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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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Olivier P. Prat
and
Ana P. Barros

Abstract

The focus of this paper is on the numerical solution of the stochastic collection equation–stochastic breakup equation (SCE–SBE) describing the evolution of raindrop spectra in warm rain. The drop size distribution (DSD) is discretized using the fixed-pivot scheme proposed by Kumar and Ramkrishna, and new discrete equations for solving collision breakup are presented. The model is evaluated using established coalescence and breakup parameterizations (kernels) available in the literature, and in that regard this paper provides a substantial review of the relevant science. The challenges posed by the need to achieve stable and accurate numerical solutions of the SCE–SBE are examined in detail. In particular, this paper focuses on the impact of varying the shape of the initial DSD on the equilibrium solution of the SCE–SBE for a wide range of rain rates and breakup kernels. The results show that, although there is no dependence of the equilibrium DSD on initial conditions for the same rain rate and breakup kernel, there is large variation in the time that it takes to reach steady state. This result suggests that, in coupled simulations of in-cloud motions and microphysics and for short time scales (<30 min) for which transient conditions prevail, the equilibrium DSD may not be attainable except for very heavy rainfall. Furthermore, simulations for the same initial conditions show a strong dependence of the dynamic evolution of the DSD on the breakup parameterization. The implication of this result is that, before the debate on the uniqueness of the shape of the equilibrium DSD can be settled, there is critical need for fundamental research including laboratory experiments to improve understanding of collisional mechanisms in DSD evolution.

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Olivier P. Prat
and
Ana P. Barros

Abstract

The objective of this study is to characterize the signature of dynamical microphysical processes on reflectivity–rainfall (ZR) relationships used for radar rainfall estimation. For this purpose, a bin model with explicit microphysics was used to perform a sensitivity analysis of the shape parameters of the drop size distribution (DSD) as a function of time and rainfall regime. Simulations show that coalescence is the dominant microphysical process for low to moderate rain intensity regimes (R < 20 mm h−1) and that the rain rate in this regime is strongly dependent on the spectral properties of the DSD (i.e., the shape). The time to equilibrium for light rainfall is at least twice as long as in the case of heavy rainfall (1 h for stratiform vis-à-vis 30 min for thunderstorms). For high-intensity rainfall (R > 20 mm h−1), collision–breakup dynamics dominate the evolution of the raindrop spectra. The time-dependent ZR relationships produced by the model converge to a universal ZR relationship for heavy intensity rainfall (A = 1257; b ∼ 1) centered on the region of ZR space defined by the ensemble of over 100 empirical ZR relationships. Given the intrinsically transient nature of the DSD for light rainfall, it is proposed that the vertical raindrop spectra and corresponding rain rates should be modeled explicitly by a microphysical model. A demonstration using a multicolumn simulation of a Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) overpass over Darwin for a stratiform event during the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) is presented.

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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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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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Xiaoming Sun
and
Ana P. Barros

Abstract

The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km × 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 1° × 1°). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Δp ~ 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.

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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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Ana P. Barros
and
Robert J. Kuligowski

Abstract

The evolution of precipitation features during a severe wintertime rainfall and flooding event associated with a cold front that crossed the central Appalachians on 19 January 1996 is illustrated through the analysis of radiosonde, rainfall, and streamflow gauge data, and WSR-88D images. Striking evidence of the linkage between heavy precipitation cells and orography was obtained by tracking the movement of the center of mass of storm precipitation, which closely followed the contours of regional orographic features. Higher intensity precipitation cells were consistently located windward of the orographic crest, and the trajectory described by the center of mass of precipitation was also consistent with the spatial arrangement of the river basins where hazardous flooding occurred. Persistent, low-intensity (⩽5 mm h−1) rainfall was registered in these basins during the 12-h period that preceded the arrival of frontal storm activity. It is argued that this prefrontal precipitation had a critical impact on watershed rainfall-runoff response and snowpack conditioning during and after the passage of the front. The intent here is to investigate the links between the observed space–time variability of rainfall and the influence of terrain features on mesoscale circulations in the lee side of the Appalachians. In particular, the viability of orographic mechanisms such as forced ascent, lee-wave interference, and precipitation scavenging of shallow orographic clouds was assessed using simple models and the available meteorological and hydrological data.

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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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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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