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Showing papers in "Meteorological Applications in 2020"


Journal ArticleDOI
TL;DR: In this article, the authors presented a new and generally applicable approach, Targeted Circulation Types (TCTs), which convolve the weather sensitivity of an impacted system of interest with the intrinsic structures of the atmospheric circulation to identify its meteorological drivers.
Abstract: Renewable electricity is a key enabling step in the decarbonisation of energy. Europe is at the forefront of renewable deployment and this has dramatically increased the weather-sensitivity of the continent’s power systems. Despite the importance of weather to energy systems, and widespread interest from both academia and industry, the meteorological drivers of European power systems remain difficult to identify and poorly understood. This study presents a new and generally applicable approach, Targeted Circulation Types (TCTs). TCTs, in contrast to standard meteorological weather-regime or circulation-typing schemes, convolve the weather-sensitivity of an impacted system of interest (in this case, the electricity system) with the intrinsic structures of the atmospheric circulation to identify its meteorological drivers. A new 38-year reconstruction of daily electricity demand and renewable supply across Europe is used to identify the winter time large-scale circulation patterns of most interest to the European electricity grid. TCTs provide greater explanatory power for power system variability and extremes compared to standard meteorological typing. Two new pairs of atmospheric patterns are highlighted, both of which have marked and extensive impacts on the European power system. The first pair resembles the meridional surface pressure dipole of the North Atlantic Oscillation but shifted eastward into Europe and noticeably strengthened, while the second pair is weaker and corresponds to surface pressure anomalies over central southern and eastern Europe. While these gross qualitative patterns are robust features of the present European power systems, the detailed circulation structures are strongly affected by the amount and location of renewables installed.

53 citations









Journal ArticleDOI
TL;DR: In this paper, the applicability of convolutional neural network (CNN) architectures for downscaling of short-range forecasts of near-surface winds on extended spatial domains is analyzed.
Abstract: We analyze the applicability of convolutional neural network (CNN) architectures for downscaling of short-range forecasts of near-surface winds on extended spatial domains. Short-range wind field forecasts (at the 100 m level) from ECMWF ERA5 reanalysis initial conditions at 31 km horizontal resolution are downscaled to mimic HRES (deterministic) short-range forecasts at 9 km resolution. We evaluate the downscaling quality of four exemplary model architectures and compare these against a multi-linear regression model. We conduct a qualitative and quantitative comparison of model predictions and examine whether the predictive skill of CNNs can be enhanced by incorporating additional atmospheric variables, such as geopotential height and forecast surface roughness, or static high-resolution fields, like land-sea mask and topography. We further propose DeepRU, a novel U-Net-based CNN architecture, which is able to infer situation-dependent wind structures that cannot be reconstructed by other models. Inferring a target 9 km resolution wind field from the low-resolution input fields over the Alpine area takes less than 10 milliseconds on our GPU target architecture, which compares favorably to an overhead in simulation time of minutes or hours between low- and high-resolution forecast simulations.

27 citations





Journal ArticleDOI
TL;DR: In the Amazon basin, extreme flooding is consistently attributed to warmer or cooler conditions in the tropical Pacific and Atlantic Oceans, with some evidence linking floods to other hydroclimatic drivers such as the Madden-Julian Oscillation (MJO) as mentioned in this paper.
Abstract: Anomalous conditions in the oceans and atmosphere have the potential to be used to enhance the predictability of flood events, enabling earlier warnings to reduce risk. In the Amazon basin, extreme flooding is consistently attributed to warmer or cooler conditions in the tropical Pacific and Atlantic Oceans, with some evidence linking floods to other hydroclimatic drivers such as the Madden-Julian Oscillation (MJO). This review evaluates the impact of several hydroclimatic drivers on rainfall and river discharge regimes independently, aggregating all of the information of previous studies to provide an up to date depiction of what we currently know and do not know about how variations in climate impact flooding in the Amazon. Additionally, 34 major flood events that have occurred since 1950 in the Amazon and their attribution to climate anomalies are documented and evaluated. This review finds that despite common agreement within the literature describing the relationship between phases of climate indices and hydrometeorological variables, results linking climate anomalies and flood hazard is often limited to correlation rather than causation, while the understanding on their usefulness for flood forecasting is weak. There is a need to better understand the ocean-atmosphere response mechanisms that led to previous flood events. In particular, examining the oceanic and atmospheric conditions preceding individual hydrological extremes as opposed to composite analysis, could provide insightful information into the magnitude and spatial distribution of anomalous SSTs required to produce extreme floods. Importantly, such analysis could provide meaningful thresholds on which to base seasonal flood forecasts.


Journal ArticleDOI
TL;DR: In this paper, the authors conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan and identified higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group.
Abstract: Funding information Ministry of Science and Technology, Grant/Award Number: MOST 107-2621-M865-001; Office of Science, Grant/Award Number: DE-SC0016605 Abstract The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low-level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low-level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective.




Journal ArticleDOI
TL;DR: In this paper, weather patterns are used as a preliminary step in producing forecasts of upper-tail precipitation threshold exceedance probabilities for the UK, where the WPs are predefined, discrete states representing daily mean sea level pressure (MSLP) over a European-North Atlantic domain.
Abstract: Medium- to long-range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper-tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea-level pressure (MSLP) over a European–North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper-tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well-known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP-based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro-meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest-matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre for Medium-range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days.





Journal ArticleDOI
TL;DR: A deep‐learning neural network model was developed to predict thunderstorm occurrence within 400 km2 South Texas domains for up to 15’hr (±2 hr accuracy) in advance and the performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models.
Abstract: Correspondence Hamid Kamangir, Department of Computing Sciences, Texas A&M University-Corpus Christi, TX, USA. Email: hkamangir@islander.tamucc.edu Abstract A deep-learning neural network (DLNN) model was developed to predict thunderstorm occurrence within 400 km South Texas domains for up to 15 hr (±2 hr accuracy) in advance. The input features were chosen primarily from numerical weather prediction model output parameters/variables; cloud-toground lightning served as the target. The deep-learning technique used was the stacked denoising autoencoder (SDAE) in order to create a higher order representation of the features. Logistic regression was then applied to the SDAE output to train the predictive model. An iterative technique was used to determine the optimal SDAE architecture. The performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models, a classifier via a combination of principal component analysis and logistic regression, and operational weather forecasters, based on the same data set.


Journal ArticleDOI
TL;DR: Among the radiation‐based models, the NF‐GP provided the best accuracy at estimating the ETo of both stations, and the other models were ranked as: ELM, SVM‐FA, Nn‐DE, NN‐PSO, MARS, RF, BT, NF‐SC, S VM and MT, respectively.