Orographic Precipitation Enhancement Research Articles

Atmospheric Rivers in South-Central Chile: Zonal and Tilted Events

The extratropical west coast of South America has one of the largest frequencies of landfalling atmospheric rivers (ARs), with dozens of events per season that account for ~50% of the annual precipitation and can produce extreme rainfall events in south-central Chile. Most ARs form an acute angle with the Andes, but, in some cases, the moist stream impinges nearly perpendicular to the mountains, referred to as zonal atmospheric rivers (ZARs). Enhanced surface-based and upper-air measurements in Concepcion (36.8° S), as well as numerical simulations, were used to characterize a ZAR and a meridionally oriented AR in July 2022. They represent extremes of the broad distribution of winter storms in this region and exhibit key features that were found in a composite analysis based on larger samples of ZARs and tilted ARs. The latter is associated with an upper-level trough, broad-scale ascent, extratropical cyclone, and cold front reaching southern Chile. Instead, ZARs are associated with tropospheric-deep, strong zonal flow and a stationary front across the South Pacific, with ascent restricted upstream of the Andes. Consequently, ZARs have minimum precipitation offshore but a marked orographic precipitation enhancement and exhibit relatively warm temperatures, thus resulting in an augmented risk of hydrometeorological extreme events.

Antecedent snowmelt and orographic precipitation contributions to water supply of Pakistan disastrous floods, 2022

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%–40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.

The rain deluge and flash floods of summer 2022 in the United Arab Emirates: Causes, analysis and perspectives on flood-risk reduction

Across the hyperarid Arabian Peninsula, preparing to face evolving risks of flash-flood hazards against projected scenarios for climate change requires both an understanding of the extreme rainfall events responsible and the implementation of appropriate mitigation/adaptation measures. In July 2022, torrential storms affected northeastern UAE. The damaging impacts prompted a broader consideration of flash-flood risks, especially for rapidly-urbanising arid landscapes. Occurring unexpectedly in mid-summer when rainfall is normally absent, the storms were produced by a combination of climatic and oceanic conditions: ITCZ position, unusual westward extension of a humid air mass from the Indian monsoonal trough, a cool sea-surface temperature anomaly that drove strong onshore moisture advection, and orographic precipitation enhancement by the coastal ranges. A foreign media suggestion that cloud seeding was partly responsible, however, is contested. Although flood emergency management was coordinated and effective, it is argued that various strategies may be strengthened to reduce future disaster risks. These include increased monitoring of mountain wadi systems to augment meteorological forecasts with hydrological information, and improved modelling to predict runoff-generation processes better in ungauged catchments. Greater capture of floodwaters to infiltrate and replenish groundwater aquifers also supports ongoing national efforts to develop sustainable solutions for addressing freshwater scarcity.

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States

Abstract The Multi-Radar Multi-Sensor (MRMS) system produces a suite of hydrometeorological products that are widely used for applications such as flash flood warning operations, water resource management, and climatological studies. The MRMS radar-based quantitative precipitation estimation (QPE) products have greater challenges in the western United States compared to the eastern two-thirds of the CONUS due to terrain-related blockages and gaps in radar coverage. Further, orographic enhancement of precipitation often occurs, which is highly variable in space and time and difficult to accurately capture with physically based approaches. A deep learning approach was applied in this study to understand the correlations between several interacting variables and to obtain a more accurate precipitation estimation in these scenarios. The model presented here is a convolutional neural network (CNN), which uses spatial information from small grids of several radar variables to predict an estimated precipitation value at the central grid point. Several case analyses are presented along with a yearlong statistical evaluation. The CNN model 24-h QPE shows higher accuracy than the MRMS radar QPE for several cool season atmospheric river events. Areas of consistent improvement from the CNN model are highlighted in the discussion along with areas where the model can be further improved. The initial findings from this work help set the foundation for further exploration of machine learning techniques and products for precipitation estimation as part of the MRMS operational system. Significance Statement This study explores the development and use of a deep learning model to generate precipitation fields in the complex terrain of the western United States. Generally, the model is able to improve on the statistical performance of existing radar-based precipitation estimation methods for several case studies and over a long-term period in 2021. We explore the patterns associated with certain areas of strong performance and suggest potential means of improving areas with weaker performance. These initial results indicate the potential of deep learning to supplement radar-based approaches in areas with observational limitations.

Vertical Motions Forced by Small-Scale Terrain and Cloud Microphysical Response in Extratropical Precipitation Systems

Abstract Airborne vertically profiling Doppler radar data and output from a ∼1-km-grid-resolution numerical simulation are used to examine how relatively small-scale terrain ridges (∼10–25 km apart and ∼0.5–1.0 km above the surrounding valleys) impact cross-mountain flow, cloud processes, and surface precipitation in deep stratiform precipitation systems. The radar data were collected along fixed flight tracks aligned with the wind, about 100 km long between the Snake River Plain and the Idaho Central Mountains, as part of the 2017 Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE). Data from repeat flight legs are composited in order to suppress transient features and retain the effect of the underlying terrain. Simulations closely match observed series of terrain-driven deep gravity waves, although the simulated wave amplitude is slightly exaggerated. The deep waves produce pockets of supercooled liquid water in the otherwise ice-dominated clouds (confirmed by flight-level observations and the model) and distort radar-derived hydrometeor trajectories. Snow particles aloft encounter several wave updrafts and downdrafts before reaching the ground. No significant wavelike modulation of radar reflectivity or model ice water content occurs. The model does indicate substantial localized precipitation enhancement (1.8–3.0 times higher than the mean) peaking just downwind of individual ridges, especially those ridges with the most intense wave updrafts, on account of shallow pockets of high liquid water content on the upwind side, leading to the growth of snow and graupel, falling out mostly downwind of the crest. Radar reflectivity values near the surface are complicated by snowmelt, but suggest a more modest enhancement downwind of individual ridges. Significance Statement Mountains in the midlatitude belt and elsewhere receive more precipitation than the surrounding lowlands. The mountain terrain often is complex, and it remains unclear exactly where this precipitation enhancement occurs, because weather radars are challenged by beam blockage and the gauge network is too sparse to capture the precipitation heterogeneity over complex terrain. This study uses airborne profiling radar and high-resolution numerical simulations for four winter storms over a series of ridges in Idaho. One key finding is that while instantaneous airborne radar transects of the cross-mountain flow, vertical drafts, and reflectivity contain much transient small-scale information, time-averaged transects look very much like the model transects. The model indicates substantial surface precipitation enhancement over terrain, peaking over and just downwind of individual ridges. Radar observations suggest less enhancement, but the radar-based assessment is uncertain. The second key conclusion is that, even though orographic gravity waves are felt all the way up into the upper troposphere, the orographic precipitation enhancement is due to processes very close to the terrain.

Quality Control Impacts on Total Precipitation Gauge Records for Montane Valley and Ridge Sites in SW Alberta, Canada

This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49–76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50–76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784–1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731–1269 mm), while the valley gauge adjustments were −4% to 1% (−39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites.

Cloud and fog deposition: Monitoring in high elevation and coastal ecosystems. The past, present, and future

Deposition of pollutants by cloud water exceeds deposition by precipitation and dry deposition in many high elevation settings. This larger loading of pollutants in these environments is due to a combination of factors such as high frequency of cloud immersion, high wind speeds, orographic enhancement of precipitation, and large leaf area of tree species typical in these environments. Fog-impacted coastal ecosystems also experience increased pollutant deposition similar to cloud-impacted high elevation sites. Therefore, development of meaningful critical load values and total nitrogen budgets for high-elevation and fog-impacted sites requires reliable cloud and fog water deposition estimates. The cost and labor intensity of cloud/fog water sample collection have made it difficult to conduct long-term studies that would provide the data needed to develop accurate estimates. Current understanding of fog formation, transport, and the role of fog and cloud deposition in hydrogeological and biogeochemical cycles is incomplete due, in part, to lack of a concerted interdisciplinary approach to the problem. Historically, these obstacles have limited interest in and collection of cloud and fog water samples. In addition to measurements of cloud/fog chemical composition, documenting fog/cloud deposition fluxes of pollutant and nutrient species requires knowledge of cloud/fog physical properties, frequency and duration of fog/cloud interception with landscapes, properties of vegetation on those landscapes, and properties of the wind that drive droplet/vegetation interactions. Because drop deposition efficiency is dependent on drop size, it is also important to consider variations in fog/cloud drop composition with drop size as species enriched in larger/smaller drops will experience enhanced/reduced deposition rates.This paper presents summary results from a small U.S. cloud water monitoring network that operated from the mid-nineties through 2011, as well as a brief qualitative review of other cloud and fog water studies conducted in the United States (including Puerto Rico), Europe, South America/Pacific, and Asia. Current collection methods are also reviewed. Recent scientific efforts by the National Atmospheric Deposition Program's (NADP) Total Deposition Science Committee and NADP's Critical Loads of Atmospheric Deposition Science Committee have identified occult (cloud/fog) deposition as a “need” in developing critical loads for ecosystems that experience significant cloud and fog impaction. To this end, remaining knowledge gaps and suggestions for future research to investigate these gaps are explored.

Learning about precipitation lapse rates from snow course data improves water balance modeling

Abstract. Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km−1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide.

Atmospheric Rivers Contribution to the Snow Accumulation Over the Southern Andes (26.5° S–37.5° S)

Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) AFB180004 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1171032 11151009

Added Value of a Dynamical Downscaling Approach for Simulating Precipitation and Temperature Over Tianshan Mountains Area, Central Asia

AbstractProgresses in climatological, ecological, and hydrological studies that focused on the Tianshan mountains area (known as “the water tower of central Asia”) have been restricted by the availability of station observations as well as high resolution and quality data set. With the aim to overcome some of these difficulties, the high‐resolution Weather Research and Forecasting (WRF) regional climate model is run over the Tianshan mountains area driven by the ERA‐Interim reanalysis. Double nesting method was used with a horizontal resolution of 40 and 8 km covering the period 1980–2018. A decade of simulation from 1980 to 1989, a period when most abundant station observations are available, is considered for validation. These downscaled results are compared against station observations, ERA‐Interim reanalysis, and three widely used spatially interpolated products in order to investigate the added value of the dynamical downscaling approach. Results of these comparisons show that the WRF‐downscaled data outperform and add significant details to ERA‐Interim reanalysis. A remarkable improvement of the WRF simulation is found at reproducing the observed seasonal cycle of daily extreme temperatures and precipitation. Due to better representation of orography, WRF simulations are able to capture extreme precipitation events that are missing in the high‐quality interpolated products. Refining the resolution from 40 to 8 km further improves the model performance, particularly at depicting orographic enhancement of precipitation. The validated WRF model can be used in future climate projections studies, and this high‐resolution as well as high‐quality climatological data set we present here is useful for impact and further downstream studies.

Variability of orographic enhancement of precipitation in the Alpine region

Climate change impacts are non uniformly distributed over the globe. Mountains have a peculiar response to large scale variations, documented by elevation gradients of surface temperature increase observed over many mountain ranges in the last decades. Significant changes of precipitation are expected in the changing climate and orographic effects are important in determining the amount of rainfall at a given location. It thus becomes particularly important to understand how orographic precipitation responds to global warming and to anthropogenic forcing. Here, using a large rain gauge dataset over the European Alpine region, we show that the distribution of annual precipitation among the lowlands and the mountains has varied over time, with an increase of the precipitation at the high elevations compared to the low elevations starting in the mid 20 century and peaking in the 1980s. The simultaneous increase and peak of anthropogenic aerosol load is discussed as a possible source for this interdecadal change. These results provide new insights to further our understanding and improve predictions of anthropic effects on mountain precipitations, which are fundamental for water security and management.

Verification of a seeder–feeder orographic precipitation enhancement scheme accounting for low‐level blocking

A subgrid parametrization scheme representing the enhancement of precipitation due to subgrid orography via the seeder–feeder (SF) effect has been modified to account for flow blocking in small Froude number situations. The scheme was validated in a set of limited‐area model simulations with a 1.5 km grid spacing, in which the orography was degraded by varying amounts. For simulations in which the largest orographic scales are still fairly well represented, the SF scheme was able to reduce the precipitation deficit by 30 to 70%. For simulations where the hills were completely subgrid, the SF scheme was still able to reduce the precipitation deficit by 10 to 30%. As well as increasing the integrated precipitation in global simulations with various grid spacings, some of the precipitation production was shifted from the convection scheme, with its very simple microphysical representation, to the microphysics scheme. In a long‐duration climate simulation, the SF scheme enhancements of orographic precipitation perturb the large‐scale hydrological cycle, as evidenced by the far‐field changes in both microphysical and convective precipitation. Changes over major mountain ranges were similar to those described for the case‐studies, so long as the upstream precipitation impinging on the mountains was not reduced by these changes in the global hydrological cycle. Increased atmospheric drying reduces column‐integrated resolved cloud water mixing ratios over mountains, reducing a positive bias in the amount of optically thick cloud with low‐ to mid‐level tops compared to ISCCP satellite observations. The associated reductions in cloud albedo slightly reduced the RMS error in the top‐of‐atmosphere outgoing short‐wave radiative fluxes over mountains compared to CERES satellite observations.

Sub-kilometer Precipitation Datasets for Snowpack and Glacier Modeling in Alpine Terrain

apturing the spatial and temporal variability of precipitation at fine scale is necessary for high-resolution modeling of snowpack and glacier mass balance in alpine terrain. In this study, we assess the impact of three sub-kilometer precipitation datasets on distributed simulations of snowpack and glacier mass balance with the detailed snowpack model Crocus for winter 2011-2012. The different precipitation datasets at 500-m grid spacing over the northern and central French Alps are coming from (i) the SAFRAN reanalysis specially developed for alpine terrain interpolated at 500-m grid spacing, (ii) the numerical weather prediction (NWP) system AROME at 2.5-km resolution downscaled with a precipitation-elevation adjustment factor and (iii) a version of AROME at 500-m grid spacing. The spatial patterns of seasonal snowfall are first analyzed for the different precipitation datasets. Large differences between SAFRAN and the two versions of AROME are found at high-altitude and in regions of strong orographic precipitation enhancement. Results of Crocus snowpack simulations are then evaluated against (i) point measurements of snow depth, (ii) maps of snow covered areas retrieved from optical satellite data (MODIS) and (iii) field measurements of winter accumulation of six glaciers. The two versions of AROME lead to an overestimation of snow depth and snow-covered area, which are substantially improved by SAFRAN. However, all the precipitation datasets lead to an underestimation of snow depth increase at the daily scale and cumulated over the season, with AROME 500 m providing the best performances at the seasonal scale. The low correlation found between the biases in snow depth and in cumulated snow depth increase illustrates that total snow depth has a limited significance for the evaluation of precipitation datasets. Measurements of glacier winter mass balance showed a systematic underestimation of high-elevation snow accumulation with SAFRAN. The two versions of AROME overestimate the winter mass balance at four glaciers and produce nearly unbiased estimation for two of them. Our study illustrates the need for improvements in the precipitation field from high-resolution NWP systems for snow and glacier modeling in alpine terrain.

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

AbstractCharacterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

Ambient Factors Controlling the Wintertime Precipitation Distribution across Mountain Ranges in the Interior Western United States. Part II: Changes in Orographic Precipitation Distribution in a Pseudo–Global Warming Simulation

AbstractTwo high-resolution (4 km) regional climate simulations over a 10-yr period are conducted to study the changes in wintertime precipitation distribution across mountain ranges in the interior western United States (IWUS) in a warming climate. One simulation represents the current climate, and another represents an ~2050 climate using a pseudo–global warming approach. The climate perturbations are derived from the ensemble mean of 15 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). These simulations provide an estimate of average changes in wintertime orographic precipitation enhancement and finescale distribution across mountain ranges. The variability in these changes among CMIP5 models is quantified using statistical downscaling relations between orographic precipitation distribution and upstream conditions, developed in Part I. The CMIP5 guidance indicates a robust warming signal (~2 K) over the IWUS by ~2050 but minor changes in relative humidity and cloud-base height. The IWUS simulations reveal a widespread increase in precipitation on account of higher precipitation rates during winter storms in this warmer climate. This precipitation increase is most significant over the mountains rather than on the surrounding plains. The increase in precipitation rate is largely due to an increase in low-level cross-mountain moisture transport. The application of the statistical relations indicates that individual CMIP5 models disagree about the magnitude and distribution of orographic precipitation change in the IWUS, although most agree with the ensemble-mean-predicted orographic precipitation increase.

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

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.

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

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.

A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region

The dependence of precipitation growth and decay on the orientation of orographic features, mesoscale flow and freezing‐level height is quantified using a 10‐year archive of composite weather radar images over the Swiss Alpine region. The mesoscale flow is described by the motion of radar precipitation echoes, computed through variational echo tracking, while the freezing‐level height is extracted from the analyses of the numerical weather prediction model COSMO. On the northern side of the Alps, the areas of growth are generally observed on the upwind slopes of the Alpine chain, while on the southern side their location also depends on factors other than the motion of precipitation, such as the convergence of low‐level flows. On the other hand, the decay of precipitation is generally found in the inner Alpine valleys and on the downwind slopes of the Alpine chain. Compared to situations characterized by low freezing level, the areas of precipitation growth penetrate more into the mountain range with high‐freezing‐level conditions. When considering a time lag of 1 h and specific flow conditions, the systematic growth and decay of precipitation can explain up to 30–40% of its total variability over the orography. The relative contribution to the total variability is lower with high freezing level because the non‐systematic variability is larger. The statistical analysis of precipitation growth and decay using large archives of composite radar images highlights the mesoscale flow conditions and geographical locations most prone to orographic precipitation enhancement, but also has potential to improve existing precipitation nowcasting systems in mountainous regions and to provide a basis for the verification of systematic biases of numerical weather prediction models.

Factors Affecting the Inland and Orographic Enhancement of Lake-Effect Precipitation over the Tug Hill Plateau

The factors affecting the inland and orographic enhancement of lake-effect precipitation are poorly understood, yet critical for operational forecasting. Here we use nine cool seasons (16 November–15 April) of radar data from the Montague/Ft. Drum, New York (KTYX), WSR-88D, the North American Regional Reanalysis (NARR), and observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine variations in lake-effect precipitation enhancement east of Lake Ontario and over the Tug Hill Plateau (hereafter Tug Hill). Key factors affecting the inland and orographic enhancement in this region include the strength of the incident boundary layer flow, the intensity of the lake-induced convective available potential energy (LCAPE), and the mode of the lake-effect system. Stronger flow favors higher precipitation rates, a precipitation maximum displaced farther downwind, and greater inland and orographic enhancement. The effects of LCAPE depend upon the strength of the flow. During periods of weak flow, higher LCAPE favors lower precipitation rates, a maximum closer to the shoreline, and lesser inland and orographic enhancement. During periods of strong flow, higher LCAPE favors higher precipitation rates, a maximum displaced farther downwind, and greater inland and orographic enhancement. Banded (nonbanded) modes favor higher (lower) precipitation rates, lesser (greater) inland and orographic enhancement, and a maximum closer to the shoreline (over Tug Hill). These results, for both manually measured and radar-estimated precipitation, are robust when many lake-effect events are considered, but substantial variability exists during individual events.

Foehn Event Triggered by an Atmospheric River Underlies Record‐Setting Temperature Along Continental Antarctica

AbstractA record‐setting temperature of 17.5°C occurred on 24 March 2015 at the Esperanza station located near the northern tip of the Antarctic Peninsula (AP). We studied the event using surface station data, satellite imagery, reanalysis data, and numerical simulations. The Moderate Resolution Imaging Spectroradiometer Antarctic Ice Shelf Image Archive provides clear evidence for disintegration and advection of sea ice, as well as the formation of melt ponds on the ice sheet surface at the base of the AP mountain range. A deep low‐pressure center over the Amundsen‐Bellingshausen Sea and a blocking ridge over the southeast Pacific provided favorable conditions for the development of an atmospheric river with a northwest‐southeast orientation, directing warm and moist air toward the AP, and triggering a widespread foehn episode. A control simulation using a regional climate model shows the existence of local topographically induced warming along the northern tip of the AP (∼60% of the full temperature signal) and the central part of the eastern AP (>90% of the full temperature signal) with respect to a simulation without topography. These modeling results suggest that more than half of the warming experienced at Esperanza can be attributed to the foehn effect (a local process), rather than to the large‐scale advection of warm air from the midlatitudes. Nevertheless, the local foehn effect also has a large‐scale advection component, since the atmospheric river provides water vapor for orographic precipitation enhancement and latent heat release, which makes it difficult to completely disentangle the role of local versus large‐scale processes in explaining the extreme event.