The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program

2009 ◽  
Vol 24 (5) ◽  
pp. 1252-1267 ◽  
Author(s):  
Christopher A. Davis ◽  
Barbara G. Brown ◽  
Randy Bullock ◽  
John Halley-Gotway
Keyword(s):  
Mesoscale Model ◽  
Wrf Model ◽  
Separation Distance ◽  
Diagnostic Evaluation ◽  
Cumulus Parameterization ◽  
False Alarms ◽  
Fuzzy Logic Algorithm ◽  
Object Based ◽  
Arw Model ◽  
Comparison Field

Abstract The authors use a procedure called the method for object-based diagnostic evaluation, commonly referred to as MODE, to compare forecasts made from two models representing separate cores of the Weather Research and Forecasting (WRF) model during the 2005 National Severe Storms Laboratory and Storm Prediction Center Spring Program. Both models, the Advanced Research WRF (ARW) and the Nonhydrostatic Mesoscale Model (NMM), were run without a traditional cumulus parameterization scheme on horizontal grid lengths of 4 km (ARW) and 4.5 km (NMM). MODE was used to evaluate 1-h rainfall accumulation from 24-h forecasts valid at 0000 UTC on 32 days between 24 April and 4 June 2005. The primary variable used for evaluation was a “total interest” derived from a fuzzy-logic algorithm that compared several attributes of forecast and observed rain features such as separation distance and spatial orientation. The maximum value of the total interest obtained by comparing an object in one field with all objects in the comparison field was retained as the quality of matching for that object. The median of the distribution of all such maximum-interest values was selected as a metric of the overall forecast quality. Results from the 32 cases suggest that, overall, the configuration of the ARW model used during the 2005 Spring Program performed slightly better than the configuration of the NMM model. The primary manifestation of the differing levels of performance was fewer false alarms, forecast rain areas with no observed counterpart, in the ARW. However, it was noted that the performance varied considerably from day to day, with most days featuring indistinguishable performance. Thus, a small number of poor NMM forecasts produced the overall difference between the two models.

scholarly journals Improvement of mesoscale numerical weather prediction WRF-ARW in the Republic of Belarus by assimilation of radar wind and reflectivity data

2020 ◽  
pp. 3-13
Author(s):  
Palina A. Zaiko ◽  
Aliaksandr N. Krasouski ◽  
Siarhei K. Barodka
Keyword(s):  
Mesoscale Model ◽  
Forecast Error ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
False Alarms ◽  
Strong Wind ◽  
Number Of False Alarms ◽  
Circulation Types

The forecasts of severe weather events obtained with the WRF numerical mesoscale model with the adapted system for assimilation of reflectivity and radial velocity data from the network of Belarusian Doppler weather radars used in Belhydromet in 2019 are analysed. A description of the system for the echo quality control based on the radar dual-polarisation characteristics and the method for three-dimensional variational assimilation (3D-VAR) used to assimilate data in the WRF model are described. The results of case studies on the simulation of precipitation and strong wind for various circulation types in Belarus with and without radar data assimilation are given. The statistical and object-oriented verification of these forecasts is provided. The results of the comprehensive assessment reveal a decrease in the forecast error for 10-m wind speed for the early forecast hours (+6 h) by 1.34 m/s, as well as a more accurate forecast of the location, orientation of the cloud systems and precipitation zones, and a decrease in the number of false alarms in the version with assimilation. A preliminary conclusion on the possibility of using the forecast results in nowcasting systems is also made.

scholarly journals Simulation of Structure, Track and Landfall of Tropical Cyclone Bijli Using WRF-ARW Model

2015 ◽  
Vol 39 (2) ◽  
pp. 157-167 ◽  
Author(s):  
KM Zahir Rayhun ◽  
DA Quadir ◽  
MA Mannan Chowdhury ◽  
MN Ahasan ◽  
MS Haque
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Convective Available Potential Energy ◽  
Wrf Model ◽  
Relative Vorticity ◽  
Cumulus Parameterization ◽  
Arw Model ◽  
Boundary Layer Scheme ◽  
Cumulus Parameterization Schemes ◽  
Academy Of Sciences

An attempt was made to simulate the structure, track, landfall and a few dynamical aspects of the tropical cyclone Bijli that formed over the Bay of Bengal using WRF-ARW model. WRF model was run in a single domain using KF cumulus parameterization schemes with WSM 3 micro physics and YSU planetary boundary layer scheme. The ARW model was run for 24, 48, 72 and 96 hrs to simulate structure, track and landfall of tropical cyclones Bijli. The different simulated parameters viz. minimum sea level pressure, maximum wind speed, convective available potential energy and relative vorticity have been studied. The results showed that the model is capable to forecast the formation of the first depression 60 - 78 hrs in advance. This indicates the high and unique predictive power of ARW model for predicting the tropical cyclone formation. The model generates a realistic structure of the tropical cyclones with high spatial details. This was possible due to the higher spatial resolution of the regional model. One of the outstanding findings of the study is that the model was successfully predicted the tracks, recurvature and probable areas and time of landfall of the selected tropical cyclone Bijli with high accuracy even in the 96 hrs predictions.Journal of Bangladesh Academy of Sciences, Vol. 39, No. 2, 157-167, 2015

Object-Based Verification of Precipitation Forecasts. Part II: Application to Convective Rain Systems

2006 ◽  
Vol 134 (7) ◽  
pp. 1785-1795 ◽  
Author(s):  
Christopher Davis ◽  
Barbara Brown ◽  
Randy Bullock
Keyword(s):  
Intensity Distribution ◽  
Wrf Model ◽  
Stage Iv ◽  
Cumulus Parameterization ◽  
High Plains ◽  
Mississippi Valley ◽  
Convective Systems ◽  
Critical Success ◽  
Object Based ◽  
Mesoscale Convective

Abstract The authors develop and apply an algorithm to define coherent areas of precipitation, emphasizing mesoscale convection, and compare properties of these areas with observations obtained from NCEP stage-IV precipitation analyses (gauge and radar combined). In Part II, fully explicit 12–36-h forecasts of rainfall from the Weather Research and Forecasting model (WRF) are evaluated. These forecasts are integrated on a 4-km mesh without a cumulus parameterization. Rain areas are defined similarly to Part I, but emphasize more intense, smaller areas. Furthermore, a time-matching algorithm is devised to group spatially and temporally coherent areas into rain systems that approximate mesoscale convective systems. In general, the WRF model produces too many rain areas with length scales of 80 km or greater. Rain systems typically last too long, and are forecast to occur 1–2 h later than observed. The intensity distribution among rain systems in the 4-km forecasts is generally too broad, especially in the late afternoon, in sharp contrast to the intensity distribution obtained on a coarser grid with parameterized convection in Part I. The model exhibits the largest positive size and intensity bias associated with systems over the Midwest and Mississippi Valley regions, but little size bias over the High Plains, Ohio Valley, and the southeast United States. For rain systems lasting 6 h or more, the critical success index for matching forecast and observed rain systems agrees closely with that obtained in a related study using manually determined rain systems.

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.

A parameterization of slantwise convection in the WRF model

2021 ◽  
Author(s):  
Ting-Chen Chen ◽  
Man-Kong Yau ◽  
Daniel J. Kirshbaum
Keyword(s):  
General Circulation ◽  
Wrf Model ◽  
Real Data ◽  
General Circulation Models ◽  
Added Value ◽  
Cumulus Parameterization ◽  
Moisture Loss ◽  
Precipitation Event ◽  
Cyclonic Storm ◽  
Slantwise Convection

Abstract In this study, we introduce a parameterization scheme for slantwise convection (SC) to be considered for models that are too coarse to resolve slantwise convection explicitly (with a horizontal grid spacing coarser than 15 km or less). This SC scheme operates in a locally defined 2D cross-section perpendicular to the deep-layer-averaged thermal wind. It applies momentum tendency to adjust the environment toward slantwise neutrality with a prescribed adjustment timescale. Condensational heating and the associated moisture loss are also considered. To evaluate the added value of the SC scheme, we implement it in the Weather Research and Forecasting (WRF) model to supplement the existing cumulus parameterization schemes for upright convection and test for two different numerical setups: a 2D idealized, unforced release of conditional symmetric instability (CSI) in an initially conditionally stable environment, and a 3D real-data precipitation event containing both CSI and conditional instability along the cold front of a cyclonic storm near the UK. Both test cases show significant improvements for the coarse-gridded (40-km) simulations when parameterizing slantwise convection. Compared to the 40-km simulations with only the upright convection scheme, the counterparts with the additional SC scheme exhibit a larger extent of CSI neutralization, generate a stronger grid-resolved slantwise circulation, and produce greater amounts of precipitation, all in better agreement with the corresponding fine-gridded reference simulations. Given the importance of slantwise convection in midlatitude weather systems, our results suggest that there exist potential benefits of parameterizing slantwise convection in general circulation models.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

scholarly journals Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lourdes Álvarez-Escudero ◽  
Yandy G. Mayor ◽  
Israel Borrajero-Montejo ◽  
Arnoldo Bezanilla-Morlot
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Weather Research And Forecasting ◽  
Tropical Storms ◽  
Precipitation Regime ◽  
Wide Debate ◽  
One Year ◽  
Cumulative Precipitation

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

scholarly journals WRF Model Experiments on the Antarctic Atmosphere in Winter

2011 ◽  
Vol 139 (4) ◽  
pp. 1279-1291 ◽  
Author(s):  
Esa-Matti Tastula ◽  
Timo Vihma
Keyword(s):  
Boundary Layer ◽  
Surface Layer ◽  
Wind Speed ◽  
Surface Pressure ◽  
Mesoscale Model ◽  
Wrf Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Bias ◽  
Radiation Parameterizations

Abstract The standard and polar versions 3.1.1 of the Weather Research and Forecasting (WRF) model, both initialized by the 40-yr ECMWF Re-Analysis (ERA-40), were run in Antarctica for July 1998. Four different boundary layer–surface layer–radiation scheme combinations were used in the standard WRF. The model results were validated against observations of the 2-m temperature, surface pressure, and 10-m wind speed at 9 coastal and 2 inland stations. The best choice for boundary layer and radiation parameterizations of the standard WRF turned out to be the Yonsei University boundary layer scheme in conjunction with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) surface layer scheme and the Rapid Radiative Transfer Model for longwave radiation. The respective temperature bias was on the order of 3°C less than the biases obtained with the other combinations. Increasing the minimum value for eddy diffusivity did, however, improve the performance of the asymmetric convective scheme by 0.8°C. Averaged over the 11 stations, the error growths in 24-h forecasts were almost identical for the standard and Polar WRF, but in 9-day forecasts Polar WRF gave a smaller 2-m temperature bias. For the Vostok station, however, the standard WRF gave a less positively biased 24-h temperature forecast. On average, the polar version gave the least biased surface pressure simulation. The wind speed simulation was characterized by low correlation values, especially under weak winds and for stations surrounded by complex topography.

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