ALBERT

Description: As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches—decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15–75 min before the lightning occurrence were used as input variables for CI detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30–45 min in advance using COMS MI data.

Description: Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Field of View (IFOV) bias correction method for Soil Moisture and Ocean Salinity (SMOS) soil moisture. To the best of our knowledge, this is the first paper to present the probabilistic presentation of SMOS soil moisture using retrieval ensembles. We illustrate that retrieval ensembles effectively mitigated the overestimation problem of SMOS soil moisture arising from brightness temperature errors over West Africa in a computationally efficient way (ensemble size: 12, no time-integration). In contrast, the existing method of Cumulative Distribution Function (CDF) matching considerably increased the SMOS biases, due to the limitations of relying on the imperfect reference data. From the validation at two semi-arid sites, Benin (moderately wet and vegetated area) and Niger (dry and sandy bare soils), it was shown that the SMOS errors arising from rain and vegetation attenuation were appropriately corrected by ensemble approaches. In Benin, the Root Mean Square Errors (RMSEs) decreased from 0.1248 m3/m3 for CDF matching to 0.0678 m3/m3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m3/m3 for CDF matching to 0.045 m3/m3 for the ensemble approach.

Description: Forests, Vol. 9, Pages 268: Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data Forests doi: 10.3390/f9050268 Authors: Junghee Lee Jungho Im Kyungmin Kim Lindi Quackenbush Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approaches—support vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species.

Description: Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014.

Description: This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO, and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper-level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2 months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal timescales. It is also found that the skill of AO forecasts during the recent period (1997–2010) is higher than that of the earlier period (1983–1996).

Description: Low-impact development (LID) methods are an important approach to storm-water mitigation. Modeling the effects of these installations using rainfall-runoff simulations can provide useful data for future design and implementation. In this study, we used the Storm Water Management Model to assess seven types of LID installations (vegetated areas, garden pots, tree filter boxes, permeable pavement, infiltration ditches, rain barrels, and infiltration blocks) at a South Korean industrial site. Using both short- and long-term simulation periods and distinct sub-basins within the study site, we were able to assess LID performance at the combined watershed, as well as at one LID facility. All LID types showed reasonable performance for storm-water runoff reduction, though rain barrels were the least effective. The effect of rainfall runoff reduction on LID facilities is changed according to rainfall depth (annual precipitation, monthly rainfall), the ratio of drainage area and facility capacity. We concluded that SWMM-LID modeling can effectively support the management of LID installations by providing additional design and planning data to better mitigate the effects of storm-water runoff.

Description: In this study, two kinds of copper micro-patterned surfaces with different heights were fabricated by using a powder injection molding (PIM) process. The micro-pattern’s size was 100 μm, and the gap size was 50 μm. The short micro-pattern’s height was 100 μm, and the height of the tall one was 380 μm. A copper powder and wax-polymer-based binder system was used to fabricate the micro-patterned surfaces. The critical heat flux (CHF) and heat transfer coefficient (HTC) during pool-boiling tests were measured with the micro-patterned surfaces and a reference plain copper surface. The CHF of short and tall micro-patterned surfaces were 1434 and 1444 kW/m2, respectively, and the plain copper surface’s CHF was 1191 kW/m2. The HTC of the plain copper surface and the PIM surface with short and tall micro-patterned surfaces were similar in value up to a heat flux 1000 kW/m2. Beyond that value, the plain surface quickly reached its CHF, while the HTC of the short micro-patterned surface achieved higher values than that of the tall micro-patterned surface. At CHF, the maximum values of HTC for the short micro-pattern, tall micro-pattern, and the plain copper surface were 68, 58, and 57 kW/m2 K.

Description: The effects of the momentum-flux ratio of propellant upon the combustion efficiency of a gas-centered-swirl-coaxial (GCSC) injector used in the combustion chamber of a full-scale 9-tonf staged-combustion-cycle engine were studied experimentally. In the combustion experiment, liquid oxygen was used as an oxidizer, and kerosene was used as fuel. The liquid oxygen and kerosene burned in the preburner drive the turbine of the turbopump under the oxidizer-rich hot-gas condition before flowing into the GCSC injector of the combustion chamber. The oxidizer-rich hot gas is mixed with liquid kerosene passed through combustion chamber’s cooling channel at the injector outlet. This mixture has a dimensionless momentum-flux ratio that depends upon the dispensing speed of the two fluids. Combustion tests were performed under varying mixture ratios and combustion pressures for different injector shapes and numbers of injectors, and the characteristic velocities and performance efficiencies of the combustion were compared. It was found that, for 61 gas-centered swirl-coaxial injectors, as the moment flux ratio increased from 9 to 23, the combustion-characteristic velocity increased linearly and the performance efficiency increased from 0.904 to 0.938. In addition, excellent combustion efficiency was observed when the combustion chamber had a large number of injectors at the same momentum-flux ratio.

Description: Fruiting body-forming members of the Basidiomycota maintain their ecological fitness against various antagonists like ascomycetous mycoparasites. To achieve that, they produce myriads of bioactive compounds, some of which are now being used as agrochemicals or pharmaceutical lead structures. Here, we screened ethyl acetate crude extracts from cultures of thirty-five mushroom species for antifungal bioactivity, for their effect on the ascomycete Saccharomyces cerevisiae and the basidiomycete Ustilago maydis. One extract that inhibited the growth of S. cerevisiae much stronger than that of U. maydis was further analyzed. For bioactive compound identification, we performed bioactivity-guided HPLC/MS fractionation. Fractions showing inhibition against S. cerevisiae but reduced activity against U. maydis were further analyzed. NMR-based structure elucidation from one such fraction revealed the polyyne we named feldin, which displays prominent antifungal bioactivity. Future studies with additional mushroom-derived eukaryotic toxic compounds or antifungals will show whether U. maydis could be used as a suitable host to shortcut an otherwise laborious production of such mushroom compounds, as could recently be shown for heterologous sesquiterpene production in U. maydis.