Using hourly rain gauge data at more than 30,000 automatic weather stations in China, in conjunction with the Climate Precipitation Center Morphing (CMORPH) precipitation product for the 2008-2010 warm seasons (from May through September), we assess the capability of the probability density function-optimal interpolation (PDF-OI) methods in generating the daily, 0.25°× 0.25°and hourly, 0.1°× 0.1°merged precipitation products between gauge observations and the CMORPH product. We find that error correlation, error variances of gauge and satellite data, and matching strategy in the PDF-OI method are dependent on the spatial and temporal resolutions of the used data. Efforts to improve the parameters and matching strategy for the hourly and 0.1°× 0.1°product have been conducted. These improvements are not only suitable to a high-frequency depiction of no-rain events, but accurately describe the error structures of hourly gauge and satellite fields. The successive merged precipitation algorithm or product is called the original PDF-OI (Orig_PDF-OI) and the improved PDF-OI, respectively. The cross-validation results show that the improved method reduces systematic bias and random errors effectively compared with both the CMORPH precipitation and the Orig_PDF-OI. The improved merged precipitation product over China at hourly, 0.1°resolution is generated from 2008 to 2010. Compared with the Orig_PDF-OI, the improved product reduces the underestimation greatly and has smaller bias and root-mean-square error, and higher spatial correlation. The improved product can better capture some varying features of hourly precipitation in heavy weather events.

Ciphertext-policy attribute-based encryption\ud (CP-ABE) is a very promising encryption technique\ud for secure data sharing in the context of cloud computing. Data\ud owner is allowed to fully control the access policy associated\ud with his data which to be shared. However, CP-ABE is limited\ud to a potential security risk that is known as key escrow problem,\ud whereby the secret keys of users have to be issued by a trusted\ud key authority. Besides, most of the existing CP-ABE schemes\ud cannot support attribute with arbitrary state. In this paper, we\ud revisit attribute-based data sharing scheme in order to solve\ud the key escrow issue but also improve the expressiveness of\ud attribute, so that the resulting scheme is more friendly to cloud\ud computing applications. We propose an improved two-party key\ud issuing protocol that can guarantee that neither key authority\ud nor cloud service provider can compromise the whole secret key\ud of a user individually. Moreover, we introduce the concept of\ud attribute with weight, being provided to enhance the expression\ud of attribute, which can not only extend the expression from\ud binary to arbitrary state, but also lighten the complexity of\ud access policy. Therefore, both storage cost and encryption\ud complexity for a ciphertext are relieved. The performance\ud analysis and the security proof show that the proposed scheme\ud is able to achieve efficient and secure data sharing in cloud\ud computin

Rainfall over northern Australia (NA) in austral summer is the largest water source of Australia. Previous studies have suggested a strong zonal-dipole trend pattern in austral summer rainfall since 1950, with rainfall increasing in northwest Australia (NWA) but decreasing in northeast Australia (NEA). The dynamics of rainfall increase in NWA was linked to sea surface temperature (SST) in the south Indian Ocean and the rainfall decrease in NEA was associated with SST in the northeast Indian Ocean.This study reports that, in contrast to a zonal-dipole trend pattern, a dominant wetting pattern over NA has recently been observed in the post-1979 satellite era. The recent NA rainfall increase also manifests as the first leading mode of summer rainfall variability over the Australian continent. Further investigation reveals that SST in the tropical western Pacific (TWP) has replaced the SST in the south and northeast Indian Ocean as the controlling factor responsible for the recent NA rainfall increase. Direct thermal forcing by increasing TWP SST gives rise to an anomalous Gill-type cyclone centered around NA, leading to anomalously high rainfall. As such, the increasing SST in the TWP induces over 50% of the observed rainfall wetting trend over NA. The increased rainfall in turn induces land surface cooling in NA. This mechanism can be confirmed with results obtained from sensitivity experiments of a numerical spectral atmospheric general circulation model. Thus, increasing SST in the TWP has contributed much of the recent summer rainfall increase and consequently the surface cooling over NA.

Manabendra Saharia (2014) Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau, This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005-2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods -arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic meanshow insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.

Extreme precipitation is one of the most devastating forms of atmospheric phenomenon, causing severe damage worldwide, and is likely to intensify in strength and occurrence in a warming climate. This contribution gives an overview of the potential and challenges associated with using weather radar data to investigate extreme precipitation. We illustrate this by presenting radar data sets for Germany, the U.S. and the UK that resolve small-scale heavy rainfall events of just a few km2 with return periods of 5 years or more. Current challenges such as relatively short radar records and radar-based QPE uncertainty are discussed. An example from a precipitation climatology derived from the German weather radar network with spatial resolution of 1 km reveals the necessity of radars for observing short-term (1–6 h) extreme precipitation. Only 17.3% of hourly heavy precipitation events that occurred in Germany from 2001 to 2018 were captured by the rain gauge station network, while 81.8% of daily events were observed. This is underlined by a similar study using data from the UK radar network for 2014. Only 36.6% (52%) of heavy hourly (daily) rain events detected by the radar network were also captured by precipitation gauging stations. Implications for the monitoring of hydrologic extremes are demonstrated over the U.S. with a continental-scale radar-based reanalysis. Hydrologic extremes are documented over ∼1000 times more locations than stream gauges, including in the majority of ungauged basins. This underlines the importance of high-resolution weather radar observations for resolving small-scale rainfall events, and the necessity of radar-based climatological data sets for understanding the small-scale and high-temporal resolution characteristics of extreme precipitation.

No abstract

LaTaON2 porous single crystals (PSCs), integrating structural coherence and porous microstructures, will warrant promising photocatalytic performance. The absence of grain boundaries in PSCs ensures rapid photocarrier transportation from bulk to the surface, thereby mitigating photocarriers’ recombination. Porous microstructures not only provide ample reachable surface to host photochemical reactions but also reinforce photon-matter interactions by additional photon reflection/scattering. Here, we have synthesized LaTaON2 PSCs via a topotactic route and show significantly improved photocatalytic performance. Efficient water oxidation into O2 has been realized by LaTaON2 PSCs with an apparent quantum efficiency as high as 5.7% at 420 ± 20 nm. Stable overall water splitting into stoichiometric H2 and O2 has also been achieved in a Z-scheme setup using LaTaON2 PSCs as the O2 evolution photocatalyst. These results not only prove that PSCs facilitate photocarrier migrations, which in turn deliver exceptional photocatalytic performance, but also imply that PSCs are useful to reinvigorate conventional semiconductor photocatalysts toward efficient solar energy conversions.

Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01 • resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01 • resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03 • × 0.03 • spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01 • resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.