Improvements to melting snow behavior in an NWP bulk microphysics scheme

2020 ◽  
Author(s):  
Emilie C. Iversen ◽  
Gregory Thompson ◽  
Bjørn Egil Nygaard
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Bimodal Distribution ◽  
Fall Velocity ◽  
Microphysics Scheme ◽  
Melting Snow ◽  
Melting Layer ◽  
Computational Performance ◽  
Fall Speed

<p>Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density, fall speed and stickiness. Given that these characteristics contribute to determine the phase and amount of precipitation reaching the ground, precisely predicting such are important in order to obtain accurate weather forecasts for which society depends on. For example, in hydrological modelling precipitation phase at the surface is a first-order driver of hydrological processes in a water shed. Also, melting snow exerts a possible threat to critical infrastructure because the wet, sticky snow may adhere to the structures and form heavy ice sleeves.</p><p>Most widely used bulk microphysical parameterization schemes part of numerical weather prediction models represent only purely solid or liquid hydrometeors, and so melting particle characteristics are either ignored or represented by parent species with simple conditions for behavior in the melting layer. The Thompson microphysics scheme is explicitly developed for forecasting winter conditions in real-time as part of the WRF model, and to maintain computational performance, the introduction of additional prognostic variables is undesirable. This research aims at improving the Thompson scheme with respect to melting snow characteristics using a physically based approximation for the snowflake melted fraction, as well as a new definition of melting level and melting particle fall velocity. A real 3D WRF case is set up to compare with in-situ measurements of hydrometeor size and fall velocity from a disdrometer and a vertically pointing Doppler radar deployed during the Olympic Mountain Experiment (OLYMPEX). The modified microphysics scheme is able to replicate the bimodal distribution of fall speed – diameter relations typical of mixed precipitation seen in disdrometer data, as well as the non-linear increase in snow fall speed with melted fraction through the melting layer.</p>

On the effective resolution of WRF simulations at microscale grid resolution.

2021 ◽  
Author(s):  
Pedro Bolgiani ◽  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Mariano Sastre ◽  
Daniel Santos-Muñoz ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Research Community ◽  
Weather Research And Forecasting ◽  
Large Uncertainty ◽  
Partial Explanation ◽  
Synoptic Conditions ◽  
Large Effort ◽  
Numerical Weather Prediction Models

<p>As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.</p>

scholarly journals Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Swagata Payra ◽  
Manju Mohan
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Winter Season ◽  
Wrf Model ◽  
Onset Time ◽  
Northwestern Part ◽  
Model Simulations ◽  
Western Disturbances ◽  
Promising Tool ◽  
Local Perturbations

The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

scholarly journals The ability of the ICE-T microphysics scheme in HARMONIE-AROME to predict aircraft icing

2021 ◽  
Author(s):  
Tim Carlsen ◽  
Morten Køltzow ◽  
Trude Storelvmo
Keyword(s):  
Water Content ◽  
Numerical Weather Prediction ◽  
Liquid Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Liquid Water Content ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Numerical Weather

Abstract In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilised by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat-CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighbourhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighbourhood areas. With a higher frequency of forecasted icing, a higher false-alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

The Life-Cycle of Cloud and Precipitation Microphysics in Radar Observation and Numerical Model

2021 ◽  
Author(s):  
Gregor Möller ◽  
Florian Ewald ◽  
Silke Groß ◽  
Martin Hagen ◽  
Christoph Knote ◽  
...  
Keyword(s):  
Prediction Models ◽  
Model Simulation ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Polarimetric Radar ◽  
Dual Frequency ◽  
Microphysics Scheme ◽  
Ka Band ◽  
Radar Observations ◽  
Conversion Rates

<p>The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty. To tackle this issue, we exploit the synergy of two polarimetric radars to provide novel observations of model microphysics parameterizations. In the framework of the IcePolCKa project (Investigation of the initiation of Convection and the Evolution of Precipitationusing simulatiOns and poLarimetric radar observations at C- and Ka-band) we use these observations to study the initiation of convection as well as the evolution of precipitation. At a distance of 23 km between the C-band PoldiRad radar of the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band Mira35 radar of the Ludwig-Maximilians-University of Munich (LMU), the two radar systems allow targeted observations and coordinated scan patterns. A second C-band radar located in Isen and operated by the German Weather Service (DWD) provides area coverage and larger spatial context. By tracking the precipitation movement, the dual-frequency and polarimetric radar observations allow us to characterize important microphysical parameters, such as predominant hydrometeor class or conversion rates between these classes over a significant fraction of the life time of a convective cell. A WRF (Weather Research and Forecasting Model) simulation setup has been established including a Europe-, a nested Germany- and a nested Munich- domain. The Munich domain covers the overlap area of our two radars Mira35 and Poldirad with a horizontal resolution of 400 m. For each of our measurement days we conduct a WRF hindcast simulation with differing microphysics schemes. To allow for a comparison between model world and observation space, we make use of the radar forward-simulator CR-SIM. The measurements so far include 240 coordinated scans of 36 different convective cells over 10 measurement days between end of April and mid July 2019 as well as 40 days of general dual-frequency volume scans between mid April and early October 2020. The performance of each microphysics scheme is analyzed through a comparison to our radar measurements on a statistical basis over all our measurements.</p>

 Evaluation of high resolution WRF forecasts for Extreme Rainfall Events over Karnataka against high density in-situ observations 

2021 ◽  
Author(s):  
Ajay Bankar ◽  
Rakesh Vasudevan
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Distinct Advantage ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Synoptic Conditions

<p><span>Extreme Rainfall Events (EREs) in India has increased many folds in recent decades. These severe weather events are generally destructive in nature causing flash floods, catastrophic loss of life and property over densely populated urban cities. Various cities in Karnataka, a southern state in India, witnessed many EREs recently. Appropriate advanced warning systems to predict these events are crucial for preparedness of mitigation strategy to reduce human casualty and socio economic loss. Mesoscale models are essential tools for developing an integrated platform for disaster warning and management. From a stakeholder/user pint of view, primary requirement to tackle ERE related damages is accurate prediction of the observed rainfall location, coverage and intensity in advance. Weather prediction models have inherent limitations imposed primarily by approximations in the model and inadequacies in data. Hence, it is important to evaluate the skill of these models for many cases under different synoptic conditions to quantify model skill before using them for operational applications. The objective of the study is to evaluate performance of the Weather Research and Forecasting (WRF) model for several ERE cases in Karnataka at different model initial conditions. The EREs were identified from the distribution of rainfall events over different regions in Karnataka and those events comes under 1% probability were considered. We examined 38 ERE’s distributed over Karnataka for the period June to November for the years 2015-2019. WRF model is configured with 3 nested domains with outer, inner and innermost domains having resolution of 12 km, 9 km and 3 km respectively. Two sets of simulations are conducted in this study, i) staring at 12 hours prior to the ERE day (i.e. -1200 UTC) & ii) starting at 0000 UTC of the ERE day. Performance of the WRF model forecast is validated against 15 minutes rainfall observations from ~6000 rain gauge stations over Karnataka. During initial hours forecasts initiated at 1200 UTC has distinct advantage in terms of accuracy compared to those initiated at 0000 UTC for most of the cases. In general, model underpredict EREs and underprediction is relatively low for forecasts initiated at 12 00 UTC.</span></p>

Secondary Ice Production in Antarctic Clouds: a process neglected in large-scale models

2020 ◽  
Author(s):  
Georgia Sotiropoulou ◽  
Etienne Vignon ◽  
Gillian Young ◽  
Thomas Lachlan-Cope ◽  
Alexis Berne ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weddell Sea ◽  
Microphysics Scheme ◽  
Ice Multiplication ◽  
Ice Production ◽  
Low Sensitivity ◽  
The Impact

<p>In-situ measurements of Antarctic clouds frequently show that ice crystal number concentrations  are much higher than the available ice-nucleating particles, suggesting that Secondary Ice Production (SIP) may be active. Here we investigate the impact of two SIP mechanisms, Hallett-Mossop (H-M)and collisional break-up (BR), on a case from the Microphysics of Antarctic Clouds (MAC) campaign in Weddell Sea using the Weather and Research Forecasting (WRF) model. H-M is already included in the default version of the Morrison microphysics scheme in WRF; for BR we implement different parameterizations and compare their performance. H-M alone is not effective enough to reproduce the observed concentrations. In contrast, BR can result in realistic ice multiplication, independently of whether H-M is active or not. In particular, the Phillips parameterization results in very good agreement with observations, but its performance depends on the prescribed rimed fraction of the colliding ice particles. Finally, our results show low sensitivity to primary ice nucleation, as long as there are enough primary ice crystals to initiate ice-ice collisions. Our findings suggest that BR is a potentially important SIP mechanism in the pristine Antarctic atmosphere that is currently not represented in weather-prediction and climate models.</p>

scholarly journals Medicane Ianos: a comparative study between WRF model and satellite measurements

2021 ◽  
Author(s):  
Stefano Federico ◽  
Albert Comellas Prat ◽  
Rosa Claudia Torcasio ◽  
Leo Pio D'Adderio ◽  
Stefano Dietrich ◽  
...  
Keyword(s):  
Water Content ◽  
Prediction Models ◽  
Quantitative Estimation ◽  
Weather Prediction ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Liquid Water Content ◽  
Correct Prediction ◽  
Satellite Measurements ◽  
North West

<p>On September 14th, 2020 a depression originated on the Libyan coasts generated a Mediterranean tropical-like cyclone (hereafter referred to as Medicane Ianos), which moved northward until it hit, with its northernmost cloud bands, the southern Italian coasts and finally bent towards Greece, where it made landfall on September 18th, 2020. Heavy precipitation and flash flooding were reported, associated to huge damages to railway, houses, and four casualties.</p><p>The correct prediction, as much as possible, of the trajectory and the intensity of these events is fundamental to prevent risks to infrastructures, natural landscapes, and people. One of the ways to evaluate the performances of the numerical weather prediction models is the comparison with satellite measurements. In particular, the mature phase of Medicane Ianos, as predicted by the weather research and forecasting (WRF) model, has been compared, for the first time, with the Global Precipitation Measurement mission Core Observatory (GPM-CO) active and passive measurements. Different microphysics schemes were used in order to investigate which is the most suitable to achieve the best forecast of Medicane Ianos considering different parameters as depression localization, reflectivity, rainfall rate, integrated liquid and ice content. The results show that all the schemes identified the precipitation bands structure of the Medicane overestimating the vertical extent of the convective structures. At the same time, all the schemes predicted an excessive columnar ice water content if compared to the one estimated from satellite measurements. It has to be highlighted that the overestimation is marked on the western precipitation bands of the Medicane eye, while a better agreement is obtained for the northern bands. Similar results are obtained for columnar liquid water content, even if the quantitative estimation is closer to the GPM measurements. Finally, all the schemes located the Medicane circulation center further north-west of its actual position.</p>

scholarly journals An algorithm for detecting the Chesapeake Bay breeze from mesoscale NWP model output

2021 ◽  
Keyword(s):  
Chesapeake Bay ◽  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Model Output ◽  
Detection Algorithms ◽  
Bay Breeze ◽  
Nwp Model

Abstract A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) model and on analyses and forecasts from the High Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. Additionally, by defining the onshore wind directions based on model land-use data, not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

The impact of SST on the weather forecast quality in the Bulgarian Antarctic Base area on Livingstone Island

2020 ◽  
Author(s):  
Boriana Chtirkova ◽  
Elisaveta Peneva
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Forecast ◽  
Horizontal Resolution ◽  
Initial Time ◽  
Test Cases ◽  
Validation Data ◽  
The Impact ◽  
Forecast Quality

<p>The weather forecast of good quality is essential for the humans living and operating in the Bulgarian Antarctic Base. The numerical weather prediction models in southern high latitude regions still need improvement as the user community is limited, little test cases are documented and validation data are scarce. Not lastly, the challenge of distributing the output results under poor internet conditions has to be addressed.</p><p>The Bulgarian Antarctic Base (BAB) is located on the Livingstone Island coast at 62⁰S and 60⁰W. The influence of the Southern ocean is significant, thus important to be correctly taken into account in the numerical forecast. The modeling system is based on the WRF model, configured in three nested domains down to 1 km horizontal resolution, centered in BAB. The main objective of the study is to quantify the Sea Surface Temperature (SST) impact and to recommend the frequency and way to perform measurements of the SST near the base. The focus is on prediction of right initial time and period of “bad” weather events like storms, frontal zones, and severe winds. Several test cases are considered with available measurements of temperature, pressure and wind speed in BAB during the summer season in 2017. The numerical 3 days forecast is performed and the model skill to capture the basic meteorological events in this period is discussed. Sensitivity experiments to SST values in the nearby marine area are concluded and the SST influence on the model forecast quality is analyzed.</p>

scholarly journals Comparison of Cloud Microphysics Schemes in a Warn-on-Forecast System Using Synthetic Satellite Objects

2018 ◽  
Vol 33 (6) ◽  
pp. 1681-1708 ◽  
Author(s):  
Thomas A. Jones ◽  
Patrick Skinner ◽  
Kent Knopfmeier ◽  
Edward Mansell ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Prediction Models ◽  
Negative Impact ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Variable Density ◽  
Cloud Microphysics ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Double Moment

AbstractForecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.

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