ESTIMATING SURFACE LONGWAVE RADIATION AND APPLICATIONS TO HIGH LATITUDE ISSUES

Two models, with distinct advantages for calculating downwelling surface longwave (DSLW) radiation under all sky conditions are presented. Both models are driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW the first model DSLW/UMD v1 utilizes a globally applicable parameterization. The second generation model DSLW/UMD v2 utilizes a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons. When computing the cloud contribution to DSLW, DSLW/UMD v1 implements a commonly used statistical model to calculate cloud vertical height while in DSLW/UMD v2 the cloud base temperature is estimated by using an independent artificial neural network based on spatially and temporally co- located MODIS and Cloudsat Cloud Profiling Radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfiner Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. Daily average estimates of DSLW for 2003 to 2009 are compared against ground measurements from the Baseline Surface Radiation Network (BSRN) and show significant improvements over currently available model estimates. DSLW/UMD v2 as optimized for Polar Regions along with a UMD develop shortwave model are used to investigate the role of radiative components in Arctic sea ice anomalies. The correlation between downwelling surface longwave and shortwave radiation and sea ice anomaly for the period from 2003 to 2007 is investigated using the latest Moderate Resolution Imagining Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. All sky downwelling surface longwave radiation (DSLW), all sky downwelling shortwave radiation (DSSW), all sky total downwelling shortwave and longwave radiation (DSSW + DSLW), and cloud total cloud forcing are individually examined to determine their respective correlation to sea ice anomaly. It is determined that these radiation components are not the primary drivers for major sea ice anomalies that occur during the investigated time frame within the 120o E to 210o E region

Observational Characterization of the Downward Atmospheric Longwave Radiation at the Surface in the City of São Paulo

This work describes the seasonal and diurnal variations of downward longwave atmospheric irradiance (LW) at the surface in São Paulo, Brazil, using 5-min-averaged values of LW, air temperature, relative humidity, and solar radiation observed continuously and simultaneously from 1997 to 2006 on a micrometeorological platform, located at the top of a 4-story building. An objective procedure, including 2-step filtering and dome emission effect correction, was used to evaluate the quality of the 9-yr-long LW dataset. The comparison between LW values observed and yielded by the Surface Radiation Budget project shows spatial and temporal agreement, indicating that monthly and annual average values of LW observed in one point of São Paulo can be used as representative of the entire metropolitan region of São Paulo. The maximum monthly averaged value of the LW is observed during summer (389 ± 14 W m-2; January), and the minimum is observed during winter (332 ± 12 W m-2; July). The effective emissivity follows the LW and shows a maximum in summer (0.907 ± 0.032; January) and a minimum in winter (0.818 ± 0.029; June). The mean cloud effect, identified objectively by comparing the monthly averaged values of the LW during clear-sky days and all-sky conditions, intensified the monthly average LW by about 32.0 ± 3.5 W m-2 and the atmospheric effective emissivity by about 0.088 ± 0.024. In August, the driest month of the year in São Paulo, the diurnal evolution of the LW shows a minimum (325 ± 11 W m-2) at 0900 LT and a maximum (345 ± 12 W m-2) at 1800 LT, which lags behind (by 4 h) the maximum diurnal variation of the screen temperature. The diurnal evolution of effective emissivity shows a minimum (0.781 ± 0.027) during daytime and a maximum (0.842 ± 0.030) during nighttime. The diurnal evolution of all-sky condition and clear-sky day differences in the effective emissivity remain relatively constant (7% ± 1%), indicating that clouds do not change the emissivity diurnal pattern. The relationship between effective emissivity and screen air temperature and between effective emissivity and water vapor is complex. During the night, when the planetary boundary layer is shallower, the effective emissivity can be estimated by screen parameters. During the day, the relationship between effective emissivity and screen parameters varies from place to place and depends on the planetary boundary layer process. Because the empirical expressions do not contain enough information about the diurnal variation of the vertical stratification of air temperature and moisture in São Paulo, they are likely to fail in reproducing the diurnal variation of the surface emissivity. The most accurate way to estimate the LW for clear-sky conditions in São Paulo is to use an expression derived from a purely empirical approach

Satellite Estimates of Surface Short-wave Fluxes: Issues of Implementation

Surface solar radiation reaching the Earth's surface is the primary forcing function of the land surface energy and water cycle. Therefore, there is a need for information on this parameter, preferably, at global scale. Satellite based estimates are now available at accuracies that meet the demands of many scientific objectives. Selection of an approach to estimate such fluxes requires consideration of trade-offs between the use of multi-spectral observations of cloud optical properties that are more difficult to implement at large scales, and methods that are simplified but easier to implement. In this study, an evaluation of such trade-offs will be performed. The University of Maryland Surface Radiation Model (UMD/SRB) has been used to reprocess five years of GOES-8 satellite observations over the United States to ensure updated calibration and improved cloud detection over snow. The UMD/SRB model was subsequently modified to allow input of information on aerosol and cloud optical depth with information from independent satellite sources. Specifically, the cloud properties from the Atmospheric Radiation Measurement (ARM) Satellite Data Analysis Program (Minnis et al., 1995) are used to drive the modified version of the model to estimate surface short-wave fluxes over the Southern Great Plain ARM sites for a twelve month period. The auxiliary data needed as model inputs such as aerosol optical depth, spectral surface albedo, water vapor and total column ozone amount were kept the same for both versions of the model. The estimated shortwave fluxes are evaluated against ground observations at the ARM Central Facility and four satellite ARM sites. During summer, the estimated fluxes based on cloud properties derived from the multi-spectral approach were in better agreement with ground measurements than those derived from the UMD/SRB model. However, in winter, the fluxes derived with the UMD/SRB model were in better agreement with ground observations than those estimated from cloud properties provided by the ARM Satellite Data Analysis Program. During the transition periods, the results were comparable

A sensor view model to investigate the influence of tree crowns on effective urban thermal anisotropy

A sensor view model is modified to include trees using a gap probability approach to estimate foliage view factors and an energy budget model for leaf surface temperatures (SUMVEG). The model is found to compare well with airborne thermal infrared (TIR) surface temperature measurements. SUMVEG is used to investigate the influence of trees on thermal anisotropy for narrow field-of-view TIR remote sensors over treed residential urban surfaces. Tests on regularly-spaced arrays of cubes on March 28 and June 21 at latitudes of 47.6°N and 25.8°N show that trees both decrease and increase anisotropy as a function of tree crown and building plan fractions. In compact geometries, anisotropy tends to decrease with tree crown plan fraction, with the opposite in open geometries, though trees taller than building height cause anisotropy to increase for all building plan fractions. These results help better understand and potentially correct urban thermal anisotropy

Process‐level evaluation of a hyper‐resolution forest snow model using distributed multi‐sensor observations

The complex dynamics of snow accumulation and melt processes under forest canopies entail major observational and modeling challenges, as they vary strongly in space and time. In this study, we present novel data sets acquired with mobile multisensor platforms in subalpine and boreal forest stands. These data sets include spatially and temporally resolved measurements of shortwave and longwave irradiance, air and snow surface temperatures, wind speed, and snow depth, all coregistered to canopy structure information. We then apply the energy balance snow model FSM2 to obtain concurrent, distributed simulations of the forest snowpack at very high (“hyper”) resolution (2 m). Our data sets allow us to assess the performance of alternative canopy representation strategies within FSM2 at the level of individual snow energy balance components and in a spatially explicit manner. We demonstrate the benefit of accounting for detailed spatial patterns of shortwave and longwave radiation transfer through the canopy and show the importance of describing wind attenuation by the canopy using stand-scale metrics. With the proposed canopy representation, snowmelt dynamics in discontinuous forest stands were successfully reproduced. Hyper-resolution simulations resolving these effects provide an optimal basis for assessing the snow-hydrological impacts of forest disturbances and for validating and improving the representation of forest snow processes in land surface models intended for coarser-scale applications

Measuring and modeling near-surface reflected and emitted radiation fluxes at the FIFE site

Information is presented pertaining to the measurement and estimation of reflected and emitted components of the radiation balance. Information is included about reflectance and transmittance of solar radiation from and through the leaves of some grass and forb prairie species, bidirectional reflectance from a prairie canopy is discussed and measured and estimated fluxes are described of incoming and outgoing longwave and shortwave radiation. Results of the study showed only very small differences in reflectances and transmittances for the adaxial and abaxial surfaces of grass species in the visible and infrared wavebands, but some differences in the infrared wavebands were noted for the forbs. Reflectance from the prairie canopy changed as a function of solar and view zenith angles in the solar principal plane with definite asymmetry about nadir. The surface temperature of prairie canopies was found to vary by as much as 5 C depending on view zenith and azimuth position and on the solar azimuth. Aerodynamic temperature calculated from measured sensible heat fluxes ranged from 0 to 3 C higher than nadir-viewed temperatures. Models were developed to estimate incoming and reflected shortwave radiation from data collected with a Barnes Modular Multiband Radiometer. Several algorithms for estimating incoming longwave radiation were evaluated and compared to actual measures of that parameter. Net radiation was calculated using the estimated components of the shortwave radiation streams, determined from the algorithms developed, and from the longwave radiation streams provided by the Brunt, modified Deacon, and the Stefan-Boltzmann models. Estimates of net radiation were compared to measured values and found to be within the measurement error of the net radiometers used in the study

Measurements and Modeling of Arctic Atmospheric Processes During the Norwegian Young Sea Ice Field Campaign

The Arctic is warming more quickly than other regions, resulting in modifications to the surface energy budget. The Norwegian Young Sea Ice (N-ICE) field campaign took place in January through June 2015, monitoring key components of the surface energy budget. This was the first field campaign to take observations of the surface energy budget during the seasonal transition from winter to spring/summer since the Surface Heat Budget of the Arctic (SHEBA) field campaign in 1997 through 1998. These observations are valuable not only due to the amount of time that has passed since the SHEBA field experiment but also because N-ICE took place on younger, thinner sea ice, and in a different part of the Arctic. This study uses data from the N-ICE experiment to recommend improvements to the Polar Weather Research and Forecasting (WRF) model. The goals of this study are: 1) compare atmospheric measurements to models, 2) investigate model cloud microphysics and boundary layer parameterizations within WRF, 3) determine what cloud properties were observed during N-ICE, 4) investigate the effects of turbulent fluxes and clouds on young sea ice, and 5) make recommendations for improving the Polar WRF model. The Polar WRF model results show that there were issues in calculating both the turbulent and radiative fluxes over first-year sea ice. The cloud conditions during N-ICE varied, and mixed-phase clouds (a mixture of water and ice) were seen throughout the entire experiment. The model, however, did not accurately simulate the radiative impacts of these clouds. To determine if the calculations of sensible and latent heat flux can be accurately estimated given the correct cloud conditions, a study was conducted comparing turbulent fluxes from the Maximum Entropy Production method and the bulk flux algorithm (based on Monin-Obukhov theory and the technique used to estimate fluxes within WRF). Sensible heat flux was accurately estimated, but all equations overestimated the amount of latent heat flux. When compared to an idealized (offline) version of the WRF model using the measured radiative fluxes as input, the WRF model estimates the turbulent fluxes well, indicating that the model is using an appropriate version of the flux equations and that most of the error within the model can be attributed to bias in the cloud properties

McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

International audienceA new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. It is a fully physical model replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapour and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 105 times greater by adopting the abaci, or look-up table, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions at several stations within the Baseline Surface Radiation Network in various climates. The bias for global irradiance comprises between −6 and 25Wm−2. The RMSE ranges from 20Wm−2 (3% of the mean observed irradiance) to 36Wm−2 (5 %) and the correlation coefficient ranges between 0.95 and 0.99. The bias for the direct irradiance comprises between −48 and +33Wm−2. The root mean square error (RMSE) ranges from 33Wm−2 (5 %) to 64Wm−2 (10 %). The correlation coefficient ranges between 0.84 and 0.98. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modelled by the MACC reanalysis

Improving the simulation of small-scale variability in radiation and land-surface parameterizations in a mesoscale numerical weather prediction model

For the simulation of subgrid-scale physical processes in mesoscale numerical weather prediction models various kinds of spatial and temporal sampling or averaging methods are employed to decrease their computational burden. These methods are applied both within the physical parameterizations, but also by restricting the number of calls to these parameterization schemes in time and space. This under-representation of small-scale variability can lead to systematic errors due to the nonlinearity of processes, and may cause inconsistencies between variables computed by the different parameterization schemes. In this work two methods are presented, which provide an efficient spatial and/or temporal sampling of heterogeneities, in the atmosphere itself and at the earth’s surface as lower boundary of atmospheric models. The first method, called adaptive radiative transfer parameterization, provides an efficient technique to compute the radiative effects in the atmosphere and at the soil surface. The second method allows for a scale-consistent coupling of atmospheric and soil-surface models, by running a high-resolution soil-vegetationatmosphere transfer model coupled to the coarser atmospheric model, connected by a novel atmospheric disaggregation scheme. Both developments incorporate small-scale variability in radiative and soil/surface processes in an efficient and consistent way. Furthermore, both methods improve the representation of the energy budget at the earth’s surface; the first by giving more accurate radiation surface net fluxes, the second by improving the turbulent exchange fluxes of sensible and latent heat. Both approaches have been implemented into the COSMO numerical weather prediction model, and tested in the COSMO-DE model configuration on a 2.8 km grid. The adaptive radiative transfer scheme takes advantage of the spatial and temporal correlations in the radiation characteristics of the atmosphere, and thus makes the parameterization computationally more efficient. The adaptive scheme generalizes the accurate radiation computations made in a fraction of the spatial and temporal space to the rest of the field. For validation three case studies with different synoptic conditions were carried out and the performance of the adaptive scheme is compared to the currently operational COSMO-DE radiation configuration, with quarter-hourly radiation computations on 2x2 averaged atmospheric columns. The reference for both schemes are frequent radiation computations on the full grid. The results show that the adaptive scheme is able to reduce the sampling errors in the surface radiation fluxes considerably and to conserve the spatial variability better, than to the operational scheme. Errors in the three-dimensional heating rates are reduced for larger averaging scales. Physical relations between the radiative quantities and cloud water or rain rates are captured better than with the operational scheme. It is shown, that these refinements also lead to improvements with respect to the dynamical development of the model simulation: the adaptive model runs show a smaller divergence from the reference model run than the currently operational scheme. One approach to deal with subgrid-scale variability at the surface in atmospheric models is the so-called mosaic approach, in which the soil and the surface are modelled on an explicit higher horizontal grid resolution than the atmospheric part. In this work a statistical downscaling scheme for the atmospheric input variables needed to drive this higher resolved soil-vegetation-atmosphere-transfer model has been developed, ensuring a scale-consistent two-way coupling between the two sub-systems in the mosaic approach. The statistical downscaling combines deterministic with stochastic modeling in a stepwise approach. Downscaling rules between atmospheric variables as predictands and surface parameters as predictors, depending on the atmospheric state, have been developed. In order to model the small-scale variability correctly, the still missing variance is estimated, and added as autocorrelated noise. The disaggregation system has been built up and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules. The approach has been extensively tested in an offline testbed by applying it to model output, but also “online” in the mesoscale COSMO model. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying the deterministic steps, root mean square errors are reduced. The stochastic step finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. These “offline” tests and also the “online“ application in fully coupled COSMO simulations in combination with the mosaic approach indicate that the mosaic approach is able to improve the performance of the turbulent surface exchange fluxes notably compared to simulations without any surface variability representation. Averaged over six case studies root mean square errors of sensible and latent heat fluxes were reduced by about 9 W/m2 and 13 W/m2, respectively, in the COSMO simulations using the 400m high-resolution COSMO model runs as reference. The application of the new downscaling scheme for the disaggregation of atmospheric forcing variables for the soil module, however, leads to only marginal improvements, despite the positive impact of the downscaling for the single terms in the flux equations. The explanation lies in a cancelling of errors for the computation of the fluxes in the standard mosaic approach, due to which the effect of the overall more realistic structure of the surface variables achieved by the distributed atmospheric forcing is mitigated. In summary, the results indicate that for operational purposes the adaptive radiation parameterization can be recommended without restriction, because it has a large positive impact and does not lead to a significant increase in computation time. The effects of the novel atmospheric disaggregation scheme are small, both with respect to the improvement for the turbulent fluxes but also with respect to computational demands. Given the additional algorithmic complexity an operational application of this downscaling algorithm can currently not be advocated. An operational application of the mosaic approach itself, however, would be beneficial due to its considerable improvement for the representation of the turbulent heat fluxes and the dynamical model development. An increase in computation time would have to be accepted, however, depending on the chosen subgrid resolution.Zwei Verfahren zur Simulation von kleinskaliger Variabilität in Strahlungs- und Landoberflächen-Parametrisierungen in einem mesoskaligen Wettervorhersagemodell Zur Simulation von subskaligen physikalischen Prozessen in numerischen Wettervorhersagemodellen werden eine Reihe von Vereinfachungen und Annahmen angewandt, um Rechenzeit zu sparen. Dies gilt zum einen für die Simulation der Prozesse innerhalb der physikalischen Parametrisierungen, aber auch für die räumliche und zeitliche Frequenz der Aufrufe dieser Parametrisierungen. Die so verursachte Vernachlässigung von kleinskaliger Variabilität kann zu systematischen Fehlern aufgrund der Nichtlinearitäten in den physikalischen Prozessen und zu Inkonsistenzen zwischen den Variablen aus den unterschiedlichen Parametrisierungen führen. In dieser Arbeit werden zwei Methoden vorgestellt, die eine effizientere Berücksichtigung von Heterogenitäten in Atmosphärenmodellen ermöglichen, zum einen innerhalb der Atmosphäre selbst, zum anderen an der Erdoberfläche als untere Randbedingung für die Atmosphäre. Die erste Methode, die adaptive Strahlungstransportparametrisierung, ist eine effektive Methode zur Berechnung der Strahlungseffekte in der Atmosphäre und an der Landoberfläche und führt zu einer Verbesserung der Erfassung von kurzfristigen kleinskaligen Änderungen im Wolkenfeld in Bezug auf Strahlungseffekte. Die zweite Methode hat das Ziel einer skalenkonsistenten Kopplung von Atmosphären- und Landoberflächenmodellen durch die Kopplung eines hochaufgelösten Boden-Vegetations-Transfermodells an das gröbere atmosphärische Modell, wobei der atmosphärische Antrieb mit Hilfe eines in dieser Arbeit entwickelten Downscalings auf die kleine Skala disaggregiert wird. Beide Methoden führen zu einer verbesserten Berechnung der Energiebilanz an der Landoberfläche; erstere durch eine realistischere Simulation der Strahlungsflüsse, zweitere durch Verbesserung der turbulenten Flüsse sensibler und latenter Wärme. Beide Ansätze wurden in das numerische Wettervorhersagemodell implementiert und in der COSMO-DE Modellkonfiguration auf einem Gitter mit 2.8 km horizontalem Gitterabstand getestet. Das Konzept der adaptiven Strahlungstransportparametrisierung macht sich die räumlichen und zeitlichen Korrelationen in den optischen Eigenschaften der Atmosphäre zunutze, wodurch eine effizientere Ausnutzung der verfügbaren Rechenzeit möglich ist. Aktuelle Strahlungsrechnungen basierend auf dem COSMO-internen komplexen Strahlungscode (basierend auf einem δ-Zweistromverfahren), die jeweils in einem Teil der Gitterpunkte vorliegen, werden ausgenutzt, um möglichst realistische Strahlungsinformationen an den restlichen Gitterpunkten zu erhalten. Zur Validierung dieses Schemas wurden drei Fallstudien mit unterschiedlichen synoptischen Bedingungen gerechnet, und die Ergebnisse des adaptiven Schemas mit Ergebnissen für das COSMO-DE-Standardschema verglichen, in welches komplexe Strahlungsrechnungen viertelstündlich auf einem vergröberten Gitter aufgerufen werden. Als Referenz wurden häufige Strahlungsrechnungen auf dem kompletten dreidimensionalen Gitter durchgeführt. Die Ergebnisse zeigen, dass das adaptive Schema in der Lage ist, die Sampling-Fehler des Standard-Verfahrens in den Netto- Strahlungsflüssen an der Landoberfläche deutlich zu reduzieren, und im Gegensatz zum operationellen COSMO-DE-Verfahren die räumliche Variabilität in den Strahlungseffekten korrekt zu simulieren. Fehler in den dreidimensionalen Heizraten werden auf größere Mittelungsskalen verringert. Auch physikalische Zusammenhänge zwischen den Strahlungsgrößen und Wolkenwasser oder Regenraten werden besser erfasst als mit dem Standardschema. Es wird gezeigt, dass diese Verbesserungen auch einen positiven Einfluss auf die dynamische Modellentwicklung haben: Modellläufe, die mit adaptiver Strahlung gerechnet werden, weichen weit weniger von den Referenzläufen ab. Eine Methode um subskalige Variabilität an der Erdoberfläche in atmosphärischen Modellen zu berücksichtigen, ist der so genannte Mosaik-Ansatz. Beim Mosaik-Ansatz wird der Boden und die Erdoberfläche auf einer explizit höheren Auflösung gerechnet als der atmosphärische Teil. In der vorliegenden Arbeit wurde ein statistisches Downscaling-Verfahren für die atmosphärischen Antriebsvariablen für dieses höher aufgelöste Boden-Vegetations- Atmosphären-Transfermodul entwickelt, um eine skalen-konsistente zwei-Wege Kopplung zwischen den beiden Sub-Systemen Atmosphäre und Erdboden/Landoberfläche unterschiedlicher Gitterweite im Mosaik-Ansatz zu ermöglichen. Dieses Disaggregationsschema kombiniert deterministische mit stochastischer Modellierung in einem schrittweisen Downscaling Verfahren. Im ersten Schritt werden bi-quadratische Splines zur Interpolation von der groben zur feinen Skala verwendet. Im zweiten Schritt werden Zusammenhänge zwischen atmosphärischen Variablen als Prädiktanden und Oberflächenparametern as Prädiktoren, abhängig vom Atmosphärenzustand, ausgenutzt. Im letzten Schritt wird die realistische kleinskalige Varianz abgeschätzt, und die fehlende Variabilität als autoregressives Rauschen generiert und hinzugefügt. Dieses Disaggregationsverfahren wurde basierend auf hoch-aufgelöstem Modelloutput aus COSMO-Modellsimulationen mit 400m horizontaler Gitterweite entwickelt und validiert, dazu wurde ein automatisches Regel-Such- System entwickelt. Das Verfahren wurde extensiv “offline” getestet, d.h. angewendet auf Modelloutput, aber auch “online”, d.h. in das mesoskalige COSMO-Modell implementiert und eine Reihe von Fallstudien durchgeführt. Angewendet auf die atmosphärischen Variablen der untersten COSMO-Modellschicht ist das Disaggregationsschema in der Lage, die Referenzfelder adäquat zu rekonstruieren. Durch die beiden deterministischen Downscaling-Schritte werden Fehler reduziert, der stochastische Downscaling-Schritt führt zu einer guten Rekonstruktion der subskaligen Varianz, jeweils in Bezug zu hochaufgelösten Referenz-Feldern. Es wird gezeigt, dass der Mosaik-Ansatz an sich zu deutlichen Verbesserungen in der Simulation der turbulenten Austauschflüsse verglichen mit Simulationen ohne Parametrisierung der subskaligen Oberflächenvariabilität führt. Gemittelt über sechs Fallstudien wird eine Verbesserung in den sensiblen und latenten Wärmeflüssen von 9 W/m2 bzw. 13 W/m2 erreicht, wiederum mit hochaufgelösten COSMO-Modellläufen als Referenz. Die Anwendung des neuen Downscaling-Verfahrens jedoch führt zu einer nur geringen zusätzlichen Verbesserung, trotz eines deutlich positiven Einflusses auf die einzelnen Terme in den Fluss-Gleichungen. Die Ursache für dieses Verhalten liegt darin, dass sich im Standard- Mosaik-Ansatz ohne atmosphärische Disaggregation die Fehler in den einzelnen Termen besser gegenseitig eliminieren, so dass der Effekt der realistischeren Struktur der verschiedenen Variablen durch das Downscaling kaum deutlich wird. Zusammenfassend kann, basierend auf den Ergebnissen in dieser Arbeit, die adaptive Strahlungsparametrisierung ohne Einschränkung für die operationelle Anwendung empfohlen werden, da sie einen deutlich positiven Einfluss hat und keine zusätzliche Rechenzeit erfordert. Der Mosaik-Ansatz an sich hat einen deutlich positiven Effekt auf die Simulation der turbulenten Wärmeflüsse, wobei jedoch ein Anstieg der Rechenzeit, abhängig von der gewählten subskaligen Auflösung, in Kauf genommen werden muss. Die Effekte des neuen atmosphärischen Disaggregation in einer kombinierten Anwendung mit dem Mosaik sind vergleichsweise klein, weswegen trotz des minimalen zusätzlichen Rechenaufwands ein operationeller Einsatz in meteorologischen Modellen nicht empfohlen ist. Das Downscaling als solches stellt jedoch ein nützliches Verfahren zur Erzeugung hoch-aufgelöster atmosphärischer Antriebsdaten für hydrologische Modelle dar