From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models

Modeling the effect of spatial variability of precipitation and basin characteristics on streamflow requires the use of distributed or semi-distributed hydrologic models. This paper addresses a DMIP 2 study that focuses on the advantages of using a semi-distributed modeling structure. We first present a revised semi-distributed structure of the NWS SACramento Soil Moisture Accounting (SAC-SMA) model that separates the routing of fast and slow response runoff components, and thus explicitly accounts for the differences between the two components. We then test four different calibration strategies that take advantage of the strengths of existing optimization algorithms (SCE-UA) and schemes (MACS). These strategies include: (1) lumped parameters and basin averaged precipitation, (2) semi-lumped parameters and distributed precipitation forcing, (3) semi-distributed parameters and distributed precipitation forcing and (4) lumped parameters and basin averaged precipitation, modified using a priori parameters of the SAC-SMA model. Finally, we explore the value of using discharge observations at interior points in model calibration by assessing gains/losses in hydrograph simulations at the basin outlet. Our investigation focuses on two key DMIP 2 science questions. Specifically, we investigate (a) the ability of the semi-distributed model structure to improve stream flow simulations at the basin outlet and (b) to provide reasonably good simulations at interior points.The semi-distributed model is calibrated for the Illinois River Basin at Siloam Springs, Arkansas using streamflow observations at the basin outlet only. The results indicate that lumped to distributed calibration strategies (1 and 4) both improve simulation at the outlet and provide meaningful streamflow predictions at interior points. In addition, the results of the complementary study, which uses interior points during the model calibration, suggest that model performance at the outlet can be further improved by using a semi-distributed structure calibrated at both interior points and the outlet, even when only a few years of historical record are available. © 2009 Elsevier B.V

Self-powered mixer for pressurized containers

Mechanical stirrer, installed entirely within tank, is powered by turbine driven by discharge flow of fluid. Contents of tank are automatically mixed whenever fluid in tank is discharged. Magnetic coupling eliminates need for shaft seal, particularly in high-pressure tanks

Daytime precipitation estimation using bispectral cloud classification system

Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach

Crude oil desulfurization

High sulfur crude oil is desulfurized by a low temperature (25-80 C.) chlorinolysis at ambient pressure in the absence of organic solvent or diluent but in the presence of water (water/oil=0.3) followed by a water and caustic wash to remove sulfur and chlorine containing reaction products. The process described can be practiced at a well site for the recovery of desulfurized oil used to generate steam for injection into the well for enhanced oil recovery

Nonconservative higher-order hydrodynamic modulation instability

The modulation instability (MI) is a universal mechanism that is responsible for the disintegration of weakly nonlinear narrow-banded wave fields and the emergence of localized extreme events in dispersive media. The instability dynamics is naturally triggered, when unstable energy side-bands located around the main energy peak are excited and then follow an exponential growth law. As a consequence of four wave mixing effect, these primary side-bands generate an infinite number of additional side-bands, forming a triangular side-band cascade. After saturation, it is expected that the system experiences a return to initial conditions followed by a spectral recurrence dynamics. Much complex nonlinear wave field motion is expected, when the secondary or successive side-band pair that are created are also located in the finite instability gain range around the main carrier frequency peak. This latter process is referred to as higher-order MI. We report a numerical and experimental study that confirm observation of higher-order MI dynamics in water waves. Furthermore, we show that the presence of weak dissipation may counter-intuitively enhance wave focusing in the second recurrent cycle of wave amplification. The interdisciplinary weakly nonlinear approach in addressing the evolution of unstable nonlinear waves dynamics may find significant resonance in other nonlinear dispersive media in physics, such as optics, solids, superfluids and plasma

Volume Stabilization and the Origin of the Inflaton Shift Symmetry in String Theory

The main problem of inflation in string theory is finding the models with a flat potential, consistent with stabilization of the volume of the compactified space. This can be achieved in the theories where the potential has (an approximate) shift symmetry in the inflaton direction. We will identify a class of models where the shift symmetry uniquely follows from the underlying mathematical structure of the theory. It is related to the symmetry properties of the corresponding coset space and the period matrix of special geometry, which shows how the gauge coupling depends on the volume and the position of the branes. In particular, for type IIB string theory on K3xT^2/Z with D3 or D7 moduli belonging to vector multiplets, the shift symmetry is a part of SO(2,2+n) symmetry of the coset space [SU(1,1)/ U(1)]x[SO(2,2+n)/(SO(2)x SO(2+n)]. The absence of a prepotential, specific for the stringy version of supergravity, plays a prominent role in this construction, which may provide a viable mechanism for the accelerated expansion and inflation in the early universe.Comment: 12 page