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Abstract
The Hovmöller diagram or the trough–ridge diagram, a simple longitude–time diagram, was designed in 1948 by Ernest Hovmöller (1912–2008) to help understand certain features in the dynamics of the atmosphere, in particular the “downstream development” phenomenon. Originally depicting the 500-hPa geopotential, today many other parameters are used, and Hovmöller diagrams have during the last 25 years found a rapidly increasing use in a wide range of atmospheric research.
Abstract
The Hovmöller diagram or the trough–ridge diagram, a simple longitude–time diagram, was designed in 1948 by Ernest Hovmöller (1912–2008) to help understand certain features in the dynamics of the atmosphere, in particular the “downstream development” phenomenon. Originally depicting the 500-hPa geopotential, today many other parameters are used, and Hovmöller diagrams have during the last 25 years found a rapidly increasing use in a wide range of atmospheric research.
Abstract
The propagation of the effect of targeted observations in numerical weather forecasts is investigated, based on results from the 2000 Winter Storm Reconnaissance (WSR00) program. In this field program, nearly 300 dropsondes were released adaptively at selected locations over the northeast Pacific on 12 separate flight days with the aim of reducing the risk of major failures in severe winter storm forecasts over the United States. The data impact was assessed by analysis–forecast experiments carried out with the T62 horizontal resolution, 28-level version of the operational global Medium Range Forecast system of the National Centers for Environmental Prediction.
In some cases, storms that reached the West Coast or Alaska were observed in an earlier phase of their development, while at other times the goal was to improve the prediction of storms that formed far downstream of the targeted region. Changes in the forecasts were the largest when landfalling systems were targeted and the baroclinic energy conversion was strong in the targeted region.
As expected from the experience accumulated during the 1999 Winter Storm Reconnaissance (WSR99) program, downstream baroclinic development played a major role in propagating the influence of the targeted data over North America. The results also show, however, that predicting the location of significant changes due to the targeted data in the forecasts can be difficult in the presence of a nonzonal large-scale flow. The strong zonal variations in the large-scale flow over the northeast Pacific during WSR00 did not reduce the positive forecast effects of the targeted data. On the contrary, the overall impact of the dropsonde data was more positive than during WSR99, when the large-scale flow was dominantly zonal on the flight days. This can be attributed to the improved prediction of the large-scale flow that led to additional improvements in the prediction of the synoptic-scale waves.
Abstract
The propagation of the effect of targeted observations in numerical weather forecasts is investigated, based on results from the 2000 Winter Storm Reconnaissance (WSR00) program. In this field program, nearly 300 dropsondes were released adaptively at selected locations over the northeast Pacific on 12 separate flight days with the aim of reducing the risk of major failures in severe winter storm forecasts over the United States. The data impact was assessed by analysis–forecast experiments carried out with the T62 horizontal resolution, 28-level version of the operational global Medium Range Forecast system of the National Centers for Environmental Prediction.
In some cases, storms that reached the West Coast or Alaska were observed in an earlier phase of their development, while at other times the goal was to improve the prediction of storms that formed far downstream of the targeted region. Changes in the forecasts were the largest when landfalling systems were targeted and the baroclinic energy conversion was strong in the targeted region.
As expected from the experience accumulated during the 1999 Winter Storm Reconnaissance (WSR99) program, downstream baroclinic development played a major role in propagating the influence of the targeted data over North America. The results also show, however, that predicting the location of significant changes due to the targeted data in the forecasts can be difficult in the presence of a nonzonal large-scale flow. The strong zonal variations in the large-scale flow over the northeast Pacific during WSR00 did not reduce the positive forecast effects of the targeted data. On the contrary, the overall impact of the dropsonde data was more positive than during WSR99, when the large-scale flow was dominantly zonal on the flight days. This can be attributed to the improved prediction of the large-scale flow that led to additional improvements in the prediction of the synoptic-scale waves.
Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability.
Mesoscale convective systems, associated with the high levels of CAPE, act to slow the motion of the trough. Hence, convection errors play an active role in the busts. The subgrid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts. The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation.
A question of relevance to the wider community is, do we have observations with sufficient accuracy to better constrain the important error structures in the initial conditions? Meanwhile, improvements to ensemble prediction systems should help us better predict the increase in forecast uncertainty.
Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability.
Mesoscale convective systems, associated with the high levels of CAPE, act to slow the motion of the trough. Hence, convection errors play an active role in the busts. The subgrid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts. The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation.
A question of relevance to the wider community is, do we have observations with sufficient accuracy to better constrain the important error structures in the initial conditions? Meanwhile, improvements to ensemble prediction systems should help us better predict the increase in forecast uncertainty.
