Maurus Borne

Previous studies showed substantial improvements in upper-level wind and mass field forecasts from assimilating Aeolus wind observations. This study extends those analyses using the improved reprocessed Aeolus dataset (version B16) in experiments with the global ECMWF forecasting system spanning more than three years. Results show that zonal wind forecasts improve through most of the troposphere during the first forecast week, with smaller gains in Northern Hemisphere midlatitudes. The impact extends into the stratosphere, reducing Root Mean Square Errors by about 0.5 %, up to 1.5 % in the tropical upper troposphere. These improvements lead to more accurate rainfall forecasts in some regions and seasons, as measured by the Fraction Skill Score (FSS) and the Stable Equitable Error in Probability Space (SEEPS). They are largest during the winter half-year in the extratropics, particularly in the Southern Hemisphere, and appear primarily at the grid scale. The largest FSS improvements, reaching several percent, occur for 5–10 d leads and heavy rainfall categories, while SEEPS indicates modest but consistent gains in categorical precipitation skill. These results show co-occurring improvements in wind and precipitation, particularly in winter, which may reflect an influence of Aeolus winds on the large-scale circulation that governs the organization of cyclones, fronts, and heavy rainfall.

Previous studies showed substantial improvements in upper-level wind and mass field forecasts from assimilating Aeolus wind observations. This study extends those analyses using the improved reprocessed Aeolus dataset (version B16) in experiments with the global ECMWF forecasting system spanning more than three years. Results show that zonal wind forecasts improve across the entire troposphere during the first forecast week, propagating gradually into the stratosphere, with average reductions in the Root Mean Square Error of about 0.5 %, reaching over 1.5 % in the tropical upper troposphere. These improvements lead to more accurate rainfall forecasts in some regions and seasons, as measured by the Fraction Skill Score (FSS) and the Stable Equitable Error in Probability Space (SEEPS). They are largest during the winter half-year in the extratropics, particularly in the Southern Hemisphere, and appear primarily at the grid scale. The largest FSS improvements, reaching several percent, occur for 5–10 day leads and heavy rainfall categories, while SEEPS indicates modest but consistent gains in categorical precipitation skill. These results suggest that assimilating Aeolus winds improves large-scale dynamics, such as jet streams and Rossby waves, leading to more accurate long-term predictions of cyclones and fronts, and ultimately better local wind and heavy rainfall forecasts.

Previous studies showed substantial improvements in upper-level wind and mass field forecasts from assimilating Aeolus wind observations. This study extends those analyses using the improved reprocessed Aeolus dataset (version B16) in experiments with the global ECMWF forecasting system spanning more than three years. Results show that zonal wind forecasts improve across the entire troposphere during the first forecast week, propagating gradually into the stratosphere, with average reductions in the Root Mean Square Error of about 0.5 %, reaching over 1.5 % in the tropical upper troposphere. These improvements lead to more accurate rainfall forecasts in some regions and seasons, as measured by the Fraction Skill Score (FSS) and the Stable Equitable Error in Probability Space (SEEPS). They are largest during the winter half-year in the extratropics, particularly in the Southern Hemisphere, and appear primarily at the grid scale. The largest FSS improvements, reaching several percent, occur for 5–10 day leads and heavy rainfall categories, while SEEPS indicates modest but consistent gains in categorical precipitation skill. These results suggest that assimilating Aeolus winds improves large-scale dynamics, such as jet streams and Rossby waves, leading to more accurate long-term predictions of cyclones and fronts, and ultimately better local wind and heavy rainfall forecasts.

Previous studies showed substantial improvements in upper-level wind and mass field forecasts from assimilating Aeolus wind observations. This study extends those analyses using the improved reprocessed Aeolus dataset (version B16) in experiments with the global ECMWF forecasting system spanning more than three years. Results show that zonal wind forecasts improve across the entire troposphere during the first forecast week, propagating gradually into the stratosphere, with average reductions in the Root Mean Square Error of about 0.5 %, reaching over 1.5 % in the tropical upper troposphere. These improvements lead to more accurate rainfall forecasts in some regions and seasons, as measured by the Fraction Skill Score (FSS) and the Stable Equitable Error in Probability Space (SEEPS). They are largest during the winter half-year in the extratropics, particularly in the Southern Hemisphere, and appear primarily at the grid scale. The largest FSS improvements, reaching several percent, occur for 5–10 day leads and heavy rainfall categories, while SEEPS indicates modest but consistent gains in categorical precipitation skill. These results suggest that assimilating Aeolus winds improves large-scale dynamics, such as jet streams and Rossby waves, leading to more accurate long-term predictions of cyclones and fronts, and ultimately better local wind and heavy rainfall forecasts.

Previous studies showed substantial improvements in upper-level wind and mass field forecasts from assimilating Aeolus wind observations. This study extends those analyses using the improved reprocessed Aeolus dataset (version B16) in experiments with the global ECMWF forecasting system spanning more than three years. Results show that zonal wind forecasts improve across the entire troposphere during the first forecast week, propagating gradually into the stratosphere, with average reductions in the Root Mean Square Error of about 0.5 %, reaching over 1.5 % in the tropical upper troposphere. These improvements lead to more accurate rainfall forecasts in some regions and seasons, as measured by the Fraction Skill Score (FSS) and the Stable Equitable Error in Probability Space (SEEPS). They are largest during the winter half-year in the extratropics, particularly in the Southern Hemisphere, and appear primarily at the grid scale. The largest FSS improvements, reaching several percent, occur for 5–10 day leads and heavy rainfall categories, while SEEPS indicates modest but consistent gains in categorical precipitation skill. These results suggest that assimilating Aeolus winds improves large-scale dynamics, such as jet streams and Rossby waves, leading to more accurate long-term predictions of cyclones and fronts, and ultimately better local wind and heavy rainfall forecasts.

Previous studies showed substantial improvements in upper-level wind and mass field forecasts from assimilating Aeolus wind observations. This study extends those analyses using the improved reprocessed Aeolus dataset (version B16) in experiments with the global ECMWF forecasting system spanning more than three years. Results show that zonal wind forecasts improve across the entire troposphere during the first forecast week, propagating gradually into the stratosphere, with average reductions in the Root Mean Square Error of about 0.5 %, reaching over 1.5 % in the tropical upper troposphere. These improvements lead to more accurate rainfall forecasts in some regions and seasons, as measured by the Fraction Skill Score (FSS) and the Stable Equitable Error in Probability Space (SEEPS). They are largest during the winter half-year in the extratropics, particularly in the Southern Hemisphere, and appear primarily at the grid scale. The largest FSS improvements, reaching several percent, occur for 5–10 day leads and heavy rainfall categories, while SEEPS indicates modest but consistent gains in categorical precipitation skill. These results suggest that assimilating Aeolus winds improves large-scale dynamics, such as jet streams and Rossby waves, leading to more accurate long-term predictions of cyclones and fronts, and ultimately better local wind and heavy rainfall forecasts.

In summer, mixed Rossby gravity waves–tropical depressions (MRG-TD) are known to drive intraseasonal humidity variability in the northeastern Atlantic troposphere, modulated by equatorial Rossby (ER) and Kelvin waves. However, their impact on dust remains poorly understood, and methods in the literature struggle to distinguish the two MRG-TD tracks. During the Clouds–Atmosphere Dynamics–Dust Interaction in West Africa (CADDIWA) campaign in September 2021, in situ data (from dropsondes and radiosondes) and remote sensing data (Global Navigation Satellite System data and Infrared Atmospheric Sounding Interferometer data) were used to investigate the three-dimensional impact of tropical waves on dust and thermodynamics over Cabo Verde. The distinct contributions of Kelvin waves, ER, and MRG-TD were isolated using frequency–wavenumber filtering of total column water vapor. The latter was efficiently split into southern- and northern-track African easterly waves using distinct frequency windows (MRG-TD1 and MRG-TD2) and enabled us to demonstrate their distinct horizontal structures and impacts. ER waves mainly impacted thermodynamics above 750 hPa, MRG-TD1 affected jet-level thermodynamics, and MRG-TD2 modulated moisture in the lower troposphere. MRG-TD2 was identified as the main driver of dust events over Cabo Verde in September 2021. Tropical cyclogenesis was linked to interactions among multiple tropical waves. Notably, a delay of up to 2 d was observed between Kelvin wave interactions with MRG-TD1 and cyclone formation, consistent with previous findings. These results highlight the critical role of tropical wave interactions in cyclogenesis and underscore their potential to improve forecasting.

Assimilating additional observations into an existing data assimilation system influences the analysis states, that serve as initial conditions for subsequent numerical weather predictions (NWP). Consequently, differences in the analysis can propagate through the forecast and impact forecast skill. The influence of individual observations on the analysis state is commonly assessed through single-observation experiments, which are computationally expensive. As an alternative, the so-called Partial Analysis Increments (PAI) diagnostic (Diefenbach et al., 2023) enables an efficient approximation of the contributions of individual observation to the resulting analysis, without requiring dedicated single-observation experiments. Instead, PAIs can be estimated for all assimilated observations using standard output obtained from the data assimilation system used by Deutscher Wetterdienst, which is based on the Local Ensemble Transform Kalman Filter (LETKF). The calculation of PAIs requires, among others, the full analysis ensemble in model and observation space as well as the first-guess departures.We apply the PAI diagnostic to a campaign re-analysis dataset that incorporates a wide range of non-operational field campaign observations which have been assimilated in addition to observations from the operational measurement network. Specifically, the campaign observations include a network of Doppler wind lidars (DWLs) deployed across southwestern Germany during the ‘Swabian MOSES 2023’ field campaign. Based on this re-analysis, we quantify the contribution of individual observations to the total analysis increments and examine the horizontal and vertical footprints of the respective PAIs, with particular emphasis on assimilated vertical profiles of the horizontal wind retrieved from the DWLs. Moreover, we compare the characteristics of assimilating DWL measurements to that of other observation types providing direct information about the wind field, such as radar radial velocities, radiosonde profiles, and airborne in-situ measurements. Our preliminary results indicate that DWLs contribute substantially to the total analysis increments, and that their spatial influence pattern is distinct from those of radar and airborne observations. Diefenbach, T., Craig, G., Keil, C., Scheck, L. and Weissmann, M. (2023): Partial analysis increments as diagnostic for LETKF data assimilation systems. Q. J. R. Meteorol. Soc., 149, 740-756, https://doi.org/10.1002/qj.4419

In summer, Mixed Rossby Gravity Waves/Tropical Disturbances (MRG-TD) are known to drive intraseasonal humidity variability in the northeastern Atlantic troposphere, modulated by Equatorial Rossby (ER) and Kelvin waves. However, their impact on dust remains poorly understood, and MRG-TD tracks are often mingled in the literature. During the Clouds-Atmosphere Dynamics-Dust Interaction in West Africa (CADDIWA) campaign in September 2021, in-situ and remote sensing data (dropsondes, radiosondes, GNSS, and IASI) were used to investigate the 3D impact of tropical waves on dust and thermodynamics over Cape Verde. The distinct contributions of Kelvin waves, ER, and MRG-TD were isolated using frequency-wave number filtering of Total Column Water Vapor (TCWV). The latter was efficiently split into southern and northern-track African Easterly Waves using distinct frequency windows (respectively MRG-TD1 and MRG-TD2) and enabled us to demonstrate their distinct horizontal structures and impacts. ER waves mainly impacted thermodynamics above 750 hPa, MRG-TD1 affected jet-level thermodynamics, and MRG-TD2 modulated moisture in the lower troposphere. MRG-TD2 was identified as the main driver of dust events over Cape Verde in September 2021. Tropical cyclogenesis was linked to interactions among multiple tropical waves. Notably, a delay of up to 2 days was observed between Kelvin wave interactions with MRG-TD1 and cyclone formation, consistent with previous findings. These results highlight the critical role of tropical wave interactions in cyclogenesis and underscore their potential for improving forecasting.

The Cabo Verde region is subject to the activity of many tropical waves during the boreal summer. They are known to favour or inhibit convective activity, and to play a role in the formation of Tropical Cyclones. A frequency-wavenumber filtering method is used to identify the different waves. A novel tracking protocol is used to distinguish African Easterly Waves propagating north and south of the African Easterly Jet based on their frequencies within the Mixed-Rossby Gravity - Tropical Disturbance (MRG-TD) domain, labeled MRG-TD1 and MRG-TD2, respectively. Based on in-situ and satellite measurements from the Cloud Atmospheric Dynamics Dust Interactions in West Africa (CADDIWA) campaign which took place in Cape Verde in September 201, the impact of each tropical type on the atmosphere vertical structure and dust content is discussed. Our results show that Equatorial Rossby waves mainly impact thermodynamics above 750 hPa, while MRG-TD1 affect jet-level thermodynamics, and MRG-TD2 modulate moisture in the lower troposphere. Dust event are mainly driven by MRG-TD2. The importance of the interaction between waves for tropical cyclogenesis is also highlighted which provides new outlooks for improving tropical cyclogenesis forecasting in the region.