Browsing by Author "Barros, AP"

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Open Access
An evaluation of the statistics of rainfall extremes in rain gauge observations, and satellite-based and reanalysis products using universal multifractals
(Journal of Hydrometeorology, 2010-04-01) Sun, X; Barros, AP
Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone-National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]-for the western United States (west of 105°W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40°N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall. © 2010 American Meteorological Society.
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Chemical composition and aerosol size distribution of the middle mountain range in the Nepal Himalayas during the 2009 pre-monsoon season
(Atmospheric Chemistry and Physics, 2010-12-15) Shrestha, P; Barros, AP; Khlystov, A
Aerosol particle number size distribution and chemical composition were measured at two low altitude sites, one urban and one relatively pristine valley, in Central Nepal during the 2009 pre-monsoon season (May-June). This is the first time that aerosol size distribution and chemical composition were measured simultaneously at lower elevations in the middle Himalayan region in Nepal. The aerosol size distribution was measured using a Scanning Mobility Particle Sizer (SMPS, 14-340 nm), and the chemical composition of the filter samples collected during the field campaign was analyzed in the laboratory. Teflon membrane filters were used for ion chromatography (IC) and watersoluble organic carbon and nitrogen analysis. Quartz fiber filters were used for organic carbon and elemental carbon analysis. Multi-lognormal fits to the measured aerosol size distribution indicated a consistent larger mode around 100 nm which is usually the oldest, most processed background aerosol. The smaller mode was located around 20 nm, which is indicative of fresh but not necessarily local aerosol. The diurnal cycle of the aerosol number concentration showed the presence of two peaks (early morning and evening), during the transitional periods of boundary layer growth and collapse. The increase in number concentration during the peak periods was observed for the entire size distribution. Although the possible contribution of local emissions in size ranges similar to the larger mode cannot be completely ruled out, another plausible explanation is the mixing of aged elevated aerosol in the residual layer during the morning period as suggested by previous studies. Similarly, the evening time concentration peaks when the boundary layer becomes shal-low concurrent with increase in local activity. A decrease in aerosol number concentration was observed during the nighttime with the development of cold (downslope) mountain winds that force the low level warmer air in the valley to rise. The mountain valley wind mechanisms induced by the topography along with the valley geometry appear to have a strong control in the diurnal cycle of the aerosol size distribution. During the sampling period, the chemical composition of PM2.5 was dominated by organic matter at both sites. Organic carbon (OC) comprised the major fraction (64-68%) of the aerosol concentration followed by ionic species (24- 26%, mainly SO2-4 and NH +4 ). Elemental Carbon (EC) compromised 7-10% of the total composition and 27% of OC was found to be water soluble at both sites. The day-to-day variability observed in the time series of aerosol composition could be explained by the synoptic scale haze that extended to the sampling region from the Indian Gangetic Plain (IGP), and rainfall occurrence. In the presence of regional scale haze during dry periods, the mean volume aerosol concentration was found to increase and so did the aerosol mass concentrations. © 2010 Author(s).
Open Access
Joint spatial variability of aerosol, clouds and rainfall in the Himalayas from satellite data
(Atmospheric Chemistry and Physics, 2010-09-14) Shrestha, P; Barros, AP
Satellite-based precipitation, Aerosol Optical Depth (AOD), Cloud Optical Depth (COD), and Aerosol Index (AI) data were used to characterize the linkages among landform and the intra-annual variability of aerosols, cloudiness and rainfall in the Himalayas using empirical orthogonal function (EOF) analysis. The first modes of AOD and AI show the presence of two branches of dust aerosol: over the Indus river basin and the Thar desert with a sharp west-east gradient parallel to the southern slopes of the Himalayas the Southern Branch; and the second against the slopes of the Tian Shan and over the Takla Makan desert in the Tibetan Plateau-the Northern branch. The third EOF mode of AOD accounts for about 7% of overall variance of AOD. It is attached to the foothills of the Himalayas east of the Aravalli range peaking in April-May-June followed by a sharp decrease in July during the first active phase of the monsoon. The first and second EOF modes of COD and precipitation show consistent patterns against the central and eastern Himalayas and along the ocean-land boundaries in western India and the Bay of Bengal. The break in cloudiness and rainfall between the winter and the monsoon seasons is captured well by the second EOF mode of COD and rainfall concurrent with the aerosol build up mode (April-May) over the region depicted by the third mode of AOD. The results show that the Aravalli range separates the two different modes of aerosol variability over northern India with dust aerosols to the west and polluted mixed aerosols to the east consistent with its role in regional circulation and precipitations patterns as per Barros et al. (2004) and Chiao and Barros (2007). SVD analysis between rainfall, COD and AOD showed a pattern of aerosol loading (resembling EOF3 of MODIS AOD) extending from 80° E∼90° E that peaks during the winter and pre-monsoon seasons and decays abruptly during the monsoon: the regions of aerosol buildup during the pre-monsoon season and the areas of high rainfall/cloudiness during the monsoon are collocated and have opposite signs suggesting aerosol-cloud-rainfall interaction. It is proposed that the third EOF of AOD maps the area where aerosol-cloud-rainfall interactions play an important role in the regional hydro-climatology. © Author(s) 2010.
Open Access
NASA GPM-Ground Validation: Integrated Precipitation and Hydrology Experiment 2014 Science Plan.
(2014-08-04) Barros, AP
Open Access
Using fractal downscaling of satellite precipitation products for hydrometeorological applications
(Journal of Atmospheric and Oceanic Technology, 2010-03-01) Tao, K; Barros, AP
The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L»l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (~25-km grid spacing) to the same resolution as the NCEP stage IV products (~4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied. © 2010 American Meteorological Society.