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.     

Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation

Statistical seasonal prediction models for the Arctic sea ice concentration (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-ice regions (of one- or multi-year sea ice). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model.     

气象预警信息一键式发布系统研究与应用

基于WebGIS、HTTP通信、数据库等技术,制定了一键式预警信息发布规范,有效整合了电子显示屏、预警大喇叭、手机等预警发布终端,开发了气象预警信息一键式发布平台,实现了地图综合显示、气象数据叠加、任意预警区域选取、一键式发布预警信息等功能,提高了预警信息发布的可视化水平,保证了多终端预警信息发布的高效性、一致性。该系统的一键式信息发布技术,为气象预警发布开拓了新思路,能够有效提升业务水平和预警发布能力。     

Geochronological and isotopic records of crustal storage and assimilation in the Wolverine Creek–Conant Creek system,Heise eruptive centre,Snake River Plain

Understanding the processes of differentiation of the Yellowstone–Snake River Plain (YSRP) rhyolites is typically impeded by the apparent lack of erupted intermediate compositions as well as the complex nature of their shallow interaction with the surrounding crust responsible for their typically low O isotopic ratios. A pair of normal-δ¹⁸O rhyolitic eruptions from the Heise eruptive centre in eastern Idaho, the Wolverine Creek Tuff and the Conant Creek Tuff, represent unique magmatic products of the Yellowstone hotspot preserving abundant vestiges of the intermediate differentiation steps leading to rhyolite generation. We address both shallow and deep processes of magma generation and storage in the two units by combining high-precision ID–TIMS U–Pb zircon geochronology, trace element, O and Hf isotopic studies of zircon, and Sr isotopic analyses of individual high-Mg# pyroxenes inherited from lower- to mid-crustal differentiation stages. The zircon geochronology confirms the derivation of both tuffs from the same rhyolitic magma reservoir erupted at 5.5941 ± 0.0097 Ma, preceded by at least 92 ± 14 ky of continuous or intermittent zircon saturation approximating the length of pre-eruptive magma accumulation in the upper crust. Some low-Mg# pyroxenes enclosing zircons predate the eruption by at least 45 ± 27 ky, illustrating the co-crystallisation of major and accessory phases in the near-liquidus rhyolitic melts of the YSRP over a significant period of time. Coeval zircon crystals are isotopically heterogeneous (two populations at εHf ~?5 and ?13), requiring the assembly of isotopically distinct melt pockets directly prior to, or during, the eruption. The primitive Mg# 60–90 pyroxenes are out of isotopic equilibrium with the host rhyolitic melt (⁸⁷Sr/⁸⁶Sr_i = 0.70889), covering a range of ⁸⁷Sr/⁸⁶Sr_i = 0.70705–0.70883 corresponding to ratios typical of the most radiogenic YSRP basalts to the least radiogenic YSRP rhyolites. Together with the low εHf in zircon, the Sr isotopic ratios illustrate limited assimilation dominated by radiogenic Archean crustal source materials incorporated into variably evolved YSRP melts as they progress towards rhyolitic compositions by assimilation–fractional crystallisation.     

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

In this paper, Kalpana-1 derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates are compared with two multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP), and India Meteorological Department (IMD) surface rain gauge (SRG)-based rainfall at meteorological sub-divisional scale over India. The performance of the summer monsoon rainfall of 2013 over Indian meteorological sub-divisions is assessed at different temporal scales. Comparison of daily accumulated rainfall over India from IMSRA shows a linear correlation of 0.72 with TMPA-3B42 and 0.70 with GSMaP estimates. IMSRA is capable to pick up daily rainfall variability over the monsoon trough region as compared to TMPA-3B42 and GSMaP products, but underestimates moderate to heavy rainfall events. Satellite-derived rainfall maps at meteorological sub-divisional scales are in reasonably good agreement with IMD-SRG based rainfall maps with some exceptions. However, IMSRA performs better than GSMaP product at meteorological sub-divisional scale and comparable with TMPA data. All the satellite-derived rainfall products underestimate orographic rainfall along the west coast, the Himalayan foothills and over the northeast India and overestimate rainfall over the southeast peninsular India. Overall results suggest that IMSRA estimates have potential for monsoon rainfall monitoring over the Indian meteorological sub-divisions and can be used for various hydro-meteorological applications.     

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.     

TEC variation over an equatorial and anomaly crest region in India during 2012 and 2013

Using the global positioning system (GPS) measurements, the total electron content (TEC) at station Bangalore (13.02°N, 77.57°E geographic; 04.44°N, 150.84°E geomagnetic), lying at the equatorial region, and station Lucknow (26.91°N, 80.95°E geographic; 17.96°N, 155.24°E geomagnetic), lying at equatorial ionospheric anomaly (EIA) crest region, have been estimated for the year 2012–2013. In order to evaluate the International Reference Ionosphere (IRI) model regarding simulation/modeling of ionospheric studies specially at equatorial and EIA crest regions, we have compared the TEC derived from the recent version of the IRI-2012 model and the older IRI-2007 with its three topside options, namely IRI-NeQuick (IRI-NeQ), IRI-2001, and IRI01-corr, with that of GPS-TEC over Bangalore and Lucknow. For the EIA station Lucknow, the IRI-2012 model with IRI-NeQ and IRI01-corr topside is found in good agreement with GPS-TEC during summer and equinox season, while the IRI-2012 model for all three topside options significantly overestimates the GPS-TEC during winter season. The IRI-2001 topside overestimates the GPS-TEC over both the stations during all seasons. The anomalous difference between the IRI-2012 model prediction and ground-based GPS-TEC in daytime hours during the winter season observed at Lucknow could be attributed to discrepancies in the slab thickness predicted by the model, which is more during the winter season as compared to summer and equinox. These large discrepancies in the slab thickness predicted by the IRI-2012 as well as the IRI-2007 model during the winter season have been supported by using the foF2 data from Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation-based measurements. We also observed that the discrepancies in the recent IRI-2012 model with respect to GPS-TEC are found to be slightly larger than those with the older IRI-2007 model over the EIA region Lucknow. However, over the equatorial region Bangalore, the discrepancy with the older model IRI-2007 was found to be larger than with the recent IRI-2012 model. This suggests that the performance of the IRI-2012 model is poorer than the IRI-2007 model at the EIA region while better at equatorial region, and that further improvements in the IRI-2012 models are required particularly in the low-latitude and EIA regions. The GPS-TEC showed disappearance of the winter anomaly during 2012–2013, while the IRI model failed to predict the disappearance of winter anomaly.     

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.