Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

Long-term estimates of parameters of the vertical distribution of cloud layers from atmospheric radiosounding data

Based on the global aerological dataset and on the method for determination of the boundaries and amount of cloudiness using the profiles of temperature and humidity obtained from the atmospheric radiosounding data [23], the estimates are computed for the parameters of atmospheric temperature- humidity separation into cloud and intercloud layers from the surface to the height of 10 km. The base and top of cloud layers and their total thickness and frequency are selected as layering parameters. The computations are based on the data for the observational period of 1964-1998. To specify the spatiotemporal features of atmospheric layering, long-term geographic distributions of mean values and standard deviations of the mentioned parameters are constructed for January and July, and the amplitude of their variations is determined.     

Spatiotemporal variations in climate moisture indices in Georgia under global warming

Using observational data from 50 weather stations in Georgia for the period of 1936-2013, the following climate indices of moisture regime are studied: maximum 1-day precipitation, maximum 5-day precipitation, the simple daily intensity index, the number of days with precipitation equal to not less than 10, 20, and 50 mm, number of consecutive wet and dry days. Geoinformation maps of the spatial structure are plotted, and the dynamics of these indices is studied for the period of global warming. Expected changes in the moisture regime in different physiographic regions in Georgia are assessed.     

Ozone content over the Russian Federation in the second quarter of 2016

The review is compiled on the basis of the operation of the total ozone (TO) monitoring system in the CIS and Baltic countries that functions in the operational regime at the Central Aerological Observatory. The monitoring system uses the data from the national network equipped with M-124 filter ozonometers under methodological supervision of the Main Geophysical Observatory. The quality of the functioning of the entire system is under the operational control based on the observations obtained from the OMI satellite equipment (NASA, the United States). The basic TO observation data are generalized for each month of the second quarter of 2016 and for the quarter as a whole. The data of routine observations of surface ozone content carried out in the Moscow region and Crimea are also presented.     

Circulation anomalies in the atmospheric centers of action during the Eastern Pacific and Central Pacific El Niño

Based on the statistical analysis the teleconnections between circulation anomalies in the atmospheric centers of action and sea surface temperature anomalies are revealed for two types of El Niño. It is demonstrated that for the Eastern Pacific El Niño stronger teleconnections are registered in the Northern Hemisphere whereas the response to the Central Pacific El Niño is much stronger in the Southern Hemisphere. The Central Pacific El Niño is characterized by the more rapid signal propagation from the tropical zone to distant regions. In some cases the pattern of interaction with the atmospheric circulation considerably differs for two types of El Niño that defines differences in the fields of weather anomalies.     

Variations in climatic and hydrological parameters in the Selenga River basin in the Russian Federation

The variations in average annual surface air temperature, precipitation, and runoff in the Selenga River basin (within Russia) are analyzed. It is demonstrated that the considerable increase in average annual temperature of surface air layers occurred in the 1980s-1990s. The decrease in peak water discharge in the rivers and the increase in the frequency of low-water periods were revealed in the forest-steppe and steppe zones of the Selenga River basin in 2001-2010. In the southwestern mountain regions (the Dzhida River basin) the river runoff increased during that period.     

Assessing Performance of Tomo-SAR and Backscattering Coefficient for Hemi-Boreal Forest Aboveground Biomass Estimation

Tomo-SAR technique has been used for hemi-boreal forest height and further forest biomass estimation through allometric equation. Backscattering coefficient especially in longer wavelength (L- or P-band) is thought as a useful parameter for hemi-boreal forest biomass retrieval. The aim of this paper is to assess the performance of vertical backscattering power and backscattering coefficient for hemi-boreal forest aboveground biomass (AGB) estimation with airborne P-band data. The test site locates in southern Sweden called Remningstorp test site, and the in-situ forest AGB ranges from 14 t/ha to 245 t/ha at stand level. Multi-baseline P-band Pol-InSAR data in repeat-path mode collected during March and May in 2007 at Remningstorp test site was used. We found that the correlation coefficient (R) between backscattering coefficient of P-band HH polarization and the in-situ forest biomass reached 0.87. The R for P-band VV backscattering power at 5 m is 0.71 and 10 m is 0.72. Backscattering coefficient in HH polarization and vertical backscattering power at 5 m and 10 m were applied to construct a model for hemi-boreal forest AGB estimation by backward step-wise regression and cross-validation approach. The results showed that the estimated forest AGB ranges from 19 to 240 t/ha, and the constructed model obtained a higher R and smaller RMSE, the value of R is 0.91, RMSE is 30.43 t/ha at Remningstorp test site.     

Practical Application of 3D Visualization Using Geotechnical Database: A Case Study Karsiyaka (Izmir) Settlement Area (Turkey)

3D geographic information system software’s (GIS) are widely used in engineering geology applications. This study was performed in the Karsiyaka settlement area for the preparation of engineering geological maps and evaluation of geological structures. Firstly, topographic maps digitized with Arcview GIS 3.2. Engineering geological maps were prepared using site works and digitized with the Rockworks 2006 programme and later stored in GIS-based computer systems. 3D modelling analysis and assessment using a geotechnical database is important to assist decision-making for land use and metro subway line planning, construction site selection, selection of water sources, etc. In this respect, the sub-surface of the study area is fully 3D visualized and useful soil class zonation maps for different depths maps are performed to be used in further studies. At last, after research at this site, the construction applications of Karsiyaka have multiplied.     

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.     

Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland,South Africa

Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m². In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r ² = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m³/m³. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m³/m³. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m³/m³). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.