To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM. © 2012 Springer-Verlag

Engineering estimates for supersonic flutter of curved shell segments

Engineering estimates for supersonic flutter of curved shell panel

An object-based approach for verification of precipitation estimation

Verification has become an integral component in the development of precipitation algorithms used in satellite-based precipitation products and evaluation of numerical weather prediction models. A number of object-based verification methods have been developed to quantify the errors related to spatial patterns and placement of precipitation. In this study, an image processing technique known as watershed transformation, capable of detecting closely spaced, but separable precipitation areas, is adopted in the object-based approach. Several key attributes of the segmented precipitation objects are selected and interest values of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall interest score is estimated from all the selected attributes and their interest values. The proposed object-based approach is implemented to validate satellite-based precipitation estimation against ground radar observations. The results indicate that the watershed segmentation technique is capable of separating the closely spaced local-scale precipitation areas. In addition, three verification metrics, including the object-based false alarm ratio, object-based missing ratio, and overall interest score, reveal the skill of precipitation estimates in depicting the spatial and geometric characteristics of the precipitation structure against observations

The spectral dimension of random brushes

We consider a class of random graphs, called random brushes, which are constructed by adding linear graphs of random lengths to the vertices of Z^d viewed as a graph. We prove that for d=2 all random brushes have spectral dimension d_s=2. For d=3 we have {5\over 2}\leq d_s\leq 3 and for d\geq 4 we have 3\leq d_s\leq d.Comment: 15 pages, 1 figur

Influence of spatial resolution on diurnal variability during the north American monsoon

Diurnal variability is an important yet poorly understood aspect of the warm-season precipitation regime over southwestern North America. In an effort to improve its understanding, diurnal variability is investigated numerically using the fifth-generation Pennsylvania State University (PSU)-NCAR Mesoscale Model (MM5). The goal herein is to determine the possible influence of spatial resolution on the diurnal cycle. The model is initialized every 48 h using the operational NCEP Eta Model 212 grid (40 km) model analysis. Model simulations are carried out at horizontal resolutions of both 9 and 3 km. Overall, the model reproduces the basic features of the diurnal cycle of rainfall over the core monsoon region of northwestern Mexico and the southwestern United States. In particular, the model captures the diurnal amplitude and phase, with heavier rainfall at high elevations along the Sierra Madre Occidental in the early-afternoon that shifts to lower elevations along the west slopes in the evening. A comparison to observations (gauge and radar data) shows that the high-resolution (3 km) model generates better rainfall distributions on time scales from monthly to hourly than the coarse-resolution (9 km) model, especially along the west slopes of the Sierra Madre Occidental. The model has difficulty with nighttime rainfall along the slopes, over the Gulf of California, and over Arizona. A comparison of surface wind data from three NCAR Integrated Sounding System (ISS) stations and the Quick Scatterometer (QuikSCAT) to the model reveals a low bias in the strength of the Gulf of California low-level jet, even at high resolution. The model results indicate that outflow from convection over northwestern Mexico can modulate the low-level jet, though the extent to which these relationships occur in nature was not investigated. © 2008 American Meteorological Society

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product