Squall Lines moving over Santarem Julia Cohen Federal University of Para, Brazil David Fitzjarrald Atmospheric Sciences Research Center/ University at Albany, USA

Wet season Fitzjarrald et al, 2008

Squall Lines initiated by the sea breeze

Mean feature of the Squall Lines (1979 to 1986) CLASSIFICATION Coastal Squall Line (CSL) = moves no more 170km. Squall Line type 1 (SL1) = moves 170 and 400 km inland. Squall Line type 2 (SL2) = moves more than 400 km inland. Max Occurrence May and August. mean length = 1400 km. mean width = 170km. Average propagation speed = 12 and 16 m/s. life time: 9, 12 and 16 hours.

00-03 UTC UTC UTC UTC (From Kousky et al. 2006, CMORPH analyses) Influence of large scale ‘mega squall lines’ on precipitation Time of ‘maximum precipitation rate’

Fitzjarrald et al, 2008

Objective The precipitation climatology of eastern Amazon reflects the relative importance of locally forced convective rain and instability line rainfall. (near Santarém these splits out by time of day). Numerical simulations of instability lines to describe instability line rainfall component.

Climatology Imagery satellite (2000 to 2006) CSL propagating < 140 km SL1 propagating 140 to 400km SL2 propagating > 400km CMORPH (0.25 o ) – 00 to 09UTC (Dec 2002 to December 2006); Reanalyses CPTEC-ETA (40km) – (2000 to 2004).

Analyse the contribution of the rain generated by squall lines when passing Santarem region.

Monthly distribution of the number of squall lines during 2000 to 2006 SLC propagated < 140 km ( stippled) SL1 propagated 140 to 400 km (white) SL2 propagated > 400 km (black) SL2 propagated > 400 km and moved over Santarem (gray)

CMORPH composite rainfall totals for storms between 00 to 09UTC 12/1/02 – 12/31/06 Propagated past Santarem Not propagated deeply into the Basin Branch north has more rainfall than branch south. Less rainfall in confluence Tapajos and Amazonas River.

SLC (fine line) SL1 (short dashed line) SL2 (heavy continuous line) SL2 over Santarém (long dashed line)

Numerical Experiment Description BRAMS = Brazilian developments on the Regional Atmospheric (RAMS) (Cotton et al., 2003). The model’s initialization was variable, each 6 hours, with the analysis of CPTEC’s global model, the radiosondes and the available surface data. The integration period was 36 hours, initiating on June, 02, 2006, at 12 UTC. Models of surface and vegetation, radiation, Cloud microphysics. Grell’s deep convective parameterization and shallow convection parameterization. Control and Topography Experiments.

3 Grids Points X = 82,113,140 Points Y = 60,89,113 Points Z = 27,27,27 Points in soil = 8 Grid increments ( 72, 24, 8 km) River and Topography (m) Grid 2 Grid 3 (included June, 03 04UTC)

Satellity Infra-Red Cannel and Rate Precipitation (mm/h) by BRAMS June UTC Grid 2 (24 km)

June, UTC Grid 3 (8 km) 

Data = Vila Franca (2.34S, 55.02W) Model = (2.63S 54.5W) Grid 2 Fitzjarrald et al, 2008 

Macapa – Close coastal Santarem Grid 2 June, UTC June, 03 06UTC

Vertical wind (cm/s) and Mixture rate all condensate June, UTC Grid 3     

Temperature potential equivalent (K)

Control (with topography) Test (without topography)

Grid S Control (with topography) Test (without topography)

CONCLUSIONS Even weak topography is important to squall propagation speed and rainfall intensity at STM. Weak meridional jet associated with inland propagation. Gravity waves trail the squall and may influence how far the squall propagates into the continent. Case study precipitation agrees with observations at Vila Franca (confluence ‘lake’) both in rainfall total and rainfall timing.

Future studies Case studies for only local convective rain. Composite rainfall transects normal to Amazon (CMORPH, reanalysis, BRAMS). Soil moisture ‘memory’ and squall line propagation. Role of trailing gravity waves in squall wake. Role of river water temperature on regional convection.