scholarly journals An Automated, Multiparameter Dryline Identification Algorithm

2015 ◽  
Vol 30 (6) ◽  
pp. 1781-1794 ◽  
Author(s):  
Adam J. Clark ◽  
Andrew MacKenzie ◽  
Amy McGovern ◽  
Valliappa Lakshmanan ◽  
Rodger A. Brown
Keyword(s):  
Wrf Model ◽  
Large Datasets ◽  
Probability Of Detection ◽  
Weather Research And Forecasting ◽  
High Plains ◽  
Identification Algorithm ◽  
Algorithm Performance ◽  
Convective Initiation ◽  
Operational Forecasting ◽  
Very Large Datasets

Abstract Moisture boundaries, or drylines, are common over the southern U.S. high plains and are one of the most important airmass boundaries for convective initiation over this region. In favorable environments, drylines can initiate storms that produce strong and violent tornadoes, large hail, lightning, and heavy rainfall. Despite their importance, there are few studies documenting climatological dryline location and frequency, or performing systematic dryline forecast evaluation, which likely stems from difficulties in objectively identifying drylines over large datasets. Previous studies have employed tedious manual identification procedures. This study aims to streamline dryline identification by developing an automated, multiparameter algorithm, which applies image-processing and pattern recognition techniques to various meteorological fields and their gradients to identify drylines. The algorithm is applied to five years of high-resolution 24-h forecasts from Weather Research and Forecasting (WRF) Model simulations valid April–June 2007–11. Manually identified dryline positions, which were available from a previous study using the same dataset, are used as truth to evaluate the algorithm performance. Generally, the algorithm performed very well. High probability of detection (POD) scores indicated that the majority of drylines were identified by the method. However, a relatively high false alarm ratio (FAR) was also found, indicating that a large number of nondryline features were also identified. Preliminary use of random forests (a machine learning technique) significantly decreased the FAR, while minimally impacting the POD. The algorithm lays the groundwork for applications including model evaluation and operational forecasting, and should enable efficient analysis of drylines from very large datasets.

scholarly journals Assessing the Predictability of Convection Initiation in the High Plains Using an Object-Based Approach

2014 ◽  
Vol 29 (2) ◽  
pp. 403-418 ◽  
Author(s):  
Brock J. Burghardt ◽  
Clark Evans ◽  
Paul J. Roebber
Keyword(s):  
Model Performance ◽  
Wrf Model ◽  
The United States ◽  
Probability Of Detection ◽  
Weather Research And Forecasting ◽  
High Plains ◽  
Object Based ◽  
Unique Object ◽  
Convection Initiation ◽  
Central High

Abstract This study investigates the short-range (0–12 h) predictability of convection initiation (CI) using the Advanced Research Weather Research and Forecasting (WRF) Model (ARW) with a horizontal grid spacing of 429 m. A unique object-based method is used to evaluate model performance for 25 cases of CI across the west-central high plains of the United States from the 2010 convective season. In the aggregate, there exists a high probability of detection but, due to the significant overproduction of CI events by the model, high false alarm and bias ratios that lead to modestly skillful forecasts. Model CI objects that are matched with observed CI objects show, on average, an early bias of about 3 min and distance errors of around 38 km. The operational utility and inherent biases of such high-resolution simulations are discussed.

scholarly journals Wind Forecasts for Rocket and Balloon Launches at the Esrange Space Center Using the WRF Model

2018 ◽  
Vol 33 (3) ◽  
pp. 813-833
Author(s):  
Ricardo Fonseca ◽  
Javier Martín-Torres ◽  
Kent Andersson
Keyword(s):  
Critical Role ◽  
Wrf Model ◽  
Arctic Region ◽  
Probability Of Detection ◽  
The Arctic ◽  
Weather Research And Forecasting ◽  
Sounding Rockets ◽  
Convective Model ◽  
Nonlocal Asymmetric ◽  
The Arctic Region

Abstract High-altitude balloons and rockets are regularly launched at the Esrange Space Center (ESC) in Kiruna, Sweden, with the aim of retrieving atmospheric data for meteorological and space studies in the Arctic region. Meteorological conditions, particularly wind direction and speed, play a critical role in the decision of whether to go ahead with or postpone a planned launch. Given the lack of high-resolution wind forecasts for this remote region, the Weather Research and Forecasting (WRF) Model is used to downscale short-term forecasts given by the Global Forecast System (GFS) for the ESC for six 5-day periods in the warm, cold, and transition seasons. Three planetary boundary layer (PBL) schemes are considered: the local Mellor–Yamada–Janjić (MYJ), the nonlocal Yonsei University (YSU), and the hybrid local–nonlocal Asymmetric Convective Model 2 (ACM2). The ACM2 scheme is found to provide the most skillful forecasts. An analysis of the WRF Model output against the launch criteria for two of the most commonly launched vehicles, the sounding rockets Veículo de Sondagem Booster-30 (VSB-30) and Improved Orion, reveals probability of detection (POD) values that always exceeds 60% with the false alarm rate (FAR) generally below 50%. It is concluded that the WRF Model, in its present configuration, can be used to generate useful 5-day wind forecasts for the launches of these two rockets. The conclusions reached here are applicable to similar sites in the Arctic and Antarctic regions.

scholarly journals Evaluation of Short-Range Precipitation Reforecasts from East Asia Regional Reanalysis

2019 ◽  
Vol 20 (2) ◽  
pp. 319-337 ◽  
Author(s):  
Eun-Gyeong Yang ◽  
Hyun Mee Kim
Keyword(s):  
East Asia ◽  
Short Range ◽  
Winter Season ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Weather Research And Forecasting ◽  
Operational Forecasts ◽  
Short Period ◽  
Regional Reanalysis

Abstract As the need for regional reanalyses emerged around the world, a short period of the East Asia Regional Reanalysis (EARR) system was recently developed based on the Unified Model (UM). In this study, the quality of the EARR is evaluated by comparing the short-range precipitation reforecasts against reforecasts of ERA-Interim (ERA-I) reanalysis and operational forecasts of the Korea Meteorological Administration (OPER). For the verification, two different periods are selected for 14 days in the summer (July 2013, denoted as 201307) and winter (February 2014, denoted as 201402). The equitable threat score (ETS) of EARR and OPER is generally greater than that of ERA-I. The frequency bias index (FBI) of EARR and OPER is overall closer to 1 than that of ERA-I for all thresholds, which indicates that EARR and OPER are much closer to the observation compared to ERA-I. For the period 201307, the ERA-I FBI is greater than 1 for lower thresholds and the probability of detection (POD) and false alarm ratio (FAR) of ERA-I are high, implying that ERA-I tends to overforecast light precipitation. In addition, using the same Weather Research and Forecasting (WRF) Model, the 6-h precipitation forecasts are integrated every 12 h (initialized from 0000/1200 UTC) for 4 months for the summer and winter season. Although the differences of ETS and FBI between EARR and ERA-I are not distinct for the summer season, overall EARR ETS is higher than ERA-I ETS, and EARR FBI is closer to 1 than ERA-I FBI. Based on several evaluations, the precipitation reforecasts of EARR are confirmed to be more accurate than those of OPER and ERA-I in East Asia.

scholarly journals A Features-Based Assessment of the Evolution of Warm Season Precipitation Forecasts from the HRRR Model over Three Years of Development

2017 ◽  
Vol 32 (5) ◽  
pp. 1841-1856 ◽  
Author(s):  
Janice L. Bytheway ◽  
Christian D. Kummerow ◽  
Curtis Alexander
Keyword(s):  
Data Assimilation ◽  
Model Development ◽  
Wrf Model ◽  
Warm Season ◽  
Convective Precipitation ◽  
Weather Research And Forecasting ◽  
Central Plains ◽  
Convective Initiation ◽  
Precipitation Forecasting ◽  
Data Assimilation System

Abstract The High Resolution Rapid Refresh (HRRR) model has been the National Weather Service’s (NWS) operational rapid update model since 2014. The HRRR has undergone continual development, including updates to the Weather Research and Forecasting (WRF) Model core, the data assimilation system, and the various physics packages in order to better represent atmospheric processes, with updated operational versions of the model being implemented approximately every spring. Given the model’s intent for use in convective precipitation forecasting, it is of interest to examine how forecasts of warm season precipitation have changed as a result of the continued model upgrades. A features-based assessment is performed on the first 6 h of HRRR quantitative precipitation forecasts (QPFs) from the 2013, 2014, and 2015 versions of the model over the U.S. central plains in an effort to understand how specific aspects of QPF performance have evolved as a result of continued model development. Significant bias changes were found with respect to precipitation intensity. Model upgrades that increased boundary layer stability and reduced the strength of the latent heating perturbations in the data assimilation were found to reduce southward biases in convective initiation, reduce the tendency for the model to overestimate heavy rainfall, and improve the representation of convective initiation.

scholarly journals Sensitivity of Northern Great Plains Convection Forecasts to Upstream and Downstream Forecast Errors

2017 ◽  
Vol 145 (6) ◽  
pp. 2141-2163 ◽  
Author(s):  
Jeremy D. Berman ◽  
Ryan D. Torn ◽  
Glen S. Romine ◽  
Morris L. Weisman
Keyword(s):  
South Dakota ◽  
Great Plains ◽  
Initial Conditions ◽  
Potential Temperature ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Northern Great Plains ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Convective Initiation

Abstract The role of earlier forecast errors on subsequent convection forecasts is evaluated for a northern Great Plains severe convective event on 11–12 June 2013 during the Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to Weather Research and Forecasting (WRF) Model ensemble forecasts with explicit convection. This case was characterized by two distinct modes of convection located 150 km apart in western Nebraska and South Dakota, which formed on either side of an axis of high, lower-tropospheric equivalent potential temperature . Convection forecasts over both regions are found to be sensitive to the position of this axis. The convection in Nebraska is sensitive to the position of the western edge of the axis near an upstream dryline, which modulates the preconvective prior to the diurnal maximum. In contrast, the convection in South Dakota is sensitive to the position of the eastern edge of the axis near a cold front, which also modulates the preconvective in that location. The position of the axis is modulated by the positions of both upstream and downstream mid- to upper-tropospheric potential vorticity anomalies, and can be traced backward in time to the initial conditions. Dropsondes sampling the region prior to convective initiation indicate that ensemble members with better representations of upstream conditions in sensitive regions are associated with better convective forecasts over Nebraska.

scholarly journals Numerical Study of the Impact of Complex Terrain and Soil Moisture on Convective Initiation

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 871
Author(s):  
Beilei Zan ◽  
Ye Yu ◽  
Longxiang Dong ◽  
Jianglin Li ◽  
Guo Zhao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Complex Terrain ◽  
Numerical Study ◽  
Dominant Role ◽  
Deep Convection ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Local Circulation ◽  
Convective Initiation ◽  
The Impact

The relative importance of topography and soil moisture on the initiation of an afternoon deep convection under weak synoptic-scale forcing was investigated using the weather research and forecasting (WRF) model with high resolution (1.33 km). The convection occurred on 29 June 2017, over the Liupan Mountains, west of the Loess Plateau. The timing and location of the convective initiation (CI) simulated by the WRF model compared well with the radar observations. It showed that the warm and humid southerly airflow under 700 hPa was divided into east and west flows due to the blockage of the Liupan Mountains. The warm and humid air on the west side was forced to climb along the slope and enhanced the humidity near the ridge. The accumulation of unstable energy in the middle and north of the ridge led to a strong vertical convergence and triggered the convection. Sensitivity experiments showed that terrain played a dominant role in triggering the convection, while the spatial heterogeneity of soil moisture played an indirect role by affecting the local circulation and the partition of surface energy.

Unsupervised dimensionality reduction for very large datasets: Are we going to the right direction?

2020 ◽  
Vol 196 ◽  
pp. 105777
Author(s):  
Jadson Jose Monteiro Oliveira ◽  
Robson Leonardo Ferreira Cordeiro
Keyword(s):  
Dimensionality Reduction ◽  
Large Datasets ◽  
Very Large Datasets ◽  
The Right

scholarly journals Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1727
Author(s):  
Valerio Capecchi ◽  
Andrea Antonini ◽  
Riccardo Benedetti ◽  
Luca Fibbi ◽  
Samantha Melani ◽  
...  
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Flash Floods ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Limited Area ◽  
Heavy Precipitation Event ◽  
The Town

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.

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