InSt: an integrated steering framework for critical weather applications

Online remote visualization and steering of critical weather applications like cyclone tracking are essential for effective and timely analysis by geographically distributed climate science community. A steering framework for controlling the high-performance simulations of critical weather events needs to take into account both the steering inputs of the scientists and the criticality needs of the application including minimum progress rate of simulations and continuous visualization of significant events. In this work, we have developed an integrated user-driven and automated steering framework InSt for simulations, online remote visualization, and analysis for critical weather applications. InSt provides the user control over various application parameters including region of interest, resolution of simulation, and frequency of data for visualization. Unlike existing efforts, our framework considers both the steering inputs and the criticality of the application, namely, the minimum progress rate needed for the application, and various resource constraints including storage space and network bandwidth to decide the best possible parameter values for simulations and visualization.

Wind generation output during cold weather-driven electricity demand peaks in Ireland

Recent cold winters and prolonged periods of low wind speeds have prompted concerns about the increasing penetration of wind generation in the Irish and other northern European power systems. On the combined Republic of Ireland and Northern Ireland system there was in excess of 1.5 GW of installed wind power in January 2010. As the penetration of these variable, non-dispatchable generators increases, power systems are becoming more sensitive to weather events on the supply side as well as on the demand side. In the temperate climate of Ireland, sensitivity of supply to weather is mainly due to wind variability while demand sensitivity is driven by space heating or cooling loads. The interplay of these two weather-driven effects is of particular concern if demand spikes driven by low temperatures coincide with periods of low winds. In December 2009 and January 2010 Ireland experienced a prolonged spell of unusually cold conditions. During much of this time, wind generation output was low due to low wind speeds. The impacts of this event are presented as a case study of the effects of weather extremes on power systems with high penetrations of variable renewable generation.

Distribution and statistics of African mesoscale convective weather systems based on the ISCCP Meteosat imagery

This paper provides for the first time an objective short-term (8 yr) climatology of African convective weather systems based on satellite imagery. Eight years of infrared International Satellite Cloud Climatology Project-European Space Agency's Meteorological Satellite (ISCCP-Meteosat) satellite imagery has been analyzed using objective feature identification, tracking, and statistical techniques for the July, August, and September periods and the region of Africa and the adjacent Atlantic ocean. This allows various diagnostics to be computed and used to study the distribution of mesoscale and synoptic-scale convective weather systems from mesoscale cloud clusters and squall lines to tropical cyclones. An 8-yr seasonal climatology (1983-90) and the seasonal cycle of this convective activity are presented and discussed. Also discussed is the dependence of organized convection for this region, on the orography, convective, and potential instability and vertical wind shear using European Centre for Medium-Range Weather Forecasts reanalysis data.

Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

Integration of Space and In Situ Observations to Study Global Climate Change

The currently available model-based global data sets of atmospheric circulation are a by-product of the daily requirement of producing initial conditions for numerical weather prediction (NWP) models. These data sets have been quite useful for studying fundamental dynamical and physical processes, and for describing the nature of the general circulation of the atmosphere. However, due to limitations in the early data assimilation systems and inconsistencies caused by numerous model changes, the available model-based global data sets may not be suitable for studying global climate change. A comprehensive analysis of global observations based on a four-dimensional data assimilation system with a realistic physical model should be undertaken to integrate space and in situ observations to produce internally consistent, homogeneous, multivariate data sets for the earth's climate system. The concept is equally applicable for producing data sets for the atmosphere, the oceans, and the biosphere, and such data sets will be quite useful for studying global climate change.

Biodiversity and green open space

Biodiversity has been defined as the totality of genes, species, and ecosystems that inhabit the earth with the field contributing to many aspects of our lives and livelihoods by providing us with food, drink, medicines and shelter, as well as contributing to improving our surrounding environment. Benefits include providing life services through improved horticultural production, improving the business and service of horticulture as well as our environment, as well as improving our health and wellbeing, and our social and cultural relationships. Threats to biodiversity can include fragmentation, degradation and deforestation of habitat, introduction of invasive and exotic species, climate change and extreme weather events, over-exploitation of our natural resources, hybridisation, genetic pollution/erosion and food security issues and human overpopulation. This chapter examines a series of examples that provide the dual aims of biodiversity conservation and horticultural production and service; namely organic horticultural cropping, turf management, and nature-based tourism, and ways of valuing biological biodiversity such as the payment of environmental services and bio-prospecting. Horticulture plays a major role in the preserving of biodiversity.

The use of remotely sensed rainfall for managing drought risk: a case study of weather index insurance in Zambia

Remotely sensed rainfall is increasingly being used to manage climate-related risk in gauge sparse regions. Applications based on such data must make maximal use of the skill of the methodology in order to avoid doing harm by providing misleading information. This is especially challenging in regions, such as Africa, which lack gauge data for validation. In this study, we show how calibrated ensembles of equally likely rainfall can be used to infer uncertainty in remotely sensed rainfall estimates, and subsequently in assessment of drought. We illustrate the methodology through a case study of weather index insurance (WII) in Zambia. Unlike traditional insurance, which compensates proven agricultural losses, WII pays out in the event that a weather index is breached. As remotely sensed rainfall is used to extend WII schemes to large numbers of farmers, it is crucial to ensure that the indices being insured are skillful representations of local environmental conditions. In our study we drive a land surface model with rainfall ensembles, in order to demonstrate how aggregation of rainfall estimates in space and time results in a clearer link with soil moisture, and hence a truer representation of agricultural drought. Although our study focuses on agricultural insurance, the methodological principles for application design are widely applicable in Africa and elsewhere.

Troposphere Calibration Techniques for Deep Space Probe Tracking

Ground-based Earth troposphere calibration systems play an important role in planetary exploration, especially to carry out radio science experiments aimed at the estimation of planetary gravity fields. In these experiments, the main observable is the spacecraft (S/C) range rate, measured from the Doppler shift of an electromagnetic wave transmitted from ground, received by the spacecraft and coherently retransmitted back to ground. If the solar corona and interplanetary plasma noise is already removed from Doppler data, the Earth troposphere remains one of the main error sources in tracking observables. Current Earth media calibration systems at NASA’s Deep Space Network (DSN) stations are based upon a combination of weather data and multidirectional, dual frequency GPS measurements acquired at each station complex. In order to support Cassini’s cruise radio science experiments, a new generation of media calibration systems were developed, driven by the need to achieve the goal of an end-to-end Allan deviation of the radio link in the order of 3×〖10〗^(-15) at 1000 s integration time. The future ESA’s Bepi Colombo mission to Mercury carries scientific instrumentation for radio science experiments (a Ka-band transponder and a three-axis accelerometer) which, in combination with the S/C telecommunication system (a X/X/Ka transponder) will provide the most advanced tracking system ever flown on an interplanetary probe. Current error budget for MORE (Mercury Orbiter Radioscience Experiment) allows the residual uncalibrated troposphere to contribute with a value of 8×〖10〗^(-15) to the two-way Allan deviation at 1000 s integration time. The current standard ESA/ESTRACK calibration system is based on a combination of surface meteorological measurements and mathematical algorithms, capable to reconstruct the Earth troposphere path delay, leaving an uncalibrated component of about 1-2% of the total delay. In order to satisfy the stringent MORE requirements, the short time-scale variations of the Earth troposphere water vapor content must be calibrated at ESA deep space antennas (DSA) with more precise and stable instruments (microwave radiometers). In parallel to this high performance instruments, ESA ground stations should be upgraded to media calibration systems at least capable to calibrate both troposphere path delay components (dry and wet) at sub-centimetre level, in order to reduce S/C navigation uncertainties. The natural choice is to provide a continuous troposphere calibration by processing GNSS data acquired at each complex by dual frequency receivers already installed for station location purposes. The work presented here outlines the troposphere calibration technique to support both Deep Space probe navigation and radio science experiments. After an introduction to deep space tracking techniques, observables and error sources, in Chapter 2 the troposphere path delay is widely investigated, reporting the estimation techniques and the state of the art of the ESA and NASA troposphere calibrations. Chapter 3 deals with an analysis of the status and the performances of the NASA Advanced Media Calibration (AMC) system referred to the Cassini data analysis. Chapter 4 describes the current release of a developed GNSS software (S/W) to estimate the troposphere calibration to be used for ESA S/C navigation purposes. During the development phase of the S/W a test campaign has been undertaken in order to evaluate the S/W performances. A description of the campaign and the main results are reported in Chapter 5. Chapter 6 presents a preliminary analysis of microwave radiometers to be used to support radio science experiments. The analysis has been carried out considering radiometric measurements of the ESA/ESTEC instruments installed in Cabauw (NL) and compared with the requirements of MORE. Finally, Chapter 7 summarizes the results obtained and defines some key technical aspects to be evaluated and taken into account for the development phase of future instrumentation.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

An Integrated Transmission-Media Noise Calibration Software For Deep-Space Radio Science Experiments

The thesis describes the implementation of a calibration, format-translation and data conditioning software for radiometric tracking data of deep-space spacecraft. All of the available propagation-media noise rejection techniques available as features in the code are covered in their mathematical formulations, performance and software implementations. Some techniques are retrieved from literature and current state of the art, while other algorithms have been conceived ex novo. All of the three typical deep-space refractive environments (solar plasma, ionosphere, troposphere) are dealt with by employing specific subroutines. Specific attention has been reserved to the GNSS-based tropospheric path delay calibration subroutine, since it is the most bulky module of the software suite, in terms of both the sheer number of lines of code, and development time. The software is currently in its final stage of development and once completed will serve as a pre-processing stage for orbit determination codes. Calibration of transmission-media noise sources in radiometric observables proved to be an essential operation to be performed of radiometric data in order to meet the more and more demanding error budget requirements of modern deep-space missions. A completely autonomous and all-around propagation-media calibration software is a novelty in orbit determination, although standalone codes are currently employed by ESA and NASA. The described S/W is planned to be compatible with the current standards for tropospheric noise calibration used by both these agencies like the AMC, TSAC and ESA IFMS weather data, and it natively works with the Tracking Data Message file format (TDM) adopted by CCSDS as standard aimed to promote and simplify inter-agency collaboration.

Laser sounding from space; report of the ESA Technology Working Group on Space Laser Sounding and Ranging

The purpose and principles of spaceborne lidar are described, giving particular attention to candidates for space deployment, including simple backscatter lidar for measuring of cloud top height, cloud extend and optical properties, differential absorption lidar providing high vertical resolution measurements of humidity, temperature and pressure, a wind profiling lidar with the unique capability of improved weather forecasting and global dynamics, and a ranging and altimeter lidar for very accurate measurement of surface features, including ground, sea and ice cap height for solid earth studies.