scholarly journals Nonlinear Characteristics of Ensemble Perturbation Evolution and Their Application to Forecasting High-Impact Events

2013 ◽  
Vol 28 (6) ◽  
pp. 1353-1365 ◽  
Author(s):  
Brian C. Ancell
Keyword(s):  
Weather Prediction ◽  
Ensemble Member ◽  
High Impact ◽  
Synoptic Scale ◽  
Wind Speeds ◽  
Weather Events ◽  
Ensemble Mean ◽  
High Impact Weather ◽  
The Mean ◽  
Impact Events

Abstract Ensemble forecasting is becoming an increasingly important aspect of numerical weather prediction. As ensemble perturbation evolution becomes more nonlinear as a forecast evolves, the ensemble mean can diverge from the model attractor on which ensemble members are constrained. In turn, the ensemble mean can become increasingly unrealistic, and although statistically best on average, it can provide poor forecast guidance for specific high-impact events. This study uses an ensemble Kalman filter to investigate this behavior at the synoptic scale for landfalling midlatitude cyclones. This work also aims to understand the best way to select “best members” closest to the mean that both behave realistically and possess the statistically beneficial qualities of the mean. It is found that substantial nonlinearity emerges within forecast times of a day, which roughly agrees with previous research addressing synoptic-scale nonlinearity more generally. The evolving nonlinearity results in unrealistic behavior of the ensemble mean that significantly underestimates precipitation and wind speeds associated with the cyclones. Choosing a single ensemble member closest to the ensemble mean over the entire forecast window provides forecasts that are unable to produce the relatively small errors of the ensemble mean. However, since different ensemble members are closest to the ensemble mean at different forecast times, the best forecast is composed of different ensemble members throughout the forecast window. The benefits and limitations of applying this methodology to improve forecasts of synoptic-scale high-impact weather events are discussed.

Diagnosing factors in parameterised and resolved convection with physical tendency output in AROME-Arctic during a Cold-Air Outbreak event

2020 ◽  
Author(s):  
Marvin Kähnert ◽  
Teresa M. Valkonen ◽  
Harald Sodemann
Keyword(s):  
Weather Prediction ◽  
Cloud Microphysics ◽  
High Impact ◽  
Surface Fluxes ◽  
The Arctic ◽  
Shallow Convection ◽  
Time Step ◽  
Cold Air ◽  
Weather Events ◽  
High Impact Weather

<p>Numerical weather prediction (NWP) models generally display comparatively low predictive skill in the Arctic. Particularly, the large impact of sub-grid scale, parameterised processes, such as surface fluxes, radiation or cloud microphysics during high-latitude weather events pose a substantial challenge for numerical modelling. Such processes are most influential during mesoscale weather events, such as polar lows, often embedded in cold air outbreaks (CAO), some of which cause high impact weather. Uncertainty in Arctic weather forecasts is thus critically dependent on parameterised processes. The strong influence from several parameterised processes also makes model forecasts particularly susceptible to compensation of errors from different parameterisations, which potentially limits model improvement.<br>Here we analyse model output of individual parameterised tendencies of wind, temperature and humidity during Arctic high-impact weather in AROME-Arctic, the operational NWP model used by the Norwegian Meteorological Institute Norway for the European Arctic. Individual tendencies describe the contribution of each applied physical parameterisation to a respective variable per model time step. We study a CAO-event taking place during 24 - 27 December 2015. This intense and widespread CAO event, reaching from the Fram Straight to Norway and affecting a particularly large portion of the Nordic seas at a time, was characterised by strong heat fluxes along the sea ice edge. <br>Model intern definitions for boundary layer type become apparent as a decisive factor in tendency contributions. Especially the interplay between the dual mass flux and the turbulence scheme is of essence here. Furthermore, sensitivity experiments, featuring a run without shallow convection and a run with a new statistical cloud scheme, show how a physically similar result is obtained by substantially different tendencies in the model.</p>

scholarly journals A Proposed Model-Based Methodology for Feature-Specific Prediction for High-Impact Weather

2011 ◽  
Vol 26 (2) ◽  
pp. 243-249 ◽  
Author(s):  
Jacob R. Carley ◽  
Benjamin R. J. Schwedler ◽  
Michael E. Baldwin ◽  
Robert J. Trapp ◽  
John Kwiatkowski ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
High Impact ◽  
Storm Event ◽  
Weather Events ◽  
Proposed Model ◽  
Forecasting Method ◽  
Specific Prediction ◽  
High Impact Weather ◽  
Verification Methodology

Abstract A feature-specific forecasting method for high-impact weather events that takes advantage of high-resolution numerical weather prediction models and spatial forecast verification methodology is proposed. An application of this method to the prediction of a severe convective storm event is given.

scholarly journals Overview of the first HyMeX Special Observation Period over Croatia

2016 ◽  
Author(s):  
Branka Ivančan-Picek ◽  
Martina Tudor ◽  
Kristian Horvath ◽  
Antonio Stanešić ◽  
Ivatek Ivatek-Šahdan
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
High Impact ◽  
Precipitation Forecast ◽  
Grid Spacing ◽  
Maximum Rainfall ◽  
Weather Events ◽  
High Impact Weather ◽  
Observation Period

Abstract. The HYdrological cycle in the Mediterranean EXperiment (HyMeX) is intended to improve the capabilities to predict high impact weather events. In its framework, the first Special Observation Period (SOP1), 5 September to 6 November 2012, was aimed to study heavy precipitation events and flash floods. Here we present high impact weather events over Croatia that occurred during SOP1. A particular attention is given to eight Intense Observation Periods (IOP)s during which high precipitation occurred over the eastern Adriatic and Dinaric Alps. During the entire SOP1, the operational models forecasts generally represented well medium intensity precipitation, while heavy precipitation was frequently underestimated by the ALADIN 8 km and overestimated at higher resolution (2 km). During IOP2 intensive rainfall event occurred in wider area of the city of Rijeka in the northern Adriatic. Short-range maximum rainfall totals have achieved maximum values ever recorded at Rijeka station since the beginning of measurements in 1958. The rainfall amount measured in intervals of 20, 30 and 40 minutes could be expected once in a more than thousand, few hundreds and hundred years respectively, and they belong to the extraordinarily rare events. The operational precipitation forecast using ALADIN model at 8 km grid spacing underestimated the rainfall intensity. Evaluation of numerical sensitivity experiments suggested that forecast was slightly enhanced by improving the initial conditions through variational data assimilation. The operational non-hydrostatic run at 2 km grid spacing using configuration with ALARO physics package further improved the forecast. This article highlights the need for an intensive observation period in the future over the Adriatic region, to validate the simulated mechanisms and improve numerical weather prediction via data assimilation and model improvements in description of microphysics and air-sea interaction.

scholarly journals Visualizing Long-Range Severe Thunderstorm Environment Guidance from CFSv2

2016 ◽  
Vol 97 (6) ◽  
pp. 1021-1031 ◽  
Author(s):  
Gregory W. Carbin ◽  
Michael K. Tippett ◽  
Samuel P. Lillo ◽  
Harold E. Brooks
Keyword(s):  
Weather Prediction ◽  
Severe Weather ◽  
Time Averaging ◽  
Forecast System ◽  
Climate Forecast System ◽  
Severe Thunderstorm ◽  
Weather Events ◽  
Ensemble Mean ◽  
Composite Parameter ◽  
High Impact Weather

Abstract Two novel approaches to extending the range of prediction for environments conducive to severe thunderstorm events are described. One approach charts Climate Forecast System, version 2 (CFSv2), run-to-run consistency of the areal extent of severe thunderstorm environments using grid counts of the supercell composite parameter (SCP). Visualization of these environments is charted for each 45-day CFSv2 run initialized at 0000 UTC. CFSv2 ensemble-mean forecast maps of SCP coverage over the contiguous United States are also produced for those forecasts meeting certain criteria for high-impact weather. The applicability of this approach to the severe weather prediction challenge is illustrated using CFSv2 output for a series of severe weather episodes occurring in March and April 2014. Another approach, possibly extending severe weather predictability from CFSv2, utilizes a run-cumulative time-averaging technique of SCP grid counts. This process is described and subjectively verified with severe weather events from early 2014.

scholarly journals Improving High-Impact Forecasts through Sensitivity-Based Ensemble Subsets: Demonstration and Initial Tests

2016 ◽  
Vol 31 (3) ◽  
pp. 1019-1036 ◽  
Author(s):  
Brian C. Ancell
Keyword(s):  
Ensemble Kalman Filter ◽  
Lead Time ◽  
Heavy Precipitation ◽  
High Impact ◽  
Forecast Errors ◽  
Observation Error ◽  
Operational Environment ◽  
Weather Events ◽  
High Impact Weather ◽  
Impact Events

Abstract Ensemble sensitivity can reveal features in the flow at early forecast times that are relevant to the predictability of chosen high-impact forecast aspects (e.g., heavy precipitation) later in time. In an operational environment, it thus might be possible to choose ensemble subsets with improved predictability over the full ensemble if members with the smallest errors in regions of large ensemble sensitivity can be identified. Since numerous observations become available hourly, such a technique is feasible and could be executed well before the next assimilation/extended forecast cycle, potentially adding valuable lead time to forecasts of high-impact weather events. Here, a sensitivity-based technique that chooses subsets of forecasts initialized from an 80-member ensemble Kalman filter (EnKF) is tested by ranking 6-h errors in sensitive regions toward improving 24-h forecasts of landfalling midlatitude cyclones on the west coast of North America. The technique is first tested within an idealized framework with one of the ensemble members serving as truth. Subsequent experiments are performed in more realistic scenarios with an independent truth run, observation error added, and sparser observations. Results show the technique can indeed produce ensemble subsets that are improved relative to the full ensemble for 24-h forecasts of landfalling cyclones. Forecast errors are found to be smallest when the greatest 70% of ensemble sensitivity magnitudes with subsets of size 5–30 members are used, as well as when only the cases of the largest forecast spread are considered. Finally, this study presents considerations for extending this technique into fully realistic situations with regard to additional high-impact events.

scholarly journals A methodology to conduct wind damage field surveys for high-impact weather events of convective origin

2020 ◽  
Vol 20 (5) ◽  
pp. 1513-1531 ◽  
Author(s):  
Oriol Rodríguez ◽  
Joan Bech ◽  
Juan de Dios Soriano ◽  
Delia Gutiérrez ◽  
Salvador Castán
Keyword(s):  
Field Studies ◽  
High Impact ◽  
Strong Wind ◽  
Field Surveys ◽  
Weather Events ◽  
Straight Line ◽  
High Impact Weather ◽  
Strong Winds ◽  
Wind Events ◽  
The One

Abstract. Post-event damage assessments are of paramount importance to document the effects of high-impact weather-related events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extent and intensity can be essential for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin (i.e. tornado, downburst and straight-line winds). It is based on previous studies and also on 136 field studies performed by the authors in Spain between 2004 and 2018. The methodology includes the collection of pictures and records of damage to human-made structures and on vegetation during the in situ visit to the affected area, as well as of available automatic weather station data, witness reports and images of the phenomenon, such as funnel cloud pictures, taken by casual observers. To synthesize the gathered data, three final deliverables are proposed: (i) a standardized text report of the analysed event, (ii) a table consisting of detailed geolocated information about each damage point and other relevant data and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, sometimes only a few hours after the event occurrence and, on many occasions, when the type of convective phenomenon was uncertain. In those uncertain cases, the information resulting from this methodology contributed effectively to discern the phenomenon type thanks to the damage pattern analysis, particularly if no witness reports were available. The application of methodologies such as the one presented here is necessary in order to build homogeneous and robust databases of severe weather cases and high-impact weather events.

A role of high impact weather events in waterborne disease outbreaks in Canada, 1975 – 2001

2006 ◽  
Vol 16 (3) ◽  
pp. 167-180 ◽  
Author(s):  
Kate M. Thomas ◽  
Dominique F. Charron ◽  
David Waltner-Toews ◽  
Corinne Schuster ◽  
Abdel R. Maarouf ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
High Impact ◽  
Waterborne Disease ◽  
Weather Events ◽  
High Impact Weather

scholarly journals THORPEX Research and the Science of Prediction

2017 ◽  
Vol 98 (4) ◽  
pp. 807-830 ◽  
Author(s):  
D. B. Parsons ◽  
M. Beland ◽  
D. Burridge ◽  
P. Bougeault ◽  
G. Brunet ◽  
...  
Keyword(s):  
Research Program ◽  
Weather Prediction ◽  
Social Science Research ◽  
Academic Research ◽  
Science Research ◽  
Academic Community ◽  
High Impact ◽  
Research Areas ◽  
Predictive Skill ◽  
High Impact Weather

Abstract The Observing System Research and Predictability Experiment (THORPEX) was a 10-yr, international research program organized by the World Meteorological Organization’s World Weather Research Program. THORPEX was motivated by the need to accelerate the rate of improvement in the accuracy of 1-day to 2-week forecasts of high-impact weather for the benefit of society, the economy, and the environment. THORPEX, which took place from 2005 to 2014, was the first major international program focusing on the advancement of global numerical weather prediction systems since the Global Atmospheric Research Program, which took place almost 40 years earlier, from 1967 through 1982. The scientific achievements of THORPEX were accomplished through bringing together scientists from operational centers, research laboratories, and the academic community to collaborate on research that would ultimately advance operational predictive skill. THORPEX included an unprecedented effort to make operational products readily accessible to the broader academic research community, with community efforts focused on problems where challenging science intersected with the potential to accelerate improvements in predictive skill. THORPEX also collaborated with other major programs to identify research areas of mutual interest, such as topics at the intersection of weather and climate. THORPEX research has 1) increased our knowledge of the global-to-regional influences on the initiation, evolution, and predictability of high-impact weather; 2) provided insight into how predictive skill depends on observing strategies and observing systems; 3) improved data assimilation and ensemble forecast systems; 4) advanced knowledge of high-impact weather associated with tropical and polar circulations and their interactions with midlatitude flows; and 5) expanded society’s use of weather information through applied and social science research.

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