Transcript Research and Development Project Improving the prediction of heavy precipitating systems over La Plata Basin LPB-ReD Presented by Alice M.

Research and Development Project
Improving the prediction of
heavy precipitating systems
over La Plata Basin
LPB-ReD
Presented by
Alice M. Grimm
Based on the draft of a preliminary RDP proposal written by
Celeste Saulo - University of Buenos Aires, CIMA, Argentina
Christopher Cunningham - CPTEC-INPE, Brazil
Alice Grimm - Federal University of Paraná, Brazil
La 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 Basin
Time scales from the very short range to the intraseasonal,
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 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 1105 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
Vera et al. 2006
 The day-to-day variability of rainfall over subtropical South America
and western Amazon basin is largely explained by northward
incursions of mid-latitude systems to the east of the Andes, even in
summer. The deep northward intrusion of midlatitude systems is
attributed to the dynamical effect of the Andes topography, which
plays a significant role on the structure and evolution of the synoptic
systems that cross South America.
Equatorward incursions of frontal systems
From Garreaud and Wallace 1998
 Cold fronts tend to be directed northward to the east of the Andes,
fostering the advance of cold air incursions into subtropical/tropical latitudes.
In summer there is large impact on the precipitation, through the equatorward
propagation (~10 ms-1) of a northwest-southeast oriented band of enhanced
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
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
summer precipitation in the central Amazonia and ~50% over subtropical
South America. These bands influence convection in the SACZ.
Fontal systems influence on tropical convection
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 quasistationary 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 3 - more frequent in austral
winter, is represented by a quasistationary 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.
Siqueira and Machado 2004; Siqueira et al. 2005
Fontal systems influence on tropical convection
Type 1
Three types of frontal system influence on tropical convection:
Type 2
Type 3
Mean cold
cloud top
fractions
Siqueira and Machado 2004; Siqueira et al. 2005
Mean 850
hPa wind
anomalies
The South American Low-Level Jet
LLJ Composites NDJF,
00 UTC
(Marengo et al. 2004)
06 UTC
12 UTC
18UTC
SALLJ spatial
structure depicted by
NOAA/P-3 missions in
SALLJEX
SALLJ diurnal
cycle at 700 hPa
depicted by SALLJEX
observations
(Nicolini et al. 2004)
From C. Vera
Synoptic Variability X SALLJ
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 25S,
 (2) events in which the jet leading edge is north of this threshold.
(1) Moisture flux
(2) Moisture flux
Precipitation
Precipitation
Nicolini et al 2002
Mesoscale Variability
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).
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)
Mesoscale Variability
In La Plata Basin Mesoscale Convective Complexes (MCCs) occur frequently
during October-April.
Summer
Autumn
Winter
Spring
• Average area: ~5105 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.
Compilation of the MCCs location as
given by several works (J. C. Conforte)
Diurnal Cycle
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.
From R. H. Johnson
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).
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
timescales to total variance of summer rainfall
Synoptic variability
Ferraz and Grimm 2004
Intraseasonal variability
Intraseasonal Variability in Summer
(10-100 day bands)
EOF1 – 16,3%
(Ferraz, 2004)
REOF1 – 10,0%
REOF2 – 7,3%
10-20 day band
REOF1 – 7,8 %
20-30 day band
REOF1 – 11,5 %
Separating into different frequencies:
30-70 day band
REOF1 – 10,6%
Intraseasonal Variability (30-70 day band)
Composites of rainfall
anomalies and vertically
integrated moisture flux
for wet and dry phases
of the first and second
rotated EOFs (“dipole”
modes), in the 30/70 day
band.
“Westerly regime”
Wet phase
MJO - Phase 4
Precip. anomalies
Grimm 2011 (in preparation)
“Easterly regime”
1st rotated mode
Ferraz and Grimm 2004
Dry phase
2nd rotated mode
The Madden-Julian Oscillation
allows some predictability, but
there are other intraseasonal
oscillations not well understood.
Intraseasonal Variability
Origin
 Not yet well understood. Spectral analyses of precipitation and OLR
show distinct peaks in the 10-20 day band, 20-25 day band, and 30-70 day
band bands.
 Remote influence? The first rotated mode seem to be related to the MJO
via tropical teleconnection, while the second rotated mode seems to be
related to wave-trains propagating southeastward from West or Central
Pacific, rounding the southern tip of South America and turning toward
the northeast, probably originated from MJO-related convection.
 Regional circulation, like the SALLJ, may be modulated by intraseasonal
fluctuations of zonal flow above the Andes and consequent fluctuations
of the orographically bound cyclone east of the Andes.
 Local forcing? Simulations with a regional climate model by Grimm et al.
(2007) show that there are links between soil moisture, surface
temperature and regional circulation that can modulate intraseasonal
oscillation thus producing interannual variability with pattern similar to
the first intraseasonal patterns. Moreover, the rough topography in
Southeast Brazil is important in shaping the associated circulation and
precipitation patterns.
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.
November REOF1
r=-0.32
January
REOF1
Grimm et al. 2007; Grimm and Zilli 2008
Mechanisms of the spring-summer relationship
Grimm, Pal and Giorgi 2007
Soil moisture * 0.5
Soil moisture * 0.5 + SST
Soil moisture * 0.5 - topo
SALLJ
Areas with
significant
variation of
extreme events:
Increase
Decrease
(Grimm and Tedeschi 2009)
Apr (+)
SALLJ
SESA
La Plata Basin floods in
May 1998 (from C. Saulo)
Apr (+)
El Niño
Jan (+)
Nov (0)
La Niña
Modulation of extreme precipitation events
by climate variability – ENSO
SESA
Central-East Brazil
Central-East Brazil
Nov (0)
Jan (+)
Modulation of extreme precipitation events
by climate variability – ENSO
REG.
MONTH AVERAGE OF EXTREME EVENTS
a
(1956-2002)
a
NOV (0)
EN (11)
LN (9)
NEUTRAL (27)
6.3
1.2
2.5
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)
RDP in the La Plata Basin
1. Motivation
 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 .
2. Current state
 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
Operational radar network over LPB.
(Courtesy of Paola Salio)
2. Current state
Weather prediction for the monsoon season
Skill for predicting
daily precipitation
during the monsoon
season in South
America is quite
variable depending
on the precipitation
threshold. There are
high probabilities of
detection for low
values of daily
precipitation but they
are overestimated, as
shown by the bias.
For larger values
there is low
probability of
detection and they
are underestimated.
(Period: monsoon
season 2007-2008.
Courtesy of Maria
Assunção Silva Dias)
2. Current state
Forecast of extreme event: South Brazil, 18-19 Aug 2011
Left: 24h precipitation accumulated in 19 Aug 2011.
Right: satellite image valid for 201108190600.
Precipitation forecast 48 h in
advance from Ensemble
Prediction System.
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 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.
(Cunningham and Escobar, 2011)
2. Current state
Forecast of extreme event: South Brazil, 18-19Aug 2011
Left: surface analysis;
Right: synoptic chart at 850 hPa.
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
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.
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.
2. Current state
Forecast of extreme event: North Argentina, Feb 2010
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).
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).
3. Some scientific questions
 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.
4. Main objectives of the proposed RDP
 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?
5. Societal impacts
 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.
6. How the RDP will undertake the research
and development?
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 PROJECT
A FDP should happen within the proposed RDP.