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PopularSummaryDeforestationis rapidly increasingin Amazonia.If deforestationcontinuesat the current rate,mostof the Amazoniantropical forestswould disappear in lessthan 100years.One questionthat arisesis whether large-scaledeforestationin Amazonia might affect the regional climate. Early modeling studiesconcludedthat widespreaddeforestationof Amazoniawould lead to decreased rainfall. More realistic and sophisticatedmesoscale modelsshowthatdeforestationcancreatelocalizedcirculationswith rising motionsover the deforested areas. The potentialimpactof thesecirculationson cloudinessandrainfall is the subjectof this paper.We analyzegeosynchronous (GOES) infrared satellite data with respectto cloudiness,andanalyzepassivemicrowavedatafrom sensorsaboardthe Tropical Rainfall MeasuringMission satellitewith respectto rainfall. We concludethat in the dry-season(August)whentheeffectsof the surfacearenot overwhelmedby largescaleweatherdisturbances, the occurrenceof cloudinessandrainfall increaseover the deforestedandnon-forested(savanna) regions.During the day,the amountof cloudiness shifts towardafternoonhoursin the deforestedandsavannaregions,ascomparedto the forestedregions. Analysis of 14 yearsof monthly estimatesfrom the Special Sensor Microwave/Imagerrevealedthat only in August did rainfall amountsincreaseover the deforested region.Many modeling studieshave concluded that widespreaddeforestationof Amazonia would leadto decreased rainfall. We analyzegeosynchronous infrared satellitedatawith respectpercentcloudiness,and analyzerain estimatesfrom microwavesensorsaboard the Tropical Rainfall MeasuringMission satellite.We concludethat in the dry-season, when the effects of the surface are not overwhelmed by synoptic-scale weather disturbances, deepconvectivecloudiness,aswell asrainfall occurrence, all increaseover the deforested and non-forested (savanna) regions. This is in responseto a local circulation initiated by the differential heating of the region's varying forestation.Analysis of the diurnal cycle of cloudinessrevealsa shift toward afternoonhoursin the deforestedandsavannaregions,comparedto the forestedregions. Analysis of 14 years of datafrom the SpecialSensorMicrowave/Imagerdatarevealedthatonly in Augustdid rainfall amountsincreaseover the deforested region.

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Popular Summary:Floods and associated landslides are one of the most widespread natural hazards on Earth, responsible for tens of thousands of deaths and billions of dollars in property damage every year. During 1993During -2002, over 1000 of the more than 2,900 natural disasters reported were due to floods. These floods and associated landslides claimed over 90,000 lives, affected over 1.4 billion people and cost about $210 billion. The impact of these disasters is often felt most acutely in less developed regions. In many countries around the world, satellite-based precipitation estimation may be the best source of rainfall data due to lack of surface observing networks.Satellite observations can be of essential value in improving our understanding of the occurrence of hazardous events and possibly in lessening their impact on local economies and in reducing injuries, if they can be used to create reliable warning systems in cost-effective ways. This article addressed these opportunities and challenges by describing a combination of satellite-based real-time precipitation estimation with land surface characteristics as input, with empirical and numerical models to map potential of landslides and floods. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25' x 0.25'.By careful integration of remote sensing and in-situ observations, and assimilation of these observations into hydrological and landslideldebris flow models with surface topographic information, prediction of useful probabilistic maps of landslide and floods for emergency management in a timely manner is possible. Early results shows that the potential exists for successful application of satellite precipitation data in improving/developing global monitoring systems for floodllandslide disaster preparedness and management. The scientific and technological prototype can be first applied in a representative test-bed and then the information deliverables for the region can be tailored to the societal and economic needs of the represented affected countries. Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitati...

[1] The development of a satellite infrared technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall in Amazonia are presented. The convective-stratiform technique (CST), calibrated by coincident, physically retrieved rain rates from the TRMM microwave imager (TMI), is applied during January-April 1999 over northern South America. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall, is presented. Results compare well (a 1-hour lag) with the diurnal cycle derived from TOGA radarestimated rainfall in Rondonia. The satellite estimates reveal that the convective rain constitutes, in the mean, 24% of the rain area while accounting for 67% of the rain volume. Estimates of the diurnal cycle for the entire Amazon Basin are in agreement with those from the TRMM precipitation radar, despite the limited sampling of the latter. The effects of geography (rivers, lakes, coasts) and topography on the diurnal cycle of convection are examined. In particular, the Amazon River, downstream of Manaus, is shown to both enhance early morning rainfall and inhibit afternoon convection. Monthly estimates from this technique, dubbed CST/TMI, are verified over a dense rain gage network in the state of Ceara, in northeast Brazil. The CST/TMI showed a high bias equal to ϩ33% of the gage mean, indicating that possibly the TMI estimates alone are also high. The root-meansquare difference (after removal of the bias) equaled 36.6% of the gage mean. The correlation coefficient was 0.77 based on 72 station months. Citation: Negri, A. J., R. F. Adler, and L. Xu, A TRMM-calibrated infrared rainfall algorithm applied over Brazil,