Ana P. Barros

Workflow Patterns

Differences in features supported by the various contemporary commercial workflow management systems point to different insights of suitability and different levels of expressive power. The challenge, which we undertake in this paper, is to systematically address workflow requirements, from basic to complex. Many of the more complex requirements identified, recur quite frequently in the analysis phases of workflow projects, however their implementation is uncertain in current products. Requirements for workflow languages are indicated through workflow patterns. In this context, patterns address business requirements in an imperative workflow style expression, but are removed from specific workflow languages. The paper describes a number of workflow patterns addressing what we believe identify comprehensive workflow functionality. These patterns provide the basis for an in-depth comparison of a number of commercially availablework flow management systems. As such, this paper can be seen as the academic response to evaluations made by prestigious consulting companies. Typically, these evaluations hardly consider the workflow modeling language and routing capabilities, and focus more on the purely technical and commercial aspects.

Dynamic modeling of orographically induced precipitation

Local orography governs the triggering of cloud formation and the enhancement of processes such as condensation and hydrometeor nucleation and growth in mountainous regions. Intense, lengthy precipitation events are typical upwind of the topographic divide, with sharply decreasing magnitude and duration on the lee side. Differences in mean annual precipitation of several hundred percent between windward slopes of orographic barriers and adjacent valleys or lee side slopes are not unusual. Because much of the streamflow in areas such as the western United States is derived from mountainous areas that are remote and often poorly instrumented, modeling of orographic precipitation has important implications for water resources management. Models of orographically induced precipitation differ by their treatment of atmospheric dynamics and by the extent to which they rely on bulk parameterization of cloud and precipitation physics. Adiabatic ascent and a direct proportionality between precipitation efficiency and orographically magnified updrafts are the most frequent assumptions in orographic precipitation modeling. Space-time discretization (i.e., resolution) is a major issue because of the high spatial variability of orographic precipitation. For a specific storm, relative errors as large as 50 to 100% are common in the forecast/hindcast of precipitation intensity and can be even larger in the case of catastrophic storms. When monthly or seasonal timescales are used to evaluate model performance, the magnitude of such errors decreases dramatically, reaching values as low as 10 to 15%. Current research is focusing on the development of data assimilation techniques to incorporate radar and satellite observations, and on the development of aggregation and disaggregation methodologies to address the implications of modeling a multiscale problem at restricted spatial and temporal resolutions.

A study of the 1999 monsoon rainfall in a mountainous region in central Nepal using TRMM products and rain gauge observations

Raingauge data from the 1999 monsoon were compared with precipitation derived from the precipitation radar (PR) and the microwave imager instruments on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The raingauges are part of a new hydrometeorological network installed in the Marsyandi river basin, which extends from the edge of the Tibetan Plateau to the Gangetic basin. TRMM-derived precipitation showed better detection of rain at low altitude stations as compared with high elevation stations, with good scores for the PR product for rain rates >0.5 mm/hr. The 3D PR rain rates suggest strong interaction between mesoscale convective systems and steep terrain at elevations of 1–2 km, which is consistent with the very high rainfall measured at those locations. Analysis of the raingauge data shows that even at altitudes as high as 4,000 m the cumulative monsoon rainfall is comparable to the highest amount recorded in the Indian subcontinent.

Parameterization of vegetation backscatter in radar-based, soil moisture estimation

Abstract The Integral Equation Model (IEM) was previously used in conjunction with an inversion model to retrieve soil moisture using multifrequency and multipolarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR). Convergence rates well above 90%, and small RMS errors were attained, for both vegetated and bare soil areas, using radar data collected during Washita 1994. However, the IEM was originally developed to describe the scattering from bare soil surfaces only, and, therefore, vegetation backscatter effects are not explicitly incorporated in the model. In this study, the problem is addressed by introducing a simple, semiempirical, vegetation scattering parameterization to the multifrequency, soil moisture inversion algorithm. The parameterization was formulated in the framework of the water–cloud model and relies on the concept of a land-cover (land-use)-based dimensionless vegetation correlation length to represent the spatial variability of vegetation across the landscape and radar-shadow effects (vegetation layovers). An application of the modified inversion model to the Washita 1994 data lead to a decrease of 32% in the RMSE, while the correlation coefficient between ground-based and SAR-derived soil moisture estimates improved from 0.84 to 0.95.

Monitoring the Monsoon in the Himalayas: Observations in Central Nepal, June 2001

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...

Experiments in short-term precipitation forecasting using artificial neural networks

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.

Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks

Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation forecasts have not yet reached the level of accuracy needed for hydrologic forecasting. Postprocessing of model output to account for local differences can enhance the accuracy and usefulness of these forecasts. Model Output Statistics have performed this important function for a number of years via regression techniques; this paper presents an alternate approach that uses artificial neural networks to produce 6-h precipitation forecasts for specific locations. Tests performed on four locations in the middle Atlantic region of the United States show that the accuracy of the forecasts produced using neural networks compares favorably with those generated using linear regression, especially for heavier precipitation amounts.

The NAME 2004 Field Campaign and Modeling Strategy

The North American Monsoon Experiment (NAME) is an internationally coordinated process study aimed at determining the sources and limits of predictability of warm-season precipitation over North America. The scientific objectives of NAME are to promote a better understanding and more realistic simulation of warm-season convective processes in complex terrain, intraseasonal variability of the monsoon, and the response of the warm-season atmospheric circulation and precipitation patterns to slowly varying, potentially predictable surface boundary conditions. During the summer of 2004, the NAME community implemented an international (United States, Mexico, Central America), multiagency (NOAA, NASA, NSF, USDA) field experiment called NAME 2004. This article presents early results from the NAME 2004 campaign and describes how the NAME modeling community will leverage the NAME 2004 data to accelerate improvements in warm-season precipitation forecasts for North America.

An Investigation of the Onsets of the 1999 and 2000 Monsoons in Central Nepal

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, reflec...

Dynamic Modeling of the Spatial Distribution of Precipitation in Remote Mountainous Areas

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 re...