Dynamics of real-time forecasting failure and recovery due to data gaps

Real-time forecasting is important to the society. It uses continuous data streams to update forecasts for sustained accuracy. But the data source is vulnerable to attacks or accidents and the dynamics of forecasting failure and recovery due to data gaps is poorly understood. As the first systematic study, a Lorenz model-based forecasting system was disrupted with data gaps of various lengths and timing. The restart time of data assimilation is found to be the most important factor. The forecasting accuracy is found not returning to the original even long after the data assimilation recovery

Determining Undersampled Coastal Tidal Harmonics using Regularized Least Squares

Satellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area, but the sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel Regularized Least-Square approach is developed to relax the limitation to the range of satellite operating conditions. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes and phases of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason-3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea-level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis

Modeling the future of irrigation: A parametric description of pressure compensating drip irrigation emitter performance

Drip irrigation is a means of distributing the exact amount of water a plant needs by dripping water directly onto the root zone. It can produce up to 90% more crops than rain-fed irrigation, and reduce water consumption by 70% compared to conventional flood irrigation. Drip irrigation may enable millions of poor farmers to rise out of poverty by growing more and higher value crops, while not contributing to overconsumption of water. Achieving this impact will require broadening the engineering knowledge required to design new, low-cost, low-power drip irrigation technology, particularly for poor, off-grid communities in developing countries. For more than 50 years, pressure compensating (PC) drip emitters—which can maintain a constant flow rate under variations in pressure, to ensure uniform water distribution on a field—have been designed and optimized empirically. This study presents a parametric model that describes the fluid and solid mechanics that govern the behavior of a common PC emitter architecture, which uses a flexible diaphragm to limit flow. The model was validated by testing nine prototypes with geometric variations, all of which matched predicted performance to within R2 = 0.85. This parametric model will enable irrigation engineers to design new drip emitters with attributes that improve performance and lower cost, which will promote the use of drip irrigation throughout the world