Jeanne M. Schneider

Climate forecasts for corn producer decision making

AbstractCorn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

Climate forecast and prediction product dissemination for agriculture in the United States

A wealth of climate forecast information and related prediction products are available, but impediments to adoption of these products by ranchers and farmers in the Unites States remain to be addressed. Impediments for agricultural applications include modest forecast skill, limited climate predictability, inappropriate forecast scale for site-specific applications, difficulties in interpretation of probabilistic forecasts by farmers and integration into agricultural decision systems, uncertainty about the value and effect of forecast information in multi-variable decision system, and generally low frequency of relevant forecasts. Various research institutions have conducted case studies of climate effects on agricultural production systems, particularly effects of historical ENSO signals in the south-eastern United States. Several studies addressed risk and economic values of seasonal climate forecasts, and others bridged the gap between current forecasting software and products and agricultural applications. These studies attest to the availability and suitability of forecast and impact-prediction software, as well as derived products for agricultural applications. Yet, little attention has been given to operational and application-specific prediction products for general agricultural use, and to an effective and affordable delivery system that reaches and resonates with the agricultural end-user (a prerequisite for adoption). The two latter impediments are the focus of this paper. Two existing approaches, the top-down and the participatory end-to-end approach for development and delivery of prediction products, are reviewed. A third approach, the hybrid approach, is emphasised and uses the top-down approach for climate forecast delivery and a participatory approach for development and delivery of farm-specific prediction information for the agricultural end-user. Suitability of such prediction products for agricultural applications and constraints to successful adoption are also discussed.

An Observational and Numerical Study of a Sheared, Convective Boundary Layer. Part I: Phoenix II Observations, Statistical Description, and Visualization

Abstract Four-dimensional velocity fields derived from dual Doppler radar observations are the basis of a description and statistical analysis of a convective, sheared planetary boundary layer during an afternoon over the High Plains of eastern Colorado. Mean velocities and momentum fluxes are calculated directly from the radar data and are verified with aircraft and tower data. Perturbation pressure and buoyancy fields are recovered for turbulent kinetic energy budgets, and for estimates of horizontal heat advection across the analysis area. The surface layer and lowest third of the observed boundary layer were similar to minimally sheared convective boundary layers, but there were significant differences in the upper two-thirds of the boundary layer. An overrunning residual mountain boundary layer merged with the locally generated convective boundary layer, producing a deep, continuously sheared layer of turbulent activity. Computer visualization reveals a complicated flow characterized by clusters of v...

Linking Climate Forecast and Watershed Runoff Prediction Using a Neural Network Approach

Potential utility of seasonal precipitation forecasts for water resources management was investigated for a wet forecast for the Fort Cobb watershed in central Oklahoma. Seasonal precipitation forecasts were expressed by ensembles of daily weather realizations, and corresponding watershed runoff was simulated by use of an artificial neural network model. Utility of a forecast was defined to be a function of the difference between runoff consistent with climatologic precipitation conditions and runoff consistent with forecast precipitation conditions. In general, runoff predictions were found to be useful for precipitation forecasts that departed by about 10% or more from normal conditions. However, average normal precipitation, antecedent hydrologic conditions, runoff response lag, and cumulative effects can dominate the forecast-based runoff prediction.

Decadal Precipitation Variations and Inflows into Fort Cobb Reservoir, Oklahoma

Changes in annual reservoir inflow and flood release volumes due to persistent multiyear dry or wet periods were investigated for the Fort Cobb Reservoir. A time-series analysis of the 1940-2004 annual precipitation revealed three dry and one wet period, called decadal variations. Corresponding strong variations were found in the reservoir inflow and flood release records. The sensitivity of watershed response, reservoir inflow and flood release volume to decadal precipitation variations suggested that water resources and water quality assessments in central Oklahoma should account for decadal precipitation variations. Along the same lines investigations based on numerical model simulations need to consider such climatic variations in the calibration, validation and application phase.