Research and Development ProjectResearch and Development Project

Improving the prediction of Improving the prediction of heavy precipitating systems heavy precipitating systems

over La Plata Basin over La Plata Basin LPB-ReDLPB-ReD

Research and Development ProjectResearch and Development Project

Improving the prediction of Improving the prediction of heavy precipitating systems heavy precipitating systems

over La Plata Basin over La Plata Basin LPB-ReDLPB-ReD

Presented byPresented byAlice M. GrimmAlice M. Grimm

Based on the draft of a preliminary RDP proposal written byBased on the draft of a preliminary RDP proposal written by

Celeste SauloCeleste Saulo - - University of Buenos Aires, CIMA, ArgentinaUniversity of Buenos Aires, CIMA, Argentina Christopher Cunningham Christopher Cunningham - - CPTEC-INPE, Brazil CPTEC-INPE, Brazil Alice GrimmAlice Grimm - - Federal University of Paraná, BrazilFederal University of Paraná, Brazil

La Plata BasinLa Plata BasinLa Plata BasinLa Plata Basin

(Grimm, 2011)- 5th largest basin in the world, 2nd in SA- Area: 3.100.000 km2

- Principal sub-basins: Paraná, Paraguay and Uruguay Rivers. - Covers parts of 5 countries: Argentina, Bolivia, Brazil, Paraguay and Uruguay.- Population > 200 millions.- Produces most of the electricity, food and exports of those 5 countries (~70% GNP).

High Impact weather in La Plata BasinHigh Impact weather in La Plata Basin Time scales from the very short range to the intraseasonal, Time scales from the very short range to the intraseasonal,

and up to a season.and up to a season.

High Impact weather in La Plata BasinHigh Impact weather in La Plata Basin Time scales from the very short range to the intraseasonal, Time scales from the very short range to the intraseasonal,

and up to a season.and up to a season.

Heavy and/or persistent rains (frequently leading to floods and landslides)

SACZ (summer) – blocking events (winter) – MCS (spring and summer) – cyclogenesis (autumn and spring)

Severe storms (tornado, wind gusts, hail, intense precipitation, lighting, etc) Late Frosts Warm/cold spells Droughts

Synoptic and mesoscale featuresSynoptic and mesoscale features Synoptic and mesoscale featuresSynoptic and mesoscale features

One of the main mechanisms supporting severe storms formation over LPB is the interaction between large-scale cold fronts and moisture advection from the Amazon. This interaction gives rise to explosive convective complexes that are amongst the largest and strongest in the world (Zipser et al., 2006).

Heavy precipitating events over LPB, are mostly associated with cyglogenesis, cut off lows, mesoscale convective systems, blocking, stationary South Atlantic Convergence Zone (SACZ).

There are major regional differences in the structure, intensity, and diurnal cycle of rainfall systems. The La Plata Basin MCSs (average area: ~5 105 km2; average lifetime: ~12 hours), are larger and more intense than the rainfall MCSs in the Amazon Basin (average area less than 1105 km2 and shorter lifetime: 3-6 hours).

MCSs are influenced by mesoscale effects such as jets and other topographically forced circulation and surface atmosphere interactions. The SALLJ is the jet with most extensive influence.

MCSs are modulated by the diurnal cycle.

One of the main mechanisms supporting severe storms formation over LPB is the interaction between large-scale cold fronts and moisture advection from the Amazon. This interaction gives rise to explosive convective complexes that are amongst the largest and strongest in the world (Zipser et al., 2006).

Heavy precipitating events over LPB, are mostly associated with cyglogenesis, cut off lows, mesoscale convective systems, blocking, stationary South Atlantic Convergence Zone (SACZ).

There are major regional differences in the structure, intensity, and diurnal cycle of rainfall systems. The La Plata Basin MCSs (average area: ~5 105 km2; average lifetime: ~12 hours), are larger and more intense than the rainfall MCSs in the Amazon Basin (average area less than 1105 km2 and shorter lifetime: 3-6 hours).

MCSs are influenced by mesoscale effects such as jets and other topographically forced circulation and surface atmosphere interactions. The SALLJ is the jet with most extensive influence.

MCSs are modulated by the diurnal cycle.

Equatorward incursions of frontal systems Equatorward incursions of frontal systems Equatorward incursions of frontal systems Equatorward incursions of frontal systems

The day-to-day variability of rainfall over subtropical South America The day-to-day variability of rainfall over subtropical South America and western Amazon basin is largely explained by northward and western Amazon basin is largely explained by northward incursions of mid-latitude systems to the east of the Andes, even in incursions of mid-latitude systems to the east of the Andes, even in summer. The deep northward intrusion of midlatitude systems is summer. The deep northward intrusion of midlatitude systems is attributed to the dynamical effect of the Andes topography, which attributed to the dynamical effect of the Andes topography, which plays a significant role on the structure and evolution of the synoptic plays a significant role on the structure and evolution of the synoptic systems that cross South America. systems that cross South America.

The day-to-day variability of rainfall over subtropical South America The day-to-day variability of rainfall over subtropical South America and western Amazon basin is largely explained by northward and western Amazon basin is largely explained by northward incursions of mid-latitude systems to the east of the Andes, even in incursions of mid-latitude systems to the east of the Andes, even in summer. The deep northward intrusion of midlatitude systems is summer. The deep northward intrusion of midlatitude systems is attributed to the dynamical effect of the Andes topography, which attributed to the dynamical effect of the Andes topography, which plays a significant role on the structure and evolution of the synoptic plays a significant role on the structure and evolution of the synoptic systems that cross South America. systems that cross South America.

Vera et al. 2006

Equatorward incursions of frontal systems Equatorward incursions of frontal systems Equatorward incursions of frontal systems Equatorward incursions of frontal systems

Cold fronts tend to be directed northward to the east of the Andes, Cold fronts tend to be directed northward to the east of the Andes, fostering the advance of cold air incursions into subtropical/tropical latitudes. fostering the advance of cold air incursions into subtropical/tropical latitudes. In summer there is large impact on the precipitation, through the equatorward In summer there is large impact on the precipitation, through the equatorward propagation (~10 mspropagation (~10 ms-1-1) of a northwest-southeast oriented band of enhanced ) of a northwest-southeast oriented band of enhanced convection ahead of the leading edge of the cool air, which tends to be convection ahead of the leading edge of the cool air, which tends to be followed by an area of suppressed convection. This synoptic scale banded followed by an area of suppressed convection. This synoptic scale banded structure, which maintains its identity for about 5 days, is the dominant mode structure, which maintains its identity for about 5 days, is the dominant mode of the day-to-day variability of deep convection, contributing with ~25% of of the day-to-day variability of deep convection, contributing with ~25% of summer precipitation in the central Amazonia and ~50% over subtropical summer precipitation in the central Amazonia and ~50% over subtropical South America. These bands influence convection in the SACZ. South America. These bands influence convection in the SACZ.

Cold fronts tend to be directed northward to the east of the Andes, Cold fronts tend to be directed northward to the east of the Andes, fostering the advance of cold air incursions into subtropical/tropical latitudes. fostering the advance of cold air incursions into subtropical/tropical latitudes. In summer there is large impact on the precipitation, through the equatorward In summer there is large impact on the precipitation, through the equatorward propagation (~10 mspropagation (~10 ms-1-1) of a northwest-southeast oriented band of enhanced ) of a northwest-southeast oriented band of enhanced convection ahead of the leading edge of the cool air, which tends to be convection ahead of the leading edge of the cool air, which tends to be followed by an area of suppressed convection. This synoptic scale banded followed by an area of suppressed convection. This synoptic scale banded structure, which maintains its identity for about 5 days, is the dominant mode structure, which maintains its identity for about 5 days, is the dominant mode of the day-to-day variability of deep convection, contributing with ~25% of of the day-to-day variability of deep convection, contributing with ~25% of summer precipitation in the central Amazonia and ~50% over subtropical summer precipitation in the central Amazonia and ~50% over subtropical South America. These bands influence convection in the SACZ. South America. These bands influence convection in the SACZ.

FFrom Garreaud and Wallace 1998rom Garreaud and Wallace 1998 FFrom Garreaud and Wallace 1998rom Garreaud and Wallace 1998

Three types of influence:Three types of influence: Type 1 - frequent in austral summer and spring, is characterized by the penetration of a cold front in subtropical South America that interacts with tropical convection and moves with it into lower tropical latitudes. Type 2 - also more frequent in austral summer, is characterized by Amazon convection and southward enhancement of convection in a quasi-stationary northwest–southeast oriented band extending from the Amazon basin to subtropical South America with the passage of a cold front in the subtropics. When this pattern remains longer than 4 days, it often characterizes the SACZ.

Type 1 - frequent in austral summer and spring, is characterized by the penetration of a cold front in subtropical South America that interacts with tropical convection and moves with it into lower tropical latitudes. Type 2 - also more frequent in austral summer, is characterized by Amazon convection and southward enhancement of convection in a quasi-stationary northwest–southeast oriented band extending from the Amazon basin to subtropical South America with the passage of a cold front in the subtropics. When this pattern remains longer than 4 days, it often characterizes the SACZ.

Fontal systems influence on tropical convectionFontal systems influence on tropical convectionFontal systems influence on tropical convectionFontal systems influence on tropical convection

Siqueira and MachadoSiqueira and Machado 2004; Siqueira et al. 2005 2004; Siqueira et al. 2005 Siqueira and MachadoSiqueira and Machado 2004; Siqueira et al. 2005 2004; Siqueira et al. 2005

Type 3 - more frequent in austral winter, is represented by a quasi-stationary cold front in subtropical South America and midlatitudes, without significant interaction with tropical convection. There are differences in MCSs for the 3 types of frontal system interaction with tropical convection. For instance, Type 2, often evolving into SACZ, has larger horizontal extent but less vertical development than Type 1.

Three types of frontal system influence on tropical convection:Three types of frontal system influence on tropical convection:

Fontal systems influence on tropical convection Fontal systems influence on tropical convection Fontal systems influence on tropical convection Fontal systems influence on tropical convection

Siqueira and MachadoSiqueira and Machado 2004; Siqueira et al. 2005 2004; Siqueira et al. 2005 Siqueira and MachadoSiqueira and Machado 2004; Siqueira et al. 2005 2004; Siqueira et al. 2005

Mean cold cloud top fractions

Mean 850 hPa wind anomalies

Type Type 11

Type Type 22

Type Type 33

The South American Low-Level Jet

SALLJ spatial structure depicted by NOAA/P-3 missions in SALLJEX

00 UTC06 UTC12 UTC18UTC

SALLJ diurnal cycle at 700 hPa depicted by SALLJEX observations (Nicolini et al. 2004)

LLJ Composites NDJF, (Marengo et al. 2004)

From C. VeraFrom C. Vera From C. VeraFrom C. Vera

The SALLJ events are conditioned by synoptic variability, and can be separated into two groups: (1) events in which the LLJ extends farther south, at least to 25S, (2) events in which the jet leading edge is north of this threshold.

The SALLJ events are conditioned by synoptic variability, and can be separated into two groups: (1) events in which the LLJ extends farther south, at least to 25S, (2) events in which the jet leading edge is north of this threshold.

Synoptic Variability X SALLJSynoptic Variability X SALLJ Synoptic Variability X SALLJSynoptic Variability X SALLJ

Nicolini et al 2002Nicolini et al 2002 Nicolini et al 2002Nicolini et al 2002

(2)(1) Moisture flux Moisture flux

Precipitation Precipitation

LPB exhibits the most extensive “hot spot” of the most intense thunderstorms on Earth, according to the TRMM data (1 January 1998 - 31 December 2004). LPB exhibits the most extensive “hot spot” of the most intense thunderstorms on Earth, according to the TRMM data (1 January 1998 - 31 December 2004).

Mesoscale VariabilityMesoscale Variability Mesoscale VariabilityMesoscale Variability

Locations of intense convective events according to different proxies for convection intensity, using the color code matching their rarity. The parameter limits for each category are indicated above each color bar. For example, of the 12.8 million PFs, only about 0.001% (128) have more than 314.7 lightning flashes per minute. (Zipser et al., BAMS, 2006)(Zipser et al., BAMS, 2006)

In La Plata Basin Mesoscale Convective Complexes (MCCs) occur frequently during October-April.In La Plata Basin Mesoscale Convective Complexes (MCCs) occur frequently during October-April.

Mesoscale VariabilityMesoscale Variability Mesoscale VariabilityMesoscale Variability

Compilation of the MCCs location as given by several works (J. C. Conforte) (J. C. Conforte)

Summer Autumn

SpringWinter

• Average area: ~5105 km2

• Average lifetime: ~12 hours• Cycle: preferentially initiate in late afternoon and mature during nighttime.• Develop east of the Andes, and move preferentially southeastward.• Intensification related with the subtropical jet and SALLJ.• More than 80% of MCCs occur during SALLJ events that penetrate farther south.

Along the northeastern coast of South America there is frequently afternoon genesis and subsequent inland propagation of coastal squall lines forced by onshore low-level flow (e.g. Cohen et al. 1995). The coastal band of convective cloudiness increases to a maximum in the late afternoon and weakens during nighttime. After inland propagation, convection is reactivated in the afternoon of the next day (Garreaud and Wallace 1997).

Diurnal Cycle Diurnal Cycle Diurnal Cycle Diurnal Cycle

From R. H. Johnson

The peak is observed at afternoon/early evening in most of the monsoon region, consistent with the more suitable thermodynamic conditions during this part of the day.

Nocturnal convection is prevalent over the subtropical plains (LPB), where it can be ascribed to the diurnal cycle of the SALLJ (Berbery and Collini 2000), to the nocturnal convergence into the valley of the Parana River basin, and to the decrease of the compensating subsidence associated with the SACZ (Silva Dias et al. 1987).

Contribution of synoptic and intraseasonal Contribution of synoptic and intraseasonal timescales to total variance of summer rainfalltimescales to total variance of summer rainfall

Contribution of synoptic and intraseasonal Contribution of synoptic and intraseasonal timescales to total variance of summer rainfalltimescales to total variance of summer rainfall

Ferraz and Grimm 2004Ferraz and Grimm 2004Ferraz and Grimm 2004Ferraz and Grimm 2004

Synoptic variabilitySynoptic variabilitySynoptic variabilitySynoptic variability Intraseasonal variabilityIntraseasonal variabilityIntraseasonal variabilityIntraseasonal variability

Intraseasonal Variability in Summer Intraseasonal Variability in Summer (10-100 day bands)(10-100 day bands)

EOF1 – 16,3%EOF1 – 16,3%EOF1 – 16,3%EOF1 – 16,3%

(Ferraz, 2004)(Ferraz, 2004)(Ferraz, 2004)(Ferraz, 2004)

10-20 day bandREOF1 – 7,8 %REOF1 – 7,8 %REOF1 – 7,8 %REOF1 – 7,8 %

REOF1 – 10,0%REOF1 – 10,0%REOF1 – 10,0%REOF1 – 10,0% REOF2 – 7,3%REOF2 – 7,3%REOF2 – 7,3%REOF2 – 7,3%

REOF1 – 10,6%REOF1 – 10,6%REOF1 – 10,6%REOF1 – 10,6%30-70 day band20-30 day band

REOF1 – 11,5 %REOF1 – 11,5 %REOF1 – 11,5 %REOF1 – 11,5 %

Separating into different frequencies:Separating into different frequencies:Separating into different frequencies:Separating into different frequencies:

11stst rotated mode rotated mode

Intraseasonal Variability Intraseasonal Variability (30-70 day band)(30-70 day band)

22ndnd rotated mode rotated mode

Composites of rainfall Composites of rainfall anomalies and vertically anomalies and vertically integrated moisture flux integrated moisture flux for wet and dry phases for wet and dry phases of the first and second of the first and second rotated EOFs (“dipole” rotated EOFs (“dipole” modes), in the 30/70 day modes), in the 30/70 day band.band.

Composites of rainfall Composites of rainfall anomalies and vertically anomalies and vertically integrated moisture flux integrated moisture flux for wet and dry phases for wet and dry phases of the first and second of the first and second rotated EOFs (“dipole” rotated EOFs (“dipole” modes), in the 30/70 day modes), in the 30/70 day band.band.

Wet phaseWet phase

Dry phaseDry phase

Ferraz and Grimm 2004Ferraz and Grimm 2004Ferraz and Grimm 2004Ferraz and Grimm 2004

“Westerly regime”

“Easterly regime”

MJO - Phase 4 MJO - Phase 4 Precip. anomaliesPrecip. anomaliesMJO - Phase 4 MJO - Phase 4 Precip. anomaliesPrecip. anomaliesGrimm 2011 (in preparation)Grimm 2011 (in preparation)Grimm 2011 (in preparation)Grimm 2011 (in preparation)

The Madden-Julian Oscillation allows some predictability, but there are other intraseasonal oscillations not well understood.

Intraseasonal VariabilityIntraseasonal Variability Origin Origin

Not yet well understood. Not yet well understood. Spectral analyses of precipitation and OLR Spectral analyses of precipitation and OLR show distinct peaks in the show distinct peaks in the 10-20 day band, 10-20 day band, 20-25 day band, and 30-70 day 20-25 day band, and 30-70 day band bands.band bands.

Remote influence? The first rotated mode seem to be related to the MJO Remote influence? The first rotated mode seem to be related to the MJO via tropical teleconnection, while the second rotated mode seems to be via tropical teleconnection, while the second rotated mode seems to be related to wave-trains propagating southeastward from West or Central related to wave-trains propagating southeastward from West or Central Pacific, rounding the southern tip of South America and turning toward Pacific, rounding the southern tip of South America and turning toward the northeast, probably originated from MJO-related convection.the northeast, probably originated from MJO-related convection.

Regional circulation, like the SALLJ, may be modulated by intraseasonal Regional circulation, like the SALLJ, may be modulated by intraseasonal fluctuations of zonal flow above the Andes and consequent fluctuations fluctuations of zonal flow above the Andes and consequent fluctuations of the orographically bound cyclone east of the Andes.of the orographically bound cyclone east of the Andes.

Local forcing? Simulations with a regional climate model by Grimm et al. Local forcing? Simulations with a regional climate model by Grimm et al. (2007) show that there are links between soil moisture, surface (2007) show that there are links between soil moisture, surface temperature and regional circulation that can modulate intraseasonal temperature and regional circulation that can modulate intraseasonal oscillation thus producing interannual variability with pattern similar to oscillation thus producing interannual variability with pattern similar to the first intraseasonal patterns. Moreover, the rough topography in the first intraseasonal patterns. Moreover, the rough topography in Southeast Brazil is important in shaping the associated circulation and Southeast Brazil is important in shaping the associated circulation and precipitation patterns. precipitation patterns.

November

January

REOF1

REOF1

r=-0.32

Grimm et al. 2007; Grimm and Zilli 2008

REGIONAL FORCING: The intraseasonal variability, might be favored or hampered, according to its phase, by local circulation anomaly set up by processes triggered by soil moisture conditions in spring, so that the first mode of interannual variability of summer precipitation is not just the rectification of intraseasonal variability or product of random sampling of intraseasonal events.

Soil moisture * 0.5Soil moisture * 0.5

Mechanisms of the spring-summer relationship Mechanisms of the spring-summer relationship

Soil moisture * 0.5 + SSTSoil moisture * 0.5 + SST Soil moisture * 0.5 - topoSoil moisture * 0.5 - topo

Grimm, Pal and Giorgi 2007Grimm, Pal and Giorgi 2007Grimm, Pal and Giorgi 2007Grimm, Pal and Giorgi 2007

SALLJ

Central-East Brazil

SESA

Modulation of extreme precipitation events Modulation of extreme precipitation events by climate variability by climate variability – ENSO – ENSO

El N

iño

El N

iño

La

Niñ

aL

a N

iña

Nov (0)Nov (0)

Nov (0)Nov (0)

Apr (+)Apr (+)

Apr (+)Apr (+)Jan (+)Jan (+)

SALLJ

Central-East Brazil

SESA

Jan (+)Jan (+)

Areas with significant variation of extreme events:

IncreaseDecrease

La Plata Basin floods in May 1998 (from C. Saulo)

(Grimm and Tedeschi 2009)

REG. MONTH AVERAGE OF EXTREME EVENTS (1956-2002)

EN (11) LN (9) NEUTRAL (27)

a NOV (0) 6.3 1.2 2.5

a

Modulation of extreme precipitation events Modulation of extreme precipitation events by climate variability by climate variability – ENSO – ENSO

ENSO-related significant changes in the frequency of extreme rainfall events are much more extensive than changes in monthly rainfall, because ENSO influence is stronger on the categories of more intense daily precipitation. (Grimm and Tedeschi 2009)

Main motivation: lack of comprehensive understanding about the processes that determine severe weather events in the La Plata Basin, and our limitation in providing skillful forecasts that would contribute to minimize their impacts.

The rainfall patterns in the La Plata Basin result from the interaction of various meteorological systems and scales. Therefore, the region is a suitable “test bed” for the assessment of model performance and the development of  specific tools for high impact weather forecasting.

Even if some of the systems that cause heavy precipitation can be reasonably well forecasted, predicted location and timing are frequently not accurate and the associated amount of precipitation is usually under predicted.

Experiments using an enhanced network of observations obtained during SALLJEX (Vera et al., 2006) suggest that when low level moisture flux is better represented, precipitation forecasts over LPB can be substantially improved (Herdies et al., 2007; 2011).

Besides improving the representation of the initial state in order to provide better forecasts over the region, we also need to improve our knowledge of the physics underlying heavy precipitating events .

Main motivation: lack of comprehensive understanding about the processes that determine severe weather events in the La Plata Basin, and our limitation in providing skillful forecasts that would contribute to minimize their impacts.

The rainfall patterns in the La Plata Basin result from the interaction of various meteorological systems and scales. Therefore, the region is a suitable “test bed” for the assessment of model performance and the development of  specific tools for high impact weather forecasting.

Even if some of the systems that cause heavy precipitation can be reasonably well forecasted, predicted location and timing are frequently not accurate and the associated amount of precipitation is usually under predicted.

Experiments using an enhanced network of observations obtained during SALLJEX (Vera et al., 2006) suggest that when low level moisture flux is better represented, precipitation forecasts over LPB can be substantially improved (Herdies et al., 2007; 2011).

Besides improving the representation of the initial state in order to provide better forecasts over the region, we also need to improve our knowledge of the physics underlying heavy precipitating events .

RDP in the La Plata Basin RDP in the La Plata Basin

1. Motivation1. Motivation RDP in the La Plata Basin RDP in the La Plata Basin

1. Motivation1. Motivation

Each operational center uses its own models to forecast at diverse ranges, but there is a lack of documentation about their actual skill for predicting extreme events. Many heavy precipitating events are not captured by state-of the art models.

Analyses of ensemble systems’ skills in the region, show that despite the combination of different forecasts, there is very low skill for predicting precipitation thresholds above 25 mm, even with only 1 day in advance.

Although available, ensemble prediction systems (EPS) are not fully used by the operational community and even less by other potential users. Most of the products derived from these systems do not concentrate on intense precipitation. The advantage of ensemble forecasting applied to severe weather has to be demonstrated through the development of specific products.

  Besides dynamical models, there are diverse techniques suitable for heavy rainfall precipitation, mainly related with the use of remote sensing. A radar network exists over Brazil and Argentina, but does not share common algorithms/systems to exchange timely information that could help providing specific alerts. The coordination of the Brazilian and Argentinean network of radars is one of the tasks of the CHUVA project. Thus, involvement in nowcasting techniques could be desirable.

Each operational center uses its own models to forecast at diverse ranges, but there is a lack of documentation about their actual skill for predicting extreme events. Many heavy precipitating events are not captured by state-of the art models.

Analyses of ensemble systems’ skills in the region, show that despite the combination of different forecasts, there is very low skill for predicting precipitation thresholds above 25 mm, even with only 1 day in advance.

Although available, ensemble prediction systems (EPS) are not fully used by the operational community and even less by other potential users. Most of the products derived from these systems do not concentrate on intense precipitation. The advantage of ensemble forecasting applied to severe weather has to be demonstrated through the development of specific products.

  Besides dynamical models, there are diverse techniques suitable for heavy rainfall precipitation, mainly related with the use of remote sensing. A radar network exists over Brazil and Argentina, but does not share common algorithms/systems to exchange timely information that could help providing specific alerts. The coordination of the Brazilian and Argentinean network of radars is one of the tasks of the CHUVA project. Thus, involvement in nowcasting techniques could be desirable.

2. Current state 2. Current state 2. Current state 2. Current state

2. Current state 2. Current state 2. Current state 2. Current state

Operational radar network over LPB. (Courtesy of Paola Salio)

Weather prediction for the monsoon seasonWeather prediction for the monsoon seasonSkill for predicting Skill for predicting daily precipitation daily precipitation during the monsoon during the monsoon season in South season in South America is quite America is quite variable depending variable depending on the precipitation on the precipitation threshold. There are threshold. There are high probabilities of high probabilities of detection for low detection for low values of daily values of daily precipitation but they precipitation but they are overestimated, as are overestimated, as shown by the bias. shown by the bias. For larger values For larger values there is low there is low probability of probability of detection and they detection and they are underestimated. are underestimated. ((Period: monsoon Period: monsoon season 2007-2008. season 2007-2008. Courtesy of Maria Courtesy of Maria Assunção Silva DiasAssunção Silva Dias))

2. Current state 2. Current state 2. Current state 2. Current state

Forecast of extreme event: Forecast of extreme event: South Brazil, 18-19 Aug 2011 South Brazil, 18-19 Aug 2011

This extreme event in western Parana State and other locations in the Southern Region affected more This extreme event in western Parana State and other locations in the Southern Region affected more than 17,000 people. The amounts of rainfall exceeded 50 mm in 24 hours (1/2 to 1/3 of the mean than 17,000 people. The amounts of rainfall exceeded 50 mm in 24 hours (1/2 to 1/3 of the mean monthly total). Those excessive rainfalls caused floods that affected more than 300 homes. Strong monthly total). Those excessive rainfalls caused floods that affected more than 300 homes. Strong winds associated with the thunderstorms ruined seven towers used for energy transmission.winds associated with the thunderstorms ruined seven towers used for energy transmission.

Left: 24h precipitation accumulated in 19 Aug 2011. Right: satellite image valid for 201108190600. Precipitation forecast 48 h in

advance from Ensemble Prediction System.

2. Current state 2. Current state 2. Current state 2. Current state

(Cunningham and Escobar, 2011)

There was an intense cold front across South Brazil, especially over southern Paraná State. There was an intense cold front across South Brazil, especially over southern Paraná State. This frontal system was caused by a cyclogenesis process, with the extratropical cyclone This frontal system was caused by a cyclogenesis process, with the extratropical cyclone located on the Atlantic Ocean, east of Buenos Aires. The northerly moisture transport was located on the Atlantic Ocean, east of Buenos Aires. The northerly moisture transport was enhanced by the low level jet, from the Amazon towards South Brazil.enhanced by the low level jet, from the Amazon towards South Brazil.

Left: surface analysis; Right: synoptic chart at 850 hPa.

2. Current state 2. Current state 2. Current state 2. Current state Forecast of extreme event: Forecast of extreme event: South Brazil, 18-19Aug 2011 South Brazil, 18-19Aug 2011

2. Current state 2. Current state 2. Current state 2. Current state

Probability of detection of a precipitation event for several thresholds. All curves display 48h forecast. Each curve displays one model of the CPTEC suite. T126_L28 is the AGCM with 100km of horizontal resolution approximately and 28 layers in the vertical. T213_L42 is the AGCM with 63km of horizontal resolution approximately and 42 layers in the vertical. T299_L64 is the AGCM with 40km of horizontal resolution approximately and 64 layers in the vertical. Acoplado is the ocean-atmosphere coupled model; the atmosphere is integrated at a T126L28 resolution. RPSAS_40 is the limited area model integrated at 40km of horizontal resolution and initiated with CPTEC’s regional analysis. GPSAS213 is the CPTEC AGCM integrated at 40km of horizontal resolution and initiated with CPTEC’s global analysis. ENS_GLOB is the Ensemble Prediction System integrated at T126L28 resolution.

Accumulated precipitation forecasts obtained with different models compared against Accumulated precipitation forecasts obtained with different models compared against observations (first row). (a) 2/Feb/2010, (b) 5/Feb/2010 and (c) 19/Feb/2010 (Suaya et al. 2010).observations (first row). (a) 2/Feb/2010, (b) 5/Feb/2010 and (c) 19/Feb/2010 (Suaya et al. 2010).Different models were unable to predict the amount of precipitation associated with diverse Different models were unable to predict the amount of precipitation associated with diverse events occurring during February 2010 (the event on February 5 reached 240 mm in one day). events occurring during February 2010 (the event on February 5 reached 240 mm in one day).

2. Current state 2. Current state 2. Current state 2. Current state Forecast of extreme event: Forecast of extreme event: North Argentina, Feb 2010 North Argentina, Feb 2010

How  early can we predict extreme precipitating events, in a probabilistic sense?

Which factors determine such predictability? (some potential factors: time of the year, soil moisture, low frequency variability (such as ENOS), wave interactions;

How well are the mechanisms leading to heavy precipitation understood / represented by current models?

How much of the forecast skill can be attributed to the resolution and convective parameterizations used?

How much can an enhanced network of observations improve the forecast of extreme events?

What is the most convenient way to combine the information from different ensemble members, so that it becomes useful for predicting extreme precipitating events?

What is the role of wetlands areas (e.g. Pantanal) in the occurrence/maintenance of extreme precipitation events and extensive flooding risk?

What is the role of biomass burning and megacity emissions in the precipitation?

What and how can we learn from model errors diagnostics? Diagnostic techniques help enhancing our understanding of the climate system, identifying problems and improving the models.

How  early can we predict extreme precipitating events, in a probabilistic sense?

Which factors determine such predictability? (some potential factors: time of the year, soil moisture, low frequency variability (such as ENOS), wave interactions;

How well are the mechanisms leading to heavy precipitation understood / represented by current models?

How much of the forecast skill can be attributed to the resolution and convective parameterizations used?

How much can an enhanced network of observations improve the forecast of extreme events?

What is the most convenient way to combine the information from different ensemble members, so that it becomes useful for predicting extreme precipitating events?

What is the role of wetlands areas (e.g. Pantanal) in the occurrence/maintenance of extreme precipitation events and extensive flooding risk?

What is the role of biomass burning and megacity emissions in the precipitation?

What and how can we learn from model errors diagnostics? Diagnostic techniques help enhancing our understanding of the climate system, identifying problems and improving the models.

3. Some scientific questions 3. Some scientific questions 3. Some scientific questions 3. Some scientific questions

To improve the assessment of state-of-art models ability to anticipate extreme weather conditions and to quantify their skill;

To analyze the sources of model errors in order to feedback the development of models itself;

To evaluate the impact of extra data in the quality of forecasts;

To use TIGGE outputs and other available ensemble systems, to develop products/ tools adequate for extreme weather forecast in the region and evaluate them in the operational context;

To foster the use of TIGGE products by the organization of training courses and workshops.

Extended prediction? Influence of climate variability?

To improve the assessment of state-of-art models ability to anticipate extreme weather conditions and to quantify their skill;

To analyze the sources of model errors in order to feedback the development of models itself;

To evaluate the impact of extra data in the quality of forecasts;

To use TIGGE outputs and other available ensemble systems, to develop products/ tools adequate for extreme weather forecast in the region and evaluate them in the operational context;

To foster the use of TIGGE products by the organization of training courses and workshops.

Extended prediction? Influence of climate variability?

4. Main objectives of the proposed RDP4. Main objectives of the proposed RDP4. Main objectives of the proposed RDP4. Main objectives of the proposed RDP

It is expected that an RDP like the one proposed here, will have immediate impacts through the implementation of state-of-the art prediction techniques at the operational services. Given that the focus will be on heavy precipitating events, and through the involvement of water resources/water administration/alert systems agencies, it is also expected that this project will help to articulate and facilitate the communication between weather forecasts community and the users of forecasts.

A regional virtual center for monitoring and forecasting severe weather for Southeastern South America could be established and maintained in the future, so as to strengthen and optimize regional networks.

It is expected that an RDP like the one proposed here, will have immediate impacts through the implementation of state-of-the art prediction techniques at the operational services. Given that the focus will be on heavy precipitating events, and through the involvement of water resources/water administration/alert systems agencies, it is also expected that this project will help to articulate and facilitate the communication between weather forecasts community and the users of forecasts.

A regional virtual center for monitoring and forecasting severe weather for Southeastern South America could be established and maintained in the future, so as to strengthen and optimize regional networks.

5. Societal impacts5. Societal impacts5. Societal impacts5. Societal impacts

Before describing how the RDP will undertake the research and development (including methodology and time line), it is necessary to carry out a workshop for discussion of the research that should be undertaken in the RDP. Some issues:

- use of ensemble forecast information for mitigation of severe weather impacts;

- techniques of ensemble forecasting applied to severe weather;

- experiments using an enhanced network of observations;

- other research and methods to achieve the objectives.

Before this workshop, a summer school for operational forecasters and end-users of the meteorological information is proposed in order to disseminate the basic concepts behind ensemble prediction and evaluation, in order to homogenize the level of knowledge of the users of information and hence motivate them.

FORECAST DEMONSTRATION PROJECTA FDP should happen within the proposed RDP.

Before describing how the RDP will undertake the research and development (including methodology and time line), it is necessary to carry out a workshop for discussion of the research that should be undertaken in the RDP. Some issues:

- use of ensemble forecast information for mitigation of severe weather impacts;

- techniques of ensemble forecasting applied to severe weather;

- experiments using an enhanced network of observations;

- other research and methods to achieve the objectives.

Before this workshop, a summer school for operational forecasters and end-users of the meteorological information is proposed in order to disseminate the basic concepts behind ensemble prediction and evaluation, in order to homogenize the level of knowledge of the users of information and hence motivate them.

FORECAST DEMONSTRATION PROJECTA FDP should happen within the proposed RDP.

6. How the RDP will undertake the research 6. How the RDP will undertake the research and development? and development?

6. How the RDP will undertake the research 6. How the RDP will undertake the research and development? and development?