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Abstract
The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.
Abstract
The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.
Abstract
In 2011, the German Federal Ministry of Transport, Building and Urban Development laid the foundation of the Hans-Ertel Centre for Weather Research [Hans-Ertel-Zentrum für Wetterforschung (HErZ)] in order to better connect fundamental meteorological research and teaching at German universities and atmospheric research centers with the needs of the German national weather service Deutscher Wetterdienst (DWD). The concept for HErZ was developed by DWD and its scientific advisory board with input from the entire German meteorological community. It foresees core research funding of about €2,000,000 yr−1 over a 12-yr period, during which time permanent research groups must be established and DWD subjects strengthened in the university curriculum. Five priority research areas were identified: atmospheric dynamics and predictability, data assimilation, model development, climate monitoring and diagnostics, and the optimal use of information from weather forecasting and climate monitoring for the benefit of society. Following an open call, five groups were selected for funding for the first 4-yr phase by an international review panel. A dual project leadership with one leader employed by the academic institute and the other by DWD ensures that research and teaching in HErZ is attuned to DWD needs and priorities, fosters a close collaboration with DWD, and facilitates the transfer of fundamental research into operations. In this article, we describe the rationale behind HErZ and the road to its establishment, present some scientific highlights from the initial five research groups, and discuss the merits and future development of this new concept to better link academic research with the needs and challenges of a national weather service.
Abstract
In 2011, the German Federal Ministry of Transport, Building and Urban Development laid the foundation of the Hans-Ertel Centre for Weather Research [Hans-Ertel-Zentrum für Wetterforschung (HErZ)] in order to better connect fundamental meteorological research and teaching at German universities and atmospheric research centers with the needs of the German national weather service Deutscher Wetterdienst (DWD). The concept for HErZ was developed by DWD and its scientific advisory board with input from the entire German meteorological community. It foresees core research funding of about €2,000,000 yr−1 over a 12-yr period, during which time permanent research groups must be established and DWD subjects strengthened in the university curriculum. Five priority research areas were identified: atmospheric dynamics and predictability, data assimilation, model development, climate monitoring and diagnostics, and the optimal use of information from weather forecasting and climate monitoring for the benefit of society. Following an open call, five groups were selected for funding for the first 4-yr phase by an international review panel. A dual project leadership with one leader employed by the academic institute and the other by DWD ensures that research and teaching in HErZ is attuned to DWD needs and priorities, fosters a close collaboration with DWD, and facilitates the transfer of fundamental research into operations. In this article, we describe the rationale behind HErZ and the road to its establishment, present some scientific highlights from the initial five research groups, and discuss the merits and future development of this new concept to better link academic research with the needs and challenges of a national weather service.
The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.
Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.
This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.
These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.
The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.
Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.
This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.
These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.
