High-resolution Rain Gauge Research Articles

Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers

The introduction of iterative ensemble smoothers (IES) for parameter calibration opens avenues for expanding parameter space in surface water hydrologic modeling. Here, we have introduced independent parameters into a model calibration experiment to estimate errors in rainfall forcing data. This approach has the potential to estimate rainfall errors using other hydrological observations and to improve model calibration. Using high-resolution rain gauge data, we estimated “real” rainfall errors across the Turkey River watershed at storm and daily scales. Tests on synthetic and real-world scenarios successfully estimated errors correlated with observed values – even at daily scales. However, a bias remained from model parameter compensation, and identifying errors was challenging for low precipitation and snowfall. Despite synthetic results showing good error correlation, the biases in parameter identification masked potential improvements in hydrological calibration. This study highlights the potential of IES to provide additional information on rainfall errors, even only using streamflow observations.

Verification of satellite and model products against a dense rain gauge network for a severe flooding event in Kumasi, Ghana

AbstractFloods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project.

An Observational Analysis of a Persistent Extreme Precipitation Event in the Post-Flood Season over a Tropical Island in China

Featuring unique tropical land–sea contrast and mesoscale terrain, Hainan Island in China is endowed with active mesoscale convections of special regional characteristics. Persistent extreme precipitation events (PEPs) during the post-flood season, triggered by multi-scale interactions among mid-latitude and tropical weather systems, exhibit notable mesoscale features, long duration and high rainfall rates with low forecasting performance. This study is motivated by a failure to forecast a PEP in two stages with distinct characteristics and predictabilities, in the post-flood season over Hainan Island on 16–18 October 2020. Based on multiple sources of remote sensing and high-resolution rain gauge records, detailed observational analyses were conducted using a flow decomposition method. Water vapor divergence (WVD) and its three components were used to investigate the spatial distribution and temporal evolution of two stages with distinct characteristics and predictabilities during this PEP. Decomposed moisture components can be used to determine how and to what extent large- and sub-synoptic scale moisture convergence contributes to PEPs in the tropics, under similar synoptic backgrounds. Joint applications of multiple sources of remote sensing data and flow decomposed WVD are proposed to further assist predicting PEPs in terms of rainfall location and evolution.

Regional sub-hourly extreme rainfall estimates in Sicily under a scale invariance framework

Design of urban drainage systems or flood risk assessment in small catchments often requires knowledge of very short-duration rainfall events (less than 1 h). Unfortunately, data for these events are often unavailable or too scarce for a reliable statistical inference. However, regularities in the temporal pattern exhibited by storm records, known as scaling properties of rainfall, could help in characterizing extreme storms at partially gauged sites better than the application of traditional statistical techniques. In this work, a scaling approach for estimating the distribution of sub-hourly extreme rainfall in Sicily (Italy) is presented based on data from high-resolution rain gauges with a short functioning period and from low-resolution rain gauges with longer samples. First, simple scaling assumption was tested for annual maxima rainfall (AMR) data from 10 min to 24 h duration, revealing that the simple scaling regime holds from 20 to 60 min for most of the stations. Then, scaling homogeneous regions were classified based on the values of the scaling exponent. In each region, this parameter was regionalized through power-law relationships with the median of 1 h AMR data. After that, regional Depth Duration Frequency (DDF) curves were developed by combining the scale-invariant framework with the generalized extreme value (GEV) probability distribution and used to estimate T-year sub-hourly extreme rainfalls at sites where only rainfall data for longer durations (≥ 1 h) were available. The regional GEV simple scaling model was validated against sub-hourly historical observations at ten rain gauges, generally yielding, in relation to the scaling exponent value, to similar or better sub-hourly estimates than empirical approach.

Heavy Precipitation Systems in Calabria Region (Southern Italy): High-Resolution Observed Rainfall and Large-Scale Atmospheric Pattern Analysis

An in-depth analysis of historical heavy rainfall fields clearly constitutes an important aspect in many related topics: as examples, mesoscale models for early warning systems and the definition of design event scenarios can be improved, with the consequent upgrading in the prediction of induced phenomena (mainly floods and landslides) into specific areas of interest. With this goal, in this work the authors focused on Calabria region (southern Italy) and classified the main precipitation systems through the analysis of selected heavy rainfall events from high resolution rain gauge network time series. Moreover, the authors investigated the relationships among the selected events and the main synoptic atmospheric patterns derived by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 Reanalysis dataset, in order to assess the possible large-scale scenarios which can induce heavy rainfall events in the study area. The obtained results highlighted: (i) the importance of areal reduction factors, rainfall intensities and amounts in order to discriminate the investigated precipitations systems for the study area; (ii) the crucial role played by the position of the averaged low-pressure areas over the Mediterranean for the synoptic systems, and by low-level temperature for the convective systems.

A Process-Based Validation of GPM IMERG and Its Sources Using a Mesoscale Rain Gauge Network in the West African Forest Zone

AbstractUsing a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG’s proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG’s overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region.

Rainfall enhancement in Karnataka state cloud seeding program “Varshadhare” 2017

Operational cloud seeding program was conducted in the Karnataka State, India during 21 August to 7 November 2017 with robust, pre-established experimental design plan following the guidelines developed under Indian national CAIPEEX program. Hygroscopic and glaciogenic seeding were carried out using areal method. A very high resolution raingauge network (spatial resolution: 5 km, temporal resolution: 15 min) was used to measure the surface rainfall. Two approaches have been used for estimating the enhancement: (i) Rainfalls within 4-h after seeding was compared at station levels with the rainfalls prior to seeding. The analysis of 618 cases showed average enhancement of 27.9% above the natural rainfall. (ii) The floating control-target area rainfall analysis was used to estimate the natural rainfall that would have occurred without seeding. For understanding maximum absolute increase in the rainfall, 7 cases in the top 5% which were well distributed within seeding period (with 4 hygroscopic and 3 glaciogenic cases) have been analyzed. The maximum increase in rainfalls in hygroscopic/glaciogenic cases were 20.8/28.1 mm which are ~5/7 times higher than the mean daily rainfall (4.3 mm) during September–October. Additional water made available due to seeding has been estimated to be 2.1 TMC. The study showed that seeding became effective in increasing rainfall under the particular dynamic and thermodynamical conditions. This is a unique study to estimate the effect of operational cloud seeding in (i) enhancement of precipitation and (ii) availability of additional water. The results of the study may find basis for the operational programs in the world.

How intermittency affects the rate at which rainfall extremes respond to changes in temperature

Abstract. A detailed analysis of how intermittency (i.e., the alternation of dry and rainy periods) modulates the rate at which sub-daily rainfall extremes depend on temperature is presented. Results show that hourly extremes tend to be predominantly controlled by peak intensity, increasing at a rate of approximately 7 % per degree in agreement with the Clausius–Clapeyron equation. However, a rapid increase in intermittency upward of 20–25 °C is shown to produce local deviations from this theoretical scaling, resulting in lower scaling rates. On the other hand, rapidly decreasing intermittency with temperature between 10 and 20° can result in higher net scaling rates than expected, potentially exceeding Clausius–Clapeyron. In general, the importance of intermittency in controlling the scaling rates of precipitation with temperature grows as we progress from hourly to daily aggregation timescales and beyond. Thermodynamic effects still play an important role in controlling the maximum water-holding capacity of the atmosphere and therefore peak rainfall intensity, but the observational evidence shows that, beyond a few hours, storm totals become increasingly dominated by dynamical factors. The conclusion is that Clausius–Clapeyron scaling alone cannot be used to reliably predict the net effective changes in rainfall extremes with temperature beyond a few hours. A more general scaling model that takes into account simultaneous changes in intermittency and peak intensity with temperature is proposed to help better disentangle these two phenomena (e.g., peak intensity and intermittency). The new model is applied to a large number of high-resolution rain gauge time series in the United States, and results show that it greatly improves the representation of rainfall extremes with temperature, producing a much more consistent and reliable picture of extremes across scales than using Clausius–Clapeyron only.

Dew measurement and estimation of rain-fed jujube (Zizyphus jujube Mill) in a semi-arid loess hilly region of China

Dew is an important water source for plants in arid and semi-arid regions. However, information on dew is scarce in such regions. In this study, we explored dew formation, amount, and duration of rain-fed jujube (Zizyphus jujube Mill) trees in a semi-arid loess hilly region of China (i.e., Mizhi County). The data included dew intensity and duration, relative humidity, temperature, and wind speed measured from 26 July to 23 October, 2012 and from 24 June to 17 October, 2013 using a micro-climate system (including dielectric leaf wetness sensors, VP-3 Relative Humidity/Temperature Sensor, High Resolution Rain Gauge, and Davis Cup Anemometer). The results show that atmospheric conditions of relative humidity of >78% and dew point temperature of 1°C–3°C are significantly favorable to dew formation. Compared with the rainfall, dew was characterized by high frequency, strong stability, and long duration. Furthermore, heavy dew accounted for a large proportion of the total amount. The empirical models (i.e., relative humidity model (RH model) and dew point depression model (DPD model)) for daily dew duration estimation performed well at 15-min intervals, with low errors ranging between 1.29 and 1.60 h, respectively. But it should be noted that the models should be calibrated firstly by determining the optimal thresholds of relatively humidity for RH model and dew point depression for DPD model. For rain-fed jujube trees in the semi-arid loess hilly regions of China, the optimal threshold of relative humidity was 78%, and the optimal upper and lower thresholds of dew point depression were 1°C and 5°C, respectively. The study further demonstrates that dew is an important water resource that cannot be ignored for rain-fed jujube trees and may affect water balance at regional scales.

Scaling and Distributional Properties of Precipitation Interamount Times

Abstract The scaling and distributional properties of precipitation interamount times (IATs) are investigated using 10 years of high-resolution rain gauge observations from the U.S. Climate Reference Network. Results show that IATs above 200 mm tend to be approximately uncorrelated and normally distributed. As one moves toward smaller scales, autocorrelation and skewness increase and distributions progressively evolve into Weibull, Gamma, lognormal, and Pareto. This procession is interpreted as a sign of increasing complexity from large to small scales in a system composed of many interacting components. It shows that, as one approaches finer scales, IATs take over more of the characteristics of power-law distributions and (multi)fractals. Regression analysis on the log moments reveals that IATs generally exhibit better scaling, that is, smaller departures from multifractality, than precipitation amounts over the same range of scales. The improvement is attributed to the fact that IATs, unlike rainfall rates, always remain positive, no matter how small the scale. In particular, the scaling is shown to be more resilient to dry periods within rain events. Nevertheless, most analyzed IAT time series still exhibited a breakpoint at about 20 mm (7 days), corresponding to the average lifetime of a low pressure system at midlatitudes. Additional breakpoints in IATs at smaller and larger time scales are possible, but could not be determined unambiguously. The results highlight the potential of IATs as a new and promising tool for the stochastic modeling, simulation, and downscaling of precipitation.

Smart Rainwater Tanks as a Rainguage Network and Dam for Flood Control

We propose the use of smart rainwater tanks (SRTs) equipped with water-level sensors, outflow monitors, and water pump controls. In our design, SRTs are connected via a network that enables monitoring data to be collected and pump-control commands to be issued to individual tanks. These connected tanks form part of a rainwater grid system (RGS). It will also be possible to monitor rainfall amount using the RGS since it functions as a high-resolution rain gauge. This is important because Heavy rainfall can cause serious attenuation of the reflectivity of rain cells monitored by X-band multi-parameter (MP) weather radar networks.A prototype SRT system has been installed in a gravel-filled 110 tons underground tank in the Oginoura Garden Suburb, which is a maisonette-type housing complex, where its functions are now being tested. Initial results indicate that measuring rainfall using the gravel-filled underground tank is not suitable due to spikes in the water level after a heavy rainfall event has occurred. So, we decide to install a void type of 5.6 tons rainwater tank to the personal house for Moriyama. It will be installed a sensors at the first quarter of 2016.For pre-discharge of rainwater before heavy rainfall, diagnosis for heavy rainfall by using SOM is installed. This diagnosis is installed on “Internet Cloud” and possible to send a order to SRTs with pumps. The accuracy of diagnosis for heavy rainfall is investigated, but it is only for Aug. 2014, it should be more investigation.

Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE) by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1) PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2) both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3) PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE). This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

Two Simple Metrics for Quantifying Rainfall Intermittency: The Burstiness and Memory of Interamount Times

Abstract Precipitation displays a remarkable variability in space and time. An important yet poorly documented aspect of this variability is intermittency. In this paper, a new way of quantifying intermittency based on the burstiness B and memory M of interamount times is proposed. The method is applied to a unique dataset of 325 high-resolution rain gauges in the United States and Europe. Results show that the M–B diagram provides useful insight into local precipitation patterns and can be used to study intermittency over a wide range of temporal scales. It is found that precipitation tends to be more intermittent in warm and dry climates with the largest observed values in the southwest of the United States (i.e., California, Nevada, Arizona, and Texas). Low-to-moderate values are reported for the northeastern United States, the United Kingdom, the Netherlands, and Germany. In the second half of the paper, the new metrics are applied to daily rainfall data for 1954–2013 to investigate regional trends in intermittency due to climate variability and global warming. No evidence is found of a global shift in intermittency but a weak trend toward burstier precipitation patterns and longer dry spells in the south of Europe (i.e., Portugal, Spain, and Italy) and an opposite trend toward steadier and more correlated precipitation patterns in Norway, Sweden, and Finland is observed.

Lightning and precipitation produced by severe weather systems over Belém, Brazil

CG Lightning flashes events monitored by a LDN of the Amazon Protection System, which included 12 LPATS IV VAISALA sensors distributed over eastern Amazonia, were analyzed during four severe rainstorm occurrences in Belem-PA-Brazil, in the 2006-2007 period. These selected case studies referred to rainfall events, which produced more than 25 mm/hour, or more than 40 mm/ 2 hours of precipitation rate totals, registered by a tipping bucket automatic high-resolution rain gauge, located at 1º 47' 53" S and 48º 30' 16" W. Centered at this location, a 30 ,10 and 5 km radius circles were drawn by means of a geographic information system, and the data from lightning occurrences within this larger area, were set apart for analysis. During these severe storms the CG lightning events, occurred almost randomly over the surrounding defined circle, previously covered by mesoscale convective systems, for all cases studied. This work also showed that the interaction between large-scale and mesoscale weather conditions have a major influence on the intensity of the storms studied cases. In addition to the enhancement of the lightning and precipitation rates, the electric activity within the larger circles can precede the rainfall at central point of the areas

Benefit of convection permitting climate model simulations in the representation of convective precipitation

A major source of uncertainty in regional climate model (RCM) simulations arises from the parameterisation of sub-grid scale convection. With increasing model resolution, approaching the so-called convection permitting scale, it is possible to switch off most of the convection parameterisations. A set of simulations using COSMO-CLM model has been carried out at different resolutions in order to investigate possible improvements and limitations resulting from increased horizontal resolution. For our analysis, 30 years were simulated in a triple nesting setup with 50, 7 and 2.8 km resolutions, with ERA40 reanalysis data at the lateral boundaries of the coarsest nest. The investigation area covers the state of Baden-Württemberg in southwestern Germany, which is a region known for abundant orographically induced convective precipitation. A very dense network of high temporal resolution rain gauges is used for evaluation of the model simulations. The purpose of this study is to examine the differences between the 7 and 2.8 km resolutions in the representation of precipitation at sub-daily timescales, and the atmospheric conditions leading to convection. Our results show that the highest resolution of RCM simulations significantly improves the representation of both hourly intensity distribution and diurnal cycle of precipitation. In addition, at convection permitting scale the atmospheric fields related to convective precipitation show a better agreement with each other. The results imply that higher spatial resolution partially improves the representation of the precipitation field, which must be the way forward for regional climate modelling.

Dynamical downscaling of ECMWF Ensemble seasonal forecasts over East Africa with RegCM3

Dynamical downscaling of ECMWF ERA‐interim reanalysis and an ensemble of May‐start ECMWF seasonal hindcasts is performed with ICTP's regional climate model (RegCM3) over the horn of Africa for a ten year period. Using ERA‐interim ‘perfect boundary’ conditions the regional model reproduced both the spatial and interannual variability of the region's rainfall and improved the global model's reproduction of year to year rainfall variability, capturing the teleconnection between ENSO and the region's precipitation pattern well. The ensembles of ECMWF seasonal hindcasts and the respective downscaled RegCM3 hindcast suite were then validated in terms of the seasonal climate and deterministic and probabilistic skill scores at a one to four month lead time. Both RegCM3 and ECMWF hindcasts reproduce the spatial and temporal rainfall variability well, but overestimate the mean and variability over the Arabian peninsula and misrepresent the teleconnection between ENSO and precipitation over the western Indian Ocean. The positive bias over the Arabian peninsula in RegCM3 and the teleconnection error between ENSO and precipitation anomalies over the western Indian Ocean are due to the propagation of errors from the driving GCMs to the regional model. Nevertheless, the probabilistic assessment (ROCS and RPSS) indicated that both ECMWF and RegCM3 have significant skill suggesting the potential utility of dynamical forecasts over the region. Comparing the skill of ECMWF and RegCM3 probabilistic hindcasts, ECMWF generally performs better on grid point by grid point comparison and at homogeneous zones over Ethiopia but RegCM3 outperforms when aggregated on a country scale and when compared against high resolution rain gauge data set.

Rainfall distribution in the Andes of southern Ecuador derived from blending weather radar data and meteorological field observations

The Andes of Ecuador show an extreme heterogeneity of spatial and temporal distribution of precipitation. The existing operational network of the national weather service is not capable of reproducing these complex patterns. By using a cost-efficient rain radar and a network of high-resolution rain gauges, the real complexity of the rainfall distribution and the meteorological processes of rainfall formation can be assessed. A blending method encompassing geostatistical tools allows to derive a comprehensive rainfall climatology for the study area. Precipitation is predominantly of the advective type, associated with humid air masses from the Amazon basin transported by the tropical easterlies. The typical form is light to heavy drizzle with long duration but lower rain rates. However, in contrast to former knowledge there is no single mechanism of rain formation for any given place. Several processes interact like small and large-scale convective cloud systems, local and regional valley/mountain breeze systems and terrain-lines of preferred moisture transport interact on various time scale. This leads to complex patterns of rainfall in space and time. Several types of characteristic weather situations are revealed by the study. They are characterized by specific combinations of local and regional atmospheric processes and interactions with the topographical configuration. They are modified by mesoscale and continental circulation patterns as the annual shift of pressure cells, the east Andean low-level Jet and katabatic flows.

Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate

AbstractThis study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.

To what extent does increased model resolution improve simulated precipitation fields? A case study of two north-Alpine heavy-rainfall events

This study considers two north-Alpine heavy-rainfall cases (20-22 May 1999 and 22-23 August 2005) in order to investigate the resolution-dependence of model skill. The simulations are conducted with the Penn State/NCAR mesoscale model MM5 with a variable number of nested domains, resulting in a finest mesh size of 9, 3, and 1 km, respectively. The results are validated against high-resolution raingauge data for western Austria and the adjacent northern foreland in Bavaria. It is found that refining the mesh size from 9 km to 1 km has a highly beneficial impact in the Alpine part of the area of investigation, which can be explained by the fact that a proper representation of the topography is a necessary precondition for simulating the observed small-scale precipitation variability. The model skill at small scales is found to be better for stable orographic precipitation than in the presence of embedded convective cells because the latter induce a stochastic component in the precipitation field. Moreover, the impact of the cloud microphysics scheme increases with increasing model resolution. In the Alpine foreland, the impact of enhancing the model resolution turns out to be small and not necessarily beneficial. There, precipitation variability is not dominated by topographic effects, and other factors (e.g. embedded convection) may not be predictable in a deterministic sense.

Sensitivity of a Mesoscale Model to Microphysical Parameterizations in the MAP SOP Events IOP2b and IOP8

Abstract The sensitivity of a mesoscale model to different microphysical parameterizations is investigated for two events of precipitation in the Mediterranean region, that is, the Mesoscale Alpine Program (MAP) intensive observation periods (IOP) 2b (19–21 September 1999) and 8 (20–22 October 1999). Simulations are performed with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5); the most commonly used bulk microphysical parameterization schemes are evaluated, with a particular focus on their impact on the forecast of rainfall. To evaluate the forecast skill, the verification is carried out quantitatively by using the observations recorded by a high-resolution rain gauge network during the MAP campaign. The results show that, for the surface rainfall forecast, all microphysical schemes produce a similar precipitation field and none of them perform significantly better than the others. The ability of different schemes to reproduce events with different ongoing microphysical processes is briefly discussed by comparing model simulations and knowledge of hydrometeor fields from radar observations. The vertical profiles of hydrometeors from two of the analyzed schemes show gross similarities with available radar observations. Last, the role of one of the parameterizations appearing in a typical bulk microphysical scheme, that is, the one of the snowfall speed, is evaluated in detail. Adjustments in the semiempirical relationships describing the fall speed of snow particles have a large impact, because a reduced snowfall speed enhances precipitation on the lee side of mountain ridges and diminishes it on the windward side. Anyway, this effect does not appear to be able to largely improve or reduce the forecast skill of the MM5 systematically; the impact of changes in the parameterization of the snow deposition velocity very likely depends on the dynamics of the event under investigation.