Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

The first space-based Doppler wind lidar (DWL) on board the Aeolus satellite was launched by the European Space Agency (ESA) on 22 August 2018 to obtain global profiles of horizontal line-of-sight (HLOS) wind speed. In this study, the Raleigh-clear and Mie-cloudy winds for periods of baseline 2B02 (from 1 October to 18 December 2018) and 2B10 (from 28 June to 31 December 2019 and from 20 April to 8 October 2020) were validated using 33 wind profilers (WPRs) installed all over Japan, two ground-based coherent Doppler wind lidars (CDWLs), and 18 GPS radiosondes (GPS-RSs). In particular, vertical and seasonal analyses were performed and discussed using WPR data. During the baseline 2B02 period, a positive bias was found to be in the ranges of 0.5 to 1.7 m s−1 for Rayleigh-clear winds and 1.6 to 2.4 m s−1 for Mie-cloudy winds using the three independent reference instruments. The statistical comparisons for the baseline 2B10 period showed smaller biases, −0.8 to 0.5 m s−1 for the Rayleigh-clear and −0.7 to 0.2 m s−1 for the Mie-cloudy winds. The vertical analysis using WPR data showed that the systematic error was slightly positive in all altitude ranges up to 11 km during the baseline 2B02 period. During the baseline 2B10 period, the systematic errors of Rayleigh-clear and Mie-cloudy winds were improved in all altitude ranges up to 11 km as compared with the baseline 2B02. Immediately after the launch of Aeolus, both Rayleigh-clear and Mie-cloudy biases were small. Within the baseline 2B02, the Rayleigh-clear and Mie-cloudy biases showed a positive trend. For the baseline 2B10, the Rayleigh-clear wind bias was generally negative for all months except August 2020, and Mie-cloudy wind bias gradually fluctuated. Both Rayleigh-clear and Mie-cloudy biases did not show a marked seasonal trend and approached zero towards September 2020. The dependence of the Rayleigh-clear wind bias on the scattering ratio was investigated, showing that there was no significant bias dependence on the scattering ratio during the baseline 2B02 and 2B10 periods. Without the estimated representativeness error associated with the comparisons using WPR observations, the Aeolus random error was determined to be 6.7 (5.1) and 6.4 (4.8) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. The main reason for the large Aeolus random errors is the lower laser energy compared to the anticipated 80 mJ. Additionally, the large representativeness error of the WPRs is probably related to the larger Aeolus random error. Using the CDWLs, the Aeolus random error estimates were in the range of 4.5 to 5.3 (2.9 to 3.2) and 4.8 to 5.2 (3.3 to 3.4) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. By taking the GPS-RS representativeness error into account, the Aeolus random error was determined to be 4.0 (3.2) and 3.0 (2.9) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively.

Correction of wind bias for the lidar on board Aeolus using telescope temperatures

The European Space Agency (ESA) Earth Explorer satellite Aeolus provides continuous profiles of the horizontal line-of-sight wind component globally from space. It was successfully launched in August 2018 with the goal to improve numerical weather prediction (NWP). Aeolus data have already been successfully assimilated into several NWP models and have already helped to significantly improve the quality of weather forecasts. To achieve this major milestone the identification and correction of several systematic error sources were necessary. One of them is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s−1. This paper presents a detailed overview of the influence of the telescope temperature variations on the Aeolus wind products and describes the approach to correct for this systematic error source in the operational near-real-time (NRT) processing. It was shown that the telescope temperature variations along the orbit are due to changes in the top-of-atmosphere reflected shortwave and outgoing longwave radiation of the Earth and the related response of the telescope's thermal control system. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. This correction scheme has been in operational use at ECMWF since April 2020 and is capable of reducing a large part of the telescope-induced wind bias. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %. Moreover, it was demonstrated that the approach of using ECMWF model-equivalent winds is justified by the fact that the global bias of model u-component winds with respect to radiosondes is smaller than 0.3 m s−1. Furthermore, this paper presents the alternative of using Aeolus ground return winds which serve as a zero-wind reference in the multiple linear regression model. The results show that the approach based on ground return winds only performs 10.8 % worse than the ECMWF model-based approach and thus has a good potential for future applications for upcoming reprocessing campaigns or even in the NRT processing of Aeolus wind products.

ALADIN laser frequency stability and its impact on the Aeolus wind error

The acquisition of atmospheric wind profiles on a global scale was realized by the launch of the Aeolus satellite, carrying the unique Atmospheric LAser Doppler INstrument (ALADIN), the first Doppler wind lidar in space. One major component of ALADIN is its high-power, ultraviolet (UV) laser transmitter, which is based on an injection-seeded, frequency-tripled Nd:YAG laser and fulfills a set of demanding requirements in terms of pulse energy, pulse length, repetition rate, and spatial and spectral beam properties. In particular, the frequency stability of the laser emission is an essential parameter which determines the performance of the lidar instrument as the Doppler frequency shifts to be detected are on the order of 108 smaller than the frequency of the emitted UV light. This article reports the assessment of the ALADIN laser frequency stability and its influence on the quality of the Aeolus wind data. Excellent frequency stability with pulse-to-pulse variations of about 10 MHz (root mean square) is evident for over more than 2 years of operations in space despite the permanent occurrence of short periods with significantly enhanced frequency noise (> 30 MHz). The latter were found to coincide with specific rotation speeds of the satellite's reaction wheels, suggesting that the root cause are micro-vibrations that deteriorate the laser stability on timescales of a few tens of seconds. Analysis of the Aeolus wind error with respect to European Centre for Medium-Range Weather Forecasts (ECMWF) model winds shows that the temporally degraded frequency stability of the ALADIN laser transmitter has only a minor influence on the wind data quality on a global scale, which is primarily due to the small percentage of wind measurements for which the frequency fluctuations are considerably enhanced. Hence, although the Mie wind bias is increased by 0.3 m s−1 at times when the frequency stability is worse than 20 MHz, the small contribution of 4 % from all Mie wind results renders this effect insignificant (< 0.1 m s−1) when all winds are considered. The impact on the Rayleigh wind bias is negligible even at high frequency noise. Similar results are demonstrated for the apparent speed of the ground returns that are measured with the Mie and Rayleigh channel of the ALADIN receiver. Here, the application of a frequency stability threshold that filters out wind observations with variations larger than 20 or 10 MHz improves the accuracy of the Mie and Rayleigh ground velocities by only 0.05 and 0.10 m s−1, respectively, however at the expense of useful ground data.

Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

The first space-based Doppler wind lidar (DWL) onboard the Aeolus satellite was launched by the European Space Agency (ESA) on 22 August 2018 to obtain global profiles of horizontal line-of-sight (HLOS) wind speed. In this study, the Raleigh-clear and Mie-cloudy winds for periods of baseline 2B02 (from 1 October to 18 December 2018) and 2B10 (from 28 June to 31 December 2019 and from 20 April to 8 October 2020) were validated using 33 wind profilers (WPRs) installed all over Japan, two ground-based coherent Doppler wind lidars (CDWLs), and 18 GPS-radiosondes (GPS-RSs). In particular, vertical and seasonal analyses were performed and discussed using WPR data. During the baseline 2B02 period, a positive bias was found to be in the ranges of 0.46–1.69 m s−1 for Rayleigh-clear winds and 1.63–2.42 m s−1 for Mie-cloudy winds using the three independent reference instruments. The biases of Rayleigh-clear and Mie-cloudy winds were in the ranges of −0.82−+0.45 m s−1 and −0.71−+0.16 m s−1 during the baseline 2B10 period, respectively. The systematic error for the baseline 2B10 was improved as compared with that for the baseline 2B02. The vertical analysis using WPR data showed that the systematic error was slightly positive in all altitude ranges up to 11 km during the baseline 2B02 period. During the baseline 2B10 period, the systematic errors of Rayleigh-clear and Mie-cloudy winds were improved in all altitude ranges up to 11 km as compared with the baseline 2B02. Immediately after the launch of Aeolus, both Rayleigh-clear and Mie-cloudy biases were small. Within the baseline 2B02, the Rayleigh-clear and Mie-cloudy biases showed a positive trend. For the baseline 2B10, the Rayleigh-clear wind bias was generally negative at all months except August 2020, and Mie-cloudy wind bias gradually fluctuated. The systematic error was close to zero with time in 2020 and did not show a marked seasonal trend. The dependence of the Rayleigh-clear wind bias on the scattering ratio was investigated, showing that the scattering ratio had a minimal effect on the systematic error of the Rayleigh-clear winds during the baseline 2B02 period. On the other hand, during the baseline 2B10 period, there was no significant bias dependence on the scattering ratio. Without the estimated representativeness error associated with the comparisons using WPR observations, the Aeolus random error was determined to be 6.71 (5.12) and 6.42 (4.80) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. The main reason for the large random errors is probably related to the large representativeness error due to the large sampling volume of the WPRs. Using the CDWLs, the Aeolus random error estimates were in the range of 4.49–5.31 (2.93–3.19) and 4.81–5.21 (3.30–3.37) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. By taking the GPS-RS representativeness error into account, the Aeolus random error was determined to be 4.01 (3.24) and 3.02 (2.89) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively.

Correction of wind bias for the lidar on-board Aeolus using telescope temperatures

The European Space Agency satellite Aeolus provides continuous profiles of the horizontal line-of-sight wind component at a global scale. It was successfully launched into space in August 2018 with the goal to improve numerical weather prediction (NWP). Aeolus data has already been successfully assimilated into several NWP models and has already helped to significantly improve the quality of weather forecasts. To achieve this major milestone the identification and correction of several systematic error sources was necessary. One of them is related to small temperatures fluctuations across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m/s. This paper presents a detailed overview of the influence of the telescope temperature variations on the Aeolus wind products and describes the approach to correct for this systematic error source in the operational near-real-time (NRT) processing. It was shown that the telescope temperature variations along the orbit are due to changes of the top-of-atmosphere short- and long-wave radiation of the Earth and the response of the telescope’s thermal control system to that. To correct for this effect ECMWF model-equivalent winds are used as bias reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. This correction scheme has been in operational use at ECMWF since April 2020 and is capable of reducing a large part of the telescope-induced wind bias. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %. Moreover, it was demonstrated that the approach of using ECMWF model-equivalent winds is justified by the fact that the global bias of models u-component winds w.r.t to radiosondes is smaller than 0.3 m/s. However, this paper also presents the alternative of using Aeolus ground return winds which serve as zero wind reference in the multiple linear regression model. The results show that the approach based on ground return winds only performs 10.8 % worse than the ECMWF model-based approach and thus has good potential for future applications for upcoming reprocessing campaigns or even in the NRT processing of Aeolus wind products.

Outdoor measurements of thoron progeny in a 232Th-rich area with deposition-based alpha track detectors and corrections for wind bias

Radon and thoron progeny are important contributors to dose from naturally occurring radionuclides, especially in high background areas and with naturally occurring radioactive material (NORM) legacy sites. Due to the short half-life of thoron, measurements of thoron progeny with a longer half-life should be used for risk and dose assessment. Deposition-based alpha track detectors for such progeny are, however, biased by air movement, especially outdoors where winds may be strong but variable. We used deposition detectors for thoron progeny and radon progeny, as well as alpha track gas detectors for 220Rn and 222Rn, outdoors within the Fen complex in Norway, an area with both elevated levels of naturally occurring radionuclides and NORM legacy sites. Different detector types were used and showed different results. We measured airflow along deposition detectors during deployment to assess wind bias and used statistical models to attain location-specific sheltering factors. These models assess how explanatory terms like point measurements with anemometer, predicted airflow along detectors, and levels of 220Rn and 222Rn explained variation in deposition detector measurements of TnP and RnP. For all the detector types, unrealistically, high equilibrium values (F) were found between progenitor noble gas and progeny before correcting for wind bias. Results suggest a magnitude of wind bias on TnP deposition detectors being a fraction of 0.74–0.96 (mean: 0.87) of the total measurement.

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the Northern Hemisphere (23.5–65∘ N) show strong variations of the Aeolus wind bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land–sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

Effects of upper-level background wind bias on Rossby waveguides, Rossby wave source hotspots, and predictability of the Silk Road pattern

&lt;p&gt;The Silk Road pattern (SRP) is a leading mode of atmospheric circulation over mid-latitude Eurasia during boreal summer. Its temporal phase is known to be unpredictable in many climate models. Previous studies have not reached a clear consensus on the role of sea surface temperature (SST) associated with SRP. To investigate role of SST on SRP formation, we begin by comparing reanalysis with seasonal hindcast experiments of the Pusan National University coupled climate model.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Although SRP cannot be predicted temporally, the ensemble runs show potential predictability in SRP related to tropical Pacific SST. While reanalysis SRP is associated with North Atlantic SST anomalies, hindcast SRP is associated with tropical Pacific SST anomalies similar to El-Nino Southern Oscillation (ENSO). To explain the different SST associations, we propose two jet biases in the climate model which may affect Rossby wave propagation. Bias in North Atlantic jet exit results in a discontinuous waveguide from North Atlantic to Asia, which may hinder propagation of waves associated with North Atlantic SST to trigger SRP. In addition, bias in subtropical western Pacific westerlies reduces the evanescent region between subtropical western Pacific and Asian jet, which may favour westward dispersion of zonally elongated waves associated with ENSO SST to trigger SRP. Therefore, we propose that the role of SST on SRP can be substantially changed depending on fidelity of model upper-level background winds.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;To investigate more quantitatively the roles of waveguides and the Rossby wave sources (RWS), we perform wave-making experiments using an idealised barotropic model prescribed with two different upper-level background winds, namely from reanalysis and from climate model. By comparing with result using reanalysis background winds, the preferred forcing locations - RWS hotspots - of SRP are identified from all the RWS associated with SRP in reanalysis. In addition to previously identified hotspots from the literature, a new hotspot in central North Pacific is discovered which can force SRP by westward dispersion of zonally elongated Rossby wave.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Wave-making result using climate model background winds reveals that the upper-level wind bias changes the RWS hotspots locations of SRP. Experimental result is consistent with theoretical analysis of waveguide bias, and support our conclusion that the relationship between SRP and SST can be substantially changed depending on model background winds bias. The impact of our study is that this sensitivity of SRP hotspots to background winds may reduce seasonal forecast skill of SRP in models with background winds bias.&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;

Potential Predictability of the Silk Road Pattern and the Role of SST as Inferred from Seasonal Hindcast Experiments of a Coupled Climate Model

The Silk Road pattern (SR) is a leading mode of atmospheric circulation over midlatitude Eurasia in boreal summer. Its temporal phase is known to be unpredictable in many models. Previous studies have not reached a clear consensus on the role of sea surface temperature (SST) associated with SR. By comparing seasonal hindcasts from the Pusan National University (PNU) coupled general circulation model with reanalysis, we investigate if there are any sources of predictability originating from the SST. It was found that the PNU model cannot predict SR temporally. In fact, SR is associated with El Niño–Southern Oscillation (ENSO) in the model hindcasts, in contrast to reanalysis results in which SR is more associated with North Atlantic SST anomalies. The PNU system, however, shows potential predictability in SR associated with tropical Pacific SST. Bias in stationary Rossby waveguides is proposed as an explanation for the SR–ENSO relationship in hindcast runs. Model upper-level wind bias in the North Atlantic results in a less continuous waveguide connecting the North Atlantic to Asia, and may hinder wave propagations induced by North Atlantic SST to trigger SR. On the other hand, model upper-level wind bias in the subtropical western Pacific may favor westward propagation of zonally elongated waves from the ENSO region to trigger SR. This study implies that the role of SST with regard to SR can be substantially changed depending on the fidelity of model upper-level background winds.

Evaluation of the WMO Solid Precipitation Intercomparison Experiment (SPICE) transfer functions for adjusting the wind bias in solid precipitation measurements

The World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) involved extensive field intercomparisons of automated instruments for measuring snow during the 2013/2014 and 2014/2015 winter seasons. A key outcome of SPICE was the development of transfer functions for the wind bias adjustment of solid precipitation measurements using various precipitation gauge and wind shield configurations. Due to the short intercomparison period, the data set was not sufficiently large to develop and evaluate transfer functions using independent precipitation measurements, although on average the adjustments were effective at reducing the bias in unshielded gauges from −33.4 % to 1.1 %. The present analysis uses data collected at eight SPICE sites over the 2015/2016 and 2016/2017 winter periods, comparing 30 min adjusted and unadjusted measurements from Geonor T-200B3 and OTT Pluvio2 precipitation gauges in different shield configurations to the WMO Double Fence Automated Reference (DFAR) for the evaluation of the transfer function. Performance is assessed in terms of relative total catch (RTC), root mean square error (RMSE), Pearson correlation (r), and percentage of events (PEs) within 0.1 mm of the DFAR. Metrics are reported for combined precipitation types and for snow only. The evaluation shows that the performance varies substantially by site. Adjusted RTC varies from 54 % to 123 %, RMSE from 0.07 to 0.38 mm, r from 0.28 to 0.94, and PEs from 37 % to 84 %, depending on precipitation phase, site, and gauge configuration (gauge and wind screen type). Generally, windier sites, such as Haukeliseter (Norway) and Bratt's Lake (Canada), exhibit a net under-adjustment (RTC of 54 % to 83 %), while the less windy sites, such as Sodankylä (Finland) and Caribou Creek (Canada), exhibit a net over-adjustment (RTC of 102 % to 123 %). Although the application of transfer functions is necessary to mitigate wind bias in solid precipitation measurements, especially at windy sites and for unshielded gauges, the variability in the performance metrics among sites suggests that the functions be applied with caution.