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Kathrin Folger and Martin Weissmann

Abstract

This study uses lidar observations from the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite to correct operational atmospheric motion vector (AMV) pressure heights. This intends to reduce the height assignment error of AMVs for their use in data assimilation. Additionally, AMVs are treated as winds in a vertical layer as proposed by several recent studies. Corrected and uncorrected AMV winds are evaluated using short-term forecasts of the global forecasting system of the German Weather Service. First, a direct lidar-based height reassignment of AMVs with collocated CALIPSO observations is evaluated. Assigning AMV winds from Meteosat-10 to ~120-hPa-deep layers below the lidar cloud top reduces the vector root-mean-square (VRMS) differences of AMVs from Meteosat-10 by 8%–15%. However, such a direct reassignment can only be applied to collocated AMV–CALIPSO observations that compose a comparably small subset of all AMVs. Second, CALIPSO observations are used to derive statistical height bias correction functions for a general height correction of all operational AMVs from Meteosat-10. Such a height bias correction achieves on average about 50% of the reduction of VRMS differences of the direct height reassignment. Results for other satellites are more ambiguous but still encouraging. Given that such a height bias correction can be applied to all AMVs from a geostationary satellite, the method exhibits a promising approach for the assimilation of AMVs in numerical weather prediction models in the future.

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Florian Harnisch and Martin Weissmann

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For the first time, joint tropical cyclone (TC) surveillance missions by several aircraft were conducted in the western North Pacific basin within the framework of The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) 2008. The collected dropsonde observations were divided into three different subsets depending on their location relative to the TC to investigate which observations are most beneficial for typhoon track forecasting. Data denial experiments with the European Centre for Medium-Range Weather Forecasts (ECMWF) global model were performed to analyze the influence of the different dropsonde subsets. In these experiments, the largest TC track forecast improvements are found for observations in the vicinity of the storm, placed at a circular ring at the outer boundary of the TC. In contrast, observations in remote regions indicated to be sensitive by singular vectors seem to have a relatively small influence with a slight positive tendency on average. Observations in the TC core and center lead to large analysis differences, but only very small mean forecast improvements. This is likely related to the fact that even modern high-resolution global models cannot fully resolve the TC center and thus can only use a relatively small part of the information provided by observations within the TC center. Times prior to landfall and recurvature are stronger affected by additional observations, while the influence on the track forecast after recurvature is relatively weak.

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Kathrin Folger and Martin Weissmann

Abstract

Atmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

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Martin Weissmann, Andreas Dörnbrack, and James D. Doyle

Abstract

A method is presented to compute the spanwise vorticity in polar coordinates from 2D vertical cross sections of high-resolution line-of-sight Doppler wind lidar observations. The method uses the continuity equation to derive the velocity component perpendicular to the observed line-of-sight velocity, which then yields the spanwise vorticity component. The results of the method are tested using a ground-based Doppler lidar, which was deployed during the Terrain-Induced Rotor Experiment (T-REX). The resulting fields can be used to identify and quantify the strength and size of vortices, such as those associated with atmospheric rotors. Furthermore, they may serve to investigate the dynamics and evolution of vortices and to evaluate numerical simulations. A demonstration of the method and comparison with high-resolution numerical simulations reveals that the derived vorticity can explain 66% of the mean-square vorticity fluctuations, has a reasonably skillful magnitude, exhibits no significant bias, and is in qualitative agreement with model-derived vorticity.

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Leonhard Scheck, Martin Weissmann, and Bernhard Mayer

Abstract

Visible satellite images contain high-resolution information about clouds that would be well suited for convective-scale data assimilation. This application requires a forward operator to generate synthetic images from the output of numerical weather prediction models. Only recently have 1D radiative transfer (RT) solvers become sufficiently fast for this purpose. Here computationally efficient methods are proposed to increase the accuracy and consistency of an operator based on the Method for Fast Satellite Image Synthesis (MFASIS) 1D RT. Two important problems are addressed: the 3D RT effects related to inclined cloud tops and the overlap of subgrid clouds. It is demonstrated that in a rotated frame of reference, an approximate solution for the 3D RT problem can be obtained by solving a computationally much cheaper 1D RT problem. Several deterministic and stochastic schemes that take the overlap of subgrid clouds into account are discussed. The impact of the inclination correction and the overlap schemes is evaluated for synthetic 0.6-μm SEVIRI images computed from operational forecasts of the German-focused COSMO (COSMO-DE) Model for a test period in May–June 2016. The cloud-top inclination correction increases the information content of the synthetic images considerably and reduces systematic errors, in particular for larger solar zenith angles. Taking subgrid cloud overlap into account is essential to avoid large systematic errors. The results obtained using several different 2D cloud overlap schemes are very similar, whereas small but significant differences are found for the most consistent 3D method, which accounts for the fact that the RT problem is solved for columns tilted toward the satellite.

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Christian Kühnlein, Andreas Dörnbrack, and Martin Weissmann

Abstract

The authors present observations of the temporal evolution of downslope windstorms with rotors and internal hydraulic jumps of unprecedented detail and spatiotemporal coverage. The observations were carried out by means of a coherent Doppler lidar in the lee of the southern Sierra Nevada range during the sixth intensive observational period of the Terrain-induced Rotor Experiment (T-REX) in 2006. Two representative flow regimes are analyzed and juxtaposed in this paper. The first case shows pulses of high-momentum air that propagate eastward through the valley with an internal hydraulic jump on the leading edge. The region downstream of the transient internal hydraulic jump is characterized by turbulence but no coherent rotor circulation was observed. During the second case, the strongest windstorm of the field campaign T-REX occurred. The observed features of this event resemble the classical notion of a rotor. Altogether, the Doppler lidar observations of both downslope flow events reveal a complex, turbulent flow that is highly transient, intermittent, 3D, and interacts with a significant along-valley flow.

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Martin Weissmann, Kathrin Folger, and Heiner Lange

Abstract

Uncertainties in the height assignment of atmospheric motion vectors (AMVs) are the main contributor to the total AMV wind error, and these uncertainties introduce errors that can be horizontally correlated over several hundred kilometers. As a consequence, only a small fraction of the available AMVs are currently used in numerical weather prediction systems. For this reason, alternative approaches for the height assignment of AMVs are investigated in this study: 1) using collocated airborne lidar observations and 2) treating AMVs as layer winds instead of winds at a discrete level. Airborne lidar observations from a field campaign in the western North Pacific Ocean region are used to demonstrate the potential of improving AMV heights in an experimental framework. On average, AMV wind errors are reduced by 10%–15% when AMV winds are assigned to a 100–150-hPa-deep layer beneath the cloud top derived from nearby lidar observations. In addition, the lidar–AMV height correction is expected to reduce the correlation of AMV errors as lidars provide independent cloud height information. This suggests that satellite lidars may be a valuable source of information for the AMV height assignment in the future. Furthermore, AMVs are compared with dropsonde and radiosonde winds averaged over vertical layers of different depth to investigate the optimal height assignment for AMVs in data assimilation. Consistent with previous studies, it is shown that AMV winds better match sounding winds vertically averaged over ~100 hPa than sounding winds at a discrete level. The comparison with deeper layers further reduces the RMS difference but introduces systematic differences of wind speeds.

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Tobias Necker, Martin Weissmann, Yvonne Ruckstuhl, Jeffrey Anderson, and Takemasa Miyoshi

Abstract

State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

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Matthias Schindler, Martin Weissmann, Andreas Schäfler, and Gabor Radnoti

Abstract

Dropsonde observations from three research aircraft in the North Atlantic region, as well as several hundred additionally launched radiosondes over Canada and Europe, were collected during the international North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) in autumn 2016. In addition, over 1000 dropsondes were deployed during NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) and Reconnaissance missions in the west Atlantic basin, supplementing the conventional observing network for several intensive observation periods. This unique dataset was assimilated within the framework of cycled data denial experiments for a 1-month period performed with the global model of the ECMWF. Results show a slightly reduced mean forecast error (1%–3%) over the northern Atlantic and Europe by assimilating these additional observations, with the most prominent error reductions being linked to Tropical Storm Karl, Cyclones Matthew and Nicole, and their subsequent interaction with the midlatitude waveguide. The evaluation of Forecast Sensitivity to Observation Impact (FSOI) indicates that the largest impact is due to dropsondes near tropical storms and cyclones, followed by dropsondes over the northern Atlantic and additional Canadian radiosondes. Additional radiosondes over Europe showed a comparatively small beneficial impact.

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Josef Schröttle, Martin Weissmann, Leonhard Scheck, and Axel Hutt

Abstract

Cloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time.

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