scholarly journals WRF Precipitation Performance and Predictability for Systematically Varied Parameterizations over Complex Terrain

2021 ◽  
Author(s):  
Julia Jeworrek ◽  
Gregory West ◽  
Roland Stull
Keyword(s):  
Complex Terrain ◽  
Wrf Model ◽  
Precipitation Intensity ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Forecast Performance ◽  
Relative Standard ◽  
Spatial Differences

AbstractPhysics parameterizations in the Weather Research and Forecasting (WRF) model are systematically varied to investigate precipitation forecast performance over the complex terrain of southwest British Columbia (BC). Comparing a full year of modelling data from over 100 WRF configurations to station observations reveals sensitivities of precipitation intensity, season, location, grid resolution, and accumulation window. The choice of cumulus and microphysics parameterizations is most important. The WSM5 microphysics scheme yields competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although the cale-aware Grell-Freitas cumulus parameterization performs better for summertime convective precipitation, the conventional Kain-Fritsch parameterization better simulates wintertime frontal precipitation, which contributes to the majority of the annual precipitation in southwest BC. Finer grid spacings have lower relative biases and a more realistic spread in precipitation intensity distribution, yet higher relative standard deviations of their errors — they produce finer spatial differences and local extrema. Finer resolutions produce the best fraction of correct-to-incorrect forecasts across all precipitation intensities, whereas the coarser 27-km domain yields the highest hit rates and equitable threat scores. Verification metrics improve greatly with longer accumulation windows — hourly precipitation values are prone to double-penalty issues, while longer accumulation windows compensate for timing errors but lose information about short-term precipitation intensities. This study provides insights regarding WRF precipitation performance in complex terrain across a wide variety of configurations, using metrics important to a range of end users.

scholarly journals Assessment of a Regional-Scale Weather Model for Hydrological Applications in South Korea

2016 ◽  
Vol 6 (2) ◽  
pp. 28
Author(s):  
Yong Jung ◽  
Yuh-Lang Lin
Keyword(s):  
Weather Prediction ◽  
Regional Scale ◽  
Wrf Model ◽  
Domain Size ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Mountainous Region ◽  
Parameterization Schemes ◽  
Plain Region ◽  
Cumulus Parameterization Schemes

<p class="1Body">In this study, a regional numerical weather prediction (NWP) model known as the Weather Research Forescasting (WRF) model was adopted to improve the quantitative precipitation forecasts (QPF) by optimizing combined microphysics and cumulus parameterization schemes. Four locations in two regions (plain region for Sangkeug and Imsil; mountainous region for Dongchun and Bunchun) in Korean Peninsula were examined for QPF for two heavy rainfall events 2006 and 2008. The maximum Index of Agreement (IOA) was 0.96 at Bunchun in 2006 using the combined Thompson microphysics and the Grell cumulus parameterization schemes. Sensitivity of QPF on domain size at Sangkeug indicated that the localized smaller domain had 55% (from 0.35 to 0.90) improved precipitation accuracy based on IOA of 2008. For the July 2006 Sangkeug event, the sensitivity to cumulus parameterization schemes for precipitation prediction cannot be ignored with finer resolutions. In mountainous region, the combined Thompson microphysics and Grell cumulus parameterization schemes make a better quantitative precipitation forecast, while in plain region, the combined Thompson microphysics and Kain-Frisch cumulus parameterization schemes are the best.</p>

Predictability of Precipitation in Complex Terrain using the WRF Model with Varying Physics Parameterizations

2020 ◽  
Author(s):  
Julia Jeworrek ◽  
Gregory West ◽  
Roland Stull
Keyword(s):  
Land Surface ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Wrf Model ◽  
The Other ◽  
Convective Precipitation ◽  
Annual Rainfall ◽  
Hourly Precipitation ◽  
Physics Parameterizations ◽  
Surface Parameterizations

&lt;p&gt;Canada&amp;#8217;s west coast topography plays a crucial role for the local precipitation patterns, which are often shaped by orographic lifting on one side of the mountains, and rain shadows on the other side. The hydroelectric infrastructure in southwest British Columbia (BC) relies heavily on the abundant rainfall of the wet season, but long lasting and heavy precipitation can cause local flooding and make reliable precipitation forecasts crucial for resource management, risk assessment, and disaster mitigation.&lt;/p&gt;&lt;p&gt;This research evaluates hourly precipitation forecasts from the Weather Research and Forecasting (WRF) model over the complex terrain of southwest BC. The model data includes a full year of daily runs across three nested domains (27-9-3 km). A selection of different parameterizations is systematically varied, including microphysics, cumulus, turbulence, and land-surface parameterizations. The resulting over 100 model configurations are evaluated with observations from ground-based quality-controlled precipitation gauges. The individual model skill of the precipitation forecasts is assessed with respect to different accumulation windows, forecast horizons, grid resolutions, and precipitation intensities. Furthermore, the ensemble mean and spread provide insight to the general error growth for precipitation forecasts in WRF.&lt;/p&gt;&lt;p&gt;Cumulus and microphysics parameterizations together determine the total precipitation in numerical weather prediction models and this study confirms the expectation that the combination of those physics parameterizations is most decisive for the precipitation forecasts. However, the boundary-layer and land-surface parameterizations have a secondary effect on precipitation skill. The verification shows that the WSM5 microphysics parameterization yields surprisingly competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although, the scale-aware Grell-Freitas cumulus parameterization performs better for summer-time convective precipitation, the conventional Kain-Fritsch parameterization performs better for winter-time frontal precipitation, which contributes to the majority of the annual rainfall in southwest BC.&lt;/p&gt;&lt;p&gt;Throughout a 3-day forecast horizon mean absolute errors are observed to grow by ~5% per forecast day. Furthermore, this study indicates that coarser resolutions suffer from larger total biases and larger random error components, however, they have slightly higher correlation coefficients. The mid-size 9-km domain yields the highest relative hit rate for significant and extreme precipitation. Verification metrics improve exponentially with longer accumulation windows: On one side, hourly precipitation values are highly prone to double-penalty issues (where a timing error can, for example, result in an over-forecast error in one hour and an under-forecast in a subsequent hour); on the other side, extended accumulation windows can compensate for timing errors, but lose information about short-term rain intensities.&lt;/p&gt;

scholarly journals The Sensitivity of Numerical Forecasts to Convective Parameterization: A Case Study of the 17 February 2004 East Coast Cyclone

2006 ◽  
Vol 21 (4) ◽  
pp. 465-488 ◽  
Author(s):  
Kelly M. Mahoney ◽  
Gary M. Lackmann
Keyword(s):  
Numerical Model ◽  
Weather Forecast ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Winter Weather ◽  
Quantitative Precipitation Forecast ◽  
Eta Model ◽  
Upper Level ◽  
Coastal Front

Abstract The sensitivity of numerical model forecasts of coastal cyclogenesis and frontogenesis to the choice of model cumulus parameterization (CP) scheme is examined for the 17 February 2004 southeastern U.S. winter weather event. This event featured a complex synoptic and mesoscale environment, as the presence of cold-air damming, a developing coastal surface cyclone, and an upper-level trough combined to present a daunting winter weather forecast scenario. The operational forecast challenge was further complicated by erratic numerical model predictions. The most poignant area of disagreement between model runs was the treatment of a coastal cyclone and an associated coastal front, features that would affect the location and timing of precipitation and influence the precipitation type. At the time of the event, it was hypothesized that the Betts–Miller–Janjić (BMJ) CP scheme was dictating the location and intensity of the initial coastal cyclone center in operational Eta Model forecasts. For this reason, forecasts for this case were rerun with the workstation Eta Model using the Kain–Fritsch (KF) CP scheme to further examine the sensitivity to this parameterization choice. Results confirm that the model CP scheme played a major role in the forecast for this case, affecting the quantitative precipitation forecast as well as the strength, location, and structure of coastal cyclogenesis and coastal frontogenesis. The Eta Model forecast using the KF CP scheme produced a relatively uniform distribution of convective precipitation oriented along the axis of an inverted trough and strong coastal front. In contrast, the BMJ forecasts resulted in a weaker coastal front and the development of multiple distinct closed cyclonic circulations in association with more localized convective precipitation centers. An additional BMJ forecast in which the shallow mixing component of the scheme was disabled bore a closer semblance to the KF forecasts relative to the original BMJ forecast. Suggestions are provided to facilitate the identification of CP-driven cyclones using standard operational model output parameters.

scholarly journals A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

2009 ◽  
Vol 24 (4) ◽  
pp. 1121-1140 ◽  
Author(s):  
Adam J. Clark ◽  
William A. Gallus ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Wrf Model ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Central United States ◽  
Statistical Consistency

Abstract An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 h. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ∼ 4 km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

scholarly journals Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

2018 ◽  
Vol 22 (1) ◽  
pp. 853-870 ◽  
Author(s):  
María Carolina Rogelis ◽  
Micha Werner
Keyword(s):  
Early Warning ◽  
Flash Flood ◽  
Weather Prediction ◽  
Wrf Model ◽  
Hydrologic Model ◽  
Added Value ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Mountainous Area ◽  
Mountainous Watersheds

Abstract. Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zero-precipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

scholarly journals Study of NWP parameterizations on extreme precipitation events over Basque Country

2016 ◽  
Vol 13 ◽  
pp. 137-144 ◽  
Author(s):  
Iván R. Gelpi ◽  
Santiago Gaztelumendi ◽  
Sheila Carreño ◽  
Roberto Hernández ◽  
Joseba Egaña
Keyword(s):  
Extreme Precipitation ◽  
Numerical Models ◽  
Basque Country ◽  
Wrf Model ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract. The Weather Research and Forecasting model (WRF), like other numerical models, can make use of several parameterization schemes. The purpose of this study is to determine how available cumulus parameterization (CP) and microphysics (MP) schemes in the WRF model simulate extreme precipitation events in the Basque Country. Possible combinations among two CP schemes (Kain–Fritsch and Betts–Miller–Janjic) and five MP (WSM3, Lin, WSM6, new Thompson and WDM6) schemes were tested. A set of simulations, corresponding to 21st century extreme precipitation events that have caused significant flood episodes have been compared with point observational data coming from the Basque Country Automatic Weather Station Mesonetwork. Configurations with Kain–Fritsch CP scheme produce better quantity of precipitation forecast (QPF) than BMJ scheme configurations. Depending on the severity level and the river basin analysed different MP schemes show the best behaviours, demonstrating that there is not a unique configuration that solve exactly all the studied events.

scholarly journals On the Importance of a Consistent Treatment of Prognostic Moisture Variables between Convective and Microphysical Parameterizations

2018 ◽  
Vol 146 (5) ◽  
pp. 1527-1548 ◽  
Author(s):  
Evelyn D. Grell ◽  
Jian-Wen Bao ◽  
David E. Kingsmill ◽  
Sara A. Michelson
Keyword(s):  
Wrf Model ◽  
Simulation Models ◽  
Cumulus Parameterization ◽  
Climate Simulation ◽  
Microphysics Scheme ◽  
Single Moment ◽  
Double Moment ◽  
Cloud Parameterization ◽  
Microphysical Processes ◽  
Consistent Treatment

Abstract Analysis of WRF Model output from experiments using two double-moment microphysics schemes is carried out to demonstrate that there can be an inconsistency between the predicted mass and number concentrations when a single-moment convective parameterization is used together with a double-moment microphysics scheme. This inconsistency may arise because the grid-scale and subgrid-scale cloud schemes generally apply different levels of complexity to the parameterized microphysical processes. In particular, when a multimoment formulation is used in the microphysics scheme and other physical parameterizations modify only the mass-related moment while the values of the second (or higher) moment for individual hydrometeors remain unchanged, an unintended modification of the particle size distribution occurs. Simulated radar reflectivity is shown to be a valuable tool in diagnosing this inconsistency. In addition, potential ways to minimize the problem are explored by including number concentration calculations in the cumulus parameterization that are consistent with the assumptions of hydrometeor sizes in the microphysics parameterization. The results of this study indicate that it is physically preferable to unify microphysical assumptions between the grid-resolved and subgrid cloud parameterization schemes in weather and climate simulation models.

scholarly journals Evaluation of Weather Research and Forecasting (WRF) Microphysics single moment class-3 and class-6 in Precipitation Forecast

2018 ◽  
Vol 150 ◽  
pp. 03007 ◽  
Author(s):  
Syeda Maria Zaidi ◽  
Jacqueline Isabella Anak Gisen
Keyword(s):  
Model Performance ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Microphysics Scheme ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Single Moment ◽  
Minimum Medium ◽  
Weather Research

In this study, the performance of two different Microphysics Scheme options in Weather Research and Forecasting (WRF) model were evaluated for the estimating the precipitation forecast. The schemes WRF single moment class-3 (WSM-3) and single moment class-6 (WSM-6) were employed to produce the minimum, medium and maximum precipitation for the selected events over the Kuantan River Basin (KRB). The obtained simulated results were compared with the observed data from eight different rainfall gauging stations. The results comparison indicate that WRF model provides better forecasting at some rainfall stations for minimum and medium rainfall events but did not produce good result during maximum rainfall overall. The WSM-6 scheme is found to produce better result compared to WSM-3. The study also found that to acquire accurate precipitation results, it is also required to test some other physics scheme parameterization to enhance the model performance.

scholarly journals Aerosol Indirect Effects on the Predicted Precipitation in a Global Weather Forecasting Model

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 392 ◽  
Author(s):  
Jung-Yoon Kang ◽  
Soo Ya Bae ◽  
Rae-Seol Park ◽  
Ji-Young Han
Keyword(s):  
Indirect Effects ◽  
Weather Forecasting ◽  
Cloud Condensation Nuclei ◽  
Cloud Water ◽  
Cumulus Parameterization ◽  
Shortwave Radiation ◽  
Convective Precipitation ◽  
Cloud Physics ◽  
Microphysics Scheme ◽  
Aerosol Indirect Effects

Aerosol indirect effects on precipitation were investigated in this study using a Global/Regional Integrated Model system (GRIMs) linked with a chemistry package devised for reducing the heavy computational burden occurring in common atmosphere–chemistry coupling models. The chemistry package was based on the Goddard Chemistry Aerosol Radiation and Transport scheme of Weather Research and Forecasting with Chemistry (WRF-Chem), and five tracers that are relatively important for cloud condensation nuclei (CCN) formation were treated as prognostic variables. For coupling with the cloud physics processes in the GRIMs, the CCN number concentrations derived from the simplified chemistry package were utilized in the cumulus parameterization scheme (CPS) and the microphysics scheme (MPS). The simulated CCN number concentrations were higher than those used in original cloud physics schemes and, overall, the amount of incoming shortwave radiation reaching the ground was indirectly reduced by an increase in clouds owing to a high CCN. The amount of heavier precipitation increased over the tropics owing to the inclusion of enhanced riming effects under deep precipitating convection. The trend regarding the changes in non-convective precipitation was mixed depending on the atmospheric conditions. The increase in small-size cloud water owing to a suppressed autoconversion led to a reduction in precipitation. More precipitation can occur when ice particles fall under high CCN conditions owing to the accretion of cloud water by snow and graupel, along with their melting.

scholarly journals Precipitation Forecast Experiments Using the Weather Research and Forecasting (WRF) Model at Gray-Zone Resolutions

2018 ◽  
Vol 33 (6) ◽  
pp. 1605-1616 ◽  
Author(s):  
Ji-Young Han ◽  
Song-You Hong
Keyword(s):  
High Resolution ◽  
Model Simulation ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Grid Size ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Gray Zone ◽  
Weather Research

Abstract In the Weather Research and Forecasting (WRF) community, a standard model setup at a grid size smaller than 5 km excludes cumulus parameterization (CP), although it is unclear how to determine a cutoff grid size where convection permitting can be assumed adequate. Also, efforts to improve high-resolution precipitation forecasts in the range of 1–10 km (the so-called gray zone for parameterized precipitation physics) have recently been made. In this study, we attempt to statistically evaluate the skill of a gray-zone CP with a focus on the quantitative precipitation forecast (QPF) in the summertime. A WRF Model simulation with the gray-zone simplified Arakawa–Schubert (GSAS) CP at 3-km spatial resolution over East Asia is evaluated for the summer of 2013 and compared with the results from a conventional setup without CP. A statistical evaluation of the 3-month simulations shows that the GSAS demonstrates a typical distribution of the QPF skill, with high (low) scores and bias in the light (heavy) precipitation category. The WRF without CP seriously suppresses light precipitation events, but its skill for heavier categories is better. Meanwhile, a new set of precipitation data, which is simply averaged precipitation from the two simulations, demonstrates the best skill in all precipitation categories. Bearing in mind that high-resolution QPF requires essential challenges in model components, along with complexity in precipitating convection mechanisms over geographically different regions, this proposed method can serve as an alternative for improving the QPF for practical usage.

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