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Ana Paula Barros

Abstract

Adaptive multilevel methods allow full coupling of atmospheric and land surface hydrological models by preserving consistency between the large-scale (atmospheric) and the regional (land) components. The methodology was investigated for three case studies involving the coupling of models with different levels of complexity and different spatial resolutions. The first case study consisted of coupling two simple models. One model provided the potential and the other the rotational components of atmospheric wind fields, which were used to drive a 3D orographic precipitation model used to investigate the long-term precipitation for the Olympic Mountains in Washington State. In the second case study, intermittent coupling (every 4 hours) of three versions of the orographic precipitation model operating at 40-m, 60-m, and 80-km resolution, respectively, was established to replicate the precipitation patterns of specifically chosen storms as they evolved across the central Sierra Nevada region. The third case study consisted of coupling the orographic precipitation model (40-km resolution) to a 1D model describing mass and energy balance conditions at the land surface for the northern and central Sierra Nevada region. Numerical coupling of the precipitation and the land surface models was implemented on a 2D finite-element mesh with 10-km resolution. One contribution of this study was the long-term simulation of the intra-annual dynamics of the hydrological cycle in a mountainous environment.

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Jason P. Giovannettone
and
Ana P. Barros

Abstract

Data from NASA’s TRMM satellite and NOAA’s GOES satellites were used to survey the orographic organization of cloud precipitation in central and southern Mexico during the monsoon with two main objectives: 1) to investigate large-scale forcing versus local landform controls, and 2) to compare the results with previous work in the Himalayas. At large scales, the modes of spatial variability of cloudiness were estimated using the empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Terrain modulation of synoptic-scale high-frequency variability (3–5- and 6–9-day cycles normally associated with the propagation of easterly waves) was found to cause higher dispersion in the EOF spectrum, with the first mode explaining less than 30% of the spatial variability in central and southern Mexico as opposed to 50% and higher in the Himalayas. A detailed analysis of the first three EOFs for 1999, an average La Niña year with above average rainfall, and for 2001, a weak La Niña year with below average rainfall, shows that landform (mountain peaks and land–ocean contrast) and large-scale circulation (moisture convergence) alternate as the key controls of regional hydrometeorology in dry and wet years, or as active and break (midsummer drought) phases of the monsoon, respectively. The diurnal cycle is the dominant time scale of variability in 2001, as it is during the midsummer drought in all years. Strong variability at time scales beyond two weeks is only present during the active phases of the monsoon. At the river basin scale, the data show increased cloudiness over the mountain ranges during the afternoon, which moves over the low-lying regions at the foot of the major orographic barriers [the Sierra Madre Occidental (SMO)/Sierra Madre del Sur (SMS) and Trans-Mexican Volcanic Belt (TMVB)], specifically the Balsas and the Rio de Santiago basins at nighttime and in the early morning. At the ridge–valley scale (∼100–200 km), robust day–night (ridge–valley) asymmetries suggest strong local controls on cloud and precipitation, with convective activity along the coastal region of the SMO and topographically forced convection at the foothills of headwater ridges in the Altiplano and the SMS. These day–night spatial shifts in cloudiness and precipitation are similar to those found in the Himalayas at the same spatial scales.

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Ana P. Barros
and
Dennis P. Lettenmaier

Abstract

Precipitation in remote mountainous areas dominates the water balance of many water-short areas of the globe, such as western North America. The inaccessibility of such environments prevents adequate measurement of the spatial distribution of precipitation and, hence, direct estimation of the water balance from observations of precipitation and runoff. Resolution constraints in atmospheric models can likewise result in large biases in prediction of the water balance for grid cells that include highly diverse topography. Modeling of the advection of moisture over topographic barriers at a spatial scale sufficient to resolve the dominant topographic features offers one method of better predicting the spatial distribution of precipitation in mountainous areas. A model is described herein that simulates Lagrangian transport of moist static energy and total water through a 3D finite-element grid, where precipitation is the only scavenging agent of both variables. The model is aimed primarily at the reproduction of the properties of high-elevation precipitation for long periods of time, but it operates at a time scale (during storm periods) of 10 min to 1 h and, therefore, is also able to reproduce the distribution of storm precipitation with an accuracy that may make it appropriate for the forecasting of extreme events. The model was tested by application to the Olympic Mountains, Washington, for a period of eight years (1967–74). Areal average precipitation, estimated through use of seasonal and annual runoff, was reproduced with errors in the 10%–15% range. Similar accuracy was achieved using point estimates of monthly precipitation from snow courses and low-elevation precipitation gauges.

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Ana Paula Barros
and
Dennis P. Lettenmaier

Abstract

A simple evaporative cooling scheme was incorporated into a dynamic model to estimate orographic precipitation in mountainous regions. The orographic precipitation model is based on the transport of atmospheric moisture and the quantification of precipitable water across a 3D representation of the terrain from the surface up to 250 hPa. Advective wind fields are computed independently and boundary conditions are extracted from radiosonde data. Precipitation rates are obtained through calibration of a spatially distributed precipitation efficiency parameter. The model was applied to the central Sierra Nevada. Results show a gain of the order of 20% in threat-score coefficients designed to measure the forecast ability of the model. Accuracy gains are largest at high elevations and during intense storms associated with warm air masses.

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Jason P. Giovannettone
and
Ana P. Barros

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.

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Ellen M. Douglas
and
Ana P. Barros
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Ellen M. Douglas
and
Ana P. Barros

Abstract

Probable maximum precipitation (PMP) is the conceptual construct that defines the magnitude of extreme storms used in the design of dams and reservoirs. In this study, the value and utility of applying multifractal analysis techniques to systematically calculate physically meaningful estimates of maximum precipitation from observations in the eastern United States is assessed. The multifractal approach is advantageous because it provides a formal framework to infer the magnitude of extreme events independent of empirical adjustments, which is called the fractal maximum precipitation (FMP), as well as an objective estimate of the associated risk. Specifically, multifractal (multiscaling) behavior of maximum accumulated precipitation at daily (327 rain gauges) and monthly (1400 rain gauges) timescales, as well as maximum accumulated 6-hourly precipitable water fluxes for the period from 1950 to 1997 were characterized. Return periods for the 3-day FMP estimates in this study ranged from 5300 to 6200 yr. The multifractal parameters were used to infer the magnitude of extreme precipitation consistent with engineering design criterion (e.g., return periods of 106 yr), the design probable maximum precipitation (DPMP). The FMP and DPMP were compared against PMP estimates for small dams in Pennsylvania using the standard methodology in engineering practice (e.g., National Weather Service Hydrometeorological Reports 51 and 52). The FMP estimates were usually, but not always, found to be lower than the standard PMP (FMP/PMP ratios ranged from 0.5 to 1.0). Furthermore, a high degree of spatial variability in these ratios points to the importance of orographic effects locally, and the need for place-based FMP estimates. DMP/PMP ratios were usually greater than one (0.96 to 2.0), thus suggesting that DPMP estimates can provide a bound of known risk to the standard PMP.

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Ana P. Barros
and
Timothy J. Lang

Abstract

The Monsoon Himalayan Precipitation Experiment (MOHPREX) occurred during June 2001 along the south slopes of the Himalayas in central Nepal. Radiosondes were launched around the clock from two sites, one in the Marsyandi River basin on the eastern footslopes of the Annapurna range, and one farther to the southwest near the border with India. The flights supported rainfall and other hydrometeorological observations (including surface winds) from the Marsyandi network that has been operated in this region since the spring of 1999. The thermodynamic profiles obtained from the soundings support the observed nocturnal maximum in rainfall during the monsoon, with total column moisture and instability maximized just before rainfall peaks. Coinciding with the appearance of a monsoon depression over central India, the onset of the monsoon in this region was characterized by a weeklong weakening of the upper-level westerlies, and an increase in moisture and convective instability. The vertical structure of convection during the project was intense at times, and frequent thunder and lightning were observed. This is suggestive of monsoon break convection, which is expected to be predominant since the monsoon had not fully matured by the end of the month. Comparisons of the MOHPREX data with the NCEP–NCAR reanalysis data reveal that upper-level winds are characterized relatively well by the reanalysis, taking into account the coarse model topography. However, moisture is severely underestimated, leading to significant underestimation of rainfall by the reanalysis. The interaction of the ambient monsoon flow with the south slopes of the Himalayas, modulated by the diurnal variability of atmospheric state, is suggested as the primary cause of the nocturnal peak in rainfall.

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Timothy J. Lang
and
Ana P. Barros

Abstract

The Marsyandi River basin in the central Nepalese Himalayas is a topographically complex region, with strong spatial gradients of precipitation over various timescales. A meteorological network consisting of 20 stations was installed at a variety of elevations (528–4435 m) in this region, and measurements of rainfall were made during the 1999 and 2000 summer monsoons. The onsets of the 1999 and 2000 monsoons in central Nepal were examined at different spatial scales by using a combination of rain gauge, Meteosat-5, Tropical Rainfall Measuring Mission (TRMM), ECMWF analysis, and Indian radiosonde data. At the network, the onsets manifested themselves as multiday rain events, which included a mixture of stratiform and convective precipitation. Moist and unstable upslope flow was associated with the occurrence of heavy rainfall. During each onset, 2-day rainfall reached as high as 462 mm, corresponding to 10%–20% of the monsoon rainfall. Differences among rain gauges were up to a factor of 8, reflecting the role of small-scale terrain features in modulating rainfall amounts. At the larger scale, the onsets were associated with monsoon depressions from the Bay of Bengal that moved close enough to the Himalayas to cause the observed upslope flow from the winds on their eastern flank. During the 1999 onset, convection in this eastern flank collided with the mountains in the vicinity of the network. In 2000 no major collision occurred, and 33%–50% less rain than 1999 fell. Analysis of observations for a 5-yr period (1997–2001) suggests that the interannual variability of the monsoon onset along the Himalayan range is linked to the trajectories and strength of these depressions.

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Robert J. Kuligowski
and
Ana P. Barros

Abstract

Accurate, timely, site-specific forecasts of precipitation are important for accurately predicting streamflow and flash floods in small drainage basins. However, presently available numerical weather prediction models do not generally provide forecasts with the accuracy and/or resolution appropriate for this task. A wide variety of approaches to small-scale, short-term precipitation forecasting have been investigated by numerous authors; this paper describes a simple precipitation forecasting model based on artificial neural networks. The model uses the radiosonde-based 700-hPa wind direction and antecedent precipitation data from a rain gauge network to generate short-term (0–6 h) precipitation forecasts for a target location. The performance of the model is illustrated for a gauge in eastern Pennsylvania.

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