Emerging La Niña conditions in the equatorial Pacific : notes for the health community

This report provides information to assist monitoring of vulnerable communities and provide time sensitive information for interventions to reduce negative health impacts. It is prudent for health decision makers to follow the situation for any developments and monitor climate/weather forecasts as part of an early warning-early action approach. Resources and recommendations for monitoring the situation are presented

Tropical cyclone genesis potential index in climate models

The potential for tropical cyclogenesis in a given ocean basin during its active season has been represented by genesis potential indices, empirically determined functions of large-scale environmental variables which influence tropical cyclone (TC) genesis. Here we examine the ability of some of today's atmospheric climate models, forced with historical observed SST over a multidecadal hindcast period, to reproduce observed values and patterns of one such genesis potential index (GP), as well as whether the GP in a given model is a good predictor of the number of TCs generated by that model. The effect of the horizontal resolution of a climate model on its GP is explored. The five analysed models are capable of reproducing the observed seasonal phasing of GP in a given region, but most of them them have a higher GP than observed. Each model has its own unique relationship between climatological GP and climatological TC number; a larger climatological GP in one model compared to others does not imply that that model has a larger climatological number of TCs. The differences among the models in the climatology of TC number thus appear to be related primarily to differences in the dynamics of the simulated storms themselves, rather than to differences in the simulated large-scale environment for genesis. The correlation of interannual anomalies in GP and number of TCs in a given basin also differs significantly from one model to the next. Experiments using the ECHAM5 model at different horizontal resolutions indicate that as resolution increases, model GP also tends to increase. Most of this increase is realized between T42 and T63

Pre-registration of CT pulmonary volumetric image data

Bakalárska práca sa zaoberá predregistráciou pľúcnych objemových CT obrazových dát. Predregistrácia je riešená metódou fázovej korelácie pri rozklade 3D obrazu na 2D rezy usporiadané za sebou. Práca ďalej popisuje geometrické transformácie, interpolácie, výpočet podobnostných kritérií, optimalizáciu registrácie obrazu a proces samotnej registrácie obrazu. Predregistračný softvér je navrhnutý v programovom prostredí MATLAB^®, kde prebieha predregistrácia 3D reálnych CT obrazových dát s dôrazom na rýchlosť procesu.This bachelor thesis is dealing with pre-registration of CT pulmonary volumetric image data. Pre-registration is solved by phase correlation method, which decomposes 3D images into 2D slices arranged in a row. It further describes the geometric transformations, interpolation, calculations of similarity criteria, optimization of registration of images and the image registration process itself. The pre-registration software runs in MATLAB^®, which works with 3D images of real CT image data with an emphasis on process speed.

Spectral Decomposition of Regulatory Thresholds for Climate-Driven Fluctuations in Hydro- and Wind Power Availability

Abstract Climate-driven fluctuations in the runoff and potential energy of surface water are generally large in comparison to the capacity of hydropower regulation, particularly when hydropower is used to balance the electricity production from covarying renewable energy sources such as wind power. To define the bounds of reservoir storage capacity, we introduce a dedicated reservoir volume that aggregates the storage capacity of several reservoirs to handle runoff from specific watersheds. We show how the storage bounds can be related to a spectrum of the climate-driven modes of variability in water availability and to the covariation between water and wind availability. A regional case study of the entire hydropower system in Sweden indicates that the longest regulation period possible to consider spans from a few days of individual subwatersheds up to several years, with an average limit of a couple of months. Watershed damping of the runoff substantially increases the longest considered regulation period and capacity. The high covariance found between the potential energy of the surface water and wind energy significantly reduces the longest considered regulation period when hydropower is used to balance the fluctuating wind power

An intercomparison of skill and overconfidence/underconfidence of the wintertime North Atlantic Oscillation in multimodel seasonal forecasts

Recent studies of individual seasonal forecast systems have shown that the wintertime North Atlantic Oscillation (NAO) can be skillfully forecast. However, it has also been suggested that these skillful forecasts tend to be underconfident, meaning that there is too high a proportion of unpredictable noise in the forecasts. We assess the skill and overconfidence/underconfidence of the seasonal forecast systems contributing to the EUROpean Seasonal to Interannual Prediction (EUROSIP) multimodel ensemble system. Five of the seven systems studied have significant skill for forecasting the wintertime NAO at 2‐ to 4‐month lead times. Four of these skillful systems are underconfident for forecasting the NAO. A multimodel ensemble (ensemble size 126 members) is both skillful and clearly underconfident. Underconfidence becomes more pronounced as the ensemble size increases. Certain years in the hindcast period are well forecast by all or most models. This implies that common teleconnections and drivers of the NAO are being captured by the EUROSIP seasonal forecasts

The poleward migration of the location of tropical cyclone maximum intensity

Temporally inconsistent and potentially unreliable global historical data hinder the detection of trends in tropical cyclone activity. This limits our confidence in evaluating proposed linkages between observed trends in tropical cyclones and in the environment. Here we mitigate this difficulty by focusing on a metric that is comparatively insensitive to past data uncertainty, and identify a pronounced poleward migration in the average latitude at which tropical cyclones have achieved their lifetime-maximum intensity over the past 30 years. The poleward trends are evident in the global historical data in both the Northern and the Southern hemispheres, with rates of 53 and 62 kilometres per decade, respectively, and are statistically significant. When considered together, the trends in each hemisphere depict a global-average migration of tropical cyclone activity away from the tropics at a rate of about one degree of latitude per decade, which lies within the range of estimates of the observed expansion of the tropics over the same period. The global migration remains evident and statistically significant under a formal data homogenization procedure, and is unlikely to be a data artefact. The migration away from the tropics is apparently linked to marked changes in the mean meridional structure of environmental vertical wind shear and potential intensity, and can plausibly be linked to tropical expansion, which is thought to have anthropogenic contributions

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2 to 5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6 to 9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2 to 5 years and 6 to 9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6 to 9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions

Improved ENSO Forecasting Using Bayesian Updating and the North American Multimodel Ensemble (NMME)

© Copyright 2017 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published workThis study assesses the forecast skill of eight North American Multi Model Ensemble (NMME) models in predicting Niño3/3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows strong dependence on lead (initial) month and target month, and is quite promising in terms of correlation, root mean square error (RMSE), the standard deviation ratio (SDRatio) and probabilistic Brier Skill Score, especially at short lead months. However, the skill decreases in target months from late spring to summer due to the “Spring Predictability Barrier.” When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Niño3/3.4 in terms of correlation, RMSE, and SDRatio. For Niño3.4, the BU-Model outperforms NMME- EM forecasts for almost all leads (1-12; particularly for short leads) and target months (from January to December). However, for Niño3, the BU-Model does not outperform NMME-EM forecasts for leads 7-11 and target months from June to October in terms of correlation and RMSE. Last, we test further potential improvements by preselecting “good” models (BU-Model-0.3) and by using principal components analysis to remove the multicollinearity among models, but these additional methodologies do not outperform the BU-Model, which produces the best forecasts of Niño3/3.4 for the 2015/2016 El Niño event