Profiling Methodology and Performance Tuning of the Met Office Unified Model for Weather and Climate Simulations

Global weather and climate modelling is a compute-intensive task that is mission-critical to government departments concerned with meteorology and climate change. The dominant component of these models is a global atmosphere model. One such model, the Met Office Unified Model (MetUM), is widely used in both Europe and Australia for this purpose. This paper describes our experiences in developing an efficient profiling methodology and scalability analysis of the MetUM version 7.5 at both low scale and high scale atmosphere grid resolutions. Variability within the execution of the MetUM and variability of the run-time of identical jobs on a highly shared cluster are taken into account. The methodology uses a lightweight profiler internal to the MetUM which we have enhanced to have minimal overhead and enables accurate profiling with only a relatively modest usage of processor time. At high-scale resolution, the MetUM scaled to core counts of 2048, with load imbalance accounting a significant fraction the loss from ideal performance. Recent patches have removed two relatively small sources of inefficiency. Internal segment size parameters gave a modest performance improvement at low-scale resolution (such as are used in climate simulation); this however was not significant a higher scales. Near-square process grid configurations tended to give the best performance. Byte-swapping optimizations vastly improved I/O performance, which has in turn a large impact on performance in operational runs

High resolution global climate modelling; the UPSCALE project, a large simulation campaign

The UPSCALE (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) project constructed and ran an ensemble of HadGEM3 (Hadley Centre Global Environment Model 3) atmosphere only global climate simulations over the period 1985–2011, at resolutions of N512 (25 km), N216 (60 km) and N96 (130 km) as used in current global weather forecasting, seasonal prediction and climate modelling respectively. Alongside these present climate simulations a parallel ensemble looking at extremes of future climate was run, using a timeslice methodology to consider conditions at the end of this century. These simulations were primarily performed using a 144 million core hour, single year grant of computing time from PRACE (the Partnership for Advanced Computing in Europe) in 2012, with additional resources supplied by the Natural Environment Research Council (NERC) and the Met Office. Almost 400 terabytes of simulation data were generated on the HERMIT supercomputer at the High Performance Computing Center Stuttgart (HLRS), and transferred to the JASMIN super-data cluster provided by the Science and Technology Facilities Council Centre for Data Archival (STFC CEDA) for analysis and storage. In this paper we describe the implementation of the project, present the technical challenges in terms of optimisation, data output, transfer and storage that such a project involves and include details of the model configuration and the composition of the UPSCALE data set. This data set is available for scientific analysis to allow assessment of the value of model resolution in both present and potential future climate conditions

Convective-scale data assimilation of thermodynamic lidar data into the weather research and forecasting model

This thesis studies the impact of assimilating temperature and humidity profiles from ground-based lidar systems and demonstrates its value for future short-range forecast. Thermodynamic profile obtained from the temperature Raman lidar and the water-vapour differential absorption lidar of the University of Hohenheim during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) project Observation Prototype Experiment (HOPE) are assimilated into the Weather Research and Forecasting model Data Assimilation (WRFDA) system by means of a new forward operator. The impact study assimilating the high-resolution thermodynamic lidar data was conducted using variational and ensemble-based data assimilation methods. The first part of the thesis describes the development of the thermodynamic lidar operator and its implementation through a deterministic DA impact study. The operator facilitates the direct assimilation of water vapour mixing ratio (WVMR), a prognostic variable in the WRF model, without conversion to relative humidity. Undesirable cross sensitivities to temperature are avoided here so that the complete information content of the observation with respect to the water vapour is provided. The assimilation experiments were performed with the three-dimensional variational (3DVAR) DA system with a rapid update cycle (RUC) with hourly frequency over ten hours. The DA experiments with the new operator outperformed the previously used relative humidity operator, and the overall humidity and temperature analyses improved. The simultaneous assimilation of temperature and WVMR resulted in a degradation of the temperature analysis compared to the improvement observed in the sole temperature assimilation experiment. The static background error covariance matrix (B) in the 3DVAR was identified as the reason behind this behaviour. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. The second part of the thesis provides a solution for overcoming the static B matrix issue. A hybrid, ensemble-based approach was applied using the Ensemble Transform Kalman Filter (ETKF) and the 3DVAR to add flow dependency to the B matrix. The hybrid experiment resulted in a 50% lower temperature and water vapour root mean square error (RMSE) than the 3DVAR experiment. Comparisons against independent radiosonde observations showed a reduction of RMSE by 26% for water vapour and 38% for temperature. The planetary boundary layer (PBL) height of the analyses also showed an improvement compared to the available ceilometer. The impact of assimilating a single lidar vertical profile spreads over a 100 km radius, which is promising for future assimilation of water vapour and temperature data from operational lidar networks for short-range weather forecasting. A forecast improvement was observed for 7 hours lead time compared with the ceilometer derived planetary boundary layer height observations and 4 hours with Global Navigation Satellite System (GNSS) derived integrated water vapour observations. With the help of sophisticated DA systems and a robust network of lidar systems, the thesis throws light on the future of short-range operational forecasting.Die Einfluss der Integration von Temperatur- und Feuchtigkeitsprofilen aus bodengestützten aktiven Lidar-Systemen wird untersucht und ihr Nutzen für künftige Kurzstreckenvorhersagen wird demonstriert. Thermodynamische Profile, die mit dem Temperatur-Raman-Lidar und dem Wasserdampf-DIAL der Universität Hohenheim während des Observation Prototype Experiment (HOPE), das Teil des Projekts High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) war, gewonnen wurden, werden mit Hilfe eines neuen Vorwärtsoperators in das Datenasimilations-System (DA) des Wetterforschungs- und -vorhersagemodells (WRF) Modells assimiliert. Die Untersuchungen zum Einfluss der Assimilation der hochauflösenden thermodynamischen Lidar-Daten wurden dabei mit Variations- und Ensemble-basierten Datenassimilationsmethoden durchgeführt. Der erste Teil des Arbeit beschreibt die Entwicklung des Lidar-Operators und seine Implementierung mit Hilfe einer deterministischen Datenassimilationsstudie. Der Operator ermöglicht die direkte Assimilation des Wasserdampf-Mischungsverhältnisses (WVMR), einer prognostischen Variable im WRF-Modell, ohne Umrechnung in relative Feuchte. Unerwünschte Querempfindlichkeiten zur Temperatur werden hierbei vermieden, und der vollständige Informationsgehalt der Beobachtung in Bezug auf den Wasserdampf wird genutzt. Das Assimilations-Experiment wurde mit einer 3-dimensionalen Variationsdatenassimilation (3DVAR) durchgeführt, wobei über einen Zeitraum von zehn Stunden jede Stunde eine 3DVAR durchgeführt wurde. Die DA-Experimente mit dem neuen Operator verbesserten die Ergebnisse gegenüber dem zuvor verwendeten Operator für die relative Luftfeuchtigkeit, und die Wasserdampf- und Temperaturanalysen wurden insgesamt optimiert. Die gleichzeitige Assimilation von Temperatur und WVMR führte dabei zu einer geringfügigen Verschlechterung des Temperaturfeldes in der Analyse, während eine Verbesserung des Temperaturfeldes beobachtet wurde, wenn die Temperatur allein assimiliert wurde. Die statische Hintergrundfehler-Kovarianzmatrix (B) in der 3DVAR wurde als Grund für dieses Verhalten identifiziert. Die Korrelation zwischen den Temperatur- und den WVMR-Variablen in der statischen und nicht-strömungsbedingten Hintergrundfehler-Kovarianzmatrix der 3DVAR begrenzte die Verbesserung im Hinblick auf die Temperatur. Der zweite Teil der Arbeit zeigt eine Lösung zur Überwindung des Problems der statischen B-Matrix auf. Es wurde ein hybrider Ansatz angewandt, der den Ensemble Transform Kalman Filter (ETKF) zusammen mit der 3DVAR verwendet, um der Hintergrundfehler-Kovarianzmatrix eine Strömungsabhängigkeit hinzuzufügen. Das Hybridexperiment führte, im Vergleich zum 3DVAR, zu einem 50% niedrigeren mittleren quadratischen Fehler (RMSE) für die Temperatur undWasserdampf. Vergleiche mit unabhängigen Radiosondenbeobachtungen zeigten eine Verringerung des RMSE um 26% für Wasserdampf und 38% für die Temperatur. Vergleiche mit Ceilometern, die während HOPE zur Verfügung standen, zeigten, dass die prognostizierte Höhe der planetarischen Grenzschicht (PBL) deutlich näher an den Beobachtungen war. Der Einflussbereich der Assimilation eines einzelnen Lidar-Vertikalprofils erstreckte sich über einen Radius von 100 km, was für die Assimilation vonWasserdampfund Temperaturdaten aus operationellen Lidar-Netzwerken für die kurzfristige Vorhersage vielversprechend ist. Eine Verbesserung der Vorhersage bezüglich der Entwicklung der planetarischen Grenzschicht konnte in den ersten 7 Stunden nach der letzten 3DVAR erreicht werden. Ein Vergleich mit vom Globalen Navigationssatellitensystem (GNSS) abgeleiteten Beobachtungen des integrierten Wasserdampfs ergab eine Verbesserung der Vorhersage während der ersten 4 Stunden nach dem letzten 3DVAR. Mit Hilfe von hochentwickelten DA-Systemen und einem robusten Netzwerk von Lidar-Systemen wirft die Arbeit ein Licht auf die Verbesserung der Zukunft der operativen Vorhersage im Nahbereich

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

An investigation into the energy and control implications of adaptive comfort in a modern office building

PhD ThesisAn investigation into the potentials of adaptive comfort in an office building is carried out using fine grained primary data and computer modelling. A comprehensive literature review and background study into energy and comfort aspects of building management provides the backdrop against which a target building is subjected to energy and comfort audit, virtual simulation and impact assessment of adaptive comfort standard (BS EN 15251: 2007). Building fabric design is also brought into focus by examining 2006 and 2010 Approved Document part L potentials against Passive House design. This is to reflect the general direction of regulatory development which tends toward zero carbon design by the end of this decade. In finishing a study of modern controls in buildings is carried out to assess the strongest contenders that next generation heating, ventilation and air-conditioning technologies will come to rely on in future buildings. An actual target building constitutes the vehicle for the work described above. A virtual model of this building was calibrated against an extensive set of actual data using version control method. The results were improved to surpass ASHRAE Guide 14. A set of different scenarios were constructed to account for improved fabric design as well as historical weather files and future weather predictions. These scenarios enabled a comparative study to investigate the effect of BS EN 15251:2007 when compared to conventional space controls. The main finding is that modern commercial buildings built to the latest UK statutory regulations can achieve considerable carbon savings through adaptive comfort standard. However these savings are only modestly improved if fabric design is enhanced to passive house levels. Adaptive comfort can also be readily deployed using current web-enabled control applications. However an actual field study is necessary to provide invaluable insight into occupants’ acceptance of this standard since winter-time space temperature results derived from BS EN 15251:2007 constitute a notable departure from CIBSE environmental guidelines