Analysis of the dynamics of extreme rainfall events in summer in southern Uruguay

Similar documents
Atmospheric patterns for heavy rain events in the Balearic Islands

Intraseasonal Variation of Visibility in Hong Kong

Benguela Niño/Niña events and their connection with southern Africa rainfall have been documented before. They involve a weakening of the trade winds

Verification of the Seasonal Forecast for the 2005/06 Winter

SIMULATION AND PREDICTION OF SUMMER MONSOON CLIMATE OVER THE INDOCHINA PENINSULA BY RSM

CHAPTER 8 NUMERICAL SIMULATIONS OF THE ITCZ OVER THE INDIAN OCEAN AND INDONESIA DURING A NORMAL YEAR AND DURING AN ENSO YEAR

Multiscale Analyses of Inland Tropical Cyclone Midlatitude Jet Interactions: Camille (1969) and Danny (1997)

SUPPLEMENTARY INFORMATION

Aviation Hazards: Thunderstorms and Deep Convection

1.4 CONNECTIONS BETWEEN PV INTRUSIONS AND TROPICAL CONVECTION. Beatriz M. Funatsu* and Darryn Waugh The Johns Hopkins University, Baltimore, MD

Charles A. Doswell III, Harold E. Brooks, and Robert A. Maddox

Impact of Eurasian spring snow decrement on East Asian summer precipitation

Thermal / Solar. When air is warmed it... Rises. Solar Energy. Evaporation. Condensation Forms Clouds

Kalimantan realistically (Figs. 8.23a-d). Also, the wind speeds of the westerly

Low-end derecho of 19 August 2017

CPTEC and NCEP Model Forecast Drift and South America during the Southern Hemisphere Summer

Simulations of Convergence Lines

General Circulation. Nili Harnik DEES, Lamont-Doherty Earth Observatory

Influence of the Western Pacific Subtropical High on summertime ozone variability in East China

Why rainfall may reduce when the ocean warms

Thunderstorms and Severe Weather. (Chapt 15)

Introduction of products for Climate System Monitoring

3. Midlatitude Storm Tracks and the North Atlantic Oscillation

Clustering Forecast System for Southern Africa SWFDP. Stephanie Landman Susanna Hopsch RES-PST-SASAS2014-LAN

Interannual Teleconnection between Ural-Siberian Blocking and the East Asian Winter Monsoon

Minor Winter Flooding Event in northwestern Pennsylvania January 2017

Fronts in November 1998 Storm

Page 1. Name:

Conference on Teleconnections in the Atmosphere and Oceans November 2008

Forecasting Local Weather

Vertical Moist Thermodynamic Structure of the MJO in AIRS Observations: An Update and A Comparison to ECMWF Interim Reanalysis

daily (0000, 0600, 1200, and 1800 UTC) National Centers for Environmental

CHAPTER 2 DATA AND METHODS. Errors using inadequate data are much less than those using no data at all. Charles Babbage, circa 1850

Evidence for Weakening of Indian Summer Monsoon and SA CORDEX Results from RegCM

EMC Probabilistic Forecast Verification for Sub-season Scales

Spatial variability of summer precipitation related to the dynamics of the Great Plains low-level jet

Warming Earth and its Atmosphere The Diurnal and Seasonal Cycles

WWRP-THORPEX African Regional Committe Meeting North Africa Case Geneva, 8-10 May 2012

CONVECTION-ALLOWING SIMULATIONS OF COLD POOLS IN THE NORTHWESTERN SAHARA

Synoptic Analysis of Total Rainfall Patterns at Azerbaijan District.

5. General Circulation Models

Divergence, Spin, and Tilt. Convergence and Divergence. Midlatitude Cyclones. Large-Scale Setting

Introduction of climate monitoring and analysis products for one-month forecast

Interannual variability of top-ofatmosphere. CERES instruments

The Ocean-Atmosphere System II: Oceanic Heat Budget

Chapter 12 Fronts & Air Masses

Trends in Intraseasonal Variability of the Indian Summer Monsoon

and 24 mm, hPa lapse rates between 3 and 4 K km 1, lifted index values

Heavy Rainfall and Flooding of 23 July 2009 By Richard H. Grumm And Ron Holmes National Weather Service Office State College, PA 16803

1. describe the two methods by which cloud droplets can grow to produce precipitation (pp );

The feature of atmospheric circulation in the extremely warm winter 2006/2007

STATISTICAL DOWNSCALING OF DAILY PRECIPITATION IN THE ARGENTINE PAMPAS REGION

Boundary layer controls on extratropical cyclone development

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

Seasonal forecasting of climate anomalies for agriculture in Italy: the TEMPIO Project

Forecast for 6/28-7/31. Forecaster: Matthew Brewer Forecast made at: 12z 6/27/2017

Impacts of Recent El Niño Modoki on Extreme Climate Conditions In East Asia and the United States during Boreal Summer

2. What are the four most common gasses in the atmosphere and their percentages?

ENVIRONMENTAL MANAGEMENT I

Transient and Eddy. Transient/Eddy Flux. Flux Components. Lecture 3: Weather/Disturbance. Transient: deviations from time mean Time Mean

Moist static energy budget diagnostics for. monsoon research. H. Annamalai

Changes of storm surge and typhoon intensities under the future global warming conditions Storm Surge Congress 2010

Science 1206 SAMPLE Test ( Weather Dynamics)

Climate and the Atmosphere

The Arctic Energy Budget

Module 9 Weather Systems

Global Atmospheric Moisture Budget

Science 1206 Chapter 1 - Inquiring about Weather

The Atmospheric Circulation

The Earth System - Atmosphere III Convection

Weather Systems III: Thunderstorms and Twisters

Chapter 9. Weather Patterns & Midlatitude Cyclones

Introduction to Meteorology & Climate. Climate & Earth System Science. Atmosphere Ocean Interactions. A: Structure of the Ocean.

24.2 Cloud Formation 2/3/2014. Orographic Lifting. Processes That Lift Air Frontal Wedging. Convergence and Localized Convective Lifting

Moisture transport to Syowa Station and Dome Fuji Station, Antarctica

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013

URBAN HEAT ISLAND IN SEOUL

8 Mechanisms for tropical rainfall responses to equatorial

COMPOSITION OF THE ATMOSPHERE

Teleconnections and Climate predictability

Weather Systems. Section

Romanian Contribution in Quantitative Precipitation Forecasts Project

Impacts of the April 2013 Mean trough over central North America

Dynamics of summer monsoon active phases and onset over Pakistan

MODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction

C

- tornadoes. Further Reading: Chapter 08 of the text book. Outline. -tropical storms. -Storm surge

Social Studies. Chapter 2 Canada s Physical Landscape

CHAPTER 2 - ATMOSPHERIC CIRCULATION & AIR/SEA INTERACTION

MJO Influence in Continental United States Temperatures

Decadal variability in the Kuroshio and Oyashio Extension frontal regions in an eddy-resolving OGCM

1 of 7 Thunderstorm Notes by Paul Sirvatka College of DuPage Meteorology. Thunderstorms

Instituto Geofisico del Perú (IGP), Lima, Peru; 2. University at Albany- State University of New York, New York, USA; 3

Severe weather. Some case studies for medium-range forecasting. T. La Rocca, Department of Synoptic Meteorology, Italian Met. Service, Rome.

11A.2 Forecasting Short Term Convective Mode And Evolution For Severe Storms Initiated Along Synoptic Boundaries

ESTUDIO NUMÉRICO DE LA PREDICTABILIDAD DE UN EVENTO DE CICLOGÉNESIS MEDITERRÁNEA MEDIANTE INVERSIÓN DE VORTICIDAD POTENCIAL

Fog forecast in Oltenia

u g z = g T y (1) f T Margules Equation for Frontal Slope

The Madden-Julian Oscillation in General Circulation Models

P0.98 Composite Analysis of Heavy-Rain-Producing Elevated Thunderstorms in the MO-KS-OK region of the United States

Transcription:

Analysis of the dynamics of extreme rainfall events in summer in southern Uruguay Matilde Ungerovich and Marcelo Barreiro UdelaR, Montevideo 8 May 2018

When I say Uruguay

When I say Uruguay When I say southern

When I say Uruguay When I say southern

When I say Uruguay When I say extreme: days in which the accumulated exceeds 90 percentile When I say southern

Objective of this investigation

Objective of this investigation Understand the main characteristics and atmospheric patterns that influence the extreme events of rainfall in summer in southern Uruguay

Motivation

Motivation Uruguay is an agricultural country, whose economy is greatly affected by the accumulation and distribution of rainfall

Motivation Uruguay is an agricultural country, whose economy is greatly affected by the accumulation and distribution of rainfall Extreme rainfall events generate social damages such as population displacement and road closures

Motivation Uruguay is an agricultural country, whose economy is greatly affected by the accumulation and distribution of rainfall Extreme rainfall events generate social damages such as population displacement and road closures There is no investigation on extreme rainfall events in Uruguay

1980 1984 1988 1992 1996 2000 2004 2008 2012 Motivation Uruguay is an agricultural country, whose economy is greatly affected by the accumulation and distribution of rainfall Extreme rainfall events generate social damages such as population displacement and road closures There is no investigation on extreme rainfall events in Uruguay Accumulated rainfall in extreme events % 0 10 20 30 40 50 60 70 80 90 100

Data

Data Daily data, period 1980-2013

Data Daily data, period 1980-2013 Observational accumulated rainfall in 15 pluviometers

Data Daily data, period 1980-2013 Observational accumulated rainfall in 15 pluviometers NCEP reanalysis 2-2.5 o x2.5 o : geopotential height, wind, temperature, latent heat

Methodology

Methodology EOF to classify the extreme events in different categories

Methodology EOF to classify the extreme events in different categories Composite analysis to study the dynamics (90% significance)

EOF- empirical orthogonal function

EOF- empirical orthogonal function The EOF was calculated considering 2m temperature

EOF- empirical orthogonal function The EOF was calculated considering 2m temperature They repesent 48% and 17% of variability EOF1 EOF2 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.2 295 300 305 310 315 320 Longitud [ ] 295 300 305 310 315 320 Longitud [ ]

EOF- empirical orthogonal function The EOF was calculated considering 2m temperature They repesent 48% and 17% of variability EOF1 EOF2 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.2 295 300 305 310 315 320 Longitud [ ] 295 300 305 310 315 320 Longitud [ ] EOF 1: temperature anomaly that leads to atmospheric (in)stability

EOF- empirical orthogonal function The EOF was calculated considering 2m temperature They repesent 48% and 17% of variability EOF1 EOF2 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.2 295 300 305 310 315 320 Longitud [ ] 295 300 305 310 315 320 Longitud [ ] EOF 1: temperature anomaly that leads to atmospheric (in)stability EOF 2: temperature gradient associated with frontal activity

EOF- empirical orthogonal function

EOF- empirical orthogonal function We separated into different groups, considering each s extreme day value in First and Second Principal component(cp)

EOF- empirical orthogonal function We separated into different groups, considering each s extreme day value in First and Second Principal component(cp) 60 20 0 20 40 20 10 0 10 20 30 PC1 PC2 clúster Rojo clúster azul clúster violeta 1 and 2 sd

Red group (PC1 +, PC2 +)

Red group (PC1 +, PC2 +) EOF1 EOF2 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.2 295 300 305 310 315 320 Longitud [ ] 295 300 305 310 315 320 Longitud [ ] Atmospheric instability + Frontal activity

Red group (PC1 +, PC2 +) 2 metre temperature anomaly

Red group (PC1 +, PC2 +) 2 metre temperature anomaly Anom P90_Grupo7, día 0, T2m Anom P90_Grupo7, día 1, T2m Anom P90_Grupo7, día 2, T2m 15 15 15 20 20 20 25 25 25 290 300 310 320 290 300 310 320 290 300 310 320 Longitud [ ] Figure 1: Day 0, Day -1, Day -2 Longitud Longitud [ ] [ ] Stationary positive temperature anomaly generating atmospheric instability

Red group (PC1 +, PC2 +) 1000 hpa geopotential anomaly

Red group (PC1 +, PC2 +) 1000 hpa geopotential anomaly Anomalía de Geopotencial, 1000hPa, día 0 Anomalía de Geopotencial, 1000hPa, día 1 Anomalía de Geopotencial, 1000hPa, día 2 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 Longitud [ ] Figure 2: Day 0, Day -1, Day -2 Longitud [ ] Longitud [ ] Surface low pressure approaching, and reaches Uruguay the day 0

Red group (PC1 +, PC2 +) Frontal Index anomaly

Red group (PC1 +, PC2 +) Frontal Index anomaly Solman & Orlanski, 2010 : T 850 x ξ 850 (1) Anomalía de IF, día 0 Anomalía de IF, día 1 Anomalía de IF, día 2 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 Longitud [ ] Figure 3: Day 0, Day -1, Day -2 Longitud [ ] Longitud [ ] Cold front approaching from the south

Red group (PC1 +, PC2 +) Latent heat anomaly, OLR anomaly

Red group (PC1 +, PC2 +) Latent heat anomaly, OLR anomaly Anom P90_Grupo7, día 0,Latent Flux Anom P90, día +1, OLR 15 15 20 20 25 25 290 300 310 320 280 290 300 310 320 Longitud [ ] longitud [ ] Figure 4: Day 0, Day 0 Negative latent flux anomaly, indicating evaporation Negative OLR, indicating rainfall in all the country

Red group (PC1 +, PC2 +) Why does it rain?

Red group (PC1 +, PC2 +) Why does it rain? Atmospheric instability due to positive temperature anomaly

Red group (PC1 +, PC2 +) Why does it rain? Atmospheric instability due to positive temperature anomaly Surface low pressure generating upward movement and condensation

Red group (PC1 +, PC2 +) Why does it rain? Atmospheric instability due to positive temperature anomaly Surface low pressure generating upward movement and condensation Even more humidity due to evaporation

Red group (PC1 +, PC2 +) Why does it rain? Atmospheric instability due to positive temperature anomaly Surface low pressure generating upward movement and condensation Even more humidity due to evaporation Frontal activity triggers the extreme rainfall event

Blue group (PC1 +, PC2 -)

Blue group (PC1 +, PC2 -) EOF1 EOF2 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.2 295 300 305 310 315 320 295 300 305 310 315 320 Longitud [ ] Atmospheric instability Longitud [ ]

Blue group (PC1 +, PC2 -) 200 hpa geopotential height anomaly

Blue group (PC1 +, PC2 -) 200 hpa geopotential height anomaly Anomalía de Geopotencial, 200hPa, día 0 Anomalía de Geopotencial, 200hPa, día 1 Anomalía de Geopotencial, 200hPa, día 2 0 0 0 20 20 20 60 60 60 80 80 80 200 250 300 350 200 250 300 350 200 250 300 350 Longitud [ ] Figure 5: Day 0, Day -1, Day -2 Longitud Longitud [ ] [ ] Stationary wave

Blue group (PC1 +, PC2 -) 1000 hpa geopotential height anomaly

Blue group (PC1 +, PC2 -) 1000 hpa geopotential height anomaly Anomalía de Geopotencial, 1000hPa, día 0 Anomalía de Geopotencial, 1000hPa, día 1 Anomalía de Geopotencial, 1000hPa, día 2 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 Longitud [ ] Figure 6: Day 0, Day -1, Day -2 Longitud [ ] Longitud [ ] Blocking situation, caused by the stationary wave generating clear skies and preventing cold fronts from reaching the area

Blue group (PC1 +, PC2 -) 2 metre temperature anomaly

Blue group (PC1 +, PC2 -) 2 metre temperature anomaly Anom P90_Grupo8, día 0, T2m Anom P90_Grupo8, día 1, T2m Anom P90_Grupo8, día 2, T2m 15 15 15 20 20 20 25 25 25 290 300 310 320 290 300 310 320 290 300 310 320 Longitud [ ] Figure 7: Day 0, Day -1, Day -2 Longitud Longitud [ ] [ ] Positive temperature anomaly intensifying in the west of Uruguay

Blue group (PC1 +, PC2 -) Heat flux anomaly

Blue group (PC1 +, PC2 -) Heat flux anomaly Anom P90_Grupo8, día 0,Latent Flux Anom P90_Grupo8, día 1,Latent Flux Anom P90_Grupo8, día 2,Latent Flux 15 15 15 20 20 20 25 25 25 290 300 310 320 290 300 310 320 290 300 310 320 Longitud [ ] Figure 8: Day 0, Day -1, Day -2 Longitud Longitud [ ] [ ] Heat transfer to the atmosphere, due to the clear sky, destabilizing the boundary layer

Blue group (PC1 +, PC2 -) OLR anomaly

Blue group (PC1 +, PC2 -) OLR anomaly Anom P90, día +1, OLR Anom P90, día 0, OLR 15 15 20 20 25 25 280 290 300 310 320 280 290 300 310 320 longitud [ ] longitud [ ] Figure 9: Day 0, Day -1 Negative OLR anomaly in the west that approaches and instensifies

Blue group (PC1 +, PC2 -) Why does it rain?

Blue group (PC1 +, PC2 -) Why does it rain? Stationary wave associated with blocking episode

Blue group (PC1 +, PC2 -) Why does it rain? Stationary wave associated with blocking episode High surface pressure favouring positive temperature anomalies and boundary layer destabilization

Blue group (PC1 +, PC2 -) Why does it rain? Stationary wave associated with blocking episode High surface pressure favouring positive temperature anomalies and boundary layer destabilization Positive temperature anomalies in the west of Uruguay, generating atmospheric instability

Blue group (PC1 +, PC2 -) Why does it rain? Stationary wave associated with blocking episode High surface pressure favouring positive temperature anomalies and boundary layer destabilization Positive temperature anomalies in the west of Uruguay, generating atmospheric instability Deep convective activity in the west of Uruguay that reaches the area

Violet group (PC1 -, PC2 +)

Violet group (PC1 -, PC2 +) EOF1 EOF2 0.2 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.2 295 300 305 310 315 320 Longitud [ ] 295 300 305 310 315 320 Longitud [ ] Frontal activity

Violet group (PC1 -, PC2 +) 200 hpa geopotential divergence

Violet group (PC1 -, PC2 +) 200 hpa geopotential divergence 200hPa P90_Grupo9_B, Anom Div, Día 0 200hPa P90_Grupo9_B, Anom Div, Día 1 200hPa P90_Grupo9_B, Anom Div, Día 2 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 Longitud [ ] Figure 10: Day 0, Day -1, Day -2 Longitud [ ] Longitud [ ] Positive divergence favouring upward movements

Violet group (PC1 -, PC2 +) 1000 hpa geopotential height anomaly

Violet group (PC1 -, PC2 +) 1000 hpa geopotential height anomaly Anomalía de Geopotencial, 1000hPa, día 0 Anomalía de Geopotencial, 1000hPa, día 1 Anomalía de Geopotencial, 1000hPa, día 2 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 Longitud [ ] Figure 11: Day 0, Day -1, Day -2 Longitud [ ] Longitud [ ] Low pressure favouring upward movement

Violet group (PC1 -, PC2 +) 850 hpa divergence

Violet group (PC1 -, PC2 +) 850 hpa divergence 850hPa P90_Grupo9_B, Anom Div, Día 0 850hPa P90_Grupo9_B, Anom Div, Día 1 850hPa P90_Grupo9_B, Anom Div, Día 2 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 Longitud [ ] Figure 12: Day 0, Day -1, Day -2 Longitud [ ] Longitud [ ] Negative divergence, indicating humidity advection

Violet group (PC1 -, PC2 +) OLR anomaly

Violet group (PC1 -, PC2 +) OLR anomaly Anom P90, día +1, OLR Anom P90, día 0, OLR Anom P90, día 1, OLR 15 15 15 20 20 20 25 25 25 280 290 300 310 320 280 290 300 310 320 280 290 300 310 320 longitud [ ] Figure 13: Day 0, Day -1, Day -2 longitud [ ] longitud [ ] Negative OLR anomaly, indicating rainfall

Violet group (PC1 -, PC2 +) Why does it rain?

Violet group (PC1 -, PC2 +) Why does it rain? 200 hpa divergence favouring upward movements

Violet group (PC1 -, PC2 +) Why does it rain? 200 hpa divergence favouring upward movements Low surface pressure favouring upward movements

Violet group (PC1 -, PC2 +) Why does it rain? 200 hpa divergence favouring upward movements Low surface pressure favouring upward movements Humidity advection

Conclusions We clasified the extreme rainfall events in 3 categories according to their 2 metre temperature configurations

Conclusions We clasified the extreme rainfall events in 3 categories according to their 2 metre temperature configurations Red extremes are characterized by a combination of atmospheric instability, evaporation and frontal activity in Uruguay

Conclusions We clasified the extreme rainfall events in 3 categories according to their 2 metre temperature configurations Red extremes are characterized by a combination of atmospheric instability, evaporation and frontal activity in Uruguay Blue extremes are characterized by a instable boundary layer in Uruguay and deep convective activity approaching from the west

Conclusions We clasified the extreme rainfall events in 3 categories according to their 2 metre temperature configurations Red extremes are characterized by a combination of atmospheric instability, evaporation and frontal activity in Uruguay Blue extremes are characterized by a instable boundary layer in Uruguay and deep convective activity approaching from the west Violet extremes are characterized by a combination of low surface pressure and humidity advection

Final conclusion We were able to achieve the objective

Final conclusion We were able to achieve the objective Questions? Suggestions?

2024 © sciencedocbox.com Privacy Policy | Terms of Service | Feedback