• Bandyopadhyay, J., cited. 2001: Water towers of the world. [Available online at http://www.peopleandplanet.net/pdoc.php?id=983.].

  • Barros, A. P., , and Lettenmaier D. P. , 1993: Dynamic modeling of the spatial distribution of precipitation in remote mountainous areas. Mon. Wea. Rev., 121 , 11951214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., , and Lang T. J. , 2003: Monitoring the monsoon in the Himalayas: Observations in central Nepal. Mon. Wea. Rev., 131 , 14081427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., , Kim G. , , Williams E. , , and Nesbitt S. W. , 2004: Probing orographic controls in the Himalayas during the monsoon using satellite imagery. Nat. Hazards Earth Syst. Sci., 4 , 2951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., , Chiao S. , , Lang T. J. , , Burbank D. , , and Putkonen J. , 2006: From weather to climate—Seasonal and interannual variability of storms and implications for erosion processes in the Himalaya. Geol. Soc. Amer. Spec. Pap., 398 , 1738.

    • Search Google Scholar
    • Export Citation
  • Bhushan, S., , and Barros A. P. , 2007: A numerical study to investigate the relationship between moisture convergence patterns and orography in central Mexico. J. Hydrometeor., 8 , 12641284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Egger, J., and Coauthors, 2005: Diurnal circulation of the Bolivian Altiplano. Part I: Observations. Mon. Wea. Rev., 133 , 911924.

  • Falvey, M., , and Garreaud R. D. , 2005: Moisture variability over the South American Altiplano during the South American Low Level Jet Experiment (SALLJEX) observing season. J. Geophy. Res., 110 , D22105. doi:10.1029/2005JD006152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferreira, R. N., , Rickenbach T. M. , , Herdies D. L. , , and Carvalho L. M. V. , 2003: Variability of South American convective cloud systems and tropospheric circulation during January–March 1998 and 1999. Mon. Wea. Rev., 131 , 961973.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferreira, N. J., , Correia A. A. , , and Ramírez M. C. V. , 2004: Synoptic scale features of the tropospheric circulation over tropical South America during the WETAMC TRMM/LBA experiment. Atmósfera, 17 , 1330.

    • Search Google Scholar
    • Export Citation
  • Figueroa, S. N., , Satyamurty P. , , and da Silva Dias P. L. , 1995: Simulations of the summer circulation over the South American region with an Eta coordinate model. J. Atmos. Sci., 52 , 15731584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gálvez, J. M., , Orozco R. K. , , Reyes C. R. , , and Douglas M. W. , 2006: Observed diurnal circulations and rainfall over the Altiplano during the SALLJEX. Proc. Eighth Int. Conf. on Southern Hemisphere Meteorology and Oceanography, Foz do Iguacu, Brazil, Amer. Meteor. Soc. and Brazilian Institute for Space Research (INPE), 1041–1047.

    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., 1999: Multiscale analysis of the summertime precipitation over the central Andes. Mon. Wea. Rev., 127 , 901921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., , and Aceituno P. , 2001: Interannual rainfall variability over the South American Altiplano. J. Climate, 14 , 27792789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., , Vuille M. , , and Clement A. C. , 2003: The climate of the Altiplano: Observed current conditions and mechanisms of past changes. Palaeogeogr. Palaeoclimatol. Palaeoecol., 194 , 522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giovannettone, J. P., , and Barros A. P. , 2008: A remote sensing survey of the role of landform on the organization of orographic precipitation in central and southern Mexico. J. Hydrometeor., 9 , 12671283.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herdies, D. L., , da Silva A. , , Silva Dias M. A. F. , , and Ferreira R. N. , 2002: Moisture budget of the bimodal pattern of the summer circulation over South America. J. Geophys. Res., 107 , 8075. doi:10.1029/2001JD000997.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., , and Carvalho L. M. V. , 2002: Active and break phases in the South American monsoon system. J. Climate, 15 , 905914.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleeman, R., 1989: A modeling study of the effect of the Andes on the summertime circulation of tropical South America. J. Atmos. Sci., 46 , 33443362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenters, J. D., , and Cook K. H. , 1995: Simulation and diagnosis of the regional summertime precipitation climatology of South America. J. Climate, 8 , 29883005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenters, J. D., , and Cook K. H. , 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev., 127 , 409431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , Kiladis G. N. , , Marengo J. A. , , Ambrizzi T. , , and Glick J. D. , 1999: Submonthly convective variability over South America and the South Atlantic convergence zone. J. Climate, 12 , 18771891.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , Kiladis G. N. , , Vera C. S. , , Saulo A. C. , , and Carvalho L. M. V. , 2004: Subseasonal variations of rainfall in South America in the vicinity of the low-level jet east of the Andes and comparison to those in the South Atlantic convergence zone. J. Climate, 17 , 38293842.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., , Fisch G. , , Morales C. , , Vendrame I. , , and Dias P. C. , 2004: Diurnal variability of rainfall in southwest Amazonia during the LBA-TRMM field campaign of the austral summer of 1999. Acta Amazonica, 34 , 593603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., , Zipser E. J. , , and Cecil D. J. , 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13 , 40874106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paegle, J., , and Mo K. C. , 1997: Alternating west and dry conditions over South America during summer. Mon. Wea. Rev., 125 , 279291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paegle, J., , Zhang C-D. , , and Baumhefner D. P. , 1987: Atmospheric response to tropical thermal forcing in real data integrations. Mon. Wea. Rev., 115 , 29752995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paegle, J., , Byerle L. A. , , and Mo K. C. , 2000: Intraseasonal modulation of South American summer precipitation. Mon. Wea. Rev., 128 , 837850.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rickenbach, T. M., , Ferreira R. N. , , Halverson J. B. , , Herdies D. L. , , and Silva Dias M. A. F. , 2002: Modulation of convection in the southwestern Amazon basin by extratropical stationary fronts. J. Geophys. Res., 107 , 8040. doi:10.1029/2000JD000263.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saulo, A. C., , Seluchi M. E. , , and Nicolini M. , 2004: A case study of a Chaco low-level jet event. Mon. Wea. Rev., 132 , 26692683.

  • Seluchi, M. E., , and Marengo J. A. , 2000: Tropical-midlatitude exchange of air masses during summer and winter in South America: Climatic aspects and examples of intense events. Int. J. Climatol., 20 , 11671190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spencer, R. W., , Goodman H. M. , , and Hood R. E. , 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6 , 254273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., 1996: Importance of low-level jets to climate: A review. J. Climate, 9 , 16981711.

  • Vera, C. S., , Vigliarolo P. K. , , and Berbery E. H. , 2002: Cold season synoptic-scale waves over subtropical South America. Mon. Wea. Rev., 130 , 684699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vera, C. S., and Coauthors, 2006: The South American low-level jet experiment. Bull. Amer. Meteor. Soc., 87 , 6377.

  • Virji, H., 1981: A preliminary study of summertime tropospheric circulation patterns over South America estimated from cloud winds. Mon. Wea. Rev., 109 , 599610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Von Storch, H., , and Zwiers F. W. , 1999: Empirical orthogonal functions. Statistical Analysis in Climate Research, Cambridge University Press, 293–301.

    • Search Google Scholar
    • Export Citation
  • Vuille, M., 1999: Atmospheric circulation over the Bolivian Altiplano during dry and wet periods and extreme phases of the Southern Oscillation. Int. J. Climatol., 19 , 15791600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vuille, M., , and Keimig F. , 2004: Interannual variability of summertime convective cloudiness and precipitation in the central Andes derived from ISCCP-B3 data. J. Climate, 17 , 33343348.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vuille, M., , Bradley R. S. , , and Keimig F. , 2000: Interannual climate variability in the central Andes and its relation to tropical Pacific and Atlantic forcing. J. Geophys. Res., 105 , 1244712460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., , and Fu R. , 2004: Influence of cross-Andes flow on the South American low-level jet. J. Climate, 17 , 12471262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, J., , and Lau K-M. , 1998: Does a monsoon exist over South America? J. Climate, 11 , 10201040.

  • View in gallery

    Schematic of synoptic-scale circulation features affecting South America and outline of study regions used for analysis. Dotted line represents the overall study region, whereas the solid lines represent the local Peru and Altiplano–Chaco study regions.

  • View in gallery

    Average monthly GOES brightness temperatures at 1545 LT over the Peru and Altiplano–Chaco study regions during (a) December 2000, (b) January 2001, and (c) February 2001.

  • View in gallery

    Anomalies of total summer precipitation (mm) over the entire study area based on the December–March (DJFM) average between 1998 and 2007 for (a) 2001 and (b) 2003.

  • View in gallery

    Magnitude and direction of 850-mb wind vectors over South America for January of (a) 2001 and (b) 2003, and topography. The white lines indicate the extent of the larger study region.

  • View in gallery

    Difference in average monthly total water vapor over the study region between January of 2001 and 2003.

  • View in gallery

    Monthly averaged GOES brightness temperatures over the Peru study region during January 2001 at local times (a) 1545 and (b) 0345 and January 2003 at (c) 1545 and (d) 0345.

  • View in gallery

    (left) EOF1 and (right) EOF2 over Peru study region for January of (a), (b) 2001 and (c), (d) 2003.

  • View in gallery

    Percent of the spatial variance of cloudiness explained by Mode 1–10 of the EOF analysis over the Peru and Altiplano–Chaco study regions and Mexico for 2001 compared to the Himalayas in 2000.

  • View in gallery

    PF2 centroid locations determined from TRMM analyses over South America at local times (a) 1200–1759 and (b) 0000–0559 during the years 1998–2004, and topography.

  • View in gallery

    Elevation mask of overall study region. Class 0 represents the ocean. Classes 1–5 range from 0 to 2500 m in intervals of 500 m. Classes 10–14 represent the same elevation intervals for land east of the Andes except in decreasing order. Along the ridge of the Andes, Classes 7–9 represent elevations from 4000 to 2500 m in intervals of 500 m, whereas Class 6 includes any topography more than 4000 m.

  • View in gallery

    Number of (a) PF1s and (b) PF2s that occurred in each class represented in Fig. 10 between 1200 and 1800 LT.

  • View in gallery

    Locations of PF1s over the Altiplano subregion between 1998 and 2004 at local times (a) 1200–1800 and (b) 0000–0600, and topography.

  • View in gallery

    Difference in GOES brightness temperatures over the Altiplano subregion between February 2001 and February 2003 at local times (a) 1045 and (b) 1945.

  • View in gallery

    Monthly averaged GOES brightness temperatures over the Altiplano subregion during January 2001 at local times (a) 1345, (b) 1645, and (c) 1845.

  • View in gallery

    Topographic map of Altiplano subregion with important river valleys emphasized in black. White contours represent a GOES brightness temperature of 253 K averaged over the month of January 2001 at 1345 LT. White arrows represent the direction of air channeling, whereas the red dashed line indicates the axis of convective development along the eastern peaks of the Altiplano Plateau.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 64 64 22
PDF Downloads 23 23 4

Probing Regional Orographic Controls of Precipitation and Cloudiness in the Central Andes Using Satellite Data

View More View Less
  • 1 Duke University, Durham, North Carolina
© Get Permissions
Full access

Abstract

Data obtained from NOAA’s Geostationary Operational Environmental Satellite (GOES) and NASA’s Tropical Rainfall Measuring Mission (TRMM) satellites were used to investigate the relationships between topography, large-scale circulation, and the climatology of precipitation and cloudiness in the Andes—specifically over Peru and the Altiplano Plateau—at diurnal, seasonal, and interannual time scales. The spatial variability of cloudiness was assessed through empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Results indicate that landform is the principal agent of the space–time variability of moist atmospheric processes in the Andes, with the first mode explaining up to 70% of all observed variability. These results substantiate the differences between “continental” (Andes and Himalayas) and “maritime” (Western Cordillera) orographic precipitation regimes, reflecting the degree to which upwind landmasses modulate moisture transport toward and across mountain barriers. GOES brightness temperatures show that afternoon convective activity during the rainy season is more intense on wet hydrometeorological years such as 2001, whereas the space–time structure of nighttime cloudiness at the foothills and outlets of deep interior valleys does not change during the monsoon and from one year to another independently of large-scale conditions. This suggests that daytime cloud formation and precipitation is strongly dependent on large-scale moisture transport. Interactions between mesoscale and ridge–valley circulations, which are locked to the topography, determine the space–time organization of clouds and precipitation at nighttime. This leads to strong clustering of precipitation features associated with enhanced convection at high elevations along the ridges and near the headwaters of the major river systems in the TRMM data.

Corresponding author address: Dr. Jason Giovannettone, Hydrologic Engineering Center, Institute of Water Resources, 609 2nd Street, Davis, CA 95616. Email: jason.p.giovannettone@usace.army.mil

Abstract

Data obtained from NOAA’s Geostationary Operational Environmental Satellite (GOES) and NASA’s Tropical Rainfall Measuring Mission (TRMM) satellites were used to investigate the relationships between topography, large-scale circulation, and the climatology of precipitation and cloudiness in the Andes—specifically over Peru and the Altiplano Plateau—at diurnal, seasonal, and interannual time scales. The spatial variability of cloudiness was assessed through empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Results indicate that landform is the principal agent of the space–time variability of moist atmospheric processes in the Andes, with the first mode explaining up to 70% of all observed variability. These results substantiate the differences between “continental” (Andes and Himalayas) and “maritime” (Western Cordillera) orographic precipitation regimes, reflecting the degree to which upwind landmasses modulate moisture transport toward and across mountain barriers. GOES brightness temperatures show that afternoon convective activity during the rainy season is more intense on wet hydrometeorological years such as 2001, whereas the space–time structure of nighttime cloudiness at the foothills and outlets of deep interior valleys does not change during the monsoon and from one year to another independently of large-scale conditions. This suggests that daytime cloud formation and precipitation is strongly dependent on large-scale moisture transport. Interactions between mesoscale and ridge–valley circulations, which are locked to the topography, determine the space–time organization of clouds and precipitation at nighttime. This leads to strong clustering of precipitation features associated with enhanced convection at high elevations along the ridges and near the headwaters of the major river systems in the TRMM data.

Corresponding author address: Dr. Jason Giovannettone, Hydrologic Engineering Center, Institute of Water Resources, 609 2nd Street, Davis, CA 95616. Email: jason.p.giovannettone@usace.army.mil

1. Introduction

More than half of the world’s population relies on mountainous regions for freshwater resources, and agricultural production in adjacent lowlands is highly dependent on the spatial and temporal distribution of precipitation at high elevations, the so-called orographic precipitation (Bandyopadhyay 2001). In this context, landform can be viewed as the organizing template by which atmospheric freshwater is captured and distributed in the landscape. The overall motivation for this work is to achieve an understanding of orographic land–atmosphere interactions in tropical regions, and the final objective is to characterize geophysiographic controls of cloudiness and their impact on precipitation processes through the use of satellite data. In this study, we focus on the Andes Mountains of South America and in particular the Altiplano region. The Altiplano extends between 15° and 22°S and has an average elevation between 3500 and 4000 m. The rainy season coincides with the austral summer during which it can receive between 60% and 90% of its annual rainfall (Garreaud et al. 2003).

Although the sources of atmospheric moisture in the mountainous regions are naturally far away from the mountains themselves, the geometry, orientation, and small-scale topographic features dictate where maximum precipitation will occur and the locations of initiation (Bhushan and Barros 2007; Egger et al. 2005; Garreaud et al. 2003; Barros and Lettenmaier 1993; Vera et al. 2002; Seluchi and Marengo 2000; Lenters and Cook 1995; Kleeman 1989; among others). This can be easily observed when studying tropical mountainous regions during a monsoon or rainy season such as in central and western Mexico (Giovannettone and Barros 2008) and the Himalayas (Barros et al. 2006; Barros et al. 2004). In the central Andes region, which includes the countries of Peru, Bolivia, Chile, and western Argentina, interactions among several synoptic-scale features provide for the synthesis of constantly changing weather patterns over this region (depicted in Fig. 1). Many studies have focused on the role of large-scale circulation features in this region of the world, such as the South American summer monsoon (SASM; Jones and Carvalho 2002; Zhou and Lau 1998), the South American convergence zone (SACZ; Liebmann et al. 2004; Ferreira et al. 2003; Herdies et al. 2002; Rickenbach et al. 2002; Paegle et al. 2000; Seluchi and Marengo 2000; Liebmann et al. 1999; Figueroa et al. 1995; Lenters and Cook 1995), the South American low-level jet (SALLJ) and the Bolivian high (Vera et al. 2006; Ferreira et al. 2004; Liebmann et al. 2004; Wang and Fu 2004; Ferreira et al. 2003; Paegle and Mo 1997), and the Madden–Julian oscillation (MJO; Liebmann et al. 2004; Paegle et al. 2000).

The main moisture source regions for the Andes Mountains of Peru and the Altiplano Plateau are the lowlands of the Amazon basin and the Atlantic Ocean to the east, whereas the Bolivian lowlands can also be an important moisture source for the Altiplano (Falvey and Garreaud 2005). Moisture advection into the Altiplano is governed primarily by the dominant direction of large-scale flow at upper levels. Enhanced easterly (westerly) flow during the austral summer (winter) season has been correlated with increased (decreased) precipitation (Vuille and Keimig 2004; Garreaud and Aceituno 2001; Vuille 1999). Altiplano convection also depends on the location of an upper-level divergent anomaly to the south and the strength of the SALLJ to the east (Vera et al. 2006; Ferreira et al. 2004; Liebmann et al. 2004; Vuille and Keimig 2004; Wang and Fu 2004; Ferreira et al. 2003; Paegle and Mo 1997). Zhou and Lau (1998) noted that during the transition from austral spring to summer, which coincides with the SASM (Jones and Carvalho 2002; Zhou and Lau 1998), the upper-level anticyclonic center over the northern Amazon shifts to the south and west over the Altiplano Plateau and becomes the Bolivian high (illustrated in Fig. 1). During anomalously rainy periods, the Bolivian high moves farther southward (Liebmann et al. 2004; Vuille and Keimig 2004; Vuille 1999). At the same time, anomalously high pressure is observed in the Sahara Desert in Africa, resulting in an enhancement of the northeasterly trade winds over the tropical Atlantic into Brazil and the Amazon. The strength and location of the SALLJ is determined by the combination of the northeasterly trade winds from the Sahara and easterly flow originating from changes in the strength of the South Atlantic subtropical high (Fig. 1). The SALLJ is a major contributor of moisture transport from the Amazon Plains into the La Plata Basin (Virji 1981; Paegle et al. 1987) and along the eastern slopes of the Andes into the Bolivian lowlands (Garreaud 1999; Saulo et al. 2004; (Vera et al. 2006) up to approximately 2500 m, thus not reaching the Altiplano per se (Falvey and Garreaud 2005). A detailed review of the influence of low-level jets on overall climate is given by Stensrud (1996).

Although the interannual variability of rainfall in the central and eastern Altiplano are controlled by the processes mentioned above, rainfall variability over the western slopes has been found to be highly correlated with the phase of the El Niño–Southern Oscillation (ENSO; Garreaud and Aceituno 2001; Vuille et al. 2000; Vuille 1999). Vuille et al. (2000) found that moisture advection into the western part of the Altiplano is reduced (increased) during an El Niño (La Niña) event. Weaker signals were found between precipitation variability over the rest of the Andes and the Altiplano and sea surface temperature anomalies (SSTAs) in the tropical Pacific.

Other interrelated circulation patterns that have been found to have an influence on moisture availability and convective activity in and around the Altiplano are 1) the existence of a bimodal weather pattern resulting from the relative strength of the SALLJ and the SACZ over southeastern Brazil and 2) a series of quasi-stationary Rossby waves (refer to Fig. 1) that propagate from the southeast Pacific Ocean over the southern Andes, where they begin to move northward toward the Bolivian high. As already discussed, when the SALLJ is aligned with the eastern slopes of the Andes and the Bolivian high is shifted farther southward, convective activity over the adjacent Altiplano Plateau is enhanced. When the northerly flow out of the Amazon basin veers to take a more northwesterly orientation and the SALLJ shifts to the southeast, the Bolivian high weakens considerably and there is a reversal resulting in increased moisture convergence and convective activity in southeastern Brazil near the SACZ and less activity over the Altiplano (Marengo et al. 2004; Herdies et al. 2002; Jones and Carvalho 2002; Rickenbach et al. 2002; Paegle and Mo 1997). Low- and high-level wind anomalies caused by the train of Rossby waves from the west can favor this bimodal pattern in either direction, depending on the direction of the anomalies (Liebmann et al. 2004; Vuille and Keimig 2004; Garreaud et al. 2003). These circulation phenomena are out of the scope of this study and, therefore, will not be discussed further.

The studies reviewed above focus on the moisture sources and convective forcing mechanisms that affect the northern Andes and the Altiplano on a large scale. High-resolution modeling studies have shown that the geometry, orientation, and small-scale topographic features along the mountain slopes dictate where precipitation maxima occur by organizing cloud formation and clustering convective activity on the landscape (Bhushan and Barros 2007; Barros et al. 2006; Egger et al. 2005; Garreaud et al. 2003; Vera et al. 2002; Seluchi and Marengo 2000; Lenters and Cook 1995; Kleeman 1989; among others). Barros et al. (2004) and Giovannettone and Barros (2008) used satellite data to map evidence of such landform controls on rainfall regime in the Himalayas and in central and southern Mexico. We now extend their previous work to the Andes.

In this manuscript, section 2 gives a short description of the remote sensing data used to illustrate the nature of convective activity in central South America and the diurnal, monthly, and interannual variabilities that exist. Analyses of the IR measurements from the Geostationary Operational Environmental Satellite (GOES) satellites on monthly, seasonal, and annual time scales over study regions in Peru and the Altiplano/Gran Chaco are performed in section 3, including an EOF analysis of the brightness temperatures in the two regions. A description of the results obtained from an analysis of precipitation features in Tropical Rainfall Measuring Mission (TRMM) satellite data, which were used as a proxy of convective activity, is given in section 4. GOES satellite data were also used in section 4 to map convective activity to orography at the ridge–valley scale. Final conclusions are summarized in section 5.

2. Data

Microwave and infrared data from satellites launched by the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) were used to perform the following analyses: TRMM (available online at http://trmm.gsfc.nasa.gov) and GOES-8 and GOES-12 (available online at http://www.goes.noaa.gov). Data from two instruments on the TRMM satellite were used in this study: the precipitation radar (PR) and the TRMM Microwave Imager (TMI). The PR has a horizontal resolution of 5 km and a swath width of 247 km postboost of the satellite in August 2001. This corresponds to 4 and 220 km preboost (available online at http://trmm.gsfc.nasa.gov). The PR provides information on the location and intensity of rainfall events. The TMI is a passive microwave sensor that allows quantitative estimates to be made of water vapor, cloud water, and rain rate. The TMI has a horizontal ground resolution of 5.0 preboost and 5.1 km postboost.

The GOES satellites are located over the equator at a longitude of 75°W and allow for constant monitoring of the development and movement of convective activity. Channel 4 (10.2–11.2 μm) from the GOES imager (an imaging radiometer) was used to provide cloud top temperatures at an absolute accuracy up to 1 K and horizontal and temporal resolutions of 4 km and in less than 26 min, respectively. A 3-h time resolution was used in this study. Although this resolution is insufficient to follow the evolution of individual storm systems, it does allow diurnal patters in overall storm movement to be made. Data from the GOES satellites were obtained at NOAA’s class Web site (available online at http://www.class.noaa.gov). The approach followed here using TRMM and GOES data replicates Giovannettone and Barros (2008).

Additional data that were needed include wind, atmospheric water vapor, and precipitation estimates. North American Regional Reanalysis (NARR) monthly wind data over western-central South America were provided by the NOAA–Cooperative Institute for Research in Environmental Sciences (CIRES) Climate Diagnostics Center in Boulder, CO, from their Web site (available online at http://www.cdc.noaa.gov/) as u and υ components at a spatial resolution of 2.5° × 2.5°. Monthly averaged estimates of water vapor summed along the entire atmospheric column were obtained through the Level 1 and Atmospheric Archive and Distribution System (LAADS) of the Goddard Space Flight Center (available online at http://ladsweb.nascom.nasa.gov/). Monthly precipitation data with a spatial resolution of 0.25° × 0.25° were accessed through the Web site of the Goddard Earth Sciences Data and Information Services Center of NASA (available online at http://disc.sci.gsfc.nasa.gov/precipitation/).

Data for all topographical maps of the study regions used in this study were obtained as digital elevation maps (DEMs) from the Global 30 Arc-Second Elevation Dataset (GTOPO30) of the U.S. Geological Survey (USGS). Study regions are identified in Fig. 1, with the larger study region outlined with the solid line and the smaller Peru and Altiplano–Chaco study regions outlined with the dotted line. The Peru study region was selected to examine the effects of easterly flow from Amazonia on the Andes, and it includes the Apurimac River Valley from 5200 to 260 m elevation (refer to Fig. 2a), which constitutes effectively the headwaters of the Amazon. The hydrometeorology of the Altiplano–Chaco study region is controlled by the SALLJ, and this region encompasses several salt flats including the world’s largest, Salar de Uyini (Fig. 2a). The regional socioeconomic importance of the Altiplano Plateau and the Gran Chaco also influenced the exact location of the study region. An even smaller Altiplano subregion (not outlined) will be mention later in this paper.

3. Large-scale controls

a. Diurnal, seasonal, and interannual variability

Remote sensing data from the GOES satellites are used in Fig. 2 to illustrate the effects of the moisture flow out of the Amazon on the Peru study region and the SALLJ on the Altiplano–Chaco study region during the rainy (austral summer) season of 2000–01. The underlying premise when using GOES brightness temperatures is that cloudiness, and in particular the distribution of cold cloud tops, can be viewed as a proxy of precipitation processes and convective activity as was done in Barros et al. (2004). Caution must be taken when trying to interpret the distribution of precipitation from brightness temperatures because not all convection produces rainfall. The monthly average brightness temperatures shown in Fig. 2 are averaged at 1545 LT, which is the time at which maximum convective activity has been observed. The initial stages of activity begin during December (Fig. 2a), whereas the peak in convection along the highest elevations occurs in January (Fig. 2b), especially over the Altiplano. As the region of maximum solar heating begins to move north in February and later, convective activity over both study regions begins to dissipate (Fig. 2c).

To illustrate the range of interannual variability in convective activity that can occur, the summer seasons (December–March) of 2001 and 2003 were analyzed and contrasted. During 2001, anomalously high total precipitation amounts (between 0 and +150 mm) occurred throughout much of the Altiplano Plateau (Fig. 3a), whereas during the summer of 2003 negative anomalies (as low as −150 mm) were prevalent (Fig. 3b). Both years were characterized by different arrangements of regional circulation features as shown in Fig. 4. The greater north–south strength of the SALLJ over the Gran Chaco can be seen during January 2001 (Fig. 4a), and the flow is more northwesterly and shifted to the east in the proximity of the SACZ in 2003 (Fig. 4b; see also (Vera et al. 2006). The effects of these differences in large-scale circulation can also be observed in the water vapor measurements shown in Fig. 5, which displays the difference in water vapor content summed throughout the entire atmospheric column in centimeters between January of 2001 and 2003 over the study region. Substantial negative differences are observed throughout the Amazon basin, whereas positive differences are seen over the ridge of the Andes and the Altiplano and south into Chile and Argentina. The results from Fig. 5 and previous figures suggest that when the north–south strength of the SALLJ is greater (2001), a decrease in moisture over the Amazon is consistent with more water vapor exiting the domain, thus creating the large-scale underpinnings of a wet rainy season. When the SALLJ is weaker (2003), moisture transport out of the Amazon basin toward the Andes and Altiplano is reduced, and thus the contribution of local evapotranspiration—that is, the precipitation recycling ratio—should be expected to increase.

A comparison of the diurnal patterns in convective activity during January of both years was performed to get a more detailed look at the effect of the position of the SALLJ with regard to precipitation over the mountain ridges and valleys. Figure 6 shows the average January brightness temperatures for 2001 and 2003 at 1545 (Figs. 6a and 6c) and 0345 LT (Figs. 6b and 6d). In the afternoon, there is a greater amount of convection (cold cloud tops) over the mountain ridges during 2001. The moisture transport from the Amazon basin is greater in 2001 (Fig. 5), thus enabling daytime convection enhanced by orographic lifting (Fig. 6a) and causing the increased precipitation shown in Fig. 3a. This suggests that large-scale moisture convergence is the limiting factor to daytime orographic rainfall. In the early morning hours, the contrast in brightness temperatures is less obvious with comparable levels of cloudiness occurring in the interior river valleys of the Apurimac, Huallaga, and Urubamba Rivers, which are against the foothills of the Andes, for both years (cf. Figs. 6b and 6d). Radiative cooling and katabatic winds lead to the establishment of convergence zones and cloud development at the outlet and confluences of deep valleys during both wet and dry years as it does in the Himalayas in Nepal and the Sierra Madre Mountains in Mexico, which remain spatially locked in the landscape year-to-year independently of large-scale forcing (Barros et al. 2006; Bhushan and Barros 2007).

Further comparisons between January 2001 and 2003 can be performed using EOF analysis techniques (described in Von Storch and Zwiers 1999) as in Barros et al. (2004) and Giovannettone and Barros (2008). All GOES brightness temperatures with a 3-h temporal resolution collected for the month of January of 2001 and 2003 were merged and used in the analysis. EOFs were calculated only over the Peru study region as a result of the computational intensity that would be required of the computer for the larger study region. The first EOF (EOF1) for both years (Figs. 7a and 7c) over the Peru region is similar and consistent with the modulation and blocking of easterly moisture influx along the Andes. The second EOF (EOF2) shows a more substantial difference in the intensity of the patterns over the Altiplano (Figs. 7b and 7d). The EOF2 for 2001 shows a strong signal at upper elevations, which is representative of mature daytime convection, whereas the EOF2 for 2003 shows a comparatively stronger nighttime signal in the deep valleys that follow the eastern boundary of the Andes against the Amazon plains (the Maranon and the Huallaga). These patterns reiterate the point that thermally driven daytime convective activity is dominant when atmospheric moisture is not the limiting ingredient of moist atmospheric processes during a relatively wet year (refer to Fig. 5), whereas strong nighttime signals exist under all conditions and are associated with modulation of moisture convergence patterns at the ridge–valley scale.

b. Precipitation regimes

A major goal of this study was to determine if the effects of topography on large-scale flow over tropical continental regions, such as the Himalayas, can be generalized for other continental regions. It is also desired to determine what type of generalization can be made between continental and maritime precipitation regimes. In this context, a continental regime is defined as a region where there is sufficient landmass upstream of a mountainous region to have an influence on storm tracks and moisture recycling. A maritime regime is defined as a mountainous area where there is little or no effect of upstream landmass on incoming weather systems. For comparison purposes, the spatial variability of cloudiness for each type of regime was assessed through EOF analysis of the GOES brightness temperatures. Figure 8 presents a comparison among representative EOF modes over central South America, Mexico (Giovannettone and Barros 2008), and the Himalayas (Barros et al. 2004). Note the dramatic differences between the maritime regime (Mexico) and the two continental regimes (Andes and Himalayas). There is a lack of one dominant mechanism over Mexico: the first EOF explains only 23% of the spatial variance in cloudiness compared to 53% in the Himalayas and 60%–67% in central South America. The different mountain orientations and proximity to the Pacific Ocean and the Gulf of Mexico produce the complex climate in Mexico, whereas in the Himalayas and the Andes the blocking effects of the two mountain ranges and upstream land–atmosphere interactions control the weather of their surrounding regions. These results suggest similar levels of convective organization and landform dependence over continental regions, whereas the effects of topography observed over maritime regions explain a smaller percentage of the cloudiness as part of a complex system resulting from large adjacent bodies of water and large-scale circulation features related to the North American monsoon and the location of the ITCZ. One implication of this result is that there is great potential in using terrain information to extrapolate clouds and precipitation distributions in continental orographic regimes.

4. Landform controls

a. Precipitation feature analysis: concentration versus landform

In terms of large-scale physical features that affect the climate over South America, the Andes Mountains stand out as the most prominent feature, particularly over Peru and the Altiplano Plateau (Egger et al. 2005; Garreaud et al. 2003; Lenters and Cook 1999). Effects of moisture transport up the eastern slopes of the Andes, taking into account the SALLJ and solar heating of the mountain slopes and the plateau itself, on the diurnal cycle of precipitation and convection over parts of the larger study region (Peru and the Altiplano Plateau) can be illustrated using remote sensing data from the TRMM satellite to identify locations of precipitation features (PFs). The PR and TMI observations used in the identification and classification of PFs are the precipitation reflectivities and polarization-corrected temperatures (PCTs), which have been adjusted to take into account changes in the geometry of the surface relative to instrument location and orientation (Spencer et al. 1989). PCTs were measured at a microwave frequency of 85.5 GHz, at which radiation emission from rainfall is minimal, whereas ice scattering effects are strong. The greater the amount of ice scattering, the larger the depression in temperature becomes, thus giving an indication of the amount of convective activity present. Using these data and an algorithm developed by Nesbitt et al. (2000), PFs are classified according to the following criteria: 1) PF1: PR ≥ 20 dBZ in at least four contiguous data bins (area ≥ 75 km2) and 2) PF2: PR ≥ 20 dBZ with PCTs ≤ 250 K in at least one bin, or PCTs ≤ 250 K in four contiguous data bins. Furthermore, PFs associated with shallow clouds and no evidence of ice scattering (i.e., PF1s) correspond to “warm rain” (WR) events; PFs associated with deep clouds and evidence of ice scattering (i.e., PF2s) correspond to “enhanced convection” (EC) events.

Figure 9 shows the diurnal pattern of the composite of geographical locations of the centroids of PF2s (EC) over the study region during the months of December–February of 1998–2004. During the afternoon hours, there is a high concentration of storms along the ridge of the Andes (Fig. 9a), including the Altiplano, whereas during the early morning hours convective activity is limited to the lower elevations along the eastern slopes (Fig. 9b). In either case, convective activity is ubiquitous throughout the Amazon basin to the east. One salient feature is Lake Titicaca’s localized effect on convection (labeled in Fig. 9). Convection is mostly absent over the lake during the daytime, and it peaks up strongly at night when the surrounding land margins show no activity. This reflects the establishment of a convergence center over the lake at the confluence of nocturnal downslope flows from surrounding ridges (Gálvez et al. 2006; Bhushan and Barros 2007; Giovannettone and Barros 2008).

To track the diurnal cycle of PF distribution with elevation in more detail, the study region was divided into 15 classes (0–14) based on elevation and location (Fig. 10). Class 0 represents the ocean. Classes 1–5 range from 0 to 2500 m in intervals of 500 m. Classes 10–14 represent the same elevation intervals for land east of the Andes except in decreasing order. Along the ridge of the Andes, Classes 7–9 represent elevations from 4000 to 2500 m in intervals of 500 m, while Class 6 includes any topography more than 4000 m. The maximum number of PF1s (WR; not shown) during the morning hours occurs along the midlevel slopes (1000–2000 m) where moisture convergence between the easterly synoptic flow and westerly downward valley flow takes place. The total number of PF1s increases from twofold to threefold during the afternoon, with the peak frequency at higher elevations (1500–3500 m) than in the morning (Fig. 11a). This corresponds to easterly synoptic flow combined with orographic lifting, thus causing convection closer to the valley ridges. There is little strong convective activity in the morning hours (not shown) with two small peaks along the mid- and upper-level slopes (1500–2000 and 3500–4000 m, respectively), whereas during the afternoon there is a dominant peak at the highest elevations (>4000 m; Fig. 11b). Inspection of the spatial distribution of PF1s (WR) and PF2s (EC) during the daytime in Fig. 11 shows that WR tends to concentrate at lower elevations along the slopes than the EC events. Similar observations were made by Barros et al. (2004) in the southern Himalayas; however, in the maritime orographic regions in the Sierra Madre of central Mexico, these diurnal patterns are not as obvious (Giovannettone and Barros 2008). The lack of precipitation observed west of the Andes along the Pacific coast is caused by strong subsidence associated with the South Pacific subtropical anticyclonic flow, where moisture is prevented from rising above the 900-hPa level (Garreaud 1999).

b. The Altiplano subregion

To zoom in on the effect of topography on the occurrence and initiation of convective activity over rough terrain, the remainder of this study will focus on the small study region of the Altiplano Plateau shown in Fig. 12, with the major topographic features identified. Figure 12 provides a more localized view of PF1 (WR) locations over the Altiplano for the same time period as in Fig. 9. The underlying premise is that inside and around the edges of the Altiplano Plateau and other prominent mountain regions, the geometry and orientation of local topographic features dictate dynamically where clouds will initially develop and where the maximum precipitation will occur. A strong day–night contrast is again evident. Between 1200 and 1800 LT, PFs were widespread throughout the Altiplano and the eastern slopes, except for a few small inactive pockets in the central Altiplano over the Salar de Uyini and Lake Poopò. Between 2400 and 0600 LT, PFs were less scattered, being limited to the river valleys of the Guapay and Pilaya Rivers along the eastern slopes of the Altiplano and the lower regions of the central Altiplano. The observed diurnal pattern is similar to that observed in Mexico (Giovannettone and Barros 2008) and the southern Himalayas (Barros et al. 2004), and it again reflects the dominant forcing mechanisms at different times of the day: 1) orographic lifting that causes convection and precipitation at higher elevations during the afternoon and 2) nocturnal downvalley flow, which leads to convergence at low elevations and enhanced convection during the early morning hours in the river valleys and salt flats. The appearance of PFs during the daytime and nighttime in the Guapay and Pilaya River is consistent with easterly moisture advection and the channeling (upslope flow) of moisture through these river valleys. The spatial arrangement of precipitation features from the TRMM data over the Altiplano subregion agree well with the GOES cloudiness observations shown in Fig. 6 over the broader Peru study region, with clouds present over the upper ridges during the daytime and aligned with deep river valleys during the nighttime. Further, these results highlight the importance of the specific positions and alignment of the Guapay and Pilaya Rivers and their respective tributaries to supply moisture into the Altiplano.

Landform controls can also be observed on an interannual time scale. An analysis of the differences in cloud brightness temperatures during February of 2001 and 2003 over the Altiplano subregion is shown in Fig. 13. Note how the Salar de Uyuni is delineated by increased cloudiness in surrounding areas by midmorning (Fig. 13a). This is consistent with solar forcing of the land surface and the establishment of thermally driven lateral circulations between the flats and the surrounding slopes leading to upward motion against the slopes and the sinking motion over the flats. At nighttime, the circulations reverse and drainage flows converge at low levels over the salt flats, leading to increased cloudiness as a function of moisture availability (that is more so in the rainy season of 2001 compared to very little in 2003; refer to Fig. 5). Gálvez et al. (2006) proposed that the lack of nighttime convection over the Salar de Uyuni during the relatively dry austral season of 2002–03 could be explained by weak nocturnal surface temperature gradients not sufficient to produce offshore circulations. The results from this analysis suggest that moisture availability is at least an additional if not an alternative limiting factor of convective activity in the same region.

The January rainy season brightness temperatures were further analyzed over the Altiplano subregion to observe the affects of local topography on the locations of convective initiation. One striking difference at 1345, the best time to observe afternoon cloud formation, is that deep clouds are confined to the eastern slopes of the Andes and do not spread into the Altiplano in 2003 (not shown), whereas in 2001 only the salt flats show weak or no signs of activity consistent with the daytime land–breeze hypothesis and as was seen in the TRMM analysis. Figure 14 illustrates this using GOES brightness temperature data averaged during three different times: a) 1345, b) 1645, and c) 1945. Note that the temperature scales are different from those used in the previous figure so that temperature gradients are more easily identified. Figure 14a gives a detailed look at the exact locations of high convective activity along the Desaguadero River and Lake Poopó at 1345. The Desaguadero River acts as a western boundary for much of the convective activity, with cloud formation being centered over the major bends in the river. At 1645 (Fig. 14b), most of the activity is west of the Desaguadero River and is more developed and widespread. The major salt flat (Salar de Uyini) is still relatively clear until 1945 (Fig. 14c) when clouds cover the entire plateau, including the salt flats.

The relationship between river position and locations of convective initiation can be further explored by examining cloud formation over the eastern slopes of the Altiplano (Fig. 15). The red dotted line in Fig. 15 represents the axis of convective development at 1345, which follows the eastern peaks of the Altiplano. The white contours correspond to brightness temperatures equal to or lower than 253 K. Note that these contours encapsulate the headwaters of all major rivers, particularly the Guapay and the Pilaya and their tributaries. It is hypothesized and illustrated by the red arrows that the channeling of moisture upstream along the river valleys (anabatic flow) causes cloud formation at their respective headwaters, as shown in the modeling experiments by Bhushan and Barros (2007). In this context, the Desaguadero River and Lake Poopó are especially relevant in that they act as the western boundary for a majority of the cloud formation and convective activity on the east side of the plateau. Because of the apparent dependence of convective development on local landform at spatial scales comparable to the width of a river valley near its headwaters, the previous figures illustrate the need for higher resolution land–atmosphere models to capture the initial locations of convective development, cloud formation, and precipitation processes, and thus produce rainfall in the right locations in the drainage network (i.e., the right watershed). The spatial scale of interest for hydrometeorological operations in mountainous regions is the ridge–valley scale.

5. Conclusions

The ultimate goal of this study is to better understand how orographic land–atmosphere interactions modulate freshwater distribution in mountainous regions to aid in future predictive and management studies of water resources. Regions in the Andes of central South America encompassing Peru and the Altiplano Plateau and large-scale moisture pathways were the focus of this study, while adding to work already performed in the Himalayas of Nepal and the Sierra Madre of Mexico. Local modulation phenomena in Peru and the Altiplano were identified to determine where in the landscape precipitation is likely to occur, thus illustrating the importance of higher resolution land–atmosphere models.

The two study regions primarily used in this study receive moisture from different sources. Region 1 (Peru) receives most of its moisture through northeasterly flow from across northern Amazonia, whereas portions of Region 2 (Altiplano Plateau) receive moisture from the Amazon basin through flows out of the north and east, while relying on the position and strength of the South American low-level jet (SALLJ). Even though the large-scale mechanisms are different, local modulation of convection by orography causes similar precipitation responses in both regions on various temporal and spatial scales.

Using cloud brightness temperatures from NOAA’s GOES satellites and precipitation data from NASA’s TRMM satellite, peak activity during relatively wet years was found to occur during the austral summer at high elevations during the afternoon and at low elevations at night and in the early morning as a result of the interaction of the large-scale flow from the east and from the topographically induced mountain/valley flow during the daytime and nighttime, respectively. During drier hydrometeorological conditions, there was a decrease in convective activity during the daytime, suggesting that the effectiveness of thermally forced daytime convection is determined by moisture availability, which in turn depends on large-scale transport. Activity levels during the nighttime were more similar to those observed in wet years, which suggests that the interactions of mesoscale and ridge–valley circulations conducive to recycling of regional evapotranspiration are a persistent feature during the South American monsoon season in the northern portion of the study region. Empirical orthogonal function analysis of the cloud brightness temperatures also revealed a strong nocturnal signal during dry years in the river valleys of Peru. In the Altiplano Plateau, however, there is nocturnal convection over lower elevations (salt flats) during wet years, whereas during dry years there is no nocturnal signal there. In contrast, the results here do not support the conclusion made by Gálvez et al. (2006) that offshore circulations over the salt flats are insufficient to produce convection. This may be true during relatively dry periods, such as in early 2003, but during years of greater moisture availability, such as in early 2001, brightness temperatures were found to be lower over the Salar de Uyuni, indicating an increase in convection. Elsewhere, the presence of vegetation may guarantee an adequate diurnal supply of moisture to the lower troposphere via latent heat fluxes leading to an evening maximum in precipitable water (PW), as discussed by Barros and Lang (2003) and Bhushan and Barros (2007). In turn, the evening and nighttime concurrence of PW and convective available potential energy (CAPE) maxima can explain the evidence of nocturnal convective activity, even when large-scale moisture advection is significantly reduced. Over the salt flats, however, vegetation is lacking; the moisture supply from evapotranspiration is, therefore, not available and in the absence of large-scale moisture transport, there is no moisture to fuel convective activity.

Regional differences in convective patterns and organization were also observed by performing an EOF analysis on the GOES brightness temperatures over the study regions. It was observed that when compared to the amount of convective organization over Mexico, the Peruvian Andes and the Altiplano show more organization and dependence on landform similar to the Himalayas in Nepal. Mexico’s maritime climate showed evidence of a greater number of mechanisms contributing to the spatial structure of cloudiness due to multiple interacting forcing mechanisms including orographic forcing, multiple synoptic circulation features, and the influence of the Atlantic and Pacific Oceans. The results support the notion of distinct continental (Himalayas and Andes) and maritime (Western Cordillera in Central America) orographic precipitation regimes for the three largest mountain ranges in monsoon climates.

The local modulation of cloudiness and convection by landform was analyzed in more detail in Peru and the Altiplano. Not only do the rising mountain slopes affect the strength and location of convection, but the river valleys also enhance convective activity through channeling and blocking. Observations over a small region in the Altiplano reveal the importance of river network morphology and location. Convection was detected on the valley ridges at the headwaters of many of the major rivers along the eastern slopes of the Altiplano (Guapay and Pilaya Rivers). These results provide some insight into why even high-resolution coupled land–atmosphere models fail to predict the exact locations of convection over rough terrain. The coarse resolutions of these models take into account the orographic effects of mountains and can predict on a large-scale where convection will occur, but the resolution needs to be finer (∼<1–5 km, depending on local ridge–valley scale arrangements) to account for the channeling effects of rivers and to be able to predict the exact locations of convective activity. It is proposed that the robust spatial distribution of major rivers and lakes provides a functional measure to evaluate hydrometeorological forecasts in mountainous areas, and therefore a minimum criterion for useful predictability in operational weather models—that is, producing rainfall in the correct drainage basin.

Acknowledgments

This work was funded in part by NASA Grant NNGO04GP02G and by the Pratt School of Engineering at Duke University. We are grateful to Dr. Nesbitt and Axel Graumann for their help with data manipulation and retrieval.

REFERENCES

  • Bandyopadhyay, J., cited. 2001: Water towers of the world. [Available online at http://www.peopleandplanet.net/pdoc.php?id=983.].

  • Barros, A. P., , and Lettenmaier D. P. , 1993: Dynamic modeling of the spatial distribution of precipitation in remote mountainous areas. Mon. Wea. Rev., 121 , 11951214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., , and Lang T. J. , 2003: Monitoring the monsoon in the Himalayas: Observations in central Nepal. Mon. Wea. Rev., 131 , 14081427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., , Kim G. , , Williams E. , , and Nesbitt S. W. , 2004: Probing orographic controls in the Himalayas during the monsoon using satellite imagery. Nat. Hazards Earth Syst. Sci., 4 , 2951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., , Chiao S. , , Lang T. J. , , Burbank D. , , and Putkonen J. , 2006: From weather to climate—Seasonal and interannual variability of storms and implications for erosion processes in the Himalaya. Geol. Soc. Amer. Spec. Pap., 398 , 1738.

    • Search Google Scholar
    • Export Citation
  • Bhushan, S., , and Barros A. P. , 2007: A numerical study to investigate the relationship between moisture convergence patterns and orography in central Mexico. J. Hydrometeor., 8 , 12641284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Egger, J., and Coauthors, 2005: Diurnal circulation of the Bolivian Altiplano. Part I: Observations. Mon. Wea. Rev., 133 , 911924.

  • Falvey, M., , and Garreaud R. D. , 2005: Moisture variability over the South American Altiplano during the South American Low Level Jet Experiment (SALLJEX) observing season. J. Geophy. Res., 110 , D22105. doi:10.1029/2005JD006152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferreira, R. N., , Rickenbach T. M. , , Herdies D. L. , , and Carvalho L. M. V. , 2003: Variability of South American convective cloud systems and tropospheric circulation during January–March 1998 and 1999. Mon. Wea. Rev., 131 , 961973.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferreira, N. J., , Correia A. A. , , and Ramírez M. C. V. , 2004: Synoptic scale features of the tropospheric circulation over tropical South America during the WETAMC TRMM/LBA experiment. Atmósfera, 17 , 1330.

    • Search Google Scholar
    • Export Citation
  • Figueroa, S. N., , Satyamurty P. , , and da Silva Dias P. L. , 1995: Simulations of the summer circulation over the South American region with an Eta coordinate model. J. Atmos. Sci., 52 , 15731584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gálvez, J. M., , Orozco R. K. , , Reyes C. R. , , and Douglas M. W. , 2006: Observed diurnal circulations and rainfall over the Altiplano during the SALLJEX. Proc. Eighth Int. Conf. on Southern Hemisphere Meteorology and Oceanography, Foz do Iguacu, Brazil, Amer. Meteor. Soc. and Brazilian Institute for Space Research (INPE), 1041–1047.

    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., 1999: Multiscale analysis of the summertime precipitation over the central Andes. Mon. Wea. Rev., 127 , 901921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., , and Aceituno P. , 2001: Interannual rainfall variability over the South American Altiplano. J. Climate, 14 , 27792789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., , Vuille M. , , and Clement A. C. , 2003: The climate of the Altiplano: Observed current conditions and mechanisms of past changes. Palaeogeogr. Palaeoclimatol. Palaeoecol., 194 , 522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giovannettone, J. P., , and Barros A. P. , 2008: A remote sensing survey of the role of landform on the organization of orographic precipitation in central and southern Mexico. J. Hydrometeor., 9 , 12671283.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herdies, D. L., , da Silva A. , , Silva Dias M. A. F. , , and Ferreira R. N. , 2002: Moisture budget of the bimodal pattern of the summer circulation over South America. J. Geophys. Res., 107 , 8075. doi:10.1029/2001JD000997.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., , and Carvalho L. M. V. , 2002: Active and break phases in the South American monsoon system. J. Climate, 15 , 905914.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleeman, R., 1989: A modeling study of the effect of the Andes on the summertime circulation of tropical South America. J. Atmos. Sci., 46 , 33443362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenters, J. D., , and Cook K. H. , 1995: Simulation and diagnosis of the regional summertime precipitation climatology of South America. J. Climate, 8 , 29883005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenters, J. D., , and Cook K. H. , 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev., 127 , 409431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , Kiladis G. N. , , Marengo J. A. , , Ambrizzi T. , , and Glick J. D. , 1999: Submonthly convective variability over South America and the South Atlantic convergence zone. J. Climate, 12 , 18771891.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , Kiladis G. N. , , Vera C. S. , , Saulo A. C. , , and Carvalho L. M. V. , 2004: Subseasonal variations of rainfall in South America in the vicinity of the low-level jet east of the Andes and comparison to those in the South Atlantic convergence zone. J. Climate, 17 , 38293842.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., , Fisch G. , , Morales C. , , Vendrame I. , , and Dias P. C. , 2004: Diurnal variability of rainfall in southwest Amazonia during the LBA-TRMM field campaign of the austral summer of 1999. Acta Amazonica, 34 , 593603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., , Zipser E. J. , , and Cecil D. J. , 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13 , 40874106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paegle, J., , and Mo K. C. , 1997: Alternating west and dry conditions over South America during summer. Mon. Wea. Rev., 125 , 279291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paegle, J., , Zhang C-D. , , and Baumhefner D. P. , 1987: Atmospheric response to tropical thermal forcing in real data integrations. Mon. Wea. Rev., 115 , 29752995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paegle, J., , Byerle L. A. , , and Mo K. C. , 2000: Intraseasonal modulation of South American summer precipitation. Mon. Wea. Rev., 128 , 837850.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rickenbach, T. M., , Ferreira R. N. , , Halverson J. B. , , Herdies D. L. , , and Silva Dias M. A. F. , 2002: Modulation of convection in the southwestern Amazon basin by extratropical stationary fronts. J. Geophys. Res., 107 , 8040. doi:10.1029/2000JD000263.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saulo, A. C., , Seluchi M. E. , , and Nicolini M. , 2004: A case study of a Chaco low-level jet event. Mon. Wea. Rev., 132 , 26692683.

  • Seluchi, M. E., , and Marengo J. A. , 2000: Tropical-midlatitude exchange of air masses during summer and winter in South America: Climatic aspects and examples of intense events. Int. J. Climatol., 20 , 11671190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spencer, R. W., , Goodman H. M. , , and Hood R. E. , 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6 , 254273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., 1996: Importance of low-level jets to climate: A review. J. Climate, 9 , 16981711.

  • Vera, C. S., , Vigliarolo P. K. , , and Berbery E. H. , 2002: Cold season synoptic-scale waves over subtropical South America. Mon. Wea. Rev., 130 , 684699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vera, C. S., and Coauthors, 2006: The South American low-level jet experiment. Bull. Amer. Meteor. Soc., 87 , 6377.

  • Virji, H., 1981: A preliminary study of summertime tropospheric circulation patterns over South America estimated from cloud winds. Mon. Wea. Rev., 109 , 599610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Von Storch, H., , and Zwiers F. W. , 1999: Empirical orthogonal functions. Statistical Analysis in Climate Research, Cambridge University Press, 293–301.

    • Search Google Scholar
    • Export Citation
  • Vuille, M., 1999: Atmospheric circulation over the Bolivian Altiplano during dry and wet periods and extreme phases of the Southern Oscillation. Int. J. Climatol., 19 , 15791600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vuille, M., , and Keimig F. , 2004: Interannual variability of summertime convective cloudiness and precipitation in the central Andes derived from ISCCP-B3 data. J. Climate, 17 , 33343348.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vuille, M., , Bradley R. S. , , and Keimig F. , 2000: Interannual climate variability in the central Andes and its relation to tropical Pacific and Atlantic forcing. J. Geophys. Res., 105 , 1244712460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., , and Fu R. , 2004: Influence of cross-Andes flow on the South American low-level jet. J. Climate, 17 , 12471262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, J., , and Lau K-M. , 1998: Does a monsoon exist over South America? J. Climate, 11 , 10201040.

Fig. 1.
Fig. 1.

Schematic of synoptic-scale circulation features affecting South America and outline of study regions used for analysis. Dotted line represents the overall study region, whereas the solid lines represent the local Peru and Altiplano–Chaco study regions.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 2.
Fig. 2.

Average monthly GOES brightness temperatures at 1545 LT over the Peru and Altiplano–Chaco study regions during (a) December 2000, (b) January 2001, and (c) February 2001.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 3.
Fig. 3.

Anomalies of total summer precipitation (mm) over the entire study area based on the December–March (DJFM) average between 1998 and 2007 for (a) 2001 and (b) 2003.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 4.
Fig. 4.

Magnitude and direction of 850-mb wind vectors over South America for January of (a) 2001 and (b) 2003, and topography. The white lines indicate the extent of the larger study region.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 5.
Fig. 5.

Difference in average monthly total water vapor over the study region between January of 2001 and 2003.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 6.
Fig. 6.

Monthly averaged GOES brightness temperatures over the Peru study region during January 2001 at local times (a) 1545 and (b) 0345 and January 2003 at (c) 1545 and (d) 0345.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 7.
Fig. 7.

(left) EOF1 and (right) EOF2 over Peru study region for January of (a), (b) 2001 and (c), (d) 2003.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 8.
Fig. 8.

Percent of the spatial variance of cloudiness explained by Mode 1–10 of the EOF analysis over the Peru and Altiplano–Chaco study regions and Mexico for 2001 compared to the Himalayas in 2000.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 9.
Fig. 9.

PF2 centroid locations determined from TRMM analyses over South America at local times (a) 1200–1759 and (b) 0000–0559 during the years 1998–2004, and topography.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 10.
Fig. 10.

Elevation mask of overall study region. Class 0 represents the ocean. Classes 1–5 range from 0 to 2500 m in intervals of 500 m. Classes 10–14 represent the same elevation intervals for land east of the Andes except in decreasing order. Along the ridge of the Andes, Classes 7–9 represent elevations from 4000 to 2500 m in intervals of 500 m, whereas Class 6 includes any topography more than 4000 m.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 11.
Fig. 11.

Number of (a) PF1s and (b) PF2s that occurred in each class represented in Fig. 10 between 1200 and 1800 LT.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 12.
Fig. 12.

Locations of PF1s over the Altiplano subregion between 1998 and 2004 at local times (a) 1200–1800 and (b) 0000–0600, and topography.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 13.
Fig. 13.

Difference in GOES brightness temperatures over the Altiplano subregion between February 2001 and February 2003 at local times (a) 1045 and (b) 1945.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 14.
Fig. 14.

Monthly averaged GOES brightness temperatures over the Altiplano subregion during January 2001 at local times (a) 1345, (b) 1645, and (c) 1845.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Fig. 15.
Fig. 15.

Topographic map of Altiplano subregion with important river valleys emphasized in black. White contours represent a GOES brightness temperature of 253 K averaged over the month of January 2001 at 1345 LT. White arrows represent the direction of air channeling, whereas the red dashed line indicates the axis of convective development along the eastern peaks of the Altiplano Plateau.

Citation: Journal of Hydrometeorology 10, 1; 10.1175/2008JHM973.1

Save