Improving Harvey Forecasts with Next-Generation Weather Satellites: Advanced Hurricane Analysis and Prediction with Assimilation of GOES-R All-Sky Radiances

AbstractHurricane Harvey brought catastrophic destruction and historical flooding to the Gulf Coast region in late August 2017. Guided by numerical weather prediction models, operational forecasters at NOAA provided outstanding forecasts of Harvey’s future path and potential for record flooding days in advance. These forecasts were valuable to the public and emergency managers in protecting lives and property. The current study shows the potential for further improving Harvey’s analysis and prediction through advanced ensemble assimilation of high-spatiotemporal all-sky infrared radiances from the newly launched, next-generation geostationary weather satellite, GOES-16. Although findings from this single-event study should be further evaluated, the results highlight the potential improvement in hurricane prediction that is possible via sustained investment in advanced observing systems, such as those from weather satellites, comprehensive data assimilation methodologies that can more effectively ingest existing and future observations, higher-resolution weather prediction models with more accurate numerics and physics, and high-performance computing facilities that can perform advanced analysis and forecasting in a timely manner.

Download Full-text

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

10.5194/gmd-2018-304 ◽

2019 ◽

Cited By ~ 1

Author(s):

Andreas Müller ◽

Willem Deconinck ◽

Christian Kühnlein ◽

Gianmarco Mengaldo ◽

Michael Lange ◽

...

Keyword(s):

Energy Efficiency ◽

High Performance Computing ◽

Energy Efficient ◽

High Performance ◽

Prediction Models ◽

Weather Prediction ◽

Building Blocks ◽

Scalable Algorithms ◽

Weather And Climate ◽

Performance Computing

Abstract. In the simulation of complex multi-scale flow problems, such as those arising in weather and climate modelling, one of the biggest challenges is to satisfy operational requirements in terms of time-to-solution and energy-to-solution yet without compromising the accuracy and stability of the calculation. These competing factors require the development of state-of-the-art algorithms that can optimally exploit the targeted underlying hardware and efficiently deliver the extreme computational capabilities typically required in operational forecast production. These algorithms should (i) minimise the energy footprint along with the time required to produce a solution, (ii) maintain a satisfying level of accuracy, (iii) be numerically stable and resilient, in case of hardware or software failure. The European Centre for Medium Range Weather Forecasts (ECMWF) is leading a project called ESCAPE (Energy-efficient SCalable Algorithms for weather Prediction on Exascale supercomputers) which is funded by Horizon 2020 (H2020) under initiative Future and Emerging Technologies in High Performance Computing (FET-HPC). The goal of the ESCAPE project is to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres and hardware vendors. This paper presents an overview of results obtained in the ESCAPE project in which weather prediction have been broken down into smaller building blocks called dwarfs. The participating weather prediction models are: IFS (Integrated Forecasting System), ALARO – a combination of AROME (Application de la Recherche à l'Opérationnel a Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International) and COSMO-EULAG – a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian/semi-Lagrangian fluid solver). The dwarfs are analysed and optimised in terms of computing performance for different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi). The ESCAPE project includes the development of new algorithms that are specifically designed for better energy efficiency and improved portability through domain specific languages. In addition, the modularity of the algorithmic framework, naturally allows testing different existing numerical approaches, and their interplay with the emerging heterogeneous hardware landscape. Throughout the paper, we will compare different numerical techniques to solve the main building blocks that constitute weather models, in terms of energy efficiency and performance, on a variety of computing technologies.

Download Full-text

Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

Monthly Weather Review ◽

10.1175/mwr-d-20-0002.1 ◽

2020 ◽

Vol 148 (11) ◽

pp. 4357-4375

Author(s):

Josef Schröttle ◽

Martin Weissmann ◽

Leonhard Scheck ◽

Axel Hutt

Keyword(s):

Lead Time ◽

Prediction Models ◽

Deep Convection ◽

Weather Prediction ◽

High Temporal Resolution ◽

Convective Scale ◽

Satellite Sensors ◽

Satellite Channels ◽

Infrared Radiances ◽

Data Assimilation System

AbstractCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time.

Download Full-text

In-situ observational network for extreme weather events in India

MAUSAM ◽

10.54302/mausam.v67i1.1145 ◽

2021 ◽

Vol 67 (1) ◽

pp. 67-76

Author(s):

GAJENDRA KUMAR ◽

SURESH CHAND ◽

R. R. MALI ◽

S. K. KUNDU ◽

A. K. BAXLA

Keyword(s):

High Performance ◽

Prediction Models ◽

Weather Prediction ◽

Extreme Weather ◽

Extreme Weather Events ◽

Natural Systems ◽

Observational Network ◽

Weather Events ◽

Vegetables And Fruits

Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

Download Full-text

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Geoscientific Model Development ◽

10.5194/gmd-12-4425-2019 ◽

2019 ◽

Vol 12 (10) ◽

pp. 4425-4441 ◽

Cited By ~ 3

Author(s):

Andreas Müller ◽

Willem Deconinck ◽

Christian Kühnlein ◽

Gianmarco Mengaldo ◽

Michael Lange ◽

...

Keyword(s):

Energy Efficient ◽

High Performance ◽

Prediction Models ◽

Weather Prediction ◽

Programming Models ◽

Scalable Algorithms ◽

Domain Specific ◽

Weather And Climate ◽

Hardware Architectures ◽

Performance Computing

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements.

Download Full-text