Journal of Hydrometeorology

Anderson, Bruce T.; Wang, Jingyun; Gopal, Suchi; Salvucci, Guido

doi: 10.1175/2009JHM1104.1pmid: N/A

The regional variability in the summertime precipitation over the southwestern United States is studied using stochastic chain-dependent models generated from 70 yr of station-based daily precipitation observations. To begin, the spatiotemporal structure of the summertime seasonal mean precipitation over the southwestern United States is analyzed using two independent spatial cluster techniques. Four optimal clusters are identified, and their structures are robust across the techniques used. Next, regional chain-dependent models—comprising a previously dependent occurrence chain, an empirical rainfall coverage distribution, and an empirical rainfall amount distribution—are constructed over each subregime and are integrated to simulate the regional daily precipitation evolution across the summer season. Results indicate that generally less than 50% of the observed interannual variance of seasonal precipitation in a given region lies outside the regional chain-dependent models’ stochastic envelope of variability; this observed variance, which is not captured by the stochastic model, is sometimes referred to as the “potentially predictable” variance. In addition, only a small fraction of observed years (between 10% and 20% over a given subregime) contain seasonal mean precipitation anomalies that contribute to this potentially predictable variance. Further results indicate that year-to-year variations in daily rainfall coverage are the largest contributors to potentially predictable seasonal mean rainfall anomalies in most regions, whereas variations in daily rainfall frequency contribute the least. A brief analysis for one region highlights how the identification of years with potentially predictable precipitation characteristics can be used to better understand large-scale circulation patterns that modulate the underlying daily rainfall processes responsible for year-to-year variations in regional rainfall.

Boushaki, Farid Ishak; Hsu, Kuo-Lin; Sorooshian, Soroosh; Park, Gi-Hyeon; Mahani, Shayesteh; Shi, Wei

doi: 10.1175/2009JHM1099.1pmid: N/A

Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States.

Zhang, Yan; Smith, James A.; Ntelekos, Alexandros A.; Baeck, Mary Lynn; Krajewski, Witold F.; Moshary, Fred

doi: 10.1175/2009JHM1046.1pmid: N/A

Heavy precipitation in the northeastern United States is examined through observational and numerical modeling analyses for a weather system that produced extreme rainfall rates and urban flash flooding over the New York–New Jersey region on 4–5 October 2006. Hydrometeorological analyses combine observations from Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars, the National Lightning Detection Network, surface observing stations in the northeastern United States, a vertically pointing lidar system, and a Joss–Waldvogel disdrometer with simulations from the Weather Research and Forecasting Model (WRF). Rainfall analyses from the Hydro-Next Generation Weather Radar (NEXRAD) system, based on observations from WSR-88D radars in State College, Pennsylvania, and Fort Dix, New Jersey, and WRF model simulations show that heavy rainfall was organized into long-lived lines of convective precipitation, with associated regions of stratiform precipitation, that develop along a frontal zone. Structure and evolution of convective storm elements that produced extreme rainfall rates over the New York–New Jersey urban corridor were influenced by the complex terrain of the central Appalachians, the diurnal cycle of convection, and the history of convective evolution in the frontal zone. Extreme rainfall rates and flash flooding were produced by a “leading line–trailing stratiform” system that was rapidly dissipating as it passed over the New York–New Jersey region. Radar, disdrometer, and lidar observations are used in combination with model analyses to examine the dynamical and cloud microphysical processes that control the spatial and temporal structure of heavy rainfall. The study illustrates key elements of the spatial and temporal distribution of rainfall that can be used to characterize flash flood hazards in the urban corridor of the northeastern United States.

Hurkmans, Ruud; Troch, Peter A.; Uijlenhoet, Remko; Torfs, Paul; Durcik, Matej

doi: 10.1175/2009JHM1133.1pmid: N/A

Understanding the long-term (interannual–decadal) variability of water availability in river basins is paramount for water resources management. Here, the authors analyze time series of simulated terrestrial water storage components, observed precipitation, and discharge spanning 74 yr in the Colorado River basin and relate them to climate indices that describe variability of sea surface temperature and sea level pressure in the tropical and extratropical Pacific. El Niño–Southern Oscillation (ENSO) indices in winter January–March (JFM) are related to winter precipitation as well as to soil moisture and discharge in the lower Colorado River basin. The low-frequency mode of the Pacific decadal oscillation (PDO) appears to be strongly correlated with deep soil moisture. During the negative PDO phase, saturated storage anomalies tend to be negative and the “amplitudes” (mean absolute anomalies) of shallow soil moisture, snow, and discharge are slightly lower compared to periods of positive PDO phases. Predicting interannual variability, therefore, strongly depends on the capability of predicting PDO regime shifts. If indeed a shift to a cool PDO phase occurred in the mid-1990s, as data suggest, the current dry conditions in the Colorado River basin may persist.

Legates, David R.; Bogart, Tianna A.

doi: 10.1175/2009JHM1086.1pmid: N/A

In applications where a monthly temporal resolution is employed, an important variable is the proportion of monthly precipitation that falls in solid form. Globally applicable equations for estimating this variable developed by previously published research are reevaluated. A revised equation developed 20 years ago by the lead author for climatological analysis works well for long-term data but not for actual monthly averages. A new equation, therefore, is developed for use with monthly data using an Arctic database of stations above 50°N latitude. These two equations have mean absolute fit errors of 0.0569 and 0.0614, respectively. The data were split into four regions—North America, northern Europe, northern Asia, and Greenland—and were also evaluated for the effect of elevation or seasonality influences. Results suggest that seasonality also is an important variable, particularly to differentiate between midwinter and transition months (i.e., October and April).