Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale Rain Areas

2006 ◽  
Vol 134 (7) ◽  
pp. 1772-1784 ◽  
Author(s):  
Christopher Davis ◽  
Barbara Brown ◽  
Randy Bullock
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Strong Dependence ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Stage Iv ◽  
Separation Distance ◽  
Weather Research And Forecasting ◽  
Rain Area ◽  
Object Based

Abstract A recently developed method of defining rain areas for the purpose of verifying precipitation produced by numerical weather prediction models is described. Precipitation objects are defined in both forecasts and observations based on a convolution (smoothing) and thresholding procedure. In an application of the new verification approach, the forecasts produced by the Weather Research and Forecasting (WRF) model are evaluated on a 22-km grid covering the continental United States during July–August 2001. Observed rainfall is derived from the stage-IV product from NCEP on a 4-km grid (averaged to a 22-km grid). It is found that the WRF produces too many large rain areas, and the spatial and temporal distribution of the rain areas reveals regional underestimates of the diurnal cycle in rain-area occurrence frequency. Objects in the two datasets are then matched according to the separation distance of their centroids. Overall, WRF rain errors exhibit no large biases in location, but do suffer from a positive size bias that maximizes during the later afternoon. This coincides with an excessive narrowing of the rainfall intensity range, consistent with the dominance of parameterized convection. Finally, matching ability has a strong dependence on object size and is interpreted as the influence of relatively predictable synoptic-scale systems on the larger areas.

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 The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions

2017 ◽  
Vol 98 (8) ◽  
pp. 1717-1737 ◽  
Author(s):  
Jordan G. Powers ◽  
Joseph B. Klemp ◽  
William C. Skamarock ◽  
Christopher A. Davis ◽  
Jimy Dudhia ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
System Modeling ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Earth System Modeling ◽  
Numerical Weather ◽  
Wide Range ◽  
Weather Research

Abstract Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

Numerical Prediction of Submesoscale Flow in the Nocturnal Stable Boundary Layer over Complex Terrain

2012 ◽  
Vol 140 (3) ◽  
pp. 956-977 ◽  
Author(s):  
Nelson L. Seaman ◽  
Brian J. Gaudet ◽  
David R. Stauffer ◽  
Larry Mahrt ◽  
Scott J. Richardson ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Stable Boundary Layers ◽  
Stable Boundary ◽  
Central Pennsylvania ◽  
Stable Conditions ◽  
Improved Model ◽  
Mean Square Errors ◽  
Numerical Weather Prediction Models

Abstract Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities. To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

scholarly journals Nonlinear Atmospheric Adjustment to Momentum Forcing

2008 ◽  
Vol 65 (3) ◽  
pp. 953-969 ◽  
Author(s):  
Adam R. Edson ◽  
Peter R. Bannon
Keyword(s):  
Lamb Wave ◽  
Prediction Models ◽  
Weather Prediction ◽  
Strong Dependence ◽  
Size Characteristic ◽  
Horizontal Momentum ◽  
Compressible Atmosphere ◽  
Isothermal Atmosphere ◽  
Numerical Weather Prediction Models ◽  
Nonlinear Numerical Model

Abstract A nonlinear, numerical model of a dry, compressible atmosphere is used to simulate the hydrostatic and geostrophic adjustment to a localized prescribed injection of momentum applied over 5 min. with a size characteristic of an isolated, deep, cumulus cloud. This theoretical study is relevant to the initialization of updrafts in compressible numerical weather prediction models. The four different forcings studied are vertical, divergent horizontal, and nondivergent horizontal momentum forcings, and a prescribed transverse circulation. These forcings are applied to an isothermal atmosphere, a nonisothermal atmosphere, and an atmosphere with a nonisothermal troposphere capped by an isothermal stratosphere. These scenarios are studied by analyzing the resulting perturbation fields and the energetics of the system. Potential vorticity is used to determine the possibility of steady atmospheric states. The energetics of the system are examined to observe the creation and propagation of atmospheric waves. Both traditional and available energetics are used to determine the presence and strength of these waves. Traditional energetics consist of kinetic, internal, and potential energies while available energetics consist of kinetic, available potential, and available elastic energies. The efficiencies are similar for these different energetics, though they represent different phenomena. The traditional energetics show a strong dependence on the presence of a Lamb wave, whereas in the available energetics the Lamb wave has little or no effect.

scholarly journals Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 834
Author(s):  
Priscila da Cunha Luz Barcellos ◽  
Marcio Cataldi
Keyword(s):  
Natural Hazards ◽  
Land Surface ◽  
Rio De Janeiro ◽  
Flash Flood ◽  
Weather Prediction ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Atmospheric Conditions ◽  
Streamflow Forecast ◽  
Janeiro State

Flash floods and extreme rains are destructive phenomena and difficult to forecast. In 2011, the mountainous region of Rio de Janeiro state suffered one of the largest natural hazards in Brazil, affecting more than 300,000 people, leaving more than 900 dead. This article simulates this natural hazard through Quantitative Precipitation Forecasting (QPF) and streamflow forecast ensemble, using 18 combinations of parameterizations between cumulus, microphysics, surface layer, planetary boundary layer, land surface and lateral contour conditions of the Weather Research and Forecasting (WRF) Model, coupling to the Soil Moisture Accounting Procedure (SMAP) hydrological model, seeking to find the best set of parametrizations for the forecasting of extreme events in the region. The results showed rainfall and streamflow forecast were underestimated by the models, reaching an error of 57.4% to QPF and 24.6% error to streamflow, and part of these errors are related to the lack of skill of the atmospheric model in predicting the intensity and the spatial-temporal distribution of rainfall. These results bring to light the limitations of numerical weather prediction, possibly due to the lack of initiatives involving the adaptation of empirical constants, intrinsic in the parametrization models, to the specific atmospheric conditions of each region of the country.

scholarly journals Approximating Input Data to a Snowmelt Model Using Weather Research and Forecasting Model Outputs in Lieu of Meteorological Measurements

2019 ◽  
Vol 20 (5) ◽  
pp. 847-862 ◽  
Author(s):  
Scott Havens ◽  
Danny Marks ◽  
Katelyn FitzGerald ◽  
Matt Masarik ◽  
Alejandro N. Flores ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Meteorological Variables ◽  
Weather Research And Forecasting ◽  
Atmospheric Flow ◽  
Temperature And Precipitation ◽  
Runoff Forecasting ◽  
Input Variables ◽  
Weather Research

Abstract Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.

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>

scholarly journals Object-Based Evaluation of a Numerical Weather Prediction Model’s Performance through Forecast Storm Characteristic Analysis

2015 ◽  
Vol 30 (6) ◽  
pp. 1451-1468 ◽  
Author(s):  
Huaqing Cai ◽  
Robert E. Dumais
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Time Of Day ◽  
Forecast Evaluation ◽  
Evaluation Tool ◽  
Convective Storm ◽  
Characteristic Analysis ◽  
Numerical Weather ◽  
Object Based

Abstract Traditional pixel-versus-pixel forecast evaluation scores such as the critical success index (CSI) provide a simple way to compare the performances of different forecasts; however, they offer little information on how to improve a particular forecast. This paper strives to demonstrate what additional information an object-based forecast evaluation tool such as the Method for Object-Based Diagnostic Evaluation (MODE) can provide in terms of assessing numerical weather prediction models’ convective storm forecasts. Forecast storm attributes evaluated by MODE in this paper include storm size, intensity, orientation, aspect ratio, complexity, and number of storms. Three weeks of the High Resolution Rapid Refresh (HRRR) model’s precipitation forecasts during the summer of 2010 over the eastern two-thirds of the contiguous United States were evaluated as an example to demonstrate the methodology. It is found that the HRRR model was able to forecast convective storm characteristics rather well either as a function of time of day or as a function of storm size, although significant bias does exist, especially in terms of storm number and storm size. Another interesting finding is that the model’s ability of forecasting new storm initiation varies substantially by regions, probably as a result of its different skills in forecasting convection driven by different forcing mechanisms (i.e., diurnal heating vs synoptic-scale frontal systems).

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.

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