Interactions between large-scale modes of climate variability that influence Australian hydroclimatic regimes

Effective management of water resources, including surface and ground water, is vital and relies on a thorough understanding of climatic and hydrological (or 'hydroclimatic') variability. In Australia hydroclimatic variability is associated with several large-scale climate modes, including remote phenomena such as El Nino - Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), and more regional climate indices such as the sub-tropical ridge (STR). Individually, the large-scale climate regimes typically associated with rainfall events are well understood. However, less is known about the interactions between, or combinations of, different large-scale conditions that influence Australian hydroclimatic regimes. These interactions are non-linear so traditional statistical frameworks may be unable to adequately characterise these relationships. Classification and Regression Trees (CART) are well suited to analysing relationships between predictor and response variables, including those based on categorical events, that may be modulated by several predictor variables acting together. By employing a more appropriate and novel statistical method this thesis aims to better understand relationships between large-scale modes of climate variability and Australian hydroclimatic regimes. In this work, tree-based models were used to classify regional Australian rainfall regimes from indices of ENSO, the IOD and the STR, yielding the following conclusions. (1) Interactions between tropical (ENSO, IOD) predictor variables and the STR control the strength of the tropical teleconnection and the influence on regional rainfall regimes in southern Australia. When tropical modes and the STR are in the same phase, rainfall regimes are continent-wide and spatially coherent. However, when indices of climate modes are in the opposite phases, i.e. El Nino combined with low STR intensity, the modulation of the tropical teleconnection by the STR is evident, as rainfall anomalies are confined to the northeast of the continent. (2) The influence of both STR intensity and position on rainfall regimes in southeastern Australia was defined. STR position was crucial for defining two distinct types of "wet" autumns, a "summer-like" ("winter-like") regime when the STR was in a southerly (northerly) position. The summer-like regime occurs at frequencies that have not changed detectably over the instrumental record. However, the frequency of the winter-like regime has declined significantly. In addition, the dry regime defined by high STR intensity has been the most frequent regime in recent years, consistent with the attribution of STR intensity as the main driver of the Millennium Drought. (3) The predictive persistence of relationships between a suite of predictor variables (indices of ENSO, IOD and the STR) and rainfall, upper-layer and lower-layer soil moisture was explored. The predictability of spring rainfall was similar using both random forests (a bootstrapping implementation of CART) and linear regression, suggesting results are not dependent on method. The key result, of possible use in seasonal forecasting, is that, deep soil moisture in spring and summer exhibits significantly more predictability than rainfall and shallow soil moisture, due to the persistence of tropical climate drivers and the removal of high-frequency variability in deep layers by natural temporal smoothing as soil moisture is transferred to deep soil layers

Quality control and bias adjustment of crowdsourced wind speed observations

Wind observations collected at citizen weather stations (CWSs) could be an invaluable resource in climate and meteorology studies, yet these observations are underutilised because scientists do not have confidence in their quality. These wind speed observations have systematic biases, likely caused by improper instrumentation and station sitings. Such systematic biases introduce spatial inconsistencies that prevent comparison of these stations spatially and limit the possible usage of the data. In this paper, we address these issues by improving and developing new methods for identifying suspect observations and adjusting systematic biases. Our complete quality control and bias adjustment procedure consists of four steps: (a) performing within-station quality control tests to check the plausible range and the temporal consistency of observations, (b) adjusting the systematic bias using empirical quantile mapping, (c) implementing between-station quality control to compare observations from neighbouring stations to identify spatially inconsistent observations, and (d) providing estimates of the true wind when CWSs falsely report zero wind speeds, as a complement to the bias adjustment. We apply these methods to CWSs from the Weather Observation Website (WOW) in the Netherlands, comparing the crowdsourced data with official data, and statistically assessing the improvements in data quality after each step. The results demonstrate that the crowdsourced wind speed data are more comparable with official data after quality control checks and bias adjustment steps. Our quality assessment methods therefore give confidence in CWSs, converting their observations into a usable data product and an invaluable resource for applications in need of additional wind observations

Statistical post-processing of wind speed forecasts using convolutional neural networks

Current statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based on KNMI's deterministic Harmonie-Arome NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using 3 different density estimation methods (quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution), and found the probabilistic forecasts based on the QS method to be best.Comment: 44 pages, 5 figure

Linear and nonlinear statistical analysis of the impact of sub-tropical ridge intensity and position on south-east Australian rainfall

The intensity and position of the sub-tropical ridge (STR) have strong relationships with rainfall variability in southern Australia. The combined effect of intensity and position in March-April-May (MAM) and June-July-August (JJA) is the focus of this research. Linear statistics were used first: area-averaged and Australia-wide spatial correlations of STR intensity and position with precipitation in south-west eastern Australia reveal that STR intensity has a much stronger and more widespread relationship with precipitation in both seasons. Over time, these relationships vary in magnitude and spatial extent with the sign of the correlation changing between two 50-year epochs. These nonlinearities were investigated further using classification trees. Area-averaged precipitation data (terciles) for south-west eastern Australia was classified on the basis of STR intensity and position. In both seasons the classification trees identify STR intensity as the primary partition defining the dry group, supporting the linear analysis. In the transition season of MAM, the time of year when the mean position of the STR is more southerly, STR position is important in distinguishing between a 'winter-like' and a 'summer-like' wet groups, providing STR intensity is low. Vector wind analyses were computed to explain the composite seasonal precipitation anomaly results in terms of different circulation patterns associated with these two wet groups. The frequency of wet and dry cases in each group was examined with changes evident over the recent years. The research confirms that STR intensity is more important than STR position in explaining inter-annual rainfall variability across southern Australia but also demonstrates the additional role of STR position in MAM. These results explain the low correlation between rainfall and STR position and why this relationship has evolved during the 20th century as the mean location of the STR has shifted south in MAM

Calibration of ecmwf seasonal ensemble precipitation reforecasts in java (Indonesia) using bias-corrected precipitation and climate indices

The seasonal precipitation forecast is one of the essential inputs for economic and agricultural activities and has significant impact on decision-making. Large-scale modes of climate variability have strong relationships with seasonal rainfall in Java and are natural candidates for use as potential predictors in a statistical postprocessing application. We explore whether using climate indices as additional predictors in the statistical postprocessing of ECMWF Seasonal Forecast System 5 (SEAS5) precipitation can improve skill. We use parametric statistical postprocessing by applying a logistic distribution-based ensemble model output statistics (EMOS) technique. We add a variety of potential predictors in the analysis, namely SEAS5 raw and empirical quantile mapping (EQM) bias-corrected precipitation, Niño-3.4 index, dipole mode index (DMI), Madden–Julian oscillation (MJO) indices, sea surface temperature (SST) around Java, and several other predictors. We analyze the period of 1981–2010, focusing on July, August, September, and October. We use the continuous ranked probability skill score (CRPSS) and Brier skill score (BSS) in a comparative verification of raw, EQM, and EMOS seasonal precipitation forecasts. We have found that it is essential to use EQM-corrected precipitation as a predictor instead of raw precipitation in the latter. Besides, Niño-3.4 and DMI forecasts are not needed as extra predictors to improve monthly precipitation forecasts for the first lead month, except for September. However, for somewhat longer lead months, in September and October when there is more skill than climatology, the model that includes only Niño-3.4 and DMI forecasts as potential predictors performs about the same compared to the model that uses only EQM-corrected precipitation as a predictor

Statistical Postprocessing of Wind Speed Forecasts Using Convolutional Neural Networks

Current statistical postprocessing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper, we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 h ahead, based on KNMI's deterministic HARMONIE-AROME NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using three different density estimation methods [quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution], and found the probabilistic forecasts based on the QS method to be best

Future changes in atmospheric rivers and Extreme precipitation in Norway

Flooding events associated with extreme precipitation have had large impacts in Norway. It is well known that these Heavy precipitation events afecting Norway (and other parts of Europe) are strongly associated with atmospheric rivers (ARs). We assess trends in Norwegian AR characteristics, and the infuence of AR variability on extreme precipitation in Norway. We first evaluate the ability of a high-resolution global climate model (EC-Earth) to simulate ARs, compared to ERA-Interim. We evaluate the EC-Earth simulated relationship between ARs and extreme precipitation in western Norway, compared to the observed relationship. We find that EC-Earth is able to simulate well the statistics of AR events and the related precipitation. The intensity and frequency of ARs making landfall in Norway both increase by the end of the century and we find a shift in seasonality of AR events in the future period. In two regions on the west coast, the majority of winter precipitation maxima are associated with AR events (> 80% of cases). Next we assess the infuence of AR variability on Extreme precipitation. A non-stationary extreme value analysis indicates that the magnitude of extreme precipitation events in these regions is associated with AR intensity. Indeed, the 1-in-20 year extreme event is 17% larger when the AR-intensity is high, compared to when it is low. There is little infuence of specifc humidity on the variability of extreme precipitation after all variables are de-trended. Finally, we fnd that the region mean temperature during winter AR events increases in the future. In the future, when the climate is generally warmer, AR days will tend to make landfall when the temperature is above the freezing point. The partitioning of more precipitation as rain, rather than as snow, can have severe impacts on fooding and water resource management

Statistical Postprocessing of Wind Speed Forecasts Using Convolutional Neural Networks

Current statistical postprocessing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper, we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 h ahead, based on KNMI's deterministic HARMONIE-AROME NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using three different density estimation methods [quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution], and found the probabilistic forecasts based on the QS method to be best