The precipitation response to variable terrain forcing over low mountain ranges in different weather regimes

Schneider, Linda; Barthlott, Christian; Barrett, Andrew I.; Hoose, Corinna

doi:10.1002/qj.3250

Cited by 23 publications

(31 citation statements)

References 51 publications

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“…In the analysis of factors that may affect the spatial patterns of the MP and EDP complexities, it is found that the altitude is a common constraint. Previous studies have shown that the topography can affect the formation and distribution of precipitation (Schneider et al ., 2018). This study shows that topography also has an impact on the complexity of precipitation.…”

Section: Discussionmentioning

confidence: 99%

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

Zhang

Liu

et al. 2020

Quart J Royal Meteoro Soc

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Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity.

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Section: Discussionmentioning

confidence: 99%

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

Zhang

Liu

et al. 2020

Quart J Royal Meteoro Soc

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“…This may create local instabilities and can be an issue when assessing the impact of topography on summer convection in a weak synoptic flow (e.g. Schneider et al, 2018). However, the fast large-scale background flow associated with Egon quickly ventilates the model and the relatively low sea-surface temperature in winter prevents spontaneous convective activity.…”

Section: Model Experimentsmentioning

confidence: 99%

Dynamics of sting‐jet storm Egon over continental Europe: Impact of surface properties and model resolution

Eisenstein

Pantillon

Knippertz

2019

Quart J Royal Meteoro Soc

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Intense Shapiro–Keyser cyclones are often accompanied by a sting jet (SJ), an air stream that descends from the cloud head into the frontal‐fracture region and can cause extreme surface gusts. Previous case‐studies have concentrated on the North Atlantic and the British Isles. Here we present the first‐ever detailed analysis of an SJ over continental Europe and investigate the influence of topography on its dynamical evolution based on observations and high‐resolution simulations using the ICOsahedral Nonhydrostatic model (ICON). Windstorm Egon intensified over the English Channel and then tracked from northern France to Poland on 12–13 January 2017, causing gusts of almost 150 km·h−1 and important damage. ICON reproduces the storm dynamics, although it delays the explosive deepening, shifts the track southward over Belgium and Germany and underestimates gusts over land. Storm characteristics show weak sensitivity to varying grid spacing between 1.6 and 6.5 km, while switching off the convection parametrization at 3.3 km grid spacing improves correlations with surface observations but deteriorates the mean error. Trajectories reveal typical SJ characteristics such as mid‐level descent, strong acceleration and conditional symmetric and other mesoscale instabilities, while evaporative cooling is stronger than in previous cases from the literature, preventing drying during descent. The SJ identification and the occurrence of mesoscale instabilities depend considerably on model resolution, convective parametrization, output frequency and employed thresholds for trajectory selection. Sensitivity experiments with modified surface characteristics show that the combined effects of warm‐air blocking by the Alps, higher roughness over land and reduced surface fluxes cause Egon to fill more quickly and to move on a faster, more northern track across Germany. While the SJ response is complex, showing some compensating effects, surface gusts strongly increase when roughness is reduced. These results suggest that weather forecasters in continental Europe should be more aware of the potential risks associated with SJs.

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“…It is a non-hydrostatic limited-area atmospheric prediction model, which operates on a rotated latitude/longitude grid with an Arakawa C-grid for horizontal differencing. First, simulations are performed with 2.8-km grid spacing on the operational COSMO-DE grid of the German Weather Service driven by 7 km COSMO-EU initial and boundary data (see Schneider et al (2018) for exact domain location).…”

Section: Numerical Modelmentioning

confidence: 99%

“…Land surface properties (e.g., land cover, terrain, and soil texture) are highly heterogeneous across a wide range of spatiotemporal scales (Santanello et al, 2018) and potential linkages between land surface variables and atmospheric variables such as temperature and precipitation are difficult to establish (e.g., Seneviratne et al, 2010). Over mountainous terrain, thermally induced wind systems and low-level convergence zones are crucial for the initiation of deep convection with prevailing weak winds (e.g., Schneider et al, 2018). They are often less well resolved in operational models, which limits the forecast capabilities in contrast to situations governed by large-scale synoptic forcing, when the forecast of precipitation is often more reliable (Baldauf et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Relative impact of aerosol, soil moisture and orographyperturbations on deep convection

Schneider¹,

Barthlott²,

Hoose³

2019

Preprint

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<p><strong>Abstract.</strong> The predictability of deep moist convection depends on many factors such as the synoptic-scale flow, the geographical region (i.e., the presence of mountains), land surface&#8211;atmosphere as well as aerosol&#8211;cloud interactions. This study addresses all these factors by investigating the relative impact of orography, soil moisture and aerosols on precipitation over Germany in different weather regimes. To this end, we conduct numerical sensitivity studies with the COnsortium for Small-sale MOdelling (COSMO) model at high spatial resolution (500&#8201;m grid spacing) for six days with weak and strong synoptic forcing. The numerical experiments consist of (i) successive smoothing of topographical features, (ii) systematic changes in the initial soil moisture fields (spatially homogeneous increase/decrease, horizontal uniform soil moisture, different realizations of dry/wet patches), and (iii) different assumptions on the ambient aerosol concentration (spatially homogeneous and heterogeneous fields). Our results show that the impact of these perturbations on precipitation is on average higher for weak than for strong synoptic forcing. Soil moisture and aerosols are each responsible for the maximum precipitation response for three of the cases, while the sensitivity to terrain forcing always shows the smallest spread. For the majority of the analyzed cases, the model produces a positive soil moisture-precipitation feedback when averaged over the entire model domain. Furthermore, correct initial values are much more important than the spatial distribution of dry and wet patches. The precipitation response to changes in the CCN concentration is more complex and case dependent. The smoothing of terrain shows weaker impacts on days with strong synoptic forcing because surface fluxes are less important and orographic ascent is still simulated reasonably well, despite missing fine-scale orographical features. We apply an object-based characterization to identify if and how the perturbations affect the structure, location, timing, and intensity of precipitation. These diagnostics reveal that the structure component is highest in the soil moisture and aerosol simulations, often due to changes in the maximum precipitation amounts. This indicates that the dominant mechanisms for convection initiation remain, but that precipitation amounts depend on the strength of the trigger mechanisms. Location and amplitude parameters are both much smaller. Still, the temporal evolution of the amplitude component correlates well with the rain rate. Our results suggest that for quantitative precipitation forecasting, both aerosols and soil moisture are of similar importance and that their inclusion in convective-scale ensemble forecasting containing classical sources of uncertainty should be assessed in the future.</p>

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The precipitation response to variable terrain forcing over low mountain ranges in different weather regimes

Cited by 23 publications

References 51 publications

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

Dynamics of sting‐jet storm Egon over continental Europe: Impact of surface properties and model resolution

Relative impact of aerosol, soil moisture and orographyperturbations on deep convection

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