30-Tages-Zeitraum des LITFASS-2003 Experiments durchgeführt. Zwei Heterogenitätsparameterisierungen, der Mosaic- und der Tile-Ansatz, sind in das LM eingebaut worden. Beide Methoden zerlegen die Oberfläche innerhalb einer atmosphärischen Gitterbox in verschiedene Untergebiete, um kleinskalige Variabilität unterhalb der Modellmaschenweite aufzulösen. Der Mosaic-Ansatz verwendet ein explizites, geographisches Untergitter, wohingegen der Tile-Ansatz die Oberfläche nach einem bestimmten Kriterium, z.B. der Landnutzung, aufteilt. Im allgemeinen benötigt der Tile-Ansatz weniger Rechenzeit, da weniger Untergebiete verwendet werden. Der Mosaic-Ansatz ist flexibler, da auch multivariate Heterogenitäten berücksichtigt werden können. Zwei wesentliche Modifikationen des operationellen Modells sind nötig, um die während LITFASS-2003 beobachteten Austauschflüsse erfolgreich zu modellieren: landnutzungsabhängige Parameter des Stomatawiderstands und der Pflanzenalbedo, sowie genaue Bodenfeuchteanalysen. Letztere lassen sich aus mehrjährigen Assimilationsläufen mit dem Bodenmodell des LM bei ausschließlichem Antrieb mit Messdaten erstellen. Diese Technik garantiert einen balancierten Modellzustand und ermöglicht es, Heterogenitätseffekte infolge regeninduzierter Bodenfeuchtevariationen wiederzugeben. Die Flussvorhersagen aller Modellläufen, die diese Modifikationen nutzen, geben die Beobachtungen im Rahmen der Messgenauigkeit gut wieder - unabhängig von der Berücksichtung von Heterogenitäten. Diese genauer modellierten Austauschflüsse reduzieren auch Fehler in den Vorhersagen des atmosphärischen Zustands. Verwendet man hochaufgelöste Modellintegrationen (z.B. mit einer Maschenweite von 1 km) als Referenz, so ergibt sich eine klare Rangfolge für die verschiedenen Parameterisierungsmethoden: Der Mosaic-Ansatz führt zu sehr genauen Flussvorhersagen, gefolgt vom Tile-Ansatz und dem operationell verwendeten Ansatz einer homogenen Oberfläche. Die Unterschiede in den vorhergesagten bodennahen Flüssen verringern sich deutlich, wenn Mittel über größere Skalen betrachtet werden. Die Rangfolge der Methoden kann durch eine Analyse kleinskaliger Varianzen in hochaufgelösten Simulationen erklärt werden: Die Varianz von Oberflächenvariablen ist deutlich größer als die von entsprechenden atmosphärischen Größen und rechtfertigt damit die dem Mosaic- und Tile-Ansatz zugrundeliegenden Annahmen, nur die Oberfläche höher aufzulösen. Während LITFASS-2003 wird ein beachtlicher Anteil der Variabilität der bodennahen Flüsse durch Bodenfeuchtevariationen erklärt, die nicht mit der Landnutzung korreliert sind. Solche kleinskaligen Heterogenitäten können nur vom Mosaic-Ansatz aufgelöst werden, nicht aber durch ein Tile-Schema

Assessing the uncertainty of soil moisture impacts on convective precipitation using a new ensemble approach

Soil moisture amount and distribution control evapotranspiration and thus impact the occurrence of convective precipitation. Many recent model studies demonstrate that changes in initial soil moisture content result in modified convective precipitation. However, to quantify the resulting precipitation changes, the chaotic behavior of the atmospheric system needs to be considered. Slight changes in the simulation setup, such as the chosen model domain, also result in modifications to the simulated precipitation field. This causes an uncertainty due to stochastic variability, which can be large compared to effects caused by soil moisture variations. By shifting the model domain, we estimate the uncertainty of the model results. Our novel uncertainty estimate includes 10 simulations with shifted model boundaries and is compared to the effects on precipitation caused by variations in soil moisture amount and local distribution. With this approach, the influence of soil moisture amount and distribution on convective precipitation is quantified. Deviations in simulated precipitation can only be attributed to soil moisture impacts if the systematic effects of soil moisture modifications are larger than the inherent simulation uncertainty at the convection-resolving scale.</p><p>We performed seven experiments with modified soil moisture amount or distribution to address the effect of soil moisture on precipitation. Each of the experiments consists of 10 ensemble members using the deep convection-resolving COSMO model with a grid spacing of 2.8 km. Only in experiments with very strong modification in soil moisture do precipitation changes exceed the model spread in amplitude, location or structure. These changes are caused by a 50 % soil moisture increase in either the whole or part of the model domain or by drying the whole model domain. Increasing or decreasing soil moisture both predominantly results in reduced precipitation rates. Replacing the soil moisture with realistic fields from different days has an insignificant influence on precipitation. The findings of this study underline the need for uncertainty estimates in soil moisture studies based on convection-resolving models

A generic gust definition and detection method based on wavelet-analysis

Wind gusts can have destructive effects on many structures and objects deemed valuable to humans. The aviation industry, for example, views gusts as a major hazard. Their destructive effect is proportional to the momentum that a specific gust imposes onto an object. The actual definition of a gust has a strong influence on how its impact can be quantified. Existing gust definitions, however, are largely based on fixed parameters describing shape requirements and thresholds and are often developed only for specific use cases. These gust definitions do not provide a direct link to the physical impact a particular gust has on a structure or object. The overall goal of this study is to provide such a direct link. The application of a wavelet-analysis to a turbulence-resolving wind velocity signal allows for the localization of signal amplitudes in the period as well as in the time domain. In this paper, we use wavelet-analysis in order to develop a straight-forward method of deriving information about gusts from a wind velocity signal. In order to define what a particular gust might be, we suggest the specification of a characteristic period and amplitude in the time-domain. We define a generic gust as a section of a wind velocity signal, where the wavelet-analysis detects that characteristic amplitude to be matched or exceeded within that characteristic period. The characteristic amplitudes and periods are generic and span a two-dimensional space of generic gust definitions. The method can be applied to turbulence resolving simulation data as well as high-resolution wind velocity measurement data. It can detect gusts of any shape, it is unbiased regarding any specific use case and invariant to changes of the mean wind. We provide a detailed description of the method, its capabilities and demonstrate its application to high resolution wind velocity measurement data

Evaluating statistical cloud schemes: what can we gain from ground-based remote sensing?

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the “perfect model approach.” This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme

The relationship between precipitation and its spatial pattern in the trades observed during EUREC 4 A

Trade wind convection organises into a rich spectrum of spatial patterns, often in conjunction with precipitation development. Which role spatial organisation plays for precipitation and vice versa is not well understood. We analyse scenes of trade-wind convection scanned by the C-band radar Poldirad during the EUREC4A field campaign to investigate how trade-wind precipitation fields are spatially organised, quantified by the cells' number, mean size, and spatial arrangement, and how this matters for precipitation characteristics. We find that the mean rain rate (i.e., the amount of precipitation in a scene) and the intensity of precipitation (mean conditional rain rate) relate differently to the spatial pattern of precipitation. Whereas the amount of precipitation increases with mean cell size or number, as it scales well with the precipitation fraction, the intensity increases predominantly with mean cell size. In dry scenes, the increase of precipitation intensity with mean cell size is stronger than in moist scenes. Dry scenes usually contain fewer cells with a higher degree of clustering than moist scenes do. High precipitation intensities hence typically occur in dry scenes with rather large, few, and strongly clustered cells, whereas high precipitation amounts typically occur in moist scenes with rather large, numerous, and weakly clustered cells. As cell size influences both the intensity and amount of precipitation, its importance is highlighted. Our analyses suggest that the cells' spatial arrangement, correlating mainly weakly with precipitation characteristics, is of second-order importance for precipitation across all regimes, but it could be important for high precipitation intensities and to maintain precipitation amounts in dry environments

Three-Dimensional Observation of Atmospheric Processes in Cities

To cope with weather and climate-induced impacts as well as with air pollution in cities, the German research programme “Urban Climate Under Change” ([UC]2) aims at developing, testing and validating a new urban climate model, which is able to cover the full range of temporal and spatial scales of urban atmospheric processes. The project “Three-dimensional Observation of Atmospheric Processes in Cities” (3DO), which forms the module B of the [UC]2 research programme, aims at acquisition of comprehensive, accurate three-dimensional observational data sets on weather, climate and air quality in the German cities of Berlin, Hamburg and Stuttgart. Data sets from long-term observations and intense observation periods allow for evaluation of the performance of a new urban climate model called PALM‑4U that is developed by the project “Model-based city planning and application in climate change” (MOSAIK), which forms the module A of the [UC]2 research programme. This article focuses on collaborative activities for compilation of existing and acquisition of new observational data within the 3DO project