Low stratiform clouds in the tropics and subtropics are a substantial cooling term in the global climate system but remain a stubborn challenge for Earth System Models (ESMs). Low clouds are represented in ESMs using parameterizations that are often based on output from high-resolution process models. But how reliable are the cloud properties and processes produced by these models? Process models are typically configured using forcing that may exhibit substantial uncertainty and be only loosely constrained by observations.
Diatoms are photosynthetic, eukaryotic microalgae found in the Bacillariophyta and other families. There are more than 200 genera of living diatoms and approximately 100,000 extant species. Because of its unique property and nano-structure, diatom offers a competitive edge over other microalgae as an economical crop for biofuel, a reinforcement agent for polymers, and an absorbent for removing contaminants and radioactive materials.
Probabilistic forecasts are fundamental tools for making decisions under uncertainty in a wide variety of fields, including short-term and seasonal-term weather and energy supply and demand. This talk will present a scheme whereby a base probabilistic forecasting system that is poorly-calibrated may be recalibrated by incorporating past performance information to produce a new forecasting system that is superior to the original one, in that it produces probability distributions that demonstrably furnish more reliable decision support than the original forecast system.
Aerosols are sometimes referred to as the most confounding cog in the climate system when it comes to prognosticating the future of the Earth's climate. Their interaction with clouds makes the problem truly wicked. Here we look at a very small sub-set of the issue – that of the effect of absorbing aerosol above cloud, a persistent feature over the southeastern Atlantic Ocean off the west coast of Africa.
This presentation will demonstrate digital methods to map soil properties such as plant available water, organic carbon content, texture, and depth of soil to name a few. Currently, the process is being utilized to map the entire nations of El Salvador, Honduras, Nicaragua and Guatemala. The process of providing predictions of spatially heterogeneous soil properties can be used both internationally and domestically for multiple implications.
This presentation will provide an overview of storm surge modeling followed by results for the 2017 Atlantic hurricane season. This talk will also present results of an impact assessment of climate change on coastal hazards in the Northwest Pacific Ocean, based on ensemble atmospheric downscaling experiments for Haiyan-like super storms in the Philippines.
Society is demanding more sustainable bioenergy and agricultural systems, but farmers who could provide both do not have sufficient information to confidently change from the status quo. This seminar will present results of novel modelling assessments that indicate the agriculturally dominate state of Iowa, USA, could achieve nearly half of state water quality improvement targets simply by converting highly unprofitable parts of corn/soybean fields to switchgrass (Panicum virgatum L.) using a precision conservation approach.
The adjoint of a numerical weather prediction model, computed as the transpose of the tangent linear approximation to the fully nonlinear model, computes the sensitivity of some aspect of the final forecast state to changes to the model state at earlier times. The adjoint is best known as an integral part of 4-dimensional variational data assimilation (4DVAR).
Satellite observations of cloudy hurricane canopies have shown a universal, daily, wave-like feature that propagates radially outward, as far as 600 km. Daytime solar heating of a hurricane's upper eyewall is surely responsible, but the mechanism for the wave was previously unknown. This seminar will discuss numerical experiments that suggest these waves are internal inertia-gravity waves, and in fact propagate through (almost!) the entire depth of the hurricane.
This presentation will summarize results of two studies that combined long term field observation data with simulation and geospatial models to study the role of conservation agriculture to mitigate land use change emissions.