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Evaluating the use of Aeolus satellite observations in the regional NWP model Harmonie-Arome

The impact of using wind speed data from the Aeolus satellite in a limited area Numerical Weather Prediction (NWP) system is being investigated using the limited area NWP model Harmonie-Arome over the Nordic region. We assimilate the Horizontal Line of Sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in Sept-Oct 2018 when the satellite was recently launched, and a later period in Apr-May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations have degraded between the first and second 5 experiment period over our domain. However observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generate flow-dependent analysis 10 increments, with the Aeolus data.


Introduction
It is well known that the quality of Numerical Weather Prediction (NWP) forecasts are dependent on the accuracy of the estimation of the initial state (Simmons and Hollingsworth, 2002). The process of combining model information, in the form of a so called background, with various types of observations for producing a model initial state is referred to as data assimilation. 15 In particular the use of satellite radiances has been demonstrated to be very important for the quality of NWP (Geer et al., 2017).
There are also satellite wind products, such as atmospheric motion vectors (AMVs) derived from tracking cloud and water vapour image sequences. However, there is clearly a lack of direct accurate wind observation for all layers of the atmosphere and available over all areas of globe. Existing observing system already provide valuable data but there are gaps in the coverage as identified by the World Meteorological Organisation (WMO, Anderson and Sato (2012)). Radiosonde locations are unevenly 20 distributed, and usually only available twice per day and mainly over land areas. Winds derived from satellite AMVs are only available where there are clouds or sufficient amounts of water vapour and can only measure the wind at these heights. Data from aircraft and air traffic control systems can sample vertical sections, but only during take-off and landing, the rest of the data comes from the height of the flight level. There is a clear gap in data coverage over remote areas like the Pacific Ocean and over the poles. 25 The Aeolus satellite is a polar orbiting wind profiler and part of the European Space Agency's (ESA) Earth Explorer mission (Stoffelen et al., 2005;Martin et al., 2020). Since the launch on 22 August 2018 it is orbiting the Earth in a sun-synchronous orbit at 320 km height providing vertical wind speed profiles measured with a Doppler wind lidar. A Doppler wind lidar is an active instrument and it derives the wind measurements by detecting the shift in the backscatter signal from the onboard laser using an instrument called ALADIN (Atmospheric Laser Doppler Instrument, see Reitebuch et al. (2009)). The winds derived 30 from the satellite measurements are perpendicular to the direction of travel, hereafter referred to as the HLOS (Horizontal Line of Sight) winds. This means that the wind measured by the Aeolus satellite is dominated by the zonal (east-west) wind component. The ALADIN instrument onboard the Aeolus satellite measures two modes of scattering, Rayleigh and Mie. The Rayleigh measurements are made in the clear atmosphere and are derived from the molecular backscatter from the atmosphere.
They reach a higher altitude than the Mie winds but also have a larger uncertainty. The Mie winds rely on measuring the cloud 35 and aerosol backscatter.The Mie winds have a stronger signal than the Rayleigh ones, and thus Mie winds are more precise and can be derived with a higher vertical resolution. They are also more concentrated to the lower part of the atmosphere.
The Aeolus satellite is the first satellite-based Doppler wind lidar mission in the world and is demonstrating the potential of this technique for obtaining global information on the vertical distribution of the wind. In particular there is a need for high resolution wind information with accurate height assignment, so the wind shear, which is important both for diagnosing 40 turbulence and for predicting developing baroclinic weather systems, can be accurately taken into account by both regional and global NWP systems.
The Aeolus satellite has now been in orbit for over two years and has been tested extensively by many weather forecasting centres around the world and has been shown to improve the forecast at, for example, the European Centre for Medium-range Weather Forecasts (ECMWF)) (Rennie and Isaksen, 2020) Similar results have been seen in testing by DWD 45 (the German weather service) and the UK Met Office. Observations from the Aeolus satellite is now used in operational global NWP systems at ECMWF, DWD, Météo-France (Martin et al., 2020;Pourret et al., 2021) and the UK Met Office, though only the Mie observations are used at the UK Met Office (Halloran, 2020).
In this study we want to evaluate the suitability of using data from the Aeolus satellite in a limited area model (LAM). To our knowledge this is the first study investigating the quality and impact of Aeolus data using a km-scale limited area model using 50 real Aeolus observations. Previous studies, for example Šavli et al. (2018) evaluated the use of pre-launch test data in a 15 km LAM using the WRF model (Weather Research and Forecasting, Skamarock et al. (2008)). We use the Harmonie-Arome model (Bengtsson et al., 2017) over the Nordic countries, covering the operational domain of MetCoOp (Meteorological Cooperation, Müller et al. (2017)). The use of Aeolus observations in limited area km-scale data assimilation differs in several aspects from global data assimilation. In particular, for a limited-area data assimilation system Aeolus data is available over the domain only 55 sometimes and the difference in spatial scales represented by the observation and by the model, respectively, is relatively large.
There are also particular operational constraints for km-scale limited area modelling, such as need for short latency of Aeolus observations. We will look at the overall impact of Aeolus data and the impact of the Rayleigh and Mie observations separately.

Description of the NWP model and study choice
We use the Harmonie-Arome (Bengtsson et al., 2017) version (cy43) of the shared Aire Limitée Adaptation dynamique De-60 veloppement InterNational (ALADIN)-High Resolution Limited Area Model (HIRLAM) NWP system. The three main components of this system are surface data assimilation, upper-air data assimilation and the forecast model. Here we focus on the upper-air data assimilation that has been prepared for assimilation of Aeolus HLOS observations. The data assimilation is applied within a 3 h data assimilation window, in which a background state is combined with various types of observations to obtain model initial states. All experiments are run over the MetCoOp domain, covering Norway, Sweden, Finland and Estonia 65 using a 2.5 km grid size and 65 vertical levels, with a model top at approximately 10 hPa. The model domain is shown in Fig. 1, which also shows the location of the available Aeolus observations. The lateral boundary conditions (LBCs) are provided by the deterministic forecast from the IFS run by ECWMF. These forecasts are launched every 6 h with a 1 h output frequency. In addition, to benefit from the high-quality large scale information from the ECMWF global forecasts in the regional MetCoOp data assimilation, a spectral large scale mixing of the background state fields with the lateral boundary ECMWF IFS fields is 70 applied.
In the main part of this paper we use the Harmonie-Arome (cy 43) standard data assimilation setup with a three-dimensional variational data assimilation (3D-Var). The data assimilation is using all available conventional observations, aircraft data and AMSU-A/MHS radiances from polar orbiting satellites NOAA18 and 19 and MetOp-A and B as well as Aeolus HLOS winds.
For the satellite radiance data a variational bias correction (VarBC) based on Dee (2005); Dee and Uppala (2009) is applied.
These are produced by Ensemble of Data Assimilation experiments (EDA, Bonavita et al. (2012)) with perturbed observations carried out with the Harmonie-Arome system applying ECMWF global EDA forecasts as lateral boundary conditions. Scaling is applied to the derived statistics in order to be in agreement with the amplitude of Harmonie-Arome + 3 h forecast errors.
Background as well as observation errors are assumed to have a Gaussian error distribution as characterized by their error where σ o,i is the observation error standard deviation, σ b,i is the background error standard deviation, L is the rejection limit and [H(x b )] i denotes the projection of the model background state x b on observation i.
The Aeolus product used in this study is the L2B wind product which provides the HLOS wind speed. This is developed by ECMWF and KNMI and the rest of the Aeolus Data, Innovation, and Science Cluster (DISC) team under contract from ESA.

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The L2B processing also provide an estimate of the observation instrument noise and corrections for temperature and pressure dependencies of the Rayleigh winds using a priori information from the ECMWF model  for both parallel experiments can therefore limit the potential impact we can see from the Aeolus data. The impact of LBC and large-scale mixing versus regional model data assimilation in Harmonie-Arome is discussed in Randriamampianina et al. (2020).
All the experiments run 3D-Var data assimilation every third hour. The model is running a 12 h forecast at the main cycles at synoptic times (00, 06, 12, 18 UTC) and only a three hour forecast for the remaining cycles. For both periods we run the same 115 set of experiments, namely -A reference experiment, where no Aeolus data is assimilated -An experiment assimilating both types of Aeolus data -An experiment assimilating only the Mie data -An experiment assimilating only the Rayleigh data 120

Characteristics of Aeolus data
The Aeolus satellite is a research satellite and the first satellite-based wind lidar mission in the world. The HLOS wind observations used in this study are L2B wind products provided by ECMWF and KNMI which are suitable for data assimilation in NWP systems . For each Aeolus measurements 20 laser pulses are accumulated corresponding to a horizontal resolution of approximately 2.9 km. The observations are then made by averaging up to 30 individual measurements 125 for both Rayleigh and Mie channels which results in a horizontally averaged wind data of 86 km. The higher signal-to-noise ratio observed for the Mie channel made it possible to have the horizontal integration length being decreased to 12 km. This was implemented in March 2019. To avoid the systematic errors caused by the Rayleigh-Brillouin scattering, corrections are made for the temperature and pressure dependence of Rayleigh data using a priori information from the ECMWF model (Dabas et al., 2008). Moreover, the wind data is retrieved in 24 bins in the vertical where the resolution varies from 0.25 km near the 130 surface to 2 km at the higher levels. Future investigations concerning the observation collection and processing, and how it can be modified to better suite the needs of km scale regional modelling, are probably needed.
The method used to derive the wind speed data from the raw measurements and the knowledge of how to best use the data in an NWP model is continually under development. Also the L2B processing software is continuously updated so there are some differences in the processing of the HLOS data for our chosen periods, most notably the correction introduced for the orbital 135 bias caused by the difference in mirror temperature . One aspect that can be improved in future versions of our km-scale data assimilation system is how we handle the different spatial scales represented by the observations and the model. To deal with model noise and spatial representation of observations, a careful evaluation of data assimilation in terms of initializing targeted spatial scales needs further evaluation for the quite different spatial characteristics of the Mie and Rayleigh winds. For example, model noise over the ocean has been demonstrated to be successfully handled by application of 140 a so-called supermodding approach (Mile et al., 2021).
There are some difference in how the HLOS data is assimilated in our Harmonie-Arome experiments. For the first period, with laser A in Sept-Oct 2018, we followed the recommendations from ECMWF (Rennie and Isaksen, 2020) and added a 1.35 ms −1 bias correction to the Mie data. Further we rejected poor quality data with large observation errors by specifying an upper limit to the observation error. These limits were set to 4.5 ms −1 for the Mie data and 8 ms −1 for the Rayleigh data. The input 145 data was also limited to one orbit per assimilation window corresponding to the orbit which had the most observations over the MetCoOp domain.
For the second period, with the laser B, the data available for the Mie was of a higher resolution (12 km horizontal distance rather than 86 km as was the case for the laser A period for both the Mie and Rayleigh data). Following our own experience with using Aeolus HLOS data (see section 5.1), we also decided to inflate the observation errors of the Mie data with a factor 150 of 1.25 and also add a lower acceptable limit on the observation error so that all observations with an error lower than 1.5 ms −1 were adjusted upwards to have an observation error of 1.5 ms −1 . The upper limit of the observation error of the Mie data was also slightly adjusted and set to 5 ms −1 rather than 4.5 ms −1 . For the Rayleigh data all observation errors below 1 ms −1 were set to 1 ms −1 and the upper limit of the observation error for the Rayleigh data was kept at 8 ms −1 . All Rayleigh winds below 850 hPa were also rejected due to the poor quality of returned signals in the boundary layer.

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The available Aeolus overpasses for the full experiments are shown in Fig. 1, where the laser A coverage is shown in the left hand panel and the laser B coverage is shown in the right hand panel. The orbits during the laser A period are more irregular than during the laser B period. The higher density of observations of the Mie observations, due to the changes in data sampling during the laser B period, is also seen by the much smaller gaps between the available observations. For both periods, the 06 UTC cycle is the cycle that has the most Aeolus observations over the MetCoOp domain. standard deviation in the Mie data has increased from 3 ms −1 to 4 ms −1 , which is comparable to the Rayleigh values in the laser A period. The accumulation length between the laser A and laser B period was reduced from 86 km to 12 km which increases the signal-to-noise ratio (SNR) which also contribute to the lower accuracy seen in the laser B data. The standard deviation of the Rayleigh data has also increased by nearly 2 ms −1 and there are also larger fluctuations in the vertical profile. This reduced quality of wind data from laser B is caused by the laser's returning energy signal which was decreasing for this 175 period.
During these two periods there is also a discrepancy in the availability of upper air wind observations. For the laser A period,   for Rayleigh data. This DFS calculation is done using all cycles of the day and for all cycles with Aeolus data every fifth day of the experiment period, which for the figure shown is the laser B period. The reason for using cycles from every fifth day is 225 to use independent weather situations in the DFS statistics. The DFS clearly shows that for the Aeolus data the relative impact (bottom row) is significantly larger than the absolute impact (top and middle rows). This is an indication that a relatively large weight is given to the Aeolus HLOS observations and that there are rather few Aeolus HLOS observations. The relative DFS shown in the lower panels of middle and right columns show, consistently with Fig. 4, that a larger weight is given to the Mie observations than to the Rayleigh observations. It should be kept in mind that large relative DFS is not necessarily a good thing  impact of all of the observations. The Rayleigh data on its own also have a larger relative than absolute impact in the DFS values, but its relative importance is the smallest of the four observation types instead of the largest. When using all Aeolus data, the relative DFS of the Aeolus data is equal to that from the radiosonde data, which has the largest absolute DFS value. If we repeat the DFS calculation, but only use the cycles for the same set of days where there are Aeolus observations (top row

Impact on forecasts
To verify the wind forecast, we compare them to radiosonde data. These are available twice per day and in the MetCoOp domain we can find up to 18 radiosonde stations. Because the different availability times of the radiosonde and Aeolus HLOS observations, only some of the forecasts which are analysed in this section start from a data assimilation where Aeolus HLOS 245 data have been used. In order to see as much impact of the Aeolus data as possible, we verify the forecasts after 6 hours, so that as far as possible the forecasts will have started from a data assimilation which contained Aeolus data. The forecasts starting at 06 and 18 UTC will generally have used Aeolus data in the data assimilation, but this is not the case for each and everyone as the Aeolus satellite track changes. The six hour forecasts from these cycles will be valid at 12 and 00 UTC and are verified against the radiosonde data. is very small and it varies with height which experiment shows the lowest value of the bias. In general the wind speed bias is 260 between 0 and -0.4 ms −1 for laser A and between 0.1 and -0.5 ms −1 for laser B. The bias for the wind direction varies with height and a negative bias is found near the surface and for the higher vertical heights.
We also looked at the O-B statistics for aircraft data for the cycles where Aeolus data was used by the background forecast to investigate the quality of the three hour forecast. Aeolus data in the assimilation). This investigation was only conducted for the laser B experiment, but we anticipate that it would give a similar result for the laser A data.
The impact on the forecast verification of using Aeolus data in the data assimilation was neutral for the other meteorological parameters, for both the laser A and laser B periods.
5 Potential for enhanced use of Aeolus data 270 5.1 Tuning of error statistics in the present assimilation system  Lindskog et al. (2006). They found a positive impact on average verification scores, and that in addition 280 a substantial positive impact is demonstrated for an individual synoptically active case by adjusting the observation error.
A new experiment was run where the background error is increased (the background is less trusted) 1 and the observation errors were also changed. Previous experiment used the observation errors as reported in the Aeolus data itself. Since the Desroziers diagnostic also indicated that the observations were given a too much weight, we manually changed all observation errors under 1.5 ms −1 so that they were considered by the data assimilation system to have a 1.5 ms −1 observation error 285 and thus given less weight in the data assimilation process. In another experiment we tested manually lowering the upper observation error as well, but this experiment showed us that the higher observation errors should be trusted and that artificially lowering them decreased the forecast skill. because the σ O will also have an impact on the other observations used in the data assimilation and the goal is also that the ratio of σ B and σ O of the used values should be close to what is estimated by the Desroziers diagnostics. Below 400 hPa this is achieved (blue dashed line in Fig. 7-right), but between 400 and 100 hPa, the ratio of the updated settings is smaller than the recommended value.
An experiment using the updated settings indicated by the Desroziers diagnostics was run for the full laser A period and it 295 resulted in similar O-B as the reference experiment, but smaller O-A values (not shown), by on average 0.45 ms −1 STDV. This means that overall the observations have been given more weight in the data assimilation and thus influence the analysis more.
Looking at the verification scores, the impact of the change in background and observation error settings is neutral.
In order to investigate whether there was a difference between the two types of HLOS observations, we ran the Desroziers diagnostic for the two experiments assimilating only Mie or only Rayleigh winds. These showed that the Desroziers diagnostic We also ran the Desroziers diagnostics for the laser B period for the experiment assimilating both Mie and Rayleih data from Aeolus. Again the diagnostics recommended an increase in the background and observation errors; though both suggested 305 increases were smaller than the ones derived from the laser A period. Since the laser B experiment used the default setting for the background error, it is not obvious why the recommended increase by the Desroziers diagnostic is smaller. It is easier to understand why the increase recommended for the observation error was smaller, since this was already increased for the Mie observations in the laser B experiment due to the shorter accumulation length (see section 3).

Refined data assimilation technique 310
The potential of an enhanced use of Aeolus HLOS wind observation by application of an enhanced data assimilation technique has been exploited. This was achieved within a single observation data assimilation framework where the currently used 3D-Var method was compared with a 4-dimensional variational data assimilation (4D-Var, Courtier et al. (1994)) framework. Two main advantages of 4D-Var as compared to 3D-Var are that observations are used at their appropriate time and that (a simplified version of) the forecast model is used when minimizing the penalty function (Gustafsson et al., 2012). The latter imply a flow 315 dependency of data assimilation corrections of the background which has clear potential advantage for Aeolus HLOS data assimilation. As described by Gustafsson et al. (2018) data assimilation is less developed for km-scale models than for the meso-beta scale models. Nevertheless, a 4-dimensional variational data assimilation methodology has been developed for the Harmonie-Arome km-scale forecasting system and has the potential to further enhance the use of observations. observation (positive direction defined to be for wind direction away from the lidar and negative direction is towards the lidar).
Here the observed value is -7.0 ms −1 , and with an assigned observation error standard deviation of approximately 0.7 ms −1 .
This corresponds to an accurate Mie Aeolus HLOS observations.
The 3D-Var background wind field at model levels around 3 km as well as the wind and temperature fields at 10 km are shown together with the location of the single Aeolus HLOS observation in Fig. 8. A frontal structure is evident along 335 the Swedish-Norwegian border with sharp gradients and an approximate North-South flow along the frontline. The single observation (marked with a dot) was positioned slightly east of the frontal area and valid at 50 min after the valid time of the 3D-Var background state.
In Fig. 9 the horizontal wind field assimilation increments at 06 UTC for model level 33 (around 3 km) induced by the single Aeolus HLOS simulated observation is shown for 3D-Var (left) and 4D-Var (right). Important differences between 3D-Var 340 and 4D-Var induced increments are that 3D-Var increments have considerably larger magnitude than the 4D-Var increments.
15  The idealized Aeolus HLOS single observation study has indicated that there can be a clear benefit in the use of Aeolus 350 HLOS observation from application of more flow dependent and advanced assimilation techniques taking the exact time of the observations better into account and it also should make better use of the model within the data assimilation process.

Conclusions
Aeolus HLOS wind profiles have been added to the 3D-Var data assimilation in a regional high-resolution NWP system. In this study we have used the Harmonie-Arome model running over the MetCoOp domain covering the Nordic countries. We 355 have used the assimilation system to investigate the quality of the Aeolus satellite winds for two different four week periods, one early in the life of the satellite in Sept-Oct 2018 and the other after more than two years of operations in April-May 2020 and the impact of the Aeolus data on the Harmonie-Arome NWP system has been investigated.
We conclude that Aeolus Mie data is demonstrated to be of considerably higher quality and more suitable for assimilation in our regional km-scale forecasting system than Rayleigh data during the two periods we studied. The Mie data are of a quality 360 comparable to radiosonde and aircraft observations for the laser A period while Rayleigh has a lower quality. For the laser B period, even though the Mie quality is lower with respect to the laser A period, it still has the same relative higher quality compared to the Rayleigh data. Another difference in the Mie data between the laser A and the laser B period is the decrease in accumulation length, from 86 km to nearer 12 km.
We have shown that the Aeolus data has an impact in the analysis as seen in the difference between O-B and O-A STDV 365 profiles as well as through DFS analysis. From the DFS analysis we see that the Mie data has a larger impact on the analysis than the Rayleigh data and that the relative impact of the Aeolus data is larger than the absolute impact. Given that the information content from the Aeolus data, as seen in the absolute DFS, is small relative to the other sources of upper air data it's not unexpected that the Aeolus data has a mostly neutral impact on the verification of forecasts, with some small improvements seen in the wind speed and direction at selected height intervals. For the laser A period we noticed somewhat better verification 370 scores if using Mie data only. This result is consistent with the analysis of the quality of Aeolus data. In future analyses of the impact of Aeolus observation we would also like to investigate the impact on the two wind components.
Different approaches for further enhancing the impact of Aeolus data by new adoptions of the Harmonie-Arome data assimilation system were investigated. Such enhancements concerned tuned error statistics and application of a refined flow dependent assimilation technique. Results from analysis of error statistics based on Desroziers approach indicated that poten-375 tial optimisations of currently used error statistics concerning Aeolus could be carried out. Application of such modifications resulted in rather neutral impact on forecast quality. A clear potential was however seen using a 4D-Var assimilation technique, allowing for taking the actual time of the observation better into account as well as using the forecast model itself within the assimilation process.
All in all, we have found that the Aeolus data overall have a small impact on the Harmonie-Arome forecast. However, there 380 are some small positive changes to the analysis which can be seen in the O-A, O-B statistics. As proposed by Stoffelen et al. (2020), having several Doppler wind lidar instrument in orbit and thus more overpasses would be more beneficial as we would have more data available and, potentially, for all our forecast cycles.
In the future, it would also be interesting to use the reprocessed data for the laser A period and rerun these experiments in order to conduct a deeper analysis of the impact of the resolution, both vertical and horizontal, versus observation quality in 385 our regional model. It would also be interesting to more fully examine the potential improvement in impact of Aeolus data on both analyses and forecasts if 4D-Var data assimilation is used for a longer trial. In addition it would be fruitful to coordinate the study with an ECMWF Aeolus experiment, to fully exploit the impact of Aeolus from regional data assimilation, LBC and large scale mixing. Moreover, in this study we mainly focused on observation error standard deviations, whereas potential observation error correlations would be a subject for future studies.

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Another aspect that can be improved in future versions of our km-scale data assimilation system is how we handle the fact that observations and model represent different spatial scales. In the case of observations representing courser spatial scales than the model this scale difference have been demonstrated to be successfully handled by application of a so-called supermodding approach (Mile et al., 2021). Competing interests. We have no competing interests