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The impact of using wind observations 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 September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second 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 generates flow-dependent analysis increments, with the Aeolus data. [ABSTRACT FROM AUTHOR]

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 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 increments, with the Aeolus data. [ABSTRACT FROM AUTHOR]

A convective‐scale ensemble system was developed to predict the occurrence of heavy convective rainfall around Singapore with a focus on the prediction of high‐impact events. The new ensemble SINGV‐EPS has been nested within two global ensembles, MOGREPS‐G (UK Met Office) and EC‐ENS (ECMWF). Predicting the occurrence of convective rainfall in an area such as Singapore is challenging and this article discusses the use of the convection‐permitting ensemble to characterize the uncertainties in the prediction of such localized heavy rainfall. First, verification of wind, temperature, and precipitation is performed for a month‐long period to assess the relative performance of each ensemble. This reveals differences, but no robust signal to say one is better than the other. The results are not statistically significant and not all variables are consistently better with one ensemble or the other. Secondly, the precipitation characteristics of SINGV‐EPS are analysed from probabilities of precipitation and variability among the ensemble members. SINGV‐EPS is sensitive to the choice of the global ensemble providing the initial conditions and boundaries. The results suggest there is benefit, in some cases, from combining the two ensembles. Thirdly, the spread of the ensemble precipitation is analysed using the dispersion Fractions Skill Score (dFSS). We compare the impact of the initial perturbations and the perturbations in lateral boundary conditions in both nesting options. The initial perturbations dominate in the beginning of the forecasts, with influence up to T+24 h, and are associated with an upscale growth of the uncertainties. The impact of the parent ensemble and lateral boundary conditions dominate at the end of the forecast and tend to influence larger scales more. [ABSTRACT FROM AUTHOR]

Since 2012 the Met Office has been running a short-range convective-scale ensemble prediction system over the United Kingdom, known as MOGREPS-UK. In this article we consider MOGREPS-UK in its past, present and future configurations. We describe the evolution of the system during its first few years as an operational model and explain the rationale behind its development. The operational configuration of MOGREPS-UK is evaluated using neighbourhood verification techniques which allow the comparison of ensemble and deterministic forecasts in a probabilistic sense. We compare the performance of MOGREPS-UK to that of the higher-resolution UK deterministic convective-scale model, the UKV, and show that over a 3-month long trial MOGREPS-UK performs better for all variables considered. Plans of future upgrade options of MOGREPS-UK that take advantage of the increased computing capacity at the Met Office are discussed. Three different options are compared: increasing the domain size (now implemented), decreasing the horizontal grid-spacing, and increasing the number of ensemble members. Objective verification results from month-long winter and summer trials show that all options have their benefits, with the most improvement seen with the increase in ensemble size, particularly for precipitation. [ABSTRACT FROM AUTHOR]

Applications of Research to Operations at Mesoscale (AROME) Airport is the high-resolution version of the French operational numerical weather prediction model AROME. The purpose of AROME Airport is to provide rapidly updated forecasts with a 500-m resolution for nowcasting over an airport and to help with the prediction of wake vortices in order to increase the efficiency of the airport. Here, the model is evaluated for the area around Paris Charles de Gaulle Airport (CDG), France. AROME Airport and other configurations of the AROME model are compared to observations during two 4-week periods, when additional observations are available for the area around CDG (May-June 2011 and September-October 2012). The root-mean-square error and the bias are calculated for the 2-m temperature, 10-m wind speed, and the vertical distribution of the wind speed using the available measurements. The performance of AROME Airport is compared to the operational configuration of the AROME model both for synoptic hours and in a more realistic setting using the forecasts from AROME Airport starting every hour. It is shown that the forecasts from AROME Airport are an improvement with respect to the operational when comparing runs from all forecast hours, particularly for the wind speed, but when comparing the synoptic hours, the results are less clear. The sensitivity of AROME Airport to its data assimilation and initial and lateral boundary conditions is also discussed. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new configuration of the Met Office convective‐scale ensemble for numerical weather prediction, for the Met Office Global and Regional Ensemble Prediction System over the United Kingdom (MOGREPS‐UK). The new version, which became operational in March 2019, uses an hourly time‐lagged configuration to take advantage of the hourly 4D‐Var data assimilation run in the deterministic UK model with variable horizontal resolution, the UKV. An 18‐member ensemble is created by running three members every hour and time‐lagging these over a 6 hr window. This configuration is compared against the previous operational configuration, a 6‐hourly convective‐scale ensemble running 12 members. The main benefits of the time‐lagged ensemble are to increase the ensemble size, to add small‐scale uncertainties in the initial conditions and to generate more timely forecasts. The time‐lagged configuration is shown to objectively improve the forecast at all lead times, with larger improvements in the first few hours. The improvement is seen in the ranked probability scores and is mainly associated with the improvements in the spread of the ensemble with an increase of about 5 to 10% in both summer and winter seasons. A larger ensemble size is necessary in the time‐lagged configuration for it to outperform or maintain as good a performance against the previous 6‐hourly configuration for all lead times. Alongside the update to an hourly configuration, the forecast length is more than doubled to 120 hr. Objective verification shows that the time‐lagged configuration performs better than the high‐resolution deterministic, UKV, and the global ensemble, MOGREPS‐G, up to T + 120 hr. Increasing the size of the time‐lagged ensemble through lagging over additional cycles leads to small but significant improvements, larger in most cases than those that can be obtained through neighbourhood processing. [ABSTRACT FROM AUTHOR]

The AROME-NWC nowcasting system has been developed in order to cover the nowcasting 0-6 h range. It is based on the existing AROME mesoscale model, from which the lateral boundary conditions and the first-guess file are taken. The difference between those two systems is basically the observation window length and a very short cut-off time. Studies have been carried out to show that in practice it is not necessary to compute a set of background-error statistics matrices as a function of the forecast lead time of the first-guess file. The spin-up of the system has been proven to be small and the 15 min cut-off to be long enough to keep a good forecast quality. This nowcasting system has been validated against the operational mesoscale model AROME: it has been shown that it performs better for nowcasting ranges as regards temperature, humidity and wind, due to its use of more recent observations. The precipitation forecasts are less satisfactory, with some improvement during the day and some degradation at night. [ABSTRACT FROM AUTHOR]

Winds measured by lidar from the Aeolus satellite are compared with winds measured by two ground-based radars – MARA in Antarctica (70.77 ∘ S, 11.73 ∘ E) and ESRAD (67.88 ∘ N, 21.10 ∘ E) in Arctic Sweden – for the period 1 July–31 December 2019. Aeolus is a demonstrator mission to test whether winds measured by Doppler lidar from space can have sufficient accuracy to contribute to improved weather forecasting. A comprehensive programme of calibration and validation has been undertaken following the satellite launch in 2018, but, so far, direct comparison with independent measurements from the Arctic or Antarctic regions have not been made. The comparison covers heights from the low troposphere to just above the tropopause. Results for each radar site are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds, sunlit ("summer") and non-sunlit ("winter") seasons, and ascending and descending satellite tracks. Horizontally projected line-of-sight (HLOS) winds from Aeolus, reprocessed using baseline 2B10, for passes within 100 km of the radar sites, are compared with HLOS winds calculated from 1 h averaged radar horizontal wind components. The agreement in most data subsets is very good, with no evidence of significant biases (<1 m s -1). Possible biases are identified for two subsets (about -2 m s -1 for the Rayleigh winds for the descending passes at MARA and about 2 m s -1 for the Mie winds for the ascending passes at ESRAD, both in winter), but these are only marginally significant. A robust significant bias of about 7 m s -1 is found for the Mie winds for the ascending tracks at MARA in summer. There is also some evidence for increased random error (by about 1 m s -1) for the Aeolus Mie winds at MARA in summer compared to winter. This might be related to the presence of sunlight scatter over the whole of Antarctica as Aeolus transits across it during summer. [ABSTRACT FROM AUTHOR]

Two atmospheric VHF radars: ESRAD (Esrange MST radar) located near Kiruna in the Swedish Arctic and MARA (Moveable Atmospheric Radar for Antarctica) at the Indian research station Maitri in Antarctica perform wind measurements in the troposphere and lower stratosphere on a regular basis. We compared horizontal winds at altitudes between about 0.5 and 14 km derived from the radar data using the full correlation analysis (FCA) technique with radiosonde observations and models. The comparison with 28 radiosondes launched from January 2017 to August 2019 showed that ESRAD underestimates the zonal and meridional winds by about 8 % and 25 %, respectively. This is likely caused by the receiver group arrangement used for the FCA together with a high level of non-white noise. A similar result was found when comparing with the regional numerical weather prediction model HARMONIE-AROME (Bengtsson et al., 2017) for the period September 2018–May 2019. The MARA winds were compared with winds from radiosondes for the period February–October 2014 (291 occasions). In contrast to ESRAD, there is no indication that MARA underestimates the winds compared to the sondes. The mean difference between the radar and radiosonde winds is close to zero for both zonal and meridional components. The comparison of MARA with the ECMWF ERA5 reanalysis for January–December 2019 reveals good agreement with the mean difference between 0.1 and - 0.5 m/s depending on the component and season. The random errors in the wind components (standard deviations over all estimates in 1 h averages) are typically 2–3 m/s for both radars. Standard deviation of the differences between radars and sondes are 3–5 m/s. [ABSTRACT FROM AUTHOR]

Winds measured by lidar from the Aeolus satellite are compared with winds measured by two ground-based radars, MARA in Antarctica (70.77° S, 11.73° E) and ESRAD (67.88° N, 21.10° E) in Arctic Sweden. Aeolus is a demonstrator mission to test whether winds measured by Doppler lidar from space can have sufficient accuracy to contribute to improved weather forecasting. A comprehensive programme of calibration and validation has been undertaken following the satellite launch in 2018 but, so far, direct comparison with independent measurements from the Arctic or Antarctic regions have not been made. The comparison covers heights from the low troposphere to just above the tropopause. Results for each radar site are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds, summer and winter, and ascending and descending satellite tracks. Horizontally-projected line-of-sight (HLOS) winds from Aeolus, for passes within 100 km from the radar sites, are compared with HLOS winds calculated from one-hour averaged radar horizontal wind components. The agreement in most data subsets is very good, with no evidence of significant biases (< 1 m s-1). Possible biases are identified for two subsets, about -2 m s-1 for MARA/Rayleigh/descending/winter winds, about 3 m s-1 for ESRAD/Mie /ascending /winter , but these are only marginally significant. A robust significant bias of about 6 m s-1 is found for MARA/Mie/ascending/summer winds. There is also some evidence for increased random error (by about 1 m s-1) for all of the Aeolus winds at MARA in summer compared to winter. This might be related to the presence of sunlight scatter over the whole of Antarctica as Aeolus transits across it during summer. [ABSTRACT FROM AUTHOR]

Two atmospheric VHF radars: ESRAD located near Kiruna in the Swedish Artic and MARA at Indian research station Maitri in Antarctica perform wind measurements in the troposphere and lower stratosphere on a regular basis. We compared horizontal winds at altitudes between about 0.5 km and 14 km derived from the radar data using the full correlation analysis technique with radiosonde observations and models. The comparison with 28 radiosondes launched from January 2017 to August 2019 showed that ESRAD underestimates the zonal and meridional winds by about 8 % and 25 %, respectively. The mean differences between the radar and radiosonde winds are -1.4 m/s and 0.4 m/s. A similar result was found when comparing with the regional NWP model HARMONIE-AROME for the period September 2018-May 2019. The ESRAD random error was estimated to be 2.8 m/s (2.4 m/s) for the zonal (meridional) component. The comparison of MARA with the ECMWF ERA5 reanalysis for January-December 2019 reveals good agreement between them with the mean difference between 0.1 m/s and -0.5 m/s depending on the component and season. The MARA random errors are 2.6 m/s and 2.3 m/s for the zonal and meridional winds, respectively. [ABSTRACT FROM AUTHOR]