How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?

We investigated the potential of the new generation of satellite precipitation product from the Global Precipitation Mission (GPM) to characterize the rainfall in Malaysia. Most satellite precipitation products have limited ability to precisely characterize the high dynamic rainfall variation that occurred at both time and scale in this humid tropical region due to the coarse grid size to meet the physical condition of the smaller land size, sub-continent and islands. Prior to the status quo, an improved satellite precipitation was required to accurately measure the rainfall and its distribution. Subsequently, the newly released of GPM precipitation product at half-hourly and 0.1° resolution served an opportunity to anticipate the aforementioned conflict. Nevertheless, related evidence was not found and therefore, this study made an initiative to fill the gap. A total of 843 rain gauges over east (Borneo) and west Malaysia (Peninsular) were used to evaluate the rainfall the GPM rainfall data. The assessment covered all critical rainy seasons which associated with Asian Monsoon including northeast (Nov. - Feb.), southwest (May - Aug.) and their subsequent inter-monsoon period (Mar. - Apr. & Sep. - Oct.). The ability of GPM to provide quantitative rainfall estimates and qualitative spatial rainfall patterns were analysed. Our results showed that the GPM had good capacity to depict the spatial rainfall patterns in less heterogeneous rainfall patterns (Spearman’s correlation, 0.591 to 0.891) compared to the clustered one (r = 0.368 to 0.721). Rainfall intensity and spatial heterogeneity that is largely driven by seasonal monsoon has significant influence on GPM ability to resolve local rainfall patterns. In quantitative rainfall estimation, large errors can be primarily associated with the rainfall intensity increment. 77% of the error variation can be explained through rainfall intensity particularly the high intensity (> 35 mm d^-1). A strong relationship between GPM rainfall and error was found from heavy (~35 mm d^-1) to violent rain (160 mm d^-1). The output of this study provides reference regarding the performance of GPM data for respective hydrology studies in this region.     

Investigation of Arsenotrophic Microbiome in Arsenic‐Affected Bangladesh Groundwater

Arsenotrophic bacteria contribute to the nutrient cycling in arsenic (As) affected groundwater. This study employed a culture‐independent and ‐dependent investigation of arsenotrophic microbiomes in As affected groundwater samples collected from Madhabpur, Sonatengra, and Union Porishod in Singair Upazila, Manikganj, Bangladesh. Total As contents, detected by Atomic Absorption Spectrophotometry (AAS) of the samples, were 47 µg/L (Madhabpur, SNGW‐1), 53 µg/L (Sonatengra, SNGW‐2), and 12 µg/L (Union porishod, SNGW‐3), whereas the control well (SNGW‐4; depths >150 m) showed As content of 6 µg/L. Denaturing Gradient Gel Electrophoresis (DGGE) analysis of the amplified 16S rRNA gene from As‐affected groundwater samples revealed the dominance of aerobic bacteria Pseudomonas within heterogeneous bacterial populations. DGGE of heterotrophic enrichments supplemented with arsenite [As (III)] for 4 weeks showed the dominance of Chryseobacterium, Flavobacterium, and Aquabacterium, whereas the dominant genera in that of autotrophic enrichments were Aeromonas, Acinetobacter, and Pseudomonas. Cultured bacteria retrieved from both autotrophic and heterotrophic enrichments were distinguished into nine genotypes belonging to Chryseobacterium, Acinetobacter, Escherichia, Pseudomonas, Stenotrophomonas, Janibacter, Staphylococcus, and Bacillus. They exhibited varying range of As(III) tolerance from 4 to 27 mM. As(III) transformation potential was confirmed within the isolates with oxidation rate as high as 0.143 mM/h for Pseudomonas sp. Sn 28. The arsenotrophic microbiome specifies their potential role in groundwater As‐cycling and their genetic information provide the scientific basis for As‐bioremediation.     

Simulation of equatorial wind field patterns with TAPM during the 1997 haze episode in Peninsular Malaysia

The large‐scale forest fires that occurred during the major El Niño event in 1997 can be counted among the worst environmental disasters in Southeast Asia. This study investigates atmospheric mesoscale features over Sumatra and the Klang Valley in Peninsular Malaysia during the resultant haze episode of September 1997 utilizing a limited area mesoscale three‐dimensional meteorological and dispersion model, The Air Pollution Model (TAPM). Mesoscale features that would not be highlighted by global numerical prediction models, such as the daily land and sea breeze conditions at the selected air pollution stations located near the west coast of Peninsular Malaysia, were predicted with an Index of Agreement of 0.3, which implied a moderate conformity between the predicted and observed values. Tracer analysis of air particles at a selected location in the Straits of Malacca verified the existence of the landward and shoreward movement of the air during the simulation of the low‐level wind field. Air pollutants from the burning areas of neighbouring Sumatra just across the straits were transported towards the Klang valley during the daytime and seawards during the nighttime, highlighting the recirculation features of aged and newer air particles over the seven days (13–18 September) of the model simulation. The presence of the central Main Range east of the Klang valley to a certain extent limited further eastward movement of the air particles. Near calm conditions at low levels were simulated from midnight to midday on 14 September, where the movement of the polluted air particles from the uncontrolled burning in Sumatra was confined within the Straits of Malacca. Turbulence within the planetary boundary layer in terms of the total kinetic energy was found to be weak from 14 to 15 September, congruent with the weak strength of low level winds that reduced the ability of the air to transport the pollutants during the period of severe smoke haze. Statistical evaluation showed that parameters such as the systematic RMSE (root mean square error) and unsystematic RMSE for the zonal wind component were slightly higher than for the meridional one, indicating higher errors between the observed and simulated zonal values. Otherwise, the equatorial meteorological parameters such as wind speed and temperature were successfully simulated by the model with comparatively high correlation coefficients, lower RMSEs and moderately high indices of agreement with observed values.     

Role of early warning systems for sustainable agriculture in Iran

Drought is a complex phenomenon in meteorology and can affect agriculture. Its impacts vary greatly since they depend not only on the magnitude, timing, duration, and frequency of rainfall deficits but also on the differing responses of various plants to water stress. The essence of good drought management is to use this range of responses to the best advantage. Iran is one of the world’s largest and most productive suppliers of food and fiber. The objective of this study was, therefore, to gather and analyze standardized information on the Role of Early Warning Systems for Sustainable Agriculture for cereals and leguminous and industrial crops in Iran environmental zones. Annual average rainfall (mm year^?1) and ET_O (mm year^?1) are 76.56 and 3001, respectively, in stations with very dry climate; 195.41 and 2249, respectively, in stations with dry climate; 343.9 and 1351, respectively, in stations with semi-dry climate; 583.8 and 1153, respectively, in stations with semi-humid climate; and 1272 and 949, respectively, in stations with humid climate. The maximum and minimum of annual average rainfall happened in Rasht (1337 mm year^?1) and Zabol (57 mm year^?1) stations, and the maximum and minimum for annual average ET_O happened in Chabahar (3909.15 mm year^?1) and Anzali harbor (890.6 mm year^?1), respectively. Therefore, 13.63 % of stations have suitable conditions for crop productions and 86.37 % are in critical conditions.     

Methods and results of an automatic analysis of a complete sample of Swift-XRT observations of GRBs

We present a homogeneous X-ray analysis of all 318 gamma-ray bursts detected by the X-ray telescope (XRT) on the Swift satellite up to 2008 July 23; this represents the largest sample of X-ray GRB data published to date. In Sections 2–3 , we detail the methods which the Swift -XRT team has developed to produce the enhanced positions, light curves, hardness ratios and spectra presented in this paper. Software using these methods continues to create such products for all new GRBs observed by the Swift -XRT. We also detail web-based tools allowing users to create these products for any object observed by the XRT, not just GRBs. In Sections 4–6 , we present the results of our analysis of GRBs, including probability distribution functions of the temporal and spectral properties of the sample. We demonstrate evidence for a consistent underlying behaviour which can produce a range of light-curve morphologies, and attempt to interpret this behaviour in the framework of external forward shock emission. We find several difficulties, in particular that reconciliation of our data with the forward shock model requires energy injection to continue for days to weeks.     

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations

The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity (Z _H), differential reflectivity (Z _DR), co-polar correlation coefficient (ρ _HV), linear depolarization ratio (LDR) and height of radar measurements (H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R ² and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R ² and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.