Regions Of Complex Terrain Research Articles

Impact of Lateral Terrestrial Water Flow on Land‐Atmosphere Interactions in the Heihe River Basin in China: Fully Coupled Modeling and Precipitation Recycling Analysis

AbstractLateral terrestrial water flow is usually not considered in regional climate modeling. This study focuses on the impact of increased hydrological model complexity for the description of the land‐atmosphere interactions in a complex terrain region. For this purpose, we apply the Weather Research and Forecasting (WRF) model with its hydrological modeling extension package WRF‐Hydro for the case of the Heihe River Basin in convection permitting atmospheric resolution (3 km). The Heihe River Basin (143,200 km2) is an arid‐semiarid inland river basin in Northwestern China. By comparing model simulations results with and without coupling for the period 2008–2010, the effect of lateral terrestrial water flow on land‐atmosphere interactions is evaluated with a joint atmospheric‐terrestrial water budget analysis, a regional precipitation recycling analysis and a fully three‐dimensional atmospheric moisture tracing method (evaporation tagging). The coupled modeling system WRF‐Hydro simulates near‐surface temperature and precipitation variability similar to the WRF model and demonstrates, in addition, its ability to reproduce daily streamflow. In the fully coupled mode, as a consequence of lateral terrestrial water flow description, the redistribution of infiltration excess in the mountainous area produces higher soil moisture content in the root zone, increases the terrestrial water storage and evapotranspiration, and decreases the total runoff. The resulting wetting and cooling in the near surface affects the regional climate by changing the regional water vapor transports and water vapor content, while, in turn, inducing precipitation differences. Overall, the fully coupled modeling increases the recycling rate, indicating that lateral terrestrial water flow influences regional climate in our study area.

Comparison of Environmental and Mesoscale Characteristics of Two Types of Mountain‐to‐Plain Precipitation Systems in the Beijing Region, China

AbstractBeijing, China, is located in a region of complex terrain with high mountain ridges to the northwest and the Bohai Sea to the southeast. The origin of convective storms occurring on the plains can often be traced to the upstream mountains. Under weakly forced conditions, these convective storms most frequently evolve into squall lines (SL) and convective clusters (CC) when reaching the plains. In this study, we analyze 18 SL and 15 CC storm systems and assess their environmental and mesoscale differences between the two phenomena. By analyzing the frequency of convective occurrence for the two types of storms based on composite radar reflectivity, it is found that the high frequencies are located in the south of Beijing for SL and near the center of Beijing for CC. Using storm‐scale reanalysis data produced by a rapid update four‐dimensional variational analysis system that assimilates Doppler radar observations, distinct features of the SL and CC storms are revealed in terms of their convective environments and mesoscale structures, such as cold pool, horizontal wind convergence, and humidity distribution. It is found that low convective inhibition and high low‐level wind speed on the plains are common to both SL and CC, whereas higher vertical shear over the plains and stronger wind speed on both mountains and plains distinguishes SL from CC. We further show that the stronger wind and vertical shear in SL generate stronger and more organized downdrafts, producing a deeper cold pool, strong outflow and convergence, which explains the formation of the high‐frequency center in the south of Beijing. In contrast, the cold pool produced in CC is shallower and weaker, resulting in weaker outflow and convergence and convective activities that are located only in central Beijing.

Coverage of China New Generation Weather Radar Network

The China Meteorological Administration has deployed the China New Generation Weather Radar (CINRAD) network for severe weather detection and to improve initial conditions for numerical weather prediction models. The CINRAD network consists of 217 radars comprising 123 S-band and 94 C-band radars over mainland China. In this paper, a high-resolution digital elevation model (DEM) and beam propagation simulations are used to compute radar beam blockage and evaluate the effective radar coverage over China. Results show that the radar coverage at a height of 1 km above ground level (AGL) is restricted in complex terrain regions. The effective coverage maps at heights of 2 km and 3 km AGL indicate that the Yangtze River Delta, the Pearl River Delta, and North China Plain have more overlapping radar coverage than other regions in China. Over eastern China, almost all areas can be sampled by more than 2 radars within 5 km above mean sea level (MSL), but the radars operating in Qinghai-Tibet Plateau still suffer from serious beam blockage caused by intervening terrain. Overall, the radars installed in western China suffer from much more severe beam blockage than those deployed in eastern China. Maps generated in this study will inform users of the CINRAD data of their limitations for use in precipitation estimation, as inputs to other weather and hydrological models, and for satellite validation studies.

Improving quantitative precipitation estimates in mountainous regions by modelling low-level seeder-feeder interactions constrained by Global Precipitation Measurement Dual-frequency Precipitation Radar measurements

A physically-based framework to address the underestimation and missed detection errors in Quantitative Precipitation Estimates (QPE) from Global Precipitation Measurement (GPM) Precipitation Radar (PR) in regions of complex terrain is presented. The framework is demonstrated using GPM Ku-PR because of its wider swath. GPM Ku-PR precipitation estimates are evaluated against ground validation (GV) observations from the long-term ground-based rain-gauge network in the Southern Appalachian Mountains. The detection and estimation errors exhibit a diurnal cycle consistent with the diurnal cycle of low-level clouds and fog (LLCF), thus suggesting the importance of low-level orographic microphysical processes. Contamination of near-surface reflectivity profiles due to ground-clutter is the major source of error in the Ku-PR QPE with spatial features that mirror landform. In particular, GPM Ku-PR drop size distribution (DSD) retrieval algorithms systematically overestimate Dm (mass-weighted mean diameter), and underestimate Nw (normalized DSD intercept) and the precipitation-rate when low-level multilayer clouds and fog are present. Second, column simulations of rainfall dynamics constrained by reflectivity measurements show an emergent relationship in Dm-Nw phase-space that explains an increase in the frequency of Dm < 1 mm in disdrometer observations due to seeder-feeder interactions (SFI) not captured by current retrieval microphysical products. To resolve ambiguity in the detection and characterization of SFI regimes, we demonstrate a physically-based framework to improve GPM Ku-PR orographic QPE that relies on a coupled microphysics-radar rainfall forward model to estimate DSD parameters using initial and boundary conditions from Ku-PR DSD estimates (Method-1), Ku-PR corrected reflectivity measurements (Method-2), and LLCF microphysics from GV observations. Model simulations using Method-2 produce realistic surface DSDs confirming that representation of SFI processes is critical. Application of the framework to GPM overpasses shows potential for robust improvement in QPE and elucidates the physical basis for improved retrievals against ground observations corresponding to which Nw increases by 3–5 dBNw, Dm decreases by approximately 0.03 mm, and rain-rate increases up to ten-fold in the presence of SFI.

Discuss on the Work Practice of Wind Farm Micro-sitting in Complex Terrain Region

Discuss on the Work Practice of Wind Farm Micro-sitting in Complex Terrain Region

Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain

Surface water floods (SWFs) that lead to household losses are mainly localized phenomena. Research on describing the associated precipitation characteristics has previously been based on case studies and on the derivation of local rainfall thresholds, but no approaches have yet been presented on the national scale. Here, we propose a new way to overcome this scaling problem. We linked a gridded precipitation dataset based on both rainfall gauges and radar data with geolocated insurance claims for all of Switzerland. We show that the absolute thresholds vary markedly over complex terrain, and we thus propose basing early warning systems for predicting damage-relevant SWF events on local quantiles of maximum intensity and the total sum of event precipitation. A threshold model based on these two parameters is able to classify rainfall events potentially leading to damage-relevant SWF events over large areas of complex terrain, including high mountains and lowland areas, and a variety of geological conditions. Our approach is an important step towards the development of impact-based early warning systems. Weather warning agencies or insurance companies can build upon these findings to find workarounds for issuing user-targeted warnings at national scale or for nowcasting purposes.

Multisurface Retracker for Swath Processing of Interferometric Radar Altimetry

Swath mode processing of CryoSat-2 Synthetic Aperture Radar Interferometric (SARIn) mode has been used to monitor elevation of areas with complex topography such as over ice sheet and ice cap margins. Swath processing relies on an accurate measure of the angle of arrival of the measured echo and, therefore, requires custom strategies in order to resolve the ambiguous phase measurement. In mountainous regions of complex terrain, it may be necessary to apply different phase ambiguities across a waveform record when returns come from different scatters distributed perpendicularly to the CryoSat-2 ground tracks. In this letter, we present modifications to the conventional swath processing method whereby a multisurface retracker is first applied to the record in order to identify potential different scattering surfaces. Phase ambiguity is then independently resolved for each of these subsurfaces. The improvements with this new method over the Karakoram glaciers are a 10% increase in the number of measurements with improvements of almost 50% for individual glaciers and a reduction in the median absolute deviation of the elevations from 20.18 to 14.69 m.

Modified Power Curves for Prediction of Power Output of Wind Farms

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan

Reliable and accurate short-term prediction of wind speed at hub height is very important to optimize the integration of wind energy into existing electrical systems. To this end, a coupled model based on the Weather Research Forecasting (WRF) model and Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics (CFD) model is proposed to improve the forecast of the wind fields over complex terrain regions. The proposed model has been validated with the quality-controlled observations of 15 turbine sites in a target wind farm in Japan. The numerical results show that the coupled model provides more precise forecasts compared to the WRF alone forecasts, with the overall improvements of 26%, 22% and 4% in mean error (ME), root mean square error (RMSE) and correlation coefficient (CC), respectively. As the first step to explore further improvement of the coupled system, the polynomial chaos expansion (PCE) approach is adopted to quantitatively evaluate the effects of several parameters in the coupled model. The statistics from the uncertainty quantification results show that the uncertainty in the inflow boundary conditions to the CFD model affects more dominantly the hub-height wind prediction in comparison with other parameters in the turbulence model, which suggests an effective approach to parameterize and assimilate the coupling interface of the model.

Multi-year surface radiative properties and vegetation parameters for hydrologic modeling in regions of complex terrain—Methodology and evaluation over the Integrated Precipitation and Hydrology Experiment 2014 domain

Study regionSoutheast US (SE US). Study focusThis study probes the propagation of errors in standard remote-sensing vegetation products caused by cloud contamination and the impact of time-variant radiative properties for correctly describing land-surface properties in hydrologic models. Spatiotemporally-varying quality-controlled vegetation attributes (i.e., leaf area index and fractional vegetation coverage) and surface radiative properties (i.e., longwave broadband emissivity and shortwave broadband albedo) were derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products over the SE US at 1 km × 1 km and hourly resolutions from 2007 to 2013. The data sets are publicly available. The impact of uncorrected standard vegetation products and static treatments of radiative properties was assessed systematically clearly illustrating improvements in simulated water and energy fluxes using the developed landscape attributes with a fully-distributed uncalibrated hydrologic model. New hydrological insights for the regionThrough simulations in the Southern Appalachian Mountains, we found that the spatiotemporal variability of radiative properties significantly influences the diurnal cycle of the surface energy budget with marked differences in sensible heat fluxes (up to 10–20%). Better performance of streamflow simulations achieved by using the improved vegetation attributes is tied to changes in rainfall interception and evapotranspiration, reflecting the importance of SE forests in the regional water cycle. The largest improvements in streamflow simulations result from larger corrections to MODIS products in the inner mountain region where cloudiness is persistent.

Role of Lateral Terrestrial Water Flow on the Regional Water Cycle in a Complex Terrain Region: Investigation With a Fully Coupled Model System

AbstractRecent developments in hydrometeorological modeling aim toward a more sophisticated treatment of terrestrial hydrologic processes. The objective of this study is to investigate the role of lateral terrestrial water flows on the regional atmospheric and terrestrial water cycle in a humid region of Central Europe. This is accomplished by evaluating model results from both the standard Weather Research and Forecasting (WRF) model and the hydrologically enhanced version WRF‐Hydro, which allows for a more comprehensive process description of the interdependencies between water and energy fluxes at the land‐atmosphere interface. To account for internal model variability, an ensemble for each model variant is generated for a 3‐month study period in the summer of 2005. The regional water cycle in the simulation domain is investigated by evaluating the ensemble results with a joint atmospheric‐terrestrial water budget analysis. We focus on six differently sized river catchments located in Southern Bavaria (Germany) and the southerly adjacent Eastern Alps, where simulation results are compared to observations of precipitation, near‐surface temperatures, and streamflow. Most prominently, WRF‐Hydro increases (near‐) surface runoff and decreases percolation, resulting in a reduced total runoff amount. Accordingly, soil moisture storage and evapotranspiration are increased. Domain‐averaged precipitation differences between WRF and WRF‐Hydro are essentially related to differences in atmospheric moisture inflow and outflow, which is the signature of internal model variability and is potentially enhanced in the WRF‐Hydro ensemble. Driven only at the boundaries of the outermost domain, the fully coupled model system shows good performance in the reproduction of observed streamflow.

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

Abstract. New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

Mars entry guidance using a semi-analytical method

Future Mars missions may require improved landed accuracy to facilitate the landing site selection and finally access a region of complex terrain with high scientific return. This paper is to develop a novel, robust, and precision entry guidance algorithm for Mars entry vehicles with low lift-to-drag ratios. In the presence of large uncertainties, the entry terminal point controller algorithm can encounter severe performance degradation due to: (1) the small perturbation assumption, and (2) theoretically ignoring deviations in the atmospheric-density model, aerodynamic-force model, etc. Based on numerical technologies and the classical variation method (VM), this work develops a semi-analytical (SA) algorithm, in which combined effects of several dynamic uncertainties now can be addressed. The terminal-downrange error is predicted by a numerical predictor such that the dependence on the reference trajectory can be reduced and then the issue caused by the small perturbation assumption can be addressed. Such a predicted terminal-downrange error is finally corrected by an analytical corrector, which is designed by the VM. It is indicative that there can be no numerical iterations in the SA algorithm. Numerical results demonstrate the effectiveness of the SA algorithm.

Assessment of CORDEX simulations over South America: added value on seasonal climatology and resolution considerations

A new set of CORDEX simulations over South America, together with their coarser-resolution driving Global Climate Models (GCMs) are used to investigate added value of Regional Climate Models (RCMs) in reproducing mean climate conditions over the continent. There are two types of simulations with different lateral boundary conditions: five hindcast simulations use re-analysis as boundary conditions, and five other historical simulations use GCMs outputs. Multi-model ensemble means and individual simulations are evaluated against two or three observation-based gridded datasets for 2-m surface air temperature and total precipitation. The analysis is performed for summer and winter, over a common period from 1990 to 2004. Results indicate that added value of RCMs is dependent on driving fields, surface properties of the area, season and variable considered. A robust added value for RCMs driven by ERA-Interim is obtained in reproducing the summer climatology of surface air temperature over tropical and subtropical latitudes. Mixed results can be seen, however, for summer precipitation climatology in both hindcast and historical experiments. For winter, there is no noticeable improvement by the RCMs for the large-scale precipitation and surface air temperature climatology. To further understand the added value of RCMs, models deviations from observation are decomposed according to different terms that reflect the observational uncertainty, the representativeness error, the interpolation error, and the actual performance of the model. Regions where these errors are not negligible, such as in complex terrain regions, among others, can be identified. There is a clear need for complementary assessment to understand better the real value added by RCMs.

An Exploration of Terrain Effects on Land Surface Phenology across the Qinghai–Tibet Plateau Using Landsat ETM+ and OLI Data

Detecting spatial patterns of land surface phenology (LSP) with high spatial and temporal resolutions is crucial for accurately estimating phenological response and feedback to climate change and biogeochemical cycles. Numerous studies have revealed LSP across the Qinghai–Tibet Plateau (QTP) using a variety of coarse-resolution satellite data. However, detailed phenological spatial patterns along with changes of mountain topography remain poorly understood, which greatly limits efforts to predict the impacts of climate change on vegetation growth and ecosystem productivity in complex terrain regions. Combining Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI (Operational Land Imager) observations in overlapping zones of adjacent images, this study detected Normalized Difference Vegetation Index (NDVI)-based LSP metrics at a spatial resolution of 30 m, and explored how LSP varied with topographic factors along a 2600 km belt transect of the central QTP. The results show that the greenup onset date showed a delayed tendency with the increase of elevation at a mean rate of 1.52 days/100 m, while the dormancy onset date indicated an advanced tendency at a mean rate of −0.59 days/100 m. In general, greenup onset date was later but dormancy onset date was earlier on shaded slopes than on sunlit slopes in the meadow area. By contrast, greenup onset date did not significantly depend on aspect in the steppe area, while dormancy onset date indicated a similar response to aspect in the steppe area with that in the meadow area. With regard to the effect of slope on vegetation phenology in the meadow area, greenup onset date was significantly delayed but dormancy onset date significantly advanced with the increase of slope on both north and south slopes. In the steppe area, however, the influence pattern of slope on vegetation phenology was the opposite. Essentially, effects of topographical parameters on LSP were controlled by temperature and moisture combinations in complex terrain.

Intercomparison of precipitation datasets for summer precipitation characteristics over East Asia

Precipitation data in the Global Precipitation Climatology Project (GPCP) and in four reanalysis datasets, ERA-Interim, MERRA, NCEP/NCAR, and JRA, are compared against the CPC Merged Precipitation (CMAP) in the cyclostationary empirical orthogonal function (CSEOF) space to evaluate these datasets in representing the summer precipitation characteristics over East Asia. CSEOF analysis is applied to each dataset, and regression analysis is performed in the CSEOF space with the CMAP data as the target. The regression analysis establishes one-to-one correspondence between the CSEOF loading vectors of the target variable and those of the predictors, i.e., GPCP and the four reanalysis datasets. The loading vectors of the GPCP data coincide almost exactly with those of the CMAP data, i.e., the two observation-based precipitation datasets represent practically identical summer precipitation characteristics over East Asia. The reanalysis datasets also reproduce the first five CSEOF modes reasonably; however, performance of NCEP/NCAR is notably lower than others. The re-constructed precipitation using the first five regressed CSEOF modes of the reanalysis datasets are well correlated with that of the CMAP data with reasonably large correlation coefficients, suggesting that these reanalysis precipitation products reliably simulate the major summer precipitation characteristics in East Asia. All of the four reanalysis products commonly show noticeable errors in representing the summer rainfall over the mid-latitude ocean to the south of Japan, the tropical western Pacific, tropical/subtropical regions including the Indochina Peninsula, India, the Maritime Continent, and regions of complex terrain especially those characterized by strong orographic slopes around the Tibetan Plateau. The errors over the regions of complex orography and coastal lines may be partially due to the inability of reanalysis models in simulating the effects of complex terrain and the lack of observations in these sparsely populated regions.

Rainfall extremes, weather and climate drivers in complex terrain: A data-driven approach based on signal enhancement methods and EV modeling

Weather and climatic characterization of rainfall extremes is both of scientific and societal value for hydrometeorological risk management, yet discrimination of local and large-scale forcing remains challenging in data-scarce environments. Here, we present an analysis framework that separates weather regime and climate controls using data-driven process identification. The approach is based on signal-to-noise separation methods and explanatory extreme value (EV) modeling of multisite rainfall extremes. The EV models integrate the temporal component of the weather/climate driver using semi-automatic parameter identification. At weather scale, the EV models are combined with a state-based Markov model to represent the spatiotemporal structure of rainfall as weather states. At climate scale, the EV models are used to search for drivers leading to the shift of weather patterns. The drivers are brought out in a climate-to-weather signal subspace, built via dimension reduction of climate model reconstructions.We apply the framework to a complex terrain region: the Western Andean Ridge in Ecuador and Peru (0–6°S) using ground data from the second half of the 20th century. Overall, we show that the framework, which does not make any prior assumption on the explanatory power of the weather and climate drivers, allows identification of well-known and new features of the regional climate in a purely data-driven fashion. Thus, the approach shows potential to identify weather controls on precipitation extremes in data-scarce and orographically complex regions in which model reconstructions are the only climate proxies.

Stratiform Precipitation Processes in Cyclones Passing over a Coastal Mountain Range

Abstract The Olympic Mountains Experiment (OLYMPEX) documented precipitation and drop size distributions (DSDs) in landfalling midlatitude cyclones with gauges and disdrometers located at various distances from the coast and at different elevations on the windward side of the mountain range. Statistics of the drop size and gauge data for the season and case study analysis of a high-rainfall-producing storm of the atmospheric river type show that DSDs during stratiform raining periods exhibit considerable variability in regions of complex terrain. Seasonal statistics show that different relative proportions of drop sizes are present, depending on synoptic and mesoscale conditions, which vary within a single storm. The most frequent DSD regime contains modest concentrations of both small and large drops with synoptic factors near their climatological norms and moderate precipitation enhancement on the lower windward slopes. The heaviest rains are the most strongly enhanced on the lower slope and have DSDs marked by large concentrations of small to medium drops and varying concentrations of large drops. During the heavy-rain period of the case examined here, the low-level flow was onshore and entirely up terrain, the melting level was ~2.5 km, and stability moist neutral so that large amounts of small raindrops were produced. At the same time, melting ice particles produced at upper levels contributed varying amounts of large drops to the DSD, depending on the subsynoptic variability of the storm structure. When the low-level flow is directed downslope and offshore, small-drop production at low altitudes is reduced or eliminated.

Field measurement using LiDAR in a wind farm of downwind turbines installed in complex terrain region

Field measurement using LiDAR in a wind farm of downwind turbines installed in complex terrain region

Toward a Polarimetric Radar Classification Scheme for Coalescence-Dominant Precipitation: Application to Complex Terrain

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.