Verifikation der Niederschlagsvorhersage für Deutschland von 2001 - 2004

Die Verifikation numerischer Modelle ist für die Verbesserung der Quantitativen Niederschlagsvorhersage (QNV) unverzichtbar. Ziel der vorliegenden Arbeit ist die Entwicklung von neuen Methoden zur Verifikation der Niederschlagsvorhersagen aus dem regionalen Modell der MeteoSchweiz (COSMO-aLMo) und des Globalmodells des Europäischen Zentrums für Mittelfristvorhersage (engl.: ECMWF). Zu diesem Zweck wurde ein neuartiger Beobachtungsdatensatz für Deutschland mit stündlicher Auflösung erzeugt und angewandt. Für die Bewertung der Modellvorhersagen wurde das neue Qualitätsmaß „SAL“ entwickelt. Der neuartige, zeitlich und räumlich hoch-aufgelöste Beobachtungsdatensatz für Deutschland wird mit der während MAP (engl.: Mesoscale Alpine Program) entwickelten Disaggregierungsmethode erstellt. Die Idee dabei ist, die zeitlich hohe Auflösung der Radardaten (stündlich) mit der Genauigkeit der Niederschlagsmenge aus Stationsmessungen (im Rahmen der Messfehler) zu kombinieren. Dieser disaggregierte Datensatz bietet neue Möglichkeiten für die quantitative Verifikation der Niederschlagsvorhersage. Erstmalig wurde eine flächendeckende Analyse des Tagesgangs des Niederschlags durchgeführt. Dabei zeigte sich, dass im Winter kein Tagesgang existiert und dies vom COSMO-aLMo gut wiedergegeben wird. Im Sommer dagegen findet sich sowohl im disaggregierten Datensatz als auch im COSMO-aLMo ein deutlicher Tagesgang, wobei der maximale Niederschlag im COSMO-aLMo zu früh zwischen 11-14 UTC im Vergleich zu 15-20 UTC in den Beobachtungen einsetzt und deutlich um das 1.5-fache überschätzt wird. Ein neues Qualitätsmaß wurde entwickelt, da herkömmliche, gitterpunkt-basierte Fehlermaße nicht mehr der Modellentwicklung Rechnung tragen. SAL besteht aus drei unabhängigen Komponenten und basiert auf der Identifikation von Niederschlagsobjekten (schwellwertabhängig) innerhalb eines Gebietes (z.B. eines Flusseinzugsgebietes). Berechnet werden Unterschiede der Niederschlagsfelder zwischen Modell und Beobachtungen hinsichtlich Struktur (S), Amplitude (A) und Ort (L) im Gebiet. SAL wurde anhand idealisierter und realer Beispiele ausführlich getestet. SAL erkennt und bestätigt bekannte Modelldefizite wie das Tagesgang-Problem oder die Simulation zu vieler relativ schwacher Niederschlagsereignisse. Es bietet zusätzlichen Einblick in die Charakteristiken der Fehler, z.B. ob es sich mehr um Fehler in der Amplitude, der Verschiebung eines Niederschlagsfeldes oder der Struktur (z.B. stratiform oder kleinskalig konvektiv) handelt. Mit SAL wurden Tages- und Stundensummen des COSMO-aLMo und des ECMWF-Modells verifiziert. SAL zeigt im statistischen Sinne speziell für stärkere (und damit für die Gesellschaft relevante Niederschlagsereignisse) eine im Vergleich zu schwachen Niederschlägen gute Qualität der Vorhersagen des COSMO-aLMo. Im Vergleich der beiden Modelle konnte gezeigt werden, dass im Globalmodell flächigere Niederschläge und damit größere Objekte vorhergesagt werden. Das COSMO-aLMo zeigt deutlich realistischere Niederschlagsstrukturen. Diese Tatsache ist aufgrund der Auflösung der Modelle nicht überraschend, konnte allerdings nicht mit herkömmlichen Fehlermaßen gezeigt werden. Die im Rahmen dieser Arbeit entwickelten Methoden sind sehr nützlich für die Verifikation der QNV zeitlich und räumlich hoch-aufgelöster Modelle. Die Verwendung des disaggregierten Datensatzes aus Beobachtungen sowie SAL als Qualitätsmaß liefern neue Einblicke in die QNV und lassen angemessenere Aussagen über die Qualität von Niederschlagsvorhersagen zu. Zukünftige Anwendungsmöglichkeiten für SAL gibt es hinsichtlich der Verifikation der neuen Generation von numerischen Wettervorhersagemodellen, die den Lebenszyklus hochreichender konvektiver Zellen explizit simulieren.

Merkmalsbezogene Verifikation hochaufgelöster Niederschlagsvorhersagen für Deutschland

Die Verifikation bewertet die Güte von quantitativen Niederschlagsvorhersagen(QNV) gegenüber Beobachtungen und liefert Hinweise auf systematische Modellfehler. Mit Hilfe der merkmals-bezogenen Technik SAL werden simulierte Niederschlagsverteilungen hinsichtlich (S)truktur, (A)mplitude und (L)ocation analysiert. Seit einigen Jahren werden numerische Wettervorhersagemodelle benutzt, mit Gitterpunktabständen, die es erlauben, hochreichende Konvektion ohne Parametrisierung zu simulieren. Es stellt sich jetzt die Frage, ob diese Modelle bessere Vorhersagen liefern. Der hoch aufgelöste stündliche Beobachtungsdatensatz, der in dieser Arbeit verwendet wird, ist eine Kombination von Radar- und Stationsmessungen. Zum einem wird damit am Beispiel der deutschen COSMO-Modelle gezeigt, dass die Modelle der neuesten Generation eine bessere Simulation des mittleren Tagesgangs aufweisen, wenn auch mit zu geringen Maximum und etwas zu spätem Auftreten. Im Gegensatz dazu liefern die Modelle der alten Generation ein zu starkes Maximum, welches erheblich zu früh auftritt. Zum anderen wird mit dem neuartigen Modell eine bessere Simulation der räumlichen Verteilung des Niederschlags, durch eine deutliche Minimierung der Luv-/Lee Proble-matik, erreicht. Um diese subjektiven Bewertungen zu quantifizieren, wurden tägliche QNVs von vier Modellen für Deutschland in einem Achtjahreszeitraum durch SAL sowie klassischen Maßen untersucht. Die höher aufgelösten Modelle simulieren realistischere Niederschlagsverteilungen(besser in S), aber bei den anderen Komponenten tritt kaum ein Unterschied auf. Ein weiterer Aspekt ist, dass das Modell mit der gröbsten Auf-lösung(ECMWF) durch den RMSE deutlich am besten bewertet wird. Darin zeigt sich das Problem des ‚Double Penalty’. Die Zusammenfassung der drei Komponenten von SAL liefert das Resultat, dass vor allem im Sommer das am feinsten aufgelöste Modell (COSMO-DE) am besten abschneidet. Hauptsächlich kommt das durch eine realistischere Struktur zustande, so dass SAL hilfreiche Informationen liefert und die subjektive Bewertung bestätigt. rnIm Jahr 2007 fanden die Projekte COPS und MAP D-PHASE statt und boten die Möglich-keit, 19 Modelle aus drei Modellkategorien hinsichtlich ihrer Vorhersageleistung in Südwestdeutschland für Akkumulationszeiträume von 6 und 12 Stunden miteinander zu vergleichen. Als Ergebnisse besonders hervorzuheben sind, dass (i) je kleiner der Gitter-punktabstand der Modelle ist, desto realistischer sind die simulierten Niederschlags-verteilungen; (ii) bei der Niederschlagsmenge wird in den hoch aufgelösten Modellen weniger Niederschlag, d.h. meist zu wenig, simuliert und (iii) die Ortskomponente wird von allen Modellen am schlechtesten simuliert. Die Analyse der Vorhersageleistung dieser Modelltypen für konvektive Situationen zeigt deutliche Unterschiede. Bei Hochdrucklagen sind die Modelle ohne Konvektionsparametrisierung nicht in der Lage diese zu simulieren, wohingegen die Modelle mit Konvektionsparametrisierung die richtige Menge, aber zu flächige Strukturen realisieren. Für konvektive Ereignisse im Zusammenhang mit Fronten sind beide Modelltypen in der Lage die Niederschlagsverteilung zu simulieren, wobei die hoch aufgelösten Modelle realistischere Felder liefern. Diese wetterlagenbezogene Unter-suchung wird noch systematischer unter Verwendung der konvektiven Zeitskala durchge-führt. Eine erstmalig für Deutschland erstellte Klimatologie zeigt einen einer Potenzfunktion folgenden Abfall der Häufigkeit dieser Zeitskala zu größeren Werten hin auf. Die SAL Ergebnisse sind für beide Bereiche dramatisch unterschiedlich. Für kleine Werte der konvektiven Zeitskala sind sie gut, dagegen werden bei großen Werten die Struktur sowie die Amplitude deutlich überschätzt. rnFür zeitlich sehr hoch aufgelöste Niederschlagsvorhersagen gewinnt der Einfluss der zeitlichen Fehler immer mehr an Bedeutung. Durch die Optimierung/Minimierung der L Komponente von SAL innerhalb eines Zeitfensters(+/-3h) mit dem Beobachtungszeit-punkt im Zentrum ist es möglich diese zu bestimmen. Es wird gezeigt, dass bei optimalem Zeitversatz die Struktur und Amplitude der QNVs für das COSMO-DE besser werden und damit die grundsätzliche Fähigkeit des Modells die Niederschlagsverteilung realistischer zu simulieren, besser gezeigt werden kann.

Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins

Providing probabilistic forecasts using Ensemble Prediction Systems has become increasingly popular in both the meteorological and hydrological communities. Compared to conventional deterministic forecasts, probabilistic forecasts may provide more reliable forecasts of a few hours to a number of days ahead, and hence are regarded as better tools for taking uncertainties into consideration and hedging against weather risks. It is essential to evaluate performance of raw ensemble forecasts and their potential values in forecasting extreme hydro-meteorological events. This study evaluates ECMWF’s medium-range ensemble forecasts of precipitation over the period 2008/01/01-2012/09/30 on a selected mid-latitude large scale river basin, the Huai river basin (ca. 270,000 km2) in central-east China. The evaluation unit is sub-basin in order to consider forecast performance in a hydrologically relevant way. The study finds that forecast performance varies with sub-basin properties, between flooding and non-flooding seasons, and with the forecast properties of aggregated time steps and lead times. Although the study does not evaluate any hydrological applications of the ensemble precipitation forecasts, its results have direct implications in hydrological forecasts should these ensemble precipitation forecasts be employed in hydrology.

The Skill of ECMWF Medium-Range Forecasts during the Year of Tropical Convection 2008

This study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) model-generated high-resolution 10-day-long predictions for the Year of Tropical Convection (YOTC) 2008. Precipitation forecast skills of the model over the tropics are evaluated against the Tropical Rainfall Measuring Mission (TRMM) estimates. It has been shown that the model was able to capture the monthly to seasonal mean features of tropical convection reasonably. Northward propagation of convective bands over the Bay of Bengal was also forecasted realistically up to 5 days in advance, including the onset phase of the monsoon during the first half of June 2008. However, large errors exist in the daily datasets especially for longer lead times over smaller domains. For shorter lead times (less than 4-5 days), forecast errors are much smaller over the oceans than over land. Moreover, the rate of increase of errors with lead time is rapid over the oceans and is confined to the regions where observed precipitation shows large day-to-day variability. It has been shown that this rapid growth of errors over the oceans is related to the spatial pattern of near-surface air temperature. This is probably due to the one-way air-sea interaction in the atmosphere-only model used for forecasting. While the prescribed surface temperature over the oceans remain realistic at shorter lead times, the pattern and hence the gradient of the surface temperature is not altered with change in atmospheric parameters at longer lead times. It has also been shown that the ECMWF model had considerable difficulties in forecasting very low and very heavy intensity of precipitation over South Asia. The model has too few grids with ``zero'' precipitation and heavy (>40 mm day(-1)) precipitation. On the other hand, drizzle-like precipitation is too frequent in the model compared to that in the TRMM datasets. Further analysis shows that a major source of error in the ECMWF precipitation forecasts is the diurnal cycle over the South Asian monsoon region. The peak intensity of precipitation in the model forecasts over land (ocean) appear about 6 (9) h earlier than that in the observations. Moreover, the amplitude of the diurnal cycle is much higher in the model forecasts compared to that in the TRMM estimates. It has been seen that the phase error of the diurnal cycle increases with forecast lead time. The error in monthly mean 3-hourly precipitation forecasts is about 2-4 times of the error in the daily mean datasets. Thus, effort should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model.

Using fractal downscaling of satellite precipitation products for hydrometeorological applications

The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L»l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (~25-km grid spacing) to the same resolution as the NCEP stage IV products (~4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied. © 2010 American Meteorological Society.

Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events

The development of NWP models with grid spacing down to 1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillful over all but the smallest scales (approximately <10–15 km). A measure of acceptable skill was defined; this was attained by the 1-km model at scales around 40–70 km, some 10–20 km less than that of the 12-km model. The biggest improvement occurred for heavier, more localized rain, despite it being more difficult to predict. The 4-km model did not improve much on the 12-km model because of the difficulties of representing convection at that resolution, which was accentuated by the spinup from 12-km fields.

Extended time weather forecasts contributes to agricultural productivity estimates

Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Parana, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information.

Thermal behavior of Ca2+ and Cu2+ oxalates obtained by precipitation in homogeneous solution from dimethyl oxalate hydrolysis

Mixed calcium and copper oxalates, with different proportions of Ca2+ and Cu2+ ions, were precipitated by dimethyl oxalate hydrolysis in homogeneous solution. The compounds were evaluated by means of scanning electron microscopy, energy dispersive X-ray spectroscopy, thermogravimetry (TG), and differential thermal analysis (DTA). The results suggested quantitative precipitation without solid solution formation. From the TG and DTA curves, it was possible to evaluate the Ca2+ ion proportion in the solid phase and to confirm the precipitation of the individual species.

Immunological studies on nucleic-acids and their components .5. Antibodies specific to a deoxyribodinucleotide sequence

Antibodies were raised in rabbits against the bovine serum albumin conjugate of dpApT. Analysis by double diffusion in agar gel and quantitative precipitation test showed the presence of antibodies specific to the hapten in the antisera. Quantitative data on the specificity of the antibodies were obtained by studying the inhibition of the binding of 3H-dpApT to the anti-sera by various nonradioactive mono- and oligonucleotides, using a nitrocellulose membrane binding assay. The antibodies were found to be highly specific for the dinucleotide sequence dpApT. The antibodies were able to bind to synthetic oligonucleotides containing the sequence dpApT and to denatured calf thymus DNA.

Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Flood risk analysis of the White Oak Creek watershed in Oak Ridge, Tennessee using MIKE SHE and MIKE 11 software

The purpose of this study was to determine the flooding potential of contaminated areas within the White Oak Creek watershed in the Oak Ridge Reservation in Tennessee. The watershed was analyzed with an integrated surface and subsurface numerical model based on MIKE SHE/MIKE 11 software. The model was calibrated and validated using five decades of historical data. A series of simulations were conducted to determine the watershed response to 25 year, 100 year and 500 year precipitation forecasts; flooding maps were generated for those events. Predicted flood events were compared to Log Pearson III flood flow frequency values for validation. This investigation also provides an improved understanding of the water fluxes between the surface and subsurface subdomains as they affect flood frequencies. In sum, this study presents crucial information to further assess the environmental risks of potential mobilization of contaminants of concern during extreme precipitation events.

The role of the Indian Ocean sector and sea surface salinity for prediction of the coupled Indo-Pacific system

The purpose of this dissertation is to evaluate the potential downstream influence of the Indian Ocean (IO) on El Niño/Southern Oscillation (ENSO) forecasts through the oceanic pathway of the Indonesian Throughflow (ITF), atmospheric teleconnections between the IO and Pacific, and assimilation of IO observations. Also the impact of sea surface salinity (SSS) in the Indo-Pacific region is assessed to try to address known problems with operational coupled model precipitation forecasts. The ITF normally drains warm fresh water from the Pacific reducing the mixed layer depths (MLD). A shallower MLD amplifies large-scale oceanic Kelvin/Rossby waves thus giving ~10% larger response and more realistic ENSO sea surface temperature (SST) variability compared to observed when the ITF is open. In order to isolate the impact of the IO sector atmospheric teleconnections to ENSO, experiments are contrasted that selectively couple/decouple the interannual forcing in the IO. The interannual variability of IO SST forcing is responsible for 3 month lagged widespread downwelling in the Pacific, assisted by off-equatorial curl, leading to warmer NINO3 SST anomaly and improved ENSO validation (significant from 3-9 months). Isolating the impact of observations in the IO sector using regional assimilation identifies large-scale warming in the IO that acts to intensify the easterlies of the Walker circulation and increases pervasive upwelling across the Pacific, cooling the eastern Pacific, and improving ENSO validation (r ~ 0.05, RMS~0.08C). Lastly, the positive impact of more accurate fresh water forcing is demonstrated to address inadequate precipitation forecasts in operational coupled models. Aquarius SSS assimilation improves the mixed layer density and enhances mixing, setting off upwelling that eventually cools the eastern Pacific after 6 months, counteracting the pervasive warming of most coupled models and significantly improving ENSO validation from 5-11 months. In summary, the ITF oceanic pathway, the atmospheric teleconnection, the impact of observations in the IO, and improved Indo-Pacific SSS are all responsible for ENSO forecast improvements, and so each aspect of this study contributes to a better overall understanding of ENSO. Therefore, the upstream influence of the IO should be thought of as integral to the functioning of ENSO phenomenon.

An investigation of goethite-seeded Al(OH)3 precipitation using in situ X-ray diffraction and Rietveld-based quantitative phase analysis

An in situ X-ray diffraction investigation of goethite-seeded Al(OH)3 precipitation from synthetic Bayer liquor at 343 K has been performed. The presence of iron oxides and oxyhydroxides in the Bayer process has implications for alumina reversion, which causes significant process losses through unwanted gibbsite precipitation, and is also relevant for the nucleation and growth of scale on mild steel process equipment. The gibbsite, bayerite and nordstrandite polymorphs of Al(OH)3 precipitated from the liquor; gibbsite appeared to precipitate first, with subsequent formation of bayerite and nordstrandite. A Rietveld-based approach to quantitative phase analysis was implemented for the determination of absolute phase abundances as a function of time, from which kinetic information for the formation of the Al(OH)3 phases was determined.