scholarly journals Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

2018 ◽  
Vol 12 (10) ◽  
pp. 3137-3160 ◽  
Author(s):  
Franziska Gerber ◽  
Nikola Besic ◽  
Varun Sharma ◽  
Rebecca Mott ◽  
Megan Daniels ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Complex Terrain ◽  
Small Scale ◽  
Near Surface ◽  
Precipitation Patterns ◽  
Simulated Precipitation ◽  
Precipitation Processes ◽  
Snow Distribution ◽  
Snow Precipitation

Abstract. Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydropower, avalanche forecasting and freshwater resources. However, it is still challenging to quantitatively forecast precipitation, especially over complex terrain where the interaction between local wind and precipitation fields strongly affects snow distribution at the mountain ridge scale. Therefore, it is essential to retrieve high-resolution information about precipitation processes over complex terrain. Here, we present very-high-resolution Weather Research and Forecasting model (WRF) simulations (COSMO–WRF), which are initialized by 2.2 km resolution Consortium for Small-scale Modeling (COSMO) analysis. To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated COSMO–WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation enhancement in the model. The high precipitation amounts also lead to a higher spatial variability in the model compared to radar estimates. Overall, an autocorrelation and scale analysis of radar and COSMO–WRF precipitation patterns at a horizontal grid spacing of 450 m show that COSMO–WRF captures the spatial variability normalized by the domain-wide variability in precipitation patterns down to the scale of a few kilometers. However, simulated precipitation patterns systematically show a lower variability on the smallest scales of a few hundred meters compared to radar estimates. A comparison of spatial variability for different model resolutions gives evidence for an improved representation of local precipitation processes at a horizontal resolution of 50 m compared to 450 m. Additionally, differences of precipitation between 2830 m above sea level and the ground indicate that near-surface processes are active in the model.

scholarly journals Spatial variability of snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

2018 ◽  
Author(s):  
Franziska Gerber ◽  
Nikola Besic ◽  
Varun Sharma ◽  
Rebecca Mott ◽  
Megan Daniels ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Small Scale ◽  
Hydro Power ◽  
Near Surface ◽  
Avalanche Forecasting ◽  
Precipitation Patterns ◽  
Flow Interactions ◽  
Wide Variability ◽  
High Precipitation ◽  
Snow Precipitation

Abstract. Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydro-power, avalanche forecasting and fresh water resources. However, the relative importance of processes such as cloud-dynamics and pure particle-flow interactions is still barely known and models are essential to investigate these processes. Here, we present very high resolution Weather Research and Forecasting model (WRF) simulations, which are initialized by 2.2 km resolution Consortium for Small-scale Modeling (COSMO) reanalysis (COSMO–WRF). To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation in the model. The high precipitation also leads to a higher spatial variability in the model at the scale of 10 km. Overall, an autocorrelation and scale analysis of radar and WRF precipitation patterns show that WRF captures the variability relative to the domain wide variability of precipitation patterns down to the scale of few kilometers, but misses quite substantial variability on the smallest scales of a few 100 meters. However, differences of precipitation between 2830 m above sea level and the ground indicate that near-surface processes are active in the model.

scholarly journals The Importance of Near-Surface Winter Precipitation Processes in Complex Alpine Terrain

2019 ◽  
Vol 20 (2) ◽  
pp. 177-196 ◽  
Author(s):  
Franziska Gerber ◽  
Rebecca Mott ◽  
Michael Lehning
Keyword(s):  
Winter Precipitation ◽  
Snow Accumulation ◽  
Windward Side ◽  
Leeward Side ◽  
Small Scale ◽  
Orographic Precipitation ◽  
Near Surface ◽  
Simulated Precipitation ◽  
Snow Precipitation ◽  
Precipitation Enhancement

Abstract In this study, near-surface snow and graupel dynamics from formation to deposition are analyzed using WRF in a large-eddy configuration. The results reveal that a horizontal grid spacing of ≤50 m is required to resolve local orographic precipitation enhancement, leeside flow separation, and thereby preferential deposition. At this resolution, precipitation patterns across mountain ridges show a high temporal and spatial variability. Simulated and observed event-mean snow precipitation across three mountain ridges in the upper Dischma valley (Davos, Switzerland) for two precipitation events show distinct patterns, which are in agreement with theoretical concepts, such as small-scale orographic precipitation enhancement or preferential deposition. We found for our case study that overall terrain–flow–precipitation interactions increase snow accumulation on the leeward side of mountain ridges by approximately 26%–28% with respect to snow accumulation on the windward side of the ridge. Cloud dynamics and mean advection may locally increase precipitation on the leeward side of the ridge by up to about 20% with respect to event-mean precipitation across a mountain ridge. Analogously, near-surface particle–flow interactions, that is, preferential deposition, may locally enhance leeward snow precipitation on the order of 10%. We further found that overall effect and relative importance of terrain–flow–precipitation interactions are strongly dependent on atmospheric humidity and stability. Weak dynamic stability is important for graupel production, which is an essential component of solid winter precipitation. A comparison to smoothed measurements of snow depth change reveals a certain agreement with simulated precipitation across mountain ridges.

Improvements to an intermediate complexity atmospheric model for high-resolution downscaling in very complex terrain.

2021 ◽  
Author(s):  
Dylan Reynolds ◽  
Bert Kruyt ◽  
Ethan Gutmann ◽  
Tobias Jonas ◽  
Michael Lehning ◽  
...  
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Large Scale ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Surface Flow ◽  
Near Surface ◽  
Precipitation Patterns ◽  
Intermediate Complexity ◽  
Nwp Model

<p>            Snow deposition patterns in complex terrain are heavily dependent on the underlying topography. This topography affects precipitating clouds at the kilometer-scale and causes changes to the wind field at the sub-kilometer scale, resulting in altered advection of falling hydrometeors. Snow particles are particularly sensitive to changes in the near-surface flow field due to their low density. Atmospheric models which run at the kilometer scale cannot resolve the actual heterogeneity of the underlying terrain, resulting in precipitation maps which do not capture terrain-affected precipitation patterns. Thus, snow-atmosphere interactions such as preferential deposition are often not resolved in precipitation data used as input to snow models. To bridge this spatial gap and resolve snow-atmosphere interactions at the sub-kilometer scale, we couple an intermediate complexity atmospheric model (ICAR) to the COSMO NWP model. Applying this model to sub-kilometer terrain (horizontal resolution of 50 and 250 m) required changes to ICAR’s computational grid, atmospheric dynamics, and boundary layer flow. As a result, the near-surface flow now accounts for surface roughness and topographically induced speed up. This has been achieved by using terrain descriptors calculated once at initialization which consider a point’s exposure or sheltering relative to surrounding terrain. In particular, the use of a 3-dimensional Sx parameter allows us to simulate areas of stagnation and recirculation on the lee of terrain features. Our approach maintains the accurate large-scale precipitation patterns from COSMO but resolves the dynamics induced by terrain at the sub-kilometer scale without adding additional computational burden. We find that solid precipitation patterns at the ridge scale, such as preferential deposition of snow, are better resolved in the high-resolution version of ICAR than the current ICAR or COSMO models. This updated version of ICAR presents a new tool to dynamically downscale NWP output for snow models and enables future studies of snow-atmosphere interactions at domain scales of 100’s of kilometers.</p>

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

2011 ◽  
Vol 139 (6) ◽  
pp. 2008-2024 ◽  
Author(s):  
Brian C. Ancell ◽  
Clifford F. Mass ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Complex Terrain ◽  
Error Variance ◽  
Small Scale ◽  
Observation Error ◽  
Grid Spacing ◽  
Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

scholarly journals Assessment of small-scale variability of rainfall and multisatellite precipitation estimates using a meso-rain gauge network measurements from southern peninsular India

2015 ◽  
Vol 12 (10) ◽  
pp. 10389-10429
Author(s):  
K. Sunilkumar ◽  
T. Narayana Rao ◽  
S. Satheeshkumar
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Diurnal Cycle ◽  
Rain Gauge ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Northeast Monsoon ◽  
Peninsular India ◽  
Precipitation Estimates ◽  
Rain Gauge Network

Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain storms (both in space and time) over a complex hilly terrain in southeast peninsular India. Three years of high-resolution gauge measurements are used to evaluate 3 hourly rainfall and sub-daily variations of four widely used multisatellite precipitation estimates (MPEs). The network consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of ~ 10 km. Morphological features of rainfall in two principal monsoon seasons (southwest monsoon: SWM and northeast monsoon: NEM) show marked seasonal differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale systems (in wet spells), whereas the contribution from small-scale systems is considerable in SWM. Rain storms with longer duration and copious rainfall are seen mostly in the western quadrants in SWM and northern quadrants in NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits marked spatiotemporal variability with strong diurnal cycle at all the stations (except for 1) during the SWM and insignificant diurnal cycle at many stations during the NEM. On average, the diurnal amplitudes are a factor 2 larger in SWM than in NEM. The 24 h harmonic explains about 70 % of total variance in SWM and only ~ 30 % in NEM. The late night-mid night peak (20:00–02:00 LT) observed during the SWM is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both the monsoons. The 1 h resolution data shows the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ~30 km in both monsoons. Evaluation of high-resolution rainfall estimates from various MPEs against the gauge rainfall indicates that all MPEs underestimate the weak and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions, however, considerable improvement is observed at 24 h resolution. Among different MPEs, Climate Prediction Centre morphing technique (CMORPH) performs better at 3 hourly resolution in both monsoons. The performance of TRMM multisatellite precipitation analysis (TMPA) is much better at daily resolution than at 3 hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insignificant diurnal cycle in NEM.

Small-Scale Signal in Mean Dynamic Topographies Applying Combined Geoid Models

2020 ◽  
Author(s):  
Frank Siegismund ◽  
Xanthi Oikonomidou ◽  
Philipp Zingerle
Keyword(s):  
High Resolution ◽  
Gulf Stream ◽  
Ocean Surface ◽  
Ocean Dynamics ◽  
Small Scale ◽  
Western Boundary ◽  
Low Resolution ◽  
Geoid Model ◽  
Near Surface ◽  
Dependent Signal

<p>The Dynamic ocean Topography (DT) describes the deviation of the true ocean surface from a hypothetical equilibrium state ocean at rest forced by gravity alone. With the geostrophic surface currents obtained from its gradients the DT is an essential parameter for describing the ocean dynamics. Observation-based global temporal Mean geodetic DTs (MDTs) are obtained from the difference of altimetric Mean Sea Surface (MSS) and the geoid height, that equipotential surface of gravity closest to the ocean surface.</p><p>The geoid is provided either as a satellite-only, or a combined model including in addition gravity anomalies derived from satellite altimetry and ground data. In recent years the focus was on satellite-only models, produced from new space-born observations obtained from the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and Ocean Circulation Explorer (GOCE) satellite missions. Moreover, combined geoid models are only cautiously used for MDT calculation, since it is still in question to what extent the gravity information obtained from altimetry is distorted by the MDT information included therein and how this translates into errors of the MDT.</p><p>Here we want to concentrate on MDT models based on recent combined geoid models. An assessment is provided based on comparisons to near-surface drifter data from the Global Drifter Program (GDP). Besides providing a general, global assessment, we focus on signal content on small scales, addressing mainly two questions: 1) Do MDTs obtained from combined geoid models contain signal for scales smaller than resolvable by the<br>satellite-only models? 2) Is there a maximum resolution beyond which no signal is detectable?</p><p>Until recently, these questions couldn't be answered since low resolution MDTs usually contained strong wavy-structured errors and thus needed a strong spatial filtering thereby killing the smallest scales resolved in the MDT. These errors, which worsen with lower resolution, are caused by Gibbs effects provoked by imperfections in bringing the high resolution ocean-only MSS models into spectral consistency with the much lower resolved global geoid model. A new methodology, however, improves the necessary globalization of the MSS. After subtraction of the geoid model, subsequent cutting-off the signal beyond a specific spherical harmonic degree and order (d/o) results in an MDT without any Gibbs effects, also for low resolution models.</p><p>To answer the questions posed above applying the new methodology, the scale-dependent signal in MDTs for different geoid models is presented for a list of cut off d/os. To minimize the influence of noise on the results we concentrate on strong signal Western Boundary Currents like the Gulf Stream and the Kuroshio. For the Gulf Stream results of a high resolution hydrodynamic model are available and presented as an independent method to estimate the scale dependent signal.</p>

scholarly journals On the Impact of NWP Model Background on Very High–Resolution Analyses in Complex Terrain

2015 ◽  
Vol 30 (4) ◽  
pp. 1077-1089 ◽  
Author(s):  
Alexander Kann ◽  
Christoph Wittmann ◽  
Benedikt Bica ◽  
Clemens Wastl
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Background Information ◽  
Small Scale ◽  
Topographic Effects ◽  
Mountainous Regions ◽  
Nwp Model ◽  
The Impact ◽  
Very High

Abstract The capability to accurately analyze the spatial distribution of temperature and wind at very high spatial (2.5–1 km) and temporal (60–5 min) resolutions is of interest in many modern techniques (e.g., nowcasting and statistical downscaling). In addition to observational data, the generation of such analyses requires background information to adequately resolve nonstatic, small-scale phenomena. Numerical weather prediction (NWP) models are of continuously increasing skill and are more capable of providing valuable information on convection-resolving scales. The present paper discusses the impact of two operational NWP models on hourly 2-m temperature and 10-m wind analyses as created by the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which includes a topographic downscaling procedure. The NWP models used for this study are a revised version of ARPEGE–ALADIN (ALARO; 4.8-km resolution) and the Applications of Research to Operations at Mesoscale (AROME; 2.5-km resolution). Based on a case study and a longer-term validation, it is shown that, generally, the finer the grid spacing of the background model and the higher the resolution of the target grid in the downscaling procedure, the slightly more accurate is the analysis. This is especially true for wind analyses in mountainous regions, where a realistic simulation of topographic effects is crucial. In the case of 2-m temperature, the impact is less pronounced, but the topographic downscaling at very high resolution at least adds detail in complex terrain. However, in the vicinity of station observations, the analysis algorithm is capable of spatially adjusting the larger biases found in the ALARO model while having a lesser effect on the downscaled AROME model.

scholarly journals An NWP Model Intercomparison of Surface Weather Parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere 1

2019 ◽  
Vol 34 (4) ◽  
pp. 959-983 ◽  
Author(s):  
Morten Køltzow ◽  
Barbara Casati ◽  
Eric Bazile ◽  
Thomas Haiden ◽  
Teresa Valkonen
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
The Arctic ◽  
Small Scale ◽  
Limited Area ◽  
Model Intercomparison ◽  
Near Surface ◽  
Surface Weather ◽  
High Resolution Models ◽  
The Impact

AbstractIncreased human activity in the Arctic calls for accurate and reliable weather predictions. This study presents an intercomparison of operational and/or high-resolution models in an attempt to establish a baseline for present-day Arctic short-range forecast capabilities for near-surface weather (pressure, wind speed, temperature, precipitation, and total cloud cover) during winter. One global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and three high-resolution, limited-area models [Applications of Research to Operations at Mesoscale (AROME)-Arctic, Canadian Arctic Prediction System (CAPS), and AROME with Météo-France setup (MF-AROME)] are evaluated. As part of the model intercomparison, several aspects of the impact of observation errors and representativeness on the verification are discussed. The results show how the forecasts differ in their spatial details and how forecast accuracy varies with region, parameter, lead time, weather, and forecast system, and they confirm many findings from mid- or lower latitudes. While some weaknesses are unique or more pronounced in some of the systems, several common model deficiencies are found, such as forecasting temperature during cloud-free, calm weather; a cold bias in windy conditions; the distinction between freezing and melting conditions; underestimation of solid precipitation; less skillful wind speed forecasts over land than over ocean; and difficulties with small-scale spatial variability. The added value of high-resolution limited area models is most pronounced for wind speed and temperature in regions with complex terrain and coastlines. However, forecast errors grow faster in the high-resolution models. This study also shows that observation errors and representativeness can account for a substantial part of the difference between forecast and observations in standard verification.

scholarly journals A Numerical Sensitivity Study on the Impact of Soil Moisture on Convection-Related Parameters and Convective Precipitation over Complex Terrain

2011 ◽  
Vol 68 (12) ◽  
pp. 2971-2987 ◽  
Author(s):  
Christian Barthlott ◽  
Norbert Kalthoff
Keyword(s):  
Soil Moisture ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Sensitivity Study ◽  
Convective Precipitation ◽  
Small Scale ◽  
Near Surface ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
The Impact

Abstract The impact of soil moisture on convection-related parameters and convective precipitation over complex terrain is studied by numerical experiments using the nonhydrostatic Consortium for Small-Scale Modeling (COSMO) model. For 1 day of the Convective and Orographically Induced Precipitation Study (COPS) conducted during summer 2007 in southwestern Germany and eastern France, initial soil moisture is varied from −50% to +50% of the reference run in steps of 5%. As synoptic-scale forcing is weak on the day under investigation, the triggering of convection is mainly due to soil–atmosphere interactions and boundary layer processes. Whereas a systematic relationship to soil moisture exists for a number of variables (e.g., latent and sensible fluxes at the ground, near-surface temperature, and humidity), a systematic increase of 24-h accumulated precipitation with increasing initial soil moisture is only present in the simulations that are drier than the reference run. The time evolution of convective precipitation can be divided into two regimes with different conditions to initiate and foster convection. Furthermore, the impact of soil moisture is different for the initiation and modification of convective precipitation. The results demonstrate the high sensitivity of numerical weather prediction to initial soil moisture fields.

Sign in / Sign up

Export Citation Format

Share Document