Complex Orography Research Articles

Sensitivity analysis of the WRF model: Assessment of performance in high resolution simulations in complex terrain in the Canary Islands

Canary Islands and other regions have been greatly damaged by weather events during the last decades. For this reason, the main duty of National Meteorological Services is to minimize socio-economic losses by forecasting adverse weather episodes with enough time in advance. To achieve this goal, the use of numerical weather prediction models is highly relevant. And, even more crucial, is to comprehend the model accuracy.In this paper, an exhaustive sensitivity analysis of the Weather Research and Forecasting (WRF) model over the Canary Islands has been carried out. The complex terrain of the archipelago makes the islands a test bench of high interest. Four scores were used to assess the accuracy of the model configurations: Bias, mean absolute error (MAE), root of the Mean Squared Error (RMSE), and the correlation coefficient (r). Initially, twenty-five WRF model configurations were considered. However, a preliminary test discarded inadequate configurations, and reduced the number to six. The variables of interest were air temperature at 2 m (T2m), maximum 1-h wind gust at 10 m and 3-h rainfall accumulation. The results indicated a systematic wind speed underestimation. This underestimation is related to the influence of the location and the complex orography. The most accurate wind forecasts were obtained using the Mellor-Yamada-Janjic Planetary Boudary Layer (PBL) scheme with the WSM6 microphysics (MP) scheme. Another major conclusion is that, for precipitation, the PBL scheme has a greater impact than the MP scheme. Finally, the results show that the Boulac – Thompson combination is the most accurate regarding T2m forecast.

High resolution modelling of severe wind patterns over the north-west Black Sea Region

The research studies the performance of the convective-permitting Harmonie model in reproducing mesoscale features of the wind regime over the north-western part of the Black Sea. It allowed establishing the optimum configuration, projection and geometry of the model's high spatial resolution area over Ukrainian part of the Black Sea, preparing a digital format for a coastline based on the resolution and conducting numerical experiments to verify informativeness and stability of computations. It also presents a detailed description of the forecasting procedure which includes a data flow from the Meteorological Archiving and Retrieving System (MARS) of the European Center, creating boundary conditions, forecast computations and a model output composition for the particular region and domain resolution.
 The results have shown that the Harmonie model with the 2.5 km spatial resolution and the 60 second time step is able to reproduce detailed spatial variability of a near-surface wind field and its evolution to the corresponding scales. In particular, the model is able to simulate mesoscale circulation features of approximately ten km over a homogeneous sea surface and to track their evolution; to monitor the position of convergence zones; to highlight the spatial characteristics of a lee-side wind attenuation band along the coast line when wind blows from the shore; to specify mesoscale wind patterns in bays and along the coastline with complex orography; to reproduce the weakening of a wind velocity over an urban area due to increased surface roughness.
 Two operational forecasting systems, GFS-WRF and ARPEGE/IFS-Harmonie are compared by the following components: numerical solvers, sub-grid parameterizations, efficiency of computer resources and intellectual potential. The GFS model output with the 25 km spatial resolution is able to correctly reproduce over the region only large-scale atmospheric patterns. However, for rapid changes in the atmospheric circulation accompanied by changes in the wind direction to the opposite and wind increase, the model simulations are delayed in terms of wind field evolution. Additionally, because of crude spatial resolution, the GFS model is unable to describe mesoscale atmospheric features due to differences in surface types, orography, thermal contrasts, etc. Comparison of the both model outputs versus observations from Odesa, Chornomorsk and Yuzhnyi port during severe wind conditions has shown that the discrepancy between the models and observations within the allowable error value (5 m/s) occurred only for Odesa port with regard to the Harmonie model for weak wind velocity. The difference partially increases for moderate wind from the shore, while for strong wind from east and south directions indicates disagreement between the model results and observations and achieves critical values of 20-25 m/s. Such values are mainly determined by the discrepancy in wind direction (up to 180°). The comparison results clearly indicate the doubtful representativeness of wind observations at Chornomorsk and Yuzhnyi stations in general, and at Odesa station in particular.

A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain

An adaptive high-temporal resolution interpolation scheme for meteorological observations is presented. It stems from a combination of linear regression, anomaly correction and clustering. A number of approaches to tackle this problem for monthly and daily data have been proposed in the past, but interpolation studies at sub-daily temporal scales are much more limited. Hourly and sub-hourly observational datasets use to present high variability that may be related to different weather conditions. In the proposed methodology, rather than considering the whole data set to perform the interpolation, data are divided in different clusters of variable size, separating regions with potential dissimilar behaviour. A linear regression model is calculated for each cluster and compared against a global model obtained considering all the observations. Only those clusters whose regression model yields a reduction of errors compared to the global model are selected. The adaptive condition lays on that several numbers of clusters are tested and the one that performs the best, in terms of Root Mean Square Error, is selected every time an interpolation is conducted. The methodology presented provides gridded analysis fields of hourly and sub-hourly intervals at 250 m of horizontal resolution. It was originally developed for a complex terrain region (Catalonia, NE Spain), and it was also demonstrated in the German Land of Baden-Württemberg and in the Italian region of Emilia-Romagna. Results show a reduction of cross-validation errors using the leave-one-out method for air temperature and dew point temperature fields and a proper representation of complex orography features. The scheme presented is implemented in Python as pyMICA and it is available as open-source software.

Investigating ground-based radar and spaceborne infrared radiometer synergy for lightning areal prediction in complex orography

A new multi-sensor approach, named PoLCast (Probability of Lightning foreCast), for predicting the lightning activity in a complex orography geographical area is proposed and discussed. The PoLCast input information are the ground-based weather radar horizontally polarized reflectivity factor and the atmospheric instability indexes, derived from the Spin Enhanced Visible Infrared Imager (SEVIRI) radiometer onboard the Meteosat Second Generation (MSG) satellite. The weather radar data are used to calculate the probability of lightning following a direct relation between the maximum values of the reflectivity factor and the lightning occurrence, whereas the atmospheric instability indexes from SEVIRI are used to constrain the probability of lighting, derived from weather radar data, and enhance such probability in cases of more unstable troposphere. To test the PoLCast methodology, the output of the Blitzortung and the “Sistema Italiano Rilevamento Fulmini” (SIRF) ground-based lightning sensor networks are used together with the C-band Mt. Midia weather radar within its 180 km diameter coverage over the Central Italy area. Both satellite and radar data are pre-processed into PoLCast to obtain a single time series in terms of the areal probability of lightning (PoL). PoLCast performances are evaluated in terms of statistical scores using 12 heterogeneous case studies over Central Italy. Even though the number of available cases is relatively limited, quantitative results show high areal PoL (from 0 to 100%) with a case-by-case variability of false alarm rate from 3 to 72%. The advantage of a multi-sensor technique, such as PoLCast, with respect to an approach using weather radar data only, becomes more evident when lightning activity is not present and the leading time of lightning forecast exceeds 2.5 h.

ДИНАМІКА ВИНИКНЕННЯ СТИХІЙНИХ ОПАДІВ НА ТЕРИТОРІЇ ЗАКАРПАТСЬКОЇ ОБЛАСТІ З 1990 ПО 2019 рр.

Heavy rainfall affects almost all spheres of human life and the economy, the intensity of this impact may depend on their type and quantity, duration and phase state, as well as geographical properties of the territory. In the paper determined the dynamics of severe precipitation (heavy and extreme rains and snowfalls) in the Transcarpathian region for 1990-2019. Information on severe precipitation was obtained from the observation points of the state hydrometeorological network of the Transcarpathian Regional Center for Hydrometeorology. Also included are data from the Pozhezhevska snow avalanche station (Ivano-Frankivsk region) as a representative observation point for the highland zone of the eastern part of the Transcarpathian region. Involvement of information from hydrological posts allowed to discover more cases of dangerous weather phenomena. The study revealed trends in the formation of heavy and extreme rains and snowfalls over the area with complex orography.

Realised added value in dynamical downscaling of Australian climate change

Coarse resolution global climate models (GCMs) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.

A multi-sensor and modeling analysis of a severe convective storm in Lake Maggiore area (northwestern Italy)

The analysis of a severe convective storm, occurred in northwestern Italy during late summer 2012, is presented here. The event was characterized by heavy precipitation, hail and intense wind gusts, causing injuries and damages to trees, buildings and infrastructures.The description of the case study draws upon a wide palette of observational data. Surface and upper-air observations helped characterize the event, emphasizing the rapid changes in the meteorological parameters in the study area; satellite data highlights the presence of deep moist convection, associated with strong updrafts; Doppler radar allows the identification of the main features of the event in terms of storm-motion, hail detection/probability and the possible presence of a mesocyclone.A high-resolution (3 km inner grid spacing) simulation with the WRF (Weather Research and Forecasting) model was performed to study the atmospheric conditions conducive to convection, as well as to estimate the main instability parameters. The main features of the storm were well reproduced with WRF; furthermore, the high values of the simulated instability parameters denote the presence of favourable conditions for severe convection.The analysis of this event shows that supercells can develop also in a pre-alpine environment, characterized by a complex orography, in particular in the sub-regions where the local morphology creates conditions favourable for severe weather development.

Impact of ocean-atmosphere coupling on regional climate: the Iberian Peninsula case

Regional models used for downscaling the European climate usually include a relatively small area of the Atlantic Ocean and are uncoupled, with the SST used as lower boundary conditions much coarser than the mesh of the regional atmospheric model. Concerns thus arise about the proper representation of the oceanic influence and the role of air-sea coupling in such experiments. A complex orography and the exposure to different air and ocean masses make the Iberian Peninsula (IP) an ideal test case for exploring the impact of including explicitly the North Atlantic in the regional domain and the added value that coupling brings to regional climate modeling. To this end, the regionally-coupled model ROM and its atmospheric component, the regional atmospheric model REMO are used in a set of coupled and uncoupled experiments forced by the ERA-Interim reanalysis and by the global climate model MPI-ESM. The atmospheric domain is the same in all simulations and includes the North Atlantic and the ocean component is global and eddy permitting. Results show that the impact of air-sea coupling on the IP winter biases can be traced back to the features of the simulated North Atlantic Ocean circulation. In summer, it is the air-sea interactions in the Mediterranean that exert the largest influence on the regional biases. Despite improvements introduced by the eddy-permitting ocean, it is suggested that a higher resolution could be needed for a correct simulation of the features of the large-scale atmospheric circulation that impact the climate of the IP.

Climate change projections of temperature and precipitation in Chile based on statistical downscaling

General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments. This is especially relevant for regions with complex orography and coastlines, such as in Chile. Downscaling techniques attempt to reduce the gap between global and regional/local scales; for instance, statistical downscaling methods establish empirical relationships between large-scale predictors and local predictands. Here, statistical downscaling was employed to generate climate change projections of daily maximum/minimum temperatures and precipitation in more than 400 locations in Chile using the analog method, which identifies the most similar or analog day based on similarities of large-scale patterns from a pool of historical records. A cross-validation framework was applied using different sets of potential predictors from the NCEP/NCAR reanalysis following the perfect prognosis approach. The best-performing set was used to downscale six different CMIP5 GCMs (forced by three representative concentration pathways, RCPs). As a result, minimum and maximum temperatures are projected to increase in the entire Chilean territory throughout all seasons. Specifically, the minimum (maximum) temperature is projected to increase by more than 2 °C (6 °C) under the RCP8.5 scenario in the austral winter by the end of the twenty-first century. Precipitation changes exhibit a larger spatial variability. By the end of the twenty-first century, a winter precipitation decrease exceeding 40% is projected under RCP8.5 in the central-southern zone, while an increase of over 60% is projected in the northern Andes.

Climatological and Hydrological Observations for the South American Andes: In situ Stations, Satellite, and Reanalysis Data Sets

Modern hydrology relies on multiple sources of information combined with climatological, hydrological and glaciological data. These data can be collected through various sources such as private initiatives by companies, research programs, and both national and international organisms. They also vary by types, e.g. in-situ measurements, satellite, reanalysis and simulated data. Recently the ANDEX research project, as a GEWEX regional program, was created to understand the processes related to the hydrological cycle and energy fluxes in the Andean region from Colombia to Patagonia. It is quite challenging to carry out this program given the complex orography and diversity of climates from tropical to sub-polar climates. This review article is a compilation of the various databases that are useful for hydrometeorological research in the South American Andes. The National Meteorological and Hydrological Services in Bolivia, Chile, Colombia, Ecuador, Peru, Venezuela and Argentina provide a large amount of data however the high-elevation areas are poorly instrumented and the number of stations varies greatly between the countries. National databases are only partially shared with the international bodies responsible for summarizing the existing data; this causes problems in term of data product assimilation. Across the entire continent, too few radiosondes are being used despite the fact that these data are crucial for validating and identifying problems in the atmospheric models. An increasing number of satellite data are available but it is difficult to assimilate them into the hydroclimatogical models suited to the adjusted spatial and temporal resolutions. Specifically, for precipitation, we recommend merged products that account for the high spatial and temporal variability across the Andes. Finally, the international ANDEX program could be an excellent opportunity to increase the knowledge of the hydrological processes in the Andes.

Effect of low-level flow and Andes mountain on the tropical and mid-latitude precipitating cloud systems: GPM observations

The effect of different directional South American low-level flow (SA-LLF) and topography on the near surface rainfall characteristics are validated in the present study. The near surface precipitation characteristics of the precipitating cloud systems (PCSs) are investigated under the influence of low-level flow (LLF) at 850 hPa and topography. Global precipitation measurement dual precipitation radar (GPM-DPR) data is used to define the PCSs. The PCSs consist of valuable information, including radar reflectivity (Ze), rain rate (RR), storm top height (STH), and DSD (drop size (Dm) and drop concentration (Nw) parameters). For LLF, we considered wind data at 850 hPa and for each PCS, the average wind angle is calculated using U and V component of wind component from European Center for Medium-Range Weather Forecast Interim data. Based on the direction of the topography, the LLF is divided into upslope, downslope, easterly and westerly LLF. The transported moisture from the Amazon to east of the Andes and strength of LLF decides the precipitation characteristics over tropical and mid-latitude PCSs. The zonal variation over the SA continent shows that the easterly and northerly LLF consist of higher fraction of PCSs with largest area (> 2000 km2). The RR and Dm are higher at the eastern flank of SA Andes, and higher RR is observed in northerly and easterly LLF and shows the role of the moisture convergence near the eastern flank of Andes. However, the differences in the near surface rainfall parameters are higher when the LLF direction is considered along the perpendicular direction of the topography. The analysis reveals that mountain and directional LLF can alter the precipitation characteristics, and mostly, the eastern flank of Andes has higher RR and Dm compared to western flank of the Andes, and highest for the orographically moisture loaded upslope and easterly LLF in tropical PCSs. Tropical PCS has higher probability of bright band and warm rain compared to mid-tropical latitude PCSs and even higher in upslope and easterly LLF. The variation in DSD parameters with RR and STH revealed the role of the LLF and complex orography in the microphysical evolution of the precipitation and suggested that it is much required to investigate them in the numerical models.

Performance evaluation of regional climate model simulations at different spatial and temporal scales over the complex orography area of the Alpine region

This work provides a significant contribution on the open debate in the climate community to establish the added value of very high-resolution configurations, characterized by a horizontal resolution below 4 km with respect to current state-of-the-art climate simulations (10–15 km). Specifically, it aims at assessing quantitative gains and losses in the performance of climate models caused by an enhancement in temporal and spatial resolution by evaluating the capability of different climate simulations in reproducing daily and sub-daily present precipitation dynamics over a complex orographic context such as the Alpine region. In this perspective, the results of three experiments (EURO-CORDEX ensemble mean, CCLM 8 and CCLM 2.2) at different spatial (~ 12, 8 and 2.2 km) and temporal (daily, 6 h and 3 h) scales are compared to gridded and point-scale observational datasets. Precipitation data are analyzed by mean of the Expert Team on Climate Change Detection and Indices indicators, as well as with statistical models able to evaluate the precipitation distribution and the extreme values for different durations of precipitation events. To objectively assess gains and losses in adopting high-resolution RCMs, data are elaborated assuming the distribution added value as metric, particularly focusing on the role of orography. The work returns, at daily scale, a gain in climate model performances moving from lower to higher horizontal resolution. At the same time, investigating the effect of the orography the simulation with the finest grid proves to better capture local precipitation dynamics at higher altitudes in terms of both sub-daily precipitation and extreme events.

Dynamically Downscaled Climate Change Projections for the South Asian Monsoon: Mean and Extreme Precipitation Changes and Physics Parameterization Impacts

AbstractThe extreme concentration of population over South Asia makes it critical to accurately understand the global warming impact on the South Asian monsoon (SAM), but the complex orography of the region makes future projections of monsoon intensity technically challenging. Here we describe a series of climate projections constructed using the Weather Research and Forecasting (WRF) Model for South Asia to dynamically downscale a global warming simulation constructed using the Community Earth System Model under the representative concentration pathway 8.5 (RCP8.5) scenario. A physics-based miniensemble is employed to investigate the sensitivity of the projected change of the SAM to the implementation of different parameterization schemes in WRF. We analyze not only the changes in mean seasonal precipitation but also the impact of the warming process on precipitation extremes. All projections are characterized by a consistent increase in average monsoon precipitation and a fattening of the tail of the daily rainfall distribution (more than a 50% decrease in the return periods of 50-yr extreme rainfall events by the end of the twenty-first century). Further analysis based on one of the WRF physics ensemble members shows that both the average rainfall intensity changes and the extreme precipitation increases are projected to be slightly larger than expectations based upon the Clausius–Clapeyron thermodynamic reference of 7% °C−1of surface warming in most parts of India. This further increase can be primarily explained by the fact that the surface warming is projected to be smaller than the warming in the midtroposphere, where a significant portion of rain originates, and dynamical effects play only a secondary role.

Wind speed interpolation for evapotranspiration assessment in complex topography area

Wind speed and direction are fundamental data for many application fields, such as power generation and hydrological modelling. Wind measurements are usually few and sparse; hence, spatial interpolation of wind data is required. However, in mountainous areas with complex orography, accurate interpolation of wind data should consider topographic effects. Due to computational constraints, fully physically based methods that solve thermodynamic and mass conservation equations in three dimensions cannot be applied for long-time simulations or very large areas, while fast empirical methods seem more suitable. The aim of this work is to compare fast empirical methods to interpolate wind speed against a physically based full atmospheric model in order to assess the impact of the introduced approximation in estimating the wind field and the potential evapotranspiration. Comparison is carried out over the area of the upper Po River basin, a predominantly alpine region located in northern Italy. Results show that empirical topographic correction can increase accuracy of interpolated wind speed in areas with complex topography, but it requires about 50% more computational time than simpler empirical methods that do not consider topography.

Lee wave detection over the Mediterranean Sea using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset

Abstract. Atmospheric gravity waves generated downstream by orography in a stratified airflow are known as lee waves. In the present study, such mesoscale patterns have been detected, over water and in clear-sky conditions, using the Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) total column water vapour (TCWV) dataset, which contains about 20 years of day and night products, obtained from the thermal infrared measurements of the Along Track Scanning Radiometer (ATSR) instrument series. The high accuracy of such data, along with the native 1 km×1 km spatial resolution, allows the investigation of small-scale features such as lee waves. In this work, we focused on the Mediterranean Sea, the largest semi-enclosed basin on the Earth. The peculiarities of this area, which is characterised by complex orography and rough coastlines, lead to the development of these structures over both land and sea. We developed an automatic tool for the rapid detection of areas with high probability of lee wave occurrence, exploiting the TCWV variability in spatial regions with a 0.15∘×0.15∘ area. Through this analysis, several occurrences of structures connected with lee waves have been observed. The waves are detected in spring, autumn and summer seasons, with TCWV values usually falling in the range of 15 to 35 kg m−2. In this article, we describe some cases over the central (Italy) and the Eastern Mediterranean Basin (Greece, Turkey and Cyprus). We compared a case of perturbed AIRWAVE TCWV fields due to lee waves occurring over the Tyrrhenian Sea on 18 July 1997 with the sea surface winds from the synthetic aperture radar (SAR), which sounded the same geographical area, finding a good agreement. Another case has been investigated in detail: on 2 August 2002 the Aegean Sea region was almost simultaneously sounded by both the second sensor of the ATSR series (ATSR-2) and the Advanced ATSR (AATSR) instruments. The AIRWAVE TCWV fields derived from the two sensors were successfully compared with the vertically integrated water vapour content simulated with the Weather Research and Forecasting (WRF) numerical model for the same time period, confirming our findings. Wave parameters such as amplitude, wavelength and phase are described through the use of the Morlet continuous wavelet transformation (CWT). The performed analysis derived typical wavelengths from 6 to 8 km and amplitudes of up to 20 kg m−2.

A convection-permitting model for the Lake Victoria Basin: evaluation and insight into the mesoscale versus synoptic atmospheric dynamics

Aiming for an improved understanding of the different factors that determine the regional climate of the Lake Victoria Basin, the COSMO-CLM regional climate model is set up in a tropical, convection-permitting configuration and is directly nested in a recent reanalysis product (ERA5). The convection-permitting simulation outperforms state-of-the-art climate integrations that rely on convection parametrisations. Yet the domain-averaged model precipitation is larger than in the multi-observational ensemble, but the latter shows large spread. Overestimations of outgoing TOA shortwave and longwave radiation are much reduced compared to the COSMO-CLM CORDEX-Africa integration, but still suggest a general underestimation of the cloud fraction or frequency. Comparing the control with a no-lake simulation, the presence of Lake Victoria implies strong intensification of over-lake rainfall, but it only slightly increases the total domain-averaged precipitation. In addition, the easterly trade winds are shown to largely affect the mesoscale circulation and precipitation patterns. During daytime, fast trades and anabatic slope winds trigger convection at the lee-wind slopes, and subsidence over the basin. Slow trades allow the stationary air to produce spontaneous convective cells and to develop anabatic winds that result in orographic precipitation. During night-time, trade winds curl around the eastern branch of the East African Rift, generating a southerly and northerly evening convergence front entering the Lake Victoria Basin. Thus, our results highlight the key importance of the easterly trade winds and the complex orography in determining the total accumulation and location of precipitation in the Lake Victoria region.

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.

A 5 km Resolution Regional Climate Simulation for Central Europe: Performance in High Mountain Areas and Seasonal, Regional and Elevation-Dependent Variations

Mountain regions with complex orography are a particular challenge for regional climate simulations. High spatial resolution is required to account for the high spatial variability in meteorological conditions. This study presents a very high-resolution regional climate simulation (5 km) using the Weather Research and Forecasting Model (WRF) for the central part of Europe including the Alps. Global boundaries are dynamically downscaled for the historical period 1980–2009 (ERA-Interim and MPI-ESM), and for the near future period 2020–2049 (MPI-ESM, scenario RCP4.5). Model results are compared to gridded observation datasets and to data from a dense meteorological station network in the Berchtesgaden Alps (Germany). Averaged for the Alps, the mean bias in temperature is about −0.3 °C, whereas precipitation is overestimated by +14% to +19%. R 2 values for hourly, daily and monthly temperature range between 0.71 and 0.99. Temporal precipitation dynamics are well reproduced at daily and monthly scales (R 2 between 0.36 and 0.85), but are not well captured at hourly scale. The spatial patterns, seasonal distributions, and elevation-dependencies of the climate change signals are investigated. Mean warming in Central Europe exhibits a temperature increase between 0.44 °C and 1.59 °C and is strongest in winter and spring. An elevation-dependent warming is found for different specific regions and seasons, but is absent in others. Annual precipitation changes between −4% and +25% in Central Europe. The change signals for humidity, wind speed, and incoming short-wave radiation are small, but they show distinct spatial and elevation-dependent patterns. On large-scale spatial and temporal averages, the presented 5 km RCM setup has in general similar biases as EURO-CORDEX simulations, but it shows very good model performance at the regional and local scale for daily meteorology, and, apart from wind-speed and precipitation, even for hourly values.

Influence of PBL parameterization schemes in WRF_ARW model on short - range precipitation's forecasts in the complex orography of Peruvian Central Andes

Abstract The study evaluated the sensitivity of the precipitation forecast in the central Andes of Peru of Weather Research and Forecasting (WRF) model to change the planetary boundary layer (PBL) schemes. In that region is located the Mantaro basin, which is one of the most important in the region. Here, the rainfall is very important to the agriculture and to the reserves of drinking water. The simulations were carried out with ten PBL schemes for 19 days in January, February, and March, between 2009 and 2012. Based on the statistical analysis (model vs. observation), the more efficient schemes were determined and analyses of the vertical profiles of some variables are shown. As a result, the schemes that most helped the model in rainfall forecasting were MYNN3 (general and north sector of the basin), Bou-Lac (central sector) and Bretherton-Park (southern sector). The model generally overestimated rainfall in the northern basin, underestimated in the center, and in the south some schemes overestimated and others underestimated. In addition, it was concluded that the boundary layer is more stable in the model than in the observations. The schemes that generated the most rainfall were those that generated a more unstable boundary layer with weaker wind speeds, at least with easterly winds. Another conclusion is that the height of the boundary layer for rainy days in the region at 18 UTC oscillates around 1000 m and that, generally, the wind's velocity changes very little or decreases within the boundary layer and increases above it.

Diurnal Variation of Rainfall in a Tropical Coastal Region with Complex Orography

We examined the diurnal cycle of the rainfall in a coastal tropical mountainous region in central Veracruz State, Mexico (18°–21° N, 95.5°–98.5° W), featuring a striking topographic gradient running from sea level at the Gulf of Mexico coast to 5000 m above sea level (m.a.s.l.) in less than 100 km horizontal distance. During the summer, this unique location leads to regular the interaction between the easterly moisture inflow and the mountainous barrier. Over the complex terrain, forced ascent leads the occurrence of maximum rainfall during the afternoon (16–19 local time, LT ≈ 1½ hours ahead of solar time in summer), first along the slope and later over the coast. Along the coastal plain, the precipitation continues until the early morning consistent with there being convergence between land breezes and the trade winds. Observations obtained during a measurement campaign from 28 June to 3 July 2015, indicate that during the early evening downslope winds move against easterly flow, likely due to katabatic outflows previously observed over the region. These features are confirmed using spatial (0.88°) and temporal (30 min) resolution CMORPH rainfall estimates, since we observed evening episodes initiating along the slope during the afternoon (14–17 LT) moving later towards the coast.