Waves to Weather (W2W)

Description:

This special collection comprises the results of the Collaborative Research Center “Waves to Weather” (W2W), which is funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) for a period of 4 years with possible extensions up to 12 years. The main topic of W2W is predictability and prediction of weather. The current scientific themes of W2W are "Upscale error growth", "Cloud-scale uncertainties", and "Predictability of local weather". It includes theoretical studies, numerical modeling, and process studies based in part on cutting edge statistical methods and visualization tools, NWP models and data collected during the field campaign NAWDEX.

The aim of W2W is to identify the limits of predictability of weather and to produce the best forecasts that are physically possible. The focus of W2W is on the most important causes of remaining uncertainties in weather prediction, which include:

  • the quick upscale growth of forecast errors from insufficiently resolved or represented processes like convection or boundary layer mixing, which modify synoptic-scale waves,
  • our limited understanding of processes in clouds, and
  • the influence of local factors on weather that influence the predictability associated with larger-scale wave disturbances.

W2W addresses these three areas in a concerted effort involving contributions from the disciplines of atmospheric dynamics, cloud physics, statistics, inverse methods and visualization.

W2W uses, and further develops a broad range of tools, including numerical models with detailed treatment of cloud processes and aerosols, and ensemble forecasts with sophisticated statistical post-processing to describe uncertainty. Improved insight has already been gained through the development of new interactive visualization methods, that enable rapid exploration of forecast ensembles to identify the sources and evolution of uncertainty in meteorologically significant features, as well as through the unprecedented dataset collected during the international field campaign NAWDEX.

W2W currently consist of eighteen individual scientific projects located in Germany (Ludwig-Maximilians University of Munich, Karlsruhe Institute of Technology, Johannes Gutenberg University in Mainz, German Aerospace Center (DLR) Oberpfaffenhofen, and University of Heidelberg).

Collection organizers:
Audine Laurian and George C. Craig, Meteorological Institute, Ludwig-Maximilians University, Munich, Germany

Waves to Weather (W2W)

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

Abstract

Precipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

Abstract

Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated precipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical postprocessing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007–14, but overall they have little added value compared to climatology. The suspicion is that parameterization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

Open access
Roderick van der Linden, Andreas H. Fink, Joaquim G. Pinto, and Tan Phan-Van

Abstract

A record-breaking rainfall event occurred in northeastern Vietnam in late July–early August 2015. The coastal region in Quang Ninh Province was hit severely, with station rainfall sums in the range of 1000–1500 mm. The heavy rainfall led to flooding and landslides, which resulted in an estimated economic loss of $108 million (U.S. dollars) and 32 fatalities. Using a multitude of data sources and ECMWF ensemble forecasts, the synoptic–dynamic development and practical predictability of the event is investigated in detail for the 4-day period from 1200 UTC 25 July to 1200 UTC 29 July 2015, during which the major portion of the rainfall was observed. A slowly moving upper-level subtropical trough and the associated surface low in the northern Gulf of Tonkin promoted sustained moisture convergence and convection over northeastern Vietnam. The humidity was advected in a moisture transport band lying across the Indochina Peninsula and emanating from a tropical storm over the Bay of Bengal. Analyses of the ECMWF ensemble forecasts clearly showed a sudden emergence of the predictability of the extreme event at lead times of 3 days that was associated with the correct forecasts of the intensity and location of the subtropical trough in the 51 ensemble members. Thus, the Quang Ninh event is a good example in which the predictability of tropical convection arises from large-scale synoptic forcing; in the present case it was due to a tropical–extratropical interaction that has not been documented before for the region and season.

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