ECHOBASE—A portable geographic fishery research database system

A geographic fishery research database system (GFRDBS), called ECHOBASE, for storing and management of data on pelagic fish resources from acoustic surveys and catch samples, combined with environmental data in a geographic context, has been developed. The system constitutes a portable and inexpensive yet powerful tool for fishery research and environmental monitoring and provides on‐board data processing and mapping capabilities.

Data are acquired from an echosounder, echo signal processor, environmental profiler (CTD probe), and Global Positioning System (GPS) receiver. The electronic digitized maps (based on C‐MAP public format files) provide active on‐screen nautical charts.

The basic GFRDBS platform is a portable PC 486 machine, which is used as an on‐board data acquisition and processing tool running under MS Windows 3.1 operating environment. The system is designed around the Borland's PARADOX package, which provides a relational database for managing acoustic, catch, and environmental data. The ECHOBASE programs were written in Borland C ++ language as a Windows application.     

Monitoring ionospheric scintillations and TEC at the Polish Polar Station on Spitsbergen: instrumentation and preliminary results

As a partial contribution to the International Polar Year, the Space Research Centre of the Polish Academy of Sciences has installed on the Polish Polar Station, located in the region of the Hornsund Bay in southern Spitsbergen, the equipment intended to monitor ionospheric scintillation and total electron content. We describe the equipment setup and the aims of the experiment. As an illustration of data analysis, we present the reconstruction of irregular structure of the ionosphere from the complex amplitude data, and the estimate of the form and drift of diffraction pattern based on the spaced receiver measurements.     

Application of predictive regional ionosphere model to medium range RTK positioning

Real Time Kinematic (RTK) GPS positioning over longer distances requires a support of atmospheric (ionospheric and tropospheric) corrections, since the atmospheric errors decorrelate with the growing distances and cannot be completely eliminated by double differencing of the satellite observations. Currently, the most commonly used approach is to derive the atmospheric corrections at the reference station network and provide them in real time to the roving receiver. Another solution, proposed here, is to use predictive atmospheric models in order to derive the atmospheric corrections. This paper presents the test results of the performance assessment of the predictive ionosphere model (UWM-IPM) application to medium-range RTK positioning. The rover data collected within 25 to 67 km from the closest reference station were processed in the kinematic mode with the support of the ionospheric corrections derived from the UWM-IPM model. The RTK solution was derived in both single-and multi-baseline modes, and compared to the two reference solutions obtained without the ionospheric corrections. All numerical tests were carried out using the MPGPS software developed in cooperation with The Ohio State University; a recent extension to the software, developed at the University of Warmia and Mazury in Olsztyn, introduces the predictive ionosphere model to the RTK solution. The test results are very promising, and indicate that predicted ionosphere corrections can effectively support medium-range RTK positioning, and allow for fast ambiguity resolution over distances of several tens of kilometers under moderate ionospheric conditions.     

Synthetic seismograms from generalized ray tracing1

A new method of numerical computation of elastic wavefields in regions containing caustics is tested. The method is an extension of the asymptotic ray theory (ART). The essential features of the method consist of the application of expressions which are well defined at caustics and expressed in terms of ray tracing combined with complex ray tracing in caustic shadows. The method and an outline of the underlying theory are briefly presented, followed by a comparison with finite differences on a test model involving a caustic cusp. The comparison reveals the unexpectedly high degree of accuracy of the new method.     

An algorithm for the optimum distribution of a regional seismic network — II. An analysis of the accuracy of location of local earthquakes depending on the number of seismic stations

Summary Seven optimal networks consisting of 4 to 10 stations are compared for a given region, where velocity-depth profiles and the distribution of seismic intensity are known. Assuming that the standard error of arrival time is _t =0.05 s and the standard errors of the parameters of velocity-depth profiles are equal to 5% of their values, the average standard errors of the origin time and focus coordinates are estimated. The application of optimum methods to the planning of seismic networks in the Lublin Coal Basin is presented, and maps of standard errors of origin time , depth and epicenter ( _xy ) for the case of an optimum network of 6 seismic stations are given.     

Magnetostratigraphic correlation of the Oxfordian–Kimmeridgian boundary

A magnetic polarity pattern for Boreal and Sub-Boreal ammonite zones of the Upper Oxfordian to Lower Kimmeridgian was established and confirmed in four British sections, including the proposed Global Boundary Stratotype Section and Point (GSSP) on the Isle of Skye (Scotland) to define the base of the international Kimmeridgian Stage. A coeval pattern for Sub-Mediterranean ammonite zones was compiled from seven sections in Poland, one German section and multi-section composites from France and Spain. The mean paleopole for the European Craton (excluding Spain) at the Oxfordian–Kimmeridgian boundary is 74.2°N, 181.3°E (Α₉₅ = 3.8°). The common magnetic polarity scale enables inter-correlation of ammonite subzones among these three faunal provinces and to the marine magnetic-anomaly M-Sequence. The proposed GSSP at the base of the Pictonia baylei Zone is near the base of an extended interval dominated by reversed polarity, which is interpreted to be Chron M26r. This GSSP level projects to the lower to middle part of the Epipeltoceras bimammatum Subzone, which is the middle subzone of this E. bimammatum Zone in the Sub-Mediterranean standard zonation. In contrast, the traditional placement of the Oxfordian–Kimmeridgian boundary in that Sub-Mediterranean standard zonation (base of Sutneria platynota Zone) is at the base of Chron M25r, or nearly 1 million years younger.     

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.     

Sea water surface energy balance in the Arctic fjord (Hornsund,SW Spitsbergen) in May–November 2014

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.     

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.