scholarly journals Probabilistic forecasts of winter thunderstorms around Amsterdam Airport Schiphol

2009 ◽  
Vol 3 (1) ◽  
pp. 39-43
Author(s):  
A. B. A. Slangen ◽  
M. J. Schmeits
Keyword(s):  
Weather Prediction ◽  
Convective Available Potential Energy ◽  
Radar Data ◽  
Primary Objective ◽  
Probabilistic Forecast ◽  
Convective Precipitation ◽  
Limited Area ◽  
Regression Equations ◽  
Forecast System ◽  
Probabilistic Forecasts

Abstract. The development and verification of a probabilistic forecast system for winter thunderstorms around Amsterdam Airport Schiphol is described. We have used Model Output Statistics (MOS) to develop the probabilistic forecast equations. The MOS system consists of 32 logistic regression equations, i.e. for two forecast periods (0–6 h and 6–12 h), four 90×80 km2 regions around Amsterdam Airport Schiphol, and four 6-h time periods. For the predictand quality-controlled Surveillance et Alerte Foudre par Interférométrie Radioélectrique (SAFIR) total lightning data were used. The potential predictors were calculated from postprocessed output of two numerical weather prediction (NWP) models – i.e. the High-Resolution Limited-Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model – and from an ensemble of advected lightning and radar data (0–6 h projections only). The predictors that are selected most often are the HIRLAM Boyden index, the square root of the ECMWF 3-h and 6-h convective precipitation sum, the HIRLAM convective available potential energy (CAPE) and two radar advection predictors. An objective verification was done, from which it can be concluded that the MOS system is skilful. The forecast system runs at the Royal Netherlands Meteorological Institute (KNMI) on an experimental basis, with the primary objective to warn aircraft pilots for potential aircraft induced lightning (AIL) risk during winter.

Probabilistic Forecasts of (Severe) Thunderstorms for the Purpose of Issuing a Weather Alarm in the Netherlands

2008 ◽  
Vol 23 (6) ◽  
pp. 1253-1267 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang ◽  
Rudolf M. van Westrhenen
Keyword(s):  
The Netherlands ◽  
Weather Prediction ◽  
Limited Area ◽  
Regression Equations ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorms ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Total Lightning

Abstract The development and verification of a new model output statistics (MOS) system is described; this system is intended to help forecasters decide whether a weather alarm for severe thunderstorms, based on high total lightning intensity, should be issued in the Netherlands. The system consists of logistic regression equations for both the probability of thunderstorms and the conditional probability of severe thunderstorms in the warm half-year (from mid-April to mid-October). These equations have been derived for 12 regions of about 90 km × 80 km each and for projections out to 12 h in advance (with 6-h periods). As a source for the predictands, reprocessed total lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset not only consisted of the combined postprocessed output from two numerical weather prediction (NWP) models, as in previous work by the first three authors, but it also contained an ensemble of advected radar and lightning data for the 0–6-h projections. The NWP model output dataset contained 17 traditional thunderstorm indices, computed from a reforecasting experiment with the High-Resolution Limited-Area Model (HIRLAM) and postprocessed output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Brier skill scores and attributes diagrams show that the skill of the MOS thunderstorm forecast system is good and that the severe thunderstorm forecast system generally is also skillful, compared to the 2000–04 climatology, and therefore, the preoperational system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in 2008.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

scholarly journals Numerical simulations of June 7, 2020 convective precipitation over Slovakia using deterministic, probabilistic, and convection-permitting approaches

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 571-607
Author(s):  
André Simon ◽  
Martin Belluš ◽  
Katarína Čatlošová ◽  
Mária Derková ◽  
Martin Dian ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Weather Prediction ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Small Scale ◽  
Limited Area ◽  
Systematic Effect ◽  
The Impact

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

scholarly journals Short-Range Numerical Weather Prediction Using Time-Lagged Ensembles

2007 ◽  
Vol 22 (3) ◽  
pp. 580-595 ◽  
Author(s):  
Chungu Lu ◽  
Huiling Yuan ◽  
Barry E. Schwartz ◽  
Stanley G. Benjamin
Keyword(s):  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Cycle Model ◽  
Forecast System ◽  
Multilinear Regression ◽  
Probabilistic Forecasts ◽  
Regression Approach ◽  
Ensemble Mean ◽  
Time Lagged

Abstract A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.

scholarly journals Intense sea-effect snowfall case on the western coast of Finland

2017 ◽  
Vol 14 ◽  
pp. 231-239 ◽  
Author(s):  
Taru Olsson ◽  
Tuuli Perttula ◽  
Kirsti Jylhä ◽  
Anna Luomaranta
Keyword(s):  
Preliminary Analysis ◽  
Temporal Evolution ◽  
Model Simulation ◽  
Weather Prediction ◽  
Convective Available Potential Energy ◽  
Horizontal Resolution ◽  
Convective Precipitation ◽  
Western Coast ◽  
Radar Images ◽  
A Value

Abstract. A new national daily snowfall record was measured in Finland on 8 January 2016 when it snowed 73 cm (31 mm as liquid water) in less than a day in Merikarvia on the western coast of Finland. The area of the most intense snowfall was very small, which is common in convective precipitation. In this work we used hourly weather radar images to identify the sea-effect snowfall case and to qualitatively estimate the performance of HARMONIE, a non-hydrostatic convection-permitting weather prediction model, in simulating the spatial and temporal evolution of the snowbands. The model simulation, including data assimilation, was run at 2.5 km horizontal resolution and 65 levels in vertical. HARMONIE was found to capture the overall sea-effect snowfall situation quite well, as both the timing and the location of the most intense snowstorm were properly simulated. Based on our preliminary analysis, the snowband case was triggered by atmospheric instability above the mostly ice-free sea and a low-level convergence zone almost perpendicular to the coastline. The simulated convective available potential energy (CAPE) reached a value of 87 J kg−1 near the site of the observed snowfall record.

scholarly journals Why do some probabilistic forecasts lack reliability?

2019 ◽  
Vol 9 ◽  
pp. A17
Author(s):  
Yûki Kubo
Keyword(s):  
Solar Flare ◽  
Skill Score ◽  
Base Rate ◽  
Probabilistic Forecast ◽  
Partial Answer ◽  
Sufficient Condition ◽  
Forecast System ◽  
Threshold Probability ◽  
Probabilistic Forecasts ◽  
Forecast Models

In this work, we investigate the reliability of the probabilistic binary forecast. We mathematically prove that a necessary, but not sufficient, condition for achieving a reliable probabilistic forecast is maximizing the Peirce Skill Score (PSS) at the threshold probability of the climatological base rate. The condition is confirmed by using artificially synthesized forecast–outcome pair data and previously published probabilistic solar flare forecast models. The condition gives a partial answer as to why some probabilistic forecast system lack reliability, because the system, which does not satisfy the proved condition, can never be reliable. Therefore, the proved condition is very important for the developers of a probabilistic forecast system. The result implies that those who want to develop a reliable probabilistic forecast system must adjust or train the system so as to maximize PSS near the threshold probability of the climatological base rate.

scholarly journals Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble System

2016 ◽  
Vol 31 (3) ◽  
pp. 957-983 ◽  
Author(s):  
Nusrat Yussouf ◽  
John S. Kain ◽  
Adam J. Clark
Keyword(s):  
Flash Flood ◽  
Stage Iv ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Flood Event ◽  
Forecast Period ◽  
Low Level ◽  
Probabilistic Forecasts ◽  
The Impact

Abstract A continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.

scholarly journals Improved nowcasting of precipitation based on convective analysis fields

2007 ◽  
Vol 10 ◽  
pp. 125-131 ◽  
Author(s):  
M. Steinheimer ◽  
T. Haiden
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Convective Available Potential Energy ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Horizontal Resolution ◽  
Specific Humidity ◽  
Convective Precipitation ◽  
Convective Boundary ◽  
Decision Algorithm

Abstract. The high-resolution analysis and nowcasting system INCA (Integrated Nowcasting through Comprehensive Analysis) developed at the Austrian national weather service provides three-dimensional fields of temperature, humidity, and wind on an hourly basis, and two-dimensional fields of precipitation rate in 15 min intervals. The system operates on a horizontal resolution of 1 km and a vertical resolution of 100–200 m. It combines surface station data, remote sensing data (radar, satellite), forecast fields of the numerical weather prediction model ALADIN, and high-resolution topographic data. An important application of the INCA system is nowcasting of convective precipitation. Based on fine-scale temperature, humidity, and wind analyses a number of convective analysis fields are routinely generated. These fields include convective boundary layer (CBL) flow convergence and specific humidity, lifted condensation level (LCL), convective available potential energy (CAPE), convective inhibition (CIN), and various convective stability indices. Based on the verification of areal precipitation nowcasts it is shown that the pure translational forecast of convective cells can be improved by using a decision algorithm which is based on a subset of the above fields, combined with satellite products.

Optimal Temporal Frequency of NSSL Phased Array Radar Observations for an Experimental Warn-on-Forecast System

2020 ◽  
Vol 35 (1) ◽  
pp. 193-214 ◽  
Author(s):  
Derek R. Stratman ◽  
Nusrat Yussouf ◽  
Youngsun Jung ◽  
Timothy A. Supinie ◽  
Ming Xue ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Phased Array ◽  
Temporal Frequency ◽  
Radar Data ◽  
Forecast System ◽  
Severe Storms ◽  
Radar Observations ◽  
Phased Array Radar ◽  
Probabilistic Forecasts ◽  
Convective Weather

Abstract A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.

scholarly journals Impact of resolution on large-eddy simulation of midlatitude summertime convection

2020 ◽  
Vol 20 (5) ◽  
pp. 2891-2910
Author(s):  
Christopher Moseley ◽  
Ieda Pscheidt ◽  
Guido Cioni ◽  
Rieke Heinze
Keyword(s):  
Large Eddy Simulation ◽  
Radar Data ◽  
Life Cycles ◽  
Convective Precipitation ◽  
Limited Area ◽  
Moist Convection ◽  
Grid Spacing ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Convective Organization

Abstract. We analyze life cycles of summertime moist convection of a large-eddy simulation (LES) in a limited-area setup over Germany. The goal is to assess the ability of the model to represent convective organization in space and time in comparison to radar data and its sensitivity to daily mean surface air temperature. A continuous period of 36 d in May and June 2016 is simulated with a grid spacing of 625 m. This period was dominated by convection over large parts of the domain on most of the days. Using convective organization indices, and a tracking algorithm for convective precipitation events, we find that an LES with 625 m grid spacing tends to underestimate the degree of convective organization and shows a weaker sensitivity of heavy convective rainfall to temperature as suggested by the radar data. An analysis of 3 d with in this period that are simulated with a finer grid spacing of 312 and 156 m showed that a grid spacing at the 100 m scale has the potential to improve the simulated diurnal cycles of convection, the mean time evolution of single convective events, and the degree of convective organization.

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