The Impact of Microphysics Parameterization in the Simulation of Two Convective Rainfall Events over the Central Andes of Peru Using WRF-ARW

The present study explores the cloud microphysics (MPs) impact on the simulation of two convective rainfall events (CREs) over the complex topography of Andes mountains, using the Weather Research and Forecasting- Advanced Research (WRF-ARW) model. The events occurred on December 29 2015 (CRE1) and January 7 2016 (CRE2). Six microphysical parameterizations (MPPs) (Thompson, WSM6, Morrison, Goddard, Milbrandt and Lin) were tested, which had been previously applied in complex orography areas. The one-way nesting technique was applied to four domains, with horizontal resolutions of 18, 6, and 3 km for the outer ones, in which cumulus and MP parameterizations were applied, while for the innermost domain, with a resolution of 0.75 km, only MP parameterization was used. It was integrated for 36 h with National Centers for Environmental Prediction (NCEP Final Operational Global Analysis (NFL) initial conditions at 00:00 UTC (Coordinated Universal Time). The simulations were verified using Geostationary Operational Environmental Satellites (GOES) brightness temperature, Ka band cloud radar, and surface meteorology variables observed at the Huancayo Observatory. All the MPPs detected the surface temperature signature of the CREs, but for CRE2, it was underestimated during its lifetime in its vicinity, matching well after the simulated event. For CRE1, all the schemes gave good estimations of 24 h precipitation, but for CRE2, Goddard and Milbrandt underestimated the 24 h precipitation in the inner domain. The Morrison and Lin configurations reproduced the general dynamics of the development of cloud systems for the two case studies. The vertical profiles of the hydrometeors simulated by different schemes showed significant differences. The best performance of the Morrison scheme for both case studies may be related to its ability to simulate the role of graupel in precipitation formation. The analysis of the maximum reflectivity field, cloud top distribution, and vertical structure of the simulated cloud field also shows that the Morrison parameterization reproduced the convective systems consistently with observations.

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Role of initial condition and parametric uncertainty in a severe hailstorm forecast

10.5194/egusphere-egu21-7406 ◽

2021 ◽

Author(s):

Patrick Kuntze ◽

Annette Miltenberger ◽

Corinna Hoose ◽

Michael Kunz

Keyword(s):

Initial Conditions ◽

Weather Prediction ◽

Parametric Uncertainty ◽

Radar Data ◽

Cloud Microphysics ◽

Initial Condition ◽

Relative Contribution ◽

Cloud Properties ◽

Set Up ◽

The Impact

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany. To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation. In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.

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Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions

Monthly Weather Review ◽

10.1175/2010mwr3487.1 ◽

2011 ◽

Vol 139 (2) ◽

pp. 403-423 ◽

Cited By ~ 82

Author(s):

Benoît Vié ◽

Olivier Nuissier ◽

Véronique Ducrocq

Keyword(s):

Boundary Conditions ◽

Case Studies ◽

Short Range ◽

Initial Conditions ◽

Ensemble Prediction ◽

Lateral Boundary ◽

Synoptic Scale ◽

Lateral Boundary Conditions ◽

Convective Scale ◽

The Impact

Abstract This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered. The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

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The relationship between landslide activity and weather: examples from Hungary

Natural Hazards and Earth System Science ◽

10.5194/nhess-3-43-2003 ◽

2003 ◽

Vol 3 (1/2) ◽

pp. 43-52 ◽

Cited By ~ 15

Author(s):

J. Szabó

Keyword(s):

Case Studies ◽

Winter Precipitation ◽

Unconsolidated Sediments ◽

Rainfall Events ◽

Landscape Type ◽

Landslide Processes ◽

Landscape Types ◽

The Impact ◽

The Relationship ◽

Precipitation Events

Abstract. The paper presents the impact of irregular rainfall events triggering landslides in the regional context of landslides in Hungary. The author’s experience, gathered from decades of observations, confirms that landslide processes are strongly correlate with precipitation events in all three landscape types (hill regions of unconsolidated sediments; high bluffs along river banks and lake shores; mountains of Tertiary stratovolcanoes). Case studies for each landscape type underline that new landslides are triggered and old ones are reactivated by extreme winter precipitation events. This assertion is valid mainly for shallow and translational slides. Wet autumns favour landsliding, while the triggering influence of intense summer rainfalls is of a subordinate nature. A recent increasing problem lies in the fact that on previously unstable slopes, stabilised during longer dry intervals, an intensive cultivation starts, thus increasing the damage caused by movements during relatively infrequent wet winters.

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Influence of Rain-Rate Initialization, Cloud Microphysics, and Cloud Torques on Hurricane Intensity

Monthly Weather Review ◽

10.1175/2010mwr3382.1 ◽

2011 ◽

Vol 139 (2) ◽

pp. 627-649 ◽

Cited By ~ 22

Author(s):

S. Pattnaik ◽

C. Inglish ◽

T. N. Krishnamurti

Keyword(s):

Angular Momentum ◽

Rain Rate ◽

Velocity Structure ◽

Mesoscale Model ◽

Initial Conditions ◽

Cloud Microphysics ◽

Hurricane Intensity ◽

Storm Intensity ◽

Intensity Changes ◽

The Impact

Abstract This study examines the impact of rain-rate initialization (RINIT), microphysical modifications, and cloud torques (in the context of angular momentum) on hurricane intensity forecasts using a mesoscale model [the Advanced Research Weather Research and Forecasting model (ARW-WRF)] at a cloud-resolving resolution of 2.7 km. The numerical simulations are performed in a triple-nested manner (25, 8.3, and 2.7 km) for Hurricane Dennis of 2005. Unless mentioned otherwise, all the results discussed are from the innermost grid with finest resolution (2.7 km). It is found that the model results obtained from the RINIT technique demonstrated robust improvement in hurricane structure, track, and intensity forecasts compared to the control experiment (CTRL; i.e., without RINIT). Thereafter, using RINIT initial conditions datasets three sensitive experiments are designed by modifying specific ice microphysical parameters (i.e., temperature-independent snow intercept parameter, doubling number of concentrations of ice, and ice crystal diameter) within the explicit parameterization scheme [i.e., the WRF Single-Moment 6-class (WSM6)]. It is shown that the experiment with enhanced ice mass concentration and temperature-independent snow intercept parameter produces the strongest and weakest storms, respectively. The results suggest that the distributions of hydrometeors are also impacted by the limited changes introduced in the microphysical scheme (e.g., the quantitative amount of snow drastically reduced to 0.1–0.2 g kg−1 when the intercept parameter of snow is made independent of temperature). It is noted that the model holds ice at a warmer temperature for a longer time with a temperature-independent intercept parameter. These variations in hydrometeor distribution in the eyewall region of the storm affect diabatic heating and vertical velocity structure and modulated the storm intensity. However, irrespective of the microphysical changes the quantitative amount of graupel hydrometeors remained nearly unaffected. Finally, the indirect effect of microphysical modifications on storm intensity through angular momentum and cloud torques is examined. A formulation to predict the short-term changes in the storm intensity using a parcel segment angular momentum budget method is developed. These results serve to elucidate the indirect impact of microphysical modifications on tropical cyclone intensity changes through modulation in cloud torque magnitude.

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The Extratropical Transition of Tropical Cyclone Lili (1996) and Its Crucial Contribution to a Moderate Extratropical Development

Monthly Weather Review ◽

10.1175/mwr2935.1 ◽

2005 ◽

Vol 133 (6) ◽

pp. 1562-1573 ◽

Cited By ~ 31

Author(s):

Anna Agustí-Panareda ◽

Suzanne L. Gray ◽

George C. Craig ◽

Chris Thorncroft

Keyword(s):

Tropical Cyclone ◽

Case Studies ◽

North Atlantic ◽

Initial Conditions ◽

Life Cycles ◽

Extratropical Transition ◽

The North ◽

Potential Vorticity Inversion ◽

The Impact

Abstract The transition that a tropical cyclone experiences as it moves into the extratropical environment (known as extratropical transition) can result in the decay or intensification of a baroclinic cyclone. The extratropical transition (ET) of Tropical Cyclone Lili (1996) in the North Atlantic resulted in a moderate extratropical development of a baroclinic cyclone. The impact of Lili in the extratropical development that occurred during its ET is investigated. Numerical experiments are performed using potential vorticity inversion and the Met Office Unified Model to forecast the extratropical development with and without the tropical cyclone in the initial conditions. In contrast with other case studies in the literature, Lili is shown to play a crucial role during its ET in the development of a baroclinic cyclone that occurred in the same region. A hypothesis of the possible scenarios of ET is presented that links the case-to-case variability of ET case studies in the literature with a classification of the life cycles of baroclinic cyclones.

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A comparison of two postprocessing approaches as part of the HEavy Precipitation forecast Postprocessing over India (HEPPI) project.

10.5194/egusphere-egu21-12403 ◽

2021 ◽

Author(s):

Michael Angus ◽

Martin Widmann ◽

Andrew Orr ◽

Gregor Leckebusch

Keyword(s):

Case Studies ◽

Heavy Rainfall ◽

Computational Cost ◽

Heavy Precipitation ◽

Precipitation Forecast ◽

Observation Data ◽

Forecast Performance ◽

Rainfall Events ◽

The Impact ◽

Mixed Distributions

Accurate predictions of heavy precipitation in India are vital for impact-orientated forecasting, and an essential requirement for mitigating the impact of damaging flood events. Operational forecasts from non-convection-permitting models can have large biases in the intensities and spatial structure of heavy precipitation, and while convection-permitting models can reduce biases, their operational use over large areas is not yet feasible. Statistical postprocessing can reduce these biases for relatively little computational cost, but few studies have focused on postprocessing monsoonal rainfall and the associated severe flooding events. As part of the Weather and Climate Science for Service Partnership India (WCSSP India), the HEavy Precipitation Forecast Postprocessing over India (HEPPI) project assesses the value of multiple postprocessing methods in this context.&#160;Here, we present an evaluation of two postprocessing approaches to determine their suitability for heavy rainfall in India: Univariate Quantile Mapping (UQM) and Ensemble Model Output Statistics (EMOS). For each method, we apply the statistical postprocessing to daily precipitation in the NCMWF 12km forecast for the 2018 and 2019 monsoon seasons individually at each grid cell within the forecast. UQM leads by construction to rainfall distributions close to the observed ones, while EMOS optimises the spread of the postprocessed ensemble without guaranteeing realistic rainfall distributions. The choice of method is therefore to some degree dependent on end user requirements.We use three rainfall observation data sets and different parametric distributions for UQM to determine the best setup. Mixed distributions, where gamma distributions are fitted separately to the bottom 90% and the top 10% of rainfall events are found to be the best choice because they are a better fit for the high rainfall values.In several case studies, an overestimation of west coast rainfall in the forecasts is corrected by UQM. Although errors linked to forecasting rainfall in the wrong location or where no rainfall has been observed at all cannot be corrected by local statistical postprocessing, the overall forecast performance is improved by the UQM approach adopted here.As in UQM, we use&#160;multiple observational datasets&#160;to determine the best EMOS setup. We select the gamma distribution, due to its suitability for both low and heavy rainfall events. Unlike in UQM, mixed distributions are unnecessary as the distribution is fitted across ensemble members at each timestep. EMOS and UQM are verified against observations and compared to each other using a variety of metrics including case studies, the Receiver Operating Characteristic and the Continuous Rank Probability Score.&#160;&#160;&#160;&#160;

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