TRR 165:
Waves to Weather
Subject Area
Geosciences
Computer Science, Systems and Electrical Engineering
Mathematics
Term
from 2015 to 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 257899354
Our ability to predict the weather up to a week or more ahead saves our societies billions of Euros annually and protects human life and property. Exponentially increasing computing power and new observations have led to continuous improvements in forecast quality over many years, but nonetheless forecasts are sometimes strikingly poor. Increasing evidence suggests that this is not merely due to deficits in our forecasting methods, because in a chaotic atmosphere, some weather situations are intrinsically hard to predict. The great challenge today is to identify the limits of predictability in different situations and produce the best forecasts that are physically possible. The Collaborative Research Center “Waves to Weather” (CRC/Transregio 165; W2W) is conceived to meet this challenge and to deliver the underpinning science urgently needed to pave the way towards a new generation of weather forecasting systems. In Phase 2, a significant breakthrough occurred, where it was possible to show that for average conditions, the intrinsic limit of predictability will be reached when the initial condition uncertainty is reduced to about 10\% of its current level. At this point the predictability is limited by rapid error growth on small scales due to diabatic processes. In Phase 3, we will build on this knowledge by identifying windows of forecast opportunity – specific atmospheric states where extended predictions are possible. Here we will apply an innovative method using a Global Control Ensemble to investigate predicability of these relatively infrequent events. Furthermore, we will develop a Regional Grand Ensemble to obtain an unprecedented, comprehensive and quantitative view of the complex network of physical processes that contribute to rapid error growth on small scales. Finally, this theoretical knowledge will be brought to practical weather prediction by developing new, computationally efficient, representations of uncertainty with hybrid methods that combine the strengths of numerical models and machine learning. As in the previous Phases, W2W brings together the necessary broad scientific expertise from the three applying universities: the Ludwig-Maximilians-Universität (LMU) in Munich, the Johannes Gutenberg-Universität (JGU) in Mainz, and the Karlsruher Institut für Technologie (KIT). This is supported by collaboration in individual projects with the Technische Universität München (TUM), the Deutsches Zentrum für Luft- und Raumfahrt (DLR) in Oberpfaffenhofen, the Universität Hamburg (UHH) and, additionally for Phase 3, the Katholische Universität Eichstätt-Ingolstadt (KU) and the Universität Wien (UW).
DFG Programme
CRC/Transregios
International Connection
Austria, Netherlands
Current projects
-
A01 - The variability and relevance of the intrinsic limit of predictability
(Project Heads
Craig, George
;
Riemer, Michael
;
Tost, Holger
;
Wirth, Volkmar
)
-
A03 - Understanding the dynamical impact of Aeolus observations
(Project Heads
Craig, George
;
Schäfler, Andreas
;
Weissmann, Martin
)
-
A06 - Forecast uncertainty near the limit of predictability
(Project Heads
Craig, George
;
Keil, Christian
;
Kober, Kirstin
;
Pongratz, Julia
)
-
A07 - Machine learning of representative features in meteorological fields
(Project Heads
Sadlo, Filip
;
Westermann, Rüdiger
)
-
A08 - Dynamical understanding of “windows of forecast opportunity”
(Project Heads
Grams, Christian
;
Riemer, Michael
;
Wirth, Volkmar
)
-
B01 - Impact of microphysical uncertainties on supercell storm predictions around the world
(Project Heads
Hoose, Corinna
;
Kunz, Michael
;
Miltenberger, Annette
;
Vogel, Bernhard
;
Weissmann, Martin
)
-
B03 - Quantifying sources of uncertainty on convective scales using a Grand Ensemble
(Project Heads
Barthlott, Christian
;
Craig, George
;
Hoose, Corinna
;
Keil, Christian
;
Westermann, Rüdiger
)
-
B04 - Bringing light to NWP – improving radiation to improve midlatitude weather prediction
(Project Heads
Mayer, Bernhard
;
Voigt, Aiko
)
-
B06 - Understanding the gap between intrinsic and practical predictability in the tropics
(Project Heads
Hoose, Corinna
;
Janjic Pfander, Tijana
;
Knippertz, Peter
)
-
B07 - “Piggybacking” clouds to analyze cloud scale parameterization uncertainty
(Project Heads
Hanke-Bourgeois, Martin
;
Spichtinger, Peter
;
Tost, Holger
)
-
B08 - Role of uncertainty in cold front microphysical processes and organization
(Project Heads
Grams, Christian
;
Hoose, Corinna
;
Miltenberger, Annette
)
-
C02 - Resource-efficient probabilistic forecasts of rainfall in tropical Africa
(Project Heads
Fink, Ph.D., Andreas
;
Gneiting, Tilmann
;
Knippertz, Peter
)
-
C03 - Three-dimensional structure of African Easterly Waves and organization of embedded convection
(Project Heads
Fink, Ph.D., Andreas
;
Riemer, Michael
;
Schömer, Elmar
)
-
C04 - Medium-Range to Subseasonal Predictability of European Heat Waves
(Project Heads
Fink, Ph.D., Andreas
;
Wirth, Volkmar
)
-
C05 - Towards a leadtime, scale and dynamical feature dependent postprocessing for wind gusts
(Project Heads
Corsmeier, Ulrich
;
Knippertz, Peter
;
Lerch, Sebastian
)
-
C08 - Subseasonal predictability facilitated through the stratosphere
(Project Heads
Birner, Thomas
;
Garny, Hella
;
Pinto, Joaquim G.
)
-
C09 - Visual feature-centric analysis from individual cases to ensembles
(Project Heads
Craig, George
;
Rautenhaus, Marc
)
-
INFZ02 - Computing Services
(Project Heads
Brinkmann, André
;
Cayoglu, Ugur
;
Craig, George
;
Keil, Christian
;
Rautenhaus, Marc
;
Redl, Robert
)
-
T04 - Development of a deep learning prototype for operational probabilistic wind gust forecasting
(Project Head
Lerch, Sebastian
)
-
Z01 - Central management
(Project Head
Craig, George
)
Completed projects
-
A02 - Impact of structured heat sources on larger scales in atmospheric dynamics
(Project Heads
Hildebrandt, Andreas
;
Lukacova, Maria
;
Spichtinger, Peter
)
-
A04 - Evolution and predictability of storm structure during extratropical transition of tropical cyclones
(Project Heads
Riemer, Michael
;
Schömer, Elmar
)
-
A05 - The role of soil moisture and surface- and subsurface water flows on predictability of convection
(Project Heads
Kober, Kirstin
;
Kunstmann, Harald
)
-
B05 - Data-driven Analysis and Learning of the Temporal Evolution of Ensemble Forecasts
(Project Head
Westermann, Rüdiger
)
-
C07 - Statistical postprocessing and stochastic physics for ensemble predictions
(Project Heads
Gneiting, Tilmann
;
Kober, Kirstin
)
-
T01 - Development of a predictability index for severe weather
events over Europe
(Project Heads
Craig, George
;
Wirth, Volkmar
)
-
T02 - Towards seamless prediction of extremes (TEX)
(Project Heads
Craig, George
;
Grams, Christian
)
-
Z01 - Central Managment
(Project Head
Craig, George
)