Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics

2005 ◽  
Vol 20 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok ◽  
Daan H. P. Vogelezang
Keyword(s):  
The Netherlands ◽  
Important Predictor ◽  
Convective Precipitation ◽  
Square Root ◽  
Model Output ◽  
Forecast System ◽  
Severe Thunderstorm ◽  
Severe Thunderstorms ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract The derivation and verification of logistic regression equations for the (conditional) probability of (severe) thunderstorms in the warm half-year (from mid-April to mid-October) in the Netherlands is described. For 12 regions of about 90 km × 80 km each, and for projections out to 48 h in advance (with 6-h periods), these equations have been derived using model output statistics (MOS). As a source for the predictands, lightning data from the Surveillance et d’Alerte Foudre par Interférométrie Radioélectrique (SAFIR) network have been used. The potential predictor dataset mainly consisted of the combined (postprocessed) output from two numerical weather prediction (NWP) models. It contained 15 traditional thunderstorm indices, computed from the High-Resolution Limited-Area Model (HIRLAM), and (postprocessed) output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The most important predictor in the thunderstorm forecast system is the square root of the ECMWF 6-h convective precipitation sum, and the most important predictor in the severe thunderstorm forecast system is the HIRLAM Boyden index. The success of the square root of the ECMWF 6-h convective precipitation sum as a thunderstorm predictor indicates that there is a strong relation between the forecast convective precipitation by the ECMWF model and the occurrence of thunderstorms, at least in the Netherlands up to 3 days in advance. The overall verification results for the 0000, 0600, 1200, and 1800 UTC runs of the MOS (severe) thunderstorm forecast system are good, and, therefore, the system was made operational at the Royal Netherlands Meteorological Institute (KNMI) in April 2004.

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.

scholarly journals Improving Week-2 Forecasts with Multimodel Reforecast Ensembles

2006 ◽  
Vol 134 (8) ◽  
pp. 2279-2284 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Xue Wei ◽  
Frédéric Vitart
Keyword(s):  
High Resolution ◽  
Real Time ◽  
State Of The Art ◽  
Horizontal Resolution ◽  
Model Output ◽  
Single Model ◽  
Global Models ◽  
Operational Forecast ◽  
Model Output Statistics ◽  
Ecmwf Model

Abstract It has recently been demonstrated that model output statistics (MOS) computed from a long retrospective dataset of ensemble “reforecasts” from a single model can significantly improve the skill of probabilistic week-2 forecasts (with the same model). In this study the technique is extended to a multimodel reforecast dataset consisting of forecasts from ECMWF and NCEP global models. Even though the ECMWF model is more advanced than the version of the NCEP model used (it has more than double the horizontal resolution and is about five years newer), the forecasts produced by the multimodel MOS technique are more skillful than those produced by the MOS technique applied to either the NCEP or ECMWF forecasts alone. These results demonstrate that the MOS reforecast approach yields benefits for week-2 forecasts that are just as large for high-resolution state-of-the-art models as they are for relatively low resolution out-of-date models. Furthermore, operational forecast centers can benefit by sharing both retrospective reforecast datasets and real-time forecasts.

scholarly journals A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

2010 ◽  
Vol 27 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Patrick N. Gatlin ◽  
Steven J. Goodman
Keyword(s):  
Severe Weather ◽  
Lightning Flash ◽  
Severe Thunderstorm ◽  
Critical Success ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Total Lightning ◽  
Jump Algorithm ◽  
Early Indication ◽  
Critical Success Index

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

scholarly journals PENENTUAN DOMAIN SPASIAL NWP DALAM PEMBANGUNAN MODEL OUTPUT STATISTICS

2013 ◽  
Vol 14 (3) ◽  
Author(s):  
Urip Haryoko ◽  
Hidayat Pawitan ◽  
Edvin Aldrian ◽  
Aji Hamim Wigena
Keyword(s):  
Model Output ◽  
Model Output Statistics

scholarly journals Deep Learning for Climate Model Output Statistics

2021 ◽  
Author(s):  
Michael Steininger ◽  
Daniel Abel ◽  
Katrin Ziegler ◽  
Anna Krause ◽  
Heiko Paeth ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Deep Learning ◽  
Climate Models ◽  
Climate Model ◽  
Model Output ◽  
Climate Change Effects ◽  
Climate Model Output ◽  
Improved Performance ◽  
Model Output Statistics

<p>Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.</p>

CHAOTIC GRAPH THEORY APPROACH FOR IDENTIFICATION OF CONVECTIVE AVAILABLE POTENTIAL ENERGY (CAPE) PATTERNS REQUIRED FOR THE GENESIS OF SEVERE THUNDERSTORMS

2007 ◽  
Vol 10 (03) ◽  
pp. 413-422 ◽  
Author(s):  
SUTAPA CHAUDHURI
Keyword(s):  
Graph Theory ◽  
Potential Energy ◽  
Deep Convection ◽  
Convective Available Potential Energy ◽  
Pressure Level ◽  
Theory Approach ◽  
Severe Thunderstorm ◽  
Available Potential Energy ◽  
Severe Thunderstorms ◽  
Conditional Instability

Severe thunderstorms are a manifestation of deep convection. Conditional instability is known to be the mechanism by which thunderstorms are formed. The energy that drives conditional instability is convective available potential energy (CAPE), which is computed with radio sonde data at each pressure level. The purpose of the present paper is to identify the pattern or shape of CAPE required for the genesis of severe thunderstorms over Kolkata (22°32′N, 88°20′E) confined within the northeastern part (20°N to 24°N latitude, 85°E to 93°E longitude) of India. The method of chaotic graph theory is adopted for this purpose. Chaotic graphs of pressure levels and CAPE are formed for thunderstorm and non-thunderstorm days. Ranks of the adjacency matrices constituted with the union of chaotic graphs of pressure levels and CAPE are computed for thunderstorm and non-thunderstorm days. The results reveal that the rank of the adjacency matrix is maximum for non-thunderstorm days and a column with all zeros occurs very quickly on severe thunderstorms days. This indicates that CAPE loses connectivity with pressure levels very early on severe thunderstorm days, showing that for the genesis of severe thunderstorms over Kolkata short, and therefore broad, CAPE is preferred.

Change in thunderstorm activity in a projected warmer future climate over Bangladesh

2021 ◽  
Author(s):  
Shahana Akter Esha ◽  
Nasreen Jahan
Keyword(s):  
Climate Model ◽  
Convective Available Potential Energy ◽  
Thunderstorm Activity ◽  
Future Climate ◽  
Convective Precipitation ◽  
Severe Thunderstorms ◽  
Wide Range ◽  
The Future ◽  
Rcp 8.5 ◽  
Increasing Risk

<p>Thunderstorms can have a wide range of impacts on modern societies and their assets. Severe thunderstorms associated with thunder squall, hail, tornado, and lightning cause extensive damage and losses to lives, especially in the densely populated sub-tropical countries like Bangladesh. In this study the future changes in thunderstorm conducive environments, in terms convective available potential energy (CAPE), have been assessed under the RCP 8.5 scenario for the selected major cities of Bangladesh. Results show an increase in CAPE for all the selected cities and in the range of 44%–106%. Later, a statistical thunderstorm frequency prediction model has been developed based on CAPE and convective precipitation and the probable scenario of thunderstorm frequency in the 21st century under future climate has been projected. The simulations were carried out for three different time slices (Early, Mid and Late 21<sup>st</sup> century) with CMCC-CM (Centro Euro-Mediterraneo per Cambiamenti Climatici Climate Model) model data. The future projection of thunderstorm shows an increase in thunderstorm frequency for all the season in a warmer future climate. But pre-monsoon and monsoon are found to be the most thunderstorm frequent season. Given the substantial damage from severe thunderstorms in the current climate, such increases imply an increasing risk of thunderstorm-related damage in this disaster-prone region of the world.</p>

LQ-Warn – Entwicklung optimierter Vorhersagen verschiedener Luftqualitätsparameter

2021 ◽  
Author(s):  
Sabine Robrecht ◽  
Robert Osinski ◽  
Ute Dauert ◽  
Andreas Lambert ◽  
Stefan Gilge ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Output ◽  
Mean Square ◽  
Post Processing ◽  
Eine Hohe ◽  
Erste Ergebnisse ◽  
Model Output Statistics ◽  
Monitoring Service

<p>Schlechte Luftqualität gefährdet die Gesundheit der Bevölkerung. Zur Information und zur Ergreifung kurzfristiger Maßnahmen zur Luftqualitätsverbesserung (z.B. Verkehrslenkung) ist eine möglichst genaue und – insbesondere in städtischen Gebieten – möglichst räumlich hochaufgelöste Luftqualitätsvorhersage notwendig. Numerische Luftqualitätsmodelle haben für diese Aufgabe in der Regel eine zu geringe räumliche Auflösung.</p> <p>Daher ist es Ziel des Projektes „LQ-Warn“ die Luftqualitätsvorhersage insbesondere im Hinblick auf die Überschreitung von Grenzwerten zu verbessern. Basierend auf den Modellergebnissen für Luftqualitätsparameter des Copernicus Atmospheric Monitoring Service (CAMS) werden zwei Ansätze verfolgt: Einerseits werden Vorhersagen mit dem regionalen chemischen Transportmodell „REM-CALGRID“ (RCG) unter Einbeziehung von CAMS-Ergebnissen und regionalen Emissionsdaten berechnet. Dabei kann eine hohe horizontale Auflösung von 2 km erzielt werden und Prognosen können für verschiedene Luftschadstoffe in stündlicher Auflösung mit bis zu 72 Stunden Vorlaufzeit erstellt werden, unter anderem für NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub>. Andererseits wird die statistische Post-Processing-Methode „Model Output Statistics“ (MOS) angewandt, um Punktvorhersagen für die Massenkonzentration der Spezies NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub> mit einer Vorlaufzeit von bis zu 96 Stunden zu berechnen. Dafür werden luftqualitätsbezogene Messungen, CAMS-Modellergebnisse und meteorologische Parameter aus dem numerischen Wettervorhersagemodell des ECMWF als Prädiktoren verwendet.</p> <p>Es werden erste Ergebnisse der mit den o.g. Ansätzen errechneten Vorhersagen präsentiert und die Vor- und Nachteile der jeweiligen Verfahren hervorgehoben. Durch die statistische Post-Processing-Methode MOS wird an den Vorhersagepunkten vor allem für die Massenkonzentration von O<sub>3 </sub>und NO<sub>2</sub> eine signifikante Verringerung des RMSE (Root Mean Square Error) im Vergleich zu den Vorhersagen des numerischen CAMS-Modells erreicht. Diese deutliche Verbesserung der Luftqualitätsvorhersage sinnvoll auf die Fläche auszudehnen ist jedoch noch eine Herausforderung. Im Gegensatz dazu zeigt die Vorhersage mit dem RCG-Modell eine geringere Verbesserung der Vorhersagegüte an einzelnen Vorhersagepunkten als der MOS-Ansatz. Stattdessen bietet das RCG-Modell zeitlich und räumlich konsistente Vorhersagen an allen Modellgitterpunkten. Kleinskalige Konzentrationsunterschiede können aufgrund der höheren Modellauflösung deutlich realistischer vorhergesagt werden als mit den CAMS-Vorhersagen. Ein weiterführendes Ziel des LQ-Warn-Projektes ist es die beiden Ansätze zu kombinieren, um die Vorteile beider nutzen zu können und eine präzise Luftqualitätsvorhersage flächendeckend für Deutschland bereitstellen zu können.</p>

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