Showing papers on "Nowcasting published in 2010"

Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

Abstract: In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed-frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high- and low-frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low-frequency variables like gross domestic product (GDP) exploiting information in a large set of higher-frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real-time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state-space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed-frequency techniques that can exploit timely information from business cycle indicators.

Lbs nowcasting sensitive advertising and promotion system and method

Abstract: A system and method for combining the delivery of advertising with weather predictions that are limited in geographical area and time, and hence which are much more precise but also more time sensitive than regular weather forecasts. The present invention is preferably implemented with “nowcasting”.

Nowcasting Challenges during the Beijing Olympics: Successes, Failures, and Implications for Future Nowcasting Systems

Abstract: The Beijing 2008 Forecast Demonstration Project (B08FDP) included a variety of nowcasting systems from China, Australia, Canada, and the United States. A goal of the B08FDP was to demonstrate state-of-the-art nowcasting systems within a mutual operational setting. The nowcasting systems were a mix of radar echo extrapolation methods, numerical models, techniques that blended numerical model and extrapolation methods, and systems incorporating forecaster input. This paper focuses on the skill of the nowcasting systems to forecast convective storms that threatened or affected the Summer Olympic Games held in Beijing, China. The topography surrounding Beijing provided unique challenges in that it often enhanced the degree and extent of storm initiation, growth, and dissipation, which took place over short time and space scales. The skill levels of the numerical techniques were inconsistent from hour to hour and day to day and it was speculated that without assimilation of real-time radar reflectivit...

A Frequent-Updating Analysis System Based on Radar, Surface, and Mesoscale Model Data for the Beijing 2008 Forecast Demonstration Project

Abstract: The Variational Doppler Radar Analysis System (VDRAS) was implemented in Beijing, China, and contributed to the Beijing 2008 Forecast Demonstration Project (B08FDP) in support of the Beijing Summer Olympics. VDRAS is a four-dimensional variational data assimilation system that produces frequently updated analyses using Doppler radar radial velocities and reflectivities, surface observations, and mesoscale model data. The system was tested in real time during the B08FDP pretrials in the summers of 2006 and 2007 and run during the Olympics to assist the 0–6-h convective weather nowcasting. This paper provides a description of the upgraded system and its Beijing implementation, an evaluation of the system performance using data collected during the pretrials, and its utility on convective weather nowcasting through two case studies. Verification of VDRAS wind against a wind profiler shows that the analyzed wind is reasonably accurate with a smaller RMS difference for 2006 than for 2007 due to better...

Nowcasting Spanish GDP Growth in Real Time: 'One and a Half Months Earlier'

Abstract: The sharp decline in economic activity registered in Spain over 2008 and 2009 has no precedents in recent history. After ten prosperous years with an average GDP growth of 3.7%, the current recession places non-judgemental forecasting models under stress. This paper evaluates the Spanish GDP nowcasting performance of combinations of small and medium-sized linear dynamic regressions with priors originating in the Bayesian VAR literature. Our forecasting procedure can be considered a timely and simple approximation to the mix of accounting tools, models and judgement used by the statistical agencies to construct aggregate GDP figures. The real time forecast evaluation conducted over the most severe phase of the recession shows that our method yields reliable real GDP growth predictions almost one and a half months before the official figures are published.

McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique

Abstract: A Variational Echo Tracking (VET) technique has been applied to four months of archived data from the South Korean radar network in order to examine the influence of the various user-selectable parameters on the skill of the resulting 20-min to 4-h nowcasts. The latter are computed over a (512 × 512) array at 2-km resolution. After correcting the original algorithm to take into account the motion of precipitation across the boundaries of such a smaller radar network, we concluded that the set of default input parameters initially assumed is very close to the optimum combination. Decreasing to (5 sx 5) or increasing to (50 × 50) the default vector density of (25 × 25), using two or three maps for velocity determination, varying the relative weights for the constraints of conservation of reflectivity and of the smoothing of the velocity vectors, and finally the application of temporal smoothing all had only marginal effects on the skill of the forecasts. The relatively small sensitivity to significant variations of the VET default parameters is a direct consequence of the fact that the major source of the loss in forecast skill cannot be attributed to errors in the forecast motion, but to the unpredictable nature of the storm growth and decay. Changing the time interval between maps, from 20 to 10 minutes, and significantly increasing the reflectivity threshold from 15 to 30 dBZ had a more noticeable reduction on the forecast skill. Comparisons with the Eulerian “zero velocity“ forecast and with a “single“ vector forecast have also been performed in order to determine the accrued skill of the VET algorithm. Because of the extensive stratiform nature of the precipitation areas affecting the Korean peninsula, the increased skill is not as large as may have been anticipated. This can be explained by the greater extent of the precipitation systems relative to the size of the radar coverage domain.

Thunderstorm Strike Probability Nowcasting

Abstract: To assist in thunderstorm warning, automated nowcasting systems have been developed that detect thunderstorm cells in radar images and propagate them forward in time to generate forecasted threat areas. Current methods, however, fail to quantify the probabilistic nature of the error structure of such forecasts. This paper introduces the Thunderstorm Environment Strike Probability Algorithm (THESPA), which forecasters can use to provide probabilistic thunderstorm nowcasts for risk assessment and emergency decision making. This method accounts for the prediction error by transforming thunderstorm nowcasts into a strike probability, or the probability that a given location will be impacted by a thunderstorm in a given period, by specifying a bivariate Gaussian distribution of speed and direction errors. This paper presents the development and analysis of the THESPA method and verifies performance using experimental data. Results from a statistical analysis of Thunderstorm Identification, Tracking, A...

Doppler weather radar based nowcasting of cyclone Ogni

Abstract: In this paper, we describe offline analysis of Indian Doppler Weather Radar (DWR) data from cyclone Ogni using a suite of radar algorithms as implemented on NEXRAD and the advanced algorithms developed jointly by the National Severe Storms Laboratory (NSSL) and the University of Oklahoma. We demonstrate the applicability of the various algorithms to Indian radar data, the improvement in the quality control and evaluate the benefit of nowcasting capabilities in Indian conditions. New information about the tropical cyclone structure, as derived from application of the algorithms is also discussed in this study. Finally, we suggest improvements that could be made to the Indian data collection strategies, networking and real-time analysis. Since this is the first study of its kind to process and utilize DWR data in a tropical climate, the suggestions on real-time analysis and data collection strategies made in this paper, would in many cases, be beneficial to other countries embarking on DWR network modernization programs.

Weather Services, Science Advances, and the Vancouver 2010 Olympic and Paralympic Winter Games

Abstract: The Olympics inspire greatness. "Swifter, Higher, Stronger" applies not only to the athletes but sets the tone for everyone associated with the games, including the weather service providers. The Winter Olympic and Paralympic Games of 2010 will be hosted by the city of Vancouver, British Columbia, from 12-28 February and 12-21 March, respectively. Alpine and Nordic events will be held on Whistler Mountain, at the new Whistler Olympic Park in the Callaghan Valley and at Cypress Mountain on the North Shore Mountains just north of the City of Vancouver (Fig.1). The challenges for winter weather forecasting and nowcasting in complex terrain within a coastal region require Olympian efforts and innovations to overcome.

Nowcasting from disaggregates in the face of location shifts

Abstract: Given a need for nowcasting, we consider how nowcasts can best be achieved, the use and timing of information, including disaggregation over variables and common features, and the role of automatic model selection for nowcasting missing disaggregates. We focus on the impact of location shifts on nowcast failure and nowcasting during breaks, using impulse saturation, its relation to intercept correction, and to robust methods to avoid systematic nowcast failure. We propose a nowcasting strategy, building models of all N disaggregate series by automatic methods, forecasting every variable each period, then testing for shifts in available measures, switching to robust forecasts of missing series when breaks are detected. Copyright © 2009 John Wiley & Sons, Ltd.

Application of Meteosat second generation data towards improving the nowcasting of convective initiation

Abstract: The prediction of convective initiation (CI) from a satellite perspective provides forecasters with a constant relatively high temporal and convective scale spatial resolution tool to help protect life and property. By monitoring infrared (IR) channel brightness temperatures, their trends and multi-spectral channel differences, the prediction of CI can be accomplished on the 0-1 h timescale. These methods, currently employed on the Geostationary Operational Environmental Satellite (GOES) system, have only recently been explored on the Meteosat Second Generation (MSG) satellite system. The additional channels and derived instability indices available on the MSG satellites may provide additional information useful to the prediction of CI. In this paper a concept is developed and discussed towards the advantages and possible applications of the inclusion of MSG specific IR spectral channels and instability information through the analysis of several convective case events over Central Europe and South Africa. Copyright  2010 Royal Meteorological Society

GDP nowcasting with ragged-edge data: a semi-parametric modeling

Abstract: This papier formalizes the process of forecasting unbalanced monthly data sets in order to obtain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This innovative approach lies on the use on non-parametric methods, based on nearest neighbors and on radial basis function approaches, ti forecast the monthly variables involved in the parametric modelling of GDP using bridge equations. A real-time experience is carried out on Euro area vintage data in order to anticipate, with an advance ranging from six to one months, the GDP flash estimate for the whole zone.

Validation Test Report for the Global Ocean Forecast System V3.0 - 1/12 deg HYCOM-NCODA: Phase II

Abstract: : Global Ocean Forecast System Version 3.0 (V3.0) is comprised of the 1/12 deg global HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA) system. It is a next-generation system capable of nowcasting and forecasting the oceanic "weather," which includes the three-dimensional ocean temperature, salinity and current structure, the surface mixed layer and the location of mesoscale features such as eddies, meandering currents and fronts. V3.0 is scheduled to replace the existing nowcast/forecast system (V2.6) based on the 1/8 deg Navy Coastal Ocean Model (NCOM), 1/32 deg Navy Layered Ocean Model (NLOM), 1/8 deg Modular Ocean Data Analysis System (MODAS) and NCODA. This Phase II report describes the validation testing performed on one-year hindcasts of V3.0 and V2.6. A few Phase I tasks (temperature vs. depth and acoustical proxy error analyses) have been re-evaluated along with new evaluations examining a) each system as a provider of boundary conditions to a regional nested model, b) 14-day forecast skill relative to climatology and persistence of temperature vs. depth, c) 14-day forecast skill of acoustical proxies, d) 14-day forecast skill of sea surface height and sea surface temperature, and e) a velocity comparison against glider and drifting buoy observations. Overall, this report has determined that GOFS V3.0 is performing equal to or notably better than GOFS V2.6. The superior performance of V3.0 is especially evident in providing boundary contitions to regional nested models, an important function of a global ocean nowcast/forecast system.

Predicting Inflation: Professional Experts Versus No-Change Forecasts

Abstract: We compare forecasts of United States inflation from the Survey of Professional Forecasters (SPF) to predictions made by simple statistical techniques. In nowcasting, economic expertise is persuasive. When projecting beyond the current quarter, novel yet simplistic probabilistic no-change forecasts are equally competitive. We further interpret surveys as ensembles of forecasts, and show that they can be used similarly to the ways in which ensemble prediction systems have transformed weather forecasting. Then we borrow another idea from weather forecasting, in that we apply statistical techniques to postprocess the SPF forecast, based on experience from the recent past. The foregoing conclusions remain unchanged after survey postprocessing.

McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part II: Real-time verification for the summer season

Abstract: The MAPLE system has been implemented in real-time in Korea since June 2008, producing forecasts up to 6 hours every 10 minutes. An object-oriented verification method has been applied for the summer season (June–July–August) over the Korean Peninsula to evaluate and understand the characteristics of the forecast results. The CRA (contiguous rain area) approach is used to decompose the total error into the different error components; location, pattern, and volume errors. The mean displacement error is smaller than 20 km up to the 3-h forecasts and increases with forecast time. The ratio between the displacement (location) error and the total error is less than 7% even for a 3-h forecast. This result indicates that MAPLE produces reliable forecast in terms of precipitation location. However, the pattern error is larger than 90% of the total error. Contingency scores that are defined with different categories of rain intensity and displacement errors show the outstanding performance up to 2.5 hours. MAPLE overpredicts rain areas with the threshold of 1 mm h−1 rain intensity throughout forecast periods. However, the heavy rainfall events are poorly predicted due to the inherent limitation of extrapolation-based nowcasting technique.

Methods and apparatus for passive tropospheric measurments utilizing a single band of frequencies adjacent to a selected millimeter wave water vapor line

Abstract: Apparatus and methods are disclosed for passive millimeter wave measurements to provide tropospheric profiles of temperature, water vapor, cloud liquid water, pressure, and refractivity utilizing a single band microwave receiver operating in the vicinity of the water vapor emission line centered at 183.31 GHz or other millimeter wave water vapor line. Ancillary meteorological measurements may be provided to refine profile outputs. Retrieval method training adapts and refines system output to provide useful information for weather nowcasting and forecasting, aviation safety, transport of pollutants, prediction of fog and other weather phenomena, and radar and optical ducting prediction.

A Nowcasting System Using Full Physics Numerical Weather Prediction Initialized with CASA and NEXRAD Radar Data

Abstract: Increasingly accurate cloud-resolving models can now be run in real-time thanks to improvements in computer speed, increases in number of processors per machine, multi-processor algorithms and advanced data assimilation techniques. While traditionally the most accurate forecasts of thunderstorms in the first 0-3 hours have come from extrapolation of echoes, the tipping point when full physics models have the advantage is moving closer to t=0 (e.g. Kong et al., 2010). Using a fullphysics model allows for the prospect of better forecasting changes in mode, direction and speed of motion and overall evolution compared to extrapolative methods.

Alternative methods for forecasting GDP

Abstract: An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the estimation of economic indicators plugged in the bridge equations, we get more accurate forecasts when using nearest neighbor method. We prove also the asymptotic normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing confidence intervals for point forecast in time series.

Thunderstorm Tracking and Monitoring on the Basis of Three Dimensional Lightning Data and Conventional and Polarimetric Radar Data

Abstract: The aim of this work is to assess the benefit of total-lightning information as independent data source for thunderstorm tracking and short-term prediction (nowcasting) of storm evolution. Special focus has been laid on the three-dimensional lightning information and the in-cloud and cloud-to-ground discrimination provided by the lightning detection network LINET. The reliability of the lightning information and its usability for nowcasting purposes have been tested both separately and in combination with other data sources which are commonly used for thunderstorm nowcasting. The new thunderstorm tracker ec-TRAM (tracking and monitoring of electrically charged cells; Meyer et al. (2009)) has been developed to identify, track, and monitor thunderstorms in high temporal and spatial resolution by combining the information of independently tracked convective ground-precipitation cells and lightning-cells to new cell objects. The algorithm builds on the autonomously operating routines rad-TRAM (tracking and monitoring of radar cells; Kober and Tafferner (2009)) and li-TRAM (tracking and monitoring of lightning cells). The latter has also been developed within this work. The new tracking algorithm has been tested based on a thunderstorm data set of more than 500 storm tracks which were recorded by ec-TRAM in southern Germany during summer 2008. It is found that the newly composed cell objects comprehensively describe simple as well as complex thunderstorm structures and the cell tracking method of ec-TRAM proves to be more coherent and stable in comparison with the tracking performances of rad-TRAM and li-TRAM. For two selected thunderstorms the time series of cell parameters monitored by ec-TRAM have been complemented with three-dimensional polarimetric radar data and satellite data to assess how the temporal evolution and parameter correlation of total lightning strokes, hydrometeor formation, ground precipitation patterns, and cloud top temperature can be used to estimate the storm state and predict its development. The parameter evolutions are found to be consistent with the current state of knowledge. A principal life-cycle scheme can be identified for the cell parameters on large time scales. The stronger fluctuating short-term parameter evolutions are found to refl ect the momentary storm dynamic. Based on the lifetime diagrams several warning parameters for subsequent storm events can be suggested. Significant cell parameter correlations, which can be parameterized, are also found in statistical analyses over the complete data set. Strong positive correlations are found between cell extension, discharge frequency, and in-cloud discharge height. Two cell regimes, sharply separated at a specific cell characteristic, can clearly be identified in all correlation diagrams. Interpreted on the basis of previous studies and in terms of the current state of knowledge, it seems most likely that the two cell-regimes refl ect the storm characteristics of different storm organization forms. The parameterized correlation curves could then be used as cell parameterizations in operational nowcasting tools to predict the dynamic evolution, duration, and danger potential of a storm, provided that the storm system can be classified. Finally, it can be concluded that this study demonstrates the usability and the promising potential of total-lightning data as reliable and independent data source for future nowcasting tools.

Improved thunderstorm weather information for pilots through ground and satellite based observing systems

Abstract: Today’s weather information for pilots on thunderstorm conditions on their flight path is insufficient. Weather charts provided by the World Area Forecasting Centres and taken onboard by pilots before take-off are based on forecasts of large scale weather models which are initialized only twice a day. The information of the charts is therefore outdated, at least with respect to thunderstorm occurrence, at the time of use. They can only provide a rough estimation of thunderstorm hazards for relatively large areas. In contrast, thunderstorms develop quickly within tenths of minutes up to an hour and their exact time of occurrence and location is more or less impossible to predict deterministically hours in advance. In this paper, the value of the satellite information on thunderstorm detection over the oceans is demonstrated by applying the DLR Cb-TRAM cloud tracker (Zinner et al., 2009) to last years occurrences of aircraft accident and incident over the Atlantic. In addition, two incidents over the European area with severe turbulence and hail encounter are investigated by satellite, radar and lighting data. The aim of the study is to demonstrate the improved information pilots would gain once the thunderstorm analyses and forecasts of the satellite and ground based systems would be brought, i.e. up-linked, to the cockpit during flight. Today, pilots have information on thunderstorm activity through onboard radar equipment which provides quite good indication on thunderstorm activity within the close range part in flight direction, about 50 miles or so, provided there is precipitation within the convective up-droughts, strong enough to give radar returns. However, the radar returns are strongly attenuated when precipitation cells are large and intense, or several cells behind one another, due to the short wave length of the radars which operate at 3 cm. In that case the pilot’s information of the situation is quite incomplete which makes it difficult for them to choose a proper path around thunderstorm cells or through a thunderstorm line. In addition there are cases where thunderstorm cells are just about to develop with weak or no returns on the radar, yet they can produce convective turbulence which can propagate to levels above the developing cells. In that case the aircraft might experience sudden turbulence without any pre-warning. Also, at high flight levels through tropical storms over the oceans, radar returns might be weak due to small droplet sizes, thereby giving a wrong indication of the severity of the storm. In contrast to this onboard radar information, remote sensing by satellite, ground based radar and lightning can provide a more complete picture of the thunderstorm situation. Ground based systems have been developed which use this data to inspect cells from above, below and multiple viewing angles thereby providing a more complete picture of the thunderstorm situation (e.g.; Tafferner et al., 2009; Senesi et al., 2009). Thunderstorms can well be detected from satellite due to their cold cloud tops and characteristic cloud shape at already early development stage, the precipitation they produce can well be detected by radar and lightning discharges by lightning detectors. References Senesi, S., Y. Guillou, A. Tafferner, and C. Forster, 2009: Cb nowcasting in FLYSAFE: Improving flight safety regarding thunderstorm hazards. WMO Symposium on Nowcasting , 30 August - 4 September 2009 , Whistler, B.C., Canada Tafferner, A. , C. Forster, S. Senesi, Y. Guillou, P. Tabary, P. Laroche, A. Delannoy, B. Lunnon, D. Turp, T. Hauf, and D. Markovic, 2009: Nowcasting thunderstorm hazards for flight operations: the CB WIMS approach in FLYSAFE. European Air and Space Conference (CEAS) , 26 - 29 Oct. 2009, Manchester, UK Zinner, T., Mannstein, H., Tafferner, A. , 2008: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteorol. Atmos. Phys. 101, 191–210

Real-Time Nowcasting of GDP: Factor Model versus Professional Forecasters

Abstract: This paper performs a fully real-time nowcasting (forecasting) exercise of US real gross domestic product (GDP) growth using Giannone, Reichlin and Small (2008) factor model framework which enables one to handle unbalanced datasets as available in real-time. To this end, we have constructed a novel real-time database of vintages from October 2000 to June 2010 for a rich panel of US variables, and can hence reproduce, for any given day in that range, the exact information that was available to a real-time forecaster. We track the daily evolution throughout the current and next quarter of the model nowcasting performance. Analogously to Giannone et al. (2008) pseudo real-time results, we find that the precision of the nowcasts increases with information releases. Furthermore, the Survey of Professional Forecasters (SPF) does not carry additional information with respect to the model best specification, suggesting that the often cited superiority of the SPF, attributable to judgment, is weak over our sample. Then, as one moves forward along the real-time data flow, the continuous updating of the model provides a more precise estimate of current quarter GDP growth and the SPF becomes stale compared to all the model specifications. These results are robust to the recent recession period.

The july 2008 rainfall estimation from barnova wsr-98 d radar using artificial neural network *

Abstract: In the last years, the artificial neural networks (ANN) have proved an attractive approach to non-linear regression problems arising in environmental modelling, such as short-term forecasting of atmospheric pollutant concentrations, rainfall run-off modelling and precipitation nowcasting using radar, satellite or meteorological data. The term nowcasting reflects the need of timely and accurate predictions of risk situations related to the development of severe meteorological events. The objective of this work is the very short term prediction of the rainfall field from radar data based on feed forward neural network approach. The radar dates used in this study were measured by the WSR-98D Doppler radar in North-East of Romania. The reflectivity data sets extend over July 2008. The ANN system with reflectivity values as input variables was trained to predict the rain rate on the ground. The output vector consists of one variable namely the rain rate measured by a rain gauge on ground level. The two available rain gauges provided the rain rate in millimetres every one hour. Data-preprocessing or the selection of input variables was performed when necessary. The efficiency of ANN network in the estimation of the rain rate on the ground in comparison with that supplied by the weather radar is evaluated.

An assessment of the quality of GPS water vapour estimates and their use in operational meteorology and climate monitoring

Abstract: The path delay between a GPS satellite and a ground based GPS receiver depends, after elimination of ionospheric effects using a combination of the two GPS frequencies, on the integral effect of the densities of dry air and water vapour along the signal path. The total delay in the signal from each satellite is known as the slant delay as the path is most likely to be non-azimuthal. The slant paths are then transferred into the vertical (or zenith) by an elevation mapping function, and this new parameter is known as the Zenith Total Delay or ZTD. ZTD gives a measure for the integrated tropospheric condition and is now widely accepted as a standard product from a network of dual frequency GPS receivers. With further calculation, taking into account surface pressure and temperature, we can then convert a portion of the ZTD into an estimate of the Integrated Water Vapour content of the atmosphere (IWV). As IWV may potentially change rapidly on a very short timescale, it is the speed at which IWV can be calculated which is of critical importance to short term meteorological forecasting. Often, rapid changes in IWV are associated with high humidity conditions caused by extreme weather events such as thunderstorms. Extreme weather events such as these are typically difficult to predict and track under current operational meteorological systems and, as they have the potential to cause great damage, it is in the interests to both the public and Met Services to significantly improve nowcasting wherever possible. As such the requirement for dense near real-time GPS networks for meteorological applications becomes apparent. Furthermore water vapour is one of the most important constituents of the atmosphere as moisture and latent heat are primarily transmitted through the water vapour phase. As well as this, water vapour is one of the most important greenhouse gases, and as such accurate monitoring of water vapour is of great importance to climatological research. This thesis assesses the quality of GPS water vapour estimates by comparison against a number of other remote sensing instruments to determine what the true value of the water vapour is and how well GPS water vapour estimates accurately represent real atmospheric fluctuations. Through these comparisons we can derive site specific bias corrections and thus, reconstruct a bias corrected time series of data for climate applications. Furthermore to ensure all biases associated with GPS processing changes are removed, a long time series of raw GPS data has been reprocessed under a consistent processing routine to again identify any climate trends in the data. Finally, this thesis addresses the question of whether near real-time GPS derived tropospheric estimates are of sufficient quality for climate applications without the need for time consuming reprocessing.

Review: A GIS approach to ingest Meteosat Second Generation data into the Local Analysis and Prediction System

Abstract: The Local Analysis and Prediction System (LAPS) is modified to ingest Meteosat Second Generation (MSG) data for cloud analysis. A first study is conducted to test the actual performance of the weather analysis software after new satellite bands are introduced. Results show that the system provides high quality cloud products such as cloud mask, cloud top height and cloudiness. A comparison with products from EUMETSAT's Nowcasting SAF shows a general underestimation of the LAPS product although the results are not conclusive. The study shows the potential of MSG data in refining the mesoscale analyses produced by LAPS. Moreover the software tools, based on open source codes for geolocation and geographical information systems, written for the transformation of MSG data into input files for LAPS have demonstrated a great flexibility and ease of use. The study opens up an avenue for successive validation and refinement of the analyses together with their improved implementation for operational nowcasting and very short range forecasting applications.

Use of TIR from Space in Operational Systems

Abstract: Thermal-infrared (TIR) observations from satellites provide Sea Surface Temperature (SST) observations at spatial resolutions ranging from 1 to 6 km and temporal resolutions of either twice daily (for polar-orbiters) or up to half-hourly for geostationary satellites, with nighttime RMS errors typically between 0.3 and 0.5°C compared with buoys. They are a valuable data source for input into operational systems for real-time SST composite products and gap-free analyses, feeding into ocean, weather and seasonal prediction models, and nowcasting/forecasting systems for ecosystem dynamics and management. This chapter reviews the current and future use and requirements of infrared satellite SST products in these operational systems. In synthesis, operational users’ goal requirements are that infrared satellite SST products be timely (within 1–24 h depending on application), of resolution 5–10 km over the open ocean and <1 km over coastal waters, and of accuracy ranging from 0.1 to 0.3°C depending on application. It should be acknowledged that each application has its own particular requirements and a single product for all systems is not possible. Satellite SST products that contain sufficient auxiliary information to allow multiple applications of the same product, such as the “L2P” single swath SST products provided for various TIR sensors by the Group for High Resolution SST (GHRSST), have proven to be particularly useful for a range of operational applications.

A short note on the nowcasting and the forecasting of Euro-area GDP using non-parametric techniques

Abstract: The aim of this paper is to introduce a new methodology to forecast the monthly economic indicators used in the Gross Domestic Product (GDP) modelling in order to improve the forecasting accuracy. Our approach is based on multivariate k-nearest neighbors method and radial basis function method for which we provide new theoretical results. We apply these two methods to compute the quarter GDP on the Euro-zone, comparing our approach, with GDP obtained when we estimate the monthly indicators with a linear model, which is often used as a benchmark.

Visibility Parameterization For Forecasting Model Applications

Abstract: this study, the visibility (Vis) parameterizations developed during Fog Remote sensing And Modelling (FRAM) projects, conducted in central and eastern Canada, and Barrow, Alaska, US will be summarized and their use for forecasting/nowcasting applications will be discussed. Parameterizations developed for reductions in visibility due to 1) fog, 2) rain, 3) snow, and 4) relative humidity (RH) during FRAM will be given and uncertainties in the parameterizations will be discussed. Observations used in this study were obtained using a fog measuring device (FMD) for fog parameterization and a Vaisala all-weather precipitation instrument called FD12P for rain and snow visibility parameterizations.

Uncertainty in Nowcasting of Radar Rainfall: a case study of the GLUE methodology

Abstract: Weather radar based nowcasting (or short term forecasting) of rainfall in urban areas is an evolving topic as it is an economically feasible solution handle some of the challenges of climate changes. The perspectives within hydrological and urban hydrological modelleing are many. In urban drainage, there is a large potential to predict rain with a certain lead time, in order to implement real time operations of urban drainage systems to facilitate storage of rain water in some parts of the drainage system in order to prevent flooding or overflow to receiving waters elsewhere. Another feasible potential is activation of warning systems of urban flooding based on rainfall nowcasting in combination with a real time urban drainage model. In the end, these solutions are ways of reducing construction or reconstructions costs of drainage systems in the changing climate. During the past few years the applications have evolved from development of methods and case studies to actual real time operation based on nowcasting, e.g. Aspegren (2001), Einfalt et al. (2004), Achleitner et al. (2009), Schellart et al. (2009) and Thorndahl et al. (2009, 2010). However, in excitement of the current development in technology, the uncertainty of rainfall nowcasting is often ignored in operation applications, and thus the risk of making wrong decisions based on defective nowcasts is impending. The uncertainty quantification is further complicated by the fact that prediction of urban rainfall requires small time scales as the runoff response is fast. Therefore, it is not sufficient to predict correct rainfall volumes, but the rain intensities have to be precise in order to apply nowcasting in urban areas. Uncertainties related to quantitative precipitation estimates from weather radars have been investigated by several authors, e.g. Borga (2002), Villarini et al. (2008) and Villarini and Krajewski (2010); and Grecu and Krajewski (2000) and Fabry and Seed (2009) investigated uncertainty related to quantitative precipitation forecasting using radars, however on a larger temporal scale than required within the area of urban hydrology. Therefore, this study will investigate the uncertainties related to nowcasting of weather radar based rainfall on a small spatial and temporal scale by implementation of a stochastic shell on top of the existing model framework (Thorndahl et al. 2009 and Thorndahl and Rasmussen 2009). The model is based on extrapolation of radar images and has its origin in the TREC and CO-TREC models (Rinehart and Garvey 1978; Mechlenburg 2000; Li et al. 1995). This is combined with the Monte Carlo based Generalized Likelihood Uncertainty Estimation (GLUE) method (Beven and Binley 1992) in which the nowcast model is conditioned on observations in order to evaluate uncertainties. GLUE has been applied within a wide range of hydrological applications and urban hydrological applications (e.g. Thorndahl et al. 2008), but as far as the authors know never in the context of nowcasting and extrapolation of radar rainfall. Besides evaluating the nowcast model as a function of the lead time and investigating the sensitivity of model parameters, it is examined, if the concept can be applied for predicting rain intensities with confidence bounds linked to each point in space and time as well as predicting probability of rain occurrence in a specific area. The main purpose is to gain knowledge on the limitations and constraints of the model during different meteorological conditions and to obtain realistic values and posterior distributions of parameters in order to, on the longer term, be able to calculate uncertainty estimates in real time nowcasting.

Mesoscale Data Assimilation Experiment in the WWRP B08RDP

Abstract: A mesoscale data assimilation experiment was performed for the Beijing 2008 Olympics Research and Development Project (B08RDP) under the World Weather Research Program (WWRP) conducted during the period around the Beijing 2008 Olympic Games.In this experiment, the Japan Meteorological Agency (JMA) hydrostatic mesoscale 4D-Var analysis system was modified and utilized to produce accurate initial fields over the China area. In addition to the conventional observations, precipitation data observed by rain-gauges were assimilated as well as data produced from a nowcasting system of the Australian Bureau of Meteorology.The analysis system with rainfall data outperformed other experiments, i.e., the mesoscale analysis without precipitation data and the JMA global analysis, with its quantitative precipitation forecast. This shows that the assimilation of precipitation data can have a positive impact on subsequent model forecasts and that it would be effective even for areas where successive rainfall observations are sparse.