Seminars / Informal seminars / Lectures by ECMWF Staff and Invited Lecturers
Seminars contribute to our ongoing educational programme and are tailored to the interests of the ECMWF scientific community.
Informal seminars are held throughout the year on a range of topics. Seminars vary in their duration, depending on the area covered, and are given by subject specialists. As with the annual seminar, this may be an ECMWF staff member or an invited lecturer.
The following is a listing of seminars/lectures that have been given this year on topics of interest to the ECMWF scientific community. See also our past informal seminars
The Joint Effort for Data assimilation Integration
Speaker: Yannick Tremolet (JCSDA, USA)
The Joint Effort for Data assimilation Integration (JEDI) aims at providing a unified data assimilation framework for all partners of the Joint Center for Satellite Data Assimilation (JCSDA) and the data assimilation community in general. The long term objective is to provide a unified framework for research and operational use, for different components of the Earth system, and for different applications, with the objective of reducing or avoiding redundant work within the community and increasing efficiency of research and of the transition from development teams to operations.
One area where this is particularly important is the use of observations. As Earth observing systems are constantly evolving and new systems launched, continuous scientific developments for exploiting the full potential of the data are necessary. Given the cost of new observing systems and their limited lifetime, it is important that this process happens quickly. Reducing duplication of work and increasing collaboration between agencies in this domain can be achieved through Unified Forward Operators (UFO) and a common Interface for Observation Data Access (IODA).
Over the last decade or two, software development technology has advanced significantly, making routine the use of complex software in everyday life. The key concept in modern software development for complex systems is the separation of concerns. In a well-designed architecture, teams can develop different aspects in parallel without interfering with other teams’ work and without breaking the components they are not working on. Scientists can be more efficient focusing on their area of expertise without having to understand all aspects of the system. This is similar to the concept of modularity. However, modern techniques (such as Object Oriented programming) extend this concept and, just as importantly, help enforce it uniformly throughout a code.
JEDI is based on the Object Oriented Prediction System (OOPS), encapsulating models and observations, which will be briefly described. Extensions towards sharing observations operators and observation related operations such as quality control across models using the UFO will also be described.
JEDI is a collaborative project with developers distributed across agencies and in several locations in different time zones. In order to facilitate collaborative work, modern software development tools are used. These tools include version control, bug and feature development tracking, automated regression testing and provide utilities for exchanging this information. The collaborative development process in JEDI will be presented before concluding with the status of the project.
Forecasting health hazards linked to heatwaves
Speaker: Claudia di Napoli (Reading University)
In recent years severe and prolonged episodes of summer heat such as the 2003 European heatwave proved that extreme high temperatures are responsible for excessed mortality in affected areas, and Heat Health Warning Systems (HHWSs) need to be put in place to mitigate the negative impacts caused by hot weather extremes on human health.
A heatwave-associated HHSW is being developed as part of the pan-European multi-hazard early warning system constructed within the HORIZON2020 ANYWHERE project (EnhANcing emergencY management and response to extreme WeatHER and climate Events). The ANYWHERE HHSW is
In this seminar the potential of UTCI forecast as a tool to predict heat-related health hazard will be explored. Heat stress conditions across Europe will be presented via UTCI maps computed from 38 years of ERA-Interim data. The association between the UTCI and summer mortality data from 17 European countries will be also discussed, and the UTCI’s ability to represent mortality patterns demonstrated for the 2003 European heatwave.
The potential of satellites and assimilation to quantify climate forcing, feedbacks and prediction in the Earth System: application to atmospheric chemistry and the carbon cycle
Speaker: Kevin Bowman (JPL, Pasadena, USA)
Anthropogenic activities since the industrial revolution have led to profound changes in atmospheric composition (e.g., carbon dioxide, methane and tropospheric ozone) and consequently the trajectory of our climate. However, the coupling of these constituents must be quantified in order to assess the efficacy of climate mitigation strategies against the backdrop of natural variability and climate feedbacks. The last decade has witnessed the launch of satellite constellations that measure Earth’s atmosphere, land, and oceans with a concomitant advance in data assimilation approaches to link these data to Earth System processes.
Using these approaches, we have attributed ozone and methane radiative forcing to global emissions at large urban scales. By incorporating both methane emissions and chemical losses, we show that the top 10% of locations with positive net methane RF are responsible for 50% of the global positive RF and the top 10% of locations with negative RF cause 60% of the global negative RF based upon an RCP 6.0 trajectory through 2050.
To understand the role of the carbon cycle in controlling the most important greenhouse gas, the NASA Carbon Monitoring System Flux (CMS-Flux) project was initiated as a coordinated effort between land surface, ocean, fossil fuel, and atmospheric scientists to develop a comprehensive a carbon cycle data assimilation system. Based upon this system, we attribute the historic atmospheric CO2 growth rate during the 2015 El Nino to spatially-resolved fluxes. We show how tropical productivity and respiration processes related to anomalously high climate variability, i.e., “extreme” events, are responsible for this growth rate and their implications for carbon-climate feedbacks.
Emergent constraints have been become an active area of research that use contemporary observations to constrain climate projections. We have developed a Bayesian formulation of this approach that explicitly accounts for the uncertainty in observations and the uncertainty between the future and present state. We explore the potential of this framework for tropospheric ozone radiative forcing and the carbon cycle.
Taken together, these advances in observations, modeling, and the methodologies to link them point to a scientifically rigorous and policy-relevant framework critically needed for the international community to address climate change.
Dr. Kevin Bowman is the Principal Investigator of the EOS Aura Tropospheric Emission Spectrometer and the NASA Carbon Monitoring System (CMS-Flux) project. He received a BEE from Auburn University in 1991, a Diplôme de Spécialisation en Traitement et Transmission des Informations at L'Ecole Supérieure d'Electricité (SUPELEC), Metz, FRANCE in 1993, and a Phd in Electrical Engineering from the Georgia Institute of Technology in 1996. He subsequently continued his career at the Jet Propulsion Laboratory in 1997. His research is centered on understanding the processes controlling atmospheric composition and their impact on climate using satellite observations, modeling, and data assimilation techniques. Dr. Bowman's broad interests have led to publications in diverse fields including air quality, carbon cycle, chemistry-climate, atmospheric hydrology, and remote sensing science. An avid musician and guitarist, Dr. Bowman is a founding member of the JPL Jazz Propulsion Band.
Using All-Sky Satellite Infrared Brightness Temperatures for Model Verification and in Ensemble Data Assimilation Systems
Speaker: Jason Otkin (Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, USA)
Infrared sensors onboard geostationary satellites provide detailed information about the cloud and water vapor fields with high spatial and temporal resolutions that make them very useful for model verification and within data assimilation systems. In the first part of this talk, results will be shown from several recent studies that used GOES infrared brightness temperatures to assess the accuracy of cloud and water vapor forecasts generated by the High Resolution Rapid Refresh (HRRR) model in real-time and as part of longer-term verification studies. The real-time GOES-based verification system provides operational forecasters objective tools to quickly assess the accuracy of current and prior HRRR model forecasts when they are creating or updating their short-range forecasts. For long-term verification, the forecast accuracy is assessed using a variety of statistical methods ranging from standard grid point metrics to neighborhood-based methods such as the Fractions Skill Score to more sophisticated object-based verification tools. Overall, the results show that the simulated brightness temperatures are too warm during the first hour of the forecast, indicating that the HRRR model initialization is deficient in upper-level clouds. This warm bias, however, is quickly replaced by a large cold bias due to the rapid generation of upper-level clouds with the negative bias often lasting for several hours before the excess cloud cover dissipates. The object-based analysis showed that the HRRR initialization contains too many small cloud objects; however, the number of cloud objects becomes too low by forecast hour 2. This behavior is consistent with the changes in the simulated brightness temperatures and indicates that the forecast cloud objects become too large after a few hours.
In the second part of this talk, output from a high-resolution ensemble data assimilation system (KENDA) is used to assess the ability of a nonlinear bias correction (NBC) method that uses a Taylor series polynomial expansion of the observation-minus-background departures to remove linear and nonlinear conditional biases from all-sky SEVIRI infrared brightness temperatures. Univariate and multivariate NBC experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the bias correction predictors. The results showed that even though the bias of the entire error distribution is equal to zero regardless of the order of the Taylor series expansion, that there are often large conditional biases that vary as a nonlinear function of the predictor value. The linear 1st order Taylor series term had the largest impact on the entire distribution as measured by reductions in the variance; however, large conditional biases often remained across the distribution. These conditional biases were typically reduced to near zero after the nonlinear 2nd (quadratic) and 3rd (cubic) order terms were used. The results showed that variables sensitive to cloud top height are effective bias predictors especially when higher order Taylor series terms are used. Comparison of statistics compiled for clear-sky and cloudy-sky matched observations revealed that nonlinear bias corrections are more important for cloudy-sky observations as signified by the much larger impact of the 2nd and 3rd order terms on the conditional biases.
LT = Lecture Theatre, LCR = Large Committee Room, MZR = Mezzanine Committee Room,
CC = Council Chamber