scholarly journals Synoptic‐scale and mesoscale controls for tornadogenesis on cold fronts: A generalised measure of tornado risk and identification of synoptic types

2020 ◽  
Vol 146 (733) ◽  
pp. 4195-4225
Author(s):  
Matthew R. Clark ◽  
Douglas J. Parker
Keyword(s):  
Synoptic Scale ◽  
Cold Fronts

scholarly journals Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

2019 ◽  
Vol 34 (4) ◽  
pp. 1137-1160 ◽  
Author(s):  
Ryan Lagerquist ◽  
Amy McGovern ◽  
David John Gagne II
Keyword(s):  
Deep Learning ◽  
Weather Prediction ◽  
Potential Temperature ◽  
Probability Of Detection ◽  
Neural Nets ◽  
Specific Humidity ◽  
Synoptic Scale ◽  
Cold Fronts ◽  
Critical Success ◽  
The North

AbstractThis paper describes the use of convolutional neural nets (CNN), a type of deep learning, to identify fronts in gridded data, followed by a novel postprocessing method that converts probability grids to objects. Synoptic-scale fronts are often associated with extreme weather in the midlatitudes. Predictors are 1000-mb (1 mb = 1 hPa) grids of wind velocity, temperature, specific humidity, wet-bulb potential temperature, and/or geopotential height from the North American Regional Reanalysis. Labels are human-drawn fronts from Weather Prediction Center bulletins. We present two experiments to optimize parameters of the CNN and object conversion. To evaluate our system, we compare the objects (predicted warm and cold fronts) with human-analyzed warm and cold fronts, matching fronts of the same type within a 100- or 250-km neighborhood distance. At 250 km our system obtains a probability of detection of 0.73, success ratio of 0.65 (or false-alarm rate of 0.35), and critical success index of 0.52. These values drastically outperform the baseline, which is a traditional method from numerical frontal analysis. Our system is not intended to replace human meteorologists, but to provide an objective method that can be applied consistently and easily to a large number of cases. Our system could be used, for example, to create climatologies and quantify the spread in forecast frontal properties across members of a numerical weather prediction ensemble.

scholarly journals A 6-yr Climatology of Fronts Affecting Helsinki, Finland, and Their Boundary Layer Structure

2013 ◽  
Vol 52 (9) ◽  
pp. 2106-2124 ◽  
Author(s):  
Victoria A. Sinclair
Keyword(s):  
Boundary Layer ◽  
Temperature Change ◽  
Frontal Zone ◽  
Layer Structure ◽  
Time Of Day ◽  
Lapse Rate ◽  
Maximum Temperature ◽  
Synoptic Scale ◽  
Boundary Layer Structure ◽  
Cold Fronts

AbstractA 6-yr climatology of the frequency, characteristics, and boundary layer structure of synoptic-scale fronts in Helsinki, Finland, was created using significant weather charts and observations from a 327-m-tall mast and from the Station for Measuring Ecosystem–Atmosphere Relationships III. In total, 855 fronts (332 cold fronts, 236 warm fronts, and 287 occluded fronts) affected Helsinki during the 6-yr period, equating to one front every 2.6 days. Seasonal and diurnal cycles were observed, with frontal frequency peaking during the cold season and during daytime. Composites of warm and cold fronts were developed to provide observationally based conceptual models of the low-level structure of fronts at the end of the North Atlantic Ocean storm track. The composite warm front displays a temperature increase of 4.0°C; a broad, forward-tilting frontal zone; and prolonged, weak-to-moderate precipitation. The composite cold front is characterized by a temperature decrease of 4.4°C, a narrow and slightly rearward-tilting frontal zone, and moderate precipitation collocated with the surface front. Relationships between frontal characteristics and the direction from which fronts approached, the season, time of day, prefrontal boundary layer lapse rate, and the location of the wind shift relative to the thermal gradient were investigated. The prefrontal lapse rate was the single most important variable in determining the temperature change, the height of the maximum temperature change, and the near-surface tilt of both warm and cold fronts. This result demonstrates the interaction between boundary layer and synoptic-scale processes that must be captured by numerical weather prediction models to accurately forecast surface fronts.

scholarly journals Use of Synthetic Aperture Radar in Finescale Surface Analysis of Synoptic-Scale Fronts at Sea

2005 ◽  
Vol 20 (3) ◽  
pp. 311-327 ◽  
Author(s):  
G. S. Young ◽  
T. N. Sikora ◽  
N. S. Winstead
Keyword(s):  
North America ◽  
Synthetic Aperture Radar ◽  
East Coast ◽  
Gulf Of Alaska ◽  
Data Sources ◽  
Synthetic Aperture ◽  
Synoptic Scale ◽  
Sar Images ◽  
Cold Fronts ◽  
Aperture Radar

Abstract The viability of synthetic aperture radar (SAR) as a tool for finescale marine meteorological surface analyses of synoptic-scale fronts is demonstrated. In particular, it is shown that SAR can reveal the presence of, and the mesoscale and microscale substructures associated with, synoptic-scale cold fronts, warm fronts, occluded fronts, and secluded fronts. The basis for these findings is the analysis of some 6000 RADARSAT-1 SAR images from the Gulf of Alaska and from off the east coast of North America. This analysis yielded 158 cases of well-defined frontal signatures: 22 warm fronts, 37 cold fronts, 3 stationary fronts, 32 occluded fronts, and 64 secluded fronts. The potential synergies between SAR and a range of other data sources are discussed for representative fronts of each type.

scholarly journals Wind power variability during the passage of cold fronts across South Africa

2019 ◽  
Vol 30 (3) ◽  
pp. 52-67 ◽  
Author(s):  
Amaris Dalton ◽  
Bernard Bekker ◽  
Andries Kruger
Keyword(s):  
South Africa ◽  
Wind Power ◽  
Cold Front ◽  
Power Production ◽  
Strong Wind ◽  
Synoptic Scale ◽  
Weather Patterns ◽  
Cold Fronts ◽  
Geographically Dispersed ◽  
Wind Resource

Wind is a naturally variable resource that fluctuates across timescales and, by the same token, the electricity generated by wind also fluctuates across timescales. At longer timescales, i.e., hours to days, synoptic-scale weather systems, notably cold fronts during South African winter months, are important instigators of strong wind conditions and variability in the wind resource. The variability of wind power production from aggregates of geographically disperse turbines for the passage of individual cold fronts over South Africa was simulated in this study. When considering wind power variability caused by synoptic-scale weather patterns, specifically cold fronts, the timescale at which analysis is conducted was found to be of great importance, as relatively small mean absolute power ramps at a ten-minute temporal resolution, order of 2-4% of simulated capacity, can result in large variations of total wind power production (at the order of 32–93% of simulated capacity) over a period of three to four days as a cold front passes. It was found that when the aggregate consists of a larger and more geographically dispersed set of turbines, as opposed to a smaller set of turbines specifically located within cold-front dominated high wind areas, variability and the mean absolute ramp rates decrease (or gets ‘smoothed’) across the timescales considered. It was finally shown that the majority of large simulated wind power ramp events observed during the winter months, especially at longer timescales, are caused by the passage of cold fronts. 

Contribution of cold fronts to seasonal rainfall in simulations over the southern La Plata Basin

2016 ◽  
Vol 68 (2-3) ◽  
pp. 243-255 ◽  
Author(s):  
EM de Jesus ◽  
RP da Rocha ◽  
MS Reboita ◽  
M Llopart ◽  
LM Mosso Dutra ◽  
...  
Keyword(s):  
Seasonal Rainfall ◽  
La Plata ◽  
Cold Fronts ◽  
La Plata Basin

scholarly journals A Synoptic Scale Perspective on Greenland Ice Core δ18O Variability and Related Teleconnection Patterns

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Norel Rimbu ◽  
Monica Ionita ◽  
Gerrit Lohmann
Keyword(s):  
Ice Core ◽  
Ice Cores ◽  
Synoptic Scale ◽  
Local Climate ◽  
Rossby Wave Breaking ◽  
Weather Patterns ◽  
Teleconnection Patterns ◽  
Climate Anomalies ◽  
The North ◽  
Oxygen Isotope Ratios

The variability of stable oxygen isotope ratios (δ18O) from Greenland ice cores is commonly linked to changes in local climate and associated teleconnection patterns. In this respect, in this study we investigate ice core δ18O variability from a synoptic scale perspective to assess the potential of such records as proxies for extreme climate variability and associated weather patterns. We show that positive (negative) δ18O anomalies in three southern and central Greenland ice cores are associated with relatively high (low) Rossby Wave Breaking (RWB) activity in the North Atlantic region. Both cyclonic and anticyclonic RWB patterns associated with high δ18O show filaments of strong moisture transport from the Atlantic Ocean towards Greenland. During such events, warm and wet conditions are recorded over southern, western and central part of Greenland. In the same time the cyclonic and anticyclonic RWB patterns show enhanced southward advection of cold polar air masses on their eastern side, leading to extreme cold conditions over Europe. The association between high δ18O winters in Greenland ice cores and extremely cold winters over Europe is partly explained by the modulation of the RWB frequency by the tropical Atlantic sea surface temperature forcing, as shown in recent modeling studies. We argue that δ18O from Greenland ice cores can be used as a proxy for RWB activity in the Atlantic European region and associated extreme weather and climate anomalies.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

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