scholarly journals SIMULATION OF DIFFERENT CUMULUS SCHEMES OF WRF MODEL FOR TWO EXTREME EVENTS OVER EGYPT

2016 ◽  
Vol 27 (Issue 2-B) ◽  
pp. 1-11
2020 ◽  
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  

Abstract. The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we evaluate land-atmosphere interactions within four simulations performed by the WRF model using three different LSMs from 1980 to 2012 over North America. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Areas showing large uncertainty in WRF simulated extreme events are also identified in a model ensemble from three different Regional Climate Model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating temperature and precipitation extreme events, which in turn will help to reduce uncertainties in climate model projections.


2021 ◽  
Author(s):  
Andrew Turner ◽  
Jennifer Fletcher ◽  
Kieran Hunt ◽  
Jayesh Phadtare ◽  
Stephen Griffiths ◽  
...  

<p>IMPROVE is motivated by the effects of orography on Indian precipitation as part of the diurnal cycle of convection, contributing to water supply, as well as its role in extreme events.  IMPROVE considers two focal regions.  The Western Ghats, which intercept the monsoon flow across the Arabian Sea, receive some of the most frequent and heaviest rainfall during summer as well as being subject to extremes such as the 2018 Kerala floods.  Meanwhile, the Himalayas play a vital role in separating dry midlatitude flows from tropical airmasses and are subject to extremes during the summer monsoon, as well as in winter due to the passage of western disturbances.  This presentation summarizes the key results of IMPROVE.  Firstly, we examine the impact of orography on the observed convective diurnal cycle and assess its simulation in models at a range of resolutions including convection-permitting scales.  MetUM and WRF model experiments are used to identify key mechanisms and test their capability at simulating scale interactions between forcing at the large scale from the BSISO and newly identified regimes of on- and offshore convection near the Western Ghats.  An additional aspect to this work is the construction of a two-layer analytical model to test the behaviour of sheared flow perpendicular to a ridge analogous to the Western Ghats.  Secondly, the role of orography in extreme events is considered.  For the Western Ghats, this focuses on the interaction between monsoon low-pressure systems and the southwesterly flow in enhancing local rainfall.  For the Himalayas, we focus on characterising interactions between tropical lows and western disturbances in enhancing the orographic precipitation.  The work in IMPROVE works towards a deeper understanding of orographic rainfall and its extremes over India and uncovering why such mechanisms may be poorly represented in models.</p>


2021 ◽  
Author(s):  
Ranjeet S. Sokhi ◽  
P. R. Tiwari ◽  
Joanna S.N. de Medeiros ◽  
Gerd A. Folberth ◽  
William J. Collins

Abstract Synoptic weather and larger scale circulation patterns are closely coupled and have a major influence on regional weather and extreme events. This study examines the role of regional circulations on meteorology and extreme events for the present and future years over Asia with the WRF model driven by HadGEM2 global model boundary conditions that includes RCP4.5 scenarios based bicentennial transient simulation. The regional scale analysis was based on boundary conditions derived from 40 years of global model outputs spanning periods of 1995-2005, 2015-2025, 2025-2035 and 2045-2055. For brevity these periods are labelled as 2000, 2020, 2030, 2050 and 'represent' decadal periods centered around the named years. Model results were compared and validated (using a number of skill metrics) against observations for the present period showing that the model is able to delineate the observed features within 95% confidence level compared to the annual mean. To characterise and quantify the changes in the circulation patterns, an Empirical Orthogonal Function (EOF) based analysis was conducted. Results indicate that wintertime minimum temperatures are projected to increase by 3-4 0 C over Asia by 2050 compared to reference period of 2000. Furthermore, anti-cyclonic activity associated with low PV anomalies and high positive temperature anomalies may be a key driver that influence the increase in frequency and duration of heat waves and droughts over SA, SEA, EA and NA regions of Asia. Overall, the modelling results suggest that regional meteorology and circulation patterns may significantly influence extremes over Asia in the future. Such impacts will have major implications for weather patterns as well as for air pollution over the region both of which will require policy responses to adjust to a changing regional climate.


2013 ◽  
Vol 26 (21) ◽  
pp. 8671-8689 ◽  
Author(s):  
Kelly Mahoney ◽  
Michael Alexander ◽  
James D. Scott ◽  
Joseph Barsugli

Abstract A high-resolution case-based approach for dynamically downscaling climate model data is presented. Extreme precipitation events are selected from regional climate model (RCM) simulations of past and future time periods. Each event is further downscaled using the Weather Research and Forecasting (WRF) Model to storm scale (1.3-km grid spacing). The high-resolution downscaled simulations are used to investigate changes in extreme precipitation projections from a past to a future climate period, as well as how projected precipitation intensity and distribution differ between the RCM scale (50-km grid spacing) and the local scale (1.3-km grid spacing). Three independent RCM projections are utilized as initial and boundary conditions to the downscaled simulations, and the results reveal considerable spread in projected changes not only among the RCMs but also in the downscaled high-resolution simulations. However, even when the RCM projections show an overall (i.e., spatially averaged) decrease in the intensity of extreme events, localized maxima in the high-resolution simulations of extreme events can remain as strong or even increase. An ingredients-based analysis of prestorm instability, moisture, and forcing for ascent illustrates that while instability and moisture tend to increase in the future simulations at both regional and local scales, local forcing, synoptic dynamics, and terrain-relative winds are quite variable. Nuanced differences in larger-scale and mesoscale dynamics are a key determinant in each event's resultant precipitation. Very high-resolution dynamical downscaling enables a more detailed representation of extreme precipitation events and their relationship to their surrounding environments with fewer parameterization-based uncertainties and provides a framework for diagnosing climate model errors.


Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 616
Author(s):  
Giuseppe Castorina ◽  
Maria Teresa Caccamo ◽  
Franco Colombo ◽  
Salvatore Magazù

Numerical weather predictions (NWP) play a fundamental role in air quality management. The transport and deposition of all the pollutants (natural and/or anthropogenic) present in the atmosphere are strongly influenced by meteorological conditions such as, for example, precipitation and winds. Furthermore, the presence of particulate matter in the atmosphere favors the physical processes of nucleation of the hydrometeors, thus increasing the risk of even extreme weather events. In this framework of reference, the present work aimed to improve the quality of weather forecasts related to extreme events through the optimization of the weather research and forecasting (WRF) model. For this purpose, the simulation results obtained using the WRF model, where physical parametrizations of the cumulus scheme can be optimized, are reported. As a case study, we considered the extreme meteorological event recorded on 25 November 2016, which affected the whole territory of Sicily and, in particular, the area of Sciacca (Agrigento). In order, to evaluate the performance of the proposed approach, we compared the WRF model outputs with data obtained by a network of radar and weather stations. The comparison was performed through statistical methods on the basis of a “contingency table”, which allowed for ascertaining the best suited physical parametrizations able to reproduce this event.


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