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1. Introduction Numerical weather prediction (NWP) has steadily improved over the last decades, allowing a multitude of socioeconomic benefits to be realized ( Bauer et al. 2015 ; Alley et al. 2019 ). While progress is unmistakable for 500-hPa geopotential heights and mean sea level pressure in the extratropics, improvements in the predictions of many other parameters are more variable ( Navascués et al. 2013 ). For example, forecasts of European cloud cover have hardly improved over the last
1. Introduction Numerical weather prediction (NWP) has steadily improved over the last decades, allowing a multitude of socioeconomic benefits to be realized ( Bauer et al. 2015 ; Alley et al. 2019 ). While progress is unmistakable for 500-hPa geopotential heights and mean sea level pressure in the extratropics, improvements in the predictions of many other parameters are more variable ( Navascués et al. 2013 ). For example, forecasts of European cloud cover have hardly improved over the last
1. Introduction The dynamical origin of the kinetic energy (KE) spectrum on the mesoscale range (wavelengths from a few kilometers up to several hundred kilometers) has been a mystery to atmospheric scientists for more than three decades ( Gage and Nastrom 1986 ; Gkioulekas and Tung 2006 ; Sun et al. 2017 ; Craig and Selz 2018 ). Understanding the dynamics underlying the mesoscale KE spectrum is not only of academic interest, but it is central to more applied aspects of numerical weather
1. Introduction The dynamical origin of the kinetic energy (KE) spectrum on the mesoscale range (wavelengths from a few kilometers up to several hundred kilometers) has been a mystery to atmospheric scientists for more than three decades ( Gage and Nastrom 1986 ; Gkioulekas and Tung 2006 ; Sun et al. 2017 ; Craig and Selz 2018 ). Understanding the dynamics underlying the mesoscale KE spectrum is not only of academic interest, but it is central to more applied aspects of numerical weather
1. Introduction Rainfall variability substantially affects societies in northern tropical Africa ( Sultan et al. 2005 ). More than 96% of cultivated land in sub-Saharan Africa is rain-fed ( FAO 2016 ). Despite this, major operational global weather prediction models still fail to deliver skillful short-range precipitation forecasts over this region ( Vogel et al. 2018 ). This corroborates the need for an improved understanding of the underlying processes involved in the generation of
1. Introduction Rainfall variability substantially affects societies in northern tropical Africa ( Sultan et al. 2005 ). More than 96% of cultivated land in sub-Saharan Africa is rain-fed ( FAO 2016 ). Despite this, major operational global weather prediction models still fail to deliver skillful short-range precipitation forecasts over this region ( Vogel et al. 2018 ). This corroborates the need for an improved understanding of the underlying processes involved in the generation of
future. Enormous population growth and urbanization occurred in Africa in recent decades and are projected to continue. Since 1950 the population quadrupled and is likely to quadruple again until the end of the twenty-first century, with then more than 4 billion inhabitants ( UNDP 2015 ). In 2050 more than 1.3 billion are expected to live in urban areas—5 times more than in 2000 ( UNDP 2015 ). In addition, Africa has been identified as the region most vulnerable to climate change and variability
future. Enormous population growth and urbanization occurred in Africa in recent decades and are projected to continue. Since 1950 the population quadrupled and is likely to quadruple again until the end of the twenty-first century, with then more than 4 billion inhabitants ( UNDP 2015 ). In 2050 more than 1.3 billion are expected to live in urban areas—5 times more than in 2000 ( UNDP 2015 ). In addition, Africa has been identified as the region most vulnerable to climate change and variability
1. Introduction Numerical weather prediction based on physical models of the atmosphere has improved continuously since its inception more than four decades ago ( Bauer et al. 2015 ). In particular, the emergence of ensemble forecasts—simulations with varying initial conditions and/or model physics—added another dimension by quantifying the flow-dependent uncertainty. Yet despite these advances the raw forecasts continue to exhibit systematic errors that need to be corrected using statistical
1. Introduction Numerical weather prediction based on physical models of the atmosphere has improved continuously since its inception more than four decades ago ( Bauer et al. 2015 ). In particular, the emergence of ensemble forecasts—simulations with varying initial conditions and/or model physics—added another dimension by quantifying the flow-dependent uncertainty. Yet despite these advances the raw forecasts continue to exhibit systematic errors that need to be corrected using statistical
circumglobal waves (e.g., Lee and Held 1993 ; Chang 1993 ). Although theoretical arguments for the evolution of Rossby waves in idealized setups had already been put forward in the middle of the twentieth century ( Dickinson 1978 , and references therein), their actual behavior and role in the atmosphere started being investigated in recent decades, facilitated by the increasing data availability and advances in computer performance [see Wirth et al. (2018) for a review of recent developments]. In this
circumglobal waves (e.g., Lee and Held 1993 ; Chang 1993 ). Although theoretical arguments for the evolution of Rossby waves in idealized setups had already been put forward in the middle of the twentieth century ( Dickinson 1978 , and references therein), their actual behavior and role in the atmosphere started being investigated in recent decades, facilitated by the increasing data availability and advances in computer performance [see Wirth et al. (2018) for a review of recent developments]. In this
over the last decade. Hagedorn et al. (2012) find that a multimodel ensemble composed of the four best participating TIGGE EPSs, which include the ECMWF ensemble, outperforms reforecast-calibrated ECMWF forecasts. For the evaluation of NWP precipitation forecast quality, TIGGE is the most complete and best available data source for the period 2007–14. Table 1 gives an overview of the nine participating TIGGE EPSs that provide accumulated precipitation forecasts. Table 1. TIGGE subensembles
over the last decade. Hagedorn et al. (2012) find that a multimodel ensemble composed of the four best participating TIGGE EPSs, which include the ECMWF ensemble, outperforms reforecast-calibrated ECMWF forecasts. For the evaluation of NWP precipitation forecast quality, TIGGE is the most complete and best available data source for the period 2007–14. Table 1 gives an overview of the nine participating TIGGE EPSs that provide accumulated precipitation forecasts. Table 1. TIGGE subensembles
Multiaircraft and ground-based observations were made over the North Atlantic in the fall of 2016 to investigate the importance of diabatic processes for midlatitude weather. Progress in understanding the processes controlling midlatitude weather is one of the factors that have contributed to a continuous improvement in the skill of medium-range weather forecasts in recent decades ( Thorpe 2004 ; Richardson et al. 2012 ; Bauer et al. 2015 ). Additionally, numerical weather prediction (NWP
Multiaircraft and ground-based observations were made over the North Atlantic in the fall of 2016 to investigate the importance of diabatic processes for midlatitude weather. Progress in understanding the processes controlling midlatitude weather is one of the factors that have contributed to a continuous improvement in the skill of medium-range weather forecasts in recent decades ( Thorpe 2004 ; Richardson et al. 2012 ; Bauer et al. 2015 ). Additionally, numerical weather prediction (NWP
1. Introduction Weather prediction has improved significantly in the past decades ( Bauer et al. 2015 ). Forecast dropouts, however, do still occur in operational numerical weather prediction models ( Rodwell et al. 2013 , 2018 ). Because of the multiscale nature of atmospheric dynamics, there may always be an intrinsic limit of predictability even if model errors and initial-condition errors occur only on the smallest resolved scale ( Lorenz 1969 ). Small-scale errors associated with moist
1. Introduction Weather prediction has improved significantly in the past decades ( Bauer et al. 2015 ). Forecast dropouts, however, do still occur in operational numerical weather prediction models ( Rodwell et al. 2013 , 2018 ). Because of the multiscale nature of atmospheric dynamics, there may always be an intrinsic limit of predictability even if model errors and initial-condition errors occur only on the smallest resolved scale ( Lorenz 1969 ). Small-scale errors associated with moist
1. Introduction Forecast skill has improved continuously over the last 40 years. The rate of improvement reached about one forecast day per decade, which means that a 6-day forecast today is as good as a 5-day forecast was 10 years ago. This considerable improvement together with its high socioeconomic impact has been recognized as a “quiet revolution” by Bauer et al. (2015) . However, such studies of past successes immediately raise the question of how far this progress will go on in the
1. Introduction Forecast skill has improved continuously over the last 40 years. The rate of improvement reached about one forecast day per decade, which means that a 6-day forecast today is as good as a 5-day forecast was 10 years ago. This considerable improvement together with its high socioeconomic impact has been recognized as a “quiet revolution” by Bauer et al. (2015) . However, such studies of past successes immediately raise the question of how far this progress will go on in the
