Large-Scale Synoptic Systems and Fog During the C-FOG Field Experiment

Boundary-Layer Meteorology - The goal of this work is to summarize synoptic meteorological conditions during the Coastal Fog (C-FOG) field project that took place onshore and offshore of the Avalon...     

The MJO’s impact on rainfall trends over the Congo rainforest

Climate Dynamics - A significant declining trend in rainfall over the Congo basin has been observed over the past three decades. Since the Madden–Julian oscillation (MJO) is a major forcing...     

Influence of Atmospheric Water Vapour on IRS NIR Measurements for Detecting Vegetation Signal. Part I: A Simulation Study Using MODTRAN

Quantitative remote sensing involving accurate estimation of vegetation properties relies greatly on the measurements of the near infrared (NIR) channel because of unique interaction property between light and leaf. It is generally assumed that the NIR measurements are made in the absence of atmospheric absorption. However, relatively weak water vapour absorption features still persist in the NIR channel, which has bearing on the quantitative estimates of the vegetation properties and long-term data series. This paper reports the results of a study that was carried out to infer the possible influence of the atmospheric water vapour (WV) on the NIR measurements (0.77–0.86 μm) of Indian Remote Sensing (IRS) satellite sensors through radiative transfer simulations using MODTRAN model. The study also suggests and evaluates the alternate band-positions for the NIR channel to improve the IRS NIR measurements. It was observed that the water absorption features present around 0.810 μm reduces the WV transmission of IRS NIR channel from 1 to 0.91 when atmospheric WV content increased from 0 to 6 g/cm² and thus hampered the NIR reflectance by 14% as compared to reference signal. A significant improvement of the order of 6.5 to 12% in the NIR reflectance and 4.2 to 7% in NDVI was observed, when IRS NIR channel was split into NIR1 (0.775–0.805 μm) and NIR2 (0.845–0.875 μm) channels by avoiding the WV absorption features. The companion paper in this issue (Pandya et al. 2011) will support results of this simulation study through the EO1-Hyperion data analysis.     

Potassium and Residue Management Options to Enhance Productivity and Soil Quality in Zero Till Maize–Wheat Rotation

Crop residue burning and imbalanced use of chemical fertilizers in intensive cereal–cereal rotations are present ecological threats in any agro‐ecosystem of the world. Therefore, identification of best suitable agricultural practices can be a feasible option. The present experiment was initiated in 2013 and consisted of four residue levels (0, 2, 4, and 6 Mg ha^?1) and five potassium (K) levels (0, 50, 100, 150% recommended dose of K and 50%RDK+K solubilizing bacteria, KSB). Crop residue (CR) and K management significantly improve crop and soil quality associated parameters. Among the treatments, maximum increase in crop growth, physiological parameters, grain yield, quality aspects, and water productivity are recorded with the application of 4–6 Mg ha^?1 CR. Application of 50%RDK+KSB also significantly increases crop and soil related parameters. Soil quality indicators (bulk density, pH, electrical conductivity, and available micronutrients) do not vary significantly with CR and K management. Change in soil organic carbon status, soil enzymes, and potassium‐solubilizing bacterial count are significantly increased with 4–6 Mg ha^?1 CR and application of 50%RDK+KSB, and this is in accordance with correlation study carried out. Therefore, it is concluded that CR retention (4–6 Mg ha^?1) and reduction of inorganic K fertilizer by 50% and inoculation of KSB enhance the soil quality indicators and thereby improve crop growth, physiological parameters, grain yield, and quality aspects along with water productivity under zero till maize–wheat rotation.     

Atmospheric Turbulence Measurements at a Coastal Zone with and without Fog

Boundary-Layer Meteorology - Measurements of atmospheric turbulence at a site in Ferryland (Newfoundland) during the C-FOG (Coastal-Fog) field campaign in September–October 2018 are used to...     

Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none’. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts.     

Tri-Stereo Image Matching using Watershed Segmentation on Disparity Space Image

Local stereo matching algorithms use winner-take-all approach to get the disparity. Many times they end up at an erroneous result. To solve this, in the present work, a novel stereo image matching technique has been developed that identifies the most likely local minimum from the several possible local minima that corresponds to true disparity. The technique uses properties of the physical continuity of the land surface by the watershed lines applied to the disparity space volume. This helps to minimize the search for the most probable local minima as the correct solution for the matching. The matching is further improved by combining the watershed lines of disparity space volume of two stereo pairs from the tri-stereo. In the present study, experiments have been carried out using the standard Middlebury stereo datasets and remotely sensed tri-stereo images. Based on this approach, the experiments are successfully carried out using the test dataset. The experimental results are compared with the results from the currently contemporary techniques of dynamic programming and semi-global matching which resulted in 2–10% improvement in density of matched points for different datasets.     

Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam

ABSTRACT

The potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models – CEEMDAN-ANN and CEEMDAN-M5-MT – with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error.