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This manuscript introduces a novel computational science approach for studying the impact of climate variability on precipitation. The approach uses an object-oriented connectivity algorithm that segments gridded near-global satellite precipitation data into four-dimensional (4D) objects (longitude, latitude, time, and intensity). These precipitation systems have distinct spatiotemporal properties that are counted, tracked, described, and stored in a searchable database. A case study of western United States precipitation systems is performed, demonstrating the unique properties and capabilities of this object-oriented database. The precipitation dataset used in the case study is the University of California, Irvine, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) from 1 March 2000 to 1 January 2011. A search of the database for all western United States precipitation systems during this time period returns 626 precipitation systems as objects. By analyzing these systems as segmented objects, joint interactions of the selected climate phenomena 1) Arctic Oscillation (AO), 2) Madden-Julian oscillation (MJO), and 3) El Niño-Southern Oscillation (ENSO) on precipitation can be shown. They directly show the increased/decreased likelihood of having precipitation systems occurring over the western United States (monthly count) during phases of these climate phenomena. It is found that specific climate phenomena impact the monthly count of the events differently, and that the joint interaction of climate phenomena of the AO-MJO and AO-ENSO is important, especially during certain months of the year. It is also found that these interactions impact the physical features of the precipitation systems themselves. [ABSTRACT FROM AUTHOR]

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to generate ensemble forecasts over the southwest United States during the 151 days of 1 November 2002 to 31 March 2003. RSM forecasts to 24 h on a 12-km grid are produced from 0000 and 1200 UTC initial conditions. Eleven ensemble members are run each forecast cycle from the NCEP Global Forecast System (GFS) ensemble analyses (one control and five pairs of bred modes) and forecast lateral boundary conditions. The model domain covers two NOAA River Forecast Centers: the California Nevada River Forecast Center (CNRFC) and the Colorado Basin River Forecast Center (CBRFC). Ensemble performance is evaluated for probabilistic forecasts of 24-h accumulated precipitation in terms of several accuracy and skill measures. Differences among several NCEP precipitation analyses are assessed along with their impact on model verification, with NCEP stage IV blended analyses selected to represent “truth.” Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region, and precipitation threshold. In general, the RSM forecasts are skillful over the CNRFC region for thresholds between 1 and 50 mm but are unskillful over the CBRFC region. The model exhibits a wet bias for all thresholds that is larger over Nevada and the CBRFC region than over California. Mitigation of such biases over the Southwest will pose serious challenges to the modeling community in view of the uncertainties inherent in verifying analyses. [ABSTRACT FROM AUTHOR]

A coupled atmosphere–land-surface mesoscale model is used to assess the responses of precipitation to soil-moisture anomalies in two regions: (1) the core region of the North American Monsoon (NAM; 105°–112°W, 24°–36°N); (2) the central–southern United States (CS-US; 85°–95°W, 30°–36°N). Results from a series of numerical experiments integrated from July to September 2000 show that precipitation increases in the NAM region in July with a prescribed wet soil-moisture anomaly; meanwhile, precipitation decreases in the CS-US region. In the following months, when the prescribed wet soil-moisture anomaly in the NAM region was removed, the increase in precipitation in the NAM region becomes weaker and shifts eastward to the CS-US region. By September, an inverse precipitation seesaw in these two regions is built up. Except for local evaporation, the transportation of atmospheric moisture affects the interaction between soil moisture and precipitation, especially in the regions and periods without the prescribed soil-moisture anomaly. The soil-moisture anomaly in the NAM region is only partially responsible for the precipitation seesaw in the southern United States. [ABSTRACT FROM AUTHOR]