Space-time Trends in U.S. Meteorological Droughts

Understanding droughts in a climate context remains a major challenge. Over the United States, different choices of observations and metrics have often produced diametrically opposite insights. This paper focuses on understanding and characterizing meteorological droughts from station measurements of precipitation. The Standardized Precipitation Index is computed and analyzed to obtain drought severity, duration and frequency. Average drought severity trends are found to be uncertain and data-dependent. Furthermore, the mean and spatial variance do not show any discernible non-stationary behavior. However, the spatial coverage of extreme meteorological droughts in the United States exhibits an increasing trend over nearly all of the last century. Furthermore, the coverage over the last half decade exceeds that of the dust bowl era. Previous literature suggests that climate extremes do not necessarily follow the trends or uncertainties exhibited by the averages. While this possibility has been suggested for droughts, this paper for the first time clearly delineates and differentiates the trends in the mean, variability and extremes of meteorological droughts in the United States, and uncovers the trends in the spatial coverage of extremes. Multiple data sets, as well as years exhibiting large, and possibly anomalous, droughts are carefully examined to characterize trends and uncertainties. Nonlinear dependence among meteorological drought attributes necessitates the use of copula-based tools from probability theory. Severity-duration-frequency curves are generated to demonstrate how these insights may be translated to design and policy

Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 205-218).Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. Recent advances in precipitation physics, Numerical Weather Prediction (NWP) models, availability of high quality remotely sensed measurements, and data dictated forecasting tools, offer the opportunity of improvements in this area. Investigative studies were performed to quantify the value of available tools and data, which indicated the promise and the pitfalls of emerging ideas. Our studies suggested that an intelligent combination of NWP model outputs and remotely sensed radar measurements, that uses process physics and data dictated tools, could improve distributed QPF. Radar measurements have distributed structure, while NWP-QPF incorporate large scale physics. Localized precipitation processes are not well handled by NWP models, and grid average NWP-QPF are not too useful for distributed QPF owing to the spatial variability of rainfall. However, forecasts for atmospheric variables from NWP have information relevant for modeling localized processes and improving distributed QPF, especially in the Summer. Certain precipitation processes like advection and large scale processes could be modeled using physically based algorithms. The physics for other processes like localized convection or residual structures are not too well understood, and data dictated tools like traditional statistical models or Artificial Neural Networks (ANN) are often more applicable.(cont.) A new strategy for distributed QPF has been proposed that utilizes information from radar and NWP. This strategy decomposes the QPF problem into component processes, and models these processes using precipitation physics and data dictated tools, as appropriate and applicable. The proposed strategy improves distributed QPF over existing techniques like radar extrapolation alone, NWP-QPF with or without statistical error correction, hybrid models that combine radar extrapolation with NWP-QPF, parameterized physically based methods, and data dictated tools alone. New insights are obtained on the component processes of distributed precipitation, the information content in radar and NWP, and the achievable precipitation predictability.by Auroop R. Ganguly.Ph.D