Integration Of TRMM Rainfall In Numerical Model For Pesticide Prediction In Subtropical Climate

Rain gauge data in developing countries are usually very limited, which constrains most of the hydrological modelling applications. The satellite based rainfall estimates could be a promising choice and hence can be used as a surrogate to ground-based rainfall. However, the usefulness of these products needs to be evaluated for hydrological application such as for pesticide predictions. The present study compares the contaminant transport simulation with the utilization of Tropical Rainfall Measuring Mission (TRMM) rainfall compared with rain gauge data from the field site. Through this study, transport trends of the pesticide, Thiram, a dithiocarbamate, at different time and depth in the fields under real field conditions for the wheat crop were compared to the numerical simulations using HYDRUS- 1D with the input of daily rainfall from the TRMM. The daily rainfall from TRMM has been utilized to simulate the pesticide concentration up to 60 cm vertical soil profile with the intervals of 15 cm. The simulated soil moisture content using ground based rainfall and TRMM derived rainfall measurements indicate an agreeable goodness of fit between the both. The overall analysis reveals that TRMM rainfall is promising for soil pesticide prediction in absence of ground based measurements of soil pesticide. Further, comparison of the model to measured field data of pesticides movement indicates that the modelling approach can provide reliable and useful estimates of the mass flux of water and non-volatile pesticide in vadose zone. Thus, the satellite-based rainfall products could also be useful for policy makers and planners while controlling inappropriate pesticide application under saturated and deficit soil moisture conditions

Reduced major axis approach for correcting GPM/GMI radiometric biases to coincide with radiative transfer simulation

Correcting radiometric biases is crucial prior to the use of satellite observations in a physically based retrieval or data assimilation system. This study proposes an algorithm - RARMA (Radiometric Adjustment using Reduced Major Axis) for correcting the radiometric biases so that the observed radiances coincide with the simulation of a radiative transfer modeL The RARMA algorithm is a static bias correction algorithm, which is developed using the reduced major axis (RMA) regression approach, NOAA\u27s Community Radiative Transfer Model (CRTM) has been used as the basis of radiative transfer simulation for adjusting the observed radiometric biases. The algorithm is experimented and applied to the recently launched Global Precipitation Measurement (GPM) mission\u27s GPM Microwave Imager (GMI), Experimental results demonstrate that radiometric biases are apparent in the GMI instrument, The RARMA algorithm has been able to correct such radiometric biases and a significant reduction of observation residuals is revealed while assessing the performance of the algorithm, The experiment is currently tested on clear scenes and over the ocean surface, where, surface emissivity is relatively easier to model. with the help of a microwave emissivity model (FASTEM-5)

ModEnzA: Accurate Identification of Metabolic Enzymes Using Function Specific Profile HMMs with Optimised Discrimination Threshold and Modified Emission Probabilities

Various enzyme identification protocols involving homology transfer by sequence-sequence or profile-sequence comparisons have been devised which utilise Swiss-Prot sequences associated with EC numbers as the training set. A profile HMM constructed for a particular EC number might select sequences which perform a different enzymatic function due to the presence of certain fold-specific residues which are conserved in enzymes sharing a common fold. We describe a protocol, ModEnzA (HMM-ModE Enzyme Annotation), which generates profile HMMs highly specific at a functional level as defined by the EC numbers by incorporating information from negative training sequences. We enrich the training dataset by mining sequences from the NCBI Non-Redundant database for increased sensitivity. We compare our method with other enzyme identification methods, both for assigning EC numbers to a genome as well as identifying protein sequences associated with an enzymatic activity. We report a sensitivity of 88% and specificity of 95% in identifying EC numbers and annotating enzymatic sequences from the E. coli genome which is higher than any other method. With the next-generation sequencing methods producing a huge amount of sequence data, the development and use of fully automated yet accurate protocols such as ModEnzA is warranted for rapid annotation of newly sequenced genomes and metagenomic sequences

Appraisal of NLDAS-2 Multi-Model Simulated Soil Moistures for Hydrological Modelling

Soil moisture is a key variable in hydrological modelling, which could be estimated by land surface modelling. However the previous studies have focused on evaluating these soil moisture estimates by using point-based measurements, and there is a lack of attention for their appraisal over basin scales particularly for hydrological applications. In this study, we carry out for the first time, a detailed evaluation of five sources of soil moisture products (NLDAS-2 multi-model simulated soil moistures: Noah, VIC, Mosaic and SAC; and a ground observation), against a widely used hydrological model Xinanjiang (XAJ) as a benchmark at a U.S. basin. Generally speaking, all products have good agreements with the hydrological soil moisture simulation, with superior performance obtained from the SAC model and the VIC model. Furthermore, the results indicate that the in-situ measurements in deeper soil layer are still usable for hydrological applications. Nevertheless further improvement is still required on the definition of land surface model layer thicknesses and the related data fusion with the remotely sensed soil moisture. The potential usage of the NLDAS-2 soil moisture datasets in real-time flood forecasting is discussed

An introduction to factor analysis for radio frequency interference detection on satellite observations

A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C- and X-band radiometer channels as effectively as the conventional PCA method