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.     

Seasonal change in body length of important small copepods and relationship with environmental factors in Jiaozhou Bay,China

Differences among species in prosome length and in species’ response to environmental factors do exist. Therefore, it is useful to examine prosome length for different copepod species in variable environments. Seasonal variations in prosome length of four small copepods and their copepodite stages in the Jiaozhou Bay were compared and the relative influence of temperature, salinity, and chlorophyll concentration were examined. Two peaks were found in the mean prosome length of Paracalanus parvus (in early winter and May). For Acartia bifilosa, the maximum values of all copepodites occurred mainly from February to April, and decreased to the bottom in July. Prosome length of Acartia pacifica peaked when it first appeared in June, then reached to the minimum in July. Parvocalanus crassirostris only appeared from late summer to autumn and the mean prosome length showed no clear changes. Correlations of adult prosome length with environmental factors were evaluated. For the four species, temperature was negatively correlated to prosome length except for P. crassirostris. But the different species varied markedly in their responds to temperature. A. bifilosa showed a more definite trend of size variation with temperature than P. parvus and A. pacifica. Correlations of prosome length with salinity were significantly positive for almost all the small copepods. The relationship between chlorophyll concentration and prosome length was complicated for these copepods, but for P. parvus, chlorophyll concentration was also an important affecting factor. Furthermore, investigation needs to be done on food quality for some copepod. These results are essential to estimate the biomass and the production, and to understand these small copepods’ population dynamics in this human-affected bay.     

Consistent seasonal snow cover depth and duration variability over the Western Himalayas (WH)

Precipitation in solid form, i.e., snow, during winter season over the Western Himalayas (WH) leads to the build-up of seasonal snow cover. Seasonal snow cover build-up (snow cover depth and duration) largely depends on atmospheric variables such as temperature, precipitation, radiation, wind, etc. Integrated (combined) influence of atmospheric variables on seasonal snow cover gets reflected in terms of spatial and temporal variability in seasonal snow cover build-up pattern. Hence spatial and temporal variability of seasonal snow cover build-up can serve as a good indicator of climate change in high altitude mountainous regions like the WH. Consistent seasonal snow cover depth and duration, delay days and early melt days of consistent seasonal snow cover at 11 stations spread across different mountain ranges over the WH were analyzed. Mean, maximum and percentiles (25th, 50th, 75th, 90th and 95th) of consistent seasonal snow cover depth and duration show decline over the WH in the recent past 2–3 decades. Consistent seasonal snow cover is found to melt early and snow cover build-up pattern is found to show changes over the WH. Decline in consistent seasonal snow cover depth, duration and changing snow cover build-up pattern over the WH in recent decades indicate that WH has undergone considerable climate change and winter weather patterns are changing in the WH.     

Characteristics of the Asian–Pacific Oscillation in Boreal Summer Simulated by BCC_CSM with Different Horizontal Resolutions

The summer Asian–Pacific Oscillation(APO) is a major teleconnection pattern that reflects the zonal thermal contrast between East Asia and the North Pacific in the upper troposphere. The performance of Beijing Climate Center Climate System Models(BCC CSMs) with different horizontal resolutions, i.e., BCC CSM1.1 and BCC CSM1.1(m), in reproducing APO interannual variability, APO-related precipitation anomalies, and associated atmospheric circulation anomalies, is evaluated.The results show that BCC CSM1.1(m) can successfully capture the interannual variability of the summer APO index. It is also more capable in reproducing the APO's spatial pattern, compared to BCC CSM1.1, due to its higher horizontal resolution. Associated with a positive APO index, the northward-shifted and intensified South Asian high, strengthened extratropical westerly jet, and tropical easterly jet in the upper troposphere, as well as the southwesterly monsoonal flow over North Africa and the Indian Ocean in the lower troposphere, are realistically represented by BCC CSM1.1(m), leading to an improvement in reproducing the increased precipitation over tropical North Africa, South Asia, and East Asia, as well as the decreased precipitation over subtropical North Africa, Japan, and North America. In contrast, these features are less consistent with observations when simulated by BCC CSM1.1. Regression analysis further indicates that surface temperature anomalies over the North Pacific and the southern and western flanks of the Tibetan Plateau are reasonably reproduced by BCC CSM1.1(m), which contributes to the substantial improvement in the simulation of the characteristics of summer APO compared to that of BCC CSM1.1.     

An algorithm to retrieve precipitation with synthetic aperture radar

This paper presents a new type of rainfall retrieval algorithm, called the model-oriented statistical and Volterra integration. It is a combination of the model-oriented statistical (MOS) and Volterra integral equation (VIE) approaches. The steps involved in this new algorithm can be briefly illustrated as follows. Firstly, information such as the start point and width of the rain is obtained through pre-analysis of the data received by synthetic aperture radar (SAR). Secondly, the VIE retrieval algorithm is employed over a short distance to obtain information on the shape of the rain. Finally, the rain rate can be calculated by using the MOS retrieval algorithm. Simulation results show that the proposed algorithm is effective and simple, and can lead to time savings of nearly 50% compared with MOS. An example of application of SAR data is also discussed, involving the retrieval of precipitation information over the South China Sea.     

Estimation of Above Ground Biomass for Central Indian Deciduous Forests Using ALOS PALSAR L-Band Data

Reliable and accurate estimates of tropical forest above ground biomass (AGB) are important to reduce uncertainties in carbon budgeting. In the present study we estimated AGB of central Indian deciduous forests of Madhya Pradesh (M.P.) state, India, using Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) L-band data of year 2010 in conjunction with field based AGB estimates using empirical models. Digital numbers of gridded 1?×?1° dual polarization (HH & HV) PALSAR mosaics for the study area were converted to normalized radar cross section (sigma naught - σ⁰). A total of 415 sampling plots (0.1 ha) data collected over the study area during 2009–10 was used in the present study. Plot-level AGB estimates using volume equations representative to the study area were computed using field inventory data. The plot-level AGB estimates were empirically modeled with the PALSAR backscatter information in HH, HV and their ratios from different forest types of the study area. The HV backscatter information showed better relation with field based AGB estimates with a coefficient of determination (R²) of 0.509 which was used to estimate spatial AGB of the study area. Results suggested a total AGB of 367.4 Mt for forests of M.P. state. Further, validation of the model was carried out using observed vs. predicted AGB estimates, which suggested a root mean square error (RMSE) of ±19.32 t/ha. The model reported robust and defensible relation for observed vs. predicted AGB values of the study area.     

Using a regional numerical weather prediction model for GNSS positioning over Brazil

The global navigation satellite system (GNSS) can provide centimeter positioning accuracy at low costs. However, in order to obtain the desired high accuracy, it is necessary to use high-quality atmospheric models. We focus on the troposphere, which is an important topic of research in Brazil where the tropospheric characteristics are unique, both spatially and temporally. There are dry regions, which lie mainly in the central part of the country. However, the most interesting area for the investigation of tropospheric models is the wet region which is located in the Amazon forest. This region substantially affects the variability of humidity over other regions of Brazil. It provides a large quantity of water vapor through the humidity convergence zone, especially for the southeast region. The interconnection and large fluxes of water vapor can generate serious deficiencies in tropospheric modeling. The CPTEC/INPE (Center for Weather Forecasting and Climate Studies/Brazilian Institute for Space Research) has been providing since July 2012 a numerical weather prediction (NWP) model for South America, known as Eta. It has yield excellent results in weather prediction but has not been used in GNSS positioning. This NWP model was evaluated in precise point positioning (PPP) and network-based positioning. Concerning PPP, the best positioning results were obtained for the station SAGA, located in Amazon region. Using the NWP model, the 3D RMS are less than 10 cm for all 24 h of data, whereas the values reach approximately 60 cm for the Hopfield model. For network-based positioning, the best results were obtained mainly when the tropospheric characteristics are critical, in which case an improvement of up to 7.2 % was obtained in 3D RMS using NWP models.     

Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability?

The global positioning system (GPS) differential code biases (DCB) provided by the International GNSS Service (IGS) show solar-cycle-like variation during 2002–2013. This study is to examine whether this variation of the GPS DCBs is associated with ionospheric variability. The GPS observations from low earth orbit (LEO) satellites including CHAMP, GRACE and Jason-1 are used to address this issue. The GPS DCBs estimated from the LEO-based observations at different orbit altitudes show a similar tendency as the IGS DCBs. However, this solar-cycle-like dependency is eliminated when the DCBs of 13 continuously operating GPS satellites are constrained to zero-mean. Our results thus revealed that ionospheric variation is not responsible for the long-term variation of the GPS DCBs. Instead, it is attributed to the GPS satellite replacement with different satellite types and the zero-mean condition imposed on all satellite DCBs.     

Investigation of Cycle Time Segments of Dragline Operation in Surface Coal Mine: A Statistical Approach

Dragline is highly capital intensive equipment to procure, operate and maintain in any surface mining operation. Given this, every second of operation of this capital intensive equipment is absolutely important. Improvement of even a single second in the total cycle time has a tremendous bearing on the overall performance of this equipment. In this light, the present paper is an endeavour to critically analyze the cycle time of dragline operations in a major surface coal mine in India. Rigorous statistical analysis has been performed on individual cycle time segments, of complete dragline cycle. The segmental cycle times have been found to be statistically significant and appear to be best represented by lognormal, normal and beta distributions. Furthermore, the mean time of the statistical distribution for segmental cycle time of dragline has revealed the dependence of cycle time on cut geometry and depth. Results have been illustrated in the form of figures, graphs and tables.